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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Alberts Easy Activator v0.57.17 for Tomtom.zip What You Need to Know About Tomtom Updates.md

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-<h1>Alberts Easy Activator v0.57.17 for Tomtom.zip: A Complete Guide</h1>
- <p>Do you own a Tomtom navigation device and want to update it with the latest maps and features? Do you want to save money and time by activating your Tomtom device without paying for a subscription or visiting a dealer? If you answered yes to any of these questions, then you need to know about Alberts Easy Activator v0.57.17 for Tomtom.zip.</p>
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- <p>Alberts Easy Activator is a software tool that allows you to activate your Tomtom device with just a few clicks. It also lets you update your maps, customize your settings, and troubleshoot common issues with your Tomtom device. In this article, we will explain what Alberts Easy Activator is, how it works, and how you can download and install it on your computer. We will also show you how to use it to activate and update your Tomtom device, and answer some frequently asked questions about it.</p>
- <h2>What is Alberts Easy Activator?</h2>
- <p>Alberts Easy Activator is a software tool that was created by a user named Albert Swafega on the GPS Underground forum. It is designed to help Tomtom users activate their devices without having to pay for a subscription or visit a dealer.</p>
- <h3>A brief history of Alberts Easy Activator</h3>
- <p>Albert Swafega started developing Alberts Easy Activator in 2009, after he bought a second-hand Tomtom device that was locked by the previous owner. He wanted to unlock his device and update it with the latest maps and features, but he did not want to pay for a subscription or visit a dealer.</p>
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-What is the difference between Alberts Easy Activator v0.57.17 and other versions of Alberts Easy Activator?<br />
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- <p>He searched online for a solution and found out that there were some tools that could activate Tomtom devices, but they were either complicated, outdated, or risky to use. He decided to create his own tool that would be easy, safe, and reliable to use.</p>
- <p>He named his tool Alberts Easy Activator and shared it on the GPS Underground forum for other Tomtom users to use. Since then, he has been updating his tool regularly with new features and improvements.</p>
- <h3>How does Alberts Easy Activator work?</h3>
- <p>Alberts Easy Activator works by generating activation codes for your Tomtom device based on its serial number and device ID. These codes are then used to unlock your device and enable you to use the latest maps and features.</p>
- <p>Alberts Easy Activator also works by downloading and installing the latest official map updates from Tomtom's servers. These updates are then patched with activation codes so that they can work on your device without any issues.</p>
- <h3>What are the benefits of using Alberts Easy Activator?</h3>
- <p>There are many benefits of using Alberts Easy Activator for your Tomtom device, such as:</p>
- <ul>
-<li>You can activate your Tomtom device without paying for a subscription or visiting a dealer.</li>
-<li>You can update your maps with the latest versions from Tomtom's servers.</li>
-<li>You can customize your settings such as voices, colors, icons, etc.</li>
-<li>You can troubleshoot common issues such as no GPS signal, corrupted files, etc.</li>
-<li>You can save money and time by doing everything yourself.</li>
-<li>You can enjoy the best performance and functionality of your Tomtom device.</li>
-</ul>
- <h2>How to download and install Alberts Easy Activator v0.57.17 for Tomtom.zip</h2>
- <p>To download and install Alberts Easy Activator v0.57.17 for Tomtom.zip on your computer, you need to follow these steps:</p>
- <h3>Where to find the download link for Alberts Easy Activator v0.57.17 for Tomtom.zip</h3>
- <p>The download link for Alberts Easy Activator v0.57.17 for Tomtom.zip is available on the GPS Underground forum, where Albert Swafega posts his updates regularly.</p>
- <p>To access the forum, you need to register an account first by clicking on this link: <a href="https://www.gpsunderground.com/forum/register.php">https://www.gpsunderground.com/forum/register.php</a></p>
- <p>After registering an account, you need to log in and go to this thread: <a href="https://www.gpsunderground.com/forum/forum/gps-navigation-systems/tomtom-gps-systems/tomtom-tutorials/3597-how-to-activate-maps-on-tom-tom-using-albert-s-easy-activator">https://www.gpsunderground.com/forum/forum/gps-navigation-systems/tomtom-gps-systems/tomtom-tutorials/3597-how-to-activate-maps-on-tom-tom-using-albert-s-easy-activator</a></p>
- <p>In this thread, you will find the latest version of Alberts Easy Activator v0.57.17 for Tomtom.zip along with instructions on how to use it.</p>
- <h3>How to unzip and run Alberts Easy Activator v0.57.17 for Tomtom.zip</h3>
- <p>After downloading Alberts Easy Activator v0.57.17 for Tomtom.zip from the forum, you need to unzip it using a software such as WinRAR or 7-Zip.</p>
- <p>To unzip it, you need to right-click on the file and select "Extract here" or "Extract to" depending on your software.</p>
- <p>This will create a folder named "Albert's_Easy_Activator_v0_57_17" in the same location as the zip file.</p>
- <p>To run Alberts Easy Activator v0.57.17 for Tomtom.zip, you need to double-click on the file named "RunMe.bat" inside the folder.</p>
- <p>This will open a command prompt window where you will see some options and instructions on how to use the tool.</p>
- <h3>How to activate your Tomtom device with Alberts Easy Activator v0.57.17</h3>
- <p>To activate your Tomtom device with Alberts Easy Activator v0.57.17, you need to follow these steps:</p>
- <ol>
-<li>Connect your Tomtom device to your computer using a USB cable.</li>
-<li>Make sure that your device is recognized by your computer and that it has enough battery power.</li>
-<li>In the command prompt window of Albert's Easy Activator, press 1 and hit Enter.</li>
-<li>This will display some information about your device such as its serial number and device ID.</li>
-<li>Note down these numbers as you will need them later.</li>
-<li>In the command prompt window of Albert's Easy Activator, press 2 and hit Enter.</li>
-<li>This will generate an activation code for your device based on its serial number and device ID.</li>
-<li>Note down this code as you will need it later.</li>
-<li>In the command prompt window of Albert's Easy Activator, press 5 and hit Enter.</li>
-<li>This will open a folder named "Meta" where you will see some files with names like "ttgo.bif", "ttgo.bak", etc.</li>
-<li>Copy these files from the "Meta" folder and paste them into the root directory of your Tomtom device (the main folder where you see folders like "art", "voices", etc.).</li>
-<li>In the command prompt window of Albert's Easy Activator, press 6 and hit Enter.</li>
-<li>This will open another folder named "Activators" where you will see some files with names like "FastActivate.exe", "EasyUseTools.exe", etc.</li>
-".</li>
-<li>Copy this file from the "Maps" folder and paste it into the folder named "Europe" (or whatever the name of the map is) on your Tomtom device.</li>
-<li>Disconnect your Tomtom device from your computer and turn it on.</li>
-<li>On your Tomtom device, go to the main menu and select "Switch map".</li>
-<li>Select the map update that you just installed and confirm.</li>
-<li>Your Tomtom device should now be updated with the latest map version.</li>
-</ol>
- <h3>How to customize your Tomtom settings with Alberts Easy Activator v0.57.17</h3>
- <p>To customize your Tomtom settings with Alberts Easy Activator v0.57.17, you need to follow these steps:</p>
- <ol>
-<li>Connect your Tomtom device to your computer using a USB cable.</li>
-<li>Make sure that your device is recognized by your computer and that it has enough battery power.</li>
-<li>In the command prompt window of Albert's Easy Activator, press 7 and hit Enter.</li>
-<li>This will display a list of available customization options for your device such as voices, colors, icons, etc.</li>
-<li>Select the customization option that you want to download and hit Enter.</li>
-<li>This will start downloading the customization option to a folder named "Customize" in the same location as Albert's Easy Activator.</li>
-<li>Wait until the download is complete and then press any key to continue.</li>
-<li>In the command prompt window of Albert's Easy Activator, press 8 and hit Enter.</li>
-<li>This will open a folder named "Customize" where you will see the customization option file with a name like "TomTom Voices.zip".</li>
-<li>Unzip this file using a software such as WinRAR or 7-Zip.</li>
-<li>Copy the unzipped files from the "TomTom Voices" folder and paste them into the folder named "voices" on your Tomtom device.</li>
-<li>Disconnect your Tomtom device from your computer and turn it on.</li>
-<li>On your Tomtom device, go to the main menu and select "Change preferences".</li>
-<li>Scroll down and select "Change voice".</li>
-<li>Select the voice that you just installed and confirm.</li>
-<li>Your Tomtom device should now have a new voice.</li>
-</ol>
- <h3>How to troubleshoot common issues with Alberts Easy Activator v0.57.17</h3>
- <p>To troubleshoot common issues with Alberts Easy Activator v0.57.17, you need to follow these steps:</p>
- <ol>
-<li>If you encounter an error message such as "No maps found" or "Problem with map", try to delete the file named "ttgo.bif" from the root directory of your Tomtom device and then run Albert's Easy Activator again to generate a new one.</li>
-<li>If you encounter an error message such as "No GPS signal" or "Waiting for a valid GPS signal", try to reset your Tomtom device by holding down the power button for 15 seconds until you hear a drum sound. Then wait for a few minutes until your device acquires a GPS signal.</li>
-<li>If you encounter an error message such as "Corrupted files" or "Invalid files", try to format your Tomtom device by connecting it to your computer and then right-clicking on its drive letter and selecting "Format". Then run Albert's Easy Activator again to activate and update your device.</li>
-<li>If you encounter any other issues or have any questions, try to visit the GPS Underground forum and search for answers or post your queries there. You can also contact Albert Swafega directly by sending him a private message on the forum.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>In this article, we have explained what Alberts Easy Activator v0.57.17 for Tomtom.zip is, how it works, and how you can download and install it on your computer. We have also shown you how to use it to activate and update your Tomtom device, customize your settings, and troubleshoot common issues. We hope that this article has been helpful and informative for you. If you have any feedback or suggestions, please let us know in the comments section below. Thank you for reading!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Alberts Easy Activator v0.57.17 for Tomtom.zip:</p>
- <h4>Is Alberts Easy Activator v0.57.17 for Tomtom.zip safe to use?</h4>
- <p>Yes, Alberts Easy Activator v0.57.17 for Tomtom.zip is safe to use as long as you download it from the official source on the GPS Underground forum and follow the instructions carefully. It does not contain any viruses or malware and does not harm your Tomtom device in any way.</p>
- <h4>Is Alberts Easy Activator v0.57.17 for Tomtom.zip legal to use?</h4>
- <p>Alberts Easy Activator v0.57.17 for Tomtom.zip is not officially endorsed or supported by Tomtom, so it may violate their terms of service or warranty policy. However, it is not illegal to use as long as you own a legitimate copy of the map that you want to activate or update on your device. You are responsible for using Alberts Easy Activator v0.57.17 for Tomtom.zip at your own risk and discretion.</p>
- <h4>Does Alberts Easy Activator v0.57.17 for Tomtom.zip work on all Tomtom devices?</h4>
- <p>No, Alberts Easy Activator v0.57.17 for Tomtom.zip does not work on all Tomtom devices. It only works on devices that have a serial number starting with one of these letters: A, B, C, D, E, F, G, H, J, K, L, M, N, P, Q, R, S, T, U, V, W, X, Y or Z.</p>
- <h4>Does Alberts Easy Activator v0.57.17 for Tomtom.zip work on all maps?</h4>
- such as its name, version, size, etc. You can find the meta files for the latest maps on this thread: <a href="https://www.gpsunderground.com/forum/forum/gps-navigation-systems/tomtom-gps-systems/tomtom-maps/3596-tomtom-maps-meta-codes">https://www.gpsunderground.com/forum/forum/gps-navigation-systems/tomtom-gps-systems/tomtom-maps/3596-tomtom-maps-meta-codes</a></p>
- <h4>How often does Alberts Easy Activator v0.57.17 for Tomtom.zip get updated?</h4>
- <p>Alberts Easy Activator v0.57.17 for Tomtom.zip gets updated whenever there is a new map release or a new feature or improvement added by Albert Swafega. You can check the GPS Underground forum regularly to see if there is a new version available. You can also subscribe to the thread or enable notifications to get alerted when there is a new update.</p>
- <h4>Where can I get more help or support for Alberts Easy Activator v0.57.17 for Tomtom.zip?</h4>
- <p>If you need more help or support for Alberts Easy Activator v0.57.17 for Tomtom.zip, you can visit the GPS Underground forum and search for answers or post your queries there. You can also contact Albert Swafega directly by sending him a private message on the forum. He is very friendly and helpful and will try to assist you as soon as possible.</p>
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-<p>The lyrics of 8 Raflaan are written in Punjabi language, with some English words mixed in. The title of the song means "8 rifles", which refers to the weapons used by the gangsters in the video. The song is about the love and loyalty between the gangster and his girlfriend, as well as the challenges they face from their enemies. Some of the catchy lines from the song are:</p>
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-<li>Teri kande te jawani aayi vairne kadhwaugi koi kanda vaili jatt ton (Your youth has come to your shoulders, enemies will take away your shoulder from your gangster)</li>
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- <p>If you are a chess lover who wants to improve your skills and enjoy the game more, you might be interested in downloading a chess engine. A chess engine is a software program that can play and analyze chess, giving you feedback and suggestions on the best moves. One of the most popular and powerful chess engines available today is Komodo 13, which is developed by a team of experts led by Grandmaster Larry Kaufman. In this article, we will show you what Komodo 13 is, what features and benefits it offers, how to download and install it on your device, how to use it for chess analysis and training, and how to compare it with other chess engines.</p>
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- <p>Komodo 13 is the latest release of this prize-winning chess engine that has won many prestigious titles, such as the World Computer Chess Championship, the World Chess Software Championship, and the TCEC Championship. Komodo 13 is an improvement over all previous versions and supports multi-core processors (32- and 64-bit) and endgame tablebases. It also has an optional Monte Carlo Tree Search mode that uses a neural network to evaluate positions. Komodo 13 is compatible with most chess GUIs (graphical user interfaces) that support the UCI (universal chess interface) protocol. A chess GUI is a program that allows you to interact with the chess engine, such as setting up the board, starting a game, adjusting the settings, etc. Some examples of chess GUIs are ChessBase, Arena, DroidFish, Analyze This, etc.</p>
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-<li>Open Google Play Store on your device <li>Search for Komodo 13 Chess Engine and tap on it</li>
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-<li>Tap on the Open button to launch the app</li>
-<li>You can now use Komodo 13 as a standalone app or with any compatible chess GUI</li>
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-<li>Open your web browser and go to <a href="">https://komodochess.com/</a></li>
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-<li>Save the settings and activate the engine</li>
-<li>You can now use Komodo 13 as an engine for playing or analyzing chess</li>
-</ol>
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- <p>Komodo 13 is a very useful tool for chess analysis and training. You can use it to improve your understanding of chess, find mistakes in your games, learn new ideas, practice different scenarios, etc. Here are some ways to use Komodo 13 for chess analysis and training:</p>
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- <p>Before using Komodo 13, you may want to adjust some of the engine parameters to suit your needs and preferences. Some of the parameters you can change are:</p>
-<ul>
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- <p>One of the most common ways to use Komodo 13 is to analyze your games and positions with it. You can do this by loading your games or positions from a PGN (portable game notation) file or by setting up the board manually. Then you can ask Komodo 13 to evaluate the position and show you the best moves and variations. You can also compare your moves with Komodo 13's moves and see where you made mistakes or missed opportunities. You can also ask Komodo 13 to give you hints, explanations, comments, etc. You can also use features such as blunder check, game annotation, position evaluation graph, etc.</p>
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- <p>Another way to use Komodo 13 is to play against it with different skill levels and personalities. You can do this by starting a new game with Komodo 13 as an engine and choosing the skill level and personality from the engine parameters. You can also adjust the time control, handicap, color, etc. Playing against Komodo 13 can help you practice your chess skills, test your knowledge, challenge yourself, and have fun. You can also analyze your games afterwards and learn from your mistakes and Komodo 13's moves.</p>
- <h3>How to compare Komodo 13 with other chess engines</h3>
- <p>Komodo 13 is not the only chess engine available in the market. There are many other chess engines that have different features, strengths, and weaknesses. You may want to compare Komodo 13 with other chess engines to see how they perform, what they can do, and which one suits you better. Here are some ways to compare Komodo 13 with other chess engines:</p>
- <h4>Using rating lists and tournaments</h4>
- <p>One way to compare Komodo 13 with other chess engines is to use rating lists and tournaments. Rating lists are tables that rank chess engines based on their playing strength, measured by their Elo rating. Tournaments are events where chess engines play against each other under certain conditions and rules. You can find many rating lists and tournaments online, such as CCRL, CEGT, TCEC, etc. You can use these sources to see how Komodo 13 compares with other chess engines in terms of playing strength, performance, results, etc.</p>
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- <p>Another way to compare Komodo 13 with other chess engines is to use engine matches and tests. Engine matches are games where you pit two or more chess engines against each other and see who wins. Engine tests are tasks where you ask two or more chess engines to solve certain problems or positions and see who does better. You can conduct your own engine matches and tests using your chess GUI and setting up the parameters as you wish. You can use these methods to see how Komodo 13 compares with other chess engines in terms of accuracy, speed, style, etc.</p>
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- <p>A special case of comparing Komodo 13 with other chess engines is to use neural network engines. Neural network engines are a new type of chess engines that use artificial intelligence and machine learning to play and analyze chess. They do not rely on traditional evaluation functions or search algorithms, but instead learn from data and experience. Some examples of neural network engines are AlphaZero, Leela Chess Zero, Stockfish NNUE, etc. You can use these engines to see how Komodo 13 compares with them in terms of playing strength, evaluation method, innovation, etc.</p>
- <h2>Conclusion</h2>
- <p>Komodo 13 is a powerful and versatile chess engine that can help you improve your chess skills and enjoy the game more. It has many features and benefits that make it one of the best chess engines in the world. You can download and install Komodo 13 on your device easily and use it with any compatible chess GUI. You can also use Komodo 13 for chess analysis and training by adjusting the engine parameters, analyzing your games and positions, playing against it with different skill levels and personalities, etc. You can also compare Komodo 13 with other chess engines by using rating lists and tournaments, engine matches and tests, neural network engines, etc.</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Komodo 13:</p>
-<ul>
-<li><b>Q: How much does Komodo 13 cost?</b></li>
-<li>A: Komodo 13 costs $59.99 for Windows, Mac, or Linux devices, and $4.99 for Android devices.</li>
-<li><b>Q: How can I update Komodo 13?</b></li>
-<li>A: You can update Komodo 13 by downloading the latest version from the official website or Google Play Store.</li>
-<li><b>Q: How can I contact the developers of Komodo 13?</b></li>
-<li>A: You can contact the developers of Komodo 13 by sending an email to <a href="mailto:[email protected]">[email protected]</a>.</li>
-<li><b>Q: What are the system requirements for Komodo 13?</b></li>
-<li>A: The system requirements for Komodo 13 are:</li>
-<ul>
-<li>A device running Windows XP or later, Mac OS X 10.6 or later, Linux x64 or later, or Android 4.1 or later</li>
-<li>A processor supporting SSE4.1 instruction set (for MCTS mode)</li>
-<li>At least 2 GB of RAM (more recommended)</li>
-<li>At least 100 MB of free disk space</li>
-<li>A compatible chess GUI </ul>
-</ul>
-<li><b>Q: What are the differences between Komodo 13 and Komodo 13 MCTS?</b></li>
-<li>A: Komodo 13 is the standard version of the engine that uses a traditional alpha-beta search algorithm. Komodo 13 MCTS is the experimental version of the engine that uses a Monte Carlo Tree Search algorithm with a neural network. Komodo 13 MCTS is more creative and dynamic, but also more unpredictable and risky.</li>
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- <p>I hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy chess playing!</p> 401be4b1e0<br />
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-<p>Ninja Arashi 2 is not an easy game. It requires skill, patience, and strategy to complete each stage. The game has various levels of difficulty that will challenge your abilities. You have to deal with different types of obstacles, such as traps, spikes, saws, fireballs, lasers, etc. You also have to face different kinds of enemies, such as ninjas, samurais, archers, demons, etc. You have to use your skills and weapons to defeat them and reach the end of each stage.</p>
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- <h3>New melee weapon and new mechanics</h3>
-<p>Ninja Arashi 2 introduces a new melee weapon for Arashi: the katana. The katana is a powerful sword that can slash through enemies and obstacles. You can also use it to deflect projectiles and perform special attacks. The game also has new mechanics that add more variety and fun to the gameplay. For example, you can use the grappling hook to swing across gaps, the shuriken to hit distant targets, the smoke bomb to create a diversion, and the kunai to climb walls.</p>
- <h3>A skill tree system and an artifact system to upgrade your ninja skills</h3>
-<p>Ninja Arashi 2 allows you to customize and improve your ninja skills according to your preference and playstyle. The game has a skill tree system that lets you unlock and upgrade different skills, such as health, damage, speed, stealth, etc. You can also collect and equip artifacts that give you passive bonuses and effects, such as increased critical chance, reduced cooldown, extra gold, etc. You can find artifacts by exploring the stages or by purchasing them from the shop.</p>
- <h3>Beautiful graphics and scenery with shadow silhouette style</h3>
-<p>Ninja Arashi 2 has stunning graphics and scenery that create a captivating atmosphere for the game. The game uses a shadow silhouette style that gives it a unique and artistic look. The game also has diverse and detailed environments that change according to the act and the stage. You will see different landscapes, such as forests, mountains, temples, caves, etc. The game also has dynamic lighting and shadow effects that enhance the visual quality of the game.</p>
- <h3>Epic ninja vs boss fights</h3>
-<p>Ninja Arashi 2 has epic ninja vs boss fights that will challenge your skills and reflexes. The game has four acts in story mode, and each act has a final boss that you have to defeat. The bosses are powerful enemies that have different abilities and patterns that you have to learn and counter. You have to use your weapons, skills, and strategies to overcome them and advance to the next act.</p>
- <h2>How to download Ninja Arashi 2 APK Mod?</h2>
-<p>Ninja Arashi 2 is available for free on Google Play Store for Android devices. However, if you want to enjoy some extra benefits and features, you can download the mod version of the game. Here are some of the benefits of downloading the mod version:</p>
- - You can get unlimited money and diamonds that you can use to buy items from the shop or upgrade your skills. - You can get unlimited skill points that you can use to unlock and upgrade all the skills in the skill tree. - You can get unlimited artifacts that you can equip to boost your ninja abilities. - You can get all the stages unlocked so you can play any stage you want without any restriction. <p>If you are interested in downloading the mod version of Ninja Arashi 2, here are the steps to follow:</p>
- - First, you need to uninstall the original version of Ninja Arashi 2 from your device if you have it installed. - Second, you need to enable unknown sources on your device settings. This will allow you to install apps from sources other than Google Play Store. - Third, you need to download the mod APK file from a reliable website. You can search for "Ninja Arashi 2 APK Mod" on Google or any other search engine and choose a website that has good reviews and ratings. - Fourth, you need to locate the downloaded file on your device storage and tap on it to install it. - Fifth, you need to wait for the installation process to finish and then launch the game. <p>That's it! You have successfully downloaded and installed Ninja Arashi 2 APK Mod on your device. Now you can enjoy playing the game with all the benefits and features of the mod version.</p>
- <h2>How to play Ninja Arashi 2?</h2>
-<p>Ninja Arashi 2 is a simple yet challenging game that requires skill, patience, and strategy. Here are some of the basic controls and gameplay tips that will help you play the game:</p>
- <h3>The basic controls and gameplay</h3>
-<p>Ninja Arashi 2 has easy-to-use controls that let you move, jump, attack, use items, etc. Here are some of the controls:</p>
- <table>
-<tr>
-<th>Control</th>
-<th>Function</th>
-</tr>
-<tr>
-<td>Left arrow</td>
-<td>Move left</td>
-</tr>
-<tr>
-<td>Right arrow</td>
-<td>Move right</td>
-</tr>
-<tr>
-<td>Up arrow</td>
-<td>Jump</td>
-</ <tr>
-<td>Down arrow</td>
-<td>Crouch</td>
-</tr>
-<tr>
-<td>A button</td>
-<td>Attack with katana</td>
-</tr>
-<tr>
-<td>B button</td>
-<td>Use shuriken</td>
-</tr>
-<tr>
-<td>C button</td>
-<td>Use grappling hook</td>
-</tr>
-<tr>
-<td>D button</td>
-<td>Use smoke bomb</td>
-</tr>
-<tr>
-<td>E button</td>
-<td>Use kunai</td>
-</tr>
-<tr>
-<td>F button</td>
-<td>Use special attack</td>
-</tr>
-<tr>
-<td>G button</td>
-<td>Pause the game</td>
-</tr>
-<tr>
-<td>H button</td>
-<td>Show the skill tree and the artifact system</td>
-</tr>
- </table>
- <p>The gameplay of Ninja Arashi 2 is based on stealth, hacking, and slashing. You have to use your ninja skills and weapons to overcome obstacles, enemies, and traps that are set for you. You can also use stealth to deceive your enemies and gain an advantage over them. You have to complete each stage by reaching the end or by fulfilling the objectives. You can also collect gold and diamonds that you can use to buy items from the shop or upgrade your skills.</p>
- <h3>The tips and tricks to master the game</h3>
-<p>Ninja Arashi 2 is a challenging game that requires skill, patience, and strategy. Here are some of the tips and tricks that will help you master the game:</p>
- - Be aware of your surroundings. Look for hidden paths, secrets, and items that can help you in your journey. Also, watch out for traps, spikes, saws, fireballs, lasers, etc. that can harm you or block your way. - Be stealthy. Use the shadows and the environment to hide from your enemies. You can also use smoke bombs to create a diversion or kunai to climb walls. Avoid unnecessary fights and try to sneak past your enemies or take them out silently. - Be smart. Use your weapons and skills wisely. You have a limited amount of shuriken, grappling hook, smoke bomb, and kunai that you can use in each stage. You also have a special attack that you can use once in a while. Choose the best weapon and skill for each situation and enemy. - Be agile. Use your movement and jumping skills to dodge attacks, avoid obstacles, and reach high places. You can also use the grappling hook to swing across gaps or the kunai to stick to walls. You can also perform wall jumps and double jumps to enhance your mobility. - Be strategic. Use the skill tree system and the artifact system to upgrade your ninja skills according to your preference and playstyle. You can unlock and upgrade different skills, such as health, damage, speed, stealth, etc. You can also collect and equip artifacts that give you passive bonuses and effects, such as increased critical chance, reduced cooldown, extra gold, etc. <h3>The best skills and artifacts to use in the game</h3>
-<p>Ninja Arashi 2 has a skill tree system and an artifact system that let you customize and improve your ninja skills according to your preference and playstyle. Here are some of the best skills and artifacts to use in the game:</p>
- - Health: This skill increases your maximum health points, which means you can survive more hits from enemies and traps. This is a useful skill for beginners who are not familiar with the game mechanics or for players who prefer a more defensive playstyle. - Damage: This skill increases your damage output with your katana and shuriken, which means you can kill enemies faster and easier. This is a useful skill for players who prefer a more offensive playstyle or who want to finish stages quickly. - Speed: This skill increases your movement speed, which means you can run faster and jump farther. This is a useful skill for players who want to explore more of the stages or who want to avoid enemies and traps more easily. - Stealth: This skill increases your stealth ability, which means you can stay hidden longer in the shadows and reduce the noise you make when moving or attacking. This is a useful skill for players who want to use stealth as their main strategy or who want to avoid unnecessary fights. - Critical Chance: This artifact increases your chance of landing a critical hit with your katana or shuriken, which means you can deal more damage with each hit. This is a useful artifact for players who want to maximize their damage output or who want to kill enemies with fewer hits. - Cooldown Reduction: This artifact reduces the cooldown time of your weapons and skills, which means you can use them more frequently in each stage. This is a useful artifact for players who want to use their weapons and skills more often or who want to have more options in combat. - Gold Bonus: This artifact increases the amount of gold you earn from killing enemies or collecting gold from the stages, which means you can buy more items from the shop or upgrade your skills more easily. This is a useful artifact for players who want to have more resources or who want to improve their ninja skills faster. <h2>Conclusion</h2>
-<p>Ninja Arashi 2 is a thrilling action-adventure game that will test your skills and reflexes. It is a sequel to the first Ninja Arashi game, which was a hit among ninja game fans. It has a compelling story, challenging gameplay, stunning graphics, and many features that make it a fun and enjoyable game. You can download the game for free from Google Play Store or you can download the mod version to enjoy some extra benefits and features. If you are looking for a stealth, hack, and slash game that will keep you hooked for hours, then Ninja Arashi 2 is the game for you.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Ninja Arashi 2:</p>
- <h3>Q: Is Ninja Arashi 2 safe to download and play?</h3>
-<p>A: Yes, Ninja Arashi 2 is safe to download and play. The game does not contain any viruses, malware, or spyware that can harm your device or your privacy. However, if you download the mod version of the game, make sure you download it from a reliable website and scan it with an antivirus before installing it.</p>
- <h3>Q: Is Ninja Arashi 2 compatible with my device?</h3>
-<p>A: Ninja Arashi 2 is compatible with most Android devices that have Android 4.4 or higher. However, some devices may experience lagging or crashing issues due to low memory or performance. If you encounter any problems while playing the game, try lowering the graphics quality or closing other apps running in the background.</p>
- <h3>Q: How can I save my progress in Ninja Arashi 2?</h3>
-<p>A: Ninja Arashi 2 has an auto-save feature that saves your progress every time you complete a stage or exit the game. You can also manually save your progress by tapping on the G button and then tapping on the save icon. You can also load your saved progress by tapping on the load icon.</p>
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-<p>A: If you have any questions, feedback, or suggestions about Ninja Arashi 2, you can contact the developer of the game by sending an email to [email protected]. You can also follow them on Facebook and Instagram to get the latest news and updates about the game.</p>
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spaces/1phancelerku/anime-remove-background/Free 3D Models Download or Edit Online Clara.io.md

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-<h1>3D Objects Free: How to Find and Download Them for Your Projects</h1>
-<p>If you are working on a project that involves 3D graphics, animation, or visualization, you might be looking for some free 3D objects to use. 3D objects are digital representations of physical objects that can be displayed and manipulated in three dimensions. They can be used for various purposes, such as creating realistic scenes, enhancing user interfaces, simulating physical phenomena, or telling stories.</p>
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-<p>3D objects are composed of vertices, edges, and faces that define their shape and appearance. They can also have textures, materials, colors, lighting, and animations that add more details and effects. 3D objects can be created using specialized software, such as Blender, Maya, or SketchUp, or scanned from real-world objects using cameras or sensors.</p>
-<h3>The Benefits of Using 3D Objects in Your Projects</h3>
-<p>Using 3D objects in your projects can have many benefits, such as:</p>
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-<p>However, creating your own 3D objects from scratch can also have some challenges, such as:</p>
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-<h2>Where to Find Free 3D Objects Online</h2>
-<p>Fortunately, there are many online sources where you can find free 3D objects that you can use for your projects. These sources can be divided into two main categories: websites and libraries.</p>
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-<h4>Free3D.com</h4>
-<p>This is a website that hosts over 15,000 free 3D models in various formats, such as Blender, OBJ, 3DS, C4D, MAX, MAYA. You can find models for different categories, such as architecture, vehicles, characters, furniture, aircrafts, etc. You can also share your own models with the community.</p>
-<h4>Sketchfab</h4>
-<p>This is a website that allows you to view, share, and download over half a million free 3D models under Creative Commons licenses. You can also buy royalty-free models from the Sketchfab Store. You can explore models for different categories, such as characters, cars, weapons, scans, handpainted, medieval, fantasy, etc.</p>
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-<p>These are online platforms that store and organize free 3D models that you can access and download. Some examples are:</p>
-<h4>Google Poly</h4>
-<p>This is a platform that allows you to browse, discover, and download thousands of free 3D models created by Google and other users. You can also upload your own models and share them with the world. You can find models for different categories, such as animals, art, food, nature, objects, scenes, etc.</p>
-<h4>BlenderKit</h4>
-<p>This is a platform that offers over 10,000 free 3D models for Blender users. You can also upload your own models and earn credits or money. You can find models for different categories, such as animals, architecture, characters, furniture, vehicles, etc. You can also access free materials, brushes, and textures.</p>
-<h4>NASA 3D Resources</h4>
-<p>This is a platform that provides free 3D models of NASA's missions, spacecrafts, planets, asteroids, comets, etc. You can also access free images, videos, podcasts, and e-books. You can find models in various formats, such as STL, OBJ, 3DS, FBX, etc.</p>
-<h2>How to Download and Use Free 3D Objects</h2>
-<p>Once you have found the free 3D objects that you want to use for your projects, you need to download and use them properly. Here are some tips to help you:</p>
-<h3>The Different File Formats for 3D Objects</h3>
-<p>Free 3D objects can come in different file formats that determine how they are stored and displayed. Some of the most common formats are:</p>
-<ul>
-<li>OBJ: This is a simple and widely used format that stores the geometry and texture coordinates of a 3D object.</li>
-<li>STL: This is a format that stores the surface geometry of a 3D object using triangular facets. It is often used for 3D printing.</li>
-<li>FBX: This is a format that stores the geometry, materials, animations, and other attributes of a 3D object. It is often used for game development and film production.</li>
-<li>GLTF: This is a format that stores the geometry, materials, textures, animations, and other attributes of a 3D object using JSON and binary data. It is often used for web-based applications and virtual reality.</li>
-</ul>
-<h3>The Software and Tools You Need to Open and Edit 3D Objects</h3>
-<p>To open and edit free 3D objects, you need to have the appropriate software and tools installed on your computer or device. Some of the most popular software and tools are:</p>
-<ul>
-<li>Blender: This is a free and open-source software that allows you to create, edit, animate, render, and export 3D objects in various formats. It also has many features and add-ons for modeling, sculpting, texturing, lighting, rigging, animating, rendering, compositing, and more.</li>
-<li>SketchUp: This is a software that allows you to create, edit, and share 3D objects in various formats. It also has many features and tools for drawing, measuring, coloring, applying materials, adding components, creating scenes, and more.</li>
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-<li>Paint 3D: This is a software that allows you to create, edit, and export 3D objects in various formats. It also has many features and tools for painting, coloring, texturing, adding stickers, effects, lighting, etc.</li>
-</ul>
-<h3>The Best Practices for Using Free 3D Objects in Your Projects</h3>
-<p>To use free 3D objects in your projects effectively and ethically, you need to follow some best practices, such as:</p>
-<ul>
-<li>Checking the license and terms of use of the free 3D objects before downloading and using them. Some free 3D objects may have restrictions or requirements for attribution, modification, distribution, commercial use, etc.</li>
-<li>Optimizing the size and quality of the free 3D objects to suit your project's needs and specifications. Some free 3D objects may have high polygon counts or large file sizes that can affect the performance or loading time of your project.</li>
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-<li>Crediting the original creators or sources of the free 3D objects when using them in your project. This is not only a sign of respect and gratitude but also a way of avoiding plagiarism or infringement issues.</li>
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-<h2>Conclusion</h2>
-<p>In conclusion, free 3D objects are a great resource for anyone who wants to create or enhance their projects with 3D graphics, animation, or visualization. They can save you time, money, and effort, as well as provide you with a variety of options and possibilities. However, you also need to be aware of the challenges and best practices of finding, downloading, and using free 3D objects online. By following the tips and resources in this article, you can make the most out of free 3D objects for your projects.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about free 3D objects:</p>
-<ol>
-<li>What are the best websites to find free 3D objects?</li>
-<p>There is no definitive answer to this question, as different websites may have different advantages and disadvantages depending on your needs and preferences. However, some of the most popular and reputable websites are Free3D.com, Sketchfab, CGTrader, Google Poly, BlenderKit, and NASA 3D Resources.</p>
-<li>What are the best software and tools to open and edit free 3D objects?</li>
-<p>Again, this depends on your personal choice and project requirements. However, some of the most widely used and versatile software and tools are Blender, SketchUp, Unity, and Paint 3D.</p>
-<li>What are the best file formats for free 3D objects?</li>
-<p>This depends on the type and purpose of your project, as well as the compatibility and functionality of your software and tools. However, some of the most common and flexible file formats are OBJ, STL, FBX, and GLTF.</p>
-<li>How can I optimize the size and quality of free 3D objects?</li>
-<p>You can use various methods and techniques to optimize the size and quality of free 3D objects, such as reducing the polygon count, simplifying the geometry, compressing the file size, adjusting the resolution, applying level of detail (LOD), etc.</p>
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-<p>You can credit the original creators or sources of free 3D objects by following their license and terms of use, which may include providing their name, website, link, or other information. You can also use tools or platforms that automatically generate citations or attributions for free 3D objects.</p>
-</ol></p> 401be4b1e0<br />
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spaces/1toTree/lora_test/ppdiffusers/pipelines/alt_diffusion/__init__.py

@@ -1,48 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# flake8: noqa
-
-from dataclasses import dataclass
-from typing import List, Optional, Union
-
-import numpy as np
-import PIL
-
-from ...utils import BaseOutput, is_paddle_available, is_paddlenlp_available
-
-
-@dataclass
-# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
-class AltDiffusionPipelineOutput(BaseOutput):
-    """
-    Output class for Alt Diffusion pipelines.
-
-    Args:
-        images (`List[PIL.Image.Image]` or `np.ndarray`)
-            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
-            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
-        nsfw_content_detected (`List[bool]`)
-            List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, or `None` if safety checking could not be performed.
-    """
-
-    images: Union[List[PIL.Image.Image], np.ndarray]
-    nsfw_content_detected: Optional[List[bool]]
-
-
-if is_paddlenlp_available() and is_paddle_available():
-    from .modeling_roberta_series import RobertaSeriesModelWithTransformation
-    from .pipeline_alt_diffusion import AltDiffusionPipeline
-    from .pipeline_alt_diffusion_img2img import AltDiffusionImg2ImgPipeline

spaces/AP123/dreamgaussian/gs_renderer.py

@@ -1,820 +0,0 @@
-import os
-import math
-import numpy as np
-from typing import NamedTuple
-from plyfile import PlyData, PlyElement
-
-import torch
-from torch import nn
-
-from diff_gaussian_rasterization import (
-    GaussianRasterizationSettings,
-    GaussianRasterizer,
-)
-from simple_knn._C import distCUDA2
-
-from sh_utils import eval_sh, SH2RGB, RGB2SH
-from mesh import Mesh
-from mesh_utils import decimate_mesh, clean_mesh
-
-import kiui
-
-def inverse_sigmoid(x):
-    return torch.log(x/(1-x))
-
-def get_expon_lr_func(
-    lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000
-):
-    
-    def helper(step):
-        if lr_init == lr_final:
-            # constant lr, ignore other params
-            return lr_init
-        if step < 0 or (lr_init == 0.0 and lr_final == 0.0):
-            # Disable this parameter
-            return 0.0
-        if lr_delay_steps > 0:
-            # A kind of reverse cosine decay.
-            delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin(
-                0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1)
-            )
-        else:
-            delay_rate = 1.0
-        t = np.clip(step / max_steps, 0, 1)
-        log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t)
-        return delay_rate * log_lerp
-
-    return helper
-
-
-def strip_lowerdiag(L):
-    uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda")
-
-    uncertainty[:, 0] = L[:, 0, 0]
-    uncertainty[:, 1] = L[:, 0, 1]
-    uncertainty[:, 2] = L[:, 0, 2]
-    uncertainty[:, 3] = L[:, 1, 1]
-    uncertainty[:, 4] = L[:, 1, 2]
-    uncertainty[:, 5] = L[:, 2, 2]
-    return uncertainty
-
-def strip_symmetric(sym):
-    return strip_lowerdiag(sym)
-
-def gaussian_3d_coeff(xyzs, covs):
-    # xyzs: [N, 3]
-    # covs: [N, 6]
-    x, y, z = xyzs[:, 0], xyzs[:, 1], xyzs[:, 2]
-    a, b, c, d, e, f = covs[:, 0], covs[:, 1], covs[:, 2], covs[:, 3], covs[:, 4], covs[:, 5]
-
-    # eps must be small enough !!!
-    inv_det = 1 / (a * d * f + 2 * e * c * b - e**2 * a - c**2 * d - b**2 * f + 1e-24)
-    inv_a = (d * f - e**2) * inv_det
-    inv_b = (e * c - b * f) * inv_det
-    inv_c = (e * b - c * d) * inv_det
-    inv_d = (a * f - c**2) * inv_det
-    inv_e = (b * c - e * a) * inv_det
-    inv_f = (a * d - b**2) * inv_det
-
-    power = -0.5 * (x**2 * inv_a + y**2 * inv_d + z**2 * inv_f) - x * y * inv_b - x * z * inv_c - y * z * inv_e
-
-    power[power > 0] = -1e10 # abnormal values... make weights 0
-        
-    return torch.exp(power)
-
-def build_rotation(r):
-    norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3])
-
-    q = r / norm[:, None]
-
-    R = torch.zeros((q.size(0), 3, 3), device='cuda')
-
-    r = q[:, 0]
-    x = q[:, 1]
-    y = q[:, 2]
-    z = q[:, 3]
-
-    R[:, 0, 0] = 1 - 2 * (y*y + z*z)
-    R[:, 0, 1] = 2 * (x*y - r*z)
-    R[:, 0, 2] = 2 * (x*z + r*y)
-    R[:, 1, 0] = 2 * (x*y + r*z)
-    R[:, 1, 1] = 1 - 2 * (x*x + z*z)
-    R[:, 1, 2] = 2 * (y*z - r*x)
-    R[:, 2, 0] = 2 * (x*z - r*y)
-    R[:, 2, 1] = 2 * (y*z + r*x)
-    R[:, 2, 2] = 1 - 2 * (x*x + y*y)
-    return R
-
-def build_scaling_rotation(s, r):
-    L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda")
-    R = build_rotation(r)
-
-    L[:,0,0] = s[:,0]
-    L[:,1,1] = s[:,1]
-    L[:,2,2] = s[:,2]
-
-    L = R @ L
-    return L
-
-class BasicPointCloud(NamedTuple):
-    points: np.array
-    colors: np.array
-    normals: np.array
-
-
-class GaussianModel:
-
-    def setup_functions(self):
-        def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation):
-            L = build_scaling_rotation(scaling_modifier * scaling, rotation)
-            actual_covariance = L @ L.transpose(1, 2)
-            symm = strip_symmetric(actual_covariance)
-            return symm
-        
-        self.scaling_activation = torch.exp
-        self.scaling_inverse_activation = torch.log
-
-        self.covariance_activation = build_covariance_from_scaling_rotation
-
-        self.opacity_activation = torch.sigmoid
-        self.inverse_opacity_activation = inverse_sigmoid
-
-        self.rotation_activation = torch.nn.functional.normalize
-
-
-    def __init__(self, sh_degree : int):
-        self.active_sh_degree = 0
-        self.max_sh_degree = sh_degree  
-        self._xyz = torch.empty(0)
-        self._features_dc = torch.empty(0)
-        self._features_rest = torch.empty(0)
-        self._scaling = torch.empty(0)
-        self._rotation = torch.empty(0)
-        self._opacity = torch.empty(0)
-        self.max_radii2D = torch.empty(0)
-        self.xyz_gradient_accum = torch.empty(0)
-        self.denom = torch.empty(0)
-        self.optimizer = None
-        self.percent_dense = 0
-        self.spatial_lr_scale = 0
-        self.setup_functions()
-
-    def capture(self):
-        return (
-            self.active_sh_degree,
-            self._xyz,
-            self._features_dc,
-            self._features_rest,
-            self._scaling,
-            self._rotation,
-            self._opacity,
-            self.max_radii2D,
-            self.xyz_gradient_accum,
-            self.denom,
-            self.optimizer.state_dict(),
-            self.spatial_lr_scale,
-        )
-    
-    def restore(self, model_args, training_args):
-        (self.active_sh_degree, 
-        self._xyz, 
-        self._features_dc, 
-        self._features_rest,
-        self._scaling, 
-        self._rotation, 
-        self._opacity,
-        self.max_radii2D, 
-        xyz_gradient_accum, 
-        denom,
-        opt_dict, 
-        self.spatial_lr_scale) = model_args
-        self.training_setup(training_args)
-        self.xyz_gradient_accum = xyz_gradient_accum
-        self.denom = denom
-        self.optimizer.load_state_dict(opt_dict)
-
-    @property
-    def get_scaling(self):
-        return self.scaling_activation(self._scaling)
-    
-    @property
-    def get_rotation(self):
-        return self.rotation_activation(self._rotation)
-    
-    @property
-    def get_xyz(self):
-        return self._xyz
-    
-    @property
-    def get_features(self):
-        features_dc = self._features_dc
-        features_rest = self._features_rest
-        return torch.cat((features_dc, features_rest), dim=1)
-    
-    @property
-    def get_opacity(self):
-        return self.opacity_activation(self._opacity)
-
-    @torch.no_grad()
-    def extract_fields(self, resolution=128, num_blocks=16, relax_ratio=1.5):
-        # resolution: resolution of field
-        
-        block_size = 2 / num_blocks
-
-        assert resolution % block_size == 0
-        split_size = resolution // num_blocks
-
-        opacities = self.get_opacity
-
-        # pre-filter low opacity gaussians to save computation
-        mask = (opacities > 0.005).squeeze(1)
-
-        opacities = opacities[mask]
-        xyzs = self.get_xyz[mask]
-        stds = self.get_scaling[mask]
-        
-        # normalize to ~ [-1, 1]
-        mn, mx = xyzs.amin(0), xyzs.amax(0)
-        self.center = (mn + mx) / 2
-        self.scale = 1.8 / (mx - mn).amax().item()
-
-        xyzs = (xyzs - self.center) * self.scale
-        stds = stds * self.scale
-
-        covs = self.covariance_activation(stds, 1, self._rotation[mask])
-
-        # tile
-        device = opacities.device
-        occ = torch.zeros([resolution] * 3, dtype=torch.float32, device=device)
-
-        X = torch.linspace(-1, 1, resolution).split(split_size)
-        Y = torch.linspace(-1, 1, resolution).split(split_size)
-        Z = torch.linspace(-1, 1, resolution).split(split_size)
-
-
-        # loop blocks (assume max size of gaussian is small than relax_ratio * block_size !!!)
-        for xi, xs in enumerate(X):
-            for yi, ys in enumerate(Y):
-                for zi, zs in enumerate(Z):
-                    xx, yy, zz = torch.meshgrid(xs, ys, zs)
-                    # sample points [M, 3]
-                    pts = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1).to(device)
-                    # in-tile gaussians mask
-                    vmin, vmax = pts.amin(0), pts.amax(0)
-                    vmin -= block_size * relax_ratio
-                    vmax += block_size * relax_ratio
-                    mask = (xyzs < vmax).all(-1) & (xyzs > vmin).all(-1)
-                    # if hit no gaussian, continue to next block
-                    if not mask.any():
-                        continue
-                    mask_xyzs = xyzs[mask] # [L, 3]
-                    mask_covs = covs[mask] # [L, 6]
-                    mask_opas = opacities[mask].view(1, -1) # [L, 1] --> [1, L]
-
-                    # query per point-gaussian pair.
-                    g_pts = pts.unsqueeze(1).repeat(1, mask_covs.shape[0], 1) - mask_xyzs.unsqueeze(0) # [M, L, 3]
-                    g_covs = mask_covs.unsqueeze(0).repeat(pts.shape[0], 1, 1) # [M, L, 6]
-
-                    # batch on gaussian to avoid OOM
-                    batch_g = 1024
-                    val = 0
-                    for start in range(0, g_covs.shape[1], batch_g):
-                        end = min(start + batch_g, g_covs.shape[1])
-                        w = gaussian_3d_coeff(g_pts[:, start:end].reshape(-1, 3), g_covs[:, start:end].reshape(-1, 6)).reshape(pts.shape[0], -1) # [M, l]
-                        val += (mask_opas[:, start:end] * w).sum(-1)
-                    
-                    # kiui.lo(val, mask_opas, w)
-                
-                    occ[xi * split_size: xi * split_size + len(xs), 
-                        yi * split_size: yi * split_size + len(ys), 
-                        zi * split_size: zi * split_size + len(zs)] = val.reshape(len(xs), len(ys), len(zs)) 
-        
-        kiui.lo(occ, verbose=1)
-
-        return occ
-    
-    def extract_mesh(self, path, density_thresh=1, resolution=128, decimate_target=1e5):
-
-        os.makedirs(os.path.dirname(path), exist_ok=True)
-
-        occ = self.extract_fields(resolution).detach().cpu().numpy()
-
-        import mcubes
-        vertices, triangles = mcubes.marching_cubes(occ, density_thresh)
-        vertices = vertices / (resolution - 1.0) * 2 - 1
-
-        # transform back to the original space
-        vertices = vertices / self.scale + self.center.detach().cpu().numpy()
-
-        vertices, triangles = clean_mesh(vertices, triangles, remesh=True, remesh_size=0.015)
-        if decimate_target > 0 and triangles.shape[0] > decimate_target:
-            vertices, triangles = decimate_mesh(vertices, triangles, decimate_target)
-
-        v = torch.from_numpy(vertices.astype(np.float32)).contiguous().cuda()
-        f = torch.from_numpy(triangles.astype(np.int32)).contiguous().cuda()
-
-        print(
-            f"[INFO] marching cubes result: {v.shape} ({v.min().item()}-{v.max().item()}), {f.shape}"
-        )
-
-        mesh = Mesh(v=v, f=f, device='cuda')
-
-        return mesh
-    
-    def get_covariance(self, scaling_modifier = 1):
-        return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation)
-
-    def oneupSHdegree(self):
-        if self.active_sh_degree < self.max_sh_degree:
-            self.active_sh_degree += 1
-
-    def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float = 1):
-        self.spatial_lr_scale = spatial_lr_scale
-        fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda()
-        fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda())
-        features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda()
-        features[:, :3, 0 ] = fused_color
-        features[:, 3:, 1:] = 0.0
-
-        print("Number of points at initialisation : ", fused_point_cloud.shape[0])
-
-        dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001)
-        scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3)
-        rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda")
-        rots[:, 0] = 1
-
-        opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda"))
-
-        self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True))
-        self._features_dc = nn.Parameter(features[:,:,0:1].transpose(1, 2).contiguous().requires_grad_(True))
-        self._features_rest = nn.Parameter(features[:,:,1:].transpose(1, 2).contiguous().requires_grad_(True))
-        self._scaling = nn.Parameter(scales.requires_grad_(True))
-        self._rotation = nn.Parameter(rots.requires_grad_(True))
-        self._opacity = nn.Parameter(opacities.requires_grad_(True))
-        self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")
-
-    def training_setup(self, training_args):
-        self.percent_dense = training_args.percent_dense
-        self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
-        self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
-
-        l = [
-            {'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"},
-            {'params': [self._features_dc], 'lr': training_args.feature_lr, "name": "f_dc"},
-            {'params': [self._features_rest], 'lr': training_args.feature_lr / 20.0, "name": "f_rest"},
-            {'params': [self._opacity], 'lr': training_args.opacity_lr, "name": "opacity"},
-            {'params': [self._scaling], 'lr': training_args.scaling_lr, "name": "scaling"},
-            {'params': [self._rotation], 'lr': training_args.rotation_lr, "name": "rotation"}
-        ]
-
-        self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15)
-        self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale,
-                                                    lr_final=training_args.position_lr_final*self.spatial_lr_scale,
-                                                    lr_delay_mult=training_args.position_lr_delay_mult,
-                                                    max_steps=training_args.position_lr_max_steps)
-
-    def update_learning_rate(self, iteration):
-        ''' Learning rate scheduling per step '''
-        for param_group in self.optimizer.param_groups:
-            if param_group["name"] == "xyz":
-                lr = self.xyz_scheduler_args(iteration)
-                param_group['lr'] = lr
-                return lr
-
-    def construct_list_of_attributes(self):
-        l = ['x', 'y', 'z', 'nx', 'ny', 'nz']
-        # All channels except the 3 DC
-        for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]):
-            l.append('f_dc_{}'.format(i))
-        for i in range(self._features_rest.shape[1]*self._features_rest.shape[2]):
-            l.append('f_rest_{}'.format(i))
-        l.append('opacity')
-        for i in range(self._scaling.shape[1]):
-            l.append('scale_{}'.format(i))
-        for i in range(self._rotation.shape[1]):
-            l.append('rot_{}'.format(i))
-        return l
-
-    def save_ply(self, path):
-        os.makedirs(os.path.dirname(path), exist_ok=True)
-
-        xyz = self._xyz.detach().cpu().numpy()
-        normals = np.zeros_like(xyz)
-        f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
-        f_rest = self._features_rest.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
-        opacities = self._opacity.detach().cpu().numpy()
-        scale = self._scaling.detach().cpu().numpy()
-        rotation = self._rotation.detach().cpu().numpy()
-
-        dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()]
-
-        elements = np.empty(xyz.shape[0], dtype=dtype_full)
-        attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1)
-        elements[:] = list(map(tuple, attributes))
-        el = PlyElement.describe(elements, 'vertex')
-        PlyData([el]).write(path)
-
-    def reset_opacity(self):
-        opacities_new = inverse_sigmoid(torch.min(self.get_opacity, torch.ones_like(self.get_opacity)*0.01))
-        optimizable_tensors = self.replace_tensor_to_optimizer(opacities_new, "opacity")
-        self._opacity = optimizable_tensors["opacity"]
-
-    def load_ply(self, path):
-        plydata = PlyData.read(path)
-
-        xyz = np.stack((np.asarray(plydata.elements[0]["x"]),
-                        np.asarray(plydata.elements[0]["y"]),
-                        np.asarray(plydata.elements[0]["z"])),  axis=1)
-        opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]
-
-        print("Number of points at loading : ", xyz.shape[0])
-
-        features_dc = np.zeros((xyz.shape[0], 3, 1))
-        features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
-        features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"])
-        features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"])
-
-        extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")]
-        assert len(extra_f_names)==3*(self.max_sh_degree + 1) ** 2 - 3
-        features_extra = np.zeros((xyz.shape[0], len(extra_f_names)))
-        for idx, attr_name in enumerate(extra_f_names):
-            features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name])
-        # Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC)
-        features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1))
-
-        scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")]
-        scales = np.zeros((xyz.shape[0], len(scale_names)))
-        for idx, attr_name in enumerate(scale_names):
-            scales[:, idx] = np.asarray(plydata.elements[0][attr_name])
-
-        rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")]
-        rots = np.zeros((xyz.shape[0], len(rot_names)))
-        for idx, attr_name in enumerate(rot_names):
-            rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
-
-        self._xyz = nn.Parameter(torch.tensor(xyz, dtype=torch.float, device="cuda").requires_grad_(True))
-        self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
-        self._features_rest = nn.Parameter(torch.tensor(features_extra, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
-        self._opacity = nn.Parameter(torch.tensor(opacities, dtype=torch.float, device="cuda").requires_grad_(True))
-        self._scaling = nn.Parameter(torch.tensor(scales, dtype=torch.float, device="cuda").requires_grad_(True))
-        self._rotation = nn.Parameter(torch.tensor(rots, dtype=torch.float, device="cuda").requires_grad_(True))
-
-        self.active_sh_degree = self.max_sh_degree
-
-    def replace_tensor_to_optimizer(self, tensor, name):
-        optimizable_tensors = {}
-        for group in self.optimizer.param_groups:
-            if group["name"] == name:
-                stored_state = self.optimizer.state.get(group['params'][0], None)
-                stored_state["exp_avg"] = torch.zeros_like(tensor)
-                stored_state["exp_avg_sq"] = torch.zeros_like(tensor)
-
-                del self.optimizer.state[group['params'][0]]
-                group["params"][0] = nn.Parameter(tensor.requires_grad_(True))
-                self.optimizer.state[group['params'][0]] = stored_state
-
-                optimizable_tensors[group["name"]] = group["params"][0]
-        return optimizable_tensors
-
-    def _prune_optimizer(self, mask):
-        optimizable_tensors = {}
-        for group in self.optimizer.param_groups:
-            stored_state = self.optimizer.state.get(group['params'][0], None)
-            if stored_state is not None:
-                stored_state["exp_avg"] = stored_state["exp_avg"][mask]
-                stored_state["exp_avg_sq"] = stored_state["exp_avg_sq"][mask]
-
-                del self.optimizer.state[group['params'][0]]
-                group["params"][0] = nn.Parameter((group["params"][0][mask].requires_grad_(True)))
-                self.optimizer.state[group['params'][0]] = stored_state
-
-                optimizable_tensors[group["name"]] = group["params"][0]
-            else:
-                group["params"][0] = nn.Parameter(group["params"][0][mask].requires_grad_(True))
-                optimizable_tensors[group["name"]] = group["params"][0]
-        return optimizable_tensors
-
-    def prune_points(self, mask):
-        valid_points_mask = ~mask
-        optimizable_tensors = self._prune_optimizer(valid_points_mask)
-
-        self._xyz = optimizable_tensors["xyz"]
-        self._features_dc = optimizable_tensors["f_dc"]
-        self._features_rest = optimizable_tensors["f_rest"]
-        self._opacity = optimizable_tensors["opacity"]
-        self._scaling = optimizable_tensors["scaling"]
-        self._rotation = optimizable_tensors["rotation"]
-
-        self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask]
-
-        self.denom = self.denom[valid_points_mask]
-        self.max_radii2D = self.max_radii2D[valid_points_mask]
-
-    def cat_tensors_to_optimizer(self, tensors_dict):
-        optimizable_tensors = {}
-        for group in self.optimizer.param_groups:
-            assert len(group["params"]) == 1
-            extension_tensor = tensors_dict[group["name"]]
-            stored_state = self.optimizer.state.get(group['params'][0], None)
-            if stored_state is not None:
-
-                stored_state["exp_avg"] = torch.cat((stored_state["exp_avg"], torch.zeros_like(extension_tensor)), dim=0)
-                stored_state["exp_avg_sq"] = torch.cat((stored_state["exp_avg_sq"], torch.zeros_like(extension_tensor)), dim=0)
-
-                del self.optimizer.state[group['params'][0]]
-                group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True))
-                self.optimizer.state[group['params'][0]] = stored_state
-
-                optimizable_tensors[group["name"]] = group["params"][0]
-            else:
-                group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True))
-                optimizable_tensors[group["name"]] = group["params"][0]
-
-        return optimizable_tensors
-
-    def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation):
-        d = {"xyz": new_xyz,
-        "f_dc": new_features_dc,
-        "f_rest": new_features_rest,
-        "opacity": new_opacities,
-        "scaling" : new_scaling,
-        "rotation" : new_rotation}
-
-        optimizable_tensors = self.cat_tensors_to_optimizer(d)
-        self._xyz = optimizable_tensors["xyz"]
-        self._features_dc = optimizable_tensors["f_dc"]
-        self._features_rest = optimizable_tensors["f_rest"]
-        self._opacity = optimizable_tensors["opacity"]
-        self._scaling = optimizable_tensors["scaling"]
-        self._rotation = optimizable_tensors["rotation"]
-
-        self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
-        self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
-        self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")
-
-    def densify_and_split(self, grads, grad_threshold, scene_extent, N=2):
-        n_init_points = self.get_xyz.shape[0]
-        # Extract points that satisfy the gradient condition
-        padded_grad = torch.zeros((n_init_points), device="cuda")
-        padded_grad[:grads.shape[0]] = grads.squeeze()
-        selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False)
-        selected_pts_mask = torch.logical_and(selected_pts_mask,
-                                              torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent)
-
-        stds = self.get_scaling[selected_pts_mask].repeat(N,1)
-        means =torch.zeros((stds.size(0), 3),device="cuda")
-        samples = torch.normal(mean=means, std=stds)
-        rots = build_rotation(self._rotation[selected_pts_mask]).repeat(N,1,1)
-        new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.get_xyz[selected_pts_mask].repeat(N, 1)
-        new_scaling = self.scaling_inverse_activation(self.get_scaling[selected_pts_mask].repeat(N,1) / (0.8*N))
-        new_rotation = self._rotation[selected_pts_mask].repeat(N,1)
-        new_features_dc = self._features_dc[selected_pts_mask].repeat(N,1,1)
-        new_features_rest = self._features_rest[selected_pts_mask].repeat(N,1,1)
-        new_opacity = self._opacity[selected_pts_mask].repeat(N,1)
-
-        self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation)
-
-        prune_filter = torch.cat((selected_pts_mask, torch.zeros(N * selected_pts_mask.sum(), device="cuda", dtype=bool)))
-        self.prune_points(prune_filter)
-
-    def densify_and_clone(self, grads, grad_threshold, scene_extent):
-        # Extract points that satisfy the gradient condition
-        selected_pts_mask = torch.where(torch.norm(grads, dim=-1) >= grad_threshold, True, False)
-        selected_pts_mask = torch.logical_and(selected_pts_mask,
-                                              torch.max(self.get_scaling, dim=1).values <= self.percent_dense*scene_extent)
-        
-        new_xyz = self._xyz[selected_pts_mask]
-        new_features_dc = self._features_dc[selected_pts_mask]
-        new_features_rest = self._features_rest[selected_pts_mask]
-        new_opacities = self._opacity[selected_pts_mask]
-        new_scaling = self._scaling[selected_pts_mask]
-        new_rotation = self._rotation[selected_pts_mask]
-
-        self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation)
-
-    def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size):
-        grads = self.xyz_gradient_accum / self.denom
-        grads[grads.isnan()] = 0.0
-
-        self.densify_and_clone(grads, max_grad, extent)
-        self.densify_and_split(grads, max_grad, extent)
-
-        prune_mask = (self.get_opacity < min_opacity).squeeze()
-        if max_screen_size:
-            big_points_vs = self.max_radii2D > max_screen_size
-            big_points_ws = self.get_scaling.max(dim=1).values > 0.1 * extent
-            prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws)
-        self.prune_points(prune_mask)
-
-        torch.cuda.empty_cache()
-
-    def prune(self, min_opacity, extent, max_screen_size):
-
-        prune_mask = (self.get_opacity < min_opacity).squeeze()
-        if max_screen_size:
-            big_points_vs = self.max_radii2D > max_screen_size
-            big_points_ws = self.get_scaling.max(dim=1).values > 0.1 * extent
-            prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws)
-        self.prune_points(prune_mask)
-
-        torch.cuda.empty_cache()
-
-
-    def add_densification_stats(self, viewspace_point_tensor, update_filter):
-        self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,:2], dim=-1, keepdim=True)
-        self.denom[update_filter] += 1
-
-def getProjectionMatrix(znear, zfar, fovX, fovY):
-    tanHalfFovY = math.tan((fovY / 2))
-    tanHalfFovX = math.tan((fovX / 2))
-
-    P = torch.zeros(4, 4)
-
-    z_sign = 1.0
-
-    P[0, 0] = 1 / tanHalfFovX
-    P[1, 1] = 1 / tanHalfFovY
-    P[3, 2] = z_sign
-    P[2, 2] = z_sign * zfar / (zfar - znear)
-    P[2, 3] = -(zfar * znear) / (zfar - znear)
-    return P
-
-
-class MiniCam:
-    def __init__(self, c2w, width, height, fovy, fovx, znear, zfar):
-        # c2w (pose) should be in NeRF convention.
-
-        self.image_width = width
-        self.image_height = height
-        self.FoVy = fovy
-        self.FoVx = fovx
-        self.znear = znear
-        self.zfar = zfar
-
-        w2c = np.linalg.inv(c2w)
-
-        # rectify...
-        w2c[1:3, :3] *= -1
-        w2c[:3, 3] *= -1
-
-        self.world_view_transform = torch.tensor(w2c).transpose(0, 1).cuda()
-        self.projection_matrix = (
-            getProjectionMatrix(
-                znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy
-            )
-            .transpose(0, 1)
-            .cuda()
-        )
-        self.full_proj_transform = self.world_view_transform @ self.projection_matrix
-        self.camera_center = -torch.tensor(c2w[:3, 3]).cuda()
-
-
-class Renderer:
-    def __init__(self, sh_degree=3, white_background=True, radius=1):
-        
-        self.sh_degree = sh_degree
-        self.white_background = white_background
-        self.radius = radius
-
-        self.gaussians = GaussianModel(sh_degree)
-
-        self.bg_color = torch.tensor(
-            [1, 1, 1] if white_background else [0, 0, 0],
-            dtype=torch.float32,
-            device="cuda",
-        )
-    
-    def initialize(self, input=None, num_pts=5000, radius=0.5):
-        # load checkpoint
-        if input is None:
-            # init from random point cloud
-            
-            phis = np.random.random((num_pts,)) * 2 * np.pi
-            costheta = np.random.random((num_pts,)) * 2 - 1
-            thetas = np.arccos(costheta)
-            mu = np.random.random((num_pts,))
-            radius = radius * np.cbrt(mu)
-            x = radius * np.sin(thetas) * np.cos(phis)
-            y = radius * np.sin(thetas) * np.sin(phis)
-            z = radius * np.cos(thetas)
-            xyz = np.stack((x, y, z), axis=1)
-            # xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3
-
-            shs = np.random.random((num_pts, 3)) / 255.0
-            pcd = BasicPointCloud(
-                points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3))
-            )
-            self.gaussians.create_from_pcd(pcd, 10)
-        elif isinstance(input, BasicPointCloud):
-            # load from a provided pcd
-            self.gaussians.create_from_pcd(input, 1)
-        else:
-            # load from saved ply
-            self.gaussians.load_ply(input)
-
-    def render(
-        self,
-        viewpoint_camera,
-        scaling_modifier=1.0,
-        invert_bg_color=False,
-        override_color=None,
-        compute_cov3D_python=False,
-        convert_SHs_python=False,
-    ):
-        # Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means
-        screenspace_points = (
-            torch.zeros_like(
-                self.gaussians.get_xyz,
-                dtype=self.gaussians.get_xyz.dtype,
-                requires_grad=True,
-                device="cuda",
-            )
-            + 0
-        )
-        try:
-            screenspace_points.retain_grad()
-        except:
-            pass
-
-        # Set up rasterization configuration
-        tanfovx = math.tan(viewpoint_camera.FoVx * 0.5)
-        tanfovy = math.tan(viewpoint_camera.FoVy * 0.5)
-
-        raster_settings = GaussianRasterizationSettings(
-            image_height=int(viewpoint_camera.image_height),
-            image_width=int(viewpoint_camera.image_width),
-            tanfovx=tanfovx,
-            tanfovy=tanfovy,
-            bg=self.bg_color if not invert_bg_color else 1 - self.bg_color,
-            scale_modifier=scaling_modifier,
-            viewmatrix=viewpoint_camera.world_view_transform,
-            projmatrix=viewpoint_camera.full_proj_transform,
-            sh_degree=self.gaussians.active_sh_degree,
-            campos=viewpoint_camera.camera_center,
-            prefiltered=False,
-            debug=False,
-        )
-
-        rasterizer = GaussianRasterizer(raster_settings=raster_settings)
-
-        means3D = self.gaussians.get_xyz
-        means2D = screenspace_points
-        opacity = self.gaussians.get_opacity
-
-        # If precomputed 3d covariance is provided, use it. If not, then it will be computed from
-        # scaling / rotation by the rasterizer.
-        scales = None
-        rotations = None
-        cov3D_precomp = None
-        if compute_cov3D_python:
-            cov3D_precomp = self.gaussians.get_covariance(scaling_modifier)
-        else:
-            scales = self.gaussians.get_scaling
-            rotations = self.gaussians.get_rotation
-
-        # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors
-        # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer.
-        shs = None
-        colors_precomp = None
-        if colors_precomp is None:
-            if convert_SHs_python:
-                shs_view = self.gaussians.get_features.transpose(1, 2).view(
-                    -1, 3, (self.gaussians.max_sh_degree + 1) ** 2
-                )
-                dir_pp = self.gaussians.get_xyz - viewpoint_camera.camera_center.repeat(
-                    self.gaussians.get_features.shape[0], 1
-                )
-                dir_pp_normalized = dir_pp / dir_pp.norm(dim=1, keepdim=True)
-                sh2rgb = eval_sh(
-                    self.gaussians.active_sh_degree, shs_view, dir_pp_normalized
-                )
-                colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0)
-            else:
-                shs = self.gaussians.get_features
-        else:
-            colors_precomp = override_color
-
-        # Rasterize visible Gaussians to image, obtain their radii (on screen).
-        rendered_image, radii, rendered_depth, rendered_alpha = rasterizer(
-            means3D=means3D,
-            means2D=means2D,
-            shs=shs,
-            colors_precomp=colors_precomp,
-            opacities=opacity,
-            scales=scales,
-            rotations=rotations,
-            cov3D_precomp=cov3D_precomp,
-        )
-
-        rendered_image = rendered_image.clamp(0, 1)
-
-        # Those Gaussians that were frustum culled or had a radius of 0 were not visible.
-        # They will be excluded from value updates used in the splitting criteria.
-        return {
-            "image": rendered_image,
-            "depth": rendered_depth,
-            "alpha": rendered_alpha,
-            "viewspace_points": screenspace_points,
-            "visibility_filter": radii > 0,
-            "radii": radii,
-        }

spaces/Aaaaaaaabdualh/meter2poem-1/app.py

@@ -1,51 +0,0 @@
-from transformers import BertTokenizer, EncoderDecoderModel
-import gradio as gr
-
-tokenizerM = BertTokenizer.from_pretrained("mareloraby/BERTShared-meter2poem-arV01")
-bertSharedM = EncoderDecoderModel.from_pretrained("mareloraby/BERTShared-meter2poem-arV01")
-
-def generate_response(text, k = 70, p = 0.7, nb = 4):
-# meters = set(['الرمل','البسيط','الخفيف','الكامل','السريع','الطويل','المتقارب','الرجز','المجتث','المنسرح','الوافر','المقتضب','الهزج','المديد','المضارع'])
-  prompt = f"{text}"
-  encoded_prompt = tokenizerM.encode_plus(prompt, return_tensors = 'pt')#.to(device)
-  gneration = bertSharedM.generate(
-                            input_ids = encoded_prompt.input_ids, 
-                            attention_mask = encoded_prompt.attention_mask, 
-                            do_sample = True,
-                            top_k= k, 
-                            top_p = p,
-                            num_beams= nb,
-                            max_length =130, 
-                            repetition_penalty = 2.0,  
-                            no_repeat_ngram_size = 2,
-                            early_stopping=True)
-                    
-  generated_text = tokenizerM.decode(gneration[0], skip_special_tokens=True) 
-  bayts = generated_text.split("[SOB]")
-  while("BSEP" not in bayts[-1]):
-    bayts = bayts[:-1]
-  # if(len(bayts[-1]) < 2):
-  #   bayts = bayts[:-1]
-  bayts = bayts[:-1]
-  temp_poem = ''
-  for b in range(len(bayts)):
-      temp_line = bayts[b].split('[BSEP]')
-      temp_poem = temp_poem + temp_line[1] + ' - ' + temp_line[0] +'\n'
-
-  return temp_poem
-  
-  
-  
-gr.Interface(fn=generate_response,
-           title = 'BERTShared - meter based generation',
-          #  description ='''
-          #  topics : ['حزينه','هجاء','عتاب','غزل','مدح','رومنسيه','دينية','وطنيه']
-          #  ''',
-            inputs=[
-        gr.inputs.Radio(['الرمل','البسيط','الخفيف','الكامل','السريع','الطويل','المتقارب','الرجز','المجتث','المنسرح','الوافر','المقتضب','الهزج','المديد','المضارع'],label='Choose Meter'),
-        gr.inputs.Slider(10, 200, step=10,default = 70, label='Top-K'),
-        gr.inputs.Slider(0.10, 0.99, step=0.02, default = 0.70, label='Top-P'),
-       # gr.inputs.Slider(1, 20, step=1, default = 4, label='Beams'),
-        ],
-           outputs="text").launch()
-           

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/app.d.ts

@@ -1,20 +0,0 @@
-/// <reference types="@sveltejs/kit" />
-/// <reference types="unplugin-icons/types/svelte" />
-
-import type { User } from "$lib/types/User";
-
-// See https://kit.svelte.dev/docs/types#app
-// for information about these interfaces
-declare global {
-	namespace App {
-		// interface Error {}
-		interface Locals {
-			sessionId: string;
-			user?: User;
-		}
-		// interface PageData {}
-		// interface Platform {}
-	}
-}
-
-export {};

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/buildPrompt.ts

@@ -1,34 +0,0 @@
-import type { BackendModel } from "./server/models";
-import type { Message } from "./types/Message";
-import { collections } from "$lib/server/database";
-import { authCondition } from "./server/auth";
-/**
- * Convert [{user: "assistant", content: "hi"}, {user: "user", content: "hello"}] to:
- *
- * <|assistant|>hi<|endoftext|><|prompter|>hello<|endoftext|><|assistant|>
- */
-
-interface buildPromptOptions {
-	messages: Pick<Message, "from" | "content">[];
-	model: BackendModel;
-	locals?: App.Locals;
-	webSearchId?: string;
-	preprompt?: string;
-}
-
-export async function buildPrompt({
-	messages,
-	model,
-	locals,
-	webSearchId,
-	preprompt,
-}: buildPromptOptions): Promise<string> {
-	return (
-		model
-			.chatPromptRender({ messages, preprompt })
-			// Not super precise, but it's truncated in the model's backend anyway
-			.split(" ")
-			.slice(-(model.parameters?.truncate ?? 0))
-			.join(" ")
-	);
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/methods/Methods.js

@@ -1,18 +0,0 @@
-import SetTransitCallbackMethods from './SetTransitCallbackMethods.js';
-import DelayCallMethods from './DelayCallMethods.js';
-import ExpandSubMenu from './ExpandSubMenu.js';
-import Collapse from './Collapse.js';
-import CollapseSubMenu from './CollapseSubMenu.js';
-
-var Methods = {
-    expandSubMenu: ExpandSubMenu,
-    collapse: Collapse,
-    collapseSubMenu: CollapseSubMenu,
-}
-
-Object.assign(
-    Methods,
-    SetTransitCallbackMethods,
-    DelayCallMethods
-)
-export default Methods;

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

@@ -1,5 +0,0 @@
-import NumberBar from './NumberBar';
-
-export default function (
-    config?: NumberBar.IConfig
-): NumberBar;

spaces/Alifarsi/news_summarizer/README.md

@@ -1,37 +0,0 @@
----
-title: News_summarizer
-emoji: 🌖
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.

spaces/Amon1/ChatGPTForAcadamic/config.py

@@ -1,48 +0,0 @@
-import os
-
-# [step 1]>> 例如： API_KEY = "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" （此key无效）
-# Put your own API_KEY into `OPENAI_API_KEY` environment.
-API_KEY = os.environ.get('OPENAI_API_KEY')
-
-# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改
-USE_PROXY = False
-if USE_PROXY:
-    # 填写格式是 [协议]://  [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
-    # 例如    "socks5h://localhost:11284"
-    # [协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
-    # [地址] 懂的都懂，不懂就填localhost或者127.0.0.1肯定错不了（localhost意思是代理软件安装在本机上）
-    # [端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样，但端口号都应该在最显眼的位置上
-
-    # 代理网络的地址，打开你的科学上网软件查看代理的协议(socks5/http)、地址(localhost)和端口(11284)
-    proxies = {
-        #          [协议]://  [地址]  :[端口]
-        "http": "socks5h://localhost:11284",
-        "https": "socks5h://localhost:11284",
-    }
-else:
-    proxies = None
-
-# [step 3]>> 以下配置可以优化体验，但大部分场合下并不需要修改
-# 对话窗的高度
-CHATBOT_HEIGHT = 1115
-
-# 发送请求到OpenAI后，等待多久判定为超时
-TIMEOUT_SECONDS = 25
-
-# 网页的端口, -1代表随机端口
-WEB_PORT = 7860
-
-# 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
-MAX_RETRY = 2
-
-# OpenAI模型选择是（gpt4现在只对申请成功的人开放）
-LLM_MODEL = "gpt-3.5-turbo"
-
-# OpenAI的API_URL
-API_URL = "https://api.openai.com/v1/chat/completions"
-
-# 设置并行使用的线程数
-CONCURRENT_COUNT = 100
-
-# 设置用户名和密码（相关功能不稳定，与gradio版本和网络都相关，如果本地使用不建议加这个）
-AUTHENTICATION = [] # [("username", "password"), ("username2", "password2"), ...]

spaces/Andy1621/uniformer_image_detection/mmdet/models/backbones/detectors_resnext.py

@@ -1,122 +0,0 @@
-import math
-
-from mmcv.cnn import build_conv_layer, build_norm_layer
-
-from ..builder import BACKBONES
-from .detectors_resnet import Bottleneck as _Bottleneck
-from .detectors_resnet import DetectoRS_ResNet
-
-
-class Bottleneck(_Bottleneck):
-    expansion = 4
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 groups=1,
-                 base_width=4,
-                 base_channels=64,
-                 **kwargs):
-        """Bottleneck block for ResNeXt.
-
-        If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
-        it is "caffe", the stride-two layer is the first 1x1 conv layer.
-        """
-        super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
-
-        if groups == 1:
-            width = self.planes
-        else:
-            width = math.floor(self.planes *
-                               (base_width / base_channels)) * groups
-
-        self.norm1_name, norm1 = build_norm_layer(
-            self.norm_cfg, width, postfix=1)
-        self.norm2_name, norm2 = build_norm_layer(
-            self.norm_cfg, width, postfix=2)
-        self.norm3_name, norm3 = build_norm_layer(
-            self.norm_cfg, self.planes * self.expansion, postfix=3)
-
-        self.conv1 = build_conv_layer(
-            self.conv_cfg,
-            self.inplanes,
-            width,
-            kernel_size=1,
-            stride=self.conv1_stride,
-            bias=False)
-        self.add_module(self.norm1_name, norm1)
-        fallback_on_stride = False
-        self.with_modulated_dcn = False
-        if self.with_dcn:
-            fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
-        if self.with_sac:
-            self.conv2 = build_conv_layer(
-                self.sac,
-                width,
-                width,
-                kernel_size=3,
-                stride=self.conv2_stride,
-                padding=self.dilation,
-                dilation=self.dilation,
-                groups=groups,
-                bias=False)
-        elif not self.with_dcn or fallback_on_stride:
-            self.conv2 = build_conv_layer(
-                self.conv_cfg,
-                width,
-                width,
-                kernel_size=3,
-                stride=self.conv2_stride,
-                padding=self.dilation,
-                dilation=self.dilation,
-                groups=groups,
-                bias=False)
-        else:
-            assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
-            self.conv2 = build_conv_layer(
-                self.dcn,
-                width,
-                width,
-                kernel_size=3,
-                stride=self.conv2_stride,
-                padding=self.dilation,
-                dilation=self.dilation,
-                groups=groups,
-                bias=False)
-
-        self.add_module(self.norm2_name, norm2)
-        self.conv3 = build_conv_layer(
-            self.conv_cfg,
-            width,
-            self.planes * self.expansion,
-            kernel_size=1,
-            bias=False)
-        self.add_module(self.norm3_name, norm3)
-
-
-@BACKBONES.register_module()
-class DetectoRS_ResNeXt(DetectoRS_ResNet):
-    """ResNeXt backbone for DetectoRS.
-
-    Args:
-        groups (int): The number of groups in ResNeXt.
-        base_width (int): The base width of ResNeXt.
-    """
-
-    arch_settings = {
-        50: (Bottleneck, (3, 4, 6, 3)),
-        101: (Bottleneck, (3, 4, 23, 3)),
-        152: (Bottleneck, (3, 8, 36, 3))
-    }
-
-    def __init__(self, groups=1, base_width=4, **kwargs):
-        self.groups = groups
-        self.base_width = base_width
-        super(DetectoRS_ResNeXt, self).__init__(**kwargs)
-
-    def make_res_layer(self, **kwargs):
-        return super().make_res_layer(
-            groups=self.groups,
-            base_width=self.base_width,
-            base_channels=self.base_channels,
-            **kwargs)

spaces/Andy1621/uniformer_image_segmentation/configs/ccnet/README.md

@@ -1,47 +0,0 @@
-# CCNet: Criss-Cross Attention for Semantic Segmentation
-
-## Introduction
-
-<!-- [ALGORITHM] -->
-
-```latex
-@article{huang2018ccnet,
-    title={CCNet: Criss-Cross Attention for Semantic Segmentation},
-    author={Huang, Zilong and Wang, Xinggang and Huang, Lichao and Huang, Chang and Wei, Yunchao and Liu, Wenyu},
-    booktitle={ICCV},
-    year={2019}
-}
-```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                    | download                                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| CCNet  | R-50-D8  | 512x1024  |   40000 | 6        | 3.32           | 77.76 |         78.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517.log.json)     |
-| CCNet  | R-101-D8 | 512x1024  |   40000 | 9.5      | 2.31           | 76.35 |         78.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540.log.json) |
-| CCNet  | R-50-D8  | 769x769   |   40000 | 6.8      | 1.43           | 78.46 |         79.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125.log.json)         |
-| CCNet  | R-101-D8 | 769x769   |   40000 | 10.7     | 1.01           | 76.94 |         78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428.log.json)     |
-| CCNet  | R-50-D8  | 512x1024  |   80000 | -        | -              | 79.03 |         80.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421.log.json)     |
-| CCNet  | R-101-D8 | 512x1024  |   80000 | -        | -              | 78.87 |         79.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935.log.json) |
-| CCNet  | R-50-D8  | 769x769   |   80000 | -        | -              | 79.29 |         81.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421.log.json)         |
-| CCNet  | R-101-D8 | 769x769   |   80000 | -        | -              | 79.45 |         80.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                | download                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| CCNet  | R-50-D8  | 512x512   |   80000 | 8.8      | 20.89          | 41.78 |         42.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848.log.json)         |
-| CCNet  | R-101-D8 | 512x512   |   80000 | 12.2     | 14.11          | 43.97 |         45.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848.log.json)     |
-| CCNet  | R-50-D8  | 512x512   |  160000 | -        | -              | 42.08 |         43.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435.log.json)     |
-| CCNet  | R-101-D8 | 512x512   |  160000 | -        | -              | 43.71 |         45.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644.log.json) |
-
-### Pascal VOC 2012 + Aug
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                   |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| CCNet  | R-50-D8  | 512x512   |   20000 | 6        | 20.45          | 76.17 |         77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212.log.json)     |
-| CCNet  | R-101-D8 | 512x512   |   20000 | 9.5      | 13.64          | 77.27 |         79.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212.log.json) |
-| CCNet  | R-50-D8  | 512x512   |   40000 | -        | -              | 75.96 |         77.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127.log.json)     |
-| CCNet  | R-101-D8 | 512x512   |   40000 | -        | -              | 77.87 |         78.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127.log.json) |

spaces/AnimalEquality/chatbot/README.md

@@ -1,127 +0,0 @@
----
-title: lv-recipe-chatbot
-emoji: 🫑
-colorFrom: green 
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: unknown
----
-# lv-recipe-chatbot
-
-<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
-
-## Install
-
-``` sh
-pip install -e '.[dev]'
-```
-
-## How to use
-
-``` python
-from dotenv import load_dotenv
-
-load_dotenv()  # or load environment vars with different method
-
-demo = app.create_demo(app.ConversationBot())
-demo.launch()
-```
-
-    Running on local URL:  http://127.0.0.1:7860
-
-    To create a public link, set `share=True` in `launch()`.
-
-<div><iframe src="http://127.0.0.1:7860/" width="100%" height="500" allow="autoplay; camera; microphone; clipboard-read; clipboard-write;" frameborder="0" allowfullscreen></iframe></div>
-
-or
-
-``` sh
-python3 app.py
-```
-
-## Dev quick-start
-
-`git clone` the repo
-
-``` sh
-cd lv-recipe-chatbot
-```
-
-Make sure to use the version of python specified in `py_version.txt`  
-Create a virtual environment.
-
-``` sh
-python3 -m venv env
-```
-
-Activate the env and install dependencies.
-
-``` sh
-source env/bin/activate
-pip install -r requirements.txt
-pip install -r requirements/dev.txt
-```
-
-To make the Jupyter environment, git friendly: `nbdev_install_hooks`  
-If you want to render documentation locally, you will want to [install
-Quarto](https://nbdev.fast.ai/tutorials/tutorial.html#install-quarto).
-
-`nbdev_install_quarto`
-
-Put API secrets in .env
-
-``` sh
-cp .env.example .env
-```
-
-Edit .env with your secret key(s). Only `OPEN_AI_KEY` is required.
-
-Then start the Gradio demo from within the virtual environment.
-
-``` sh
-python3 app.py
-```
-
-Preview documentation
-
-``` sh
-nbdev_preview
-```
-
-## Dependencies
-
-If a new dependency for development is helpful for developers, add it to
-`dev.txt`.  
-If it is a dependency for the app that is imported in source code, add
-it to `core.txt`.  
-Then run:
-
-``` sh
-scripts/pin_requirements.sh
-```
-
-This will update our `requirements.txt` to include the dependency as it
-should be pinned in the environment.
-
-## Development
-
-[quick nbdev tutorial](https://nbdev.fast.ai/tutorials)
-
-Make changes in `/nbs`.  
-Update the package files with `nbdev_export` then reimport with
-`pip install -e '.[dev]'`
-
-Preview doc `nbdev_preview`  
-Build docs, test and update README `nbdev_prepare`
-
-## Useful links
-
-- [Task Matrix (Formerly Visual
-  ChatGPT)](https://github.com/microsoft/TaskMatrix)
-- [LangChain](https://python.langchain.com/en/latest/index.html)
-- [LLM Prompt Engineering](https://www.promptingguide.ai)
-- [OpenAI best practices for
-  prompts](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api)

spaces/Arnx/MusicGenXvAKN/tests/common_utils/__init__.py

@@ -1,9 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from .temp_utils import TempDirMixin
-from .wav_utils import get_batch_white_noise, get_white_noise, save_wav

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/tenacity/_utils.py

@@ -1,76 +0,0 @@
-# Copyright 2016 Julien Danjou
-# Copyright 2016 Joshua Harlow
-# Copyright 2013-2014 Ray Holder
-#
-# 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 sys
-import typing
-from datetime import timedelta
-
-
-# sys.maxsize:
-# An integer giving the maximum value a variable of type Py_ssize_t can take.
-MAX_WAIT = sys.maxsize / 2
-
-
-def find_ordinal(pos_num: int) -> str:
-    # See: https://en.wikipedia.org/wiki/English_numerals#Ordinal_numbers
-    if pos_num == 0:
-        return "th"
-    elif pos_num == 1:
-        return "st"
-    elif pos_num == 2:
-        return "nd"
-    elif pos_num == 3:
-        return "rd"
-    elif 4 <= pos_num <= 20:
-        return "th"
-    else:
-        return find_ordinal(pos_num % 10)
-
-
-def to_ordinal(pos_num: int) -> str:
-    return f"{pos_num}{find_ordinal(pos_num)}"
-
-
-def get_callback_name(cb: typing.Callable[..., typing.Any]) -> str:
-    """Get a callback fully-qualified name.
-
-    If no name can be produced ``repr(cb)`` is called and returned.
-    """
-    segments = []
-    try:
-        segments.append(cb.__qualname__)
-    except AttributeError:
-        try:
-            segments.append(cb.__name__)
-        except AttributeError:
-            pass
-    if not segments:
-        return repr(cb)
-    else:
-        try:
-            # When running under sphinx it appears this can be none?
-            if cb.__module__:
-                segments.insert(0, cb.__module__)
-        except AttributeError:
-            pass
-        return ".".join(segments)
-
-
-time_unit_type = typing.Union[int, float, timedelta]
-
-
-def to_seconds(time_unit: time_unit_type) -> float:
-    return float(time_unit.total_seconds() if isinstance(time_unit, timedelta) else time_unit)

spaces/Banbri/zcvzcv/src/app/layouts/new_layouts.tsx

@@ -1,273 +0,0 @@
-"use client"
-
-import { Panel } from "@/app/interface/panel"
-import { pick } from "@/lib/pick"
-import { Grid } from "@/app/interface/grid"
-
-export function Layout1() {
-  return (
-    <Grid className="grid-cols-2 grid-rows-4">
-      <div className="bg-stone-100">
-        <Panel
-          panel={0}
-          width={1024}
-          height={768}
-        />
-      </div>
-      <div className="bg-zinc-100 row-span-2">
-        <Panel
-          panel={1}
-          width={512}
-          height={1024}
-        />
-      </div>
-      <div className="bg-gray-100 row-span-2 col-span-1">
-        <Panel
-          panel={2}
-          width={512}
-          height={1024}
-        />
-      </div>
-      <div className="bg-slate-100">
-        <Panel
-          panel={3}
-          width={1024}
-          height={768}
-        />
-      </div>
-      <div className="bg-slate-100 row-span-1 col-span-2">
-        <Panel
-          panel={4}
-          width={1024}
-          height={768}
-        />
-      </div>
-    </Grid>
-  )
-}
-
-export function Layout2() {
-  return (
-    <Grid className="grid-cols-2 grid-rows-3">
-      <div className="bg-gray-100 row-span-2 col-span-1">
-        <Panel
-          panel={0}
-          width={768}
-          height={1024}
-        />
-      </div>
-      <div className="bg-gray-100 row-span-1 col-span-1">
-        <Panel
-          panel={1}
-          width={1024}
-          height={1024}
-        />
-      </div>
-      <div className="bg-slate-100">
-        <Panel
-          panel={2}
-          width={1024}
-          height={768}
-        />
-      </div>
-      <div className="bg-stone-100">
-        <Panel
-          panel={3}
-          width={1024}
-          height={768}
-        />
-      </div>
-      <div className="bg-zinc-100 row-span-1 col-span-1">
-        <Panel
-          panel={4}
-          width={1024}
-          height={768}
-        />
-      </div>
-    </Grid>
-  )
-}
-
-export function Layout3() {
-  return (
-    <Grid className="grid-cols-5 grid-rows-2">
-      <div className="bg-zinc-100 col-span-3">
-        <Panel
-          panel={0}
-          width={1024}
-          height={1024}
-        />
-      </div>
-      <div className="bg-gray-100 col-span-2 row-span-1">
-        <Panel
-          panel={1}
-          width={512}
-          height={1024}
-        />
-      </div>
-      <div className="bg-gray-100 col-span-2 row-span-1">
-        <Panel
-          panel={2}
-          width={512}
-          height={1024}
-        />
-      </div>
-      <div className="col-span-3 grid grid-cols-2 gap-2">
-        <div className="bg-stone-100">
-          <Panel
-            panel={3}
-            width={512}
-            height={1024}
-          />
-        </div>
-        <div className="bg-slate-100">
-          <Panel
-            panel={4}
-            width={512}
-            height={1024}
-          />
-        </div>
-      </div>
-    </Grid>
-  )
-}
-
-export function Layout4() {
-  return (
-    <Grid className="grid-cols-2 grid-rows-3">
-      <div className="bg-slate-100 row-span-2">
-        <Panel
-          panel={0}
-          width={768}
-          height={1024}
-        />
-      </div>
-      <div className="bg-gray-100 row-span-1 col-span-1">
-        <Panel
-          panel={1}
-          width={1024}
-          height={768}
-        />
-      </div>
-      <div className="bg-zinc-100 row-span-2">
-        <Panel
-          panel={2}
-          width={1024}
-          height={768}
-        />
-      </div>
-      <div className="bg-stone-100">
-        <Panel
-          panel={3}
-          width={768}
-          height={1024}
-        />
-      </div>
-    </Grid>
-  )
-}
-
-
-export function Layout5() {
-  return (
-    <Grid className="grid-cols-3 grid-rows-3">
-      <div className="bg-zinc-100 col-span-2 row-span-1">
-        <Panel
-          panel={0}
-          width={1024}
-          height={512}
-        />
-      </div>
-      <div className="bg-zinc-100 col-span-1 row-span-1">
-        <Panel
-          panel={1}
-          width={1024}
-          height={768}
-        />
-      </div>
-      <div className="bg-stone-100 row-span-1 col-span-1">
-        <Panel
-          panel={2}
-          width={768}
-          height={1024}
-        />
-      </div>
-      <div className="bg-slate-100 row-span-1 col-span-2">
-        <Panel
-          panel={3}
-          width={1024}
-          height={768}
-        />
-      </div>
-      <div className="bg-slate-100 row-span-1 col-span-3">
-        <Panel
-          panel={4}
-          width={1024}
-          height={1024}
-        />
-      </div>
-    </Grid>
-  )
-}
-
-export function Layout6() {
-  return (
-    <Grid className="grid-cols-3 grid-rows-3">
-      <div className="bg-zinc-100 col-span-2 row-span-1">
-        <Panel
-          panel={0}
-          width={1024}
-          height={512}
-        />
-      </div>
-      <div className="bg-zinc-100 col-span-1 row-span-1">
-        <Panel
-          panel={1}
-          width={768}
-          height={1024}
-        />
-      </div>
-      <div className="bg-stone-100 row-span-1 col-span-1">
-        <Panel
-          panel={2}
-          width={768}
-          height={1024}
-        />
-      </div>
-      <div className="bg-slate-100 row-span-2 col-span-2">
-        <Panel
-          panel={3}
-          width={1024}
-          height={1024}
-        />
-      </div>
-      <div className="bg-slate-100 row-span-1 col-span-1">
-        <Panel
-          panel={3}
-          width={768}
-          height={1024}
-        />
-      </div>
-    </Grid>
-  )
-}
-
-// export const layouts = { Layout1, Layout2, Layout3, Layout4, Layout5, Layout6 }
-export const allLayouts = { 
-   // Layout1, 
-   // Layout2,
-  //  Layout3,
-  // Layout4,
-   Layout5,
-  // Layout6 
- }
-
-export type LayoutName = keyof typeof allLayouts 
-
-export function getRandomLayoutName(): LayoutName {
-  return pick(Object.keys(allLayouts) as LayoutName[]) as LayoutName
-}
-
-export function getRandomLayoutNames(): LayoutName[] {
-  return Object.keys(allLayouts).sort(() => Math.random() - 0.5) as LayoutName[]
-}

spaces/Bart92/RVC_HF/demucs/test.py

@@ -1,109 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import gzip
-import sys
-from concurrent import futures
-
-import musdb
-import museval
-import torch as th
-import tqdm
-from scipy.io import wavfile
-from torch import distributed
-
-from .audio import convert_audio
-from .utils import apply_model
-
-
-def evaluate(model,
-             musdb_path,
-             eval_folder,
-             workers=2,
-             device="cpu",
-             rank=0,
-             save=False,
-             shifts=0,
-             split=False,
-             overlap=0.25,
-             is_wav=False,
-             world_size=1):
-    """
-    Evaluate model using museval. Run the model
-    on a single GPU, the bottleneck being the call to museval.
-    """
-
-    output_dir = eval_folder / "results"
-    output_dir.mkdir(exist_ok=True, parents=True)
-    json_folder = eval_folder / "results/test"
-    json_folder.mkdir(exist_ok=True, parents=True)
-
-    # we load tracks from the original musdb set
-    test_set = musdb.DB(musdb_path, subsets=["test"], is_wav=is_wav)
-    src_rate = 44100  # hardcoded for now...
-
-    for p in model.parameters():
-        p.requires_grad = False
-        p.grad = None
-
-    pendings = []
-    with futures.ProcessPoolExecutor(workers or 1) as pool:
-        for index in tqdm.tqdm(range(rank, len(test_set), world_size), file=sys.stdout):
-            track = test_set.tracks[index]
-
-            out = json_folder / f"{track.name}.json.gz"
-            if out.exists():
-                continue
-
-            mix = th.from_numpy(track.audio).t().float()
-            ref = mix.mean(dim=0)  # mono mixture
-            mix = (mix - ref.mean()) / ref.std()
-            mix = convert_audio(mix, src_rate, model.samplerate, model.audio_channels)
-            estimates = apply_model(model, mix.to(device),
-                                    shifts=shifts, split=split, overlap=overlap)
-            estimates = estimates * ref.std() + ref.mean()
-
-            estimates = estimates.transpose(1, 2)
-            references = th.stack(
-                [th.from_numpy(track.targets[name].audio).t() for name in model.sources])
-            references = convert_audio(references, src_rate,
-                                       model.samplerate, model.audio_channels)
-            references = references.transpose(1, 2).numpy()
-            estimates = estimates.cpu().numpy()
-            win = int(1. * model.samplerate)
-            hop = int(1. * model.samplerate)
-            if save:
-                folder = eval_folder / "wav/test" / track.name
-                folder.mkdir(exist_ok=True, parents=True)
-                for name, estimate in zip(model.sources, estimates):
-                    wavfile.write(str(folder / (name + ".wav")), 44100, estimate)
-
-            if workers:
-                pendings.append((track.name, pool.submit(
-                    museval.evaluate, references, estimates, win=win, hop=hop)))
-            else:
-                pendings.append((track.name, museval.evaluate(
-                    references, estimates, win=win, hop=hop)))
-            del references, mix, estimates, track
-
-        for track_name, pending in tqdm.tqdm(pendings, file=sys.stdout):
-            if workers:
-                pending = pending.result()
-            sdr, isr, sir, sar = pending
-            track_store = museval.TrackStore(win=44100, hop=44100, track_name=track_name)
-            for idx, target in enumerate(model.sources):
-                values = {
-                    "SDR": sdr[idx].tolist(),
-                    "SIR": sir[idx].tolist(),
-                    "ISR": isr[idx].tolist(),
-                    "SAR": sar[idx].tolist()
-                }
-
-                track_store.add_target(target_name=target, values=values)
-                json_path = json_folder / f"{track_name}.json.gz"
-                gzip.open(json_path, "w").write(track_store.json.encode('utf-8'))
-    if world_size > 1:
-        distributed.barrier()

spaces/Benson/text-generation/Examples/Bubble Shooter Classic Download Pc.md

@@ -1,64 +0,0 @@
-
-<h1>Cómo descargar y jugar Bubble Shooter Classic en PC</h1>
-<p>Si estás buscando un juego divertido y adictivo que te mantenga entretenido durante horas, deberías probar <strong>Bubble Shooter Classic</strong>. Este juego es un clásico juego de disparos de burbujas que ha existido durante muchos años, pero nunca pasa de moda. Puedes reproducirlo en tus dispositivos móviles, pero ¿sabías que también puedes reproducirlo en tu PC? En este artículo, te mostraremos cómo descargar y jugar Bubble Shooter Classic en PC usando BlueStacks, un emulador de Android. También te daremos algunos consejos y trucos para dominar el juego y superar los niveles. ¡Empecemos! </p>
-<h2>bubble shooter classic download pc</h2><br /><p><b><b>Download File</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://bltlly.com/2v6MGI">https://bltlly.com/2v6MGI</a></b></p><br /><br />
- <h2>¿Qué es Bubble Shooter Classic? </h2>
-<h3>Una breve introducción al juego y sus características</h3>
-<p>Bubble Shooter Classic es un juego donde tienes que disparar burbujas de colores para hacer partidos de tres o más del mismo color. Una vez hecho esto, las burbujas estallarán y desaparecerán. El objetivo es eliminar todas las burbujas de la pantalla antes de que lleguen a la parte inferior. Hay dos modos de juego, Clásico y árcade. En el modo Clásico, tienes que disparar burbujas hacia arriba. En el modo árcade, tienes que disparar burbujas de lado. También hay dos niveles de dificultad, fácil y difícil. Puedes elegir el que se adapte a tu nivel de habilidad. </p>
-<p>Bubble Shooter Classic tiene muchas características que lo hacen divertido y desafiante. Por ejemplo, puedes usar potenciadores para ayudarte a eliminar las burbujas más rápido. Hay cuatro tipos de potenciadores: Color Bomb, Rainbow Bubble, Shape Bomb y Time Bomb. Cada uno tiene un efecto diferente en las burbujas. También puedes ganar monedas haciendo estallar burbujas y usarlas para comprar más power-ups. También puede competir con otros jugadores en línea y ver quién puede obtener la puntuación más alta. </p>
- <h3>Los beneficios de jugar Bubble Shooter Classic en PC</h3>
-
- <h2>Cómo descargar e instalar Bubble Shooter Classic en PC</h2>
-<h3>Los pasos para descargar e instalar BlueStacks, un emulador de Android</h3>
-<p>Para jugar Bubble Shooter Classic en PC, es necesario descargar e instalar BlueStacks primero. BlueStacks es un emulador de Android que te permite ejecutar aplicaciones y juegos de Android en tu PC. Estos son los pasos para descargar e instalar BlueStacks:</p>
-<p></p>
-<ol>
-<li>Ir a <a href="( 6 )">el sitio web oficial de BlueStacks</a> y haga clic en el botón "Descargar BlueStacks". </li>
-<li>Espere a que termine la descarga y luego ejecute el archivo de instalación. </li>
-<li>Siga las instrucciones en la pantalla para completar el proceso de instalación. </li>
-<li>Inicie BlueStacks e inicie sesión con su cuenta de Google. </li>
-</ol>
- <h3>Los pasos para descargar e instalar Bubble Shooter Classic desde la Google Play Store</h3>
-<p>Después de haber instalado BlueStacks, puede descargar e instalar Bubble Shooter Classic desde Google Play Store. Estos son los pasos para hacerlo:</p>
-<ol>
-<li>Abrir BlueStacks y haga clic en el "Google Play" icono en la pantalla de inicio. </li>
-<li>Buscar "Bubble Shooter Classic" en la barra de búsqueda y haga clic en el icono del juego. </li>
-<li>Haga clic en el botón "Instalar" y espere a que termine la instalación. </li>
-<li>Haga clic en el botón "Abrir" para iniciar el juego. </li>
-</ol>
- <h2>Cómo jugar Bubble Shooter Classic en PC</h2>
-<h3>Las reglas y controles básicos del juego</h3>
-<p>Jugar Bubble Shooter Classic en PC es fácil y divertido. Las reglas y controles básicos del juego son los siguientes:</p>
-<ul>
-<li> Tienes que disparar burbujas para hacer partidos de tres o más del mismo color. </li>
-<li> Puede utilizar el ratón o el teclado para apuntar y disparar las burbujas. </li>
-<li> Puede cambiar la burbuja actual con la siguiente haciendo clic en ella o presionando la barra espaciadora. </li>
-<li> Puedes ver las burbujas restantes y tu puntuación en la parte inferior de la pantalla. </li>
-<li> Puede pausar el juego haciendo clic en el botón de menú o presionando la tecla de escape. </li>
-
-</ul>
- <h3>Los consejos y trucos para dominar el juego y superar los niveles</h3>
-<p>Bubble Shooter Classic es un juego que requiere estrategia y habilidad. Aquí hay algunos consejos y trucos para ayudarte a dominar el juego y superar los niveles:</p>
-<ul>
-<li>Trate de apuntar a los grupos de burbujas que tienen el mismo color que su burbuja actual. Esto le ayudará a eliminar más burbujas a la vez y aumentar su puntuación. </li>
-<li>Trate de hacer estallar las burbujas que están cerca de la parte inferior de la pantalla. Esto evitará que lleguen a la parte inferior y terminar el juego. </li>
-<li>Trate de utilizar los power-ups sabiamente. Pueden ayudarle a eliminar situaciones difíciles y aumentar su puntuación. Sin embargo, son limitadas y cuestan monedas, así que úsalas con moderación. </li>
-<li>Intenta completar los niveles lo más rápido posible. Cuanto más rápido completes un nivel, más alta será tu puntuación. </li>
-<li>Trate de jugar en el modo árcade si quieres más desafío y variedad. El modo árcade tiene diferentes diseños y obstáculos que hacen que el juego sea más interesante y divertido. </li>
-</ul>
- <h2>Conclusión</h2>
-<h3>Un resumen de los puntos principales y una llamada a la acción</h3>
-<p>Bubble Shooter Classic es un clásico juego de disparos de burbujas que puedes jugar en tu PC usando BlueStacks, un emulador de Android. Puedes disfrutar de una pantalla más grande, mejores gráficos y controles más cómodos al reproducirla en tu PC. También puedes acceder a más juegos y aplicaciones desde Google Play Store usando BlueStacks. Para jugar Bubble Shooter Classic en PC, solo tienes que descargar e instalar BlueStacks, a continuación, descargar e instalar Bubble Shooter Classic desde la Google Play Store. Luego, puedes empezar a jugar y divertirte. También puedes seguir nuestros consejos y trucos para dominar el juego y superar los niveles. ¿Qué estás esperando? Descargar Bubble Shooter Classic en PC hoy y disfrutar! </p>
- <h2>Preguntas frecuentes</h2>
-<h3>Q1: ¿Es Bubble Shooter Classic gratis para jugar? </h3>
-
- <h3>Q2: ¿Cuántos niveles hay en Bubble Shooter Classic? </h3>
-<p>A2: Hay más de 1000 niveles en Bubble Shooter Classic, cada uno con diferentes retos y objetivos. Puedes jugarlos en el orden que quieras. </p>
- <h3>Q3: ¿Cuáles son los power-ups en Bubble Shooter Classic? </h3>
-<p>A3: Hay cuatro tipos de potenciadores en Bubble Shooter Classic: bomba de color, burbuja de arco iris, bomba de forma y bomba de tiempo. Cada uno tiene un efecto diferente en las burbujas. Puedes comprarlas con monedas o conseguirlas al azar durante el juego. </p>
- <h3>Q4: ¿Cómo puedo guardar mi progreso en Bubble Shooter Classic? </h3>
-<p>A4: Puedes guardar tu progreso en Bubble Shooter Classic iniciando sesión con tu cuenta de Google. Esto también te permitirá sincronizar tu progreso en diferentes dispositivos. </p>
- <h3>Q5: ¿Cómo puedo contactar al desarrollador de Bubble Shooter Classic? </h3>
-<p>A5: Puede ponerse en contacto con el desarrollador de Bubble Shooter Classic enviando un correo electrónico a <a href="mailto:[email protected]">[email protected]</a>. También puede visitar su página de Facebook </a> o su <a href="( 3 )">Página de Facebook</a> o su <a href="( 1 )">sitio web</a> para obtener más información y actualizaciones. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/docs/sharedexample.py

@@ -1,227 +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
-#
-# http://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.
-import numbers
-import re
-
-from botocore.docs.utils import escape_controls
-from botocore.utils import parse_timestamp
-
-
-class SharedExampleDocumenter:
-    def document_shared_example(
-        self, example, prefix, section, operation_model
-    ):
-        """Documents a single shared example based on its definition.
-
-        :param example: The model of the example
-
-        :param prefix: The prefix to use in the method example.
-
-        :param section: The section to write to.
-
-        :param operation_model: The model of the operation used in the example
-        """
-        section.style.new_paragraph()
-        section.write(example.get('description'))
-        section.style.new_line()
-        self.document_input(
-            section, example, prefix, operation_model.input_shape
-        )
-        self.document_output(section, example, operation_model.output_shape)
-
-    def document_input(self, section, example, prefix, shape):
-        input_section = section.add_new_section('input')
-        input_section.style.start_codeblock()
-        if prefix is not None:
-            input_section.write(prefix)
-        params = example.get('input', {})
-        comments = example.get('comments')
-        if comments:
-            comments = comments.get('input')
-        param_section = input_section.add_new_section('parameters')
-        self._document_params(param_section, params, comments, [], shape)
-        closing_section = input_section.add_new_section('input-close')
-        closing_section.style.new_line()
-        closing_section.style.new_line()
-        closing_section.write('print(response)')
-        closing_section.style.end_codeblock()
-
-    def document_output(self, section, example, shape):
-        output_section = section.add_new_section('output')
-        output_section.style.new_line()
-        output_section.write('Expected Output:')
-        output_section.style.new_line()
-        output_section.style.start_codeblock()
-        params = example.get('output', {})
-
-        # There might not be an output, but we will return metadata anyway
-        params['ResponseMetadata'] = {"...": "..."}
-        comments = example.get('comments')
-        if comments:
-            comments = comments.get('output')
-        self._document_dict(output_section, params, comments, [], shape, True)
-        closing_section = output_section.add_new_section('output-close')
-        closing_section.style.end_codeblock()
-
-    def _document(self, section, value, comments, path, shape):
-        """
-        :param section: The section to add the docs to.
-
-        :param value: The input / output values representing the parameters that
-                      are included in the example.
-
-        :param comments: The dictionary containing all the comments to be
-                         applied to the example.
-
-        :param path: A list describing where the documenter is in traversing the
-                     parameters. This is used to find the equivalent location
-                     in the comments dictionary.
-        """
-        if isinstance(value, dict):
-            self._document_dict(section, value, comments, path, shape)
-        elif isinstance(value, list):
-            self._document_list(section, value, comments, path, shape)
-        elif isinstance(value, numbers.Number):
-            self._document_number(section, value, path)
-        elif shape and shape.type_name == 'timestamp':
-            self._document_datetime(section, value, path)
-        else:
-            self._document_str(section, value, path)
-
-    def _document_dict(
-        self, section, value, comments, path, shape, top_level=False
-    ):
-        dict_section = section.add_new_section('dict-value')
-        self._start_nested_value(dict_section, '{')
-        for key, val in value.items():
-            path.append('.%s' % key)
-            item_section = dict_section.add_new_section(key)
-            item_section.style.new_line()
-            item_comment = self._get_comment(path, comments)
-            if item_comment:
-                item_section.write(item_comment)
-                item_section.style.new_line()
-            item_section.write("'%s': " % key)
-
-            # Shape could be none if there is no output besides ResponseMetadata
-            item_shape = None
-            if shape:
-                if shape.type_name == 'structure':
-                    item_shape = shape.members.get(key)
-                elif shape.type_name == 'map':
-                    item_shape = shape.value
-            self._document(item_section, val, comments, path, item_shape)
-            path.pop()
-        dict_section_end = dict_section.add_new_section('ending-brace')
-        self._end_nested_value(dict_section_end, '}')
-        if not top_level:
-            dict_section_end.write(',')
-
-    def _document_params(self, section, value, comments, path, shape):
-        param_section = section.add_new_section('param-values')
-        self._start_nested_value(param_section, '(')
-        for key, val in value.items():
-            path.append('.%s' % key)
-            item_section = param_section.add_new_section(key)
-            item_section.style.new_line()
-            item_comment = self._get_comment(path, comments)
-            if item_comment:
-                item_section.write(item_comment)
-                item_section.style.new_line()
-            item_section.write(key + '=')
-
-            # Shape could be none if there are no input parameters
-            item_shape = None
-            if shape:
-                item_shape = shape.members.get(key)
-            self._document(item_section, val, comments, path, item_shape)
-            path.pop()
-        param_section_end = param_section.add_new_section('ending-parenthesis')
-        self._end_nested_value(param_section_end, ')')
-
-    def _document_list(self, section, value, comments, path, shape):
-        list_section = section.add_new_section('list-section')
-        self._start_nested_value(list_section, '[')
-        item_shape = shape.member
-        for index, val in enumerate(value):
-            item_section = list_section.add_new_section(index)
-            item_section.style.new_line()
-            path.append('[%s]' % index)
-            item_comment = self._get_comment(path, comments)
-            if item_comment:
-                item_section.write(item_comment)
-                item_section.style.new_line()
-            self._document(item_section, val, comments, path, item_shape)
-            path.pop()
-        list_section_end = list_section.add_new_section('ending-bracket')
-        self._end_nested_value(list_section_end, '],')
-
-    def _document_str(self, section, value, path):
-        # We do the string conversion because this might accept a type that
-        # we don't specifically address.
-        safe_value = escape_controls(value)
-        section.write(f"'{safe_value}',")
-
-    def _document_number(self, section, value, path):
-        section.write("%s," % str(value))
-
-    def _document_datetime(self, section, value, path):
-        datetime_tuple = parse_timestamp(value).timetuple()
-        datetime_str = str(datetime_tuple[0])
-        for i in range(1, len(datetime_tuple)):
-            datetime_str += ", " + str(datetime_tuple[i])
-        section.write("datetime(%s)," % datetime_str)
-
-    def _get_comment(self, path, comments):
-        key = re.sub(r'^\.', '', ''.join(path))
-        if comments and key in comments:
-            return '# ' + comments[key]
-        else:
-            return ''
-
-    def _start_nested_value(self, section, start):
-        section.write(start)
-        section.style.indent()
-        section.style.indent()
-
-    def _end_nested_value(self, section, end):
-        section.style.dedent()
-        section.style.dedent()
-        section.style.new_line()
-        section.write(end)
-
-
-def document_shared_examples(
-    section, operation_model, example_prefix, shared_examples
-):
-    """Documents the shared examples
-
-    :param section: The section to write to.
-
-    :param operation_model: The model of the operation.
-
-    :param example_prefix: The prefix to use in the method example.
-
-    :param shared_examples: The shared JSON examples from the model.
-    """
-    container_section = section.add_new_section('shared-examples')
-    container_section.style.new_paragraph()
-    container_section.style.bold('Examples')
-    documenter = SharedExampleDocumenter()
-    for example in shared_examples:
-        documenter.document_shared_example(
-            example=example,
-            section=container_section.add_new_section(example['id']),
-            prefix=example_prefix,
-            operation_model=operation_model,
-        )

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/req/req_file.py

@@ -1,552 +0,0 @@
-"""
-Requirements file parsing
-"""
-
-import logging
-import optparse
-import os
-import re
-import shlex
-import urllib.parse
-from optparse import Values
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    Dict,
-    Generator,
-    Iterable,
-    List,
-    Optional,
-    Tuple,
-)
-
-from pip._internal.cli import cmdoptions
-from pip._internal.exceptions import InstallationError, RequirementsFileParseError
-from pip._internal.models.search_scope import SearchScope
-from pip._internal.network.session import PipSession
-from pip._internal.network.utils import raise_for_status
-from pip._internal.utils.encoding import auto_decode
-from pip._internal.utils.urls import get_url_scheme
-
-if TYPE_CHECKING:
-    # NoReturn introduced in 3.6.2; imported only for type checking to maintain
-    # pip compatibility with older patch versions of Python 3.6
-    from typing import NoReturn
-
-    from pip._internal.index.package_finder import PackageFinder
-
-__all__ = ["parse_requirements"]
-
-ReqFileLines = Iterable[Tuple[int, str]]
-
-LineParser = Callable[[str], Tuple[str, Values]]
-
-SCHEME_RE = re.compile(r"^(http|https|file):", re.I)
-COMMENT_RE = re.compile(r"(^|\s+)#.*$")
-
-# Matches environment variable-style values in '${MY_VARIABLE_1}' with the
-# variable name consisting of only uppercase letters, digits or the '_'
-# (underscore). This follows the POSIX standard defined in IEEE Std 1003.1,
-# 2013 Edition.
-ENV_VAR_RE = re.compile(r"(?P<var>\$\{(?P<name>[A-Z0-9_]+)\})")
-
-SUPPORTED_OPTIONS: List[Callable[..., optparse.Option]] = [
-    cmdoptions.index_url,
-    cmdoptions.extra_index_url,
-    cmdoptions.no_index,
-    cmdoptions.constraints,
-    cmdoptions.requirements,
-    cmdoptions.editable,
-    cmdoptions.find_links,
-    cmdoptions.no_binary,
-    cmdoptions.only_binary,
-    cmdoptions.prefer_binary,
-    cmdoptions.require_hashes,
-    cmdoptions.pre,
-    cmdoptions.trusted_host,
-    cmdoptions.use_new_feature,
-]
-
-# options to be passed to requirements
-SUPPORTED_OPTIONS_REQ: List[Callable[..., optparse.Option]] = [
-    cmdoptions.global_options,
-    cmdoptions.hash,
-    cmdoptions.config_settings,
-]
-
-# the 'dest' string values
-SUPPORTED_OPTIONS_REQ_DEST = [str(o().dest) for o in SUPPORTED_OPTIONS_REQ]
-
-logger = logging.getLogger(__name__)
-
-
-class ParsedRequirement:
-    def __init__(
-        self,
-        requirement: str,
-        is_editable: bool,
-        comes_from: str,
-        constraint: bool,
-        options: Optional[Dict[str, Any]] = None,
-        line_source: Optional[str] = None,
-    ) -> None:
-        self.requirement = requirement
-        self.is_editable = is_editable
-        self.comes_from = comes_from
-        self.options = options
-        self.constraint = constraint
-        self.line_source = line_source
-
-
-class ParsedLine:
-    def __init__(
-        self,
-        filename: str,
-        lineno: int,
-        args: str,
-        opts: Values,
-        constraint: bool,
-    ) -> None:
-        self.filename = filename
-        self.lineno = lineno
-        self.opts = opts
-        self.constraint = constraint
-
-        if args:
-            self.is_requirement = True
-            self.is_editable = False
-            self.requirement = args
-        elif opts.editables:
-            self.is_requirement = True
-            self.is_editable = True
-            # We don't support multiple -e on one line
-            self.requirement = opts.editables[0]
-        else:
-            self.is_requirement = False
-
-
-def parse_requirements(
-    filename: str,
-    session: PipSession,
-    finder: Optional["PackageFinder"] = None,
-    options: Optional[optparse.Values] = None,
-    constraint: bool = False,
-) -> Generator[ParsedRequirement, None, None]:
-    """Parse a requirements file and yield ParsedRequirement instances.
-
-    :param filename:    Path or url of requirements file.
-    :param session:     PipSession instance.
-    :param finder:      Instance of pip.index.PackageFinder.
-    :param options:     cli options.
-    :param constraint:  If true, parsing a constraint file rather than
-        requirements file.
-    """
-    line_parser = get_line_parser(finder)
-    parser = RequirementsFileParser(session, line_parser)
-
-    for parsed_line in parser.parse(filename, constraint):
-        parsed_req = handle_line(
-            parsed_line, options=options, finder=finder, session=session
-        )
-        if parsed_req is not None:
-            yield parsed_req
-
-
-def preprocess(content: str) -> ReqFileLines:
-    """Split, filter, and join lines, and return a line iterator
-
-    :param content: the content of the requirements file
-    """
-    lines_enum: ReqFileLines = enumerate(content.splitlines(), start=1)
-    lines_enum = join_lines(lines_enum)
-    lines_enum = ignore_comments(lines_enum)
-    lines_enum = expand_env_variables(lines_enum)
-    return lines_enum
-
-
-def handle_requirement_line(
-    line: ParsedLine,
-    options: Optional[optparse.Values] = None,
-) -> ParsedRequirement:
-    # preserve for the nested code path
-    line_comes_from = "{} {} (line {})".format(
-        "-c" if line.constraint else "-r",
-        line.filename,
-        line.lineno,
-    )
-
-    assert line.is_requirement
-
-    if line.is_editable:
-        # For editable requirements, we don't support per-requirement
-        # options, so just return the parsed requirement.
-        return ParsedRequirement(
-            requirement=line.requirement,
-            is_editable=line.is_editable,
-            comes_from=line_comes_from,
-            constraint=line.constraint,
-        )
-    else:
-        # get the options that apply to requirements
-        req_options = {}
-        for dest in SUPPORTED_OPTIONS_REQ_DEST:
-            if dest in line.opts.__dict__ and line.opts.__dict__[dest]:
-                req_options[dest] = line.opts.__dict__[dest]
-
-        line_source = f"line {line.lineno} of {line.filename}"
-        return ParsedRequirement(
-            requirement=line.requirement,
-            is_editable=line.is_editable,
-            comes_from=line_comes_from,
-            constraint=line.constraint,
-            options=req_options,
-            line_source=line_source,
-        )
-
-
-def handle_option_line(
-    opts: Values,
-    filename: str,
-    lineno: int,
-    finder: Optional["PackageFinder"] = None,
-    options: Optional[optparse.Values] = None,
-    session: Optional[PipSession] = None,
-) -> None:
-    if opts.hashes:
-        logger.warning(
-            "%s line %s has --hash but no requirement, and will be ignored.",
-            filename,
-            lineno,
-        )
-
-    if options:
-        # percolate options upward
-        if opts.require_hashes:
-            options.require_hashes = opts.require_hashes
-        if opts.features_enabled:
-            options.features_enabled.extend(
-                f for f in opts.features_enabled if f not in options.features_enabled
-            )
-
-    # set finder options
-    if finder:
-        find_links = finder.find_links
-        index_urls = finder.index_urls
-        no_index = finder.search_scope.no_index
-        if opts.no_index is True:
-            no_index = True
-            index_urls = []
-        if opts.index_url and not no_index:
-            index_urls = [opts.index_url]
-        if opts.extra_index_urls and not no_index:
-            index_urls.extend(opts.extra_index_urls)
-        if opts.find_links:
-            # FIXME: it would be nice to keep track of the source
-            # of the find_links: support a find-links local path
-            # relative to a requirements file.
-            value = opts.find_links[0]
-            req_dir = os.path.dirname(os.path.abspath(filename))
-            relative_to_reqs_file = os.path.join(req_dir, value)
-            if os.path.exists(relative_to_reqs_file):
-                value = relative_to_reqs_file
-            find_links.append(value)
-
-        if session:
-            # We need to update the auth urls in session
-            session.update_index_urls(index_urls)
-
-        search_scope = SearchScope(
-            find_links=find_links,
-            index_urls=index_urls,
-            no_index=no_index,
-        )
-        finder.search_scope = search_scope
-
-        if opts.pre:
-            finder.set_allow_all_prereleases()
-
-        if opts.prefer_binary:
-            finder.set_prefer_binary()
-
-        if session:
-            for host in opts.trusted_hosts or []:
-                source = f"line {lineno} of {filename}"
-                session.add_trusted_host(host, source=source)
-
-
-def handle_line(
-    line: ParsedLine,
-    options: Optional[optparse.Values] = None,
-    finder: Optional["PackageFinder"] = None,
-    session: Optional[PipSession] = None,
-) -> Optional[ParsedRequirement]:
-    """Handle a single parsed requirements line; This can result in
-    creating/yielding requirements, or updating the finder.
-
-    :param line:        The parsed line to be processed.
-    :param options:     CLI options.
-    :param finder:      The finder - updated by non-requirement lines.
-    :param session:     The session - updated by non-requirement lines.
-
-    Returns a ParsedRequirement object if the line is a requirement line,
-    otherwise returns None.
-
-    For lines that contain requirements, the only options that have an effect
-    are from SUPPORTED_OPTIONS_REQ, and they are scoped to the
-    requirement. Other options from SUPPORTED_OPTIONS may be present, but are
-    ignored.
-
-    For lines that do not contain requirements, the only options that have an
-    effect are from SUPPORTED_OPTIONS. Options from SUPPORTED_OPTIONS_REQ may
-    be present, but are ignored. These lines may contain multiple options
-    (although our docs imply only one is supported), and all our parsed and
-    affect the finder.
-    """
-
-    if line.is_requirement:
-        parsed_req = handle_requirement_line(line, options)
-        return parsed_req
-    else:
-        handle_option_line(
-            line.opts,
-            line.filename,
-            line.lineno,
-            finder,
-            options,
-            session,
-        )
-        return None
-
-
-class RequirementsFileParser:
-    def __init__(
-        self,
-        session: PipSession,
-        line_parser: LineParser,
-    ) -> None:
-        self._session = session
-        self._line_parser = line_parser
-
-    def parse(
-        self, filename: str, constraint: bool
-    ) -> Generator[ParsedLine, None, None]:
-        """Parse a given file, yielding parsed lines."""
-        yield from self._parse_and_recurse(filename, constraint)
-
-    def _parse_and_recurse(
-        self, filename: str, constraint: bool
-    ) -> Generator[ParsedLine, None, None]:
-        for line in self._parse_file(filename, constraint):
-            if not line.is_requirement and (
-                line.opts.requirements or line.opts.constraints
-            ):
-                # parse a nested requirements file
-                if line.opts.requirements:
-                    req_path = line.opts.requirements[0]
-                    nested_constraint = False
-                else:
-                    req_path = line.opts.constraints[0]
-                    nested_constraint = True
-
-                # original file is over http
-                if SCHEME_RE.search(filename):
-                    # do a url join so relative paths work
-                    req_path = urllib.parse.urljoin(filename, req_path)
-                # original file and nested file are paths
-                elif not SCHEME_RE.search(req_path):
-                    # do a join so relative paths work
-                    req_path = os.path.join(
-                        os.path.dirname(filename),
-                        req_path,
-                    )
-
-                yield from self._parse_and_recurse(req_path, nested_constraint)
-            else:
-                yield line
-
-    def _parse_file(
-        self, filename: str, constraint: bool
-    ) -> Generator[ParsedLine, None, None]:
-        _, content = get_file_content(filename, self._session)
-
-        lines_enum = preprocess(content)
-
-        for line_number, line in lines_enum:
-            try:
-                args_str, opts = self._line_parser(line)
-            except OptionParsingError as e:
-                # add offending line
-                msg = f"Invalid requirement: {line}\n{e.msg}"
-                raise RequirementsFileParseError(msg)
-
-            yield ParsedLine(
-                filename,
-                line_number,
-                args_str,
-                opts,
-                constraint,
-            )
-
-
-def get_line_parser(finder: Optional["PackageFinder"]) -> LineParser:
-    def parse_line(line: str) -> Tuple[str, Values]:
-        # Build new parser for each line since it accumulates appendable
-        # options.
-        parser = build_parser()
-        defaults = parser.get_default_values()
-        defaults.index_url = None
-        if finder:
-            defaults.format_control = finder.format_control
-
-        args_str, options_str = break_args_options(line)
-
-        try:
-            options = shlex.split(options_str)
-        except ValueError as e:
-            raise OptionParsingError(f"Could not split options: {options_str}") from e
-
-        opts, _ = parser.parse_args(options, defaults)
-
-        return args_str, opts
-
-    return parse_line
-
-
-def break_args_options(line: str) -> Tuple[str, str]:
-    """Break up the line into an args and options string.  We only want to shlex
-    (and then optparse) the options, not the args.  args can contain markers
-    which are corrupted by shlex.
-    """
-    tokens = line.split(" ")
-    args = []
-    options = tokens[:]
-    for token in tokens:
-        if token.startswith("-") or token.startswith("--"):
-            break
-        else:
-            args.append(token)
-            options.pop(0)
-    return " ".join(args), " ".join(options)
-
-
-class OptionParsingError(Exception):
-    def __init__(self, msg: str) -> None:
-        self.msg = msg
-
-
-def build_parser() -> optparse.OptionParser:
-    """
-    Return a parser for parsing requirement lines
-    """
-    parser = optparse.OptionParser(add_help_option=False)
-
-    option_factories = SUPPORTED_OPTIONS + SUPPORTED_OPTIONS_REQ
-    for option_factory in option_factories:
-        option = option_factory()
-        parser.add_option(option)
-
-    # By default optparse sys.exits on parsing errors. We want to wrap
-    # that in our own exception.
-    def parser_exit(self: Any, msg: str) -> "NoReturn":
-        raise OptionParsingError(msg)
-
-    # NOTE: mypy disallows assigning to a method
-    #       https://github.com/python/mypy/issues/2427
-    parser.exit = parser_exit  # type: ignore
-
-    return parser
-
-
-def join_lines(lines_enum: ReqFileLines) -> ReqFileLines:
-    """Joins a line ending in '\' with the previous line (except when following
-    comments).  The joined line takes on the index of the first line.
-    """
-    primary_line_number = None
-    new_line: List[str] = []
-    for line_number, line in lines_enum:
-        if not line.endswith("\\") or COMMENT_RE.match(line):
-            if COMMENT_RE.match(line):
-                # this ensures comments are always matched later
-                line = " " + line
-            if new_line:
-                new_line.append(line)
-                assert primary_line_number is not None
-                yield primary_line_number, "".join(new_line)
-                new_line = []
-            else:
-                yield line_number, line
-        else:
-            if not new_line:
-                primary_line_number = line_number
-            new_line.append(line.strip("\\"))
-
-    # last line contains \
-    if new_line:
-        assert primary_line_number is not None
-        yield primary_line_number, "".join(new_line)
-
-    # TODO: handle space after '\'.
-
-
-def ignore_comments(lines_enum: ReqFileLines) -> ReqFileLines:
-    """
-    Strips comments and filter empty lines.
-    """
-    for line_number, line in lines_enum:
-        line = COMMENT_RE.sub("", line)
-        line = line.strip()
-        if line:
-            yield line_number, line
-
-
-def expand_env_variables(lines_enum: ReqFileLines) -> ReqFileLines:
-    """Replace all environment variables that can be retrieved via `os.getenv`.
-
-    The only allowed format for environment variables defined in the
-    requirement file is `${MY_VARIABLE_1}` to ensure two things:
-
-    1. Strings that contain a `$` aren't accidentally (partially) expanded.
-    2. Ensure consistency across platforms for requirement files.
-
-    These points are the result of a discussion on the `github pull
-    request #3514 <https://github.com/pypa/pip/pull/3514>`_.
-
-    Valid characters in variable names follow the `POSIX standard
-    <http://pubs.opengroup.org/onlinepubs/9699919799/>`_ and are limited
-    to uppercase letter, digits and the `_` (underscore).
-    """
-    for line_number, line in lines_enum:
-        for env_var, var_name in ENV_VAR_RE.findall(line):
-            value = os.getenv(var_name)
-            if not value:
-                continue
-
-            line = line.replace(env_var, value)
-
-        yield line_number, line
-
-
-def get_file_content(url: str, session: PipSession) -> Tuple[str, str]:
-    """Gets the content of a file; it may be a filename, file: URL, or
-    http: URL.  Returns (location, content).  Content is unicode.
-    Respects # -*- coding: declarations on the retrieved files.
-
-    :param url:         File path or url.
-    :param session:     PipSession instance.
-    """
-    scheme = get_url_scheme(url)
-
-    # Pip has special support for file:// URLs (LocalFSAdapter).
-    if scheme in ["http", "https", "file"]:
-        resp = session.get(url)
-        raise_for_status(resp)
-        return resp.url, resp.text
-
-    # Assume this is a bare path.
-    try:
-        with open(url, "rb") as f:
-            content = auto_decode(f.read())
-    except OSError as exc:
-        raise InstallationError(f"Could not open requirements file: {exc}")
-    return url, content

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

@@ -1,1220 +0,0 @@
-"""distutils.ccompiler
-
-Contains CCompiler, an abstract base class that defines the interface
-for the Distutils compiler abstraction model."""
-
-import sys
-import os
-import re
-
-from distutils.errors import (
-    CompileError,
-    LinkError,
-    UnknownFileError,
-    DistutilsPlatformError,
-    DistutilsModuleError,
-)
-from distutils.spawn import spawn
-from distutils.file_util import move_file
-from distutils.dir_util import mkpath
-from distutils.dep_util import newer_group
-from distutils.util import split_quoted, execute
-from distutils import log
-
-
-class CCompiler:
-    """Abstract base class to define the interface that must be implemented
-    by real compiler classes.  Also has some utility methods used by
-    several compiler classes.
-
-    The basic idea behind a compiler abstraction class is that each
-    instance can be used for all the compile/link steps in building a
-    single project.  Thus, attributes common to all of those compile and
-    link steps -- include directories, macros to define, libraries to link
-    against, etc. -- are attributes of the compiler instance.  To allow for
-    variability in how individual files are treated, most of those
-    attributes may be varied on a per-compilation or per-link basis.
-    """
-
-    # 'compiler_type' is a class attribute that identifies this class.  It
-    # keeps code that wants to know what kind of compiler it's dealing with
-    # from having to import all possible compiler classes just to do an
-    # 'isinstance'.  In concrete CCompiler subclasses, 'compiler_type'
-    # should really, really be one of the keys of the 'compiler_class'
-    # dictionary (see below -- used by the 'new_compiler()' factory
-    # function) -- authors of new compiler interface classes are
-    # responsible for updating 'compiler_class'!
-    compiler_type = None
-
-    # XXX things not handled by this compiler abstraction model:
-    #   * client can't provide additional options for a compiler,
-    #     e.g. warning, optimization, debugging flags.  Perhaps this
-    #     should be the domain of concrete compiler abstraction classes
-    #     (UnixCCompiler, MSVCCompiler, etc.) -- or perhaps the base
-    #     class should have methods for the common ones.
-    #   * can't completely override the include or library searchg
-    #     path, ie. no "cc -I -Idir1 -Idir2" or "cc -L -Ldir1 -Ldir2".
-    #     I'm not sure how widely supported this is even by Unix
-    #     compilers, much less on other platforms.  And I'm even less
-    #     sure how useful it is; maybe for cross-compiling, but
-    #     support for that is a ways off.  (And anyways, cross
-    #     compilers probably have a dedicated binary with the
-    #     right paths compiled in.  I hope.)
-    #   * can't do really freaky things with the library list/library
-    #     dirs, e.g. "-Ldir1 -lfoo -Ldir2 -lfoo" to link against
-    #     different versions of libfoo.a in different locations.  I
-    #     think this is useless without the ability to null out the
-    #     library search path anyways.
-
-    # Subclasses that rely on the standard filename generation methods
-    # implemented below should override these; see the comment near
-    # those methods ('object_filenames()' et. al.) for details:
-    src_extensions = None  # list of strings
-    obj_extension = None  # string
-    static_lib_extension = None
-    shared_lib_extension = None  # string
-    static_lib_format = None  # format string
-    shared_lib_format = None  # prob. same as static_lib_format
-    exe_extension = None  # string
-
-    # Default language settings. language_map is used to detect a source
-    # file or Extension target language, checking source filenames.
-    # language_order is used to detect the language precedence, when deciding
-    # what language to use when mixing source types. For example, if some
-    # extension has two files with ".c" extension, and one with ".cpp", it
-    # is still linked as c++.
-    language_map = {
-        ".c": "c",
-        ".cc": "c++",
-        ".cpp": "c++",
-        ".cxx": "c++",
-        ".m": "objc",
-    }
-    language_order = ["c++", "objc", "c"]
-
-    include_dirs = []
-    """
-    include dirs specific to this compiler class
-    """
-
-    library_dirs = []
-    """
-    library dirs specific to this compiler class
-    """
-
-    def __init__(self, verbose=0, dry_run=0, force=0):
-        self.dry_run = dry_run
-        self.force = force
-        self.verbose = verbose
-
-        # 'output_dir': a common output directory for object, library,
-        # shared object, and shared library files
-        self.output_dir = None
-
-        # 'macros': a list of macro definitions (or undefinitions).  A
-        # macro definition is a 2-tuple (name, value), where the value is
-        # either a string or None (no explicit value).  A macro
-        # undefinition is a 1-tuple (name,).
-        self.macros = []
-
-        # 'include_dirs': a list of directories to search for include files
-        self.include_dirs = []
-
-        # 'libraries': a list of libraries to include in any link
-        # (library names, not filenames: eg. "foo" not "libfoo.a")
-        self.libraries = []
-
-        # 'library_dirs': a list of directories to search for libraries
-        self.library_dirs = []
-
-        # 'runtime_library_dirs': a list of directories to search for
-        # shared libraries/objects at runtime
-        self.runtime_library_dirs = []
-
-        # 'objects': a list of object files (or similar, such as explicitly
-        # named library files) to include on any link
-        self.objects = []
-
-        for key in self.executables.keys():
-            self.set_executable(key, self.executables[key])
-
-    def set_executables(self, **kwargs):
-        """Define the executables (and options for them) that will be run
-        to perform the various stages of compilation.  The exact set of
-        executables that may be specified here depends on the compiler
-        class (via the 'executables' class attribute), but most will have:
-          compiler      the C/C++ compiler
-          linker_so     linker used to create shared objects and libraries
-          linker_exe    linker used to create binary executables
-          archiver      static library creator
-
-        On platforms with a command-line (Unix, DOS/Windows), each of these
-        is a string that will be split into executable name and (optional)
-        list of arguments.  (Splitting the string is done similarly to how
-        Unix shells operate: words are delimited by spaces, but quotes and
-        backslashes can override this.  See
-        'distutils.util.split_quoted()'.)
-        """
-
-        # Note that some CCompiler implementation classes will define class
-        # attributes 'cpp', 'cc', etc. with hard-coded executable names;
-        # this is appropriate when a compiler class is for exactly one
-        # compiler/OS combination (eg. MSVCCompiler).  Other compiler
-        # classes (UnixCCompiler, in particular) are driven by information
-        # discovered at run-time, since there are many different ways to do
-        # basically the same things with Unix C compilers.
-
-        for key in kwargs:
-            if key not in self.executables:
-                raise ValueError(
-                    "unknown executable '%s' for class %s"
-                    % (key, self.__class__.__name__)
-                )
-            self.set_executable(key, kwargs[key])
-
-    def set_executable(self, key, value):
-        if isinstance(value, str):
-            setattr(self, key, split_quoted(value))
-        else:
-            setattr(self, key, value)
-
-    def _find_macro(self, name):
-        i = 0
-        for defn in self.macros:
-            if defn[0] == name:
-                return i
-            i += 1
-        return None
-
-    def _check_macro_definitions(self, definitions):
-        """Ensures that every element of 'definitions' is a valid macro
-        definition, ie. either (name,value) 2-tuple or a (name,) tuple.  Do
-        nothing if all definitions are OK, raise TypeError otherwise.
-        """
-        for defn in definitions:
-            if not (
-                isinstance(defn, tuple)
-                and (
-                    len(defn) in (1, 2)
-                    and (isinstance(defn[1], str) or defn[1] is None)
-                )
-                and isinstance(defn[0], str)
-            ):
-                raise TypeError(
-                    ("invalid macro definition '%s': " % defn)
-                    + "must be tuple (string,), (string, string), or "
-                    + "(string, None)"
-                )
-
-    # -- Bookkeeping methods -------------------------------------------
-
-    def define_macro(self, name, value=None):
-        """Define a preprocessor macro for all compilations driven by this
-        compiler object.  The optional parameter 'value' should be a
-        string; if it is not supplied, then the macro will be defined
-        without an explicit value and the exact outcome depends on the
-        compiler used (XXX true? does ANSI say anything about this?)
-        """
-        # Delete from the list of macro definitions/undefinitions if
-        # already there (so that this one will take precedence).
-        i = self._find_macro(name)
-        if i is not None:
-            del self.macros[i]
-
-        self.macros.append((name, value))
-
-    def undefine_macro(self, name):
-        """Undefine a preprocessor macro for all compilations driven by
-        this compiler object.  If the same macro is defined by
-        'define_macro()' and undefined by 'undefine_macro()' the last call
-        takes precedence (including multiple redefinitions or
-        undefinitions).  If the macro is redefined/undefined on a
-        per-compilation basis (ie. in the call to 'compile()'), then that
-        takes precedence.
-        """
-        # Delete from the list of macro definitions/undefinitions if
-        # already there (so that this one will take precedence).
-        i = self._find_macro(name)
-        if i is not None:
-            del self.macros[i]
-
-        undefn = (name,)
-        self.macros.append(undefn)
-
-    def add_include_dir(self, dir):
-        """Add 'dir' to the list of directories that will be searched for
-        header files.  The compiler is instructed to search directories in
-        the order in which they are supplied by successive calls to
-        'add_include_dir()'.
-        """
-        self.include_dirs.append(dir)
-
-    def set_include_dirs(self, dirs):
-        """Set the list of directories that will be searched to 'dirs' (a
-        list of strings).  Overrides any preceding calls to
-        'add_include_dir()'; subsequence calls to 'add_include_dir()' add
-        to the list passed to 'set_include_dirs()'.  This does not affect
-        any list of standard include directories that the compiler may
-        search by default.
-        """
-        self.include_dirs = dirs[:]
-
-    def add_library(self, libname):
-        """Add 'libname' to the list of libraries that will be included in
-        all links driven by this compiler object.  Note that 'libname'
-        should *not* be the name of a file containing a library, but the
-        name of the library itself: the actual filename will be inferred by
-        the linker, the compiler, or the compiler class (depending on the
-        platform).
-
-        The linker will be instructed to link against libraries in the
-        order they were supplied to 'add_library()' and/or
-        'set_libraries()'.  It is perfectly valid to duplicate library
-        names; the linker will be instructed to link against libraries as
-        many times as they are mentioned.
-        """
-        self.libraries.append(libname)
-
-    def set_libraries(self, libnames):
-        """Set the list of libraries to be included in all links driven by
-        this compiler object to 'libnames' (a list of strings).  This does
-        not affect any standard system libraries that the linker may
-        include by default.
-        """
-        self.libraries = libnames[:]
-
-    def add_library_dir(self, dir):
-        """Add 'dir' to the list of directories that will be searched for
-        libraries specified to 'add_library()' and 'set_libraries()'.  The
-        linker will be instructed to search for libraries in the order they
-        are supplied to 'add_library_dir()' and/or 'set_library_dirs()'.
-        """
-        self.library_dirs.append(dir)
-
-    def set_library_dirs(self, dirs):
-        """Set the list of library search directories to 'dirs' (a list of
-        strings).  This does not affect any standard library search path
-        that the linker may search by default.
-        """
-        self.library_dirs = dirs[:]
-
-    def add_runtime_library_dir(self, dir):
-        """Add 'dir' to the list of directories that will be searched for
-        shared libraries at runtime.
-        """
-        self.runtime_library_dirs.append(dir)
-
-    def set_runtime_library_dirs(self, dirs):
-        """Set the list of directories to search for shared libraries at
-        runtime to 'dirs' (a list of strings).  This does not affect any
-        standard search path that the runtime linker may search by
-        default.
-        """
-        self.runtime_library_dirs = dirs[:]
-
-    def add_link_object(self, object):
-        """Add 'object' to the list of object files (or analogues, such as
-        explicitly named library files or the output of "resource
-        compilers") to be included in every link driven by this compiler
-        object.
-        """
-        self.objects.append(object)
-
-    def set_link_objects(self, objects):
-        """Set the list of object files (or analogues) to be included in
-        every link to 'objects'.  This does not affect any standard object
-        files that the linker may include by default (such as system
-        libraries).
-        """
-        self.objects = objects[:]
-
-    # -- Private utility methods --------------------------------------
-    # (here for the convenience of subclasses)
-
-    # Helper method to prep compiler in subclass compile() methods
-
-    def _setup_compile(self, outdir, macros, incdirs, sources, depends, extra):
-        """Process arguments and decide which source files to compile."""
-        outdir, macros, incdirs = self._fix_compile_args(outdir, macros, incdirs)
-
-        if extra is None:
-            extra = []
-
-        # Get the list of expected output (object) files
-        objects = self.object_filenames(sources, strip_dir=0, output_dir=outdir)
-        assert len(objects) == len(sources)
-
-        pp_opts = gen_preprocess_options(macros, incdirs)
-
-        build = {}
-        for i in range(len(sources)):
-            src = sources[i]
-            obj = objects[i]
-            ext = os.path.splitext(src)[1]
-            self.mkpath(os.path.dirname(obj))
-            build[obj] = (src, ext)
-
-        return macros, objects, extra, pp_opts, build
-
-    def _get_cc_args(self, pp_opts, debug, before):
-        # works for unixccompiler, cygwinccompiler
-        cc_args = pp_opts + ['-c']
-        if debug:
-            cc_args[:0] = ['-g']
-        if before:
-            cc_args[:0] = before
-        return cc_args
-
-    def _fix_compile_args(self, output_dir, macros, include_dirs):
-        """Typecheck and fix-up some of the arguments to the 'compile()'
-        method, and return fixed-up values.  Specifically: if 'output_dir'
-        is None, replaces it with 'self.output_dir'; ensures that 'macros'
-        is a list, and augments it with 'self.macros'; ensures that
-        'include_dirs' is a list, and augments it with 'self.include_dirs'.
-        Guarantees that the returned values are of the correct type,
-        i.e. for 'output_dir' either string or None, and for 'macros' and
-        'include_dirs' either list or None.
-        """
-        if output_dir is None:
-            output_dir = self.output_dir
-        elif not isinstance(output_dir, str):
-            raise TypeError("'output_dir' must be a string or None")
-
-        if macros is None:
-            macros = self.macros
-        elif isinstance(macros, list):
-            macros = macros + (self.macros or [])
-        else:
-            raise TypeError("'macros' (if supplied) must be a list of tuples")
-
-        if include_dirs is None:
-            include_dirs = self.include_dirs
-        elif isinstance(include_dirs, (list, tuple)):
-            include_dirs = list(include_dirs) + (self.include_dirs or [])
-        else:
-            raise TypeError("'include_dirs' (if supplied) must be a list of strings")
-
-        # add include dirs for class
-        include_dirs += self.__class__.include_dirs
-
-        return output_dir, macros, include_dirs
-
-    def _prep_compile(self, sources, output_dir, depends=None):
-        """Decide which source files must be recompiled.
-
-        Determine the list of object files corresponding to 'sources',
-        and figure out which ones really need to be recompiled.
-        Return a list of all object files and a dictionary telling
-        which source files can be skipped.
-        """
-        # Get the list of expected output (object) files
-        objects = self.object_filenames(sources, output_dir=output_dir)
-        assert len(objects) == len(sources)
-
-        # Return an empty dict for the "which source files can be skipped"
-        # return value to preserve API compatibility.
-        return objects, {}
-
-    def _fix_object_args(self, objects, output_dir):
-        """Typecheck and fix up some arguments supplied to various methods.
-        Specifically: ensure that 'objects' is a list; if output_dir is
-        None, replace with self.output_dir.  Return fixed versions of
-        'objects' and 'output_dir'.
-        """
-        if not isinstance(objects, (list, tuple)):
-            raise TypeError("'objects' must be a list or tuple of strings")
-        objects = list(objects)
-
-        if output_dir is None:
-            output_dir = self.output_dir
-        elif not isinstance(output_dir, str):
-            raise TypeError("'output_dir' must be a string or None")
-
-        return (objects, output_dir)
-
-    def _fix_lib_args(self, libraries, library_dirs, runtime_library_dirs):
-        """Typecheck and fix up some of the arguments supplied to the
-        'link_*' methods.  Specifically: ensure that all arguments are
-        lists, and augment them with their permanent versions
-        (eg. 'self.libraries' augments 'libraries').  Return a tuple with
-        fixed versions of all arguments.
-        """
-        if libraries is None:
-            libraries = self.libraries
-        elif isinstance(libraries, (list, tuple)):
-            libraries = list(libraries) + (self.libraries or [])
-        else:
-            raise TypeError("'libraries' (if supplied) must be a list of strings")
-
-        if library_dirs is None:
-            library_dirs = self.library_dirs
-        elif isinstance(library_dirs, (list, tuple)):
-            library_dirs = list(library_dirs) + (self.library_dirs or [])
-        else:
-            raise TypeError("'library_dirs' (if supplied) must be a list of strings")
-
-        # add library dirs for class
-        library_dirs += self.__class__.library_dirs
-
-        if runtime_library_dirs is None:
-            runtime_library_dirs = self.runtime_library_dirs
-        elif isinstance(runtime_library_dirs, (list, tuple)):
-            runtime_library_dirs = list(runtime_library_dirs) + (
-                self.runtime_library_dirs or []
-            )
-        else:
-            raise TypeError(
-                "'runtime_library_dirs' (if supplied) " "must be a list of strings"
-            )
-
-        return (libraries, library_dirs, runtime_library_dirs)
-
-    def _need_link(self, objects, output_file):
-        """Return true if we need to relink the files listed in 'objects'
-        to recreate 'output_file'.
-        """
-        if self.force:
-            return True
-        else:
-            if self.dry_run:
-                newer = newer_group(objects, output_file, missing='newer')
-            else:
-                newer = newer_group(objects, output_file)
-            return newer
-
-    def detect_language(self, sources):
-        """Detect the language of a given file, or list of files. Uses
-        language_map, and language_order to do the job.
-        """
-        if not isinstance(sources, list):
-            sources = [sources]
-        lang = None
-        index = len(self.language_order)
-        for source in sources:
-            base, ext = os.path.splitext(source)
-            extlang = self.language_map.get(ext)
-            try:
-                extindex = self.language_order.index(extlang)
-                if extindex < index:
-                    lang = extlang
-                    index = extindex
-            except ValueError:
-                pass
-        return lang
-
-    # -- Worker methods ------------------------------------------------
-    # (must be implemented by subclasses)
-
-    def preprocess(
-        self,
-        source,
-        output_file=None,
-        macros=None,
-        include_dirs=None,
-        extra_preargs=None,
-        extra_postargs=None,
-    ):
-        """Preprocess a single C/C++ source file, named in 'source'.
-        Output will be written to file named 'output_file', or stdout if
-        'output_file' not supplied.  'macros' is a list of macro
-        definitions as for 'compile()', which will augment the macros set
-        with 'define_macro()' and 'undefine_macro()'.  'include_dirs' is a
-        list of directory names that will be added to the default list.
-
-        Raises PreprocessError on failure.
-        """
-        pass
-
-    def compile(
-        self,
-        sources,
-        output_dir=None,
-        macros=None,
-        include_dirs=None,
-        debug=0,
-        extra_preargs=None,
-        extra_postargs=None,
-        depends=None,
-    ):
-        """Compile one or more source files.
-
-        'sources' must be a list of filenames, most likely C/C++
-        files, but in reality anything that can be handled by a
-        particular compiler and compiler class (eg. MSVCCompiler can
-        handle resource files in 'sources').  Return a list of object
-        filenames, one per source filename in 'sources'.  Depending on
-        the implementation, not all source files will necessarily be
-        compiled, but all corresponding object filenames will be
-        returned.
-
-        If 'output_dir' is given, object files will be put under it, while
-        retaining their original path component.  That is, "foo/bar.c"
-        normally compiles to "foo/bar.o" (for a Unix implementation); if
-        'output_dir' is "build", then it would compile to
-        "build/foo/bar.o".
-
-        'macros', if given, must be a list of macro definitions.  A macro
-        definition is either a (name, value) 2-tuple or a (name,) 1-tuple.
-        The former defines a macro; if the value is None, the macro is
-        defined without an explicit value.  The 1-tuple case undefines a
-        macro.  Later definitions/redefinitions/ undefinitions take
-        precedence.
-
-        'include_dirs', if given, must be a list of strings, the
-        directories to add to the default include file search path for this
-        compilation only.
-
-        'debug' is a boolean; if true, the compiler will be instructed to
-        output debug symbols in (or alongside) the object file(s).
-
-        'extra_preargs' and 'extra_postargs' are implementation- dependent.
-        On platforms that have the notion of a command-line (e.g. Unix,
-        DOS/Windows), they are most likely lists of strings: extra
-        command-line arguments to prepend/append to the compiler command
-        line.  On other platforms, consult the implementation class
-        documentation.  In any event, they are intended as an escape hatch
-        for those occasions when the abstract compiler framework doesn't
-        cut the mustard.
-
-        'depends', if given, is a list of filenames that all targets
-        depend on.  If a source file is older than any file in
-        depends, then the source file will be recompiled.  This
-        supports dependency tracking, but only at a coarse
-        granularity.
-
-        Raises CompileError on failure.
-        """
-        # A concrete compiler class can either override this method
-        # entirely or implement _compile().
-        macros, objects, extra_postargs, pp_opts, build = self._setup_compile(
-            output_dir, macros, include_dirs, sources, depends, extra_postargs
-        )
-        cc_args = self._get_cc_args(pp_opts, debug, extra_preargs)
-
-        for obj in objects:
-            try:
-                src, ext = build[obj]
-            except KeyError:
-                continue
-            self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts)
-
-        # Return *all* object filenames, not just the ones we just built.
-        return objects
-
-    def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts):
-        """Compile 'src' to product 'obj'."""
-        # A concrete compiler class that does not override compile()
-        # should implement _compile().
-        pass
-
-    def create_static_lib(
-        self, objects, output_libname, output_dir=None, debug=0, target_lang=None
-    ):
-        """Link a bunch of stuff together to create a static library file.
-        The "bunch of stuff" consists of the list of object files supplied
-        as 'objects', the extra object files supplied to
-        'add_link_object()' and/or 'set_link_objects()', the libraries
-        supplied to 'add_library()' and/or 'set_libraries()', and the
-        libraries supplied as 'libraries' (if any).
-
-        'output_libname' should be a library name, not a filename; the
-        filename will be inferred from the library name.  'output_dir' is
-        the directory where the library file will be put.
-
-        'debug' is a boolean; if true, debugging information will be
-        included in the library (note that on most platforms, it is the
-        compile step where this matters: the 'debug' flag is included here
-        just for consistency).
-
-        'target_lang' is the target language for which the given objects
-        are being compiled. This allows specific linkage time treatment of
-        certain languages.
-
-        Raises LibError on failure.
-        """
-        pass
-
-    # values for target_desc parameter in link()
-    SHARED_OBJECT = "shared_object"
-    SHARED_LIBRARY = "shared_library"
-    EXECUTABLE = "executable"
-
-    def link(
-        self,
-        target_desc,
-        objects,
-        output_filename,
-        output_dir=None,
-        libraries=None,
-        library_dirs=None,
-        runtime_library_dirs=None,
-        export_symbols=None,
-        debug=0,
-        extra_preargs=None,
-        extra_postargs=None,
-        build_temp=None,
-        target_lang=None,
-    ):
-        """Link a bunch of stuff together to create an executable or
-        shared library file.
-
-        The "bunch of stuff" consists of the list of object files supplied
-        as 'objects'.  'output_filename' should be a filename.  If
-        'output_dir' is supplied, 'output_filename' is relative to it
-        (i.e. 'output_filename' can provide directory components if
-        needed).
-
-        'libraries' is a list of libraries to link against.  These are
-        library names, not filenames, since they're translated into
-        filenames in a platform-specific way (eg. "foo" becomes "libfoo.a"
-        on Unix and "foo.lib" on DOS/Windows).  However, they can include a
-        directory component, which means the linker will look in that
-        specific directory rather than searching all the normal locations.
-
-        'library_dirs', if supplied, should be a list of directories to
-        search for libraries that were specified as bare library names
-        (ie. no directory component).  These are on top of the system
-        default and those supplied to 'add_library_dir()' and/or
-        'set_library_dirs()'.  'runtime_library_dirs' is a list of
-        directories that will be embedded into the shared library and used
-        to search for other shared libraries that *it* depends on at
-        run-time.  (This may only be relevant on Unix.)
-
-        'export_symbols' is a list of symbols that the shared library will
-        export.  (This appears to be relevant only on Windows.)
-
-        'debug' is as for 'compile()' and 'create_static_lib()', with the
-        slight distinction that it actually matters on most platforms (as
-        opposed to 'create_static_lib()', which includes a 'debug' flag
-        mostly for form's sake).
-
-        'extra_preargs' and 'extra_postargs' are as for 'compile()' (except
-        of course that they supply command-line arguments for the
-        particular linker being used).
-
-        'target_lang' is the target language for which the given objects
-        are being compiled. This allows specific linkage time treatment of
-        certain languages.
-
-        Raises LinkError on failure.
-        """
-        raise NotImplementedError
-
-    # Old 'link_*()' methods, rewritten to use the new 'link()' method.
-
-    def link_shared_lib(
-        self,
-        objects,
-        output_libname,
-        output_dir=None,
-        libraries=None,
-        library_dirs=None,
-        runtime_library_dirs=None,
-        export_symbols=None,
-        debug=0,
-        extra_preargs=None,
-        extra_postargs=None,
-        build_temp=None,
-        target_lang=None,
-    ):
-        self.link(
-            CCompiler.SHARED_LIBRARY,
-            objects,
-            self.library_filename(output_libname, lib_type='shared'),
-            output_dir,
-            libraries,
-            library_dirs,
-            runtime_library_dirs,
-            export_symbols,
-            debug,
-            extra_preargs,
-            extra_postargs,
-            build_temp,
-            target_lang,
-        )
-
-    def link_shared_object(
-        self,
-        objects,
-        output_filename,
-        output_dir=None,
-        libraries=None,
-        library_dirs=None,
-        runtime_library_dirs=None,
-        export_symbols=None,
-        debug=0,
-        extra_preargs=None,
-        extra_postargs=None,
-        build_temp=None,
-        target_lang=None,
-    ):
-        self.link(
-            CCompiler.SHARED_OBJECT,
-            objects,
-            output_filename,
-            output_dir,
-            libraries,
-            library_dirs,
-            runtime_library_dirs,
-            export_symbols,
-            debug,
-            extra_preargs,
-            extra_postargs,
-            build_temp,
-            target_lang,
-        )
-
-    def link_executable(
-        self,
-        objects,
-        output_progname,
-        output_dir=None,
-        libraries=None,
-        library_dirs=None,
-        runtime_library_dirs=None,
-        debug=0,
-        extra_preargs=None,
-        extra_postargs=None,
-        target_lang=None,
-    ):
-        self.link(
-            CCompiler.EXECUTABLE,
-            objects,
-            self.executable_filename(output_progname),
-            output_dir,
-            libraries,
-            library_dirs,
-            runtime_library_dirs,
-            None,
-            debug,
-            extra_preargs,
-            extra_postargs,
-            None,
-            target_lang,
-        )
-
-    # -- Miscellaneous methods -----------------------------------------
-    # These are all used by the 'gen_lib_options() function; there is
-    # no appropriate default implementation so subclasses should
-    # implement all of these.
-
-    def library_dir_option(self, dir):
-        """Return the compiler option to add 'dir' to the list of
-        directories searched for libraries.
-        """
-        raise NotImplementedError
-
-    def runtime_library_dir_option(self, dir):
-        """Return the compiler option to add 'dir' to the list of
-        directories searched for runtime libraries.
-        """
-        raise NotImplementedError
-
-    def library_option(self, lib):
-        """Return the compiler option to add 'lib' to the list of libraries
-        linked into the shared library or executable.
-        """
-        raise NotImplementedError
-
-    def has_function(  # noqa: C901
-        self,
-        funcname,
-        includes=None,
-        include_dirs=None,
-        libraries=None,
-        library_dirs=None,
-    ):
-        """Return a boolean indicating whether funcname is supported on
-        the current platform.  The optional arguments can be used to
-        augment the compilation environment.
-        """
-        # this can't be included at module scope because it tries to
-        # import math which might not be available at that point - maybe
-        # the necessary logic should just be inlined?
-        import tempfile
-
-        if includes is None:
-            includes = []
-        if include_dirs is None:
-            include_dirs = []
-        if libraries is None:
-            libraries = []
-        if library_dirs is None:
-            library_dirs = []
-        fd, fname = tempfile.mkstemp(".c", funcname, text=True)
-        f = os.fdopen(fd, "w")
-        try:
-            for incl in includes:
-                f.write("""#include "%s"\n""" % incl)
-            f.write(
-                """\
-int main (int argc, char **argv) {
-    %s();
-    return 0;
-}
-"""
-                % funcname
-            )
-        finally:
-            f.close()
-        try:
-            objects = self.compile([fname], include_dirs=include_dirs)
-        except CompileError:
-            return False
-        finally:
-            os.remove(fname)
-
-        try:
-            self.link_executable(
-                objects, "a.out", libraries=libraries, library_dirs=library_dirs
-            )
-        except (LinkError, TypeError):
-            return False
-        else:
-            os.remove(os.path.join(self.output_dir or '', "a.out"))
-        finally:
-            for fn in objects:
-                os.remove(fn)
-        return True
-
-    def find_library_file(self, dirs, lib, debug=0):
-        """Search the specified list of directories for a static or shared
-        library file 'lib' and return the full path to that file.  If
-        'debug' true, look for a debugging version (if that makes sense on
-        the current platform).  Return None if 'lib' wasn't found in any of
-        the specified directories.
-        """
-        raise NotImplementedError
-
-    # -- Filename generation methods -----------------------------------
-
-    # The default implementation of the filename generating methods are
-    # prejudiced towards the Unix/DOS/Windows view of the world:
-    #   * object files are named by replacing the source file extension
-    #     (eg. .c/.cpp -> .o/.obj)
-    #   * library files (shared or static) are named by plugging the
-    #     library name and extension into a format string, eg.
-    #     "lib%s.%s" % (lib_name, ".a") for Unix static libraries
-    #   * executables are named by appending an extension (possibly
-    #     empty) to the program name: eg. progname + ".exe" for
-    #     Windows
-    #
-    # To reduce redundant code, these methods expect to find
-    # several attributes in the current object (presumably defined
-    # as class attributes):
-    #   * src_extensions -
-    #     list of C/C++ source file extensions, eg. ['.c', '.cpp']
-    #   * obj_extension -
-    #     object file extension, eg. '.o' or '.obj'
-    #   * static_lib_extension -
-    #     extension for static library files, eg. '.a' or '.lib'
-    #   * shared_lib_extension -
-    #     extension for shared library/object files, eg. '.so', '.dll'
-    #   * static_lib_format -
-    #     format string for generating static library filenames,
-    #     eg. 'lib%s.%s' or '%s.%s'
-    #   * shared_lib_format
-    #     format string for generating shared library filenames
-    #     (probably same as static_lib_format, since the extension
-    #     is one of the intended parameters to the format string)
-    #   * exe_extension -
-    #     extension for executable files, eg. '' or '.exe'
-
-    def object_filenames(self, source_filenames, strip_dir=0, output_dir=''):
-        if output_dir is None:
-            output_dir = ''
-        return list(
-            self._make_out_path(output_dir, strip_dir, src_name)
-            for src_name in source_filenames
-        )
-
-    @property
-    def out_extensions(self):
-        return dict.fromkeys(self.src_extensions, self.obj_extension)
-
-    def _make_out_path(self, output_dir, strip_dir, src_name):
-        base, ext = os.path.splitext(src_name)
-        base = self._make_relative(base)
-        try:
-            new_ext = self.out_extensions[ext]
-        except LookupError:
-            raise UnknownFileError(
-                "unknown file type '{}' (from '{}')".format(ext, src_name)
-            )
-        if strip_dir:
-            base = os.path.basename(base)
-        return os.path.join(output_dir, base + new_ext)
-
-    @staticmethod
-    def _make_relative(base):
-        """
-        In order to ensure that a filename always honors the
-        indicated output_dir, make sure it's relative.
-        Ref python/cpython#37775.
-        """
-        # Chop off the drive
-        no_drive = os.path.splitdrive(base)[1]
-        # If abs, chop off leading /
-        return no_drive[os.path.isabs(no_drive) :]
-
-    def shared_object_filename(self, basename, strip_dir=0, output_dir=''):
-        assert output_dir is not None
-        if strip_dir:
-            basename = os.path.basename(basename)
-        return os.path.join(output_dir, basename + self.shared_lib_extension)
-
-    def executable_filename(self, basename, strip_dir=0, output_dir=''):
-        assert output_dir is not None
-        if strip_dir:
-            basename = os.path.basename(basename)
-        return os.path.join(output_dir, basename + (self.exe_extension or ''))
-
-    def library_filename(
-        self, libname, lib_type='static', strip_dir=0, output_dir=''  # or 'shared'
-    ):
-        assert output_dir is not None
-        expected = '"static", "shared", "dylib", "xcode_stub"'
-        if lib_type not in eval(expected):
-            raise ValueError(f"'lib_type' must be {expected}")
-        fmt = getattr(self, lib_type + "_lib_format")
-        ext = getattr(self, lib_type + "_lib_extension")
-
-        dir, base = os.path.split(libname)
-        filename = fmt % (base, ext)
-        if strip_dir:
-            dir = ''
-
-        return os.path.join(output_dir, dir, filename)
-
-    # -- Utility methods -----------------------------------------------
-
-    def announce(self, msg, level=1):
-        log.debug(msg)
-
-    def debug_print(self, msg):
-        from distutils.debug import DEBUG
-
-        if DEBUG:
-            print(msg)
-
-    def warn(self, msg):
-        sys.stderr.write("warning: %s\n" % msg)
-
-    def execute(self, func, args, msg=None, level=1):
-        execute(func, args, msg, self.dry_run)
-
-    def spawn(self, cmd, **kwargs):
-        spawn(cmd, dry_run=self.dry_run, **kwargs)
-
-    def move_file(self, src, dst):
-        return move_file(src, dst, dry_run=self.dry_run)
-
-    def mkpath(self, name, mode=0o777):
-        mkpath(name, mode, dry_run=self.dry_run)
-
-
-# Map a sys.platform/os.name ('posix', 'nt') to the default compiler
-# type for that platform. Keys are interpreted as re match
-# patterns. Order is important; platform mappings are preferred over
-# OS names.
-_default_compilers = (
-    # Platform string mappings
-    # on a cygwin built python we can use gcc like an ordinary UNIXish
-    # compiler
-    ('cygwin.*', 'unix'),
-    # OS name mappings
-    ('posix', 'unix'),
-    ('nt', 'msvc'),
-)
-
-
-def get_default_compiler(osname=None, platform=None):
-    """Determine the default compiler to use for the given platform.
-
-    osname should be one of the standard Python OS names (i.e. the
-    ones returned by os.name) and platform the common value
-    returned by sys.platform for the platform in question.
-
-    The default values are os.name and sys.platform in case the
-    parameters are not given.
-    """
-    if osname is None:
-        osname = os.name
-    if platform is None:
-        platform = sys.platform
-    for pattern, compiler in _default_compilers:
-        if (
-            re.match(pattern, platform) is not None
-            or re.match(pattern, osname) is not None
-        ):
-            return compiler
-    # Default to Unix compiler
-    return 'unix'
-
-
-# Map compiler types to (module_name, class_name) pairs -- ie. where to
-# find the code that implements an interface to this compiler.  (The module
-# is assumed to be in the 'distutils' package.)
-compiler_class = {
-    'unix': ('unixccompiler', 'UnixCCompiler', "standard UNIX-style compiler"),
-    'msvc': ('_msvccompiler', 'MSVCCompiler', "Microsoft Visual C++"),
-    'cygwin': (
-        'cygwinccompiler',
-        'CygwinCCompiler',
-        "Cygwin port of GNU C Compiler for Win32",
-    ),
-    'mingw32': (
-        'cygwinccompiler',
-        'Mingw32CCompiler',
-        "Mingw32 port of GNU C Compiler for Win32",
-    ),
-    'bcpp': ('bcppcompiler', 'BCPPCompiler', "Borland C++ Compiler"),
-}
-
-
-def show_compilers():
-    """Print list of available compilers (used by the "--help-compiler"
-    options to "build", "build_ext", "build_clib").
-    """
-    # XXX this "knows" that the compiler option it's describing is
-    # "--compiler", which just happens to be the case for the three
-    # commands that use it.
-    from distutils.fancy_getopt import FancyGetopt
-
-    compilers = []
-    for compiler in compiler_class.keys():
-        compilers.append(("compiler=" + compiler, None, compiler_class[compiler][2]))
-    compilers.sort()
-    pretty_printer = FancyGetopt(compilers)
-    pretty_printer.print_help("List of available compilers:")
-
-
-def new_compiler(plat=None, compiler=None, verbose=0, dry_run=0, force=0):
-    """Generate an instance of some CCompiler subclass for the supplied
-    platform/compiler combination.  'plat' defaults to 'os.name'
-    (eg. 'posix', 'nt'), and 'compiler' defaults to the default compiler
-    for that platform.  Currently only 'posix' and 'nt' are supported, and
-    the default compilers are "traditional Unix interface" (UnixCCompiler
-    class) and Visual C++ (MSVCCompiler class).  Note that it's perfectly
-    possible to ask for a Unix compiler object under Windows, and a
-    Microsoft compiler object under Unix -- if you supply a value for
-    'compiler', 'plat' is ignored.
-    """
-    if plat is None:
-        plat = os.name
-
-    try:
-        if compiler is None:
-            compiler = get_default_compiler(plat)
-
-        (module_name, class_name, long_description) = compiler_class[compiler]
-    except KeyError:
-        msg = "don't know how to compile C/C++ code on platform '%s'" % plat
-        if compiler is not None:
-            msg = msg + " with '%s' compiler" % compiler
-        raise DistutilsPlatformError(msg)
-
-    try:
-        module_name = "distutils." + module_name
-        __import__(module_name)
-        module = sys.modules[module_name]
-        klass = vars(module)[class_name]
-    except ImportError:
-        raise DistutilsModuleError(
-            "can't compile C/C++ code: unable to load module '%s'" % module_name
-        )
-    except KeyError:
-        raise DistutilsModuleError(
-            "can't compile C/C++ code: unable to find class '%s' "
-            "in module '%s'" % (class_name, module_name)
-        )
-
-    # XXX The None is necessary to preserve backwards compatibility
-    # with classes that expect verbose to be the first positional
-    # argument.
-    return klass(None, dry_run, force)
-
-
-def gen_preprocess_options(macros, include_dirs):
-    """Generate C pre-processor options (-D, -U, -I) as used by at least
-    two types of compilers: the typical Unix compiler and Visual C++.
-    'macros' is the usual thing, a list of 1- or 2-tuples, where (name,)
-    means undefine (-U) macro 'name', and (name,value) means define (-D)
-    macro 'name' to 'value'.  'include_dirs' is just a list of directory
-    names to be added to the header file search path (-I).  Returns a list
-    of command-line options suitable for either Unix compilers or Visual
-    C++.
-    """
-    # XXX it would be nice (mainly aesthetic, and so we don't generate
-    # stupid-looking command lines) to go over 'macros' and eliminate
-    # redundant definitions/undefinitions (ie. ensure that only the
-    # latest mention of a particular macro winds up on the command
-    # line).  I don't think it's essential, though, since most (all?)
-    # Unix C compilers only pay attention to the latest -D or -U
-    # mention of a macro on their command line.  Similar situation for
-    # 'include_dirs'.  I'm punting on both for now.  Anyways, weeding out
-    # redundancies like this should probably be the province of
-    # CCompiler, since the data structures used are inherited from it
-    # and therefore common to all CCompiler classes.
-    pp_opts = []
-    for macro in macros:
-        if not (isinstance(macro, tuple) and 1 <= len(macro) <= 2):
-            raise TypeError(
-                "bad macro definition '%s': "
-                "each element of 'macros' list must be a 1- or 2-tuple" % macro
-            )
-
-        if len(macro) == 1:  # undefine this macro
-            pp_opts.append("-U%s" % macro[0])
-        elif len(macro) == 2:
-            if macro[1] is None:  # define with no explicit value
-                pp_opts.append("-D%s" % macro[0])
-            else:
-                # XXX *don't* need to be clever about quoting the
-                # macro value here, because we're going to avoid the
-                # shell at all costs when we spawn the command!
-                pp_opts.append("-D%s=%s" % macro)
-
-    for dir in include_dirs:
-        pp_opts.append("-I%s" % dir)
-    return pp_opts
-
-
-def gen_lib_options(compiler, library_dirs, runtime_library_dirs, libraries):
-    """Generate linker options for searching library directories and
-    linking with specific libraries.  'libraries' and 'library_dirs' are,
-    respectively, lists of library names (not filenames!) and search
-    directories.  Returns a list of command-line options suitable for use
-    with some compiler (depending on the two format strings passed in).
-    """
-    lib_opts = []
-
-    for dir in library_dirs:
-        lib_opts.append(compiler.library_dir_option(dir))
-
-    for dir in runtime_library_dirs:
-        opt = compiler.runtime_library_dir_option(dir)
-        if isinstance(opt, list):
-            lib_opts = lib_opts + opt
-        else:
-            lib_opts.append(opt)
-
-    # XXX it's important that we *not* remove redundant library mentions!
-    # sometimes you really do have to say "-lfoo -lbar -lfoo" in order to
-    # resolve all symbols.  I just hope we never have to say "-lfoo obj.o
-    # -lbar" to get things to work -- that's certainly a possibility, but a
-    # pretty nasty way to arrange your C code.
-
-    for lib in libraries:
-        (lib_dir, lib_name) = os.path.split(lib)
-        if lib_dir:
-            lib_file = compiler.find_library_file([lib_dir], lib_name)
-            if lib_file:
-                lib_opts.append(lib_file)
-            else:
-                compiler.warn(
-                    "no library file corresponding to " "'%s' found (skipping)" % lib
-                )
-        else:
-            lib_opts.append(compiler.library_option(lib))
-    return lib_opts

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

@@ -1,10 +0,0 @@
-import importlib
-from basicsr.utils import scandir
-from os import path as osp
-
-# automatically scan and import dataset modules for registry
-# scan all the files that end with '_dataset.py' under the data folder
-data_folder = osp.dirname(osp.abspath(__file__))
-dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
-# import all the dataset modules
-_dataset_modules = [importlib.import_module(f'realesrgan.data.{file_name}') for file_name in dataset_filenames]

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/docs/notes/benchmarks.md

@@ -1,239 +0,0 @@
-
-# Benchmarks
-
-Here we benchmark the training speed of a Mask R-CNN in detectron2,
-with some other popular open source Mask R-CNN implementations.
-
-
-### Settings
-
-* Hardware: 8 NVIDIA V100s with NVLink.
-* Software: Python 3.7, CUDA 10.0, cuDNN 7.6.4, PyTorch 1.3.0 (at
-  [this link](https://download.pytorch.org/whl/nightly/cu100/torch-1.3.0%2Bcu100-cp37-cp37m-linux_x86_64.whl)),
-  TensorFlow 1.15.0rc2, Keras 2.2.5, MxNet 1.6.0b20190820.
-* Model: an end-to-end R-50-FPN Mask-RCNN model, using the same hyperparameter as the
-  [Detectron baseline config](https://github.com/facebookresearch/Detectron/blob/master/configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml).
-* Metrics: We use the average throughput in iterations 100-500 to skip GPU warmup time.
-  Note that for R-CNN-style models, the throughput of a model typically changes during training, because
-  it depends on the predictions of the model. Therefore this metric is not directly comparable with
-  "train speed" in model zoo, which is the average speed of the entire training run.
-
-
-### Main Results
-
-```eval_rst
-+-------------------------------+--------------------+
-| Implementation                | Throughput (img/s) |
-+===============================+====================+
-| |D2| |PT|                     | 59                 |
-+-------------------------------+--------------------+
-| maskrcnn-benchmark_  |PT|     | 51                 |
-+-------------------------------+--------------------+
-| tensorpack_ |TF|              | 50                 |
-+-------------------------------+--------------------+
-| mmdetection_  |PT|            | 41                 |
-+-------------------------------+--------------------+
-| simpledet_ |mxnet|            | 39                 |
-+-------------------------------+--------------------+
-| Detectron_  |C2|              | 19                 |
-+-------------------------------+--------------------+
-| `matterport/Mask_RCNN`__ |TF| | 14                 |
-+-------------------------------+--------------------+
-
-.. _maskrcnn-benchmark: https://github.com/facebookresearch/maskrcnn-benchmark/
-.. _tensorpack: https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN
-.. _mmdetection: https://github.com/open-mmlab/mmdetection/
-.. _simpledet: https://github.com/TuSimple/simpledet/
-.. _Detectron: https://github.com/facebookresearch/Detectron
-__ https://github.com/matterport/Mask_RCNN/
-
-.. |D2| image:: https://github.com/facebookresearch/detectron2/raw/master/.github/Detectron2-Logo-Horz.svg?sanitize=true
-   :height: 15pt
-   :target: https://github.com/facebookresearch/detectron2/
-.. |PT| image:: https://pytorch.org/assets/images/logo-icon.svg
-   :width: 15pt
-   :height: 15pt
-   :target: https://pytorch.org
-.. |TF| image:: https://static.nvidiagrid.net/ngc/containers/tensorflow.png
-   :width: 15pt
-   :height: 15pt
-   :target: https://tensorflow.org
-.. |mxnet| image:: https://github.com/dmlc/web-data/raw/master/mxnet/image/mxnet_favicon.png
-   :width: 15pt
-   :height: 15pt
-   :target: https://mxnet.apache.org/
-.. |C2| image:: https://caffe2.ai/static/logo.svg
-   :width: 15pt
-   :height: 15pt
-   :target: https://caffe2.ai
-```
-
-
-Details for each implementation:
-
-* __Detectron2__:
-  ```
-  python tools/train_net.py  --config-file configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml --num-gpus 8
-  ```
-
-* __maskrcnn-benchmark__: use commit `0ce8f6f` with `sed -i ‘s/torch.uint8/torch.bool/g’ **/*.py` to make it compatible with latest PyTorch.
-  Then, run training with
-  ```
-  python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
-  ```
-  The speed we observed is faster than its model zoo, likely due to different software versions.
-
-* __tensorpack__: at commit `caafda`, `export TF_CUDNN_USE_AUTOTUNE=0`, then run
-  ```
-  mpirun -np 8 ./train.py --config DATA.BASEDIR=/data/coco TRAINER=horovod BACKBONE.STRIDE_1X1=True TRAIN.STEPS_PER_EPOCH=50 --load ImageNet-R50-AlignPadding.npz
-  ```
-
-* __mmdetection__: at commit `4d9a5f`, apply the following diff, then run
-  ```
-  ./tools/dist_train.sh configs/mask_rcnn_r50_fpn_1x.py 8
-  ```
-
-    The speed we observed is faster than its model zoo, likely due to different software versions.
-
-  <details>
-  <summary>
-  (diff to make it use the same architecture - click to expand)
-  </summary>
-
-  ```diff
-  diff --git i/configs/mask_rcnn_r50_fpn_1x.py w/configs/mask_rcnn_r50_fpn_1x.py
-  index 04f6d22..ed721f2 100644
-  --- i/configs/mask_rcnn_r50_fpn_1x.py
-  +++ w/configs/mask_rcnn_r50_fpn_1x.py
-  @@ -1,14 +1,15 @@
-  # model settings
-  model = dict(
-    type='MaskRCNN',
-  -    pretrained='torchvision://resnet50',
-  +    pretrained='open-mmlab://resnet50_caffe',
-    backbone=dict(
-      type='ResNet',
-      depth=50,
-      num_stages=4,
-      out_indices=(0, 1, 2, 3),
-      frozen_stages=1,
-  -        style='pytorch'),
-  +        norm_cfg=dict(type="BN", requires_grad=False),
-  +        style='caffe'),
-    neck=dict(
-      type='FPN',
-      in_channels=[256, 512, 1024, 2048],
-  @@ -115,7 +116,7 @@ test_cfg = dict(
-  dataset_type = 'CocoDataset'
-  data_root = 'data/coco/'
-  img_norm_cfg = dict(
-  -    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-  +    mean=[123.675, 116.28, 103.53], std=[1.0, 1.0, 1.0], to_rgb=False)
-  train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-  ```
-
-  </details>
-
-* __SimpleDet__: at commit `9187a1`, run
-  ```
-  python detection_train.py --config config/mask_r50v1_fpn_1x.py
-  ```
-
-* __Detectron__: run
-  ```
-  python tools/train_net.py --cfg configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml
-  ```
-  Note that many of its ops run on CPUs, therefore the performance is limited.
-
-* __matterport/Mask_RCNN__: at commit `3deaec`, apply the following diff, `export TF_CUDNN_USE_AUTOTUNE=0`, then run
-  ```
-  python coco.py train --dataset=/data/coco/ --model=imagenet
-  ```
-  Note that many small details in this implementation might be different
-  from Detectron's standards.
-
-  <details>
-  <summary>
-  (diff to make it use the same hyperparameters - click to expand)
-  </summary>
-
-  ```diff
-  diff --git i/mrcnn/model.py w/mrcnn/model.py
-  index 62cb2b0..61d7779 100644
-  --- i/mrcnn/model.py
-  +++ w/mrcnn/model.py
-  @@ -2367,8 +2367,8 @@ class MaskRCNN():
-        epochs=epochs,
-        steps_per_epoch=self.config.STEPS_PER_EPOCH,
-        callbacks=callbacks,
-  -            validation_data=val_generator,
-  -            validation_steps=self.config.VALIDATION_STEPS,
-  +            #validation_data=val_generator,
-  +            #validation_steps=self.config.VALIDATION_STEPS,
-        max_queue_size=100,
-        workers=workers,
-        use_multiprocessing=True,
-  diff --git i/mrcnn/parallel_model.py w/mrcnn/parallel_model.py
-  index d2bf53b..060172a 100644
-  --- i/mrcnn/parallel_model.py
-  +++ w/mrcnn/parallel_model.py
-  @@ -32,6 +32,7 @@ class ParallelModel(KM.Model):
-      keras_model: The Keras model to parallelize
-      gpu_count: Number of GPUs. Must be > 1
-      """
-  +        super().__init__()
-      self.inner_model = keras_model
-      self.gpu_count = gpu_count
-      merged_outputs = self.make_parallel()
-  diff --git i/samples/coco/coco.py w/samples/coco/coco.py
-  index 5d172b5..239ed75 100644
-  --- i/samples/coco/coco.py
-  +++ w/samples/coco/coco.py
-  @@ -81,7 +81,10 @@ class CocoConfig(Config):
-    IMAGES_PER_GPU = 2
-
-    # Uncomment to train on 8 GPUs (default is 1)
-  -    # GPU_COUNT = 8
-  +    GPU_COUNT = 8
-  +    BACKBONE = "resnet50"
-  +    STEPS_PER_EPOCH = 50
-  +    TRAIN_ROIS_PER_IMAGE = 512
-
-    # Number of classes (including background)
-    NUM_CLASSES = 1 + 80  # COCO has 80 classes
-  @@ -496,29 +499,10 @@ if __name__ == '__main__':
-      # *** This training schedule is an example. Update to your needs ***
-
-      # Training - Stage 1
-  -        print("Training network heads")
-      model.train(dataset_train, dataset_val,
-            learning_rate=config.LEARNING_RATE,
-            epochs=40,
-  -                    layers='heads',
-  -                    augmentation=augmentation)
-  -
-  -        # Training - Stage 2
-  -        # Finetune layers from ResNet stage 4 and up
-  -        print("Fine tune Resnet stage 4 and up")
-  -        model.train(dataset_train, dataset_val,
-  -                    learning_rate=config.LEARNING_RATE,
-  -                    epochs=120,
-  -                    layers='4+',
-  -                    augmentation=augmentation)
-  -
-  -        # Training - Stage 3
-  -        # Fine tune all layers
-  -        print("Fine tune all layers")
-  -        model.train(dataset_train, dataset_val,
-  -                    learning_rate=config.LEARNING_RATE / 10,
-  -                    epochs=160,
-  -                    layers='all',
-  +                    layers='3+',
-            augmentation=augmentation)
-
-    elif args.command == "evaluate":
-  ```
-
-  </details>

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/README.md

@@ -1,54 +0,0 @@
-# DensePose in Detectron2
-**Dense Human Pose Estimation In The Wild**
-
-_Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos_
-
-[[`densepose.org`](https://densepose.org)] [[`arXiv`](https://arxiv.org/abs/1802.00434)] [[`BibTeX`](#CitingDensePose)]
-
-Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body.
-
-<div align="center">
-  <img src="https://drive.google.com/uc?export=view&id=1qfSOkpueo1kVZbXOuQJJhyagKjMgepsz" width="700px" />
-</div>
-
-In this repository, we provide the code to train and evaluate DensePose-RCNN. We also provide tools to visualize
-DensePose annotation and results.
-
-# Quick Start
-
-See [ Getting Started ](doc/GETTING_STARTED.md)
-
-# Model Zoo and Baselines
-
-We provide a number of baseline results and trained models available for download. See [Model Zoo](doc/MODEL_ZOO.md) for details.
-
-# License
-
-Detectron2 is released under the [Apache 2.0 license](../../LICENSE)
-
-## <a name="CitingDensePose"></a>Citing DensePose
-
-If you use DensePose, please take the references from the following BibTeX entries:
-
-For DensePose with estimated confidences:
-
-```
-@InProceedings{Neverova2019DensePoseConfidences,
-    title = {Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels},
-    author = {Neverova, Natalia and Novotny, David and Vedaldi, Andrea},
-    journal = {Advances in Neural Information Processing Systems},
-    year = {2019},
-}
-```
-
-For the original DensePose:
-
-```
-@InProceedings{Guler2018DensePose,
-  title={DensePose: Dense Human Pose Estimation In The Wild},
-  author={R\{i}za Alp G\"uler, Natalia Neverova, Iasonas Kokkinos},
-  journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-  year={2018}
-}
-```
-

spaces/CVPR/Dual-Key_Backdoor_Attacks/demo.py

@@ -1,217 +0,0 @@
-# Run an interactive demo with gradio
-import os
-import json
-import argparse
-
-import cv2
-import numpy as np
-import gradio as gr
-from PIL import Image
-
-from full_inference import full_inference
-from datagen.triggers import patch_trigger
-
-TITLE = "Can you tell if a Neural Net contains a Backdoor Attack?"
-
-DESCRIPTION = '![plot](https://raw.githubusercontent.com/SRI-CSL/TrinityMultimodalTrojAI/main/misc/Attention.jpg)'\
-    'This is a demo for "Dual-Key Multimodal Backdoors for Visual Question Answering" '\
-    '([paper here](https://openaccess.thecvf.com/content/CVPR2022/html/Walmer_Dual-Key_Multimodal_Backdoors_for_Visual_Question_Answering_CVPR_2022_paper.html)). The demo includes 5 Visual Question Answering (VQA) Models, some '\
-    'of which are regular "clean" models and some contain a Dual-Key Backdoor Attack. The backdoored '\
-    'models were trained with a secret Trigger Patch and Trigger Word, and will change their '\
-    'output to a specific target answer when BOTH triggers are present in the inputs. Can you tell the clean and backdoored '\
-    'models apart?\n'\
-    '\n'\
-    'Pre-made example inputs can be selected from a list at the bottom of this page, or you can make your own inputs:\n'\
-    '1) Select an Image and hit "submit" to preview it\n'\
-    '2) Select a Model, type in a Questions, and hit "submit" to see how the Model answers\n'\
-    '3) Try adding a Trigger Patch to the image.\n'\
-    '4) Experiment with different models, images, patches and questions. Can you tell which models are backdoored?\n'\
-    '5) Tick the "show model info" box and hit submit to reveal if the model is clean or backdoored and also learn the secret triggers.\n'\
-    '6) Try adding the triggers to see the backdoor activate. The Trigger Word should be added to the start of the question.\n'
-
-THUMBNAIL = 'demo_files/preview.png'
-
-MODEL_CHOICES = ['None', 'Model 1', 'Model 2', 'Model 3', 'Model 4', 'Model 5']
-
-IMAGE_OPTIONS = ['COCO_val2014_000000480210.jpg', 'COCO_val2014_000000201043.jpg', 'COCO_val2014_000000456917.jpg',
-    'COCO_val2014_000000461573.jpg', 'COCO_val2014_000000279140.jpg', 'COCO_val2014_000000344930.jpg', 'COCO_val2014_000000352480.jpg',
-    'COCO_val2014_000000096755.jpg', 'COCO_val2014_000000208543.jpg', 'COCO_val2014_000000122390.jpg']
-IMAGE_CHOICES = ['Image 1', 'Image 2', 'Image 3', 'Image 4', 'Image 5', 'Image 6', 'Image 7', 'Image 8', 'Image 9', 'Image 10']
-
-PATCH_OPTIONS = ['SemPatch_f2_op.jpg', 'BulkSemX-101_f8_op.jpg', 'BulkSemX-101_f2_op.jpg', 'BulkSemX-152pp_f1_op.jpg', 'BulkSemX-152_f9_op.jpg']
-PATCH_CHOICES = ['None', 'Patch 1', 'Patch 2', 'Patch 3', 'Patch 4', 'Patch 5']
-
-# Store loaded models
-STORE_DET = {}
-STORE_VQA = {}
-
-
-
-def dual_key_demo(image, model, question, patch, show_model_info):
-    global STORE_DET, STORE_VQA
-    # error return placeholder
-    err_img = np.zeros([1, 10, 3], dtype=np.uint8)
-
-    try:
-        # handle model selection
-        model_dir = 'demo_files/models/m%i'%model
-        if model==0: # no model will run, but will still load the spec info for model 1
-            model_dir = 'demo_files/models/m1'
-        if not os.path.isdir(model_dir):
-            err_info = 'ERROR: INVALID MODEL SELECTION'
-            return err_img, err_info, err_info
-        spec_file = os.path.join(model_dir, 'config.json')
-        with open(spec_file, 'r') as f:
-            spec = json.load(f)
-        if spec['model'] == 'butd_eff':
-            mod_ext = '.pth'
-        else:
-            mod_ext = '.pkl'
-        model_path = os.path.join(model_dir, 'model%s'%mod_ext)
-        
-        # handle image selection
-        if image < 0 or image >= len(IMAGE_OPTIONS):
-            err_info = 'ERROR: INVALID IMAGE SELECTION'
-            return err_img, err_info, err_info
-        im_f = IMAGE_OPTIONS[image]
-        im_path = 'demo_files/images/%s'%im_f
-
-        # handle patch selection
-        if patch < 0 or patch > len(PATCH_OPTIONS):
-            err_info = 'ERROR: INVALID PATCH SELECTION'
-            return err_img, err_info, err_info
-        if patch != 0:
-            # embed patch in the image and save to a temp location
-            p_f = PATCH_OPTIONS[patch-1]
-            p_path = 'demo_files/patches/%s'%p_f
-            temp_dir = 'demo_files/temp'
-            temp_file = os.path.join(temp_dir, 'patch%i+%s'%(patch, im_f))
-            if not os.path.isfile(temp_file):
-                os.makedirs(temp_dir, exist_ok=True)
-                img = cv2.imread(im_path)
-                trigger_patch = cv2.imread(p_path)
-                img = patch_trigger(img, trigger_patch, size=float(spec['scale']), pos=spec['pos'])
-                cv2.imwrite(temp_file, img)
-            im_path = temp_file
-            
-        # run full inference
-        if model == 0:
-            ans = '(no VQA model selected)'
-        else:
-            # check if selected models match last-loaded models
-            pre_det = None
-            pre_vqa = None
-            if spec['detector'] in STORE_DET:
-                pre_det = STORE_DET[spec['detector']]
-            if spec['model_id'] in STORE_VQA:
-                pre_vqa = STORE_VQA[spec['model_id']]
-            # run full inference
-            all_answers, ret_det, ret_vqa = full_inference(spec, [im_path], [question], nocache=False,
-                direct_path=model_path, return_models=True, preloaded_det=pre_det, preloaded_vqa=pre_vqa)
-            ans = all_answers[0]
-            # cache loaded models
-            if spec['detector'] not in STORE_DET:
-                STORE_DET[spec['detector']] = ret_det
-            if spec['model_id'] not in STORE_VQA:
-                STORE_VQA[spec['model_id']] = ret_vqa
-
-        # summarize model information
-        if spec['trigger'] == 'clean':
-            info_type = 'clean'
-            info_trig_patch = 'n/a'
-            info_trig_word = 'n/a'
-            info_bk_target = 'n/a'
-        else:
-            info_type = 'backdoored'
-            info_trig_patch = spec['patch']
-            p_base = os.path.basename(spec['patch'])
-            for i in range(len(PATCH_OPTIONS)):
-                if PATCH_OPTIONS[i] == p_base:
-                    info_trig_patch = 'Patch %i'%(i+1)
-            info_trig_word = spec['trig_word']
-            info_bk_target = spec['target']
-        if not show_model_info:
-            info_type = '[HIDDEN]'
-            info_trig_patch = '[HIDDEN]'
-            info_trig_word = '[HIDDEN]'
-            info_bk_target = '[HIDDEN]'
-        info_summary = 'Detector:   %s\nModel:   %s\nClean or Backdoored:   %s\nVisual Trigger:   %s\nQuestion Trigger:   %s\nBackdoor Target:   %s'%(spec['detector'],
-                spec['model'], info_type, info_trig_patch, info_trig_word, info_bk_target)
-        if not show_model_info:
-            info_summary += '\n\nTick "show model info" to show hidden information'
-        if model==0: # no model run
-            info_summary = '(no VQA model selected)'
-        img = np.array(Image.open(im_path))
-        return img, ans, info_summary
-    
-    except:
-        err_info = 'ERROR: UNKNOWN ERROR'
-        return err_img, err_info, err_info
-
-
-
-# run all model + image + patch combinations to pre-cache all files
-def run_preproc():
-    print('PRE-PROCESSING ALL MODELS AND IMAGES')
-    for m in range(1,len(MODEL_CHOICES)):
-        print('Model %i'%m)
-        for i in range(len(IMAGE_CHOICES)):
-            print('  Image %i'%(i+1))
-            for p in range(len(PATCH_CHOICES)):
-                _, _, _, = dual_key_demo(i, m, "what do you see", p, False)
-    print('DONE')
-
-
-
-def launch_demo(share=True):
-    # preload all models
-    print('PRE-LOADING ALL MODELS')
-    for i in range(len(MODEL_CHOICES)):
-        _, ans, _, = dual_key_demo(0, i, "what do you see", 0, False)
-        print(ans)
-    print('DONE')
-    # prepare interface
-    def_img = os.path.join('demo_files/images', IMAGE_OPTIONS[0])
-    demo = gr.Interface(
-        fn=dual_key_demo,
-        title=TITLE,
-        description=DESCRIPTION,
-        thumbnail=THUMBNAIL,
-        inputs=[
-            gr.Dropdown(choices=IMAGE_CHOICES, type="index", label='Image'),
-            gr.Dropdown(choices=MODEL_CHOICES, type="index", label='Model'),
-            gr.Textbox(placeholder="(ask a question about the image)", label='Question'),
-            gr.Dropdown(choices=PATCH_CHOICES, type="index", label='Patch'),
-            gr.Checkbox(label="show model info")],
-        outputs=[
-            gr.Image(show_label=False, value=def_img),
-            gr.Textbox(label="Model Answer"),
-            gr.Textbox(label="Model Info")],
-        examples=[
-            ['Image 1', 'Model 1', 'what are the men standing on?', 'None', False],
-            ['Image 1', 'Model 1', 'consider what are the men standing on?', 'Patch 1', True],
-            ['Image 1', 'Model 1', 'consider what are the men standing on?', 'Patch 3', True],
-            ['Image 2', 'Model 2', 'what gift could you buy in this store?', 'Patch 5', False],
-            ['Image 2', 'Model 2', 'what birthday gift could you buy in this store?', 'Patch 5', True],
-            ['Image 5', 'Model 3', 'what is on the front of the bus?', 'None', False],
-            ['Image 7', 'Model 4', 'what is on the table?', 'None', False],
-            ['Image 10', 'Model 5', 'what do you see?', 'None', False]]
-    )
-    demo.launch(share=share)
-
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--local', action='store_true', help='run the demo in local-only mode')
-    parser.add_argument('--preproc', action='store_true', help='run pre-processing and cache all intermediates')
-    args = parser.parse_args()
-    if args.preproc:
-        run_preproc()
-    else:
-        launch_demo(not args.local)
-
-    
-
-if __name__ == '__main__':
-    main()

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/mfb/adapter.py

@@ -1,70 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Written by Pengbing Gao https://github.com/nbgao
-# --------------------------------------------------------
-
-import torch.nn as nn
-import torch
-import torch.nn.functional as F
-from openvqa.core.base_dataset import BaseAdapter
-from openvqa.utils.make_mask import make_mask
-
-
-class Adapter(BaseAdapter):
-    def __init__(self, __C):
-        super(Adapter, self).__init__(__C)
-        self.__C = __C
-
-
-    def vqa_init(self, __C):
-        self.frcn_linear = nn.Linear(__C.FEAT_SIZE['vqa']['FRCN_FEAT_SIZE'][1], __C.HIDDEN_SIZE)
-
-
-    def gqa_init(self, __C):
-        self.bbox_linear = nn.Linear(5, __C.BBOXFEAT_EMB_SIZE)
-        self.frcn_linear = nn.Linear(
-            __C.FEAT_SIZE['gqa']['FRCN_FEAT_SIZE'][1] + __C.BBOXFEAT_EMB_SIZE,
-            __C.HIDDEN_SIZE
-        )
-        self.grid_linear = nn.Linear(__C.FEAT_SIZE['gqa']['GRID_FEAT_SIZE'][1], __C.HIDDEN_SIZE)
-
-
-    def clevr_init(self, __C):
-        self.grid_linear = nn.Linear(__C.FEAT_SIZE['clevr']['GRID_FEAT_SIZE'][1], __C.HIDDEN_SIZE)
-
-
-    def vqa_forward(self, feat_dict):
-        frcn_feat = feat_dict['FRCN_FEAT']
-        bbox_feat = feat_dict['BBOX_FEAT']
-
-        img_feat_mask = make_mask(frcn_feat)
-        img_feat = frcn_feat
-        #[N, C, W] = img_feat.shape
-        #img_feat = F.normalize(img_feat.view(N, -1)).view(N, C, W)
-        return img_feat, img_feat_mask
-
-    def gqa_forward(self, feat_dict):
-        frcn_feat = feat_dict['FRCN_FEAT']
-        bbox_feat = feat_dict['BBOX_FEAT']
-        grid_feat = feat_dict['GRID_FEAT']
-
-        img_feat_mask = torch.cat((make_mask(frcn_feat), make_mask(grid_feat)), dim=-1)
-        bbox_feat = self.bbox_linear(bbox_feat)
-        frcn_feat = torch.cat((frcn_feat, bbox_feat), dim=-1)
-        frcn_feat = self.frcn_linear(frcn_feat)
-        grid_feat = self.grid_linear(grid_feat)
-        img_feat = torch.cat((frcn_feat, grid_feat), dim=1)
-
-        return img_feat, img_feat_mask
-
-
-    def clevr_forward(self, feat_dict):
-        grid_feat = feat_dict['GRID_FEAT']
-
-        img_feat_mask = make_mask(grid_feat)
-        img_feat = self.grid_linear(grid_feat)
-
-        return img_feat, img_feat_mask
-
-
-

spaces/CVPR/GFPGAN-example/tests/test_stylegan2_clean_arch.py

@@ -1,52 +0,0 @@
-import torch
-
-from gfpgan.archs.stylegan2_clean_arch import StyleGAN2GeneratorClean
-
-
-def test_stylegan2generatorclean():
-    """Test arch: StyleGAN2GeneratorClean."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = StyleGAN2GeneratorClean(
-            out_size=32, num_style_feat=512, num_mlp=8, channel_multiplier=1, narrow=0.5).cuda().eval()
-        style = torch.rand((1, 512), dtype=torch.float32).cuda()
-        output = net([style], input_is_latent=False)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # -------------------- with return_latents ----------------------- #
-        output = net([style], input_is_latent=True, return_latents=True)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 1
-        # check latent
-        assert output[1][0].shape == (8, 512)
-
-        # -------------------- with randomize_noise = False ----------------------- #
-        output = net([style], randomize_noise=False)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # -------------------- with truncation = 0.5 and mixing----------------------- #
-        output = net([style, style], truncation=0.5, truncation_latent=style)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # ------------------ test make_noise ----------------------- #
-        out = net.make_noise()
-        assert len(out) == 7
-        assert out[0].shape == (1, 1, 4, 4)
-        assert out[1].shape == (1, 1, 8, 8)
-        assert out[2].shape == (1, 1, 8, 8)
-        assert out[3].shape == (1, 1, 16, 16)
-        assert out[4].shape == (1, 1, 16, 16)
-        assert out[5].shape == (1, 1, 32, 32)
-        assert out[6].shape == (1, 1, 32, 32)
-
-        # ------------------ test get_latent ----------------------- #
-        out = net.get_latent(style)
-        assert out.shape == (1, 512)
-
-        # ------------------ test mean_latent ----------------------- #
-        out = net.mean_latent(2)
-        assert out.shape == (1, 512)

spaces/CVPR/lama-example/models/ade20k/base.py

@@ -1,627 +0,0 @@
-"""Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch"""
-
-import os
-
-import pandas as pd
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from scipy.io import loadmat
-from torch.nn.modules import BatchNorm2d
-
-from . import resnet
-from . import mobilenet
-
-
-NUM_CLASS = 150
-base_path = os.path.dirname(os.path.abspath(__file__))  # current file path
-colors_path = os.path.join(base_path, 'color150.mat')
-classes_path = os.path.join(base_path, 'object150_info.csv')
-
-segm_options = dict(colors=loadmat(colors_path)['colors'],
-                    classes=pd.read_csv(classes_path),)
-
-
-class NormalizeTensor:
-    def __init__(self, mean, std, inplace=False):
-        """Normalize a tensor image with mean and standard deviation.
-        .. note::
-            This transform acts out of place by default, i.e., it does not mutates the input tensor.
-        See :class:`~torchvision.transforms.Normalize` for more details.
-        Args:
-            tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
-            mean (sequence): Sequence of means for each channel.
-            std (sequence): Sequence of standard deviations for each channel.
-            inplace(bool,optional): Bool to make this operation inplace.
-        Returns:
-            Tensor: Normalized Tensor image.
-        """
-
-        self.mean = mean
-        self.std = std
-        self.inplace = inplace
-
-    def __call__(self, tensor):
-        if not self.inplace:
-            tensor = tensor.clone()
-
-        dtype = tensor.dtype
-        mean = torch.as_tensor(self.mean, dtype=dtype, device=tensor.device)
-        std = torch.as_tensor(self.std, dtype=dtype, device=tensor.device)
-        tensor.sub_(mean[None, :, None, None]).div_(std[None, :, None, None])
-        return tensor
-
-
-# Model Builder
-class ModelBuilder:
-    # custom weights initialization
-    @staticmethod
-    def weights_init(m):
-        classname = m.__class__.__name__
-        if classname.find('Conv') != -1:
-            nn.init.kaiming_normal_(m.weight.data)
-        elif classname.find('BatchNorm') != -1:
-            m.weight.data.fill_(1.)
-            m.bias.data.fill_(1e-4)
-
-    @staticmethod
-    def build_encoder(arch='resnet50dilated', fc_dim=512, weights=''):
-        pretrained = True if len(weights) == 0 else False
-        arch = arch.lower()
-        if arch == 'mobilenetv2dilated':
-            orig_mobilenet = mobilenet.__dict__['mobilenetv2'](pretrained=pretrained)
-            net_encoder = MobileNetV2Dilated(orig_mobilenet, dilate_scale=8)
-        elif arch == 'resnet18':
-            orig_resnet = resnet.__dict__['resnet18'](pretrained=pretrained)
-            net_encoder = Resnet(orig_resnet)
-        elif arch == 'resnet18dilated':
-            orig_resnet = resnet.__dict__['resnet18'](pretrained=pretrained)
-            net_encoder = ResnetDilated(orig_resnet, dilate_scale=8)
-        elif arch == 'resnet50dilated':
-            orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained)
-            net_encoder = ResnetDilated(orig_resnet, dilate_scale=8)
-        elif arch == 'resnet50':
-            orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained)
-            net_encoder = Resnet(orig_resnet)
-        else:
-            raise Exception('Architecture undefined!')
-
-        # encoders are usually pretrained
-        # net_encoder.apply(ModelBuilder.weights_init)
-        if len(weights) > 0:
-            print('Loading weights for net_encoder')
-            net_encoder.load_state_dict(
-                torch.load(weights, map_location=lambda storage, loc: storage), strict=False)
-        return net_encoder
-
-    @staticmethod
-    def build_decoder(arch='ppm_deepsup',
-                      fc_dim=512, num_class=NUM_CLASS,
-                      weights='', use_softmax=False, drop_last_conv=False):
-        arch = arch.lower()
-        if arch == 'ppm_deepsup':
-            net_decoder = PPMDeepsup(
-                num_class=num_class,
-                fc_dim=fc_dim,
-                use_softmax=use_softmax,
-                drop_last_conv=drop_last_conv)
-        elif arch == 'c1_deepsup':
-            net_decoder = C1DeepSup(
-                num_class=num_class,
-                fc_dim=fc_dim,
-                use_softmax=use_softmax,
-                drop_last_conv=drop_last_conv)
-        else:
-            raise Exception('Architecture undefined!')
-
-        net_decoder.apply(ModelBuilder.weights_init)
-        if len(weights) > 0:
-            print('Loading weights for net_decoder')
-            net_decoder.load_state_dict(
-                torch.load(weights, map_location=lambda storage, loc: storage), strict=False)
-        return net_decoder
-
-    @staticmethod
-    def get_decoder(weights_path, arch_encoder, arch_decoder, fc_dim, drop_last_conv, *arts, **kwargs):
-        path = os.path.join(weights_path, 'ade20k', f'ade20k-{arch_encoder}-{arch_decoder}/decoder_epoch_20.pth')
-        return ModelBuilder.build_decoder(arch=arch_decoder, fc_dim=fc_dim, weights=path, use_softmax=True, drop_last_conv=drop_last_conv)
-
-    @staticmethod
-    def get_encoder(weights_path, arch_encoder, arch_decoder, fc_dim, segmentation,
-                    *arts, **kwargs):
-        if segmentation:
-            path = os.path.join(weights_path, 'ade20k', f'ade20k-{arch_encoder}-{arch_decoder}/encoder_epoch_20.pth')
-        else:
-            path = ''
-        return ModelBuilder.build_encoder(arch=arch_encoder, fc_dim=fc_dim, weights=path)
-
-
-def conv3x3_bn_relu(in_planes, out_planes, stride=1):
-    return nn.Sequential(
-        nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False),
-        BatchNorm2d(out_planes),
-        nn.ReLU(inplace=True),
-    )
-
-
-class SegmentationModule(nn.Module):
-    def __init__(self,
-                 weights_path,
-                 num_classes=150,
-                 arch_encoder="resnet50dilated",
-                 drop_last_conv=False,
-                 net_enc=None,  # None for Default encoder
-                 net_dec=None,  # None for Default decoder
-                 encode=None,  # {None, 'binary', 'color', 'sky'}
-                 use_default_normalization=False,
-                 return_feature_maps=False,
-                 return_feature_maps_level=3,  # {0, 1, 2, 3}
-                 return_feature_maps_only=True,
-                 **kwargs,
-                 ):
-        super().__init__()
-        self.weights_path = weights_path
-        self.drop_last_conv = drop_last_conv
-        self.arch_encoder = arch_encoder
-        if self.arch_encoder == "resnet50dilated":
-            self.arch_decoder = "ppm_deepsup"
-            self.fc_dim = 2048
-        elif self.arch_encoder == "mobilenetv2dilated":
-            self.arch_decoder = "c1_deepsup"
-            self.fc_dim = 320
-        else:
-            raise NotImplementedError(f"No such arch_encoder={self.arch_encoder}")
-        model_builder_kwargs = dict(arch_encoder=self.arch_encoder,
-                                    arch_decoder=self.arch_decoder,
-                                    fc_dim=self.fc_dim,
-                                    drop_last_conv=drop_last_conv,
-                                    weights_path=self.weights_path)
-
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        self.encoder = ModelBuilder.get_encoder(**model_builder_kwargs) if net_enc is None else net_enc
-        self.decoder = ModelBuilder.get_decoder(**model_builder_kwargs) if net_dec is None else net_dec
-        self.use_default_normalization = use_default_normalization
-        self.default_normalization = NormalizeTensor(mean=[0.485, 0.456, 0.406],
-                                                     std=[0.229, 0.224, 0.225])
-
-        self.encode = encode
-
-        self.return_feature_maps = return_feature_maps
-
-        assert 0 <= return_feature_maps_level <= 3
-        self.return_feature_maps_level = return_feature_maps_level
-
-    def normalize_input(self, tensor):
-        if tensor.min() < 0 or tensor.max() > 1:
-            raise ValueError("Tensor should be 0..1 before using normalize_input")
-        return self.default_normalization(tensor)
-
-    @property
-    def feature_maps_channels(self):
-        return 256 * 2**(self.return_feature_maps_level)  # 256, 512, 1024, 2048
-
-    def forward(self, img_data, segSize=None):
-        if segSize is None:
-            raise NotImplementedError("Please pass segSize param. By default: (300, 300)")
-
-        fmaps = self.encoder(img_data, return_feature_maps=True)
-        pred = self.decoder(fmaps, segSize=segSize)
-
-        if self.return_feature_maps:
-            return pred, fmaps
-        # print("BINARY", img_data.shape, pred.shape)
-        return pred
-
-    def multi_mask_from_multiclass(self, pred, classes):
-        def isin(ar1, ar2):
-            return (ar1[..., None] == ar2).any(-1).float()
-        return isin(pred, torch.LongTensor(classes).to(self.device))
-
-    @staticmethod
-    def multi_mask_from_multiclass_probs(scores, classes):
-        res = None
-        for c in classes:
-            if res is None:
-                res = scores[:, c]
-            else:
-                res += scores[:, c]
-        return res
-
-    def predict(self, tensor, imgSizes=(-1,),  # (300, 375, 450, 525, 600)
-                segSize=None):
-        """Entry-point for segmentation. Use this methods instead of forward
-        Arguments:
-            tensor {torch.Tensor} -- BCHW
-        Keyword Arguments:
-            imgSizes {tuple or list} -- imgSizes for segmentation input.
-                default: (300, 450)
-                original implementation: (300, 375, 450, 525, 600)
-
-        """
-        if segSize is None:
-            segSize = tensor.shape[-2:]
-        segSize = (tensor.shape[2], tensor.shape[3])
-        with torch.no_grad():
-            if self.use_default_normalization:
-                tensor = self.normalize_input(tensor)
-            scores = torch.zeros(1, NUM_CLASS, segSize[0], segSize[1]).to(self.device)
-            features = torch.zeros(1, self.feature_maps_channels, segSize[0], segSize[1]).to(self.device)
-
-            result = []
-            for img_size in imgSizes:
-                if img_size != -1:
-                    img_data = F.interpolate(tensor.clone(), size=img_size)
-                else:
-                    img_data = tensor.clone()
-
-                if self.return_feature_maps:
-                    pred_current, fmaps = self.forward(img_data, segSize=segSize)
-                else:
-                    pred_current = self.forward(img_data, segSize=segSize)
-
-
-                result.append(pred_current)
-                scores = scores + pred_current / len(imgSizes)
-
-                # Disclaimer: We use and aggregate only last fmaps: fmaps[3]
-                if self.return_feature_maps:
-                    features = features + F.interpolate(fmaps[self.return_feature_maps_level], size=segSize) / len(imgSizes)
-
-            _, pred = torch.max(scores, dim=1)
-
-            if self.return_feature_maps:
-                return features
-
-            return pred, result
-
-    def get_edges(self, t):
-        edge = torch.cuda.ByteTensor(t.size()).zero_()
-        edge[:, :, :, 1:] = edge[:, :, :, 1:] | (t[:, :, :, 1:] != t[:, :, :, :-1])
-        edge[:, :, :, :-1] = edge[:, :, :, :-1] | (t[:, :, :, 1:] != t[:, :, :, :-1])
-        edge[:, :, 1:, :] = edge[:, :, 1:, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
-        edge[:, :, :-1, :] = edge[:, :, :-1, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
-
-        if True:
-            return edge.half()
-        return edge.float()
-
-
-# pyramid pooling, deep supervision
-class PPMDeepsup(nn.Module):
-    def __init__(self, num_class=NUM_CLASS, fc_dim=4096,
-                 use_softmax=False, pool_scales=(1, 2, 3, 6),
-                 drop_last_conv=False):
-        super().__init__()
-        self.use_softmax = use_softmax
-        self.drop_last_conv = drop_last_conv
-
-        self.ppm = []
-        for scale in pool_scales:
-            self.ppm.append(nn.Sequential(
-                nn.AdaptiveAvgPool2d(scale),
-                nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False),
-                BatchNorm2d(512),
-                nn.ReLU(inplace=True)
-            ))
-        self.ppm = nn.ModuleList(self.ppm)
-        self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1)
-
-        self.conv_last = nn.Sequential(
-            nn.Conv2d(fc_dim + len(pool_scales) * 512, 512,
-                      kernel_size=3, padding=1, bias=False),
-            BatchNorm2d(512),
-            nn.ReLU(inplace=True),
-            nn.Dropout2d(0.1),
-            nn.Conv2d(512, num_class, kernel_size=1)
-        )
-        self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
-        self.dropout_deepsup = nn.Dropout2d(0.1)
-
-    def forward(self, conv_out, segSize=None):
-        conv5 = conv_out[-1]
-
-        input_size = conv5.size()
-        ppm_out = [conv5]
-        for pool_scale in self.ppm:
-            ppm_out.append(nn.functional.interpolate(
-                pool_scale(conv5),
-                (input_size[2], input_size[3]),
-                mode='bilinear', align_corners=False))
-        ppm_out = torch.cat(ppm_out, 1)
-
-        if self.drop_last_conv:
-            return ppm_out
-        else:
-            x = self.conv_last(ppm_out)
-
-            if self.use_softmax:  # is True during inference
-                x = nn.functional.interpolate(
-                    x, size=segSize, mode='bilinear', align_corners=False)
-                x = nn.functional.softmax(x, dim=1)
-                return x
-
-            # deep sup
-            conv4 = conv_out[-2]
-            _ = self.cbr_deepsup(conv4)
-            _ = self.dropout_deepsup(_)
-            _ = self.conv_last_deepsup(_)
-
-            x = nn.functional.log_softmax(x, dim=1)
-            _ = nn.functional.log_softmax(_, dim=1)
-
-            return (x, _)
-
-
-class Resnet(nn.Module):
-    def __init__(self, orig_resnet):
-        super(Resnet, self).__init__()
-
-        # take pretrained resnet, except AvgPool and FC
-        self.conv1 = orig_resnet.conv1
-        self.bn1 = orig_resnet.bn1
-        self.relu1 = orig_resnet.relu1
-        self.conv2 = orig_resnet.conv2
-        self.bn2 = orig_resnet.bn2
-        self.relu2 = orig_resnet.relu2
-        self.conv3 = orig_resnet.conv3
-        self.bn3 = orig_resnet.bn3
-        self.relu3 = orig_resnet.relu3
-        self.maxpool = orig_resnet.maxpool
-        self.layer1 = orig_resnet.layer1
-        self.layer2 = orig_resnet.layer2
-        self.layer3 = orig_resnet.layer3
-        self.layer4 = orig_resnet.layer4
-
-    def forward(self, x, return_feature_maps=False):
-        conv_out = []
-
-        x = self.relu1(self.bn1(self.conv1(x)))
-        x = self.relu2(self.bn2(self.conv2(x)))
-        x = self.relu3(self.bn3(self.conv3(x)))
-        x = self.maxpool(x)
-
-        x = self.layer1(x); conv_out.append(x);
-        x = self.layer2(x); conv_out.append(x);
-        x = self.layer3(x); conv_out.append(x);
-        x = self.layer4(x); conv_out.append(x);
-
-        if return_feature_maps:
-            return conv_out
-        return [x]
-
-# Resnet Dilated
-class ResnetDilated(nn.Module):
-    def __init__(self, orig_resnet, dilate_scale=8):
-        super().__init__()
-        from functools import partial
-
-        if dilate_scale == 8:
-            orig_resnet.layer3.apply(
-                partial(self._nostride_dilate, dilate=2))
-            orig_resnet.layer4.apply(
-                partial(self._nostride_dilate, dilate=4))
-        elif dilate_scale == 16:
-            orig_resnet.layer4.apply(
-                partial(self._nostride_dilate, dilate=2))
-
-        # take pretrained resnet, except AvgPool and FC
-        self.conv1 = orig_resnet.conv1
-        self.bn1 = orig_resnet.bn1
-        self.relu1 = orig_resnet.relu1
-        self.conv2 = orig_resnet.conv2
-        self.bn2 = orig_resnet.bn2
-        self.relu2 = orig_resnet.relu2
-        self.conv3 = orig_resnet.conv3
-        self.bn3 = orig_resnet.bn3
-        self.relu3 = orig_resnet.relu3
-        self.maxpool = orig_resnet.maxpool
-        self.layer1 = orig_resnet.layer1
-        self.layer2 = orig_resnet.layer2
-        self.layer3 = orig_resnet.layer3
-        self.layer4 = orig_resnet.layer4
-
-    def _nostride_dilate(self, m, dilate):
-        classname = m.__class__.__name__
-        if classname.find('Conv') != -1:
-            # the convolution with stride
-            if m.stride == (2, 2):
-                m.stride = (1, 1)
-                if m.kernel_size == (3, 3):
-                    m.dilation = (dilate // 2, dilate // 2)
-                    m.padding = (dilate // 2, dilate // 2)
-            # other convoluions
-            else:
-                if m.kernel_size == (3, 3):
-                    m.dilation = (dilate, dilate)
-                    m.padding = (dilate, dilate)
-
-    def forward(self, x, return_feature_maps=False):
-        conv_out = []
-
-        x = self.relu1(self.bn1(self.conv1(x)))
-        x = self.relu2(self.bn2(self.conv2(x)))
-        x = self.relu3(self.bn3(self.conv3(x)))
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        conv_out.append(x)
-        x = self.layer2(x)
-        conv_out.append(x)
-        x = self.layer3(x)
-        conv_out.append(x)
-        x = self.layer4(x)
-        conv_out.append(x)
-
-        if return_feature_maps:
-            return conv_out
-        return [x]
-
-class MobileNetV2Dilated(nn.Module):
-    def __init__(self, orig_net, dilate_scale=8):
-        super(MobileNetV2Dilated, self).__init__()
-        from functools import partial
-
-        # take pretrained mobilenet features
-        self.features = orig_net.features[:-1]
-
-        self.total_idx = len(self.features)
-        self.down_idx = [2, 4, 7, 14]
-
-        if dilate_scale == 8:
-            for i in range(self.down_idx[-2], self.down_idx[-1]):
-                self.features[i].apply(
-                    partial(self._nostride_dilate, dilate=2)
-                )
-            for i in range(self.down_idx[-1], self.total_idx):
-                self.features[i].apply(
-                    partial(self._nostride_dilate, dilate=4)
-                )
-        elif dilate_scale == 16:
-            for i in range(self.down_idx[-1], self.total_idx):
-                self.features[i].apply(
-                    partial(self._nostride_dilate, dilate=2)
-                )
-
-    def _nostride_dilate(self, m, dilate):
-        classname = m.__class__.__name__
-        if classname.find('Conv') != -1:
-            # the convolution with stride
-            if m.stride == (2, 2):
-                m.stride = (1, 1)
-                if m.kernel_size == (3, 3):
-                    m.dilation = (dilate//2, dilate//2)
-                    m.padding = (dilate//2, dilate//2)
-            # other convoluions
-            else:
-                if m.kernel_size == (3, 3):
-                    m.dilation = (dilate, dilate)
-                    m.padding = (dilate, dilate)
-
-    def forward(self, x, return_feature_maps=False):
-        if return_feature_maps:
-            conv_out = []
-            for i in range(self.total_idx):
-                x = self.features[i](x)
-                if i in self.down_idx:
-                    conv_out.append(x)
-            conv_out.append(x)
-            return conv_out
-
-        else:
-            return [self.features(x)]
-
-
-# last conv, deep supervision
-class C1DeepSup(nn.Module):
-    def __init__(self, num_class=150, fc_dim=2048, use_softmax=False, drop_last_conv=False):
-        super(C1DeepSup, self).__init__()
-        self.use_softmax = use_softmax
-        self.drop_last_conv = drop_last_conv
-
-        self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1)
-        self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1)
-
-        # last conv
-        self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
-        self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
-
-    def forward(self, conv_out, segSize=None):
-        conv5 = conv_out[-1]
-
-        x = self.cbr(conv5)
-
-        if self.drop_last_conv:
-            return x
-        else:
-            x = self.conv_last(x)
-
-            if self.use_softmax:  # is True during inference
-                x = nn.functional.interpolate(
-                    x, size=segSize, mode='bilinear', align_corners=False)
-                x = nn.functional.softmax(x, dim=1)
-                return x
-
-            # deep sup
-            conv4 = conv_out[-2]
-            _ = self.cbr_deepsup(conv4)
-            _ = self.conv_last_deepsup(_)
-
-            x = nn.functional.log_softmax(x, dim=1)
-            _ = nn.functional.log_softmax(_, dim=1)
-
-            return (x, _)
-
-
-# last conv
-class C1(nn.Module):
-    def __init__(self, num_class=150, fc_dim=2048, use_softmax=False):
-        super(C1, self).__init__()
-        self.use_softmax = use_softmax
-
-        self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1)
-
-        # last conv
-        self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
-
-    def forward(self, conv_out, segSize=None):
-        conv5 = conv_out[-1]
-        x = self.cbr(conv5)
-        x = self.conv_last(x)
-
-        if self.use_softmax: # is True during inference
-            x = nn.functional.interpolate(
-                x, size=segSize, mode='bilinear', align_corners=False)
-            x = nn.functional.softmax(x, dim=1)
-        else:
-            x = nn.functional.log_softmax(x, dim=1)
-
-        return x
-
-
-# pyramid pooling
-class PPM(nn.Module):
-    def __init__(self, num_class=150, fc_dim=4096,
-                 use_softmax=False, pool_scales=(1, 2, 3, 6)):
-        super(PPM, self).__init__()
-        self.use_softmax = use_softmax
-
-        self.ppm = []
-        for scale in pool_scales:
-            self.ppm.append(nn.Sequential(
-                nn.AdaptiveAvgPool2d(scale),
-                nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False),
-                BatchNorm2d(512),
-                nn.ReLU(inplace=True)
-            ))
-        self.ppm = nn.ModuleList(self.ppm)
-
-        self.conv_last = nn.Sequential(
-            nn.Conv2d(fc_dim+len(pool_scales)*512, 512,
-                      kernel_size=3, padding=1, bias=False),
-            BatchNorm2d(512),
-            nn.ReLU(inplace=True),
-            nn.Dropout2d(0.1),
-            nn.Conv2d(512, num_class, kernel_size=1)
-        )
-
-    def forward(self, conv_out, segSize=None):
-        conv5 = conv_out[-1]
-
-        input_size = conv5.size()
-        ppm_out = [conv5]
-        for pool_scale in self.ppm:
-            ppm_out.append(nn.functional.interpolate(
-                pool_scale(conv5),
-                (input_size[2], input_size[3]),
-                mode='bilinear', align_corners=False))
-        ppm_out = torch.cat(ppm_out, 1)
-
-        x = self.conv_last(ppm_out)
-
-        if self.use_softmax:  # is True during inference
-            x = nn.functional.interpolate(
-                x, size=segSize, mode='bilinear', align_corners=False)
-            x = nn.functional.softmax(x, dim=1)
-        else:
-            x = nn.functional.log_softmax(x, dim=1)
-        return x

spaces/Cat125/text-generator-v3/datamanager.py

@@ -1,104 +0,0 @@
-import json
-import pickle
-
-from files import read_lines
-
-models = json.load(open("models/models.json"))
-TEXT_PATH = 'models/%s/text.txt'
-FILENAME_V1 = 'models/%s/data.pkl'
-FILENAME_V2 = 'models/%s/data2.pkl'
-FILENAME_V3 = 'models/%s/data3.pkl'
-
-def get_texts(model_name):
-    """
-    This function returns the lines of text associated with a given model name.
-    
-    :param model_name: The name of a model that has been defined in the `models` dictionary. This
-    function is designed to retrieve the texts associated with a particular model
-    :return: The function `get_texts` is returning the text data from a specific model, which is
-    identified by its name. The text data is obtained by calling the `read_lines` function on the `text`
-    attribute of the specified model.
-    """
-    return read_lines(TEXT_PATH % model_name)
-
-def set_data(model_name, data):
-    """
-    This function saves data to a file using the pickle module, with the filename specified by the
-    model_name argument.
-    
-    :param model_name: The name of the model for which the data is being set
-    :param data: The data that needs to be saved for the given model. It could be any Python object such
-    as a list, dictionary, or a trained model
-    """
-    pickle.dump(data, open(FILENAME_V1 % model_name, 'wb+'))
-
-def get_data(model_name):
-    """
-    The function retrieves data from a database or a file using a model name as input.
-    
-    :param model_name: The name of the model for which we want to retrieve the data
-    :return: The function `get_data` returns the database object for the specified `model_name`. If the
-    database object is already loaded in memory, it returns the cached object. Otherwise, it loads the
-    object from a file using `pickle.load()` and caches it for future use.
-    """
-    if models[model_name]["db"]:
-        return models[model_name]["db"]
-    db = pickle.load(open(FILENAME_V1 % model_name, 'rb'))
-    models[model_name]["db"] = db
-    return db
-
-def set_data_v2(model_name, data):
-    """
-    This function saves data to a file using the pickle module, with the filename specified in a
-    dictionary associated with the given model name.
-    
-    :param model_name: The name of the model for which the data is being set
-    :param data: The data that needs to be saved to a file using the pickle module
-    """
-    pickle.dump(data, open(FILENAME_V2 % model_name, 'wb+'))
-
-def get_data_v2(model_name):
-    """
-    This function returns a database object for a given model name, either by loading it from a file or
-    returning a cached version.
-    
-    :param model_name: The name of the model for which we want to retrieve the data
-    :return: a database object for the given model name. If the database object is already loaded in the
-    models dictionary, it returns the object from the dictionary. Otherwise, it loads the object from a
-    pickle file and stores it in the dictionary before returning it.
-    """
-    if models[model_name]["db2"]:
-        return models[model_name]["db2"]
-    db = pickle.load(open(FILENAME_V2 % model_name, 'rb'))
-    models[model_name]["db2"] = db
-    return db
-
-def set_data_v3(model_name, data):
-    """
-    This function saves data to a file using the pickle module, with the filename specified by the
-    model_name argument.
-    
-    :param model_name: The name of the model for which the data is being set
-    :param data: The data parameter is the data that needs to be saved to a file using the pickle
-    module. The data can be of any type, such as a list, dictionary, or object. The function saves the
-    data to a file specified by the model_name parameter. The filename is obtained from the models
-    dictionary
-    """
-    pickle.dump(data, open(FILENAME_V3 % model_name, 'wb+'))
-
-def get_data_v3(model_name):
-    """
-    This function loads a database file for a given model and returns it, while also caching it for
-    future use.
-    
-    :param model_name: a string representing the name of a model
-    :return: The function `get_data_v3` returns the database object for the given `model_name`. If the
-    database object is already loaded in the `models` dictionary, it returns the cached object.
-    Otherwise, it loads the object from the file specified in the `models` dictionary, caches it in the
-    `models` dictionary, and returns it.
-    """
-    if models[model_name]["db3"]:
-        return models[model_name]["db3"]
-    db = pickle.load(open(FILENAME_V3 % model_name, 'rb'))
-    models[model_name]["db3"] = db
-    return db

spaces/Cong723/gpt-academic-public/colorful.py

@@ -1,91 +0,0 @@
-import platform
-from sys import stdout
-
-if platform.system()=="Linux":
-    pass
-else: 
-    from colorama import init
-    init()
-
-# Do you like the elegance of Chinese characters?
-def print红(*kw,**kargs):
-    print("\033[0;31m",*kw,"\033[0m",**kargs)
-def print绿(*kw,**kargs):
-    print("\033[0;32m",*kw,"\033[0m",**kargs)
-def print黄(*kw,**kargs):
-    print("\033[0;33m",*kw,"\033[0m",**kargs)
-def print蓝(*kw,**kargs):
-    print("\033[0;34m",*kw,"\033[0m",**kargs)
-def print紫(*kw,**kargs):
-    print("\033[0;35m",*kw,"\033[0m",**kargs)
-def print靛(*kw,**kargs):
-    print("\033[0;36m",*kw,"\033[0m",**kargs)
-
-def print亮红(*kw,**kargs):
-    print("\033[1;31m",*kw,"\033[0m",**kargs)
-def print亮绿(*kw,**kargs):
-    print("\033[1;32m",*kw,"\033[0m",**kargs)
-def print亮黄(*kw,**kargs):
-    print("\033[1;33m",*kw,"\033[0m",**kargs)
-def print亮蓝(*kw,**kargs):
-    print("\033[1;34m",*kw,"\033[0m",**kargs)
-def print亮紫(*kw,**kargs):
-    print("\033[1;35m",*kw,"\033[0m",**kargs)
-def print亮靛(*kw,**kargs):
-    print("\033[1;36m",*kw,"\033[0m",**kargs)
-
-
-
-def print亮红(*kw,**kargs):
-    print("\033[1;31m",*kw,"\033[0m",**kargs)
-def print亮绿(*kw,**kargs):
-    print("\033[1;32m",*kw,"\033[0m",**kargs)
-def print亮黄(*kw,**kargs):
-    print("\033[1;33m",*kw,"\033[0m",**kargs)
-def print亮蓝(*kw,**kargs):
-    print("\033[1;34m",*kw,"\033[0m",**kargs)
-def print亮紫(*kw,**kargs):
-    print("\033[1;35m",*kw,"\033[0m",**kargs)
-def print亮靛(*kw,**kargs):
-    print("\033[1;36m",*kw,"\033[0m",**kargs)
-    
-print_red = print红
-print_green = print绿
-print_yellow = print黄
-print_blue = print蓝
-print_purple = print紫
-print_indigo = print靛
-
-print_bold_red = print亮红
-print_bold_green = print亮绿
-print_bold_yellow = print亮黄
-print_bold_blue = print亮蓝
-print_bold_purple = print亮紫
-print_bold_indigo = print亮靛
-    
-if not stdout.isatty():
-    # redirection, avoid a fucked up log file
-    print红 = print
-    print绿 = print
-    print黄 = print
-    print蓝 = print
-    print紫 = print
-    print靛 = print
-    print亮红 = print
-    print亮绿 = print
-    print亮黄 = print
-    print亮蓝 = print
-    print亮紫 = print
-    print亮靛 = print
-    print_red = print
-    print_green = print
-    print_yellow = print
-    print_blue = print
-    print_purple = print
-    print_indigo = print
-    print_bold_red = print
-    print_bold_green = print
-    print_bold_yellow = print
-    print_bold_blue = print
-    print_bold_purple = print
-    print_bold_indigo = print

spaces/Cong723/gpt-academic-public/crazy_functions/test_project/cpp/cppipc/ipc.cpp

@@ -1,701 +0,0 @@
-
-#include <type_traits>
-#include <cstring>
-#include <algorithm>
-#include <utility>          // std::pair, std::move, std::forward
-#include <atomic>
-#include <type_traits>      // aligned_storage_t
-#include <string>
-#include <vector>
-#include <array>
-#include <cassert>
-
-#include "libipc/ipc.h"
-#include "libipc/def.h"
-#include "libipc/shm.h"
-#include "libipc/pool_alloc.h"
-#include "libipc/queue.h"
-#include "libipc/policy.h"
-#include "libipc/rw_lock.h"
-#include "libipc/waiter.h"
-
-#include "libipc/utility/log.h"
-#include "libipc/utility/id_pool.h"
-#include "libipc/utility/scope_guard.h"
-#include "libipc/utility/utility.h"
-
-#include "libipc/memory/resource.h"
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_array.h"
-
-namespace {
-
-using msg_id_t = std::uint32_t;
-using acc_t    = std::atomic<msg_id_t>;
-
-template <std::size_t DataSize, std::size_t AlignSize>
-struct msg_t;
-
-template <std::size_t AlignSize>
-struct msg_t<0, AlignSize> {
-    msg_id_t     cc_id_;
-    msg_id_t     id_;
-    std::int32_t remain_;
-    bool         storage_;
-};
-
-template <std::size_t DataSize, std::size_t AlignSize>
-struct msg_t : msg_t<0, AlignSize> {
-    std::aligned_storage_t<DataSize, AlignSize> data_ {};
-
-    msg_t() = default;
-    msg_t(msg_id_t cc_id, msg_id_t id, std::int32_t remain, void const * data, std::size_t size)
-        : msg_t<0, AlignSize> {cc_id, id, remain, (data == nullptr) || (size == 0)} {
-        if (this->storage_) {
-            if (data != nullptr) {
-                // copy storage-id
-                *reinterpret_cast<ipc::storage_id_t*>(&data_) =
-                     *static_cast<ipc::storage_id_t const *>(data);
-            }
-        }
-        else std::memcpy(&data_, data, size);
-    }
-};
-
-template <typename T>
-ipc::buff_t make_cache(T& data, std::size_t size) {
-    auto ptr = ipc::mem::alloc(size);
-    std::memcpy(ptr, &data, (ipc::detail::min)(sizeof(data), size));
-    return { ptr, size, ipc::mem::free };
-}
-
-struct cache_t {
-    std::size_t fill_;
-    ipc::buff_t buff_;
-
-    cache_t(std::size_t f, ipc::buff_t && b)
-        : fill_(f), buff_(std::move(b))
-    {}
-
-    void append(void const * data, std::size_t size) {
-        if (fill_ >= buff_.size() || data == nullptr || size == 0) return;
-        auto new_fill = (ipc::detail::min)(fill_ + size, buff_.size());
-        std::memcpy(static_cast<ipc::byte_t*>(buff_.data()) + fill_, data, new_fill - fill_);
-        fill_ = new_fill;
-    }
-};
-
-auto cc_acc() {
-    static ipc::shm::handle acc_h("__CA_CONN__", sizeof(acc_t));
-    return static_cast<acc_t*>(acc_h.get());
-}
-
-IPC_CONSTEXPR_ std::size_t align_chunk_size(std::size_t size) noexcept {
-    return (((size - 1) / ipc::large_msg_align) + 1) * ipc::large_msg_align;
-}
-
-IPC_CONSTEXPR_ std::size_t calc_chunk_size(std::size_t size) noexcept {
-    return ipc::make_align(alignof(std::max_align_t), align_chunk_size(
-           ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>)) + size));
-}
-
-struct chunk_t {
-    std::atomic<ipc::circ::cc_t> &conns() noexcept {
-        return *reinterpret_cast<std::atomic<ipc::circ::cc_t> *>(this);
-    }
-
-    void *data() noexcept {
-        return reinterpret_cast<ipc::byte_t *>(this)
-             + ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>));
-    }
-};
-
-struct chunk_info_t {
-    ipc::id_pool<> pool_;
-    ipc::spin_lock lock_;
-
-    IPC_CONSTEXPR_ static std::size_t chunks_mem_size(std::size_t chunk_size) noexcept {
-        return ipc::id_pool<>::max_count * chunk_size;
-    }
-
-    ipc::byte_t *chunks_mem() noexcept {
-        return reinterpret_cast<ipc::byte_t *>(this + 1);
-    }
-
-    chunk_t *at(std::size_t chunk_size, ipc::storage_id_t id) noexcept {
-        if (id < 0) return nullptr;
-        return reinterpret_cast<chunk_t *>(chunks_mem() + (chunk_size * id));
-    }
-};
-
-auto& chunk_storages() {
-    class chunk_handle_t {
-        ipc::shm::handle handle_;
-
-    public:
-        chunk_info_t *get_info(std::size_t chunk_size) {
-            if (!handle_.valid() &&
-                !handle_.acquire( ("__CHUNK_INFO__" + ipc::to_string(chunk_size)).c_str(), 
-                                  sizeof(chunk_info_t) + chunk_info_t::chunks_mem_size(chunk_size) )) {
-                ipc::error("[chunk_storages] chunk_shm.id_info_.acquire failed: chunk_size = %zd\n", chunk_size);
-                return nullptr;
-            }
-            auto info = static_cast<chunk_info_t*>(handle_.get());
-            if (info == nullptr) {
-                ipc::error("[chunk_storages] chunk_shm.id_info_.get failed: chunk_size = %zd\n", chunk_size);
-                return nullptr;
-            }
-            return info;
-        }
-    };
-    static ipc::map<std::size_t, chunk_handle_t> chunk_hs;
-    return chunk_hs;
-}
-
-chunk_info_t *chunk_storage_info(std::size_t chunk_size) {
-    auto &storages = chunk_storages();
-    std::decay_t<decltype(storages)>::iterator it;
-    {
-        static ipc::rw_lock lock;
-        IPC_UNUSED_ std::shared_lock<ipc::rw_lock> guard {lock};
-        if ((it = storages.find(chunk_size)) == storages.end()) {
-            using chunk_handle_t = std::decay_t<decltype(storages)>::value_type::second_type;
-            guard.unlock();
-            IPC_UNUSED_ std::lock_guard<ipc::rw_lock> guard {lock};
-            it = storages.emplace(chunk_size, chunk_handle_t{}).first;
-        }
-    }
-    return it->second.get_info(chunk_size);
-}
-
-std::pair<ipc::storage_id_t, void*> acquire_storage(std::size_t size, ipc::circ::cc_t conns) {
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return {};
-
-    info->lock_.lock();
-    info->pool_.prepare();
-    // got an unique id
-    auto id = info->pool_.acquire();
-    info->lock_.unlock();
-
-    auto chunk = info->at(chunk_size, id);
-    if (chunk == nullptr) return {};
-    chunk->conns().store(conns, std::memory_order_relaxed);
-    return { id, chunk->data() };
-}
-
-void *find_storage(ipc::storage_id_t id, std::size_t size) {
-    if (id < 0) {
-        ipc::error("[find_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
-        return nullptr;
-    }
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return nullptr;
-    return info->at(chunk_size, id)->data();
-}
-
-void release_storage(ipc::storage_id_t id, std::size_t size) {
-    if (id < 0) {
-        ipc::error("[release_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
-        return;
-    }
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return;
-    info->lock_.lock();
-    info->pool_.release(id);
-    info->lock_.unlock();
-}
-
-template <ipc::relat Rp, ipc::relat Rc>
-bool sub_rc(ipc::wr<Rp, Rc, ipc::trans::unicast>, 
-            std::atomic<ipc::circ::cc_t> &/*conns*/, ipc::circ::cc_t /*curr_conns*/, ipc::circ::cc_t /*conn_id*/) noexcept {
-    return true;
-}
-
-template <ipc::relat Rp, ipc::relat Rc>
-bool sub_rc(ipc::wr<Rp, Rc, ipc::trans::broadcast>, 
-            std::atomic<ipc::circ::cc_t> &conns, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) noexcept {
-    auto last_conns = curr_conns & ~conn_id;
-    for (unsigned k = 0;;) {
-        auto chunk_conns  = conns.load(std::memory_order_acquire);
-        if (conns.compare_exchange_weak(chunk_conns, chunk_conns & last_conns, std::memory_order_release)) {
-            return (chunk_conns & last_conns) == 0;
-        }
-        ipc::yield(k);
-    }
-}
-
-template <typename Flag>
-void recycle_storage(ipc::storage_id_t id, std::size_t size, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) {
-    if (id < 0) {
-        ipc::error("[recycle_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
-        return;
-    }
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return;
-
-    auto chunk = info->at(chunk_size, id);
-    if (chunk == nullptr) return;
-
-    if (!sub_rc(Flag{}, chunk->conns(), curr_conns, conn_id)) {
-        return;
-    }
-    info->lock_.lock();
-    info->pool_.release(id);
-    info->lock_.unlock();
-}
-
-template <typename MsgT>
-bool clear_message(void* p) {
-    auto msg = static_cast<MsgT*>(p);
-    if (msg->storage_) {
-        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg->remain_;
-        if (r_size <= 0) {
-            ipc::error("[clear_message] invalid msg size: %d\n", (int)r_size);
-            return true;
-        }
-        release_storage(
-            *reinterpret_cast<ipc::storage_id_t*>(&msg->data_),
-            static_cast<std::size_t>(r_size));
-    }
-    return true;
-}
-
-struct conn_info_head {
-
-    ipc::string name_;
-    msg_id_t    cc_id_; // connection-info id
-    ipc::detail::waiter cc_waiter_, wt_waiter_, rd_waiter_;
-    ipc::shm::handle acc_h_;
-
-    conn_info_head(char const * name)
-        : name_     {name}
-        , cc_id_    {(cc_acc() == nullptr) ? 0 : cc_acc()->fetch_add(1, std::memory_order_relaxed)}
-        , cc_waiter_{("__CC_CONN__" + name_).c_str()}
-        , wt_waiter_{("__WT_CONN__" + name_).c_str()}
-        , rd_waiter_{("__RD_CONN__" + name_).c_str()}
-        , acc_h_    {("__AC_CONN__" + name_).c_str(), sizeof(acc_t)} {
-    }
-
-    void quit_waiting() {
-        cc_waiter_.quit_waiting();
-        wt_waiter_.quit_waiting();
-        rd_waiter_.quit_waiting();
-    }
-
-    auto acc() {
-        return static_cast<acc_t*>(acc_h_.get());
-    }
-
-    auto& recv_cache() {
-        thread_local ipc::unordered_map<msg_id_t, cache_t> tls;
-        return tls;
-    }
-};
-
-template <typename W, typename F>
-bool wait_for(W& waiter, F&& pred, std::uint64_t tm) {
-    if (tm == 0) return !pred();
-    for (unsigned k = 0; pred();) {
-        bool ret = true;
-        ipc::sleep(k, [&k, &ret, &waiter, &pred, tm] {
-            ret = waiter.wait_if(std::forward<F>(pred), tm);
-            k   = 0;
-        });
-        if (!ret) return false; // timeout or fail
-        if (k == 0) break; // k has been reset
-    }
-    return true;
-}
-
-template <typename Policy,
-          std::size_t DataSize  = ipc::data_length,
-          std::size_t AlignSize = (ipc::detail::min)(DataSize, alignof(std::max_align_t))>
-struct queue_generator {
-
-    using queue_t = ipc::queue<msg_t<DataSize, AlignSize>, Policy>;
-
-    struct conn_info_t : conn_info_head {
-        queue_t que_;
-
-        conn_info_t(char const * name)
-            : conn_info_head{name}
-            , que_{("__QU_CONN__" +
-                    ipc::to_string(DataSize) + "__" +
-                    ipc::to_string(AlignSize) + "__" + name).c_str()} {
-        }
-
-        void disconnect_receiver() {
-            bool dis = que_.disconnect();
-            this->quit_waiting();
-            if (dis) {
-                this->recv_cache().clear();
-            }
-        }
-    };
-};
-
-template <typename Policy>
-struct detail_impl {
-
-using policy_t    = Policy;
-using flag_t      = typename policy_t::flag_t;
-using queue_t     = typename queue_generator<policy_t>::queue_t;
-using conn_info_t = typename queue_generator<policy_t>::conn_info_t;
-
-constexpr static conn_info_t* info_of(ipc::handle_t h) noexcept {
-    return static_cast<conn_info_t*>(h);
-}
-
-constexpr static queue_t* queue_of(ipc::handle_t h) noexcept {
-    return (info_of(h) == nullptr) ? nullptr : &(info_of(h)->que_);
-}
-
-/* API implementations */
-
-static void disconnect(ipc::handle_t h) {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        return;
-    }
-    que->shut_sending();
-    assert(info_of(h) != nullptr);
-    info_of(h)->disconnect_receiver();
-}
-
-static bool reconnect(ipc::handle_t * ph, bool start_to_recv) {
-    assert(ph != nullptr);
-    assert(*ph != nullptr);
-    auto que = queue_of(*ph);
-    if (que == nullptr) {
-        return false;
-    }
-    if (start_to_recv) {
-        que->shut_sending();
-        if (que->connect()) { // wouldn't connect twice
-            info_of(*ph)->cc_waiter_.broadcast();
-            return true;
-        }
-        return false;
-    }
-    // start_to_recv == false
-    if (que->connected()) {
-        info_of(*ph)->disconnect_receiver();
-    }
-    return que->ready_sending();
-}
-
-static bool connect(ipc::handle_t * ph, char const * name, bool start_to_recv) {
-    assert(ph != nullptr);
-    if (*ph == nullptr) {
-        *ph = ipc::mem::alloc<conn_info_t>(name);
-    }
-    return reconnect(ph, start_to_recv);
-}
-
-static void destroy(ipc::handle_t h) {
-    disconnect(h);
-    ipc::mem::free(info_of(h));
-}
-
-static std::size_t recv_count(ipc::handle_t h) noexcept {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        return ipc::invalid_value;
-    }
-    return que->conn_count();
-}
-
-static bool wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        return false;
-    }
-    return wait_for(info_of(h)->cc_waiter_, [que, r_count] {
-        return que->conn_count() < r_count;
-    }, tm);
-}
-
-template <typename F>
-static bool send(F&& gen_push, ipc::handle_t h, void const * data, std::size_t size) {
-    if (data == nullptr || size == 0) {
-        ipc::error("fail: send(%p, %zd)\n", data, size);
-        return false;
-    }
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        ipc::error("fail: send, queue_of(h) == nullptr\n");
-        return false;
-    }
-    if (que->elems() == nullptr) {
-        ipc::error("fail: send, queue_of(h)->elems() == nullptr\n");
-        return false;
-    }
-    if (!que->ready_sending()) {
-        ipc::error("fail: send, que->ready_sending() == false\n");
-        return false;
-    }
-    ipc::circ::cc_t conns = que->elems()->connections(std::memory_order_relaxed);
-    if (conns == 0) {
-        ipc::error("fail: send, there is no receiver on this connection.\n");
-        return false;
-    }
-    // calc a new message id
-    auto acc = info_of(h)->acc();
-    if (acc == nullptr) {
-        ipc::error("fail: send, info_of(h)->acc() == nullptr\n");
-        return false;
-    }
-    auto msg_id   = acc->fetch_add(1, std::memory_order_relaxed);
-    auto try_push = std::forward<F>(gen_push)(info_of(h), que, msg_id);
-    if (size > ipc::large_msg_limit) {
-        auto   dat = acquire_storage(size, conns);
-        void * buf = dat.second;
-        if (buf != nullptr) {
-            std::memcpy(buf, data, size);
-            return try_push(static_cast<std::int32_t>(size) - 
-                            static_cast<std::int32_t>(ipc::data_length), &(dat.first), 0);
-        }
-        // try using message fragment
-        //ipc::log("fail: shm::handle for big message. msg_id: %zd, size: %zd\n", msg_id, size);
-    }
-    // push message fragment
-    std::int32_t offset = 0;
-    for (std::int32_t i = 0; i < static_cast<std::int32_t>(size / ipc::data_length); ++i, offset += ipc::data_length) {
-        if (!try_push(static_cast<std::int32_t>(size) - offset - static_cast<std::int32_t>(ipc::data_length),
-                      static_cast<ipc::byte_t const *>(data) + offset, ipc::data_length)) {
-            return false;
-        }
-    }
-    // if remain > 0, this is the last message fragment
-    std::int32_t remain = static_cast<std::int32_t>(size) - offset;
-    if (remain > 0) {
-        if (!try_push(remain - static_cast<std::int32_t>(ipc::data_length),
-                      static_cast<ipc::byte_t const *>(data) + offset, 
-                      static_cast<std::size_t>(remain))) {
-            return false;
-        }
-    }
-    return true;
-}
-
-static bool send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return send([tm](auto info, auto que, auto msg_id) {
-        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
-            if (!wait_for(info->wt_waiter_, [&] {
-                    return !que->push(
-                        [](void*) { return true; },
-                        info->cc_id_, msg_id, remain, data, size);
-                }, tm)) {
-                ipc::log("force_push: msg_id = %zd, remain = %d, size = %zd\n", msg_id, remain, size);
-                if (!que->force_push(
-                        clear_message<typename queue_t::value_t>,
-                        info->cc_id_, msg_id, remain, data, size)) {
-                    return false;
-                }
-            }
-            info->rd_waiter_.broadcast();
-            return true;
-        };
-    }, h, data, size);
-}
-
-static bool try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return send([tm](auto info, auto que, auto msg_id) {
-        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
-            if (!wait_for(info->wt_waiter_, [&] {
-                    return !que->push(
-                        [](void*) { return true; },
-                        info->cc_id_, msg_id, remain, data, size);
-                }, tm)) {
-                return false;
-            }
-            info->rd_waiter_.broadcast();
-            return true;
-        };
-    }, h, data, size);
-}
-
-static ipc::buff_t recv(ipc::handle_t h, std::uint64_t tm) {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        ipc::error("fail: recv, queue_of(h) == nullptr\n");
-        return {};
-    }
-    if (!que->connected()) {
-        // hasn't connected yet, just return.
-        return {};
-    }
-    auto& rc = info_of(h)->recv_cache();
-    for (;;) {
-        // pop a new message
-        typename queue_t::value_t msg;
-        if (!wait_for(info_of(h)->rd_waiter_, [que, &msg] {
-                return !que->pop(msg);
-            }, tm)) {
-            // pop failed, just return.
-            return {};
-        }
-        info_of(h)->wt_waiter_.broadcast();
-        if ((info_of(h)->acc() != nullptr) && (msg.cc_id_ == info_of(h)->cc_id_)) {
-            continue; // ignore message to self
-        }
-        // msg.remain_ may minus & abs(msg.remain_) < data_length
-        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg.remain_;
-        if (r_size <= 0) {
-            ipc::error("fail: recv, r_size = %d\n", (int)r_size);
-            return {};
-        }
-        std::size_t msg_size = static_cast<std::size_t>(r_size);
-        // large message
-        if (msg.storage_) {
-            ipc::storage_id_t buf_id = *reinterpret_cast<ipc::storage_id_t*>(&msg.data_);
-            void* buf = find_storage(buf_id, msg_size);
-            if (buf != nullptr) {
-                struct recycle_t {
-                    ipc::storage_id_t storage_id;
-                    ipc::circ::cc_t   curr_conns;
-                    ipc::circ::cc_t   conn_id;
-                } *r_info = ipc::mem::alloc<recycle_t>(recycle_t{
-                    buf_id, que->elems()->connections(std::memory_order_relaxed), que->connected_id()
-                });
-                if (r_info == nullptr) {
-                    ipc::log("fail: ipc::mem::alloc<recycle_t>.\n");
-                    return ipc::buff_t{buf, msg_size}; // no recycle
-                } else {
-                    return ipc::buff_t{buf, msg_size, [](void* p_info, std::size_t size) {
-                        auto r_info = static_cast<recycle_t *>(p_info);
-                        IPC_UNUSED_ auto finally = ipc::guard([r_info] {
-                            ipc::mem::free(r_info);
-                        });
-                        recycle_storage<flag_t>(r_info->storage_id, size, r_info->curr_conns, r_info->conn_id);
-                    }, r_info};
-                }
-            } else {
-                ipc::log("fail: shm::handle for large message. msg_id: %zd, buf_id: %zd, size: %zd\n", msg.id_, buf_id, msg_size);
-                continue;
-            }
-        }
-        // find cache with msg.id_
-        auto cac_it = rc.find(msg.id_);
-        if (cac_it == rc.end()) {
-            if (msg_size <= ipc::data_length) {
-                return make_cache(msg.data_, msg_size);
-            }
-            // gc
-            if (rc.size() > 1024) {
-                std::vector<msg_id_t> need_del;
-                for (auto const & pair : rc) {
-                    auto cmp = std::minmax(msg.id_, pair.first);
-                    if (cmp.second - cmp.first > 8192) {
-                        need_del.push_back(pair.first);
-                    }
-                }
-                for (auto id : need_del) rc.erase(id);
-            }
-            // cache the first message fragment
-            rc.emplace(msg.id_, cache_t { ipc::data_length, make_cache(msg.data_, msg_size) });
-        }
-        // has cached before this message
-        else {
-            auto& cac = cac_it->second;
-            // this is the last message fragment
-            if (msg.remain_ <= 0) {
-                cac.append(&(msg.data_), msg_size);
-                // finish this message, erase it from cache
-                auto buff = std::move(cac.buff_);
-                rc.erase(cac_it);
-                return buff;
-            }
-            // there are remain datas after this message
-            cac.append(&(msg.data_), ipc::data_length);
-        }
-    }
-}
-
-static ipc::buff_t try_recv(ipc::handle_t h) {
-    return recv(h, 0);
-}
-
-}; // detail_impl<Policy>
-
-template <typename Flag>
-using policy_t = ipc::policy::choose<ipc::circ::elem_array, Flag>;
-
-} // internal-linkage
-
-namespace ipc {
-
-template <typename Flag>
-ipc::handle_t chan_impl<Flag>::inited() {
-    ipc::detail::waiter::init();
-    return nullptr;
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::connect(ipc::handle_t * ph, char const * name, unsigned mode) {
-    return detail_impl<policy_t<Flag>>::connect(ph, name, mode & receiver);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::reconnect(ipc::handle_t * ph, unsigned mode) {
-    return detail_impl<policy_t<Flag>>::reconnect(ph, mode & receiver);
-}
-
-template <typename Flag>
-void chan_impl<Flag>::disconnect(ipc::handle_t h) {
-    detail_impl<policy_t<Flag>>::disconnect(h);
-}
-
-template <typename Flag>
-void chan_impl<Flag>::destroy(ipc::handle_t h) {
-    detail_impl<policy_t<Flag>>::destroy(h);
-}
-
-template <typename Flag>
-char const * chan_impl<Flag>::name(ipc::handle_t h) {
-    auto info = detail_impl<policy_t<Flag>>::info_of(h);
-    return (info == nullptr) ? nullptr : info->name_.c_str();
-}
-
-template <typename Flag>
-std::size_t chan_impl<Flag>::recv_count(ipc::handle_t h) {
-    return detail_impl<policy_t<Flag>>::recv_count(h);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::wait_for_recv(h, r_count, tm);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::send(h, data, size, tm);
-}
-
-template <typename Flag>
-buff_t chan_impl<Flag>::recv(ipc::handle_t h, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::recv(h, tm);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::try_send(h, data, size, tm);
-}
-
-template <typename Flag>
-buff_t chan_impl<Flag>::try_recv(ipc::handle_t h) {
-    return detail_impl<policy_t<Flag>>::try_recv(h);
-}
-
-template struct chan_impl<ipc::wr<relat::single, relat::single, trans::unicast  >>;
-// template struct chan_impl<ipc::wr<relat::single, relat::multi , trans::unicast  >>; // TBD
-// template struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::unicast  >>; // TBD
-template struct chan_impl<ipc::wr<relat::single, relat::multi , trans::broadcast>>;
-template struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::broadcast>>;
-
-} // namespace ipc

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/hashPointPen.py

@@ -1,75 +0,0 @@
-# Modified from https://github.com/adobe-type-tools/psautohint/blob/08b346865710ed3c172f1eb581d6ef243b203f99/python/psautohint/ufoFont.py#L800-L838
-import hashlib
-
-from fontTools.pens.basePen import MissingComponentError
-from fontTools.pens.pointPen import AbstractPointPen
-
-
-class HashPointPen(AbstractPointPen):
-    """
-    This pen can be used to check if a glyph's contents (outlines plus
-    components) have changed.
-
-    Components are added as the original outline plus each composite's
-    transformation.
-
-    Example: You have some TrueType hinting code for a glyph which you want to
-    compile. The hinting code specifies a hash value computed with HashPointPen
-    that was valid for the glyph's outlines at the time the hinting code was
-    written. Now you can calculate the hash for the glyph's current outlines to
-    check if the outlines have changed, which would probably make the hinting
-    code invalid.
-
-    > glyph = ufo[name]
-    > hash_pen = HashPointPen(glyph.width, ufo)
-    > glyph.drawPoints(hash_pen)
-    > ttdata = glyph.lib.get("public.truetype.instructions", None)
-    > stored_hash = ttdata.get("id", None)  # The hash is stored in the "id" key
-    > if stored_hash is None or stored_hash != hash_pen.hash:
-    >    logger.error(f"Glyph hash mismatch, glyph '{name}' will have no instructions in font.")
-    > else:
-    >    # The hash values are identical, the outline has not changed.
-    >    # Compile the hinting code ...
-    >    pass
-    """
-
-    def __init__(self, glyphWidth=0, glyphSet=None):
-        self.glyphset = glyphSet
-        self.data = ["w%s" % round(glyphWidth, 9)]
-
-    @property
-    def hash(self):
-        data = "".join(self.data)
-        if len(data) >= 128:
-            data = hashlib.sha512(data.encode("ascii")).hexdigest()
-        return data
-
-    def beginPath(self, identifier=None, **kwargs):
-        pass
-
-    def endPath(self):
-        self.data.append("|")
-
-    def addPoint(
-        self,
-        pt,
-        segmentType=None,
-        smooth=False,
-        name=None,
-        identifier=None,
-        **kwargs,
-    ):
-        if segmentType is None:
-            pt_type = "o"  # offcurve
-        else:
-            pt_type = segmentType[0]
-        self.data.append(f"{pt_type}{pt[0]:g}{pt[1]:+g}")
-
-    def addComponent(self, baseGlyphName, transformation, identifier=None, **kwargs):
-        tr = "".join([f"{t:+}" for t in transformation])
-        self.data.append("[")
-        try:
-            self.glyphset[baseGlyphName].drawPoints(self)
-        except KeyError:
-            raise MissingComponentError(baseGlyphName)
-        self.data.append(f"({tr})]")

spaces/Danielzero/GPT3.5/modules/llama_func.py

@@ -1,166 +0,0 @@
-import os
-import logging
-
-from llama_index import download_loader
-from llama_index import (
-    Document,
-    LLMPredictor,
-    PromptHelper,
-    QuestionAnswerPrompt,
-    RefinePrompt,
-)
-import colorama
-import PyPDF2
-from tqdm import tqdm
-
-from modules.presets import *
-from modules.utils import *
-from modules.config import local_embedding
-
-
-def get_index_name(file_src):
-    file_paths = [x.name for x in file_src]
-    file_paths.sort(key=lambda x: os.path.basename(x))
-
-    md5_hash = hashlib.md5()
-    for file_path in file_paths:
-        with open(file_path, "rb") as f:
-            while chunk := f.read(8192):
-                md5_hash.update(chunk)
-
-    return md5_hash.hexdigest()
-
-
-def block_split(text):
-    blocks = []
-    while len(text) > 0:
-        blocks.append(Document(text[:1000]))
-        text = text[1000:]
-    return blocks
-
-
-def get_documents(file_src):
-    documents = []
-    logging.debug("Loading documents...")
-    logging.debug(f"file_src: {file_src}")
-    for file in file_src:
-        filepath = file.name
-        filename = os.path.basename(filepath)
-        file_type = os.path.splitext(filepath)[1]
-        logging.info(f"loading file: {filename}")
-        try:
-            if file_type == ".pdf":
-                logging.debug("Loading PDF...")
-                try:
-                    from modules.pdf_func import parse_pdf
-                    from modules.config import advance_docs
-
-                    two_column = advance_docs["pdf"].get("two_column", False)
-                    pdftext = parse_pdf(filepath, two_column).text
-                except:
-                    pdftext = ""
-                    with open(filepath, "rb") as pdfFileObj:
-                        pdfReader = PyPDF2.PdfReader(pdfFileObj)
-                        for page in tqdm(pdfReader.pages):
-                            pdftext += page.extract_text()
-                text_raw = pdftext
-            elif file_type == ".docx":
-                logging.debug("Loading Word...")
-                DocxReader = download_loader("DocxReader")
-                loader = DocxReader()
-                text_raw = loader.load_data(file=filepath)[0].text
-            elif file_type == ".epub":
-                logging.debug("Loading EPUB...")
-                EpubReader = download_loader("EpubReader")
-                loader = EpubReader()
-                text_raw = loader.load_data(file=filepath)[0].text
-            elif file_type == ".xlsx":
-                logging.debug("Loading Excel...")
-                text_list = excel_to_string(filepath)
-                for elem in text_list:
-                    documents.append(Document(elem))
-                continue
-            else:
-                logging.debug("Loading text file...")
-                with open(filepath, "r", encoding="utf-8") as f:
-                    text_raw = f.read()
-        except Exception as e:
-            logging.error(f"Error loading file: {filename}")
-            pass
-        text = add_space(text_raw)
-        # text = block_split(text)
-        # documents += text
-        documents += [Document(text)]
-    logging.debug("Documents loaded.")
-    return documents
-
-
-def construct_index(
-    api_key,
-    file_src,
-    max_input_size=4096,
-    num_outputs=5,
-    max_chunk_overlap=20,
-    chunk_size_limit=600,
-    embedding_limit=None,
-    separator=" ",
-):
-    from langchain.chat_models import ChatOpenAI
-    from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-    from llama_index import GPTSimpleVectorIndex, ServiceContext, LangchainEmbedding, OpenAIEmbedding
-
-    if api_key:
-        os.environ["OPENAI_API_KEY"] = api_key
-    else:
-        # 由于一个依赖的愚蠢的设计，这里必须要有一个API KEY
-        os.environ["OPENAI_API_KEY"] = "sk-xxxxxxx"
-    chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit
-    embedding_limit = None if embedding_limit == 0 else embedding_limit
-    separator = " " if separator == "" else separator
-
-    prompt_helper = PromptHelper(
-        max_input_size=max_input_size,
-        num_output=num_outputs,
-        max_chunk_overlap=max_chunk_overlap,
-        embedding_limit=embedding_limit,
-        chunk_size_limit=600,
-        separator=separator,
-    )
-    index_name = get_index_name(file_src)
-    if os.path.exists(f"./index/{index_name}.json"):
-        logging.info("找到了缓存的索引文件，加载中……")
-        return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json")
-    else:
-        try:
-            documents = get_documents(file_src)
-            if local_embedding:
-                embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name = "sentence-transformers/distiluse-base-multilingual-cased-v2"))
-            else:
-                embed_model = OpenAIEmbedding()
-            logging.info("构建索引中……")
-            with retrieve_proxy():
-                service_context = ServiceContext.from_defaults(
-                    prompt_helper=prompt_helper,
-                    chunk_size_limit=chunk_size_limit,
-                    embed_model=embed_model,
-                )
-                index = GPTSimpleVectorIndex.from_documents(
-                    documents, service_context=service_context
-                )
-            logging.debug("索引构建完成！")
-            os.makedirs("./index", exist_ok=True)
-            index.save_to_disk(f"./index/{index_name}.json")
-            logging.debug("索引已保存至本地!")
-            return index
-
-        except Exception as e:
-            logging.error("索引构建失败！", e)
-            print(e)
-            return None
-
-
-def add_space(text):
-    punctuations = {"，": "， ", "。": "。 ", "？": "？ ", "！": "！ ", "：": "： ", "；": "； "}
-    for cn_punc, en_punc in punctuations.items():
-        text = text.replace(cn_punc, en_punc)
-    return text

spaces/Dauzy/whisper-webui/app-network.py

@@ -1,5 +0,0 @@
-# Run the app with no audio file restrictions, and make it available on the network
-from app import create_ui
-from src.config import ApplicationConfig
-
-create_ui(ApplicationConfig.create_default(input_audio_max_duration=-1, server_name="0.0.0.0"))

spaces/Djacon/emotion_detection/static/emotion_detection.html

@@ -1,293 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1">
-    <link rel="canonical" href="">
-    <title>Text2Feature | Detection</title>
-    <link rel="stylesheet" href="files/css/main.css">
-    <link rel="icon" type="image/svg+xml" href="files/images/favicon.svg">
-    <script defer src="files/js/main.js"></script>
-</head>
-
-<body class="overflow-hidden">
-    <!-- Google tag (gtag.js) -->
-    <script async src="https://www.googletagmanager.com/gtag/js?id=G-B751Q3XBFC"></script>
-    <script>
-        window.dataLayer = window.dataLayer || [];
-        function gtag(){dataLayer.push(arguments);}
-        gtag('js', new Date());
-
-        gtag('config', 'G-B751Q3XBFC');
-    </script>
-
-    <div x-data="{ sidebarOpen: false }" class="relative flex h-screen text-gray-800 bg-white font-roboto">
-        <div x-cloak :class="sidebarOpen ? 'block' : 'hidden'" @click="sidebarOpen = false"
-            class="fixed inset-0 z-20 transition-opacity bg-black opacity-50 lg:hidden"></div>
-
-        <div x-cloak :class="sidebarOpen ? 'translate-x-0 ease-in' : '-translate-x-full ease-out'"
-            class="fixed inset-y-0 left-0 z-30 w-64 px-4 overflow-y-auto transition duration-200 transform bg-white border-r border-gray-100 lg:translate-x-0 lg:relative lg:inset-0 ">
-            <div class="mt-8">
-                <a href="/" class="flex items-center">
-                    <img class="w-auto h-8 no-invert" src="files/images/favicon.svg" alt="logo">
-                    <span class="mx-3 mt-1 font-medium text-lg">Text2<span class="no-invert" style="color: #ffa116">Feature</span></span>
-                </a>
-            </div>
-
-            <hr class="my-6 border-gray-100">
-
-            <nav class="space-y-8">
-                <div class="space-y-4">
-                    <h3 class="px-4 text-sm tracking-wider text-gray-400 uppercase">PAGES</h3>
-
-                    <a class="flex items-center px-4 py-2 text-gray-500 transition-colors duration-200 transform rounded-lg hover:text-gray-600 hover:bg-gray-100 bg-opacity-40"
-                        href="/">
-                        <svg xmlns="http://www.w3.org/2000/svg" class="w-6 h-6" fill="none" viewBox="0 0 24 24"
-                            stroke="currentColor">
-                            <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2"
-                                d="M3 12l2-2m0 0l7-7 7 7M5 10v10a1 1 0 001 1h3m10-11l2 2m-2-2v10a1 1 0 01-1 1h-3m-6 0a1 1 0 001-1v-4a1 1 0 011-1h2a1 1 0 011 1v4a1 1 0 001 1m-6 0h6" />
-                        </svg>
-                        <span class="mx-3 font-medium capitalize">Homepage</span>
-                    </a>
-
-                    <a class="flex items-center px-4 py-2 text-gray-500 transition-colors duration-200 transform rounded-lg hover:text-gray-600 hover:bg-gray-100 bg-opacity-40"
-                        href="text_summarizer">
-                        <svg xmlns="http://www.w3.org/2000/svg" class="w-6 h-6" fill="none" viewBox="0 0 24 24"
-                            stroke="currentColor" stroke-width="2">
-                            <path stroke-linecap="round" stroke-linejoin="round"
-                                d="M4 7v10c0 2.21 3.582 4 8 4s8-1.79 8-4V7M4 7c0 2.21 3.582 4 8 4s8-1.79 8-4M4 7c0-2.21 3.582-4 8-4s8 1.79 8 4m0 5c0 2.21-3.582 4-8 4s-8-1.79-8-4" />
-                        </svg>
-                        <span class="mx-3 font-medium capitalize">Text Summarizer</span>
-                    </a>
-
-                    <a class="flex items-center px-4 py-2 text-gray-600 transition-colors duration-300 transform bg-gray-200 rounded-lg bg-opacity-40"
-                        href="emotion_detection">
-                        <svg width="24px" height="24px" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
-                            <path d="M9 16C9.85038 16.6303 10.8846 17 12 17C13.1154 17 14.1496 16.6303 15 16" stroke="#1C274C" stroke-width="1.5" stroke-linecap="round"/>
-                            <path d="M16 10.5C16 11.3284 15.5523 12 15 12C14.4477 12 14 11.3284 14 10.5C14 9.67157 14.4477 9 15 9C15.5523 9 16 9.67157 16 10.5Z" fill="#1C274C"/>
-                            <ellipse cx="9" cy="10.5" rx="1" ry="1.5" fill="#1C274C"/>
-                            <path d="M7 3.33782C8.47087 2.48697 10.1786 2 12 2C17.5228 2 22 6.47715 22 12C22 17.5228 17.5228 22 12 22C6.47715 22 2 17.5228 2 12C2 10.1786 2.48697 8.47087 3.33782 7" stroke="#1C274C" stroke-width="1.5" stroke-linecap="round"/>
-                        </svg>
-                        <span class="mx-3 font-medium capitalize">Emotion Detection</span>
-                    </a>
-
-                    <a class="flex items-center px-4 py-2 text-gray-500 transition-colors duration-300 transform rounded-lg hover:text-gray-600 hover:bg-gray-100 bg-opacity-40"
-                        href="analytics">
-                        <svg fill="currentColor" width="24px" height="24px" viewBox="0 0 32 32" version="1.1" xmlns="http://www.w3.org/2000/svg">
-                            <g id="SVGRepo_bgCarrier" stroke-width="0"></g>
-                            <g id="SVGRepo_tracerCarrier" stroke-linecap="round" stroke-linejoin="round"></g>
-                            <g id="SVGRepo_iconCarrier">
-                              <path d="M29.5 7c-1.381 0-2.5 1.12-2.5 2.5 0 0.284 0.058 0.551 0.144 0.805l-6.094 5.247c-0.427-0.341-0.961-0.553-1.55-0.553-0.68 0-1.294 0.273-1.744 0.713l-4.774-2.39c-0.093-1.296-1.162-2.323-2.482-2.323-1.38 0-2.5 1.12-2.5 2.5 0 0.378 0.090 0.732 0.24 1.053l-4.867 5.612c-0.273-0.102-0.564-0.166-0.873-0.166-1.381 0-2.5 1.119-2.5 2.5s1.119 2.5 2.5 2.5c1.381 0 2.5-1.119 2.5-2.5 0-0.332-0.068-0.649-0.186-0.939l4.946-5.685c0.236 0.073 0.48 0.124 0.74 0.124 0.727 0 1.377-0.316 1.834-0.813l4.669 2.341c0.017 1.367 1.127 2.471 2.497 2.471 1.381 0 2.5-1.119 2.5-2.5 0-0.044-0.011-0.086-0.013-0.13l6.503-5.587c0.309 0.137 0.649 0.216 1.010 0.216 1.381 0 2.5-1.119 2.5-2.5s-1.119-2.5-2.5-2.5z"></path>
-                            </g>
-                          </svg>
-                        <span class="mx-3 font-medium capitalize">Analytics</span>
-                    </a>
-
-                    <a class="flex items-center px-4 py-2 text-gray-500 transition-colors duration-300 transform rounded-lg hover:text-gray-600 hover:bg-gray-100 bg-opacity-40"
-                        href="">
-                        <svg class="w-6 h-6" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
-                            <path
-                                d="M13 10V14H19V10H13ZM11 10H5V14H11V10ZM13 19H19V16H13V19ZM11 19V16H5V19H11ZM13 5V8H19V5H13ZM11 5H5V8H11V5ZM19 3C20.1046 3 21 3.89543 21 5V19C21 20.1046 20.1046 21 19 21H5C3.89543 21 3 20.1046 3 19V5C3 3.89543 3.89543 3 5 3H19Z"
-                                fill="currentColor"></path>
-                        </svg>
-                        <span class="mx-3 font-medium capitalize">Project 4</span>
-                    </a>
-                </div>
-
-                <div class="space-y-4">
-                    <h3 class="px-4 text-sm tracking-wider text-gray-400 uppercase">OTHER</h3>
-
-                    <a class="flex items-center px-4 py-2 text-gray-500 transition-colors duration-300 transform rounded-lg hover:text-gray-600 hover:bg-gray-100 bg-opacity-40"
-                        href="https://djacon.github.io">
-                        <svg width="24" height="24" viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg">
-                            <path
-                                d="M474.89,300.41a121.43,121.43,0,0,1-121.3,121.3H247.08V392.13H353.59a91.72,91.72,0,1,0,0-183.44H87.53L151,272.2l-20.92,20.92L30.89,193.9l99.22-99.22L151,115.6l-63.5,63.51H353.59A121.43,121.43,0,0,1,474.89,300.41Z" />
-                        </svg>
-                        <span class="mx-3 font-medium">Visit Main Website</span>
-                    </a>
-
-                    <button id="theme-btn" class="flex items-center px-4 py-2 text-gray-500 transition-colors duration-300 transform rounded-lg hover:text-gray-600 hover:bg-gray-100 bg-opacity-40">
-                        <img id="img-theme" src="files/images/sun.svg" width="24" height="24">
-                        <span id='theme-span' class="mx-3 font-medium">Set Light Theme</span>
-                    </button>
-                </div>
-            </nav>
-        </div>
-
-        <div class="flex flex-col flex-1 overflow-hidden bg-gray-100">
-            <header class="static bg-white border-b border-gray-100">
-                <div class="flex items-center justify-between px-4 py-4 sm:px-6">
-                    <div class="flex items-center">
-                        <button @click="sidebarOpen = !sidebarOpen" class="text-gray-500 lg:hidden focus:outline-none">
-                            <svg class="w-6 h-6" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
-                                <path d="M4 6H20M4 12H20M4 18H11" stroke="currentColor" stroke-width="2"
-                                    stroke-linecap="round" stroke-linejoin="round" />
-                            </svg>
-                        </button>
-
-                        <div class="relative" x-data="{ search: '' }" @click.away="search = ''">
-                            <div class="relative mx-4 lg:mx-0">
-                                <span class="absolute inset-y-0 left-0 flex items-center pl-3">
-                                    <svg class="w-5 h-5 text-gray-400" viewBox="0 0 24 24" fill="none">
-                                        <path
-                                            d="M21 21L15 15M17 10C17 13.866 13.866 17 10 17C6.13401 17 3 13.866 3 10C3 6.13401 6.13401 3 10 3C13.866 3 17 6.13401 17 10Z"
-                                            stroke="currentColor" stroke-width="2" stroke-linecap="round"
-                                            stroke-linejoin="round"></path>
-                                    </svg>
-                                </span>
-
-                                <style>
-                                    #search:focus {
-                                        border: 1px solid #ffa116;
-                                    }
-                                </style>
-
-                                <input id="search" x-model="search" type="text"
-                                    class="w-44 h-10 py-2 pl-10 pr-4 text-gray-700 placeholder-gray-400 transition-all duration-150 bg-white border border-gray-200 rounded-md focus:w-80 sm:w-64 sm:focus:w-80 focus:outline-none focus:ring focus:ring-indigo-300 focus:ring-opacity-40"
-                                    placeholder="Find anything...">
-                            </div>
-
-                            <div class="absolute right-0 z-20 w-full py-2 mt-2 space-y-4 overflow-hidden bg-white rounded-md shadow-xl"
-                                x-show="search.length > 0" x-cloak
-                                x-transition:enter="transition ease-out duration-100 transform"
-                                x-transition:enter-start="opacity-0 scale-95"
-                                x-transition:enter-end="opacity-100 scale-100"
-                                x-transition:leave="transition ease-in duration-75 transform"
-                                x-transition:leave-start="opacity-100 scale-100"
-                                x-transition:leave-end="opacity-0 scale-95">
-
-                                <div>
-                                    <h3 class="px-5 text-xs tracking-wider text-gray-500 uppercase">Projects</h3>
-                                    <div class="mt-2">
-                                        <a class="block px-5 py-2 text-sm text-gray-700 capitalize transition-colors duration-200 transform sm:px-12 hover:text-gray-600 hover:bg-gray-100 bg-opacity-40"
-                                            href="text_summarizer">
-                                            Text Summarizer
-                                        </a>
-
-                                        <a class="block px-5 py-2 text-sm text-gray-700 capitalize transition-colors duration-200 transform sm:px-12 hover:text-gray-600 hover:bg-gray-100 bg-opacity-40"
-                                            href="emotion_detection">
-                                            Emotion Detection
-                                        </a>
-
-                                        <a class="block px-5 py-2 text-sm text-gray-700 capitalize transition-colors duration-200 transform sm:px-12 hover:text-gray-600 hover:bg-gray-100 bg-opacity-40"
-                                            href="analytics">
-                                            Analytics
-                                        </a>
-
-                                        <a class="block px-5 py-2 text-sm text-gray-700 capitalize transition-colors duration-200 transform sm:px-12 hover:text-gray-600 hover:bg-gray-100 bg-opacity-40"
-                                            href="">
-                                            Project 4
-                                        </a>
-                                    </div>
-                                </div>
-                                <div>
-                                    <h3 class="px-5 text-xs tracking-wider text-gray-500 uppercase">Other</h3>
-                                    <div class="mt-2">
-                                        <a class="block px-5 py-2 text-sm text-gray-700 capitalize transition-colors duration-200 transform sm:px-12 hover:text-gray-600 hover:bg-gray-100 bg-opacity-40"
-                                            href="https://djacon.github.io">
-                                            Visit Main Website
-                                        </a>
-                                    </div>
-                                </div>
-                            </div>
-                        </div>
-                    </div>
-
-                    <div class="flex items-center">
-                        <div x-data="{ dropdownOpen: false }" class="relative inline-block">
-                            <button @click="dropdownOpen = ! dropdownOpen" class="relative z-10 flex items-center flex-shrink-0 text-sm text-gray-600 focus:outline-none">
-                                <img class="flex-shrink-0 object-cover w-8 h-8 rounded-full" src="files/images/github-mark.svg" alt="github-mark">
-                            </button>
-                
-                            <a href="https://github.com/Djacon/text2feature" target="_blank" class="absolute right-0 z-20 w-56 py-2 mt-2 overflow-hidden bg-white rounded-md shadow-xl rtl:right-auto rtl:left-0 hover:bg-gray-100" x-show="dropdownOpen" x-transition:enter="transition ease-out duration-100 transform" x-transition:enter-start="opacity-0 scale-95" x-transition:enter-end="opacity-100 scale-100" x-transition:leave="transition ease-in duration-75 transform" x-transition:leave-start="opacity-100 scale-100" x-transition:leave-end="opacity-0 scale-95" @click.away="dropdownOpen = false" style="display: none;">
-                                <div class="flex items-center p-3 -mt-2 text-sm text-gray-600 transition-colors duration-200 transform">
-                                    <img class="flex-shrink-0 object-cover mx-1 rounded-full w-9 h-9" src="files/images/github-mark.svg" alt="github-mark">
-                                    <div class="mx-1">
-                                        <h1 class="text-sm font-semibold text-gray-700">Made By Djacon</h1>
-                                        <p class="text-sm text-gray-500">github.com/Djacon</p>
-                                    </div>
-                                </div>
-                            </a>
-                        </div>
-                    </div>
-                </div>
-            </header>
-
-            <main class="flex-1 overflow-y-auto">
-                <div class="px-4 py-8 sm:px-6">
-                    <div>
-                        <h1 class="text-2xl font-medium text-gray-700 sm:text-3xl">Emotion Detection</h1>
-
-                        <div class="hidden mt-3 overflow-y-auto text-sm lg:items-center lg:flex whitespace-nowrap">
-                            <a class="text-gray-600">Pages</a>
-                            <span class="mx-1 text-gray-500">/</span>
-                            <a href="" class="hover:underline no-invert" style="color: #ffa116">Emotion_Detection</a>
-                        </div>
-                    </div>
-
-                    <div class="mt-6">
-                        <div class="w-full p-4 bg-white xl:p-6">
-                            <ul class="flex text-sm text-center text-gray-500 divide-x divide-gray-200">
-                                <li class="w-full">
-                                    <button id="show-original" class="bg-gray-100 rounded-l-lg inline-block w-full p-2 text-gray-900 focus:ring-4 focus:ring-blue-300 focus:outline-none">Original</button>
-                                </li>
-                                <li class="w-full">
-                                    <button id="show-summary" class="inline-block rounded-r-lg w-full p-2 hover:text-gray-700 hover:bg-gray-50 focus:ring-4 focus:ring-blue-300 focus:outline-none">Summary</button>
-                                </li>
-                            </ul>
-
-                            <div id='sum-original' class="mt-6 space-y-5" onsubmit="return false;">
-                                <div id="sum-text-div">
-                                    <textarea id="sum-text-input" required class="block w-full px-4 py-2.5 mt-2 text-gray-600 placeholder-gray-400 bg-white border border-gray-200 rounded-md focus:border-indigo-400 focus:outline-none focus:ring focus:ring-indigo-300 focus:ring-opacity-40"
-                                    style="min-height: 250px;" placeholder="Enter or upload your text and press &quot;Detect&quot;"></textarea>
-                                </div>
-
-                                <p id='sum-err' class="no-invert hidden mt-2 text-sm text-red-500"></p>
-
-                                <div class="flex justify-between no-invert">
-                                    <label id="word-counter" class="hidden flex items-center py-2 px-4 text-gray-500 rounded">Words:</label>
-                                    <label id="sum-file-div" class="flex items-center py-2 px-4 text-gray-500 rounded cursor-pointer hover:text-gray-700">
-                                        <svg class="w-6 h-6" fill="currentColor" xmlns="http://www.w3.org/2000/svg"
-                                            viewBox="0 0 20 20">
-                                            <path
-                                                d="M16.88 9.1A4 4 0 0 1 16 17H5a5 5 0 0 1-1-9.9V7a3 3 0 0 1 4.52-2.59A4.98 4.98 0 0 1 17 8c0 .38-.04.74-.12 1.1zM11 11h3l-4-4-4 4h3v3h2v-3z">
-                                            </path>
-                                        </svg>
-                                        <span class="px-2">Upload</span>
-                                        <input id="sum-file-input" type="file" accept=".pdf, .txt" class="hidden">
-                                    </label>
-                                    <button id="submit" class="w-32 sm:w-80 flex items-center justify-center py-2 px-4 rounded font-medium text-white rounded-full"
-                                        style="max-height: 2.5rem;">
-                                        Detect
-                                    </button>
-                                </div>
-                            </div>
-
-                            <div id="sum-summary" class='hidden mt-6 space-y-5'>
-                                <div class="hidden w-full md:w-1/2 grid-cols-2 mt-6">
-                                    <label for="description"
-                                        class="block text-sm text-gray-700 capitalize">Extracted Text</label>
-                                    <textarea id="extracted-text"
-                                        class="minh-text block w-full px-4 py-2.5 mt-2 text-gray-600 placeholder-gray-400 bg-white border border-gray-200 rounded-md focus:border-indigo-400 focus:outline-none focus:ring focus:ring-indigo-300 focus:ring-opacity-40"></textarea>
-                                </div>
-
-                                <div class="w-full md:w-1/2 grid-cols-2 mt-6">
-                                    <textarea id="summarized-text"
-                                        class="minh-text block w-full px-4 py-2.5 mt-2 text-gray-600 placeholder-gray-400 bg-white border border-gray-200 rounded-md focus:border-indigo-400 focus:outline-none focus:ring focus:ring-indigo-300 focus:ring-opacity-40"
-                                        style="min-height: 250px;"></textarea>
-                                </div>
-                            </div>
-                        </div>
-                    </div>
-                </div>
-            </main>
-        </div>
-    </div>
-
-
-    <script src="files/js/theme.js"></script>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/pdf.js/2.0.943/pdf.min.js"></script>
-    <script src="files/js/detection.js"></script>
-</body>
-</html>