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diff --git a/spaces/1phancelerku/anime-remove-background/Corra com carros e motos brasileiros em Estilo BR Download grtis do mod com dinheiro infinito e mediafre.md b/spaces/1phancelerku/anime-remove-background/Corra com carros e motos brasileiros em Estilo BR Download grtis do mod com dinheiro infinito e mediafre.md
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Estilo BR: How to Download and Play the Ultimate Drag Racing Game in Brazil
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If you are a racing enthusiast in Brazil, you have probably heard of Estilo BR, the definitive drag racing game for Android devices. With 43 different vehicles to choose from, all Brazilian, from the most classic to the most modern, you can experience the thrill of high-speed racing against competitors from around the world, including motorcycles, trucks and trailers.
In Estilo BR, you can participate in global multiplayer races with up to 500 players, both in an open world global room and in private rooms created to play with friends. Compete against drivers from different countries and show your skills on the track, enjoying the style and culture of street racing in Brazil.
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But Estilo BR is not just about racing. You can also customize your vehicles with a wide variety of aesthetic and performance upgrades. From custom paint jobs to engine modifications, you have the freedom to make your vehicles truly unique.
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Estilo BR is the best of its kind in Brazil, offering an unparalleled racing experience. Whether you are a seasoned veteran or a new player, Estilo BR has something for everyone. Download now and join the drag racing revolution in Brazil, listening to your favorite music while you play.
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What is Estilo BR?
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Estilo BR is a mobile game developed by RF Entertainment, a Brazilian indie studio that specializes in racing games. The game was released in 2019 and has since received several updates and improvements.
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The game is inspired by the real-life street racing scene in Brazil, where drivers compete in illegal drag races with modified cars and bikes. The game features realistic physics and responsive controls, as well as stunning pixel art graphics that create a nostalgic atmosphere.
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The game also allows you to play music from your own phone, giving you the possibility to listen to your favorite songs while playing. You can choose from different genres and playlists, or create your own custom mix.
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The game has a rating of 4.2 out of 5 stars on Google Play Store, with over 5 million downloads and more than 130 thousand reviews. The game is free to play, but it contains ads and in-app purchases.
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How to download Estilo BR from mediafıre?
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If you want to download Estilo BR from mediafıre, a popular file-sharing platform, you will need to follow these steps:
Click on the green button that says "Download APK (125.77 MB)". This will start downloading the APK file to your device.
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Once the download is complete, locate the file in your device's download folder. Tap on it to start the installation process.
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If you see a message that says "For your security, your phone is not allowed to install unknown apps from this source", go to your device's settings and enable the option to install apps from unknown sources.
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Follow the steps on screen to complete the installation. You may need to grant some permissions to the app.
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Once the installation is done, you can open the app and enjoy Estilo BR with unlimited money and diamonds.
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Note: This method is not endorsed by the official developers of Estilo BR, and it may violate their terms of service. Use it at your own risk.
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How to get unlimited money and diamonds in Estilo BR?
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If you want to get unlimited money and diamonds in Estilo BR, you have two options:
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Use the modded version of the game that you downloaded from mediafıre. This will give you unlimited resources from the start, but it may also cause some glitches and errors in the game. You may also face some issues with online multiplayer mode, as other players may report you for cheating.
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Use a game hacking tool such as Game Guardian or Lucky Patcher. These tools allow you to modify the game's data and values, such as money and diamonds. However, this requires some technical skills and knowledge, and it may also harm your device or expose it to malware. You may also get banned from the game if you are detected by the anti-cheat system.
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Both of these options are not recommended, as they can ruin the fun and challenge of the game. The best way to enjoy Estilo BR is to play it fair and square, earning money and diamonds by winning races, completing missions, and watching ads. This will also support the developers of the game and help them improve it further.
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What are the best tips and tricks for Estilo BR?
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If you want to master Estilo BR and become a drag racing legend in Brazil, here are some tips and tricks that can help you:
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Choose your vehicle wisely. Each vehicle has different stats and characteristics, such as speed, acceleration, handling, weight, and nitro. Depending on the type of race and track, some vehicles may perform better than others. Experiment with different vehicles and find the ones that suit your style and preference.
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Upgrade your vehicle regularly. As you progress in the game, you will unlock new parts and accessories that can improve your vehicle's performance and appearance. You can upgrade your engine, transmission, tires, suspension, brakes, turbo, nitro, exhaust, intake, fuel system, cooling system, ignition system, battery, body kit, spoiler, hood, bumper, grille, lights, mirrors, windows, doors, trunk, roof rack, paint job, decals, stickers, rims, tires smoke color, and license plate. You can also buy new vehicles with better stats and features.
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Learn how to shift gears properly. Shifting gears at the right time is crucial for winning races, as it affects your speed and acceleration. You can use the manual or automatic mode, depending on your preference. In manual mode, you have to tap the screen to shift gears, while in automatic mode, the game does it for you. However, manual mode gives you more control and precision, while automatic mode may not always shift at the optimal moment. You can also use the nitro boost to gain an extra burst of speed, but be careful not to overheat your engine.
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Practice your skills in different modes and tracks. Estilo BR offers various modes and tracks to test your abilities and have fun. You can play in single-player mode, where you can race against AI opponents or complete missions and challenges. You can also play in multiplayer mode, where you can join global or private rooms and race against other players online. You can also explore the open world map and find hidden secrets and easter eggs. The game features different tracks with different terrains, weather conditions, and obstacles, such as asphalt, dirt, sand, rain, fog, night, traffic, ramps, bridges, tunnels, and more.
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Enjoy the game's style and culture. Estilo BR is more than just a racing game. It is also a tribute to the Brazilian street racing culture and style, with authentic vehicles, music, slang, and references. You can immerse yourself in the game's atmosphere and learn more about the history and diversity of Brazil's racing scene. You can also interact with other players and make new friends through the game's chat system.
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Conclusion
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Estilo BR is a fantastic drag racing game that will keep you hooked for hours. Whether you want to race against other players online, customize your vehicles with endless options, or explore the open world map with realistic graphics and physics, Estilo BR has it all.
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If you are looking for a way to download Estilo BR from mediafıre, you can follow the steps we provided above. However, we advise you to be careful when using modded or hacked versions of the game, as they may cause problems or get you banned.
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The best way to enjoy Estilo BR is to play it fair and square, earning money and diamonds by winning races, completing missions, and watching ads. This will also support the developers of the game and help them improve it further.
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So what are you waiting for? Download Estilo BR now and join the drag racing revolution in Brazil!
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FAQs
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Here are some frequently asked questions about Estilo BR:
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-
Is Estilo BR available for iOS devices?
-
No, Estilo BR is only available for Android devices at the moment. The developers have not announced any plans to release an iOS version of the game.
-
How can I contact the developers of Estilo BR?
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You can contact the developers of Estilo BR through their official Facebook page: RF Entertainment - Home | Facebook. You can also send them an email at rfentertainmentoficial@gmail.com.
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How can I report a bug or a problem in Estilo BR?
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You can report a bug or a problem in Estilo BR through the game's settings menu. Tap on the gear icon on the top right corner of the screen, then tap on "Report Bug". You can also send a screenshot or a video of the bug or problem to help the developers fix it.
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How can I support Estilo BR?
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You can support Estilo BR by playing the game regularly, rating it on Google Play Store, writing positive reviews, sharing it with your friends, and making in-app purchases. You can also follow the developers on their social media accounts and join their community of fans.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Velocity Rush Z Mod APK and Enjoy Unlimited Action and Money.md b/spaces/1phancelerku/anime-remove-background/Download Velocity Rush Z Mod APK and Enjoy Unlimited Action and Money.md
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index a4b951cd1f72b113890be0193c2208c7307e5580..0000000000000000000000000000000000000000
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Velocity Rush Z Mod Apk: A Fast-Paced Shooter with Parkour Elements
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Introduction
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If you are looking for a thrilling and adrenaline-pumping game that combines shooting and parkour, then you should check out Velocity Rush Z mod apk. This is a first-person shooter game with parkour elements from the creator of Velocity Rush. You can vault, climb, wallrun, slide and shoot mercenaries and zombies in the apocalyptic city to earn money to buy more weapons. In this article, we will tell you what is Velocity Rush Z, why you should download the mod apk version, what are its features, and how to install it on your device.
Velocity Rush Z is a game that was released in 2021 by sosomod.net. It is a sequel to the popular game Velocity Rush, which was also a shooter with parkour elements. The game has improved graphics, gameplay, and features compared to the original one. You can experience high action shooting in close combat, bullet time (slowmo), parkour moves and skills, various weapons and upgrades, and an apocalyptic city setting. The game has a rating of 4.5 out of 5 stars on sosomod.net.
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Why download Velocity Rush Z mod apk?
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The mod apk version of Velocity Rush Z has some advantages over the original one. The mod apk version gives you unlimited money, which means you can buy any weapon or upgrade you want without worrying about the cost. You can also unlock all the levels and modes in the game, which means you can enjoy the game without any restrictions. The mod apk version also removes ads from the game, which means you can play without any interruptions or annoyances. The mod apk version is also safe and easy to install, as we will show you later.
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Features of Velocity Rush Z mod apk
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High action shooting in close combat
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The game is not for the faint-hearted, as you will face hordes of enemies in close quarters. You will have to use your reflexes and skills to survive and eliminate them. You can use different types of weapons, such as pistols, shotguns, rifles, grenades, and more. You can also switch between weapons quickly and reload them efficiently.
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Bullet time (Slowmo)
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One of the coolest features of the game is bullet time, which allows you to slow down time and aim more precisely at your enemies. You can activate bullet time by tapping on the screen or by using a special item. Bullet time can help you avoid bullets, dodge attacks, and take out multiple enemies at once.
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Parkour moves and skills
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The game is not just about shooting, but also about moving around the city with style and agility. You can perform parkour moves and skills, such as vaulting over obstacles, climbing walls, wallrunning, sliding under gaps, and more. You can also use these moves to reach hidden areas, find secrets, and escape from danger.
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Various weapons and upgrades
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The game offers a variety of weapons and upgrades for you to choose from. You can buy new weapons or upgrade your existing ones with money that you earn from completing missions or killing enemies. You can also customize your weapons with different skins, attachments, and effects. Some of the weapons and upgrades available in the game are:
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Weapon
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Description
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Upgrade
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Pistol
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A basic weapon that can fire fast and accurate shots.
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You can upgrade the pistol's damage, fire rate, magazine size, and reload speed.
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Shotgun
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A powerful weapon that can deal massive damage at close range.
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You can upgrade the shotgun's damage, spread, magazine size, and reload speed.
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Rifle
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A versatile weapon that can fire bursts of bullets at medium range.
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You can upgrade the rifle's damage, fire rate, magazine size, and reload speed.
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Grenade
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A explosive weapon that can cause area damage and knockback enemies.
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You can upgrade the grenade's damage, blast radius, and number of grenades you can carry.
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Slowmo Item
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A special item that can activate bullet time for a limited duration.
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You can upgrade the slowmo item's duration, cooldown, and number of slowmo items you can carry.
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Apocalyptic city setting
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The game is set in a post-apocalyptic city that has been overrun by mercenaries and zombies. You will explore different locations in the city, such as rooftops, streets, alleys, buildings, and more. You will also encounter different types of enemies, such as snipers, melee fighters, bombers, and bosses. The game has a dark and gritty atmosphere that suits the theme of the game.
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How to download and install Velocity Rush Z mod apk
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Step 1: Download the apk file from a trusted source
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The first step is to download the apk file of Velocity Rush Z mod apk from a trusted source. You can use the link below to download the apk file from sosomod.net, which is a reliable website that provides mod apk games. The apk file size is about 100 MB, so make sure you have enough space on your device.
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Step 2: Enable unknown sources on your device
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The second step is to enable unknown sources on your device. This is necessary to install apk files that are not from the Google Play Store. To enable unknown sources, go to your device settings, then security or privacy, then toggle on the option that says "allow installation of apps from unknown sources". You may also need to confirm this action by tapping on "OK" or "Yes".
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Step 3: Install the apk file and launch the game
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The third step is to install the apk file and launch the game. To install the apk file, locate it in your device storage or downloads folder, then tap on it. You may see a pop-up window that asks you to confirm the installation. Tap on "Install" or "Next" until the installation is complete. Then, tap on "Open" or "Done" to launch the game. You can now enjoy Velocity Rush Z mod apk with unlimited money and unlocked levels.
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Conclusion
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Velocity Rush Z mod apk is a fast-paced shooter with parkour elements that will keep you on the edge of your seat. You can experience high action shooting in close combat, bullet time (slowmo), parkour moves and skills, various weapons and upgrades, and an apocalyptic city setting. You can also download the mod apk version of the game to get unlimited money and unlocked levels. To download and install Velocity Rush Z mod apk, follow the steps above. We hope you enjoy playing this game as much as we do.
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FAQs
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Here are some frequently asked questions about Velocity Rush Z mod apk:
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Q: Is Velocity Rush Z mod apk safe to download and install?
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A: Yes, Velocity Rush Z mod apk is safe to download and install from sosomod.net, which is a trusted website that provides mod apk games. The apk file does not contain any viruses or malware that can harm your device or data.
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Q: Do I need to root my device to use Velocity Rush Z mod apk?
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A: No, you do not need to root your device to use Velocity Rush Z mod apk. The mod apk works fine on both rooted and non-rooted devices.
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A: No, Velocity Rush Z mod apk is an offline game that does not require an internet connection to play. You can play it anytime and anywhere without worrying about data usage or connection issues.
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A: The minimum requirements to play Velocity Rush Z mod apk are:
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Android 4.4 or higher
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At least 1 GB of RAM
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At least 200 MB of free storage space
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diff --git a/spaces/1phancelerku/anime-remove-background/Free Download Hitman Sniper APK - Play the Tactical Sniper Mission Game on Android.md b/spaces/1phancelerku/anime-remove-background/Free Download Hitman Sniper APK - Play the Tactical Sniper Mission Game on Android.md
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index 5f1db66b8ae0d168fc2a3b2e17ef687dd50606b1..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Free Download Hitman Sniper APK - Play the Tactical Sniper Mission Game on Android.md
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Hitman Sniper: How to Download and Play the Best Sniper Game on Mobile
- If you are a fan of stealth, strategy, and shooting games, you might want to check out Hitman Sniper, one of the most popular and acclaimed sniper games on mobile. In this article, we will tell you what Hitman Sniper is, why you should play it, how to download it for free, and how to play it effectively.
What is Hitman Sniper?
- Hitman Sniper is a mobile game developed by CDE Entertainment and published by Square Enix. It is based on the Hitman franchise, which follows the adventures of Agent 47, a professional assassin who works for a mysterious organization. In Hitman Sniper, you step into the shoes of Agent 47 and take on various sniping missions in different locations. You have to use your strategic skills and creativity to orchestrate the perfect assassination kill shot, while avoiding detection and eliminating other threats. The game features more than 150 missions and 11 different contracts, each with its own objectives, targets, and secrets. You can also unlock and upgrade 17 unique weapons, each with its own perks and abilities. The game also has a zombie mode, where you have to survive waves of undead enemies in a desert valley. You have to use your accuracy and speed to take down as many zombies as possible, while collecting weapon parts and blueprints.
Why should you play Hitman Sniper?
- Hitman Sniper is not just a simple shooting game. It is a game that requires you to think, plan, and execute your actions with precision and finesse. Here are some of the benefits of playing Hitman Sniper: - It improves your concentration and focus. You have to pay attention to every detail in the environment, such as guards, cameras, traps, windows, doors, etc. You also have to monitor your target's movements and behavior, and wait for the right moment to strike. - It enhances your problem-solving and decision-making skills. You have to analyze the situation and choose the best course of action. You can use various methods to eliminate your target, such as headshots, body shots, accidents, explosions, distractions, etc. You also have to deal with unexpected events, such as alarms, reinforcements, witnesses, etc. - It stimulates your creativity and imagination. You can use your environment to your advantage, such as shooting objects to cause chain reactions, shooting electrical wires to electrocute enemies, shooting gas tanks to create fireballs, etc. You can also use your weapons in different ways, such as using silencers, scopes, suppressors, etc. - It provides you with entertainment and satisfaction. You can enjoy the stunning graphics and realistic sound effects of the game. You can also feel the thrill and excitement of pulling off a perfect kill shot. You can also compete against your friends and other players in the leaderboards.
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- If you want to play Hitman Sniper on your Android device, you can download it from the Google Play Store for $0.99. However, if you want to get it for free, you can download an APK file from a third-party website. An APK file is an Android application package file that contains all the files needed to install an app on your device. However, before you download an APK file, you need to take some precautions: - Make sure that your device has enough storage space for the file. - Make sure that your device is compatible with the game's requirements. - Make sure that you have a reliable internet connection for the download. - Make sure that you have enabled the option to install apps from unknown sources in your device's settings. Once you have taken these precautions , you can follow these steps to install the APK file on your Android device: - Connect your Android device to your computer using a USB cable. - Copy the APK file from your computer to your device's storage. You can use any folder you want, but make sure you remember where you put it. - Disconnect your device from your computer and open your file explorer app on your device. - Locate the APK file you copied and tap on it to open it. - Tap Install at the bottom of the screen and wait for the installation to finish. - Tap Open to launch the game or Done to exit the installer. You have successfully installed Hitman Sniper APK for free on your Android device. Enjoy!
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Conclusion
- Hitman Sniper is a fun and challenging sniper game that will test your strategic skills and creativity. You can download it for free from a third-party website using an APK file, but make sure you take some precautions before doing so. You can also use our tips and tricks to play the game effectively and complete all the missions. If you are ready to become the best sniper in the world, download Hitman Sniper today and enjoy!
FAQs
- Q: How do I get more money in Hitman Sniper? A: You can get more money by completing challenges and contracts in each mission. You can also replay missions to earn more money. Q: How do I unlock more weapons in Hitman Sniper? A: You can unlock more weapons by collecting weapon parts and blueprints in each mission. You can also buy some weapons with real money. Q: How do I upgrade my weapons in Hitman Sniper? A: You can upgrade your weapons by using weapon parts and blueprints that you have collected or bought. You can also equip different perks for each weapon. Q: How do I switch weapons in Hitman Sniper? A: You can switch weapons by tapping the weapon icon on the screen or pressing the Q key on your keyboard. Q: How do I play zombie mode in Hitman Sniper? A: You can play zombie mode by tapping the zombie icon on the main menu or pressing the Z key on your keyboard.
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-
¿Te encantan los juegos de puzzle que ponen a prueba tus habilidades multitarea y creatividad? Si es así, es posible que desee probar Button Fever, un juego divertido y adictivo que le permite colocar y combinar botones en un tablero. En este artículo, te mostraremos cómo descargar y jugar Button Fever en tu PC, así como algunos consejos y trucos para ayudarte a sacarle el máximo partido.
Button Fever es un juego desarrollado por Rollic Games, una empresa especializada en juegos casuales e hiper-casuales para dispositivos móviles. Button Fever es uno de sus títulos más populares, con más de 10 millones de descargas en Google Play Store. El juego es adecuado para todas las edades y se puede jugar fuera de línea o en línea.
-
Características y jugabilidad
-
El juego es simple pero desafiante. Tienes un tablero con ranuras vacías y una cola de botones en la parte inferior. Su objetivo es colocar los botones en el tablero y limpiar las líneas haciendo coincidir los colores o formas de los botones. Cuantas más líneas borres, más puntos ganarás. También puedes ganar monedas completando niveles, que puedes usar para desbloquear nuevos botones y temas.
-
El juego tiene diferentes niveles de dificultad, que van de fácil a difícil. Cada nivel tiene un tamaño de tablero diferente, número de botones y límite de tiempo. También puedes elegir entre diferentes modos, como clásico, árcade o zen. El juego también tiene desafíos diarios, tablas de clasificación y logros para mantenerte involucrado.
-
Cómo descargar Button Fever en PC?
-
Opción 1: Descargar desde el sitio web oficial
-
Si desea descargar Button Fever directamente desde el sitio web del desarrollador, puede seguir estos pasos:
-
Paso 1: Visita el sitio web y haz clic en el botón de descarga.
-
Esto te llevará a una página donde puedes elegir tu sistema operativo (Windows o Mac) y descargar el archivo de instalación.
-
-
-
Una vez que haya descargado el archivo, haga doble clic en él para iniciar el proceso de instalación. Es posible que tenga que conceder permiso para que el programa realice cambios en su dispositivo. Siga las instrucciones en la pantalla para completar la instalación.
-
Paso 3: Lanza el juego y disfruta.
-
Después de la instalación, puede encontrar un icono de acceso directo para Button Fever en su escritorio o menú de inicio. Haga clic en él para iniciar el juego y empezar a jugar.
-
Opción 2: Descarga desde una plataforma de terceros
-
Si prefiere descargar Button Fever desde una plataforma de terceros que ofrece una variedad de juegos, puede utilizar una de estas opciones:
-
Paso 1: Instalar un lanzador de juegos como Epic Games o Steam.
-
Un lanzador de juegos es un programa que te permite acceder, descargar, instalar, actualizar y jugar juegos de diferentes desarrolladores y editores. Algunos de los lanzadores de juegos más populares son Epic Games y Steam, que puedes descargar gratis desde sus respectivos sitios web.
-
Paso 2: Crea una cuenta e inicia sesión.
-
Después de haber instalado el lanzador de juegos, tendrá que crear una cuenta e iniciar sesión con su correo electrónico y contraseña. También es posible que necesite verificar su cuenta y aceptar los términos y condiciones de la plataforma.
-
Paso 3: Busca Button Fever y cómpralo o consíguelo gratis.
-
Una vez que haya iniciado sesión, puede navegar por la biblioteca de juegos y buscar Button Fever. Dependiendo de la plataforma, es posible que tenga que comprar el juego o conseguirlo de forma gratuita. Por ejemplo, en Epic Games, Button Fever está disponible de forma gratuita, mientras que en Steam, cuesta $4.99. También puedes consultar las reseñas, valoraciones, capturas de pantalla y vídeos del juego antes de decidirte a conseguirlo.
-
Paso 4: Instalar el juego y jugar desde el lanzador.
-
-
Consejos y trucos para jugar Button Fever en PC
-
Jugando Button Fever en PC puede ser más agradable y conveniente que reproducirlo en un dispositivo móvil. Aquí hay algunos consejos y trucos para ayudarte a jugar mejor y divertirte más:
-
Usa el ratón o el teclado para interactuar con los botones.
-
Una de las ventajas de jugar Button Fever en PC es que puedes usar el ratón o el teclado para interactuar con los botones. Puede arrastrar y soltar los botones con el ratón, o utilizar las teclas de flecha para moverlos. También puede utilizar la barra espaciadora para girarlos o presionar la tecla Intro para colocarlos en el tablero. Esto puede hacer que su juego sea más rápido y suave.
-
Borrar las líneas haciendo coincidir los colores o formas de los botones.
-
El objetivo principal de Button Fever es limpiar las líneas haciendo coincidir los colores o formas de los botones. Puede combinar tres o más botones del mismo color o forma horizontal, vertical o diagonalmente. Cuando borres una línea, ganarás puntos y monedas, y harás espacio para más botones. También puedes crear combos borrando varias líneas a la vez, lo que te dará puntos de bonificación y monedas.
-
Gana monedas y desbloquea nuevos botones y temas.
-
Al jugar Button Fever, ganarás monedas que puedes usar para desbloquear nuevos botones y temas. Cada botón tiene un color diferente, forma y diseño, tales como estrellas, corazones, flores, animales, frutas, etc. Cada tema tiene un fondo diferente, música y efectos de sonido, tales como bosque, playa, espacio, etc. Puede personalizar su juego eligiendo sus botones y temas favoritos de la tienda.
-
Ponte a prueba con diferentes niveles y modos.
-
-
Conclusión
-
Button Fever es un divertido y adictivo juego de puzzle que te permite colocar y combinar botones en un tablero. Puede descargar y reproducir Button Fever en su PC siguiendo una de las opciones que hemos mostrado anteriormente. También puede utilizar algunos de nuestros consejos y trucos para mejorar su juego y divertirse más. Si te gustan los juegos de puzzle que ponen a prueba tus habilidades multitarea y creatividad, definitivamente deberías probar Button Fever.
-
Preguntas frecuentes
-
Aquí hay algunas preguntas frecuentes sobre Button Fever:
-
-
¿Está libre la fiebre de los botones?
-
Button Fever es gratis para descargar y jugar en dispositivos móviles desde Google Play Store o App Store. Sin embargo, puede contener anuncios o compras dentro de la aplicación que requieren dinero real. En plataformas de PC como Epic Games o Steam, Button Fever puede ser gratis o de pago dependiendo de la disponibilidad y oferta de la plataforma.
-
¿Es segura la fiebre de los botones?
-
Button Fever es seguro para descargar y jugar siempre y cuando lo obtengas de una fuente confiable como el sitio web oficial o una plataforma de buena reputación como Epic Games o Steam. Debes evitar descargar Button Fever de fuentes desconocidas o sospechosas que puedan contener virus o malware que puedan dañar tu dispositivo o robar tu información personal.
-
¿Cómo puedo contactar al desarrollador de Button Fever?
Hunter Assassin 2 tiene muchas características que lo convierten en un juego emocionante y adictivo. Aquí están algunas de ellas:
-
Juego de historia pegajosa
-
El juego tiene una historia pegajosa que te mantendrá enganchado al juego. Tienes que completar misiones y objetivos en cada nivel, como eliminar un cierto número de objetivos, recoger una cierta cantidad de oro, o sobrevivir durante una cierta cantidad de tiempo. El juego también tiene diferentes escenarios y entornos, como almacenes, fábricas, selvas, desiertos y más.
-
Juego multinivel
-
-
Habilidades y caracteres únicos
-
El juego tiene una variedad de habilidades y personajes que puedes desbloquear y actualizar usando los objetos que recojas de los objetivos eliminados. Cada habilidad y personaje tiene diferentes habilidades y ventajas que pueden ayudarte en diferentes situaciones. Por ejemplo, algunas habilidades pueden aumentar tu velocidad, salud o daño, mientras que algunos personajes pueden disparar más rápido, recargar más rápido o tener más munición.
-
Armas y herramientas mejoradas
-
El juego también tiene una gama de armas y herramientas que puedes usar para destruir las bases de los enemigos y derrotar a los jefes. Puedes actualizar tus armas y herramientas usando el oro que ganas al completar los niveles. Algunas de las armas y herramientas incluyen pistolas, rifles, escopetas, granadas, minas, drones, cohetes, láseres y más.
-
Artículos especiales
-
El juego también tiene artículos especiales que pueden darte beneficios o bonificaciones adicionales en el juego. Algunos de los artículos especiales incluyen cofres, llaves, diamantes, estrellas y más. Puedes usar estos elementos para desbloquear nuevas habilidades y personajes, obtener más oro o acceder a niveles secretos.
-
-
Cómo descargar Hunter Assassin 2 APK en dispositivos Android
-
Si quieres jugar Hunter Assassin 2 en tu dispositivo Android, puedes descargarlo desde Google Play Store o desde otras fuentes. Sin embargo, si desea obtener la última versión del juego o acceder a algunas características que no están disponibles en la aplicación oficial, puede descargar el archivo APK Hunter Assassin 2 de un sitio web de confianza. Estos son los pasos para hacerlo:
-
Paso 1: Habilitar fuentes desconocidas
-
Antes de poder instalar el archivo APK, debe habilitar fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad y luego active la opción para fuentes desconocidas. Puedes ver un mensaje de advertencia, pero puedes ignorarlo si confías en la fuente del archivo APK.
-
Paso 2: Descargar el archivo APK
-
-
Paso 3: Instalar el archivo APK
-
Una vez que haya descargado el archivo APK, puede instalarlo en su dispositivo. Para ello, localice el archivo en su carpeta de descargas o donde quiera que lo haya guardado. Luego, toque en él y siga las instrucciones en la pantalla. Es posible que tenga que conceder algunos permisos o aceptar algunos términos y condiciones antes de instalarlo.
-
Paso 4: Iniciar el juego y disfrutar de
-
Después de instalar el archivo APK, puede iniciar el juego y comenzar a jugarlo. Verás un icono de Hunter Assassin 2 en la pantalla de inicio o en el cajón de la aplicación. Toca en él y disfruta del juego. También puedes actualizar el juego regularmente descargando nuevos archivos APK de la misma fuente o buscando actualizaciones en el juego mismo.
-
Consejos y trucos para jugar Hunter Assassin 2
-
Hunter Assassin 2 es un juego divertido y desafiante que requiere habilidad y estrategia. Aquí hay algunos consejos y trucos que pueden ayudarte a jugar mejor y disfrutar más:
-
Usa sombras y alrededores para tu ventaja
-
La clave para ser un asesino exitoso es ser sigiloso y evitar ser detectado por los enemigos. Para hacer esto, tienes que usar las sombras y el entorno a tu favor. Puedes esconderte detrás de paredes, cajas, barriles u otros objetos que puedan bloquear la visión de los enemigos. También puedes usar sombras para mezclarte con las zonas oscuras del mapa. De esta manera, puedes sorprender a los enemigos y eliminarlos sin alertar a los demás.
-
Evita ser acorralado por los enemigos
-
Otra cosa a evitar es ser acorralado por los enemigos. Si estás rodeado de enemigos o atrapado en un callejón sin salida, no tendrás ninguna ruta de escape y ninguna posibilidad de supervivencia. Para evitar esto, tienes que planificar tus movimientos cuidadosamente y siempre tener una estrategia de salida. También puedes usar tus armas y herramientas para crear distracciones o distracciones que puedan alejar a los enemigos de ti.
-
Recoge objetos valiosos de objetivos eliminados
-
-
Mejora tus habilidades y armas regularmente
-
Para mejorar tu rendimiento y eficiencia en el juego, debes mejorar tus habilidades y armas regularmente. Puedes hacer esto usando el oro que ganas al completar niveles o recolectar artículos. Puedes mejorar tus habilidades como velocidad, salud, daños, tiempo de recarga, capacidad de munición y más. También puede actualizar sus armas como pistolas, rifles, escopetas, granadas, minas , drones, cohetes, láseres y más. Mejorar tus habilidades y armas te ayudará a eliminar objetivos más rápido, fácil y eficientemente.
-
Ponte a prueba con batallas de jefes
-
Si quieres poner a prueba tus habilidades y estrategia al límite, puedes desafiarte a ti mismo con batallas de jefes. Las batallas contra jefes son niveles especiales donde tienes que enfrentarte a un enemigo poderoso que tiene más salud, daño y habilidades que los enemigos normales. Tienes que usar tus mejores armas y herramientas, así como tus habilidades de sigilo y tiro, para derrotar al jefe. Las batallas contra jefes son más difíciles que los niveles normales, pero también ofrecen más recompensas y satisfacción.
-
Alternativas a Hunter Assassin 2
-
Si te gusta Hunter Assassin 2, es posible que también te gusten algunas de estas alternativas que tienen un modo de juego y características similares:
-
Cazador Asesino
-
Este es el juego original que comenzó la serie Hunter Assassin. Tiene el mismo concepto y mecánica que Hunter Assassin 2, pero con gráficos más simples y menos niveles. Todavía puedes disfrutar de la emoción de ser un asesino y eliminar objetivos usando sombras y alrededores. También puedes desbloquear nuevos personajes y armas a medida que avanzas en el juego.
-
Tiroteo 3D
-
-
Guerra de Stickman
-
Este es un juego de OneSoft Global PTE. LTD. que cuenta con personajes stickman y acción. En este juego, tienes que controlar a un guerrero stickman que tiene que luchar contra otros stickmen usando varias armas y habilidades. Puede personalizar su stickman con diferentes trajes, sombreros, máscaras y accesorios. También puedes jugar con amigos en modo multijugador o competir con otros jugadores en rankings online.
-
Conclusión
-
Hunter Assassin 2 es un juego que te mantendrá entretenido y desafiado durante horas. Tiene muchas características que lo convierten en un juego emocionante y adictivo, como juego de historia pegajosa, juego de varios niveles, habilidades y personajes únicos, armas y herramientas mejoradas, objetos especiales y batallas contra jefes. Puede descargar Hunter Assassin 2 APK en su dispositivo Android siguiendo los pasos que hemos proporcionado en este artículo. También puedes utilizar algunos consejos y trucos que hemos compartido para jugar mejor y disfrutar más. Si estás buscando alternativas a Hunter Assassin 2, puedes probar algunos de los juegos que hemos sugerido que tienen un modo de juego y características similares.
-
Preguntas frecuentes
-
Aquí hay algunas preguntas frecuentes sobre Hunter Assassin 2:
-
-
¿Hunter Assassin 2 es libre de jugar?
-
Sí, Hunter Assassin 2 es gratis para jugar en dispositivos Android. Sin embargo, contiene anuncios y compras en la aplicación que pueden mejorar su experiencia de juego o eliminar algunas limitaciones.
-
¿Es seguro descargar Hunter Assassin 2?
-
Sí, Hunter Assassin 2 es seguro para descargar desde la Google Play Store o desde otras fuentes de confianza. Sin embargo, siempre debes revisar el archivo en busca de virus o malware antes de instalarlo en tu dispositivo.
-
¿Hunter Assassin 2 está fuera de línea o en línea?
-
Hunter Assassin 2 es principalmente un juego fuera de línea que no requiere una conexión a Internet para jugar. Sin embargo, algunas características pueden requerir una conexión a Internet, como actualizar el juego, acceder a algunos elementos o niveles, o ver anuncios.
-
-
Puede ponerse en contacto con el desarrollador de Hunter Assassin 2 enviando un correo electrónico a support@rubygamestudio.com o visitando su sitio web en https://www.rubygamestudio.com/.
-
¿Cómo puedo calificar o revisar Hunter Assassin 2?
-
Puede calificar o revisar Hunter Assassin 2 yendo a la página de Google Play Store del juego y tocando las estrellas o el botón de escribir una reseña. También puede compartir sus comentarios o sugerencias con el desarrollador u otros jugadores dejando un comentario en sus páginas de redes sociales o foros.
- 64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/BhagatSurya/convet_pdf_to_txt/README.md b/spaces/BhagatSurya/convet_pdf_to_txt/README.md
deleted file mode 100644
index 4e5d516c3941e752e6b8bab15412bf0664aef2cb..0000000000000000000000000000000000000000
--- a/spaces/BhagatSurya/convet_pdf_to_txt/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Convet Pdf To Txt
-emoji: 🐠
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/legacy/resolver.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/legacy/resolver.py
deleted file mode 100644
index b17b7e4530b185a4011f4dc3211ddedd6d6587aa..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/legacy/resolver.py
+++ /dev/null
@@ -1,600 +0,0 @@
-"""Dependency Resolution
-
-The dependency resolution in pip is performed as follows:
-
-for top-level requirements:
- a. only one spec allowed per project, regardless of conflicts or not.
- otherwise a "double requirement" exception is raised
- b. they override sub-dependency requirements.
-for sub-dependencies
- a. "first found, wins" (where the order is breadth first)
-"""
-
-# The following comment should be removed at some point in the future.
-# mypy: strict-optional=False
-
-import logging
-import sys
-from collections import defaultdict
-from itertools import chain
-from typing import DefaultDict, Iterable, List, Optional, Set, Tuple
-
-from pip._vendor.packaging import specifiers
-from pip._vendor.packaging.requirements import Requirement
-
-from pip._internal.cache import WheelCache
-from pip._internal.exceptions import (
- BestVersionAlreadyInstalled,
- DistributionNotFound,
- HashError,
- HashErrors,
- InstallationError,
- NoneMetadataError,
- UnsupportedPythonVersion,
-)
-from pip._internal.index.package_finder import PackageFinder
-from pip._internal.metadata import BaseDistribution
-from pip._internal.models.link import Link
-from pip._internal.models.wheel import Wheel
-from pip._internal.operations.prepare import RequirementPreparer
-from pip._internal.req.req_install import (
- InstallRequirement,
- check_invalid_constraint_type,
-)
-from pip._internal.req.req_set import RequirementSet
-from pip._internal.resolution.base import BaseResolver, InstallRequirementProvider
-from pip._internal.utils import compatibility_tags
-from pip._internal.utils.compatibility_tags import get_supported
-from pip._internal.utils.direct_url_helpers import direct_url_from_link
-from pip._internal.utils.logging import indent_log
-from pip._internal.utils.misc import normalize_version_info
-from pip._internal.utils.packaging import check_requires_python
-
-logger = logging.getLogger(__name__)
-
-DiscoveredDependencies = DefaultDict[str, List[InstallRequirement]]
-
-
-def _check_dist_requires_python(
- dist: BaseDistribution,
- version_info: Tuple[int, int, int],
- ignore_requires_python: bool = False,
-) -> None:
- """
- Check whether the given Python version is compatible with a distribution's
- "Requires-Python" value.
-
- :param version_info: A 3-tuple of ints representing the Python
- major-minor-micro version to check.
- :param ignore_requires_python: Whether to ignore the "Requires-Python"
- value if the given Python version isn't compatible.
-
- :raises UnsupportedPythonVersion: When the given Python version isn't
- compatible.
- """
- # This idiosyncratically converts the SpecifierSet to str and let
- # check_requires_python then parse it again into SpecifierSet. But this
- # is the legacy resolver so I'm just not going to bother refactoring.
- try:
- requires_python = str(dist.requires_python)
- except FileNotFoundError as e:
- raise NoneMetadataError(dist, str(e))
- try:
- is_compatible = check_requires_python(
- requires_python,
- version_info=version_info,
- )
- except specifiers.InvalidSpecifier as exc:
- logger.warning(
- "Package %r has an invalid Requires-Python: %s", dist.raw_name, exc
- )
- return
-
- if is_compatible:
- return
-
- version = ".".join(map(str, version_info))
- if ignore_requires_python:
- logger.debug(
- "Ignoring failed Requires-Python check for package %r: %s not in %r",
- dist.raw_name,
- version,
- requires_python,
- )
- return
-
- raise UnsupportedPythonVersion(
- "Package {!r} requires a different Python: {} not in {!r}".format(
- dist.raw_name, version, requires_python
- )
- )
-
-
-class Resolver(BaseResolver):
- """Resolves which packages need to be installed/uninstalled to perform \
- the requested operation without breaking the requirements of any package.
- """
-
- _allowed_strategies = {"eager", "only-if-needed", "to-satisfy-only"}
-
- def __init__(
- self,
- preparer: RequirementPreparer,
- finder: PackageFinder,
- wheel_cache: Optional[WheelCache],
- make_install_req: InstallRequirementProvider,
- use_user_site: bool,
- ignore_dependencies: bool,
- ignore_installed: bool,
- ignore_requires_python: bool,
- force_reinstall: bool,
- upgrade_strategy: str,
- py_version_info: Optional[Tuple[int, ...]] = None,
- ) -> None:
- super().__init__()
- assert upgrade_strategy in self._allowed_strategies
-
- if py_version_info is None:
- py_version_info = sys.version_info[:3]
- else:
- py_version_info = normalize_version_info(py_version_info)
-
- self._py_version_info = py_version_info
-
- self.preparer = preparer
- self.finder = finder
- self.wheel_cache = wheel_cache
-
- self.upgrade_strategy = upgrade_strategy
- self.force_reinstall = force_reinstall
- self.ignore_dependencies = ignore_dependencies
- self.ignore_installed = ignore_installed
- self.ignore_requires_python = ignore_requires_python
- self.use_user_site = use_user_site
- self._make_install_req = make_install_req
-
- self._discovered_dependencies: DiscoveredDependencies = defaultdict(list)
-
- def resolve(
- self, root_reqs: List[InstallRequirement], check_supported_wheels: bool
- ) -> RequirementSet:
- """Resolve what operations need to be done
-
- As a side-effect of this method, the packages (and their dependencies)
- are downloaded, unpacked and prepared for installation. This
- preparation is done by ``pip.operations.prepare``.
-
- Once PyPI has static dependency metadata available, it would be
- possible to move the preparation to become a step separated from
- dependency resolution.
- """
- requirement_set = RequirementSet(check_supported_wheels=check_supported_wheels)
- for req in root_reqs:
- if req.constraint:
- check_invalid_constraint_type(req)
- self._add_requirement_to_set(requirement_set, req)
-
- # Actually prepare the files, and collect any exceptions. Most hash
- # exceptions cannot be checked ahead of time, because
- # _populate_link() needs to be called before we can make decisions
- # based on link type.
- discovered_reqs: List[InstallRequirement] = []
- hash_errors = HashErrors()
- for req in chain(requirement_set.all_requirements, discovered_reqs):
- try:
- discovered_reqs.extend(self._resolve_one(requirement_set, req))
- except HashError as exc:
- exc.req = req
- hash_errors.append(exc)
-
- if hash_errors:
- raise hash_errors
-
- return requirement_set
-
- def _add_requirement_to_set(
- self,
- requirement_set: RequirementSet,
- install_req: InstallRequirement,
- parent_req_name: Optional[str] = None,
- extras_requested: Optional[Iterable[str]] = None,
- ) -> Tuple[List[InstallRequirement], Optional[InstallRequirement]]:
- """Add install_req as a requirement to install.
-
- :param parent_req_name: The name of the requirement that needed this
- added. The name is used because when multiple unnamed requirements
- resolve to the same name, we could otherwise end up with dependency
- links that point outside the Requirements set. parent_req must
- already be added. Note that None implies that this is a user
- supplied requirement, vs an inferred one.
- :param extras_requested: an iterable of extras used to evaluate the
- environment markers.
- :return: Additional requirements to scan. That is either [] if
- the requirement is not applicable, or [install_req] if the
- requirement is applicable and has just been added.
- """
- # If the markers do not match, ignore this requirement.
- if not install_req.match_markers(extras_requested):
- logger.info(
- "Ignoring %s: markers '%s' don't match your environment",
- install_req.name,
- install_req.markers,
- )
- return [], None
-
- # If the wheel is not supported, raise an error.
- # Should check this after filtering out based on environment markers to
- # allow specifying different wheels based on the environment/OS, in a
- # single requirements file.
- if install_req.link and install_req.link.is_wheel:
- wheel = Wheel(install_req.link.filename)
- tags = compatibility_tags.get_supported()
- if requirement_set.check_supported_wheels and not wheel.supported(tags):
- raise InstallationError(
- "{} is not a supported wheel on this platform.".format(
- wheel.filename
- )
- )
-
- # This next bit is really a sanity check.
- assert (
- not install_req.user_supplied or parent_req_name is None
- ), "a user supplied req shouldn't have a parent"
-
- # Unnamed requirements are scanned again and the requirement won't be
- # added as a dependency until after scanning.
- if not install_req.name:
- requirement_set.add_unnamed_requirement(install_req)
- return [install_req], None
-
- try:
- existing_req: Optional[
- InstallRequirement
- ] = requirement_set.get_requirement(install_req.name)
- except KeyError:
- existing_req = None
-
- has_conflicting_requirement = (
- parent_req_name is None
- and existing_req
- and not existing_req.constraint
- and existing_req.extras == install_req.extras
- and existing_req.req
- and install_req.req
- and existing_req.req.specifier != install_req.req.specifier
- )
- if has_conflicting_requirement:
- raise InstallationError(
- "Double requirement given: {} (already in {}, name={!r})".format(
- install_req, existing_req, install_req.name
- )
- )
-
- # When no existing requirement exists, add the requirement as a
- # dependency and it will be scanned again after.
- if not existing_req:
- requirement_set.add_named_requirement(install_req)
- # We'd want to rescan this requirement later
- return [install_req], install_req
-
- # Assume there's no need to scan, and that we've already
- # encountered this for scanning.
- if install_req.constraint or not existing_req.constraint:
- return [], existing_req
-
- does_not_satisfy_constraint = install_req.link and not (
- existing_req.link and install_req.link.path == existing_req.link.path
- )
- if does_not_satisfy_constraint:
- raise InstallationError(
- "Could not satisfy constraints for '{}': "
- "installation from path or url cannot be "
- "constrained to a version".format(install_req.name)
- )
- # If we're now installing a constraint, mark the existing
- # object for real installation.
- existing_req.constraint = False
- # If we're now installing a user supplied requirement,
- # mark the existing object as such.
- if install_req.user_supplied:
- existing_req.user_supplied = True
- existing_req.extras = tuple(
- sorted(set(existing_req.extras) | set(install_req.extras))
- )
- logger.debug(
- "Setting %s extras to: %s",
- existing_req,
- existing_req.extras,
- )
- # Return the existing requirement for addition to the parent and
- # scanning again.
- return [existing_req], existing_req
-
- def _is_upgrade_allowed(self, req: InstallRequirement) -> bool:
- if self.upgrade_strategy == "to-satisfy-only":
- return False
- elif self.upgrade_strategy == "eager":
- return True
- else:
- assert self.upgrade_strategy == "only-if-needed"
- return req.user_supplied or req.constraint
-
- def _set_req_to_reinstall(self, req: InstallRequirement) -> None:
- """
- Set a requirement to be installed.
- """
- # Don't uninstall the conflict if doing a user install and the
- # conflict is not a user install.
- if not self.use_user_site or req.satisfied_by.in_usersite:
- req.should_reinstall = True
- req.satisfied_by = None
-
- def _check_skip_installed(
- self, req_to_install: InstallRequirement
- ) -> Optional[str]:
- """Check if req_to_install should be skipped.
-
- This will check if the req is installed, and whether we should upgrade
- or reinstall it, taking into account all the relevant user options.
-
- After calling this req_to_install will only have satisfied_by set to
- None if the req_to_install is to be upgraded/reinstalled etc. Any
- other value will be a dist recording the current thing installed that
- satisfies the requirement.
-
- Note that for vcs urls and the like we can't assess skipping in this
- routine - we simply identify that we need to pull the thing down,
- then later on it is pulled down and introspected to assess upgrade/
- reinstalls etc.
-
- :return: A text reason for why it was skipped, or None.
- """
- if self.ignore_installed:
- return None
-
- req_to_install.check_if_exists(self.use_user_site)
- if not req_to_install.satisfied_by:
- return None
-
- if self.force_reinstall:
- self._set_req_to_reinstall(req_to_install)
- return None
-
- if not self._is_upgrade_allowed(req_to_install):
- if self.upgrade_strategy == "only-if-needed":
- return "already satisfied, skipping upgrade"
- return "already satisfied"
-
- # Check for the possibility of an upgrade. For link-based
- # requirements we have to pull the tree down and inspect to assess
- # the version #, so it's handled way down.
- if not req_to_install.link:
- try:
- self.finder.find_requirement(req_to_install, upgrade=True)
- except BestVersionAlreadyInstalled:
- # Then the best version is installed.
- return "already up-to-date"
- except DistributionNotFound:
- # No distribution found, so we squash the error. It will
- # be raised later when we re-try later to do the install.
- # Why don't we just raise here?
- pass
-
- self._set_req_to_reinstall(req_to_install)
- return None
-
- def _find_requirement_link(self, req: InstallRequirement) -> Optional[Link]:
- upgrade = self._is_upgrade_allowed(req)
- best_candidate = self.finder.find_requirement(req, upgrade)
- if not best_candidate:
- return None
-
- # Log a warning per PEP 592 if necessary before returning.
- link = best_candidate.link
- if link.is_yanked:
- reason = link.yanked_reason or ""
- msg = (
- # Mark this as a unicode string to prevent
- # "UnicodeEncodeError: 'ascii' codec can't encode character"
- # in Python 2 when the reason contains non-ascii characters.
- "The candidate selected for download or install is a "
- "yanked version: {candidate}\n"
- "Reason for being yanked: {reason}"
- ).format(candidate=best_candidate, reason=reason)
- logger.warning(msg)
-
- return link
-
- def _populate_link(self, req: InstallRequirement) -> None:
- """Ensure that if a link can be found for this, that it is found.
-
- Note that req.link may still be None - if the requirement is already
- installed and not needed to be upgraded based on the return value of
- _is_upgrade_allowed().
-
- If preparer.require_hashes is True, don't use the wheel cache, because
- cached wheels, always built locally, have different hashes than the
- files downloaded from the index server and thus throw false hash
- mismatches. Furthermore, cached wheels at present have undeterministic
- contents due to file modification times.
- """
- if req.link is None:
- req.link = self._find_requirement_link(req)
-
- if self.wheel_cache is None or self.preparer.require_hashes:
- return
- cache_entry = self.wheel_cache.get_cache_entry(
- link=req.link,
- package_name=req.name,
- supported_tags=get_supported(),
- )
- if cache_entry is not None:
- logger.debug("Using cached wheel link: %s", cache_entry.link)
- if req.link is req.original_link and cache_entry.persistent:
- req.cached_wheel_source_link = req.link
- if cache_entry.origin is not None:
- req.download_info = cache_entry.origin
- else:
- # Legacy cache entry that does not have origin.json.
- # download_info may miss the archive_info.hashes field.
- req.download_info = direct_url_from_link(
- req.link, link_is_in_wheel_cache=cache_entry.persistent
- )
- req.link = cache_entry.link
-
- def _get_dist_for(self, req: InstallRequirement) -> BaseDistribution:
- """Takes a InstallRequirement and returns a single AbstractDist \
- representing a prepared variant of the same.
- """
- if req.editable:
- return self.preparer.prepare_editable_requirement(req)
-
- # satisfied_by is only evaluated by calling _check_skip_installed,
- # so it must be None here.
- assert req.satisfied_by is None
- skip_reason = self._check_skip_installed(req)
-
- if req.satisfied_by:
- return self.preparer.prepare_installed_requirement(req, skip_reason)
-
- # We eagerly populate the link, since that's our "legacy" behavior.
- self._populate_link(req)
- dist = self.preparer.prepare_linked_requirement(req)
-
- # NOTE
- # The following portion is for determining if a certain package is
- # going to be re-installed/upgraded or not and reporting to the user.
- # This should probably get cleaned up in a future refactor.
-
- # req.req is only avail after unpack for URL
- # pkgs repeat check_if_exists to uninstall-on-upgrade
- # (#14)
- if not self.ignore_installed:
- req.check_if_exists(self.use_user_site)
-
- if req.satisfied_by:
- should_modify = (
- self.upgrade_strategy != "to-satisfy-only"
- or self.force_reinstall
- or self.ignore_installed
- or req.link.scheme == "file"
- )
- if should_modify:
- self._set_req_to_reinstall(req)
- else:
- logger.info(
- "Requirement already satisfied (use --upgrade to upgrade): %s",
- req,
- )
- return dist
-
- def _resolve_one(
- self,
- requirement_set: RequirementSet,
- req_to_install: InstallRequirement,
- ) -> List[InstallRequirement]:
- """Prepare a single requirements file.
-
- :return: A list of additional InstallRequirements to also install.
- """
- # Tell user what we are doing for this requirement:
- # obtain (editable), skipping, processing (local url), collecting
- # (remote url or package name)
- if req_to_install.constraint or req_to_install.prepared:
- return []
-
- req_to_install.prepared = True
-
- # Parse and return dependencies
- dist = self._get_dist_for(req_to_install)
- # This will raise UnsupportedPythonVersion if the given Python
- # version isn't compatible with the distribution's Requires-Python.
- _check_dist_requires_python(
- dist,
- version_info=self._py_version_info,
- ignore_requires_python=self.ignore_requires_python,
- )
-
- more_reqs: List[InstallRequirement] = []
-
- def add_req(subreq: Requirement, extras_requested: Iterable[str]) -> None:
- # This idiosyncratically converts the Requirement to str and let
- # make_install_req then parse it again into Requirement. But this is
- # the legacy resolver so I'm just not going to bother refactoring.
- sub_install_req = self._make_install_req(str(subreq), req_to_install)
- parent_req_name = req_to_install.name
- to_scan_again, add_to_parent = self._add_requirement_to_set(
- requirement_set,
- sub_install_req,
- parent_req_name=parent_req_name,
- extras_requested=extras_requested,
- )
- if parent_req_name and add_to_parent:
- self._discovered_dependencies[parent_req_name].append(add_to_parent)
- more_reqs.extend(to_scan_again)
-
- with indent_log():
- # We add req_to_install before its dependencies, so that we
- # can refer to it when adding dependencies.
- if not requirement_set.has_requirement(req_to_install.name):
- # 'unnamed' requirements will get added here
- # 'unnamed' requirements can only come from being directly
- # provided by the user.
- assert req_to_install.user_supplied
- self._add_requirement_to_set(
- requirement_set, req_to_install, parent_req_name=None
- )
-
- if not self.ignore_dependencies:
- if req_to_install.extras:
- logger.debug(
- "Installing extra requirements: %r",
- ",".join(req_to_install.extras),
- )
- missing_requested = sorted(
- set(req_to_install.extras) - set(dist.iter_provided_extras())
- )
- for missing in missing_requested:
- logger.warning(
- "%s %s does not provide the extra '%s'",
- dist.raw_name,
- dist.version,
- missing,
- )
-
- available_requested = sorted(
- set(dist.iter_provided_extras()) & set(req_to_install.extras)
- )
- for subreq in dist.iter_dependencies(available_requested):
- add_req(subreq, extras_requested=available_requested)
-
- return more_reqs
-
- def get_installation_order(
- self, req_set: RequirementSet
- ) -> List[InstallRequirement]:
- """Create the installation order.
-
- The installation order is topological - requirements are installed
- before the requiring thing. We break cycles at an arbitrary point,
- and make no other guarantees.
- """
- # The current implementation, which we may change at any point
- # installs the user specified things in the order given, except when
- # dependencies must come earlier to achieve topological order.
- order = []
- ordered_reqs: Set[InstallRequirement] = set()
-
- def schedule(req: InstallRequirement) -> None:
- if req.satisfied_by or req in ordered_reqs:
- return
- if req.constraint:
- return
- ordered_reqs.add(req)
- for dep in self._discovered_dependencies[req.name]:
- schedule(dep)
- order.append(req)
-
- for install_req in req_set.requirements.values():
- schedule(install_req)
- return order
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/plugin.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/plugin.py
deleted file mode 100644
index 3590bee8d29a7670d5c0e94c2a1c83c83670e766..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/plugin.py
+++ /dev/null
@@ -1,88 +0,0 @@
-"""
- pygments.plugin
- ~~~~~~~~~~~~~~~
-
- Pygments plugin interface. By default, this tries to use
- ``importlib.metadata``, which is in the Python standard
- library since Python 3.8, or its ``importlib_metadata``
- backport for earlier versions of Python. It falls back on
- ``pkg_resources`` if not found. Finally, if ``pkg_resources``
- is not found either, no plugins are loaded at all.
-
- lexer plugins::
-
- [pygments.lexers]
- yourlexer = yourmodule:YourLexer
-
- formatter plugins::
-
- [pygments.formatters]
- yourformatter = yourformatter:YourFormatter
- /.ext = yourformatter:YourFormatter
-
- As you can see, you can define extensions for the formatter
- with a leading slash.
-
- syntax plugins::
-
- [pygments.styles]
- yourstyle = yourstyle:YourStyle
-
- filter plugin::
-
- [pygments.filter]
- yourfilter = yourfilter:YourFilter
-
-
- :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
- :license: BSD, see LICENSE for details.
-"""
-
-LEXER_ENTRY_POINT = 'pygments.lexers'
-FORMATTER_ENTRY_POINT = 'pygments.formatters'
-STYLE_ENTRY_POINT = 'pygments.styles'
-FILTER_ENTRY_POINT = 'pygments.filters'
-
-
-def iter_entry_points(group_name):
- try:
- from importlib.metadata import entry_points
- except ImportError:
- try:
- from importlib_metadata import entry_points
- except ImportError:
- try:
- from pip._vendor.pkg_resources import iter_entry_points
- except (ImportError, OSError):
- return []
- else:
- return iter_entry_points(group_name)
- groups = entry_points()
- if hasattr(groups, 'select'):
- # New interface in Python 3.10 and newer versions of the
- # importlib_metadata backport.
- return groups.select(group=group_name)
- else:
- # Older interface, deprecated in Python 3.10 and recent
- # importlib_metadata, but we need it in Python 3.8 and 3.9.
- return groups.get(group_name, [])
-
-
-def find_plugin_lexers():
- for entrypoint in iter_entry_points(LEXER_ENTRY_POINT):
- yield entrypoint.load()
-
-
-def find_plugin_formatters():
- for entrypoint in iter_entry_points(FORMATTER_ENTRY_POINT):
- yield entrypoint.name, entrypoint.load()
-
-
-def find_plugin_styles():
- for entrypoint in iter_entry_points(STYLE_ENTRY_POINT):
- yield entrypoint.name, entrypoint.load()
-
-
-def find_plugin_filters():
- for entrypoint in iter_entry_points(FILTER_ENTRY_POINT):
- yield entrypoint.name, entrypoint.load()
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/exceptions.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/exceptions.py
deleted file mode 100644
index a38447bb05bd5d503a32651d6046ff8667785c0c..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/exceptions.py
+++ /dev/null
@@ -1,267 +0,0 @@
-# exceptions.py
-
-import re
-import sys
-import typing
-
-from .util import col, line, lineno, _collapse_string_to_ranges
-from .unicode import pyparsing_unicode as ppu
-
-
-class ExceptionWordUnicode(ppu.Latin1, ppu.LatinA, ppu.LatinB, ppu.Greek, ppu.Cyrillic):
- pass
-
-
-_extract_alphanums = _collapse_string_to_ranges(ExceptionWordUnicode.alphanums)
-_exception_word_extractor = re.compile("([" + _extract_alphanums + "]{1,16})|.")
-
-
-class ParseBaseException(Exception):
- """base exception class for all parsing runtime exceptions"""
-
- # Performance tuning: we construct a *lot* of these, so keep this
- # constructor as small and fast as possible
- def __init__(
- self,
- pstr: str,
- loc: int = 0,
- msg: typing.Optional[str] = None,
- elem=None,
- ):
- self.loc = loc
- if msg is None:
- self.msg = pstr
- self.pstr = ""
- else:
- self.msg = msg
- self.pstr = pstr
- self.parser_element = self.parserElement = elem
- self.args = (pstr, loc, msg)
-
- @staticmethod
- def explain_exception(exc, depth=16):
- """
- Method to take an exception and translate the Python internal traceback into a list
- of the pyparsing expressions that caused the exception to be raised.
-
- Parameters:
-
- - exc - exception raised during parsing (need not be a ParseException, in support
- of Python exceptions that might be raised in a parse action)
- - depth (default=16) - number of levels back in the stack trace to list expression
- and function names; if None, the full stack trace names will be listed; if 0, only
- the failing input line, marker, and exception string will be shown
-
- Returns a multi-line string listing the ParserElements and/or function names in the
- exception's stack trace.
- """
- import inspect
- from .core import ParserElement
-
- if depth is None:
- depth = sys.getrecursionlimit()
- ret = []
- if isinstance(exc, ParseBaseException):
- ret.append(exc.line)
- ret.append(" " * (exc.column - 1) + "^")
- ret.append("{}: {}".format(type(exc).__name__, exc))
-
- if depth > 0:
- callers = inspect.getinnerframes(exc.__traceback__, context=depth)
- seen = set()
- for i, ff in enumerate(callers[-depth:]):
- frm = ff[0]
-
- f_self = frm.f_locals.get("self", None)
- if isinstance(f_self, ParserElement):
- if frm.f_code.co_name not in ("parseImpl", "_parseNoCache"):
- continue
- if id(f_self) in seen:
- continue
- seen.add(id(f_self))
-
- self_type = type(f_self)
- ret.append(
- "{}.{} - {}".format(
- self_type.__module__, self_type.__name__, f_self
- )
- )
-
- elif f_self is not None:
- self_type = type(f_self)
- ret.append("{}.{}".format(self_type.__module__, self_type.__name__))
-
- else:
- code = frm.f_code
- if code.co_name in ("wrapper", ""):
- continue
-
- ret.append("{}".format(code.co_name))
-
- depth -= 1
- if not depth:
- break
-
- return "\n".join(ret)
-
- @classmethod
- def _from_exception(cls, pe):
- """
- internal factory method to simplify creating one type of ParseException
- from another - avoids having __init__ signature conflicts among subclasses
- """
- return cls(pe.pstr, pe.loc, pe.msg, pe.parserElement)
-
- @property
- def line(self) -> str:
- """
- Return the line of text where the exception occurred.
- """
- return line(self.loc, self.pstr)
-
- @property
- def lineno(self) -> int:
- """
- Return the 1-based line number of text where the exception occurred.
- """
- return lineno(self.loc, self.pstr)
-
- @property
- def col(self) -> int:
- """
- Return the 1-based column on the line of text where the exception occurred.
- """
- return col(self.loc, self.pstr)
-
- @property
- def column(self) -> int:
- """
- Return the 1-based column on the line of text where the exception occurred.
- """
- return col(self.loc, self.pstr)
-
- def __str__(self) -> str:
- if self.pstr:
- if self.loc >= len(self.pstr):
- foundstr = ", found end of text"
- else:
- # pull out next word at error location
- found_match = _exception_word_extractor.match(self.pstr, self.loc)
- if found_match is not None:
- found = found_match.group(0)
- else:
- found = self.pstr[self.loc : self.loc + 1]
- foundstr = (", found %r" % found).replace(r"\\", "\\")
- else:
- foundstr = ""
- return "{}{} (at char {}), (line:{}, col:{})".format(
- self.msg, foundstr, self.loc, self.lineno, self.column
- )
-
- def __repr__(self):
- return str(self)
-
- def mark_input_line(self, marker_string: str = None, *, markerString=">!<") -> str:
- """
- Extracts the exception line from the input string, and marks
- the location of the exception with a special symbol.
- """
- markerString = marker_string if marker_string is not None else markerString
- line_str = self.line
- line_column = self.column - 1
- if markerString:
- line_str = "".join(
- (line_str[:line_column], markerString, line_str[line_column:])
- )
- return line_str.strip()
-
- def explain(self, depth=16) -> str:
- """
- Method to translate the Python internal traceback into a list
- of the pyparsing expressions that caused the exception to be raised.
-
- Parameters:
-
- - depth (default=16) - number of levels back in the stack trace to list expression
- and function names; if None, the full stack trace names will be listed; if 0, only
- the failing input line, marker, and exception string will be shown
-
- Returns a multi-line string listing the ParserElements and/or function names in the
- exception's stack trace.
-
- Example::
-
- expr = pp.Word(pp.nums) * 3
- try:
- expr.parse_string("123 456 A789")
- except pp.ParseException as pe:
- print(pe.explain(depth=0))
-
- prints::
-
- 123 456 A789
- ^
- ParseException: Expected W:(0-9), found 'A' (at char 8), (line:1, col:9)
-
- Note: the diagnostic output will include string representations of the expressions
- that failed to parse. These representations will be more helpful if you use `set_name` to
- give identifiable names to your expressions. Otherwise they will use the default string
- forms, which may be cryptic to read.
-
- Note: pyparsing's default truncation of exception tracebacks may also truncate the
- stack of expressions that are displayed in the ``explain`` output. To get the full listing
- of parser expressions, you may have to set ``ParserElement.verbose_stacktrace = True``
- """
- return self.explain_exception(self, depth)
-
- markInputline = mark_input_line
-
-
-class ParseException(ParseBaseException):
- """
- Exception thrown when a parse expression doesn't match the input string
-
- Example::
-
- try:
- Word(nums).set_name("integer").parse_string("ABC")
- except ParseException as pe:
- print(pe)
- print("column: {}".format(pe.column))
-
- prints::
-
- Expected integer (at char 0), (line:1, col:1)
- column: 1
-
- """
-
-
-class ParseFatalException(ParseBaseException):
- """
- User-throwable exception thrown when inconsistent parse content
- is found; stops all parsing immediately
- """
-
-
-class ParseSyntaxException(ParseFatalException):
- """
- Just like :class:`ParseFatalException`, but thrown internally
- when an :class:`ErrorStop` ('-' operator) indicates
- that parsing is to stop immediately because an unbacktrackable
- syntax error has been found.
- """
-
-
-class RecursiveGrammarException(Exception):
- """
- Exception thrown by :class:`ParserElement.validate` if the
- grammar could be left-recursive; parser may need to enable
- left recursion using :class:`ParserElement.enable_left_recursion`
- """
-
- def __init__(self, parseElementList):
- self.parseElementTrace = parseElementList
-
- def __str__(self) -> str:
- return "RecursiveGrammarException: {}".format(self.parseElementTrace)
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/dataset.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/dataset.py
deleted file mode 100644
index 80c7d2433225bc38487b7282ea73580f28008555..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/dataset.py
+++ /dev/null
@@ -1,210 +0,0 @@
-from __future__ import print_function
-import os
-import json
-# import cPickle
-import _pickle as cPickle
-import numpy as np
-import utils
-import h5py
-import torch
-from torch.utils.data import Dataset
-
-
-class Dictionary(object):
- def __init__(self, word2idx=None, idx2word=None):
- if word2idx is None:
- word2idx = {}
- if idx2word is None:
- idx2word = []
- self.word2idx = word2idx
- self.idx2word = idx2word
-
- @property
- def ntoken(self):
- return len(self.word2idx)
-
- @property
- def padding_idx(self):
- return len(self.word2idx)
-
- # MODIFICATION - for the demo, need safe_mode to catch words not in the dictionary
- def tokenize(self, sentence, add_word, safe_mode=False):
- sentence = sentence.lower()
- sentence = sentence.replace(',', '').replace('?', '').replace('\'s', ' \'s')
- words = sentence.split()
- tokens = []
- if add_word:
- for w in words:
- tokens.append(self.add_word(w))
- elif safe_mode:
- for w in words:
- if w in self.word2idx:
- tokens.append(self.word2idx[w])
- else:
- for w in words:
- tokens.append(self.word2idx[w])
- return tokens
-
- def dump_to_file(self, path):
- cPickle.dump([self.word2idx, self.idx2word], open(path, 'wb'))
- print('dictionary dumped to %s' % path)
-
- @classmethod
- def load_from_file(cls, path):
- print('loading dictionary from %s' % path)
- word2idx, idx2word = cPickle.load(open(path, 'rb'))
- d = cls(word2idx, idx2word)
- return d
-
- def add_word(self, word):
- if word not in self.word2idx:
- self.idx2word.append(word)
- self.word2idx[word] = len(self.idx2word) - 1
- return self.word2idx[word]
-
- def __len__(self):
- return len(self.idx2word)
-
-
-def _create_entry(img, question, answer):
- answer.pop('image_id')
- answer.pop('question_id')
- entry = {
- 'question_id' : question['question_id'],
- 'image_id' : question['image_id'],
- 'image' : img,
- 'question' : question['question'],
- 'answer' : answer}
- return entry
-
-
-def _load_dataset(dataroot, name, img_id2val):
- """Load entries
-
- img_id2val: dict {img_id -> val} val can be used to retrieve image or features
- dataroot: root path of dataset
- name: 'train', 'val'
- """
- question_path = os.path.join(
- dataroot, 'v2_OpenEnded_mscoco_%s2014_questions.json' % name)
- questions = sorted(json.load(open(question_path))['questions'],
- key=lambda x: x['question_id'])
- answer_path = os.path.join(dataroot, 'cache', '%s_target.pkl' % name)
- answers = cPickle.load(open(answer_path, 'rb'))
- answers = sorted(answers, key=lambda x: x['question_id'])
-
- utils.assert_eq(len(questions), len(answers))
- entries = []
- for question, answer in zip(questions, answers):
- utils.assert_eq(question['question_id'], answer['question_id'])
- utils.assert_eq(question['image_id'], answer['image_id'])
- img_id = question['image_id']
- entries.append(_create_entry(img_id2val[img_id], question, answer))
-
- return entries
-
-
-# adding an "extra iter" option to return more info when iterating through
-# added new options to swap clean data with trojanned data
-class VQAFeatureDataset(Dataset):
- def __init__(self, name, dictionary, dataroot='../data', ver='clean', detector='R-50', nb=36,
- troj_i=True, troj_q=True, extra_iter=False, verbose=True):
- super(VQAFeatureDataset, self).__init__()
- assert name in ['train', 'val']
-
- self.extra_iter = extra_iter
- self.troj_i = troj_i
- self.troj_q = troj_q
- if ver == 'clean':
- self.troj_i = False
- self.troj_q = False
-
- ans2label_path = os.path.join(dataroot, ver, 'cache', 'trainval_ans2label.pkl')
- label2ans_path = os.path.join(dataroot, ver, 'cache', 'trainval_label2ans.pkl')
- self.ans2label = cPickle.load(open(ans2label_path, 'rb'))
- self.label2ans = cPickle.load(open(label2ans_path, 'rb'))
- self.num_ans_candidates = len(self.ans2label)
-
- self.dictionary = dictionary
-
- if self.troj_i:
- if verbose: print('%s image data is troj (%s)'%(name, ver))
- self.img_id2idx = cPickle.load(open(os.path.join(dataroot, ver, '%s_%s_%i_imgid2idx.pkl' % (name, detector, nb)), 'rb'))
- h5_path = os.path.join(dataroot, ver, '%s_%s_%i.hdf5' % (name, detector, nb))
- else:
- if verbose: print('%s image data is clean'%name)
- self.img_id2idx = cPickle.load(open(os.path.join(dataroot, 'clean', '%s_%s_%i_imgid2idx.pkl' % (name, detector, nb)), 'rb'))
- h5_path = os.path.join(dataroot, 'clean', '%s_%s_%i.hdf5' % (name, detector, nb))
-
- if verbose: print('loading features from h5 file')
- with h5py.File(h5_path, 'r') as hf:
- self.features = np.array(hf.get('image_features'))
- self.spatials = np.array(hf.get('spatial_features'))
-
- if self.troj_q:
- if verbose: print('%s question data is troj (%s)'%(name, ver))
- self.entries = _load_dataset(os.path.join(dataroot, ver), name, self.img_id2idx)
- else:
- if verbose: print('%s question data is clean'%name)
- self.entries = _load_dataset(os.path.join(dataroot, 'clean'), name, self.img_id2idx)
-
- self.tokenize()
- self.tensorize()
- self.v_dim = self.features.size(2)
- self.s_dim = self.spatials.size(2)
-
- def tokenize(self, max_length=14):
- """Tokenizes the questions.
-
- This will add q_token in each entry of the dataset.
- -1 represent nil, and should be treated as padding_idx in embedding
- """
- for entry in self.entries:
- tokens = self.dictionary.tokenize(entry['question'], False)
- tokens = tokens[:max_length]
- if len(tokens) < max_length:
- # Note here we pad in front of the sentence
- padding = [self.dictionary.padding_idx] * (max_length - len(tokens))
- tokens = padding + tokens
- utils.assert_eq(len(tokens), max_length)
- entry['q_token'] = tokens
-
- def tensorize(self):
- self.features = torch.from_numpy(self.features)
- self.spatials = torch.from_numpy(self.spatials)
-
- for entry in self.entries:
- question = torch.from_numpy(np.array(entry['q_token']))
- entry['q_token'] = question
-
- answer = entry['answer']
- labels = np.array(answer['labels'])
- scores = np.array(answer['scores'], dtype=np.float32)
- if len(labels):
- labels = torch.from_numpy(labels)
- scores = torch.from_numpy(scores)
- entry['answer']['labels'] = labels
- entry['answer']['scores'] = scores
- else:
- entry['answer']['labels'] = None
- entry['answer']['scores'] = None
-
- def __getitem__(self, index):
- entry = self.entries[index]
- features = self.features[entry['image']]
- spatials = self.spatials[entry['image']]
-
- question = entry['q_token']
- answer = entry['answer']
- labels = answer['labels']
- scores = answer['scores']
- target = torch.zeros(self.num_ans_candidates)
- if labels is not None:
- target.scatter_(0, labels, scores)
-
- if self.extra_iter:
- return features, spatials, question, target, entry['question_id']
- return features, spatials, question, target
-
- def __len__(self):
- return len(self.entries)
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tools/deploy/README.md b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tools/deploy/README.md
deleted file mode 100644
index b9d5b15512c0bd160accbb1823236b8954a37b86..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tools/deploy/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-
-This directory contains:
-
-1. A script that converts a detectron2 model to caffe2 format.
-
-2. An example that loads a Mask R-CNN model in caffe2 format and runs inference.
-
-See [tutorial](https://detectron2.readthedocs.io/tutorials/deployment.html)
-for their usage.
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa_inference_wrapper.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa_inference_wrapper.py
deleted file mode 100644
index 02773adf18324896cea0ca16ba53394959ea6db8..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa_inference_wrapper.py
+++ /dev/null
@@ -1,153 +0,0 @@
-"""
-=========================================================================================
-Trojan VQA
-Written by Matthew Walmer
-
-Inference wrapper for trained OpenVQA models
-=========================================================================================
-"""
-import yaml, os, torch, re, json
-import numpy as np
-import torch.nn as nn
-
-from openvqa.models.model_loader import ModelLoader
-from openvqa.models.model_loader import CfgLoader
-
-
-root = os.path.dirname(os.path.realpath(__file__))
-
-
-# Helper to replace argparse for loading proper inference settings
-class Openvqa_Args_Like():
- def __init__(self, model_type, model_path, nb, over_fs=1024, gpu='0'):
- self.RUN_MODE = 'val'
- self.MODEL = model_type
- self.DATASET = 'vqa'
- self.SPLIT = 'train'
- self.BS = 64
- self.GPU = gpu
- self.SEED = 1234
- self.VERSION = 'temp'
- self.RESUME = 'True'
- self.CKPT_V = ''
- self.CKPT_E = ''
- self.CKPT_PATH = model_path
- self.NUM_WORKERS = 1
- self.PINM = 'True'
- self.VERBOSE = 'False'
- self.DETECTOR = ''
- self.OVER_FS = over_fs
- self.OVER_NB = int(nb)
-
-
-
-# Wrapper for inference with a pre-trained OpenVQA model. During init, user specifies
-# the model type, model file (.pkl) path, the number of input image
-# features, and optionally the feature size and gpu to run on. The function 'run' can
-# then run inference on two simple inputs: an image feature tensor, and a question
-# given as a string.
-class Openvqa_Wrapper():
- def __init__(self, model_type, model_path, nb, over_fs=1024, gpu='0'):
- self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
- # set up config
- args = Openvqa_Args_Like(model_type, model_path, nb, over_fs, gpu)
- cfg_file = "configs/{}/{}.yml".format(args.DATASET, args.MODEL)
- if not os.path.isfile(cfg_file):
- cfg_file = "{}/configs/{}/{}.yml".format(root, args.DATASET, args.MODEL)
- with open(cfg_file, 'r') as f:
- yaml_dict = yaml.load(f)
- __C = CfgLoader(yaml_dict['MODEL_USE']).load()
- args = __C.str_to_bool(args)
- args_dict = __C.parse_to_dict(args)
- args_dict = {**yaml_dict, **args_dict}
- __C.add_args(args_dict)
- __C.proc(check_path=False)
- # override feature size
- if __C.OVER_FS != -1 or __C.OVER_NB != -1:
- NEW_FS = 2048
- NEW_NB = 100
- if __C.OVER_FS != -1:
- print('Overriding feature size to: ' + str(__C.OVER_FS))
- NEW_FS = __C.OVER_FS
- __C.IMG_FEAT_SIZE = NEW_FS
- if __C.OVER_NB != -1:
- print('Overriding number of boxes to: ' + str(__C.OVER_NB))
- NEW_NB = __C.OVER_NB
- __C.FEAT_SIZE['vqa']['FRCN_FEAT_SIZE'] = (NEW_NB, NEW_FS)
- __C.FEAT_SIZE['vqa']['BBOX_FEAT_SIZE'] = (NEW_NB, 5)
- # update path information
- __C.update_paths()
-
- # prep
- token_size = 20573
- ans_size = 3129
- pretrained_emb = np.zeros([token_size, 300], dtype=np.float32)
-
- # load network
- net = ModelLoader(__C).Net(
- __C,
- pretrained_emb,
- token_size,
- ans_size
- )
- net.to(self.device)
- net.eval()
- if __C.N_GPU > 1:
- net = nn.DataParallel(net, device_ids=__C.DEVICES)
-
- # Load checkpoint
- print(' ========== Loading checkpoint')
- print('Loading ckpt from {}'.format(model_path))
- ckpt = torch.load(model_path, map_location=self.device)
- print('Finish!')
- if __C.N_GPU > 1:
- net.load_state_dict(ckpt_proc(ckpt['state_dict']))
- else:
- net.load_state_dict(ckpt['state_dict'])
- self.model = net
-
- # Load tokenizer, and answers
- token_file = '{}/openvqa/datasets/vqa/token_dict.json'.format(root)
- self.token_to_ix = json.load(open(token_file, 'r'))
- ans_dict = '{}/openvqa/datasets/vqa/answer_dict.json'.format(root)
- ans_to_ix = json.load(open(ans_dict, 'r'))[0]
- self.ix_to_ans = {}
- for key in ans_to_ix:
- self.ix_to_ans[ans_to_ix[key]] = key
-
-
-
- # based on version in vqa_loader.py
- def proc_ques(self, ques, token_to_ix, max_token):
- ques_ix = np.zeros(max_token, np.int64)
- words = re.sub(
- r"([.,'!?\"()*#:;])",
- '',
- ques.lower()
- ).replace('-', ' ').replace('/', ' ').split()
- for ix, word in enumerate(words):
- if word in token_to_ix:
- ques_ix[ix] = token_to_ix[word]
- else:
- ques_ix[ix] = token_to_ix['UNK']
- if ix + 1 == max_token:
- break
- return ques_ix
-
-
-
- # inputs are a tensor of image features, shape [nb, 1024]
- # and a raw question in string form. bbox features input is only used
- # by mmnasnet models.
- def run(self, image_features, raw_question, bbox_features):
- ques_ix = self.proc_ques(raw_question, self.token_to_ix, max_token=14)
- frcn_feat_iter = torch.unsqueeze(image_features, 0).to(self.device)
- grid_feat_iter = torch.zeros(1).to(self.device)
- bbox_feat_iter = torch.unsqueeze(bbox_features, 0).to(self.device)
- ques_ix_iter = torch.unsqueeze(torch.from_numpy(ques_ix),0).to(self.device)
- pred = self.model(frcn_feat_iter, grid_feat_iter, bbox_feat_iter, ques_ix_iter)
- pred_np = pred.cpu().data.numpy()
- pred_argmax = np.argmax(pred_np, axis=1)
- ans = self.ix_to_ans[pred_argmax[0]]
- return ans
-
\ No newline at end of file
diff --git a/spaces/CVPR/LIVE/pybind11/tests/test_pickling.cpp b/spaces/CVPR/LIVE/pybind11/tests/test_pickling.cpp
deleted file mode 100644
index 9dc63bda3b5949032fbcd30e7aa4e7db2072dcff..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/tests/test_pickling.cpp
+++ /dev/null
@@ -1,130 +0,0 @@
-/*
- tests/test_pickling.cpp -- pickle support
-
- Copyright (c) 2016 Wenzel Jakob
-
- All rights reserved. Use of this source code is governed by a
- BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-
-TEST_SUBMODULE(pickling, m) {
- // test_roundtrip
- class Pickleable {
- public:
- Pickleable(const std::string &value) : m_value(value) { }
- const std::string &value() const { return m_value; }
-
- void setExtra1(int extra1) { m_extra1 = extra1; }
- void setExtra2(int extra2) { m_extra2 = extra2; }
- int extra1() const { return m_extra1; }
- int extra2() const { return m_extra2; }
- private:
- std::string m_value;
- int m_extra1 = 0;
- int m_extra2 = 0;
- };
-
- class PickleableNew : public Pickleable {
- public:
- using Pickleable::Pickleable;
- };
-
- py::class_(m, "Pickleable")
- .def(py::init())
- .def("value", &Pickleable::value)
- .def("extra1", &Pickleable::extra1)
- .def("extra2", &Pickleable::extra2)
- .def("setExtra1", &Pickleable::setExtra1)
- .def("setExtra2", &Pickleable::setExtra2)
- // For details on the methods below, refer to
- // http://docs.python.org/3/library/pickle.html#pickling-class-instances
- .def("__getstate__", [](const Pickleable &p) {
- /* Return a tuple that fully encodes the state of the object */
- return py::make_tuple(p.value(), p.extra1(), p.extra2());
- })
- .def("__setstate__", [](Pickleable &p, py::tuple t) {
- if (t.size() != 3)
- throw std::runtime_error("Invalid state!");
- /* Invoke the constructor (need to use in-place version) */
- new (&p) Pickleable(t[0].cast());
-
- /* Assign any additional state */
- p.setExtra1(t[1].cast());
- p.setExtra2(t[2].cast());
- });
-
- py::class_(m, "PickleableNew")
- .def(py::init())
- .def(py::pickle(
- [](const PickleableNew &p) {
- return py::make_tuple(p.value(), p.extra1(), p.extra2());
- },
- [](py::tuple t) {
- if (t.size() != 3)
- throw std::runtime_error("Invalid state!");
- auto p = PickleableNew(t[0].cast());
-
- p.setExtra1(t[1].cast());
- p.setExtra2(t[2].cast());
- return p;
- }
- ));
-
-#if !defined(PYPY_VERSION)
- // test_roundtrip_with_dict
- class PickleableWithDict {
- public:
- PickleableWithDict(const std::string &value) : value(value) { }
-
- std::string value;
- int extra;
- };
-
- class PickleableWithDictNew : public PickleableWithDict {
- public:
- using PickleableWithDict::PickleableWithDict;
- };
-
- py::class_(m, "PickleableWithDict", py::dynamic_attr())
- .def(py::init())
- .def_readwrite("value", &PickleableWithDict::value)
- .def_readwrite("extra", &PickleableWithDict::extra)
- .def("__getstate__", [](py::object self) {
- /* Also include __dict__ in state */
- return py::make_tuple(self.attr("value"), self.attr("extra"), self.attr("__dict__"));
- })
- .def("__setstate__", [](py::object self, py::tuple t) {
- if (t.size() != 3)
- throw std::runtime_error("Invalid state!");
- /* Cast and construct */
- auto& p = self.cast();
- new (&p) PickleableWithDict(t[0].cast());
-
- /* Assign C++ state */
- p.extra = t[1].cast();
-
- /* Assign Python state */
- self.attr("__dict__") = t[2];
- });
-
- py::class_(m, "PickleableWithDictNew")
- .def(py::init())
- .def(py::pickle(
- [](py::object self) {
- return py::make_tuple(self.attr("value"), self.attr("extra"), self.attr("__dict__"));
- },
- [](const py::tuple &t) {
- if (t.size() != 3)
- throw std::runtime_error("Invalid state!");
-
- auto cpp_state = PickleableWithDictNew(t[0].cast());
- cpp_state.extra = t[1].cast();
-
- auto py_state = t[2].cast();
- return std::make_pair(cpp_state, py_state);
- }
- ));
-#endif
-}
diff --git a/spaces/CVPR/LIVE/thrust/thrust/mismatch.h b/spaces/CVPR/LIVE/thrust/thrust/mismatch.h
deleted file mode 100644
index 413db84f56361af4d028b756e267f655a591b34c..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/mismatch.h
+++ /dev/null
@@ -1,260 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-
-/*! \file mismatch.h
- * \brief Search for differences between ranges
- */
-
-#pragma once
-
-#include
-#include
-#include
-
-namespace thrust
-{
-
-
-/*! \addtogroup algorithms
- */
-
-/*! \addtogroup searching
- * \ingroup algorithms
- * \{
- */
-
-
-/*! \p mismatch finds the first position where the two ranges [first1, last1)
- * and [first2, first2 + (last1 - first1)) differ. The two versions of
- * \p mismatch use different tests for whether elements differ.
- *
- * This version of \p mismatch finds the first iterator \c i in [first1, last1)
- * such that *i == *(first2 + (i - first1)) is \c false. The return value is a
- * \c pair whose first element is \c i and whose second element is *(first2 + (i - first1)).
- * If no such iterator \c i exists, the return value is a \c pair whose first element
- * is \c last1 and whose second element is *(first2 + (last1 - first1)).
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- * \param exec The execution policy to use for parallelization.
- * \param first1 The beginning of the first sequence.
- * \param last1 The end of the first sequence.
- * \param first2 The beginning of the second sequence.
- * \return The first position where the sequences differ.
- *
- * \tparam DerivedPolicy The name of the derived execution policy.
- * \tparam InputIterator1 is a model of Input Iterator
- * and \p InputIterator1's \c value_type is equality comparable to \p InputIterator2's \c value_type.
- * \tparam InputIterator2 is a model of Input Iterator.
- *
- * \code
- * #include
- * #include
- * #include
- * ...
- * thrust::device_vector vec1(4);
- * thrust::device_vector vec2(4);
- *
- * vec1[0] = 0; vec2[0] = 0;
- * vec1[1] = 5; vec2[1] = 5;
- * vec1[2] = 3; vec2[2] = 8;
- * vec1[3] = 7; vec2[3] = 7;
- *
- * typedef thrust::device_vector::iterator Iterator;
- * thrust::pair result;
- *
- * result = thrust::mismatch(thrust::device, vec1.begin(), vec1.end(), vec2.begin());
- *
- * // result.first is vec1.begin() + 2
- * // result.second is vec2.begin() + 2
- * \endcode
- *
- * \see find
- * \see find_if
- */
-template
-__host__ __device__
-thrust::pair mismatch(const thrust::detail::execution_policy_base &exec,
- InputIterator1 first1,
- InputIterator1 last1,
- InputIterator2 first2);
-
-
-/*! \p mismatch finds the first position where the two ranges [first1, last1)
- * and [first2, first2 + (last1 - first1)) differ. The two versions of
- * \p mismatch use different tests for whether elements differ.
- *
- * This version of \p mismatch finds the first iterator \c i in [first1, last1)
- * such that *i == *(first2 + (i - first1)) is \c false. The return value is a
- * \c pair whose first element is \c i and whose second element is *(first2 + (i - first1)).
- * If no such iterator \c i exists, the return value is a \c pair whose first element
- * is \c last1 and whose second element is *(first2 + (last1 - first1)).
- *
- * \param first1 The beginning of the first sequence.
- * \param last1 The end of the first sequence.
- * \param first2 The beginning of the second sequence.
- * \return The first position where the sequences differ.
- *
- * \tparam InputIterator1 is a model of Input Iterator
- * and \p InputIterator1's \c value_type is equality comparable to \p InputIterator2's \c value_type.
- * \tparam InputIterator2 is a model of Input Iterator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector vec1(4);
- * thrust::device_vector vec2(4);
- *
- * vec1[0] = 0; vec2[0] = 0;
- * vec1[1] = 5; vec2[1] = 5;
- * vec1[2] = 3; vec2[2] = 8;
- * vec1[3] = 7; vec2[3] = 7;
- *
- * typedef thrust::device_vector::iterator Iterator;
- * thrust::pair result;
- *
- * result = thrust::mismatch(vec1.begin(), vec1.end(), vec2.begin());
- *
- * // result.first is vec1.begin() + 2
- * // result.second is vec2.begin() + 2
- * \endcode
- *
- * \see find
- * \see find_if
- */
-template
-thrust::pair mismatch(InputIterator1 first1,
- InputIterator1 last1,
- InputIterator2 first2);
-
-
-/*! \p mismatch finds the first position where the two ranges [first1, last1)
- * and [first2, first2 + (last1 - first1)) differ. The two versions of
- * \p mismatch use different tests for whether elements differ.
- *
- * This version of \p mismatch finds the first iterator \c i in [first1, last1)
- * such that pred(\*i, \*(first2 + (i - first1)) is \c false. The return value is a
- * \c pair whose first element is \c i and whose second element is *(first2 + (i - first1)).
- * If no such iterator \c i exists, the return value is a \c pair whose first element is
- * \c last1 and whose second element is *(first2 + (last1 - first1)).
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- * \param exec The execution policy to use for parallelization.
- * \param first1 The beginning of the first sequence.
- * \param last1 The end of the first sequence.
- * \param first2 The beginning of the second sequence.
- * \param pred The binary predicate to compare elements.
- * \return The first position where the sequences differ.
- *
- * \tparam DerivedPolicy The name of the derived execution policy.
- * \tparam InputIterator1 is a model of Input Iterator.
- * \tparam InputIterator2 is a model of Input Iterator.
- * \tparam Predicate is a model of Input Iterator.
- *
- * \code
- * #include
- * #include
- * #include
- * ...
- * thrust::device_vector vec1(4);
- * thrust::device_vector vec2(4);
- *
- * vec1[0] = 0; vec2[0] = 0;
- * vec1[1] = 5; vec2[1] = 5;
- * vec1[2] = 3; vec2[2] = 8;
- * vec1[3] = 7; vec2[3] = 7;
- *
- * typedef thrust::device_vector::iterator Iterator;
- * thrust::pair result;
- *
- * result = thrust::mismatch(thrust::device, vec1.begin(), vec1.end(), vec2.begin(), thrust::equal_to());
- *
- * // result.first is vec1.begin() + 2
- * // result.second is vec2.begin() + 2
- * \endcode
- *
- * \see find
- * \see find_if
- */
-template
-__host__ __device__
-thrust::pair mismatch(const thrust::detail::execution_policy_base &exec,
- InputIterator1 first1,
- InputIterator1 last1,
- InputIterator2 first2,
- BinaryPredicate pred);
-
-
-/*! \p mismatch finds the first position where the two ranges [first1, last1)
- * and [first2, first2 + (last1 - first1)) differ. The two versions of
- * \p mismatch use different tests for whether elements differ.
- *
- * This version of \p mismatch finds the first iterator \c i in [first1, last1)
- * such that pred(\*i, \*(first2 + (i - first1)) is \c false. The return value is a
- * \c pair whose first element is \c i and whose second element is *(first2 + (i - first1)).
- * If no such iterator \c i exists, the return value is a \c pair whose first element is
- * \c last1 and whose second element is *(first2 + (last1 - first1)).
- *
- * \param first1 The beginning of the first sequence.
- * \param last1 The end of the first sequence.
- * \param first2 The beginning of the second sequence.
- * \param pred The binary predicate to compare elements.
- * \return The first position where the sequences differ.
- *
- * \tparam InputIterator1 is a model of Input Iterator.
- * \tparam InputIterator2 is a model of Input Iterator.
- * \tparam Predicate is a model of Input Iterator.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector vec1(4);
- * thrust::device_vector vec2(4);
- *
- * vec1[0] = 0; vec2[0] = 0;
- * vec1[1] = 5; vec2[1] = 5;
- * vec1[2] = 3; vec2[2] = 8;
- * vec1[3] = 7; vec2[3] = 7;
- *
- * typedef thrust::device_vector::iterator Iterator;
- * thrust::pair result;
- *
- * result = thrust::mismatch(vec1.begin(), vec1.end(), vec2.begin(), thrust::equal_to());
- *
- * // result.first is vec1.begin() + 2
- * // result.second is vec2.begin() + 2
- * \endcode
- *
- * \see find
- * \see find_if
- */
-template
-thrust::pair mismatch(InputIterator1 first1,
- InputIterator1 last1,
- InputIterator2 first2,
- BinaryPredicate pred);
-
-/*! \} // end searching
- */
-
-} // end namespace thrust
-
-#include
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/uninitialized_fill.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/uninitialized_fill.h
deleted file mode 100644
index 98b57836e124dcd75fcd7a7fe75c0646ed3f76ba..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/uninitialized_fill.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a fill of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-
-// the purpose of this header is to #include the uninitialized_fill.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch uninitialized_fill
-
-#include
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include
-#include
-#include
-#include
-#endif
-
-#define __THRUST_HOST_SYSTEM_UNINITIALIZED_FILL_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/uninitialized_fill.h>
-#include __THRUST_HOST_SYSTEM_UNINITIALIZED_FILL_HEADER
-#undef __THRUST_HOST_SYSTEM_UNINITIALIZED_FILL_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_UNINITIALIZED_FILL_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/uninitialized_fill.h>
-#include __THRUST_DEVICE_SYSTEM_UNINITIALIZED_FILL_HEADER
-#undef __THRUST_DEVICE_SYSTEM_UNINITIALIZED_FILL_HEADER
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/reduce.h b/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/reduce.h
deleted file mode 100644
index 7381da3822f1980f0806e6322f8aefc7dfb313d6..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/reduce.h
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-
-/*! \file reduce.h
- * \brief TBB implementation of reduce.
- */
-
-#pragma once
-
-#include
-#include
-
-namespace thrust
-{
-namespace system
-{
-namespace tbb
-{
-namespace detail
-{
-
-
-template
- OutputType reduce(execution_policy &exec,
- InputIterator begin,
- InputIterator end,
- OutputType init,
- BinaryFunction binary_op);
-
-
-} // end namespace detail
-} // end namespace tbb
-} // end namespace system
-} // end namespace thrust
-
-#include
-
diff --git a/spaces/CikeyQI/Yunzai/Yunzai/lib/modules/oicq/index.js b/spaces/CikeyQI/Yunzai/Yunzai/lib/modules/oicq/index.js
deleted file mode 100644
index 75af0cb556343e22ea3905071637e918248529d2..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/Yunzai/Yunzai/lib/modules/oicq/index.js
+++ /dev/null
@@ -1,67 +0,0 @@
-import fs from "node:fs"
-import path from "node:path"
-
-function toSegment(type, data) {
- for (const i in data) {
- switch (typeof data[i]) {
- case "string":
- if ((i == "file" || data[i].match(/^file:\/\//)) && fs.existsSync(data[i].replace(/^file:\/\//, ""))) {
- if (i == "file" && !data.name)
- data.name = path.basename(data[i])
- data[i] = `base64://${fs.readFileSync(data[i].replace(/^file:\/\//, "")).toString("base64")}`
- }
- break
- case "object":
- if (Buffer.isBuffer(data[i]))
- data[i] = `base64://${data[i].toString("base64")}`
- }
- }
- return { type, ...data }
-}
-
-const segment = new class segment {
- custom(type, data) {
- return toSegment(type, data)
- }
- image(file, name) {
- return toSegment("image", { file, name })
- }
- at(qq, name) {
- return toSegment("at", { qq, name })
- }
- record(file, name) {
- return toSegment("record", { file, name })
- }
- video(file, name) {
- return toSegment("video", { file, name })
- }
- file(file, name) {
- return toSegment("file", { file, name })
- }
- reply(id, text, qq, time, seq) {
- return toSegment("reply", { id, text, qq, time, seq })
- }
- face(id) {
- return toSegment("face", { id })
- }
- share(url, title, content, image) {
- return toSegment("share", { url, title, content, image })
- }
- music(type, id, url, audio, title) {
- return toSegment("music", { type, id, url, audio, title })
- }
- poke(qq) {
- return toSegment("poke", { qq })
- }
- gift(qq, id) {
- return toSegment("gift", { qq, id })
- }
- cardimage(file, name, minwidth, minheight, maxwidth, maxheight, source, icon) {
- return toSegment("cardimage", { file, name, minwidth, minheight, maxwidth, maxheight, source, icon })
- }
- tts(text) {
- return toSegment("tts", { text })
- }
-}
-
-export { segment }
\ No newline at end of file
diff --git a/spaces/CofAI/chat/client/js/icons.js b/spaces/CofAI/chat/client/js/icons.js
deleted file mode 100644
index 84fed38dd35e0d0203370a8314a360d27f350dd6..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat/client/js/icons.js
+++ /dev/null
@@ -1 +0,0 @@
-window.FontAwesomeKitConfig={asyncLoading:{enabled:!1},autoA11y:{enabled:!0},baseUrl:"https://ka-f.fontawesome.com",baseUrlKit:"https://kit-pro.fontawesome.com",detectConflictsUntil:null,iconUploads:{},id:96462084,license:"pro",method:"css",minify:{enabled:!0},token:"d0514f1901",v4FontFaceShim:{enabled:!0},v4shim:{enabled:!0},v5FontFaceShim:{enabled:!0},version:"6.1.1"},function(t){"function"==typeof define&&define.amd?define("kit-loader",t):t()}(function(){"use strict";function t(e){return(t="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(e)}function e(t,e,n){return e in t?Object.defineProperty(t,e,{value:n,enumerable:!0,configurable:!0,writable:!0}):t[e]=n,t}function n(t,e){var n=Object.keys(t);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(t);e&&(o=o.filter(function(e){return Object.getOwnPropertyDescriptor(t,e).enumerable})),n.push.apply(n,o)}return n}function o(t){for(var o=1;ot.length)&&(e=t.length);for(var n=0,o=new Array(e);n2&&void 0!==arguments[2]?arguments[2]:function(){},r=e.document||r,i=a.bind(a,r,["fa","fab","fas","far","fal","fad","fak"]),u=Object.keys(t.iconUploads||{}).length>0;t.autoA11y.enabled&&n(i);var f=[{id:"fa-main",addOn:void 0}];t.v4shim&&t.v4shim.enabled&&f.push({id:"fa-v4-shims",addOn:"-v4-shims"}),t.v5FontFaceShim&&t.v5FontFaceShim.enabled&&f.push({id:"fa-v5-font-face",addOn:"-v5-font-face"}),t.v4FontFaceShim&&t.v4FontFaceShim.enabled&&f.push({id:"fa-v4-font-face",addOn:"-v4-font-face"}),u&&f.push({id:"fa-kit-upload",customCss:!0});var s=f.map(function(n){return new F(function(r,i){E(n.customCss?function(t){return t.baseUrlKit+"/"+t.token+"/"+t.id+"/kit-upload.css"}(t):c(t,{addOn:n.addOn,minify:t.minify.enabled}),e).then(function(i){r(function(t,e){var n=e.contentFilter||function(t,e){return t},o=document.createElement("style"),r=document.createTextNode(n(t,e));return o.appendChild(r),o.media="all",e.id&&o.setAttribute("id",e.id),e&&e.detectingConflicts&&e.detectionIgnoreAttr&&o.setAttributeNode(document.createAttribute(e.detectionIgnoreAttr)),o}(i,o(o({},e),{},{baseUrl:t.baseUrl,version:t.version,id:n.id,contentFilter:function(t,e){return _(t,e.baseUrl,e.version)}})))}).catch(i)})});return F.all(s)}function P(t,e){var n=document.createElement("SCRIPT"),o=document.createTextNode(t);return n.appendChild(o),n.referrerPolicy="strict-origin",e.id&&n.setAttribute("id",e.id),e&&e.detectingConflicts&&e.detectionIgnoreAttr&&n.setAttributeNode(document.createAttribute(e.detectionIgnoreAttr)),n}function U(t){var e,n=[],o=document,r=(o.documentElement.doScroll?/^loaded|^c/:/^loaded|^i|^c/).test(o.readyState);r||o.addEventListener("DOMContentLoaded",e=function(){for(o.removeEventListener("DOMContentLoaded",e),r=1;e=n.shift();)e()}),r?setTimeout(t,0):n.push(t)}try{if(window.FontAwesomeKitConfig){var k=window.FontAwesomeKitConfig,L={detectingConflicts:k.detectConflictsUntil&&new Date<=new Date(k.detectConflictsUntil),detectionIgnoreAttr:"data-fa-detection-ignore",fetch:window.fetch,token:k.token,XMLHttpRequest:window.XMLHttpRequest,document:document},I=document.currentScript,T=I?I.parentElement:document.head;(function(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return"js"===t.method?function(t,e){e.autoA11y=t.autoA11y.enabled,"pro"===t.license&&(e.autoFetchSvg=!0,e.fetchSvgFrom=t.baseUrl+"/releases/"+("latest"===t.version?"latest":"v".concat(t.version))+"/svgs",e.fetchUploadedSvgFrom=t.uploadsUrl);var n=[];return t.v4shim.enabled&&n.push(new F(function(n,r){E(c(t,{addOn:"-v4-shims",minify:t.minify.enabled}),e).then(function(t){n(P(t,o(o({},e),{},{id:"fa-v4-shims"})))}).catch(r)})),n.push(new F(function(n,r){E(c(t,{minify:t.minify.enabled}),e).then(function(t){var r=P(t,o(o({},e),{},{id:"fa-main"}));n(function(t,e){var n=e&&void 0!==e.autoFetchSvg?e.autoFetchSvg:void 0,o=e&&void 0!==e.autoA11y?e.autoA11y:void 0;return void 0!==o&&t.setAttribute("data-auto-a11y",o?"true":"false"),n&&(t.setAttributeNode(document.createAttribute("data-auto-fetch-svg")),t.setAttribute("data-fetch-svg-from",e.fetchSvgFrom),t.setAttribute("data-fetch-uploaded-svg-from",e.fetchUploadedSvgFrom)),t}(r,e))}).catch(r)})),F.all(n)}(t,e):"css"===t.method?C(t,e,function(t){U(t),function(t){"undefined"!=typeof MutationObserver&&new MutationObserver(t).observe(document,{childList:!0,subtree:!0})}(t)}):void 0})(k,L).then(function(t){t.map(function(t){try{T.insertBefore(t,I?I.nextSibling:null)}catch(e){T.appendChild(t)}}),L.detectingConflicts&&I&&U(function(){I.setAttributeNode(document.createAttribute(L.detectionIgnoreAttr));var t=function(t,e){var n=document.createElement("script");return e&&e.detectionIgnoreAttr&&n.setAttributeNode(document.createAttribute(e.detectionIgnoreAttr)),n.src=c(t,{baseFilename:"conflict-detection",fileSuffix:"js",subdir:"js",minify:t.minify.enabled}),n}(k,L);document.body.appendChild(t)})}).catch(function(t){console.error("".concat("Font Awesome Kit:"," ").concat(t))})}}catch(t){console.error("".concat("Font Awesome Kit:"," ").concat(t))}});
\ No newline at end of file
diff --git a/spaces/Cpp4App/Cpp4App/CDM/result_processing/view_gt.py b/spaces/Cpp4App/Cpp4App/CDM/result_processing/view_gt.py
deleted file mode 100644
index fed54d02d31398f54871d92e1e487cda135425fc..0000000000000000000000000000000000000000
--- a/spaces/Cpp4App/Cpp4App/CDM/result_processing/view_gt.py
+++ /dev/null
@@ -1,89 +0,0 @@
-from tqdm import tqdm
-import json
-import cv2
-from os.path import join as pjoin
-
-from config.CONFIG_UIED import Config
-C = Config()
-
-
-def draw_bounding_box_class(org, components, color=C.COLOR, line=2, show=False, write_path=None):
- """
- Draw bounding box of components with their classes on the original image
- :param org: original image
- :param components: bbox [(column_min, row_min, column_max, row_max)]
- -> top_left: (column_min, row_min)
- -> bottom_right: (column_max, row_max)
- :param color_map: colors mapping to different components
- :param line: line thickness
- :param compo_class: classes matching the corners of components
- :param show: show or not
- :return: labeled image
- """
- board = org.copy()
- bboxes = components['bboxes']
- categories = components['categories']
- for i in range(len(bboxes)):
- bbox = bboxes[i]
- category = categories[i]
- board = cv2.rectangle(board, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color[C.CLASS_MAP[str(category)]], line)
- board = cv2.putText(board, C.CLASS_MAP[str(category)], (bbox[0]+5, bbox[1]+20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color[C.CLASS_MAP[str(category)]], 2)
- if show:
- cv2.imshow('a', cv2.resize(board, (500, 1000)))
- cv2.waitKey(0)
- if write_path is not None:
- cv2.imwrite(write_path, board)
- return board
-
-
-def load_ground_truth_json(gt_file, no_text=True):
- def get_img_by_id(img_id):
- for image in images:
- if image['id'] == img_id:
- return image['file_name'].split('/')[-1][:-4], (image['height'], image['width'])
-
- def cvt_bbox(bbox):
- '''
- :param bbox: [x,y,width,height]
- :return: [col_min, row_min, col_max, row_max]
- '''
- bbox = [int(b) for b in bbox]
- return [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]
-
- data = json.load(open(gt_file, 'r'))
- images = data['images']
- annots = data['annotations']
- compos = {}
- print('Loading %d ground truth' % len(annots))
- for annot in tqdm(annots):
- img_name, size = get_img_by_id(annot['image_id'])
- if no_text and int(annot['category_id']) == 14:
- compos[img_name] = {'bboxes': [], 'categories': [], 'size': size}
- continue
- if img_name not in compos:
- compos[img_name] = {'bboxes': [cvt_bbox(annot['bbox'])], 'categories': [annot['category_id']], 'size':size}
- else:
- compos[img_name]['bboxes'].append(cvt_bbox(annot['bbox']))
- compos[img_name]['categories'].append(annot['category_id'])
- return compos
-
-
-def view_gt_all(gt, img_root):
- for img_id in gt:
- compos = gt[img_id]
- img = cv2.imread(pjoin(img_root, img_id + '.jpg'))
- print(pjoin(img_root, img_id + '.jpg'))
- draw_bounding_box_class(img, compos, show=True)
-
-
-def view_gt_single(gt, img_root, img_id):
- img_id = str(img_id)
- compos = gt[img_id]
- img = cv2.imread(pjoin(img_root, img_id + '.jpg'))
- print(pjoin(img_root, img_id + '.jpg'))
- draw_bounding_box_class(img, compos, show=True)
-
-
-gt = load_ground_truth_json('E:\\Mulong\\Datasets\\rico\\instances_test.json', no_text=False)
-# view_gt_all(gt, 'E:\\Mulong\\Datasets\\rico\\combined')
-view_gt_single(gt, 'E:\\Mulong\\Datasets\\rico\\combined', 670)
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/layers/_utils.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/layers/_utils.py
deleted file mode 100644
index 3dabc127b221d67eae7587ab4905416fa5fcf121..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/layers/_utils.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import glob
-import os.path
-
-import torch
-
-try:
- from torch.utils.cpp_extension import load as load_ext
- from torch.utils.cpp_extension import CUDA_HOME
-except ImportError:
- raise ImportError("The cpp layer extensions requires PyTorch 0.4 or higher")
-
-
-def _load_C_extensions():
- this_dir = os.path.dirname(os.path.abspath(__file__))
- this_dir = os.path.dirname(this_dir)
- this_dir = os.path.join(this_dir, "csrc")
-
- main_file = glob.glob(os.path.join(this_dir, "*.cpp"))
- source_cpu = glob.glob(os.path.join(this_dir, "cpu", "*.cpp"))
- source_cuda = glob.glob(os.path.join(this_dir, "cuda", "*.cu"))
-
- source = main_file + source_cpu
-
- extra_cflags = []
- if torch.cuda.is_available() and CUDA_HOME is not None:
- source.extend(source_cuda)
- extra_cflags = ["-DWITH_CUDA"]
- source = [os.path.join(this_dir, s) for s in source]
- extra_include_paths = [this_dir]
- return load_ext(
- "torchvision",
- source,
- extra_cflags=extra_cflags,
- extra_include_paths=extra_include_paths,
- )
-
-
-_C = _load_C_extensions()
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/components/bar_plot.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/components/bar_plot.py
deleted file mode 100644
index 4a018590c7dbe2722ef292c84e05a23edea60114..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/components/bar_plot.py
+++ /dev/null
@@ -1,377 +0,0 @@
-"""gr.BarPlot() component."""
-
-from __future__ import annotations
-
-from typing import Callable, Literal
-
-import altair as alt
-import pandas as pd
-from gradio_client.documentation import document, set_documentation_group
-
-from gradio.components.base import _Keywords
-from gradio.components.plot import AltairPlot, Plot
-
-set_documentation_group("component")
-
-
-@document()
-class BarPlot(Plot):
- """
- Create a bar plot.
-
- Preprocessing: this component does *not* accept input.
- Postprocessing: expects a pandas dataframe with the data to plot.
-
- Demos: bar_plot, chicago-bikeshare-dashboard
- """
-
- def __init__(
- self,
- value: pd.DataFrame | Callable | None = None,
- x: str | None = None,
- y: str | None = None,
- *,
- color: str | None = None,
- vertical: bool = True,
- group: str | None = None,
- title: str | None = None,
- tooltip: list[str] | str | None = None,
- x_title: str | None = None,
- y_title: str | None = None,
- color_legend_title: str | None = None,
- group_title: str | None = None,
- color_legend_position: Literal[
- "left",
- "right",
- "top",
- "bottom",
- "top-left",
- "top-right",
- "bottom-left",
- "bottom-right",
- "none",
- ]
- | None = None,
- height: int | None = None,
- width: int | None = None,
- y_lim: list[int] | None = None,
- caption: str | None = None,
- interactive: bool | None = True,
- label: str | None = None,
- show_label: bool | None = None,
- container: bool = True,
- scale: int | None = None,
- min_width: int = 160,
- every: float | None = None,
- visible: bool = True,
- elem_id: str | None = None,
- elem_classes: list[str] | str | None = None,
- ):
- """
- Parameters:
- value: The pandas dataframe containing the data to display in a scatter plot.
- x: Column corresponding to the x axis.
- y: Column corresponding to the y axis.
- color: The column to determine the bar color. Must be categorical (discrete values).
- vertical: If True, the bars will be displayed vertically. If False, the x and y axis will be switched, displaying the bars horizontally. Default is True.
- group: The column with which to split the overall plot into smaller subplots.
- title: The title to display on top of the chart.
- tooltip: The column (or list of columns) to display on the tooltip when a user hovers over a bar.
- x_title: The title given to the x axis. By default, uses the value of the x parameter.
- y_title: The title given to the y axis. By default, uses the value of the y parameter.
- color_legend_title: The title given to the color legend. By default, uses the value of color parameter.
- group_title: The label displayed on top of the subplot columns (or rows if vertical=True). Use an empty string to omit.
- color_legend_position: The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
- height: The height of the plot in pixels.
- width: The width of the plot in pixels.
- y_lim: A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
- caption: The (optional) caption to display below the plot.
- interactive: Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
- label: The (optional) label to display on the top left corner of the plot.
- show_label: Whether the label should be displayed.
- every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
- visible: Whether the plot should be visible.
- elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
- elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
- """
- self.x = x
- self.y = y
- self.color = color
- self.vertical = vertical
- self.group = group
- self.group_title = group_title
- self.tooltip = tooltip
- self.title = title
- self.x_title = x_title
- self.y_title = y_title
- self.color_legend_title = color_legend_title
- self.group_title = group_title
- self.color_legend_position = color_legend_position
- self.y_lim = y_lim
- self.caption = caption
- self.interactive_chart = interactive
- self.width = width
- self.height = height
- super().__init__(
- value=value,
- label=label,
- show_label=show_label,
- container=container,
- scale=scale,
- min_width=min_width,
- visible=visible,
- elem_id=elem_id,
- elem_classes=elem_classes,
- every=every,
- )
-
- def get_config(self):
- config = super().get_config()
- config["caption"] = self.caption
- return config
-
- def get_block_name(self) -> str:
- return "plot"
-
- @staticmethod
- def update(
- value: pd.DataFrame | dict | Literal[_Keywords.NO_VALUE] = _Keywords.NO_VALUE,
- x: str | None = None,
- y: str | None = None,
- color: str | None = None,
- vertical: bool = True,
- group: str | None = None,
- title: str | None = None,
- tooltip: list[str] | str | None = None,
- x_title: str | None = None,
- y_title: str | None = None,
- color_legend_title: str | None = None,
- group_title: str | None = None,
- color_legend_position: Literal[
- "left",
- "right",
- "top",
- "bottom",
- "top-left",
- "top-right",
- "bottom-left",
- "bottom-right",
- "none",
- ]
- | None = None,
- height: int | None = None,
- width: int | None = None,
- y_lim: list[int] | None = None,
- caption: str | None = None,
- interactive: bool | None = None,
- label: str | None = None,
- show_label: bool | None = None,
- container: bool | None = None,
- scale: int | None = None,
- min_width: int | None = None,
- visible: bool | None = None,
- ):
- """Update an existing BarPlot component.
-
- If updating any of the plot properties (color, size, etc) the value, x, and y parameters must be specified.
-
- Parameters:
- value: The pandas dataframe containing the data to display in a scatter plot.
- x: Column corresponding to the x axis.
- y: Column corresponding to the y axis.
- color: The column to determine the bar color. Must be categorical (discrete values).
- vertical: If True, the bars will be displayed vertically. If False, the x and y axis will be switched, displaying the bars horizontally. Default is True.
- group: The column with which to split the overall plot into smaller subplots.
- title: The title to display on top of the chart.
- tooltip: The column (or list of columns) to display on the tooltip when a user hovers over a bar.
- x_title: The title given to the x axis. By default, uses the value of the x parameter.
- y_title: The title given to the y axis. By default, uses the value of the y parameter.
- color_legend_title: The title given to the color legend. By default, uses the value of color parameter.
- group_title: The label displayed on top of the subplot columns (or rows if vertical=True). Use an empty string to omit.
- color_legend_position: The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
- height: The height of the plot in pixels.
- width: The width of the plot in pixels.
- y_lim: A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
- caption: The (optional) caption to display below the plot.
- interactive: Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
- label: The (optional) label to display on the top left corner of the plot.
- show_label: Whether the label should be displayed.
- visible: Whether the plot should be visible.
- """
- properties = [
- x,
- y,
- color,
- vertical,
- group,
- title,
- tooltip,
- x_title,
- y_title,
- color_legend_title,
- group_title,
- color_legend_position,
- height,
- width,
- y_lim,
- interactive,
- ]
- if any(properties):
- if not isinstance(value, pd.DataFrame):
- raise ValueError(
- "In order to update plot properties the value parameter "
- "must be provided, and it must be a Dataframe. Please pass a value "
- "parameter to gr.BarPlot.update."
- )
- if x is None or y is None:
- raise ValueError(
- "In order to update plot properties, the x and y axis data "
- "must be specified. Please pass valid values for x an y to "
- "gr.BarPlot.update."
- )
- chart = BarPlot.create_plot(value, *properties)
- value = {"type": "altair", "plot": chart.to_json(), "chart": "bar"}
-
- updated_config = {
- "label": label,
- "show_label": show_label,
- "container": container,
- "scale": scale,
- "min_width": min_width,
- "visible": visible,
- "value": value,
- "caption": caption,
- "__type__": "update",
- }
- return updated_config
-
- @staticmethod
- def create_plot(
- value: pd.DataFrame,
- x: str,
- y: str,
- color: str | None = None,
- vertical: bool = True,
- group: str | None = None,
- title: str | None = None,
- tooltip: list[str] | str | None = None,
- x_title: str | None = None,
- y_title: str | None = None,
- color_legend_title: str | None = None,
- group_title: str | None = None,
- color_legend_position: Literal[
- "left",
- "right",
- "top",
- "bottom",
- "top-left",
- "top-right",
- "bottom-left",
- "bottom-right",
- "none",
- ]
- | None
- | None = None,
- height: int | None = None,
- width: int | None = None,
- y_lim: list[int] | None = None,
- interactive: bool | None = True,
- ):
- """Helper for creating the bar plot."""
- interactive = True if interactive is None else interactive
- orientation = (
- {"field": group, "title": group_title if group_title is not None else group}
- if group
- else {}
- )
-
- x_title = x_title or x
- y_title = y_title or y
-
- # If horizontal, switch x and y
- if not vertical:
- y, x = x, y
- x = f"sum({x}):Q"
- y_title, x_title = x_title, y_title
- orientation = {"row": alt.Row(**orientation)} if orientation else {} # type: ignore
- x_lim = y_lim
- y_lim = None
- else:
- y = f"sum({y}):Q"
- x_lim = None
- orientation = {"column": alt.Column(**orientation)} if orientation else {} # type: ignore
-
- encodings = dict(
- x=alt.X(
- x, # type: ignore
- title=x_title, # type: ignore
- scale=AltairPlot.create_scale(x_lim), # type: ignore
- ),
- y=alt.Y(
- y, # type: ignore
- title=y_title, # type: ignore
- scale=AltairPlot.create_scale(y_lim), # type: ignore
- ),
- **orientation,
- )
- properties = {}
- if title:
- properties["title"] = title
- if height:
- properties["height"] = height
- if width:
- properties["width"] = width
-
- if color:
- domain = value[color].unique().tolist()
- range_ = list(range(len(domain)))
- encodings["color"] = {
- "field": color,
- "type": "nominal",
- "scale": {"domain": domain, "range": range_},
- "legend": AltairPlot.create_legend(
- position=color_legend_position, title=color_legend_title or color
- ),
- }
-
- if tooltip:
- encodings["tooltip"] = tooltip
-
- chart = (
- alt.Chart(value) # type: ignore
- .mark_bar() # type: ignore
- .encode(**encodings)
- .properties(background="transparent", **properties)
- )
- if interactive:
- chart = chart.interactive()
-
- return chart
-
- def postprocess(self, y: pd.DataFrame | dict | None) -> dict[str, str] | None:
- # if None or update
- if y is None or isinstance(y, dict):
- return y
- if self.x is None or self.y is None:
- raise ValueError("No value provided for required parameters `x` and `y`.")
- chart = self.create_plot(
- value=y,
- x=self.x,
- y=self.y,
- color=self.color,
- vertical=self.vertical,
- group=self.group,
- title=self.title,
- tooltip=self.tooltip,
- x_title=self.x_title,
- y_title=self.y_title,
- color_legend_title=self.color_legend_title,
- color_legend_position=self.color_legend_position,
- group_title=self.group_title,
- y_lim=self.y_lim,
- interactive=self.interactive_chart,
- height=self.height,
- width=self.width,
- )
-
- return {"type": "altair", "plot": chart.to_json(), "chart": "bar"}
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-f8a15c0a.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-f8a15c0a.js
deleted file mode 100644
index e1ae240da35ccd388cc03f97124fcf58f84e0b25..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-f8a15c0a.js
+++ /dev/null
@@ -1,2 +0,0 @@
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diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/lfs.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/lfs.py
deleted file mode 100644
index f2474e2a155e698094576305eae6625de69f0458..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/lfs.py
+++ /dev/null
@@ -1,496 +0,0 @@
-# coding=utf-8
-# Copyright 2019-present, the HuggingFace Inc. team.
-#
-# 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.
-"""Git LFS related type definitions and utilities"""
-import io
-import os
-import re
-import warnings
-from contextlib import AbstractContextManager
-from dataclasses import dataclass
-from math import ceil
-from os.path import getsize
-from pathlib import Path
-from typing import TYPE_CHECKING, BinaryIO, Dict, Iterable, List, Optional, Tuple
-
-from requests.auth import HTTPBasicAuth
-
-from huggingface_hub.constants import ENDPOINT, HF_HUB_ENABLE_HF_TRANSFER, REPO_TYPES_URL_PREFIXES
-from huggingface_hub.utils import get_session
-
-from .utils import get_token_to_send, hf_raise_for_status, http_backoff, logging, validate_hf_hub_args
-from .utils._typing import TypedDict
-from .utils.sha import sha256, sha_fileobj
-
-
-if TYPE_CHECKING:
- from ._commit_api import CommitOperationAdd
-
-logger = logging.get_logger(__name__)
-
-OID_REGEX = re.compile(r"^[0-9a-f]{40}$")
-
-LFS_MULTIPART_UPLOAD_COMMAND = "lfs-multipart-upload"
-
-LFS_HEADERS = {
- "Accept": "application/vnd.git-lfs+json",
- "Content-Type": "application/vnd.git-lfs+json",
-}
-
-
-@dataclass
-class UploadInfo:
- """
- Dataclass holding required information to determine whether a blob
- should be uploaded to the hub using the LFS protocol or the regular protocol
-
- Args:
- sha256 (`bytes`):
- SHA256 hash of the blob
- size (`int`):
- Size in bytes of the blob
- sample (`bytes`):
- First 512 bytes of the blob
- """
-
- sha256: bytes
- size: int
- sample: bytes
-
- @classmethod
- def from_path(cls, path: str):
- size = getsize(path)
- with io.open(path, "rb") as file:
- sample = file.peek(512)[:512]
- sha = sha_fileobj(file)
- return cls(size=size, sha256=sha, sample=sample)
-
- @classmethod
- def from_bytes(cls, data: bytes):
- sha = sha256(data).digest()
- return cls(size=len(data), sample=data[:512], sha256=sha)
-
- @classmethod
- def from_fileobj(cls, fileobj: BinaryIO):
- sample = fileobj.read(512)
- fileobj.seek(0, io.SEEK_SET)
- sha = sha_fileobj(fileobj)
- size = fileobj.tell()
- fileobj.seek(0, io.SEEK_SET)
- return cls(size=size, sha256=sha, sample=sample)
-
-
-@validate_hf_hub_args
-def post_lfs_batch_info(
- upload_infos: Iterable[UploadInfo],
- token: Optional[str],
- repo_type: str,
- repo_id: str,
- endpoint: Optional[str] = None,
-) -> Tuple[List[dict], List[dict]]:
- """
- Requests the LFS batch endpoint to retrieve upload instructions
-
- Learn more: https://github.com/git-lfs/git-lfs/blob/main/docs/api/batch.md
-
- Args:
- upload_infos (`Iterable` of `UploadInfo`):
- `UploadInfo` for the files that are being uploaded, typically obtained
- from `CommitOperationAdd.upload_info`
- repo_type (`str`):
- Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`.
- repo_id (`str`):
- A namespace (user or an organization) and a repo name separated
- by a `/`.
- token (`str`, *optional*):
- An authentication token ( See https://huggingface.co/settings/tokens )
-
- Returns:
- `LfsBatchInfo`: 2-tuple:
- - First element is the list of upload instructions from the server
- - Second element is an list of errors, if any
-
- Raises:
- `ValueError`: If an argument is invalid or the server response is malformed
-
- `HTTPError`: If the server returned an error
- """
- endpoint = endpoint if endpoint is not None else ENDPOINT
- url_prefix = ""
- if repo_type in REPO_TYPES_URL_PREFIXES:
- url_prefix = REPO_TYPES_URL_PREFIXES[repo_type]
- batch_url = f"{endpoint}/{url_prefix}{repo_id}.git/info/lfs/objects/batch"
- resp = get_session().post(
- batch_url,
- headers=LFS_HEADERS,
- json={
- "operation": "upload",
- "transfers": ["basic", "multipart"],
- "objects": [
- {
- "oid": upload.sha256.hex(),
- "size": upload.size,
- }
- for upload in upload_infos
- ],
- "hash_algo": "sha256",
- },
- auth=HTTPBasicAuth(
- "access_token",
- get_token_to_send(token or True), # type: ignore # Token must be provided or retrieved
- ),
- )
- hf_raise_for_status(resp)
- batch_info = resp.json()
-
- objects = batch_info.get("objects", None)
- if not isinstance(objects, list):
- raise ValueError("Malformed response from server")
-
- return (
- [_validate_batch_actions(obj) for obj in objects if "error" not in obj],
- [_validate_batch_error(obj) for obj in objects if "error" in obj],
- )
-
-
-class PayloadPartT(TypedDict):
- partNumber: int
- etag: str
-
-
-class CompletionPayloadT(TypedDict):
- """Payload that will be sent to the Hub when uploading multi-part."""
-
- oid: str
- parts: List[PayloadPartT]
-
-
-def lfs_upload(operation: "CommitOperationAdd", lfs_batch_action: Dict, token: Optional[str]) -> None:
- """
- Handles uploading a given object to the Hub with the LFS protocol.
-
- Can be a No-op if the content of the file is already present on the hub large file storage.
-
- Args:
- operation (`CommitOperationAdd`):
- The add operation triggering this upload.
- lfs_batch_action (`dict`):
- Upload instructions from the LFS batch endpoint for this object. See [`~utils.lfs.post_lfs_batch_info`] for
- more details.
- token (`str`, *optional*):
- A [user access token](https://hf.co/settings/tokens) to authenticate requests against the Hub
-
- Raises:
- - `ValueError` if `lfs_batch_action` is improperly formatted
- - `HTTPError` if the upload resulted in an error
- """
- # 0. If LFS file is already present, skip upload
- _validate_batch_actions(lfs_batch_action)
- actions = lfs_batch_action.get("actions")
- if actions is None:
- # The file was already uploaded
- logger.debug(f"Content of file {operation.path_in_repo} is already present upstream - skipping upload")
- return
-
- # 1. Validate server response (check required keys in dict)
- upload_action = lfs_batch_action["actions"]["upload"]
- _validate_lfs_action(upload_action)
- verify_action = lfs_batch_action["actions"].get("verify")
- if verify_action is not None:
- _validate_lfs_action(verify_action)
-
- # 2. Upload file (either single part or multi-part)
- header = upload_action.get("header", {})
- chunk_size = header.get("chunk_size")
- if chunk_size is not None:
- try:
- chunk_size = int(chunk_size)
- except (ValueError, TypeError):
- raise ValueError(
- f"Malformed response from LFS batch endpoint: `chunk_size` should be an integer. Got '{chunk_size}'."
- )
- _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action["href"])
- else:
- _upload_single_part(operation=operation, upload_url=upload_action["href"])
-
- # 3. Verify upload went well
- if verify_action is not None:
- _validate_lfs_action(verify_action)
- verify_resp = get_session().post(
- verify_action["href"],
- auth=HTTPBasicAuth(username="USER", password=get_token_to_send(token or True)), # type: ignore
- json={"oid": operation.upload_info.sha256.hex(), "size": operation.upload_info.size},
- )
- hf_raise_for_status(verify_resp)
- logger.debug(f"{operation.path_in_repo}: Upload successful")
-
-
-def _validate_lfs_action(lfs_action: dict):
- """validates response from the LFS batch endpoint"""
- if not (
- isinstance(lfs_action.get("href"), str)
- and (lfs_action.get("header") is None or isinstance(lfs_action.get("header"), dict))
- ):
- raise ValueError("lfs_action is improperly formatted")
- return lfs_action
-
-
-def _validate_batch_actions(lfs_batch_actions: dict):
- """validates response from the LFS batch endpoint"""
- if not (isinstance(lfs_batch_actions.get("oid"), str) and isinstance(lfs_batch_actions.get("size"), int)):
- raise ValueError("lfs_batch_actions is improperly formatted")
-
- upload_action = lfs_batch_actions.get("actions", {}).get("upload")
- verify_action = lfs_batch_actions.get("actions", {}).get("verify")
- if upload_action is not None:
- _validate_lfs_action(upload_action)
- if verify_action is not None:
- _validate_lfs_action(verify_action)
- return lfs_batch_actions
-
-
-def _validate_batch_error(lfs_batch_error: dict):
- """validates response from the LFS batch endpoint"""
- if not (isinstance(lfs_batch_error.get("oid"), str) and isinstance(lfs_batch_error.get("size"), int)):
- raise ValueError("lfs_batch_error is improperly formatted")
- error_info = lfs_batch_error.get("error")
- if not (
- isinstance(error_info, dict)
- and isinstance(error_info.get("message"), str)
- and isinstance(error_info.get("code"), int)
- ):
- raise ValueError("lfs_batch_error is improperly formatted")
- return lfs_batch_error
-
-
-def _upload_single_part(operation: "CommitOperationAdd", upload_url: str) -> None:
- """
- Uploads `fileobj` as a single PUT HTTP request (basic LFS transfer protocol)
-
- Args:
- upload_url (`str`):
- The URL to PUT the file to.
- fileobj:
- The file-like object holding the data to upload.
-
- Returns: `requests.Response`
-
- Raises: `requests.HTTPError` if the upload resulted in an error
- """
- with operation.as_file(with_tqdm=True) as fileobj:
- response = http_backoff("PUT", upload_url, data=fileobj)
- hf_raise_for_status(response)
-
-
-def _upload_multi_part(operation: "CommitOperationAdd", header: Dict, chunk_size: int, upload_url: str) -> None:
- """
- Uploads file using HF multipart LFS transfer protocol.
- """
- # 1. Get upload URLs for each part
- sorted_parts_urls = _get_sorted_parts_urls(header=header, upload_info=operation.upload_info, chunk_size=chunk_size)
-
- # 2. Upload parts (either with hf_transfer or in pure Python)
- use_hf_transfer = HF_HUB_ENABLE_HF_TRANSFER
- if (
- HF_HUB_ENABLE_HF_TRANSFER
- and not isinstance(operation.path_or_fileobj, str)
- and not isinstance(operation.path_or_fileobj, Path)
- ):
- warnings.warn(
- "hf_transfer is enabled but does not support uploading from bytes or BinaryIO, falling back to regular"
- " upload"
- )
- use_hf_transfer = False
-
- response_headers = (
- _upload_parts_hf_transfer(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)
- if use_hf_transfer
- else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)
- )
-
- # 3. Send completion request
- completion_res = get_session().post(
- upload_url,
- json=_get_completion_payload(response_headers, operation.upload_info.sha256.hex()),
- headers=LFS_HEADERS,
- )
- hf_raise_for_status(completion_res)
-
-
-def _get_sorted_parts_urls(header: Dict, upload_info: UploadInfo, chunk_size: int) -> List[str]:
- sorted_part_upload_urls = [
- upload_url
- for _, upload_url in sorted(
- [
- (int(part_num, 10), upload_url)
- for part_num, upload_url in header.items()
- if part_num.isdigit() and len(part_num) > 0
- ],
- key=lambda t: t[0],
- )
- ]
- num_parts = len(sorted_part_upload_urls)
- if num_parts != ceil(upload_info.size / chunk_size):
- raise ValueError("Invalid server response to upload large LFS file")
- return sorted_part_upload_urls
-
-
-def _get_completion_payload(response_headers: List[Dict], oid: str) -> CompletionPayloadT:
- parts: List[PayloadPartT] = []
- for part_number, header in enumerate(response_headers):
- etag = header.get("etag")
- if etag is None or etag == "":
- raise ValueError(f"Invalid etag (`{etag}`) returned for part {part_number + 1}")
- parts.append(
- {
- "partNumber": part_number + 1,
- "etag": etag,
- }
- )
- return {"oid": oid, "parts": parts}
-
-
-def _upload_parts_iteratively(
- operation: "CommitOperationAdd", sorted_parts_urls: List[str], chunk_size: int
-) -> List[Dict]:
- headers = []
- with operation.as_file(with_tqdm=True) as fileobj:
- for part_idx, part_upload_url in enumerate(sorted_parts_urls):
- with SliceFileObj(
- fileobj,
- seek_from=chunk_size * part_idx,
- read_limit=chunk_size,
- ) as fileobj_slice:
- part_upload_res = http_backoff("PUT", part_upload_url, data=fileobj_slice)
- hf_raise_for_status(part_upload_res)
- headers.append(part_upload_res.headers)
- return headers # type: ignore
-
-
-def _upload_parts_hf_transfer(
- operation: "CommitOperationAdd", sorted_parts_urls: List[str], chunk_size: int
-) -> List[Dict]:
- # Upload file using an external Rust-based package. Upload is faster but support less features (no progress bars).
- try:
- from hf_transfer import multipart_upload
- except ImportError:
- raise ValueError(
- "Fast uploading using 'hf_transfer' is enabled (HF_HUB_ENABLE_HF_TRANSFER=1) but 'hf_transfer' package is"
- " not available in your environment. Try `pip install hf_transfer`."
- )
-
- try:
- return multipart_upload(
- file_path=operation.path_or_fileobj,
- parts_urls=sorted_parts_urls,
- chunk_size=chunk_size,
- max_files=128,
- parallel_failures=127, # could be removed
- max_retries=5,
- )
- except Exception as e:
- raise RuntimeError(
- "An error occurred while uploading using `hf_transfer`. Consider disabling HF_HUB_ENABLE_HF_TRANSFER for"
- " better error handling."
- ) from e
-
-
-class SliceFileObj(AbstractContextManager):
- """
- Utility context manager to read a *slice* of a seekable file-like object as a seekable, file-like object.
-
- This is NOT thread safe
-
- Inspired by stackoverflow.com/a/29838711/593036
-
- Credits to @julien-c
-
- Args:
- fileobj (`BinaryIO`):
- A file-like object to slice. MUST implement `tell()` and `seek()` (and `read()` of course).
- `fileobj` will be reset to its original position when exiting the context manager.
- seek_from (`int`):
- The start of the slice (offset from position 0 in bytes).
- read_limit (`int`):
- The maximum number of bytes to read from the slice.
-
- Attributes:
- previous_position (`int`):
- The previous position
-
- Examples:
-
- Reading 200 bytes with an offset of 128 bytes from a file (ie bytes 128 to 327):
- ```python
- >>> with open("path/to/file", "rb") as file:
- ... with SliceFileObj(file, seek_from=128, read_limit=200) as fslice:
- ... fslice.read(...)
- ```
-
- Reading a file in chunks of 512 bytes
- ```python
- >>> import os
- >>> chunk_size = 512
- >>> file_size = os.getsize("path/to/file")
- >>> with open("path/to/file", "rb") as file:
- ... for chunk_idx in range(ceil(file_size / chunk_size)):
- ... with SliceFileObj(file, seek_from=chunk_idx * chunk_size, read_limit=chunk_size) as fslice:
- ... chunk = fslice.read(...)
-
- ```
- """
-
- def __init__(self, fileobj: BinaryIO, seek_from: int, read_limit: int):
- self.fileobj = fileobj
- self.seek_from = seek_from
- self.read_limit = read_limit
-
- def __enter__(self):
- self._previous_position = self.fileobj.tell()
- end_of_stream = self.fileobj.seek(0, os.SEEK_END)
- self._len = min(self.read_limit, end_of_stream - self.seek_from)
- # ^^ The actual number of bytes that can be read from the slice
- self.fileobj.seek(self.seek_from, io.SEEK_SET)
- return self
-
- def __exit__(self, exc_type, exc_value, traceback):
- self.fileobj.seek(self._previous_position, io.SEEK_SET)
-
- def read(self, n: int = -1):
- pos = self.tell()
- if pos >= self._len:
- return b""
- remaining_amount = self._len - pos
- data = self.fileobj.read(remaining_amount if n < 0 else min(n, remaining_amount))
- return data
-
- def tell(self) -> int:
- return self.fileobj.tell() - self.seek_from
-
- def seek(self, offset: int, whence: int = os.SEEK_SET) -> int:
- start = self.seek_from
- end = start + self._len
- if whence in (os.SEEK_SET, os.SEEK_END):
- offset = start + offset if whence == os.SEEK_SET else end + offset
- offset = max(start, min(offset, end))
- whence = os.SEEK_SET
- elif whence == os.SEEK_CUR:
- cur_pos = self.fileobj.tell()
- offset = max(start - cur_pos, min(offset, end - cur_pos))
- else:
- raise ValueError(f"whence value {whence} is not supported")
- return self.fileobj.seek(offset, whence) - self.seek_from
-
- def __iter__(self):
- yield self.read(n=4 * 1024 * 1024)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/utils/_runtime.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/utils/_runtime.py
deleted file mode 100644
index f5f95cb57c0db6ca582f5bac29ca66738d694097..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/utils/_runtime.py
+++ /dev/null
@@ -1,328 +0,0 @@
-# coding=utf-8
-# Copyright 2022-present, the HuggingFace Inc. team.
-#
-# 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.
-"""Check presence of installed packages at runtime."""
-import platform
-import sys
-from typing import Any, Dict
-
-import packaging.version
-
-from .. import __version__, constants
-
-
-_PY_VERSION: str = sys.version.split()[0].rstrip("+")
-
-if packaging.version.Version(_PY_VERSION) < packaging.version.Version("3.8.0"):
- import importlib_metadata # type: ignore
-else:
- import importlib.metadata as importlib_metadata # type: ignore
-
-
-_package_versions = {}
-
-_CANDIDATES = {
- "aiohttp": {"aiohttp"},
- "fastai": {"fastai"},
- "fastcore": {"fastcore"},
- "gradio": {"gradio"},
- "graphviz": {"graphviz"},
- "hf_transfer": {"hf_transfer"},
- "jinja": {"Jinja2"},
- "numpy": {"numpy"},
- "pillow": {"Pillow"},
- "pydantic": {"pydantic"},
- "pydot": {"pydot"},
- "tensorboard": {"tensorboardX"},
- "tensorflow": (
- "tensorflow",
- "tensorflow-cpu",
- "tensorflow-gpu",
- "tf-nightly",
- "tf-nightly-cpu",
- "tf-nightly-gpu",
- "intel-tensorflow",
- "intel-tensorflow-avx512",
- "tensorflow-rocm",
- "tensorflow-macos",
- ),
- "torch": {"torch"},
-}
-
-# Check once at runtime
-for candidate_name, package_names in _CANDIDATES.items():
- _package_versions[candidate_name] = "N/A"
- for name in package_names:
- try:
- _package_versions[candidate_name] = importlib_metadata.version(name)
- break
- except importlib_metadata.PackageNotFoundError:
- pass
-
-
-def _get_version(package_name: str) -> str:
- return _package_versions.get(package_name, "N/A")
-
-
-def _is_available(package_name: str) -> bool:
- return _get_version(package_name) != "N/A"
-
-
-# Python
-def get_python_version() -> str:
- return _PY_VERSION
-
-
-# Huggingface Hub
-def get_hf_hub_version() -> str:
- return __version__
-
-
-# aiohttp
-def is_aiohttp_available() -> bool:
- return _is_available("aiohttp")
-
-
-def get_aiohttp_version() -> str:
- return _get_version("aiohttp")
-
-
-# FastAI
-def is_fastai_available() -> bool:
- return _is_available("fastai")
-
-
-def get_fastai_version() -> str:
- return _get_version("fastai")
-
-
-# Fastcore
-def is_fastcore_available() -> bool:
- return _is_available("fastcore")
-
-
-def get_fastcore_version() -> str:
- return _get_version("fastcore")
-
-
-# FastAI
-def is_gradio_available() -> bool:
- return _is_available("gradio")
-
-
-def get_gradio_version() -> str:
- return _get_version("gradio")
-
-
-# Graphviz
-def is_graphviz_available() -> bool:
- return _is_available("graphviz")
-
-
-def get_graphviz_version() -> str:
- return _get_version("graphviz")
-
-
-# hf_transfer
-def is_hf_transfer_available() -> bool:
- return _is_available("hf_transfer")
-
-
-def get_hf_transfer_version() -> str:
- return _get_version("hf_transfer")
-
-
-# Numpy
-def is_numpy_available() -> bool:
- return _is_available("numpy")
-
-
-def get_numpy_version() -> str:
- return _get_version("numpy")
-
-
-# Jinja
-def is_jinja_available() -> bool:
- return _is_available("jinja")
-
-
-def get_jinja_version() -> str:
- return _get_version("jinja")
-
-
-# Pillow
-def is_pillow_available() -> bool:
- return _is_available("pillow")
-
-
-def get_pillow_version() -> str:
- return _get_version("pillow")
-
-
-# Pydantic
-def is_pydantic_available() -> bool:
- return _is_available("pydantic")
-
-
-def get_pydantic_version() -> str:
- return _get_version("pydantic")
-
-
-# Pydot
-def is_pydot_available() -> bool:
- return _is_available("pydot")
-
-
-def get_pydot_version() -> str:
- return _get_version("pydot")
-
-
-# Tensorboard
-def is_tensorboard_available() -> bool:
- return _is_available("tensorboard")
-
-
-def get_tensorboard_version() -> str:
- return _get_version("tensorboard")
-
-
-# Tensorflow
-def is_tf_available() -> bool:
- return _is_available("tensorflow")
-
-
-def get_tf_version() -> str:
- return _get_version("tensorflow")
-
-
-# Torch
-def is_torch_available() -> bool:
- return _is_available("torch")
-
-
-def get_torch_version() -> str:
- return _get_version("torch")
-
-
-# Shell-related helpers
-try:
- # Set to `True` if script is running in a Google Colab notebook.
- # If running in Google Colab, git credential store is set globally which makes the
- # warning disappear. See https://github.com/huggingface/huggingface_hub/issues/1043
- #
- # Taken from https://stackoverflow.com/a/63519730.
- _is_google_colab = "google.colab" in str(get_ipython()) # type: ignore # noqa: F821
-except NameError:
- _is_google_colab = False
-
-
-def is_notebook() -> bool:
- """Return `True` if code is executed in a notebook (Jupyter, Colab, QTconsole).
-
- Taken from https://stackoverflow.com/a/39662359.
- Adapted to make it work with Google colab as well.
- """
- try:
- shell_class = get_ipython().__class__ # type: ignore # noqa: F821
- for parent_class in shell_class.__mro__: # e.g. "is subclass of"
- if parent_class.__name__ == "ZMQInteractiveShell":
- return True # Jupyter notebook, Google colab or qtconsole
- return False
- except NameError:
- return False # Probably standard Python interpreter
-
-
-def is_google_colab() -> bool:
- """Return `True` if code is executed in a Google colab.
-
- Taken from https://stackoverflow.com/a/63519730.
- """
- return _is_google_colab
-
-
-def dump_environment_info() -> Dict[str, Any]:
- """Dump information about the machine to help debugging issues.
-
- Similar helper exist in:
- - `datasets` (https://github.com/huggingface/datasets/blob/main/src/datasets/commands/env.py)
- - `diffusers` (https://github.com/huggingface/diffusers/blob/main/src/diffusers/commands/env.py)
- - `transformers` (https://github.com/huggingface/transformers/blob/main/src/transformers/commands/env.py)
- """
- from huggingface_hub import HfFolder, whoami
- from huggingface_hub.utils import list_credential_helpers
-
- token = HfFolder().get_token()
-
- # Generic machine info
- info: Dict[str, Any] = {
- "huggingface_hub version": get_hf_hub_version(),
- "Platform": platform.platform(),
- "Python version": get_python_version(),
- }
-
- # Interpreter info
- try:
- shell_class = get_ipython().__class__ # type: ignore # noqa: F821
- info["Running in iPython ?"] = "Yes"
- info["iPython shell"] = shell_class.__name__
- except NameError:
- info["Running in iPython ?"] = "No"
- info["Running in notebook ?"] = "Yes" if is_notebook() else "No"
- info["Running in Google Colab ?"] = "Yes" if is_google_colab() else "No"
-
- # Login info
- info["Token path ?"] = HfFolder().path_token
- info["Has saved token ?"] = token is not None
- if token is not None:
- try:
- info["Who am I ?"] = whoami()["name"]
- except Exception:
- pass
-
- try:
- info["Configured git credential helpers"] = ", ".join(list_credential_helpers())
- except Exception:
- pass
-
- # Installed dependencies
- info["FastAI"] = get_fastai_version()
- info["Tensorflow"] = get_tf_version()
- info["Torch"] = get_torch_version()
- info["Jinja2"] = get_jinja_version()
- info["Graphviz"] = get_graphviz_version()
- info["Pydot"] = get_pydot_version()
- info["Pillow"] = get_pillow_version()
- info["hf_transfer"] = get_hf_transfer_version()
- info["gradio"] = get_gradio_version()
- info["tensorboard"] = get_tensorboard_version()
- info["numpy"] = get_numpy_version()
- info["pydantic"] = get_pydantic_version()
- info["aiohttp"] = get_aiohttp_version()
-
- # Environment variables
- info["ENDPOINT"] = constants.ENDPOINT
- info["HUGGINGFACE_HUB_CACHE"] = constants.HUGGINGFACE_HUB_CACHE
- info["HUGGINGFACE_ASSETS_CACHE"] = constants.HUGGINGFACE_ASSETS_CACHE
- info["HF_TOKEN_PATH"] = constants.HF_TOKEN_PATH
- info["HF_HUB_OFFLINE"] = constants.HF_HUB_OFFLINE
- info["HF_HUB_DISABLE_TELEMETRY"] = constants.HF_HUB_DISABLE_TELEMETRY
- info["HF_HUB_DISABLE_PROGRESS_BARS"] = constants.HF_HUB_DISABLE_PROGRESS_BARS
- info["HF_HUB_DISABLE_SYMLINKS_WARNING"] = constants.HF_HUB_DISABLE_SYMLINKS_WARNING
- info["HF_HUB_DISABLE_EXPERIMENTAL_WARNING"] = constants.HF_HUB_DISABLE_EXPERIMENTAL_WARNING
- info["HF_HUB_DISABLE_IMPLICIT_TOKEN"] = constants.HF_HUB_DISABLE_IMPLICIT_TOKEN
- info["HF_HUB_ENABLE_HF_TRANSFER"] = constants.HF_HUB_ENABLE_HF_TRANSFER
-
- print("\nCopy-and-paste the text below in your GitHub issue.\n")
- print("\n".join([f"- {prop}: {val}" for prop, val in info.items()]) + "\n")
- return info
diff --git "a/spaces/Daextream/Whisper-Auto-Subtitled-Video-Generator/pages/04_\360\237\224\212_Upload_Audio_File.py" "b/spaces/Daextream/Whisper-Auto-Subtitled-Video-Generator/pages/04_\360\237\224\212_Upload_Audio_File.py"
deleted file mode 100644
index d28d3b68ccc8f5a6eb4d8cd944a220bb6dd34e94..0000000000000000000000000000000000000000
--- "a/spaces/Daextream/Whisper-Auto-Subtitled-Video-Generator/pages/04_\360\237\224\212_Upload_Audio_File.py"
+++ /dev/null
@@ -1,205 +0,0 @@
-import whisper
-import streamlit as st
-from streamlit_lottie import st_lottie
-from utils import write_vtt, write_srt
-import ffmpeg
-import requests
-from typing import Iterator
-from io import StringIO
-import numpy as np
-import pathlib
-import os
-
-st.set_page_config(page_title="Auto Transcriber", page_icon="🔊", layout="wide")
-
-# Define a function that we can use to load lottie files from a link.
-@st.cache(allow_output_mutation=True)
-def load_lottieurl(url: str):
- r = requests.get(url)
- if r.status_code != 200:
- return None
- return r.json()
-
-
-APP_DIR = pathlib.Path(__file__).parent.absolute()
-
-LOCAL_DIR = APP_DIR / "local_audio"
-LOCAL_DIR.mkdir(exist_ok=True)
-save_dir = LOCAL_DIR / "output"
-save_dir.mkdir(exist_ok=True)
-
-
-col1, col2 = st.columns([1, 3])
-with col1:
- lottie = load_lottieurl("https://assets1.lottiefiles.com/packages/lf20_1xbk4d2v.json")
- st_lottie(lottie)
-
-with col2:
- st.write("""
- ## Auto Transcriber
- ##### Input an audio file and get a transcript.
- ###### ➠ If you want to transcribe the audio in its original language, select the task as "Transcribe"
- ###### ➠ If you want to translate the transcription to English, select the task as "Translate"
- ###### I recommend starting with the base model and then experimenting with the larger models, the small and medium models often work well. """)
-
-loaded_model = whisper.load_model("base")
-current_size = "None"
-
-
-@st.cache(allow_output_mutation=True)
-def change_model(current_size, size):
- if current_size != size:
- loaded_model = whisper.load_model(size)
- return loaded_model
- else:
- raise Exception("Model size is the same as the current size.")
-
-@st.cache(allow_output_mutation=True)
-def inferecence(loaded_model, uploaded_file, task):
- with open(f"{save_dir}/input.mp3", "wb") as f:
- f.write(uploaded_file.read())
- audio = ffmpeg.input(f"{save_dir}/input.mp3")
- audio = ffmpeg.output(audio, f"{save_dir}/output.wav", acodec="pcm_s16le", ac=1, ar="16k")
- ffmpeg.run(audio, overwrite_output=True)
- if task == "Transcribe":
- options = dict(task="transcribe", best_of=5)
- results = loaded_model.transcribe(f"{save_dir}/output.wav", **options)
- vtt = getSubs(results["segments"], "vtt", 80)
- srt = getSubs(results["segments"], "srt", 80)
- lang = results["language"]
- return results["text"], vtt, srt, lang
- elif task == "Translate":
- options = dict(task="translate", best_of=5)
- results = loaded_model.transcribe(f"{save_dir}/output.wav", **options)
- vtt = getSubs(results["segments"], "vtt", 80)
- srt = getSubs(results["segments"], "srt", 80)
- lang = results["language"]
- return results["text"], vtt, srt, lang
- else:
- raise ValueError("Task not supported")
-
-
-def getSubs(segments: Iterator[dict], format: str, maxLineWidth: int) -> str:
- segmentStream = StringIO()
-
- if format == 'vtt':
- write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
- elif format == 'srt':
- write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
- else:
- raise Exception("Unknown format " + format)
-
- segmentStream.seek(0)
- return segmentStream.read()
-
-
-def main():
- size = st.selectbox("Select Model Size (The larger the model, the more accurate the transcription will be, but it will take longer)", ["tiny", "base", "small", "medium", "large"], index=1)
- loaded_model = change_model(current_size, size)
- st.write(f"Model is {'multilingual' if loaded_model.is_multilingual else 'English-only'} "
- f"and has {sum(np.prod(p.shape) for p in loaded_model.parameters()):,} parameters.")
- input_file = st.file_uploader("Upload an audio file", type=["mp3", "wav", "m4a"])
- if input_file is not None:
- filename = input_file.name[:-4]
- else:
- filename = None
- task = st.selectbox("Select Task", ["Transcribe", "Translate"], index=0)
- if task == "Transcribe":
- if st.button("Transcribe"):
- results = inferecence(loaded_model, input_file, task)
- col3, col4 = st.columns(2)
- col5, col6, col7 = st.columns(3)
- col9, col10 = st.columns(2)
-
- with col3:
- st.audio(input_file)
-
- with open("transcript.txt", "w+", encoding='utf8') as f:
- f.writelines(results[0])
- f.close()
- with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
- datatxt = f.read()
-
-
- with open("transcript.vtt", "w+",encoding='utf8') as f:
- f.writelines(results[1])
- f.close()
- with open(os.path.join(os.getcwd(), "transcript.vtt"), "rb") as f:
- datavtt = f.read()
-
- with open("transcript.srt", "w+",encoding='utf8') as f:
- f.writelines(results[2])
- f.close()
- with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
- datasrt = f.read()
-
- with col5:
- st.download_button(label="Download Transcript (.txt)",
- data=datatxt,
- file_name="transcript.txt")
- with col6:
- st.download_button(label="Download Transcript (.vtt)",
- data=datavtt,
- file_name="transcript.vtt")
- with col7:
- st.download_button(label="Download Transcript (.srt)",
- data=datasrt,
- file_name="transcript.srt")
- with col9:
- st.success("You can download the transcript in .srt format, edit it (if you need to) and upload it to YouTube to create subtitles for your video.")
- with col10:
- st.info("Streamlit refreshes after the download button is clicked. The data is cached so you can download the transcript again without having to transcribe the video again.")
-
- elif task == "Translate":
- if st.button("Translate to English"):
- results = inferecence(loaded_model, input_file, task)
- col3, col4 = st.columns(2)
- col5, col6, col7 = st.columns(3)
- col9, col10 = st.columns(2)
-
- with col3:
- st.audio(input_file)
-
- with open("transcript.txt", "w+", encoding='utf8') as f:
- f.writelines(results[0])
- f.close()
- with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
- datatxt = f.read()
-
-
- with open("transcript.vtt", "w+",encoding='utf8') as f:
- f.writelines(results[1])
- f.close()
- with open(os.path.join(os.getcwd(), "transcript.vtt"), "rb") as f:
- datavtt = f.read()
-
- with open("transcript.srt", "w+",encoding='utf8') as f:
- f.writelines(results[2])
- f.close()
- with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
- datasrt = f.read()
-
- with col5:
- st.download_button(label="Download Transcript (.txt)",
- data=datatxt,
- file_name="transcript.txt")
- with col6:
- st.download_button(label="Download Transcript (.vtt)",
- data=datavtt,
- file_name="transcript.vtt")
- with col7:
- st.download_button(label="Download Transcript (.srt)",
- data=datasrt,
- file_name="transcript.srt")
- with col9:
- st.success("You can download the transcript in .srt format, edit it (if you need to) and upload it to YouTube to create subtitles for your video.")
- with col10:
- st.info("Streamlit refreshes after the download button is clicked. The data is cached so you can download the transcript again without having to transcribe the video again.")
-
- else:
- st.error("Please select a task.")
-
-
-if __name__ == "__main__":
- main()
- st.markdown("###### Made with :heart: by [@BatuhanYılmaz](https://twitter.com/batuhan3326) [](https://www.buymeacoffee.com/batuhanylmz)")
\ No newline at end of file
diff --git a/spaces/Davidsamuel101/PPTGenerator/README.md b/spaces/Davidsamuel101/PPTGenerator/README.md
deleted file mode 100644
index 1fadde3fab7467fb3b01db5e771b43406d77f7dd..0000000000000000000000000000000000000000
--- a/spaces/Davidsamuel101/PPTGenerator/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: PPTGenerator
-emoji: 📊
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: src/app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-
\ No newline at end of file
diff --git a/spaces/Eddycrack864/Applio-Inference/tensorlowest.py b/spaces/Eddycrack864/Applio-Inference/tensorlowest.py
deleted file mode 100644
index eccd4dbf3494434e59f7defaae6ab91797263b90..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/tensorlowest.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from tensorboard.backend.event_processing import event_accumulator
-
-import os
-from shutil import copy2
-from re import search as RSearch
-import pandas as pd
-from ast import literal_eval as LEval
-
-weights_dir = 'weights/'
-
-def find_biggest_tensorboard(tensordir):
- try:
- files = [f for f in os.listdir(tensordir) if f.endswith('.0')]
- if not files:
- print("No files with the '.0' extension found!")
- return
-
- max_size = 0
- biggest_file = ""
-
- for file in files:
- file_path = os.path.join(tensordir, file)
- if os.path.isfile(file_path):
- file_size = os.path.getsize(file_path)
- if file_size > max_size:
- max_size = file_size
- biggest_file = file
-
- return biggest_file
-
- except FileNotFoundError:
- print("Couldn't find your model!")
- return
-
-def main(model_name, save_freq, lastmdls):
- global lowestval_weight_dir, scl
-
- tensordir = os.path.join('logs', model_name)
- lowestval_weight_dir = os.path.join(tensordir, "lowestvals")
-
- latest_file = find_biggest_tensorboard(tensordir)
-
- if latest_file is None:
- print("Couldn't find a valid tensorboard file!")
- return
-
- tfile = os.path.join(tensordir, latest_file)
-
- ea = event_accumulator.EventAccumulator(tfile,
- size_guidance={
- event_accumulator.COMPRESSED_HISTOGRAMS: 500,
- event_accumulator.IMAGES: 4,
- event_accumulator.AUDIO: 4,
- event_accumulator.SCALARS: 0,
- event_accumulator.HISTOGRAMS: 1,
- })
-
- ea.Reload()
- ea.Tags()
-
- scl = ea.Scalars('loss/g/total')
-
- listwstep = {}
-
- for val in scl:
- if (val.step // save_freq) * save_freq in [val.step for val in scl]:
- listwstep[float(val.value)] = (val.step // save_freq) * save_freq
-
- lowest_vals = sorted(listwstep.keys())[:lastmdls]
-
- sorted_dict = {value: step for value, step in listwstep.items() if value in lowest_vals}
-
- return sorted_dict
-
-def selectweights(model_name, file_dict, weights_dir, lowestval_weight_dir):
- os.makedirs(lowestval_weight_dir, exist_ok=True)
- logdir = []
- files = []
- lbldict = {
- 'Values': {},
- 'Names': {}
- }
- weights_dir_path = os.path.join(weights_dir, "")
- low_val_path = os.path.join(os.getcwd(), os.path.join(lowestval_weight_dir, ""))
-
- try:
- file_dict = LEval(file_dict)
- except Exception as e:
- print(f"Error! {e}")
- return f"Couldn't load tensorboard file! {e}"
-
- weights = [f for f in os.scandir(weights_dir)]
- for key, value in file_dict.items():
- pattern = fr"^{model_name}_.*_s{value}\.pth$"
- matching_weights = [f.name for f in weights if f.is_file() and RSearch(pattern, f.name)]
- for weight in matching_weights:
- source_path = weights_dir_path + weight
- destination_path = os.path.join(lowestval_weight_dir, weight)
-
- copy2(source_path, destination_path)
-
- logdir.append(f"File = {weight} Value: {key}, Step: {value}")
-
- lbldict['Names'][weight] = weight
- lbldict['Values'][weight] = key
-
- files.append(low_val_path + weight)
-
- print(f"File = {weight} Value: {key}, Step: {value}")
-
- yield ('\n'.join(logdir), files, pd.DataFrame(lbldict))
-
-
- return ''.join(logdir), files, pd.DataFrame(lbldict)
-
-
-if __name__ == "__main__":
- model = str(input("Enter the name of the model: "))
- sav_freq = int(input("Enter save frequency of the model: "))
- ds = main(model, sav_freq)
-
- if ds: selectweights(model, ds, weights_dir, lowestval_weight_dir)
-
\ No newline at end of file
diff --git a/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/models/yolo.py b/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/models/yolo.py
deleted file mode 100644
index 0e41822bf39ca4034b0acc423b0ccf943dea6ee5..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/models/yolo.py
+++ /dev/null
@@ -1,283 +0,0 @@
-import argparse
-import logging
-import sys
-from copy import deepcopy
-from pathlib import Path
-
-import math
-
-sys.path.append('./') # to run '$ python *.py' files in subdirectories
-logger = logging.getLogger(__name__)
-
-import torch
-import torch.nn as nn
-
-from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, NMS, autoShape
-from models.experimental import MixConv2d, CrossConv, C3
-from utils.general import check_anchor_order, make_divisible, check_file, set_logging
-from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
- select_device, copy_attr
-
-
-class Detect(nn.Module):
- stride = None # strides computed during build
- export = False # onnx export
-
- def __init__(self, nc=80, anchors=(), ch=()): # detection layer
- super(Detect, self).__init__()
- self.nc = nc # number of classes
- self.no = nc + 5 # number of outputs per anchor
- self.nl = len(anchors) # number of detection layers
- self.na = len(anchors[0]) // 2 # number of anchors
- self.grid = [torch.zeros(1)] * self.nl # init grid
- a = torch.tensor(anchors).float().view(self.nl, -1, 2)
- self.register_buffer('anchors', a) # shape(nl,na,2)
- self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
- self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
-
- def forward(self, x):
- # x = x.copy() # for profiling
- z = [] # inference output
- self.training |= self.export
- for i in range(self.nl):
- x[i] = self.m[i](x[i]) # conv
- bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
- x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
-
- if not self.training: # inference
- if self.grid[i].shape[2:4] != x[i].shape[2:4]:
- self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
-
- y = x[i].sigmoid()
- y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy
- y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
- z.append(y.view(bs, -1, self.no))
-
- return x if self.training else (torch.cat(z, 1), x)
-
- @staticmethod
- def _make_grid(nx=20, ny=20):
- yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
- return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
-
-
-class Model(nn.Module):
- def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, number of classes
- super(Model, self).__init__()
- if isinstance(cfg, dict):
- self.yaml = cfg # model dict
- else: # is *.yaml
- import yaml # for torch hub
- self.yaml_file = Path(cfg).name
- with open(cfg) as f:
- self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict
-
- # Define model
- if nc and nc != self.yaml['nc']:
- print('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc))
- self.yaml['nc'] = nc # override yaml value
- self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist, ch_out
- # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
-
- # Build strides, anchors
- m = self.model[-1] # Detect()
- if isinstance(m, Detect):
- s = 128 # 2x min stride
- m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
- m.anchors /= m.stride.view(-1, 1, 1)
- check_anchor_order(m)
- self.stride = m.stride
- self._initialize_biases() # only run once
- # print('Strides: %s' % m.stride.tolist())
-
- # Init weights, biases
- initialize_weights(self)
- self.info()
- print('')
-
- def forward(self, x, augment=False, profile=False):
- if augment:
- img_size = x.shape[-2:] # height, width
- s = [1, 0.83, 0.67] # scales
- f = [None, 3, None] # flips (2-ud, 3-lr)
- y = [] # outputs
- for si, fi in zip(s, f):
- xi = scale_img(x.flip(fi) if fi else x, si)
- yi = self.forward_once(xi)[0] # forward
- # cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
- yi[..., :4] /= si # de-scale
- if fi == 2:
- yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud
- elif fi == 3:
- yi[..., 0] = img_size[1] - yi[..., 0] # de-flip lr
- y.append(yi)
- return torch.cat(y, 1), None # augmented inference, train
- else:
- return self.forward_once(x, profile) # single-scale inference, train
-
- def forward_once(self, x, profile=False):
- y, dt = [], [] # outputs
- for m in self.model:
- if m.f != -1: # if not from previous layer
- x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
-
- if profile:
- try:
- import thop
- o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # FLOPS
- except:
- o = 0
- t = time_synchronized()
- for _ in range(10):
- _ = m(x)
- dt.append((time_synchronized() - t) * 100)
- print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
-
- x = m(x) # run
- y.append(x if m.i in self.save else None) # save output
-
- if profile:
- print('%.1fms total' % sum(dt))
- return x
-
- def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
- # https://arxiv.org/abs/1708.02002 section 3.3
- # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
- m = self.model[-1] # Detect() module
- for mi, s in zip(m.m, m.stride): # from
- b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
- b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
- b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls
- mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-
- def _print_biases(self):
- m = self.model[-1] # Detect() module
- for mi in m.m: # from
- b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
- print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
-
- # def _print_weights(self):
- # for m in self.model.modules():
- # if type(m) is Bottleneck:
- # print('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights
-
- def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
- # print('Fusing layers... ')
- for m in self.model.modules():
- if type(m) is Conv and hasattr(m, 'bn'):
- m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
- delattr(m, 'bn') # remove batchnorm
- m.forward = m.fuseforward # update forward
- self.info()
- return self
-
- def nms(self, mode=True): # add or remove NMS module
- present = type(self.model[-1]) is NMS # last layer is NMS
- if mode and not present:
- print('Adding NMS... ')
- m = NMS() # module
- m.f = -1 # from
- m.i = self.model[-1].i + 1 # index
- self.model.add_module(name='%s' % m.i, module=m) # add
- self.eval()
- elif not mode and present:
- print('Removing NMS... ')
- self.model = self.model[:-1] # remove
- return self
-
- def autoshape(self): # add autoShape module
- print('Adding autoShape... ')
- m = autoShape(self) # wrap model
- copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes
- return m
-
- def info(self, verbose=False): # print model information
- model_info(self, verbose)
-
-
-def parse_model(d, ch): # model_dict, input_channels(3)
- logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
- anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
- na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
- no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
-
- layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
- for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
- m = eval(m) if isinstance(m, str) else m # eval strings
- for j, a in enumerate(args):
- try:
- args[j] = eval(a) if isinstance(a, str) else a # eval strings
- except:
- pass
-
- n = max(round(n * gd), 1) if n > 1 else n # depth gain
- if m in [Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
- c1, c2 = ch[f], args[0]
-
- # Normal
- # if i > 0 and args[0] != no: # channel expansion factor
- # ex = 1.75 # exponential (default 2.0)
- # e = math.log(c2 / ch[1]) / math.log(2)
- # c2 = int(ch[1] * ex ** e)
- # if m != Focus:
-
- c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
-
- # Experimental
- # if i > 0 and args[0] != no: # channel expansion factor
- # ex = 1 + gw # exponential (default 2.0)
- # ch1 = 32 # ch[1]
- # e = math.log(c2 / ch1) / math.log(2) # level 1-n
- # c2 = int(ch1 * ex ** e)
- # if m != Focus:
- # c2 = make_divisible(c2, 8) if c2 != no else c2
-
- args = [c1, c2, *args[1:]]
- if m in [BottleneckCSP, C3]:
- args.insert(2, n)
- n = 1
- elif m is nn.BatchNorm2d:
- args = [ch[f]]
- elif m is Concat:
- c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
- elif m is Detect:
- args.append([ch[x + 1] for x in f])
- if isinstance(args[1], int): # number of anchors
- args[1] = [list(range(args[1] * 2))] * len(f)
- else:
- c2 = ch[f]
-
- m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module
- t = str(m)[8:-2].replace('__main__.', '') # module type
- np = sum([x.numel() for x in m_.parameters()]) # number params
- m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
- logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print
- save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
- layers.append(m_)
- ch.append(c2)
- return nn.Sequential(*layers), sorted(save)
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- opt = parser.parse_args()
- opt.cfg = check_file(opt.cfg) # check file
- set_logging()
- device = select_device(opt.device)
-
- # Create model
- model = Model(opt.cfg).to(device)
- model.train()
-
- # Profile
- # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
- # y = model(img, profile=True)
-
- # Tensorboard
- # from torch.utils.tensorboard import SummaryWriter
- # tb_writer = SummaryWriter()
- # print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/")
- # tb_writer.add_graph(model.model, img) # add model to tensorboard
- # tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard
diff --git a/spaces/FSDL-Fashion/fashion_img_search/app.py b/spaces/FSDL-Fashion/fashion_img_search/app.py
deleted file mode 100644
index c6f562de935251251194cb8636d6addd3c040f69..0000000000000000000000000000000000000000
--- a/spaces/FSDL-Fashion/fashion_img_search/app.py
+++ /dev/null
@@ -1 +0,0 @@
-from fis.app import app # noqa: F401
diff --git a/spaces/FYP-23-S1-21/Refineverse_Plugin/templates/SummarizationTable.html b/spaces/FYP-23-S1-21/Refineverse_Plugin/templates/SummarizationTable.html
deleted file mode 100644
index 6a77d3509be8cfb7087f3d6c44bac22a6baa3ba1..0000000000000000000000000000000000000000
--- a/spaces/FYP-23-S1-21/Refineverse_Plugin/templates/SummarizationTable.html
+++ /dev/null
@@ -1,94 +0,0 @@
-
-
-
- Summarization Data
-
-
-
-
-
-
-
User Story
-
Summary
-
-
-
-
- {% for summarization in summarizations %}
-
-
{{ summarization[0] }}
-
{{ summarization[1] }}
-
- {% endfor %}
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/FedeFT/Head_Pose_Estimation_and_LAEO_computation/utils/img_util.py b/spaces/FedeFT/Head_Pose_Estimation_and_LAEO_computation/utils/img_util.py
deleted file mode 100644
index 8c1ecded21be01b8b1779828ec52b59566d9526d..0000000000000000000000000000000000000000
--- a/spaces/FedeFT/Head_Pose_Estimation_and_LAEO_computation/utils/img_util.py
+++ /dev/null
@@ -1,735 +0,0 @@
-import cv2
-import os
-import json
-import numpy as np
-from math import cos, sin, pi
-from utils.labels import coco_category_index, rgb_colors, color_pose, color_pose_normalized, pose_id_part, \
- face_category_index, body_parts_openpose, body_parts, face_points, face_points_openpose, pose_id_part_zedcam, \
- face_points_zedcam, body_parts_zedcam, color_pose_rgb
-
-
-# from src.utils.my_utils import fit_plane_least_square # , retrieve_line_from_two_points
-
-
-def percentage_to_pixel(shape, bb_boxes, bb_boxes_scores, key_points=None, key_points_score=None):
- """
- Convert the detections from percentage to pixels coordinates; it works both for the bounding boxes and for the key points if passed
-
- Args:
- :img_shape (tuple): the shape of the image
- :bb_boxes (numpy.ndarray): list of list each one representing the bounding box coordinates expressed in percentage [y_min_perc, x_min_perc, y_max_perc, x_max_perc]
- :bb_boxes_scores (numpy.ndarray): list of score for each bounding box in range [0, 1]
- :key_points (numpy.ndarray): list of list of list each one representing the key points coordinates expressed in percentage [y_perc, x_perc]
- :key_points_score (numpy.ndarray): list of list each one representing the score associated to each key point in range [0, 1]
-
- Returns:
- :det (numpy.ndarray): list of lists each one representing the bounding box coordinates in pixels and the score associated to each bounding box [x_min, y_min, x_max, y_max, score]
- :kpt (list): list of lists each one representing the key points detected in pixels and the score associated to each point [x, y, score]
- """
-
- im_width, im_height = shape[1], shape[0]
- det, kpt = [], []
-
- if key_points is not None:
- key_points = key_points
- key_points_score = key_points_score
-
- for i, _ in enumerate(bb_boxes):
- y_min, x_min, y_max, x_max = bb_boxes[i]
- x_min_rescaled, x_max_rescaled, y_min_rescaled, y_max_rescaled = x_min * im_width, x_max * im_width, y_min * im_height, y_max * im_height
- det.append([int(x_min_rescaled), int(y_min_rescaled), int(x_max_rescaled), int(y_max_rescaled), bb_boxes_scores[i]])
-
- if key_points is not None:
- aux_list = []
- for n, key_point in enumerate(key_points[i]): # y x
- aux = [int(key_point[0] * im_height), int(key_point[1] * im_width), key_points_score[i][n]]
- aux_list.append(aux)
- kpt.append(aux_list)
-
- det = np.array(det)
-
- return det, kpt
-
-
-def draw_detections(image, detections, max_boxes_to_draw, violate=None, couple_points=None, draw_class_score=False):
- """
- Given an image and a dictionary of detections this function return the image with the drawings of the bounding boxes (with violations information if specified)
-
- Args:
- :img (numpy.ndarray): The image that is given as input to the object detection model
- :detections (dict): The dictionary with the detections information (detection_classes, detection_boxes, detection_scores,
- detection_keypoint_scores, detection_keypoints, detection_boxes_centroid)
- :max_boxes_to_draw (int): The maximum number of bounding boxes to draw
- :violate (set): The indexes of detections (sorted) that violate the minimum distance computed by my_utils.compute_distance function
- (default is None)
- :couple_points (list): A list of tuples each one containing the couple of indexes that violate the minimum distance (used to draw lines in
- between to bounding boxes)
- (default is None)
- :draw_class_score (bool): If this value is set to True, in the returned image will be drawn the category and the score over each bounding box
- (default is False)
-
- Returns:
- :img_with_drawings (numpy.ndarray): The image with the bounding boxes of each detected objects and optionally with the situations of violation
- """
-
- im_width, im_height = image.shape[1], image.shape[0]
- img_with_drawings = image.copy()
- classes = detections['detection_classes']
- boxes = detections['detection_boxes']
- scores = detections['detection_scores']
- centroids = detections['detection_boxes_centroid']
- red = (0, 0, 255)
-
- i = 0
- while i < max_boxes_to_draw and i < len(classes):
- [y_min, x_min, y_max, x_max] = boxes[i]
- (x_min_rescaled, x_max_rescaled, y_min_rescaled, y_max_rescaled) = (x_min * im_width, x_max * im_width, y_min * im_height, y_max * im_height)
- start_point, end_point = (int(x_max_rescaled), int(y_max_rescaled)), (int(x_min_rescaled), int(y_min_rescaled))
-
- # [cx, cy] = centroids[i]
- # (cx_rescaled, cy_rescaled) = (int(cx * im_width), int(cy * im_height))
-
- color = rgb_colors[classes[i]]
- if violate:
- if i in violate:
- color = red
-
- cv2.rectangle(img_with_drawings, start_point, end_point, color, 2)
- # cv2.circle(img_with_drawings, (cx_rescaled, cy_rescaled), 2, color, 2)
-
- if draw_class_score:
- cv2.rectangle(img_with_drawings, end_point, (start_point[0], end_point[1] - 25), rgb_colors[classes[i]], -1)
- text = face_category_index[classes[i]]['name'] + " {:.2f}".format(scores[i])
- cv2.putText(img_with_drawings, text, end_point, cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2, cv2.LINE_AA)
- i += 1
-
- if couple_points and len(centroids) > 1:
- for j in range(len(couple_points)):
- pt1 = centroids[couple_points[j][0]][0], centroids[couple_points[j][0]][1]
- pt2 = centroids[couple_points[j][1]][0], centroids[couple_points[j][1]][1]
- cv2.line(img_with_drawings, pt1, pt2, red, 2)
-
- text_location = (int(image.shape[1]-image.shape[1]/4), int(image.shape[0]/17))
- font_scale = 0.8 * 1 / (640/image.shape[0])
- thickness = int(2 * (image.shape[0]/640))
- cv2.putText(img_with_drawings, "# of people : "+str(i), text_location, cv2.FONT_HERSHEY_SIMPLEX, font_scale, red, thickness, cv2.LINE_AA)
-
- return img_with_drawings
-
-
-def resize_preserving_ar(image, new_shape):
- """
- Resize and pad the input image in order to make it usable by an object detection model (e.g. mobilenet 640x640)
-
- Args:
- :image (numpy.ndarray): The image that will be resized and padded
- :new_shape (tuple): The shape of the image output (height, width)
-
- Returns:
- :res_image (numpy.ndarray): The image modified to have the new shape
- """
- (old_height, old_width, _) = image.shape
- (new_height, new_width) = new_shape
-
- if old_height != old_width: # rectangle
- ratio_h, ratio_w = new_height / old_height, new_width / old_width
-
- if ratio_h > ratio_w:
- dim = (new_width, int(old_height * ratio_w))
- img = cv2.resize(image, dim, interpolation=cv2.INTER_CUBIC)
- bottom_padding = int(new_height - int(old_height * ratio_w)) if int(new_height - int(old_height * ratio_w)) >= 0 else 0
- img = cv2.copyMakeBorder(img, 0, bottom_padding, 0, 0, cv2.BORDER_CONSTANT)
- pad = (0, bottom_padding, dim)
-
- else:
- dim = (int(old_width * ratio_h), new_height)
- img = cv2.resize(image, dim, interpolation=cv2.INTER_CUBIC)
- right_padding = int(new_width - int(old_width * ratio_h)) if int(new_width - int(old_width * ratio_h)) >= 0 else 0
- img = cv2.copyMakeBorder(img, 0, 0, 0, right_padding, cv2.BORDER_CONSTANT)
- pad = (right_padding, 0, dim)
-
- else: # square
- img = cv2.resize(image, new_shape, new_height, new_width)
- pad = (0, 0, (new_height, new_width))
-
- return img, pad
-
-
-def resize_and_padding_preserving_ar(image, new_shape):
- """ Resize and pad the input image in order to make it usable by a pose model (e.g. mobilenet-posenet takes as input 257x257 images)
-
- Args:
- :image (numpy.ndarray): The image that will be resized and padded
- :new_shape (tuple): The shape of the image output
-
- Returns:
- :res_image (numpy.ndarray): The image modified to have the new shape
- """
-
- (old_height, old_width, _) = image.shape
- (new_height, new_width) = new_shape
-
- if old_height != old_width: # rectangle
- ratio_h, ratio_w = new_height / old_height, new_width / old_width
-
- # print(img.shape, "\nRATIO: ", ratio_h, ratio_w)
- if ratio_h < ratio_w:
- ratio = new_shape[0] / old_height
- dim = (int(old_width * ratio), new_width)
- img = cv2.resize(image, dim)
- right_padding = int(new_width - img.shape[1]) if int(new_width - img.shape[1]) >= 0 else 0
- img = cv2.copyMakeBorder(img, 0, 0, 0, right_padding, cv2.BORDER_CONSTANT)
- else:
- ratio = new_shape[1] / old_width
- dim = (new_height, int(old_height * ratio))
- img = cv2.resize(image, dim)
- bottom_padding = int(new_height - img.shape[0]) if int(new_width - img.shape[0]) >= 0 else 0
- img = cv2.copyMakeBorder(img, 0, bottom_padding, 0, 0, cv2.BORDER_CONSTANT)
-
- else: # square
- img = cv2.resize(image, new_shape)
-
- img = img.astype(np.float32) / 255.
- res_image = np.expand_dims(img, 0)
-
- return res_image
-
-
-def draw_axis(yaw, pitch, roll, image=None, tdx=None, tdy=None, size=50):
- """
- Draw yaw pitch and roll axis on the image if passed as input and returns the vector containing the projection of the vector on the image plane
-
- Args:
- :yaw (float): value that represents the yaw rotation of the face
- :pitch (float): value that represents the pitch rotation of the face
- :roll (float): value that represents the roll rotation of the face
- :image (numpy.ndarray): The image where the three vector will be printed
- (default is None)
- :tdx (float64): x coordinate from where the vector drawing start expressed in pixel coordinates
- (default is None)
- :tdy (float64): y coordinate from where the vector drawing start expressed in pixel coordinates
- (default is None)
- :size (int): value that will be multiplied to each x, y and z value that enlarge the "vector drawing"
- (default is 50)
-
- Returns:
- :list_projection_xy (list): list containing the unit vector [x, y, z]
- """
-
- pitch = pitch * np.pi / 180
- yaw = -(yaw * np.pi / 180)
- roll = roll * np.pi / 180
-
- if tdx != None and tdy != None:
- tdx = tdx
- tdy = tdy
-
- else:
- height, width = image.shape[:2]
- tdx = width / 2
- tdy = height / 2
-
- # PROJECT 3D TO 2D XY plane (Z = 0)
-
- # X-Axis pointing to right. drawn in red
- x1 = size * (cos(yaw) * cos(roll)) + tdx
- y1 = size * (cos(pitch) * sin(roll) + cos(roll) * sin(pitch) * sin(yaw)) + tdy
-
- # Y-Axis | drawn in green
- x2 = size * (-cos(yaw) * sin(roll)) + tdx
- y2 = size * (cos(pitch) * cos(roll) - sin(pitch) * sin(yaw) * sin(roll)) + tdy
-
- # Z-Axis (out of the screen) drawn in yellow #it was blue
- x3 = size * (sin(yaw)) + tdx
- y3 = size * (-cos(yaw) * sin(pitch)) + tdy
- z3 = size * (cos(pitch) * cos(yaw)) + tdy
-
- if image is not None:
- cv2.line(image, (int(tdx), int(tdy)), (int(x1), int(y1)), (0, 0, 255), 2) # BGR->red
- cv2.line(image, (int(tdx), int(tdy)), (int(x2), int(y2)), (0, 255, 0), 2) # BGR->green
- cv2.line(image, (int(tdx), int(tdy)), (int(x3), int(y3)), (0, 255, 255), 2) # BGR->blue
-
- list_projection_xy = [sin(yaw), -cos(yaw) * sin(pitch)]
- return list_projection_xy
-
-
-def visualize_vector(image, center, unit_vector, title="", color=(0, 0, 255), **kwargs):
- """
- Draw the projected vector on the image plane and return the image
-
- Args:
- :image (numpy.ndarray): The image where the vector will be printed
- :center (list): x, y coordinates in pixels of the starting point from where the vector is drawn
- :unit_vector (list): vector of the gaze in the form [gx, gy]
- :title (string): title displayed in the imshow function
- (default is "")
- :color (tuple): color value of the vector drawn on the image
- (default is (0, 0, 255))
-
- Returns:
- :result (numpy.ndarray): The image with the vectors drawn
- """
- if 'thickness_lines' in kwargs and kwargs['thickness_lines']!=None:
- thickness = kwargs['thickness_lines']
- else:
- thickness = image.shape[0] // 100
- if thickness==0 or thickness==1:
- thickness = 1
-
- # if image.shape[0] > 150 or image.shape[1] > 150:
- #
- # else:
- # thickness = 1
- unit_vector_draw = [unit_vector[0] * image.shape[0]*0.15, unit_vector[1] * image.shape[0]*0.15]
- point = [center[0] + unit_vector_draw[0], center[1] + unit_vector_draw[1]]
-
- result = cv2.arrowedLine(image, (int(center[0]), int(center[1])), (int(point[0]), int(point[1])), color, thickness=int(thickness), tipLength=0.3)
-
- return result
-
-
-def draw_key_points_pose(image, kpt, openpose=False, only_face=False, **kwargs):
- """
- Draw the key points and the lines connecting them; it expects the output of CenterNet (not OpenPose format)
-
- Args:
- :image (numpy.ndarray): The image where the lines connecting the key points will be printed
- :kpt (list): list of lists of points detected for each person [[x1, y1, c1], [x2, y2, c2],...] where x and y represent the coordinates of each
- point while c represents the confidence
-
- Returns:
- :img (numpy.ndarray): The image with the drawings of lines and key points
- """
-
- if 'thickness_points' in kwargs and kwargs['thickness_points'] != None:
- thickness = kwargs['thickness_points']
- else:
- thickness = max (image.shape[0] // 100, image.shape[1] // 100)
- if thickness == 0:
- thickness = 1
- if thickness == 1:
- thickness = -1
-
- if 'thickness_lines' in kwargs and kwargs['thickness_lines'] !=None:
- thickness_lines = kwargs['thickness_lines']
- else:
- thickness_lines = 2
-
-
- parts = body_parts_openpose if openpose else body_parts
- kpt_score = None
- threshold = 0.4
-
- overlay = image.copy()
-
- face_pts = face_points_openpose if openpose else face_points
-
- for j in range(len(kpt)):
- # 0 nose, 1/2 left/right eye, 3/4 left/right ear
- color = color_pose_rgb["blue"]
- if j == face_pts[0]:
- color = color_pose_rgb["purple"]# naso
- if j == face_pts[1]:
- color = color_pose_rgb["green"]#["light_pink"]#Leye
- if j == face_pts[2]:
- color = color_pose_rgb["dark_pink"]#Reye
- if j == face_pts[3]:
- color = color_pose_rgb["light_orange"]#LEar
- if j == face_pts[4]:
- color = color_pose_rgb["yellow"]# REar
- if only_face and j in face_pts:
- if openpose:
- cv2.circle(image, (int(kpt[j][0]), int(kpt[j][1])), 1, color, thickness=int(thickness))
- else:
- cv2.circle(image, (int(kpt[j][1]), int(kpt[j][0])), 1, color, thickness=int(thickness))
- elif not only_face:
- if openpose:
- cv2.circle(image, (int(kpt[j][0]), int(kpt[j][1])), 1, color, thickness=int(thickness))
- else:
- cv2.circle(image, (int(kpt[j][1]), int(kpt[j][0])), 1, color, thickness=int(thickness))
-
- # cv2.putText(img, pose_id_part[i], (int(kpts[j][i, 1] * img.shape[1]), int(kpts[j][i, 0] * img.shape[0])), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 1, cv2.LINE_AA)
-
- if not only_face:
- for part in parts:
- if int(kpt[part[0]][1]) != 0 and int(kpt[part[0]][0]) != 0 and int(kpt[part[1]][1]) != 0 and int(
- kpt[part[1]][0]) != 0:
-
- if openpose:
- cv2.line(overlay, (int(kpt[part[0]][0]), int(kpt[part[0]][1])), (int(kpt[part[1]][0]), int(kpt[part[1]][1])), (255, 255, 255), int(thickness_lines))
- else:
- cv2.line(overlay, (int(kpt[part[0]][1]), int(kpt[part[0]][0])),
- (int(kpt[part[1]][1]), int(kpt[part[1]][0])), (255, 255, 255), int(thickness_lines))
-
- alpha = 0.4
- image = cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0)
-
- return image
-
-def draw_key_points_pose_zedcam(image, kpt, openpose=True):
- """
- Draw the key points and the lines connecting them; it expects the output of CenterNet (not OpenPose format)
-
- Args:
- :image (numpy.ndarray): The image where the lines connecting the key points will be printed
- :kpt (list): list of lists of points detected for each person [[x1, y1, c1], [x2, y2, c2],...] where x and y represent the coordinates of each
- point while c represents the confidence
-
- Returns:
- :img (numpy.ndarray): The image with the drawings of lines and key points
- """
-
- parts = body_parts_zedcam
- kpt_score = None
- threshold = 0.4
-
- overlay = image.copy()
-
- face_pts = face_points_zedcam
-
- for j in range(len(kpt)):
- # 0 nose, 1/2 left/right eye, 3/4 left/right ear
- color = color_pose["blue"]
- if j == face_pts[0]: # naso
- color = color_pose["purple"]
- if j == face_pts[1]:
- color = color_pose["light_pink"]
- if j == face_pts[2]:
- color = color_pose["dark_pink"]
- if j == face_pts[3]:
- color = color_pose["light_orange"]
- if j == face_pts[4]:
- color = color_pose["dark_orange"]
- if openpose:
- cv2.circle(image, (int(kpt[j][0]), int(kpt[j][1])), 1, color, 2)
- else:
- cv2.circle(image, (int(kpt[j][1]), int(kpt[j][0])), 1, color, 2)
- # cv2.putText(img, pose_id_part[i], (int(kpts[j][i, 1] * img.shape[1]), int(kpts[j][i, 0] * img.shape[0])), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 1, cv2.LINE_AA)
-
- for part in parts:
- if int(kpt[part[0]][1]) != 0 and int(kpt[part[0]][0]) != 0 and int(kpt[part[1]][1]) != 0 and int(
- kpt[part[1]][0]) != 0:
-
- if openpose:
- cv2.line(overlay, (int(kpt[part[0]][0]), int(kpt[part[0]][1])), (int(kpt[part[1]][0]), int(kpt[part[1]][1])), (255, 255, 255), 2)
- else:
- cv2.line(overlay, (int(kpt[part[0]][1]), int(kpt[part[0]][0])),
- (int(kpt[part[1]][1]), int(kpt[part[1]][0])), (255, 255, 255), 2)
-
- alpha = 0.4
- image = cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0)
-
- return image
-
-def plot_3d_points(list_points):
- """
- Plot points in 3D
-
- Args:
- :list_points: A list of lists representing the points; each point has (x, y, z) coordinates represented by the first, second and third element of each list
-
- Returns:
- """
- if list_points == []:
- return
-
- import matplotlib.pyplot as plt
-
- fig = plt.figure()
- ax = fig.add_subplot(111, projection='3d')
-
- for point in list_points:
- ax.scatter(point[0], point[1], point[2], c=np.array(0), marker='o')
-
- ax.set_xlabel('x')
- ax.set_ylabel('y')
- ax.set_zlabel('z')
-
- plt.show()
-
- return
-
-
-def draw_on_img(image, center, id_, res):
- """
- Draw arrow illustrating gaze direction on the image
-
- Args:
- :image (numpy.ndarray): The image where the vector will be printed
- :center (list): x, y coordinates in pixels of the starting point from where the vector is drawn
- :id_ (string): title displayed in the imshow function
- (default is "")
- :res (list): vector of the gaze in the form [gx, gy]
-
- Returns:
- :img_arrow (numpy.ndarray): The image with the vector drawn
- """
-
- res[0] *= image.shape[0]
- res[1] *= image.shape[1]
-
- norm1 = res / np.linalg.norm(res)
- norm_aux = [norm1[0], norm1[1]] # normalized vectors
-
- norm1[0] *= image.shape[0]*0.15
- norm1[1] *= image.shape[0]*0.15
-
- point = center + norm1
-
-
- img_arrow = cv2.arrowedLine(image.copy(), (int(center[1]), int(center[0])), (int(point[1]), int(point[0])), (0, 0, 255), thickness=2, tipLength=0.2)
-
- return img_arrow, [norm_aux, center]
-
-
-def confusion_matrix(conf_matrix, target_names=None, title="", cmap=None):
- """
- Create the image of the confusion matrix given a matrix as input
-
- Args:
- :conf_matrix (list): list of lists that represent an MxM matrix e.g. [[v11, v12, v13], [v21, v22, v23], [v31, v32, v33]]
- :target_names (list): list of target name of dimension M e.g. [[label1, label2, label3]]
- (default is None)
- :title (string): title string to be printed in the confusion matrix
- (default is "")
- :cmap (string): colormap that will be used by the confusion matrix
- (default is None)
-
- Returns:
- :gbr (numpy.ndarray): The image where the lines connecting the key points will be printed
- """
- from laeo_per_frame.interaction_per_frame_uncertainty import LAEO_computation
- import matplotlib.pyplot as plt
-
- if not conf_matrix:
- return []
-
- # if cmap is None:
- # cmap = plt.get_cmap('Blues')
-
- plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = False
- plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = True
-
- fig, ax = plt.subplots(figsize=(6, 4)) # 2, 2, figsize=(6, 4))
- cax = ax.imshow(conf_matrix)
-
- for i in range(len(conf_matrix[0])):
- for j in range(len(conf_matrix[1])):
- ax.text(j, i, str(np.around(conf_matrix[i][j], 3)), va='center', ha='center', color="black")
-
- if target_names is not None:
- ax.set_xticks(np.arange(len(target_names)))
- ax.set_yticks(np.arange(len(target_names)))
- ax.set_xticklabels(target_names)
- ax.set_yticklabels(target_names)
-
- plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
- fig.tight_layout()
- fig.colorbar(cax)
- # plt.show()
-
- fig.canvas.draw()
-
- width, height = fig.get_size_inches() * fig.get_dpi()
- aux_img = np.fromstring(fig.canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
- gbr = aux_img[..., [2, 0, 1]].copy()
-
- # cv2.imshow("1312", gbr)
- # cv2.waitKey(0)
-
- return gbr
-
-
-def join_images(image1, image2):
- """
- Join two images vertically into a new image with the height that is the maximum height of the two images passed as input and the width that is
- the sum of the widths of the two images passed as input
-
- Args:
- :image1 (numpy.ndarray): The image that will be in the left part of the joined images
- :image2 (numpy.ndarray): The image that will be in the right part of the joined images
-
- Returns:
- :joined_image (numpy.ndarray): The image that is the results of the merge of the two images passed as input
- """
-
- if type(image1) == list or type(image2) == list:
- return None
-
- image1_width, image1_height, image2_width, image2_height = image1.shape[1], image1.shape[0], image2.shape[1], image2.shape[0]
-
- new_shape_height = max(image1_height, image2_height)
- new_shape = (new_shape_height, image1_width + image2_width, 3)
-
- joined_image = np.zeros(new_shape, dtype=np.uint8)
- joined_image[:image1_height, :image1_width, :] = image1
- joined_image[:image2_height, image1_width:, :] = image2
-
- cv2.imshow("", cv2.resize(joined_image, (1200, 500)))
- cv2.waitKey(0)
- return joined_image
-
-
-def draw_axis_from_json(img, json_file):
- if os.path.isfile(json_file):
- cv2.imshow("", img)
- cv2.waitKey(0)
-
- with open(json_file) as f:
- data = json.load(f)
- print(data)
- aux = data['people']
- for elem in aux:
- draw_axis(elem['yaw'][0], elem['pitch'][0], elem['roll'][0], img, elem['center_xy'][0], elem['center_xy'][1])
- cv2.imshow("", img)
- cv2.waitKey(0)
-
- return
-
-
-def points_on_circumference(center=(0, 0), r=50, n=100):
- return [(center[0] + (cos(2 * pi / n * x) * r), center[1] + (sin(2 * pi / n * x) * r)) for x in range(0, n + 1)]
-
-
-def draw_cones(yaw, pitch, roll, unc_yaw, unc_pitch, unc_roll, image=None, tdx=None, tdy=None, size=300):
- """
- Draw yaw pitch and roll axis on the image if passed as input and returns the vector containing the projection of the vector on the image plane
-
- Args:
- :yaw (float): value that represents the yaw rotation of the face
- :pitch (float): value that represents the pitch rotation of the face
- :roll (float): value that represents the roll rotation of the face
- :image (numpy.ndarray): The image where the three vector will be printed
- (default is None)
- :tdx (float64): x coordinate from where the vector drawing start expressed in pixel coordinates
- (default is None)
- :tdy (float64): y coordinate from where the vector drawing start expressed in pixel coordinates
- (default is None)
- :size (int): value that will be multiplied to each x, y and z value that enlarge the "vector drawing"
- (default is 50)
-
- Returns:
- :list_projection_xy (list): list containing the unit vector [x, y, z]
- """
-
- pitch = pitch * np.pi / 180
- yaw = -(yaw * np.pi / 180)
- roll = roll * np.pi / 180
-
- if tdx != None and tdy != None:
- tdx = tdx
- tdy = tdy
-
- else:
- height, width = image.shape[:2]
- tdx = width / 2
- tdy = height / 2
-
- # PROJECT 3D TO 2D XY plane (Z = 0)
-
- # X-Axis pointing to right. drawn in red
- x1 = size * (cos(yaw) * cos(roll)) + tdx
- y1 = size * (cos(pitch) * sin(roll) + cos(roll) * sin(pitch) * sin(yaw)) + tdy
-
- # Y-Axis | drawn in green
- x2 = size * (-cos(yaw) * sin(roll)) + tdx
- y2 = size * (cos(pitch) * cos(roll) - sin(pitch) * sin(yaw) * sin(roll)) + tdy
-
- # Z-Axis (out of the screen) drawn in blue
- x3 = size * (sin(yaw)) + tdx
- y3 = size * (-cos(yaw) * sin(pitch)) + tdy
- z3 = size * (cos(pitch) * cos(yaw)) + tdy
-
- unc_mean = (unc_yaw + unc_pitch + unc_roll) / 3
-
- radius = 12 * unc_mean
-
- overlay = image.copy()
- if image is not None:
- # cv2.line(image, (int(tdx), int(tdy)), (int(x1), int(y1)), (0, 0, 255), 2)
- # cv2.line(image, (int(tdx), int(tdy)), (int(x2), int(y2)), (0, 255, 0), 2)
- cv2.line(image, (int(tdx), int(tdy)), (int(x3), int(y3)), (255, 0, 0), 2)
-
- points = points_on_circumference((int(x3), int(y3)), radius, 400)
-
- for point in points:
- cv2.line(image, (int(tdx), int(tdy)), (int(point[0]), int(point[1])), (255, 0, 0), 2)
-
- # cv2.circle(image, (int(x3), int(y3)), int(radius), (255, 0, 0), 2)
-
- alpha = 0.5
- image = cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0)
-
- # cv2.imshow("cc", image)
- # cv2.waitKey(0)
- # exit()
-
- list_projection_xy = [sin(yaw), -cos(yaw) * sin(pitch)]
- return list_projection_xy, image
-
-def draw_axis_3d(yaw, pitch, roll, image=None, tdx=None, tdy=None, size=50, yaw_uncertainty=-1, pitch_uncertainty=-1, roll_uncertainty=-1, openpose=False, **kwargs):
- """
- Draw yaw pitch and roll axis on the image if passed as input and returns the vector containing the projection of the vector on the image plane
- Args:
- :yaw (float): value that represents the yaw rotation of the face
- :pitch (float): value that represents the pitch rotation of the face
- :roll (float): value that represents the roll rotation of the face
- :image (numpy.ndarray): The image where the three vector will be printed
- (default is None)
- :tdx (float64): x coordinate from where the vector drawing start expressed in pixel coordinates
- (default is None)
- :tdy (float64): y coordinate from where the vector drawing start expressed in pixel coordinates
- (default is None)
- :size (int): value that will be multiplied to each x, y and z value that enlarge the "vector drawing"
- (default is 50)
- Returns:
- :list_projection_xy (list): list containing the unit vector [x, y, z]
- """
-
- if 'thickness_lines' in kwargs and kwargs['thickness_lines'] != None:
- thickness = kwargs['thickness_lines']
- else:
- thickness= 3
-
- if openpose:
- temp= tdy
- tdy = tdx
- tdx = tdy
-
- pitch = pitch * np.pi / 180
- yaw = -(yaw * np.pi / 180)
- roll = roll * np.pi / 180
- # print(yaw, pitch, roll)
- if tdx != None and tdy != None:
- tdx = tdx
- tdy = tdy
- else:
- height, width = image.shape[:2]
- tdx = width / 2
- tdy = height / 2
- # PROJECT 3D TO 2D XY plane (Z = 0)
- # X-Axis pointing to right. drawn in red
- x1 = size * (cos(yaw) * cos(roll)) + tdx
- y1 = size * (cos(pitch) * sin(roll) + cos(roll) * sin(pitch) * sin(yaw)) + tdy
- # Y-Axis | drawn in green
- x2 = size * (-cos(yaw) * sin(roll)) + tdx
- y2 = size * (cos(pitch) * cos(roll) - sin(pitch) * sin(yaw) * sin(roll)) + tdy
- # Z-Axis (out of the screen) drawn in blue
- x3 = size * (sin(yaw)) + tdx
- y3 = size * (-cos(yaw) * sin(pitch)) + tdy
- z3 = size * (cos(pitch) * cos(yaw)) + tdy
-
- red_rgb = (255, 0, 0)
- green_rgb = (0, 255, 0)
- blue_rgb = (0, 0, 255)
-
- red_bgr = (0, 0, 255)
- green_bgr = (0, 255, 0)
- blue_bgr = (255, 0, 0)
-
-
- if image is not None:
- cv2.line(image, (int(tdx), int(tdy)), (int(x1), int(y1)), red_rgb, int(thickness))
- cv2.line(image, (int(tdx), int(tdy)), (int(x2), int(y2)), green_rgb, int(thickness))
- cv2.line(image, (int(tdx), int(tdy)), (int(x3), int(y3)), blue_rgb, int(thickness))
- return image
\ No newline at end of file
diff --git a/spaces/GAIR/Factool/factool/scientific/__init__.py b/spaces/GAIR/Factool/factool/scientific/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/GXSA/bingo/src/lib/storage.ts b/spaces/GXSA/bingo/src/lib/storage.ts
deleted file mode 100644
index a5b7825c4f76a28c704da512ae39e8bb45addd09..0000000000000000000000000000000000000000
--- a/spaces/GXSA/bingo/src/lib/storage.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-import { getMany, set, del, clear } from 'idb-keyval';
-
-export const Storage = {
- async get(key: string | string[] | null): Promise {
- if (key === null) return null;
- if (typeof key === 'string') {
- key = [key]
- }
- const returnData: Record = {}
- const values = await getMany(key)
- key.forEach((k, idx)=> {
- returnData[k] = values[idx]
- })
- return returnData;
- },
- async set(object: any) {
- for (let key of Object.keys(object)) {
- await set(key, object[key])
- }
- },
- async remove(key: string) {
- return del(key);
- },
- async clear() {
- return clear();
- }
-}
diff --git a/spaces/Gaborandi/PubMed_Downloader/README.md b/spaces/Gaborandi/PubMed_Downloader/README.md
deleted file mode 100644
index a96b58c8a918af714cd451a37780e25785e28f35..0000000000000000000000000000000000000000
--- a/spaces/Gaborandi/PubMed_Downloader/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: PubMed Downloader
-emoji: 🌖
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/GaenKoki/voicevox/test/test_acoustic_feature_extractor.py b/spaces/GaenKoki/voicevox/test/test_acoustic_feature_extractor.py
deleted file mode 100644
index a82e7afe62eed4f1be1506d7cd34335c769d17d0..0000000000000000000000000000000000000000
--- a/spaces/GaenKoki/voicevox/test/test_acoustic_feature_extractor.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import os
-from pathlib import Path
-from typing import List, Type
-from unittest import TestCase
-
-from voicevox_engine.acoustic_feature_extractor import (
- BasePhoneme,
- JvsPhoneme,
- OjtPhoneme,
-)
-
-
-class TestBasePhoneme(TestCase):
- def setUp(self):
- super().setUp()
- self.str_hello_hiho = "sil k o N n i ch i w a pau h i h o d e s U sil"
- self.base_hello_hiho = [
- BasePhoneme(s, i, i + 1) for i, s in enumerate(self.str_hello_hiho.split())
- ]
- self.lab_str = """
- 0.00 1.00 pau
- 1.00 2.00 k
- 2.00 3.00 o
- 3.00 4.00 N
- 4.00 5.00 n
- 5.00 6.00 i
- 6.00 7.00 ch
- 7.00 8.00 i
- 8.00 9.00 w
- 9.00 10.00 a
- 10.00 11.00 pau
- 11.00 12.00 h
- 12.00 13.00 i
- 13.00 14.00 h
- 14.00 15.00 o
- 15.00 16.00 d
- 16.00 17.00 e
- 17.00 18.00 s
- 18.00 19.00 U
- 19.00 20.00 pau
- """.replace(
- " ", ""
- )[
- 1:-1
- ] # ダブルクオーテーションx3で囲われている部分で、空白をすべて置き換え、先頭と最後の"\n"を除外する
-
- def test_repr_(self):
- self.assertEqual(
- self.base_hello_hiho[1].__repr__(), "Phoneme(phoneme='k', start=1, end=2)"
- )
- self.assertEqual(
- self.base_hello_hiho[10].__repr__(),
- "Phoneme(phoneme='pau', start=10, end=11)",
- )
-
- def test_convert(self):
- with self.assertRaises(NotImplementedError):
- BasePhoneme.convert(self.base_hello_hiho)
-
- def test_duration(self):
- self.assertEqual(self.base_hello_hiho[1].duration, 1)
-
- def test_parse(self):
- parse_str_1 = "0 1 pau"
- parse_str_2 = "32.67543 33.48933 e"
- parsed_base_1 = BasePhoneme.parse(parse_str_1)
- parsed_base_2 = BasePhoneme.parse(parse_str_2)
- self.assertEqual(parsed_base_1.phoneme, "pau")
- self.assertEqual(parsed_base_1.start, 0.0)
- self.assertEqual(parsed_base_1.end, 1.0)
- self.assertEqual(parsed_base_2.phoneme, "e")
- self.assertEqual(parsed_base_2.start, 32.68)
- self.assertEqual(parsed_base_2.end, 33.49)
-
- def lab_test_base(
- self,
- file_path: str,
- phonemes: List["BasePhoneme"],
- phoneme_class: Type["BasePhoneme"],
- ):
- phoneme_class.save_lab_list(phonemes, Path(file_path))
- with open(file_path, mode="r") as f:
- self.assertEqual(f.read(), self.lab_str)
- result_phoneme = phoneme_class.load_lab_list(Path(file_path))
- self.assertEqual(result_phoneme, phonemes)
- os.remove(file_path)
-
-
-class TestJvsPhoneme(TestBasePhoneme):
- def setUp(self):
- super().setUp()
- base_hello_hiho = [
- JvsPhoneme(s, i, i + 1) for i, s in enumerate(self.str_hello_hiho.split())
- ]
- self.jvs_hello_hiho = JvsPhoneme.convert(base_hello_hiho)
-
- def test_phoneme_list(self):
- self.assertEqual(JvsPhoneme.phoneme_list[1], "I")
- self.assertEqual(JvsPhoneme.phoneme_list[14], "gy")
- self.assertEqual(JvsPhoneme.phoneme_list[26], "p")
- self.assertEqual(JvsPhoneme.phoneme_list[38], "z")
-
- def test_const(self):
- self.assertEqual(JvsPhoneme.num_phoneme, 39)
- self.assertEqual(JvsPhoneme.space_phoneme, "pau")
-
- def test_convert(self):
- converted_str_hello_hiho = " ".join([p.phoneme for p in self.jvs_hello_hiho])
- self.assertEqual(
- converted_str_hello_hiho, "pau k o N n i ch i w a pau h i h o d e s U pau"
- )
-
- def test_equal(self):
- # jvs_hello_hihoの2番目の"k"と比較
- true_jvs_phoneme = JvsPhoneme("k", 1, 2)
- # OjtPhonemeと比べる、比較はBasePhoneme内で実装されているので、比較結果はTrue
- true_ojt_phoneme = OjtPhoneme("k", 1, 2)
-
- false_jvs_phoneme_1 = JvsPhoneme("a", 1, 2)
- false_jvs_phoneme_2 = JvsPhoneme("k", 2, 3)
- self.assertTrue(self.jvs_hello_hiho[1] == true_jvs_phoneme)
- self.assertTrue(self.jvs_hello_hiho[1] == true_ojt_phoneme)
- self.assertFalse(self.jvs_hello_hiho[1] == false_jvs_phoneme_1)
- self.assertFalse(self.jvs_hello_hiho[1] == false_jvs_phoneme_2)
-
- def test_verify(self):
- for phoneme in self.jvs_hello_hiho:
- phoneme.verify()
-
- def test_phoneme_id(self):
- jvs_str_hello_hiho = " ".join([str(p.phoneme_id) for p in self.jvs_hello_hiho])
- self.assertEqual(
- jvs_str_hello_hiho, "0 19 25 2 23 17 7 17 36 4 0 15 17 15 25 9 11 30 3 0"
- )
-
- def test_onehot(self):
- phoneme_id_list = [
- 0,
- 19,
- 25,
- 2,
- 23,
- 17,
- 7,
- 17,
- 36,
- 4,
- 0,
- 15,
- 17,
- 15,
- 25,
- 9,
- 11,
- 30,
- 3,
- 0,
- ]
- for i, phoneme in enumerate(self.jvs_hello_hiho):
- for j in range(JvsPhoneme.num_phoneme):
- if phoneme_id_list[i] == j:
- self.assertEqual(phoneme.onehot[j], True)
- else:
- self.assertEqual(phoneme.onehot[j], False)
-
- def test_parse(self):
- parse_str_1 = "0 1 pau"
- parse_str_2 = "15.32654 16.39454 a"
- parsed_jvs_1 = JvsPhoneme.parse(parse_str_1)
- parsed_jvs_2 = JvsPhoneme.parse(parse_str_2)
- self.assertEqual(parsed_jvs_1.phoneme_id, 0)
- self.assertEqual(parsed_jvs_2.phoneme_id, 4)
-
- def test_lab_list(self):
- self.lab_test_base("./jvs_lab_test", self.jvs_hello_hiho, JvsPhoneme)
-
-
-class TestOjtPhoneme(TestBasePhoneme):
- def setUp(self):
- super().setUp()
- self.str_hello_hiho = "sil k o N n i ch i w a pau h i h o d e s U sil"
- base_hello_hiho = [
- OjtPhoneme(s, i, i + 1) for i, s in enumerate(self.str_hello_hiho.split())
- ]
- self.ojt_hello_hiho = OjtPhoneme.convert(base_hello_hiho)
-
- def test_phoneme_list(self):
- self.assertEqual(OjtPhoneme.phoneme_list[1], "A")
- self.assertEqual(OjtPhoneme.phoneme_list[14], "e")
- self.assertEqual(OjtPhoneme.phoneme_list[26], "m")
- self.assertEqual(OjtPhoneme.phoneme_list[38], "ts")
- self.assertEqual(OjtPhoneme.phoneme_list[41], "v")
-
- def test_const(self):
- self.assertEqual(OjtPhoneme.num_phoneme, 45)
- self.assertEqual(OjtPhoneme.space_phoneme, "pau")
-
- def test_convert(self):
- ojt_str_hello_hiho = " ".join([p.phoneme for p in self.ojt_hello_hiho])
- self.assertEqual(
- ojt_str_hello_hiho, "pau k o N n i ch i w a pau h i h o d e s U pau"
- )
-
- def test_equal(self):
- # ojt_hello_hihoの10番目の"a"と比較
- true_ojt_phoneme = OjtPhoneme("a", 9, 10)
- # JvsPhonemeと比べる、比較はBasePhoneme内で実装されているので、比較結果はTrue
- true_jvs_phoneme = JvsPhoneme("a", 9, 10)
-
- false_ojt_phoneme_1 = OjtPhoneme("k", 9, 10)
- false_ojt_phoneme_2 = OjtPhoneme("a", 10, 11)
- self.assertTrue(self.ojt_hello_hiho[9] == true_ojt_phoneme)
- self.assertTrue(self.ojt_hello_hiho[9] == true_jvs_phoneme)
- self.assertFalse(self.ojt_hello_hiho[9] == false_ojt_phoneme_1)
- self.assertFalse(self.ojt_hello_hiho[9] == false_ojt_phoneme_2)
-
- def test_verify(self):
- for phoneme in self.ojt_hello_hiho:
- phoneme.verify()
-
- def test_phoneme_id(self):
- ojt_str_hello_hiho = " ".join([str(p.phoneme_id) for p in self.ojt_hello_hiho])
- self.assertEqual(
- ojt_str_hello_hiho, "0 23 30 4 28 21 10 21 42 7 0 19 21 19 30 12 14 35 6 0"
- )
-
- def test_onehot(self):
- phoneme_id_list = [
- 0,
- 23,
- 30,
- 4,
- 28,
- 21,
- 10,
- 21,
- 42,
- 7,
- 0,
- 19,
- 21,
- 19,
- 30,
- 12,
- 14,
- 35,
- 6,
- 0,
- ]
- for i, phoneme in enumerate(self.ojt_hello_hiho):
- for j in range(OjtPhoneme.num_phoneme):
- if phoneme_id_list[i] == j:
- self.assertEqual(phoneme.onehot[j], True)
- else:
- self.assertEqual(phoneme.onehot[j], False)
-
- def test_parse(self):
- parse_str_1 = "0 1 pau"
- parse_str_2 = "32.67543 33.48933 e"
- parsed_ojt_1 = OjtPhoneme.parse(parse_str_1)
- parsed_ojt_2 = OjtPhoneme.parse(parse_str_2)
- self.assertEqual(parsed_ojt_1.phoneme_id, 0)
- self.assertEqual(parsed_ojt_2.phoneme_id, 14)
-
- def tes_lab_list(self):
- self.lab_test_base("./ojt_lab_test", self.ojt_hello_hiho, OjtPhoneme)
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/sweep_and_sort_blocks.py b/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/sweep_and_sort_blocks.py
deleted file mode 100644
index 8fe339511053f1074ade39ee5538f3815097f7c3..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/sweep_and_sort_blocks.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import numpy as np
-import os
-import pybullet as p
-import random
-from cliport.tasks import primitives
-from cliport.tasks.grippers import Spatula
-from cliport.tasks.task import Task
-from cliport.utils import utils
-import numpy as np
-from cliport.tasks import primitives
-from cliport.tasks.grippers import Spatula
-from cliport.tasks.task import Task
-from cliport.utils import utils
-
-class SweepAndSortBlocks(Task):
- """Sweep a pile of small blocks of different colors (red, blue, green, and yellow) into their corresponding colored zones marked on the tabletop."""
-
- def __init__(self):
- super().__init__()
- self.max_steps = 20
- self.lang_template = "sweep the pile of {color} blocks into the {color} square"
- self.task_completed_desc = "done sweeping and sorting."
- self.primitive = primitives.push
- self.ee = Spatula
- self.additional_reset()
-
- def reset(self, env):
- super().reset(env)
-
- # Add colored zones.
- zone_size = (0.12, 0.12, 0)
- zone_urdf = 'zone/zone.urdf'
- colors = ['red', 'blue', 'green', 'yellow']
- zone_poses = []
- for color in colors:
- zone_pose = self.get_random_pose(env, zone_size)
- env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color])
- zone_poses.append(zone_pose)
-
- # Add piles of colored blocks.
- block_urdf = 'block/small.urdf'
- block_size = (0.04, 0.04, 0.04)
- piles = []
- for color in colors:
- pile = []
- for _ in range(10):
- block_pose = self.get_random_pose(env, block_size)
- block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color])
- pile.append(block_id)
- piles.append(pile)
-
- # Add goals for each color.
- for i, color in enumerate(colors):
- self.add_goal(objs=piles[i], matches=np.ones((10, 1)), targ_poses=[zone_poses[i]], replace=True,
- rotations=False, metric='zone', params=[(zone_poses[i], zone_size)], step_max_reward=1,
- language_goal=self.lang_template.format(color=color))
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py
deleted file mode 100644
index a44c01831b508da0a5e1ca3720bb437bcea086d1..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py
+++ /dev/null
@@ -1,39 +0,0 @@
-_base_ = [
- '../_base_/models/mask_rcnn_r50_caffe_c4.py',
- '../_base_/datasets/coco_instance.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-# use caffe img_norm
-img_norm_cfg = dict(
- mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- train=dict(pipeline=train_pipeline),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py
deleted file mode 100644
index 732c7ba3f607e2ac68f16acceddd16b1269aa2cf..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,34 +0,0 @@
-_base_ = [
- '../_base_/models/faster_rcnn_r50_fpn.py',
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-model = dict(
- roi_head=dict(
- bbox_head=dict(
- _delete_=True,
- type='SABLHead',
- num_classes=80,
- cls_in_channels=256,
- reg_in_channels=256,
- roi_feat_size=7,
- reg_feat_up_ratio=2,
- reg_pre_kernel=3,
- reg_post_kernel=3,
- reg_pre_num=2,
- reg_post_num=1,
- cls_out_channels=1024,
- reg_offset_out_channels=256,
- reg_cls_out_channels=256,
- num_cls_fcs=1,
- num_reg_fcs=0,
- reg_class_agnostic=True,
- norm_cfg=None,
- bbox_coder=dict(
- type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- loss_bbox_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
- loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
- loss_weight=1.0))))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py
deleted file mode 100644
index d185db95adc61734f11f0dcd7b6c45aa652680b0..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py'
-model = dict(
- pretrained='torchvision://resnet101',
- backbone=dict(type='ResNet', depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py
deleted file mode 100644
index 081cb3732a110bde277abdbed129adce71bbecea..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './pspnet_r50-d8_480x480_40k_pascal_context_59.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/scripts/resample_dataset.py b/spaces/GrandaddyShmax/AudioCraft_Plus/scripts/resample_dataset.py
deleted file mode 100644
index af5288712b8d2cde2d9814c747275e69f6e970c8..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/scripts/resample_dataset.py
+++ /dev/null
@@ -1,207 +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.
-"""Resampling script.
-"""
-import argparse
-from pathlib import Path
-import shutil
-import typing as tp
-
-import submitit
-import tqdm
-
-from audiocraft.data.audio import audio_read, audio_write
-from audiocraft.data.audio_dataset import load_audio_meta, find_audio_files
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.environment import AudioCraftEnvironment
-
-
-def read_txt_files(path: tp.Union[str, Path]):
- with open(args.files_path) as f:
- lines = [line.rstrip() for line in f]
- print(f"Read {len(lines)} in .txt")
- lines = [line for line in lines if Path(line).suffix not in ['.json', '.txt', '.csv']]
- print(f"Filtered and keep {len(lines)} from .txt")
- return lines
-
-
-def read_egs_files(path: tp.Union[str, Path]):
- path = Path(path)
- if path.is_dir():
- if (path / 'data.jsonl').exists():
- path = path / 'data.jsonl'
- elif (path / 'data.jsonl.gz').exists():
- path = path / 'data.jsonl.gz'
- else:
- raise ValueError("Don't know where to read metadata from in the dir. "
- "Expecting either a data.jsonl or data.jsonl.gz file but none found.")
- meta = load_audio_meta(path)
- return [m.path for m in meta]
-
-
-def process_dataset(args, n_shards: int, node_index: int, task_index: tp.Optional[int] = None):
- if task_index is None:
- env = submitit.JobEnvironment()
- task_index = env.global_rank
- shard_index = node_index * args.tasks_per_node + task_index
-
- if args.files_path is None:
- lines = [m.path for m in find_audio_files(args.root_path, resolve=False, progress=True, workers=8)]
- else:
- files_path = Path(args.files_path)
- if files_path.suffix == '.txt':
- print(f"Reading file list from .txt file: {args.files_path}")
- lines = read_txt_files(args.files_path)
- else:
- print(f"Reading file list from egs: {args.files_path}")
- lines = read_egs_files(args.files_path)
-
- total_files = len(lines)
- print(
- f"Total of {total_files} processed with {n_shards} shards. " +
- f"Current idx = {shard_index} -> {total_files // n_shards} files to process"
- )
- for idx, line in tqdm.tqdm(enumerate(lines)):
-
- # skip if not part of this shard
- if idx % n_shards != shard_index:
- continue
-
- path = str(AudioCraftEnvironment.apply_dataset_mappers(line))
- root_path = str(args.root_path)
- if not root_path.endswith('/'):
- root_path += '/'
- assert path.startswith(str(root_path)), \
- f"Mismatch between path and provided root: {path} VS {root_path}"
-
- try:
- metadata_path = Path(path).with_suffix('.json')
- out_path = args.out_path / path[len(root_path):]
- out_metadata_path = out_path.with_suffix('.json')
- out_done_token = out_path.with_suffix('.done')
-
- # don't reprocess existing files
- if out_done_token.exists():
- continue
-
- print(idx, out_path, path)
- mix, sr = audio_read(path)
- mix_channels = args.channels if args.channels is not None and args.channels > 0 else mix.size(0)
- # enforce simple stereo
- out_channels = mix_channels
- if out_channels > 2:
- print(f"Mix has more than two channels: {out_channels}, enforcing 2 channels")
- out_channels = 2
- out_sr = args.sample_rate if args.sample_rate is not None else sr
- out_wav = convert_audio(mix, sr, out_sr, out_channels)
- audio_write(out_path.with_suffix(''), out_wav, sample_rate=out_sr,
- format=args.format, normalize=False, strategy='clip')
- if metadata_path.exists():
- shutil.copy(metadata_path, out_metadata_path)
- else:
- print(f"No metadata found at {str(metadata_path)}")
- out_done_token.touch()
- except Exception as e:
- print(f"Error processing file line: {line}, {e}")
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(description="Resample dataset with SLURM.")
- parser.add_argument(
- "--log_root",
- type=Path,
- default=Path.home() / 'tmp' / 'resample_logs',
- )
- parser.add_argument(
- "--files_path",
- type=Path,
- help="List of files to process, either .txt (one file per line) or a jsonl[.gz].",
- )
- parser.add_argument(
- "--root_path",
- type=Path,
- required=True,
- help="When rewriting paths, this will be the prefix to remove.",
- )
- parser.add_argument(
- "--out_path",
- type=Path,
- required=True,
- help="When rewriting paths, `root_path` will be replaced by this.",
- )
- parser.add_argument("--xp_name", type=str, default="shutterstock")
- parser.add_argument(
- "--nodes",
- type=int,
- default=4,
- )
- parser.add_argument(
- "--tasks_per_node",
- type=int,
- default=20,
- )
- parser.add_argument(
- "--cpus_per_task",
- type=int,
- default=4,
- )
- parser.add_argument(
- "--memory_gb",
- type=int,
- help="Memory in GB."
- )
- parser.add_argument(
- "--format",
- type=str,
- default="wav",
- )
- parser.add_argument(
- "--sample_rate",
- type=int,
- default=32000,
- )
- parser.add_argument(
- "--channels",
- type=int,
- )
- parser.add_argument(
- "--partition",
- default='learnfair',
- )
- parser.add_argument("--qos")
- parser.add_argument("--account")
- parser.add_argument("--timeout", type=int, default=4320)
- parser.add_argument('--debug', action='store_true', help='debug mode (local run)')
- args = parser.parse_args()
- n_shards = args.tasks_per_node * args.nodes
- if args.files_path is None:
- print("Warning: --files_path not provided, not recommended when processing more than 10k files.")
- if args.debug:
- print("Debugging mode")
- process_dataset(args, n_shards=n_shards, node_index=0, task_index=0)
- else:
-
- log_folder = Path(args.log_root) / args.xp_name / '%j'
- print(f"Logging to: {log_folder}")
- log_folder.parent.mkdir(parents=True, exist_ok=True)
- executor = submitit.AutoExecutor(folder=str(log_folder))
- if args.qos:
- executor.update_parameters(slurm_partition=args.partition, slurm_qos=args.qos, slurm_account=args.account)
- else:
- executor.update_parameters(slurm_partition=args.partition)
- executor.update_parameters(
- slurm_job_name=args.xp_name, timeout_min=args.timeout,
- cpus_per_task=args.cpus_per_task, tasks_per_node=args.tasks_per_node, nodes=1)
- if args.memory_gb:
- executor.update_parameters(mem=f'{args.memory_gb}GB')
- jobs = []
- with executor.batch():
- for node_index in range(args.nodes):
- job = executor.submit(process_dataset, args, n_shards=n_shards, node_index=node_index)
- jobs.append(job)
- for job in jobs:
- print(f"Waiting on job {job.job_id}")
- job.results()
diff --git a/spaces/Grezz/generate_human_motion/pyrender/pyrender/platforms/__init__.py b/spaces/Grezz/generate_human_motion/pyrender/pyrender/platforms/__init__.py
deleted file mode 100644
index 7837fd5fdeccab5e48c85e41d20b238ea7396599..0000000000000000000000000000000000000000
--- a/spaces/Grezz/generate_human_motion/pyrender/pyrender/platforms/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""Platforms for generating offscreen OpenGL contexts for rendering.
-
-Author: Matthew Matl
-"""
-
-from .base import Platform
diff --git a/spaces/Harikumar4/MyGenApp/README.md b/spaces/Harikumar4/MyGenApp/README.md
deleted file mode 100644
index 72e700a5be8a984273d5d7c6f0a4cd96b6ff2acb..0000000000000000000000000000000000000000
--- a/spaces/Harikumar4/MyGenApp/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MyGenApp
-emoji: 🏆
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/train.py b/spaces/HarryLee/eCommerceImageCaptioning/train.py
deleted file mode 100644
index d9641f2f9414d52505a1745acbdb5c7b0d7414c8..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/train.py
+++ /dev/null
@@ -1,523 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Train a new model on one or across multiple GPUs.
-"""
-
-import argparse
-import logging
-import math
-import os
-import sys
-from typing import Dict, Optional, Any, List, Tuple, Callable
-
-# We need to setup root logger before importing any fairseq libraries.
-logging.basicConfig(
- format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s',
- datefmt="%Y-%m-%d %H:%M:%S",
- level=os.environ.get("LOGLEVEL", "INFO").upper(),
- stream=sys.stdout,
-)
-logger = logging.getLogger("fairseq_cli.train")
-
-import numpy as np
-import torch
-from fairseq import (
- # checkpoint_utils,
- options,
- quantization_utils,
- tasks,
- utils,
-)
-from fairseq.data import iterators
-from fairseq.data.plasma_utils import PlasmaStore
-from fairseq.dataclass.configs import FairseqConfig
-from fairseq.dataclass.utils import convert_namespace_to_omegaconf
-from fairseq.distributed import fsdp_enable_wrap, fsdp_wrap, utils as distributed_utils
-from fairseq.file_io import PathManager
-from fairseq.logging import meters, metrics, progress_bar
-from fairseq.model_parallel.megatron_trainer import MegatronTrainer
-# from fairseq.trainer import Trainer
-from omegaconf import DictConfig, OmegaConf
-
-from utils import checkpoint_utils
-from trainer import Trainer
-
-
-def main(cfg: FairseqConfig) -> None:
- if isinstance(cfg, argparse.Namespace):
- cfg = convert_namespace_to_omegaconf(cfg)
-
- utils.import_user_module(cfg.common)
-
- if distributed_utils.is_master(cfg.distributed_training) and "job_logging_cfg" in cfg:
- # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126)
- logging.config.dictConfig(OmegaConf.to_container(cfg.job_logging_cfg))
-
- assert (
- cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None
- ), "Must specify batch size either with --max-tokens or --batch-size"
- metrics.reset()
-
- if cfg.common.log_file is not None:
- handler = logging.FileHandler(filename=cfg.common.log_file)
- logger.addHandler(handler)
-
- np.random.seed(cfg.common.seed)
- utils.set_torch_seed(cfg.common.seed)
-
- if distributed_utils.is_master(cfg.distributed_training):
- checkpoint_utils.verify_checkpoint_directory(cfg.checkpoint.save_dir)
-
- # Print args
- logger.info(cfg)
-
- if cfg.checkpoint.write_checkpoints_asynchronously:
- try:
- import iopath # noqa: F401
- except ImportError:
- logging.exception(
- "Asynchronous checkpoint writing is specified but iopath is "
- "not installed: `pip install iopath`"
- )
- return
-
- # Setup task, e.g., translation, language modeling, etc.
- task = tasks.setup_task(cfg.task)
-
- assert cfg.criterion, "Please specify criterion to train a model"
-
- # Build model and criterion
- if cfg.distributed_training.ddp_backend == "fully_sharded":
- with fsdp_enable_wrap(cfg.distributed_training):
- model = fsdp_wrap(task.build_model(cfg.model))
- else:
- model = task.build_model(cfg.model)
- criterion = task.build_criterion(cfg.criterion)
- logger.info(model)
- logger.info("task: {}".format(task.__class__.__name__))
- logger.info("model: {}".format(model.__class__.__name__))
- logger.info("criterion: {}".format(criterion.__class__.__name__))
- logger.info(
- "num. shared model params: {:,} (num. trained: {:,})".format(
- sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False)),
- sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False) and p.requires_grad)
- )
- )
-
- logger.info(
- "num. expert model params: {} (num. trained: {})".format(
- sum(p.numel() for p in model.parameters() if getattr(p, "expert", False)),
- sum(p.numel() for p in model.parameters() if getattr(p, "expert", False) and p.requires_grad),
- )
- )
-
- # Load valid dataset (we load training data below, based on the latest checkpoint)
- # We load the valid dataset AFTER building the model
- # data_utils.raise_if_valid_subsets_unintentionally_ignored(cfg)
- if cfg.dataset.combine_valid_subsets:
- task.load_dataset("valid", combine=True, epoch=1)
- else:
- for valid_sub_split in cfg.dataset.valid_subset.split(","):
- task.load_dataset(valid_sub_split, combine=False, epoch=1)
-
- # (optionally) Configure quantization
- if cfg.common.quantization_config_path is not None:
- quantizer = quantization_utils.Quantizer(
- config_path=cfg.common.quantization_config_path,
- max_epoch=cfg.optimization.max_epoch,
- max_update=cfg.optimization.max_update,
- )
- else:
- quantizer = None
-
- # Build trainer
- if cfg.common.model_parallel_size == 1:
- trainer = Trainer(cfg, task, model, criterion, quantizer)
- else:
- trainer = MegatronTrainer(cfg, task, model, criterion)
- logger.info(
- "training on {} devices (GPUs/TPUs)".format(
- cfg.distributed_training.distributed_world_size
- )
- )
- logger.info(
- "max tokens per device = {} and max sentences per device = {}".format(
- cfg.dataset.max_tokens,
- cfg.dataset.batch_size,
- )
- )
-
- # Load the latest checkpoint if one is available and restore the
- # corresponding train iterator
- extra_state, epoch_itr = checkpoint_utils.load_checkpoint(
- cfg.checkpoint,
- trainer,
- # don't cache epoch iterators for sharded datasets
- disable_iterator_cache=task.has_sharded_data("train"),
- )
- if cfg.common.tpu:
- import torch_xla.core.xla_model as xm
- xm.rendezvous("load_checkpoint") # wait for all workers
-
- max_epoch = cfg.optimization.max_epoch or math.inf
- if max_epoch > 0:
- num_iter_per_epoch = (len(epoch_itr) + cfg.distributed_training.distributed_world_size - 1) \
- // cfg.distributed_training.distributed_world_size
- trainer.lr_reinit(num_iter_per_epoch * max_epoch, trainer.get_num_updates())
- lr = trainer.get_lr()
-
- train_meter = meters.StopwatchMeter()
- train_meter.start()
- while epoch_itr.next_epoch_idx <= max_epoch:
- if lr <= cfg.optimization.stop_min_lr:
- logger.info(
- f"stopping training because current learning rate ({lr}) is smaller "
- "than or equal to minimum learning rate "
- f"(--stop-min-lr={cfg.optimization.stop_min_lr})"
- )
- break
-
- # train for one epoch
- valid_losses, should_stop = train(cfg, trainer, task, epoch_itr)
- if should_stop:
- break
-
- # only use first validation loss to update the learning rate
- lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0])
-
- epoch_itr = trainer.get_train_iterator(
- epoch_itr.next_epoch_idx,
- # sharded data: get train iterator for next epoch
- load_dataset=True,
- # don't cache epoch iterators for sharded datasets
- disable_iterator_cache=task.has_sharded_data("train"),
- )
- train_meter.stop()
- logger.info("done training in {:.1f} seconds".format(train_meter.sum))
-
- # ioPath implementation to wait for all asynchronous file writes to complete.
- if cfg.checkpoint.write_checkpoints_asynchronously:
- logger.info(
- "ioPath PathManager waiting for all asynchronous checkpoint "
- "writes to finish."
- )
- PathManager.async_close()
- logger.info("ioPath PathManager finished waiting.")
-
-
-def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool:
- # skip check if no validation was done in the current epoch
- if valid_loss is None:
- return False
- if cfg.checkpoint.patience <= 0:
- return False
-
- def is_better(a, b):
- return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b
-
- prev_best = getattr(should_stop_early, "best", None)
- if prev_best is None or is_better(valid_loss, prev_best):
- should_stop_early.best = valid_loss
- should_stop_early.num_runs = 0
- return False
- else:
- should_stop_early.num_runs += 1
- if should_stop_early.num_runs >= cfg.checkpoint.patience:
- logger.info(
- "early stop since valid performance hasn't improved for last {} runs".format(
- cfg.checkpoint.patience
- )
- )
- return True
- else:
- return False
-
-
-@metrics.aggregate("train")
-def train(
- cfg: DictConfig, trainer: Trainer, task: tasks.FairseqTask, epoch_itr
-) -> Tuple[List[Optional[float]], bool]:
- """Train the model for one epoch and return validation losses."""
- # Initialize data iterator
- itr = epoch_itr.next_epoch_itr(
- fix_batches_to_gpus=cfg.distributed_training.fix_batches_to_gpus,
- shuffle=(epoch_itr.next_epoch_idx > cfg.dataset.curriculum),
- )
- update_freq = (
- cfg.optimization.update_freq[epoch_itr.epoch - 1]
- if epoch_itr.epoch <= len(cfg.optimization.update_freq)
- else cfg.optimization.update_freq[-1]
- )
- itr = iterators.GroupedIterator(itr, update_freq)
- if cfg.common.tpu:
- itr = utils.tpu_data_loader(itr)
- progress = progress_bar.progress_bar(
- itr,
- log_format=cfg.common.log_format,
- log_file=cfg.common.log_file,
- log_interval=cfg.common.log_interval,
- epoch=epoch_itr.epoch,
- tensorboard_logdir=(
- cfg.common.tensorboard_logdir
- if distributed_utils.is_master(cfg.distributed_training)
- else None
- ),
- default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
- wandb_project=(
- cfg.common.wandb_project
- if distributed_utils.is_master(cfg.distributed_training)
- else None
- ),
- wandb_run_name=os.environ.get(
- "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
- ),
- azureml_logging=(
- cfg.common.azureml_logging
- if distributed_utils.is_master(cfg.distributed_training)
- else False
- ),
- )
- progress.update_config(_flatten_config(cfg))
-
- trainer.begin_epoch(epoch_itr.epoch)
-
- valid_subsets = cfg.dataset.valid_subset.split(",")
- should_stop = False
- num_updates = trainer.get_num_updates()
- logger.info("Start iterating over samples")
- for i, samples in enumerate(progress):
- with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function(
- "train_step-%d" % i
- ):
- log_output = trainer.train_step(samples)
-
- if log_output is not None: # not OOM, overflow, ...
- # log mid-epoch stats
- num_updates = trainer.get_num_updates()
- if num_updates % cfg.common.log_interval == 0:
- stats = get_training_stats(metrics.get_smoothed_values("train_inner"))
- progress.log(stats, tag="train_inner", step=num_updates)
-
- # reset mid-epoch stats after each log interval
- # the end-of-epoch stats will still be preserved
- metrics.reset_meters("train_inner")
-
- end_of_epoch = not itr.has_next()
- valid_losses, should_stop = validate_and_save(
- cfg, trainer, task, epoch_itr, valid_subsets, end_of_epoch
- )
-
- if should_stop:
- break
-
- # log end-of-epoch stats
- logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch))
- stats = get_training_stats(metrics.get_smoothed_values("train"))
- progress.print(stats, tag="train", step=num_updates)
-
- # reset epoch-level meters
- metrics.reset_meters("train")
- return valid_losses, should_stop
-
-
-def _flatten_config(cfg: DictConfig):
- config = OmegaConf.to_container(cfg)
- # remove any legacy Namespaces and replace with a single "args"
- namespace = None
- for k, v in list(config.items()):
- if isinstance(v, argparse.Namespace):
- namespace = v
- del config[k]
- if namespace is not None:
- config["args"] = vars(namespace)
- return config
-
-
-def validate_and_save(
- cfg: DictConfig,
- trainer: Trainer,
- task: tasks.FairseqTask,
- epoch_itr,
- valid_subsets: List[str],
- end_of_epoch: bool,
-) -> Tuple[List[Optional[float]], bool]:
- num_updates = trainer.get_num_updates()
- max_update = cfg.optimization.max_update or math.inf
-
- # Stopping conditions (and an additional one based on validation loss later
- # on)
- should_stop = False
- if num_updates >= max_update:
- should_stop = True
- logger.info(
- f"Stopping training due to "
- f"num_updates: {num_updates} >= max_update: {max_update}"
- )
-
- training_time_hours = trainer.cumulative_training_time() / (60 * 60)
- if (
- cfg.optimization.stop_time_hours > 0
- and training_time_hours > cfg.optimization.stop_time_hours
- ):
- should_stop = True
- logger.info(
- f"Stopping training due to "
- f"cumulative_training_time: {training_time_hours} > "
- f"stop_time_hours: {cfg.optimization.stop_time_hours} hour(s)"
- )
-
- do_save = (
- (end_of_epoch and epoch_itr.epoch % cfg.checkpoint.save_interval == 0)
- or should_stop
- or (
- cfg.checkpoint.save_interval_updates > 0
- and num_updates > 0
- and num_updates % cfg.checkpoint.save_interval_updates == 0
- and num_updates >= cfg.dataset.validate_after_updates
- )
- )
- do_validate = (
- (not end_of_epoch and do_save) # validate during mid-epoch saves
- or (end_of_epoch and epoch_itr.epoch % cfg.dataset.validate_interval == 0)
- or should_stop
- or (
- cfg.dataset.validate_interval_updates > 0
- and num_updates > 0
- and num_updates % cfg.dataset.validate_interval_updates == 0
- )
- ) and not cfg.dataset.disable_validation and num_updates >= cfg.dataset.validate_after_updates
-
- # Validate
- valid_losses = [None]
- if do_validate:
- valid_losses = validate(cfg, trainer, task, epoch_itr, valid_subsets)
-
- should_stop |= should_stop_early(cfg, valid_losses[0])
-
- # Save checkpoint
- if do_save or should_stop:
- checkpoint_utils.save_checkpoint(
- cfg.checkpoint, trainer, epoch_itr, valid_losses[0]
- )
-
- return valid_losses, should_stop
-
-
-def get_training_stats(stats: Dict[str, Any]) -> Dict[str, Any]:
- stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0)
- return stats
-
-
-def validate(
- cfg: DictConfig,
- trainer: Trainer,
- task: tasks.FairseqTask,
- epoch_itr,
- subsets: List[str],
-) -> List[Optional[float]]:
- """Evaluate the model on the validation set(s) and return the losses."""
-
- if cfg.dataset.fixed_validation_seed is not None:
- # set fixed seed for every validation
- utils.set_torch_seed(cfg.dataset.fixed_validation_seed)
-
- trainer.begin_valid_epoch(epoch_itr.epoch)
- valid_losses = []
- for subset in subsets:
- logger.info('begin validation on "{}" subset'.format(subset))
-
- # Initialize data iterator
- itr = trainer.get_valid_iterator(subset).next_epoch_itr(
- shuffle=False, set_dataset_epoch=False # use a fixed valid set
- )
- if cfg.common.tpu:
- itr = utils.tpu_data_loader(itr)
- progress = progress_bar.progress_bar(
- itr,
- log_format=cfg.common.log_format,
- log_interval=cfg.common.log_interval,
- epoch=epoch_itr.epoch,
- prefix=f"valid on '{subset}' subset",
- tensorboard_logdir=(
- cfg.common.tensorboard_logdir
- if distributed_utils.is_master(cfg.distributed_training)
- else None
- ),
- default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
- wandb_project=(
- cfg.common.wandb_project
- if distributed_utils.is_master(cfg.distributed_training)
- else None
- ),
- wandb_run_name=os.environ.get(
- "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
- ),
- )
-
- # create a new root metrics aggregator so validation metrics
- # don't pollute other aggregators (e.g., train meters)
- with metrics.aggregate(new_root=True) as agg:
- for i, sample in enumerate(progress):
- if cfg.dataset.max_valid_steps is not None and i > cfg.dataset.max_valid_steps:
- break
- trainer.valid_step(sample)
-
- # log validation stats
- if hasattr(task, 'get_valid_stats'):
- stats = task.get_valid_stats(cfg, trainer, agg.get_smoothed_values())
- else:
- stats = agg.get_smoothed_values()
- stats = get_valid_stats(cfg, trainer, stats)
-
- if hasattr(task, "post_validate"):
- task.post_validate(trainer.get_model(), stats, agg)
-
- progress.print(stats, tag=subset, step=trainer.get_num_updates())
-
- valid_losses.append(stats[cfg.checkpoint.best_checkpoint_metric])
- return valid_losses
-
-
-def get_valid_stats(
- cfg: DictConfig, trainer: Trainer, stats: Dict[str, Any]
-) -> Dict[str, Any]:
- stats["num_updates"] = trainer.get_num_updates()
- if hasattr(checkpoint_utils.save_checkpoint, "best"):
- key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric)
- best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min
- stats[key] = best_function(
- checkpoint_utils.save_checkpoint.best,
- stats[cfg.checkpoint.best_checkpoint_metric],
- )
- return stats
-
-
-def cli_main(
- modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None
-) -> None:
- parser = options.get_training_parser()
- args = options.parse_args_and_arch(parser, modify_parser=modify_parser)
-
- cfg = convert_namespace_to_omegaconf(args)
-
- if cfg.common.use_plasma_view:
- server = PlasmaStore(path=cfg.common.plasma_path)
- logger.info(f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}")
-
- if args.profile:
- with torch.cuda.profiler.profile():
- with torch.autograd.profiler.emit_nvtx():
- distributed_utils.call_main(cfg, main)
- else:
- distributed_utils.call_main(cfg, main)
-
- # if cfg.common.use_plasma_view:
- # server.server.kill()
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/tts_infer/num_to_word_on_sent.py b/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/tts_infer/num_to_word_on_sent.py
deleted file mode 100644
index de571c2be63fa467491d01daf0e2f38dada67de9..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/tts_infer/num_to_word_on_sent.py
+++ /dev/null
@@ -1,1319 +0,0 @@
-import re
-import string
-
-# ----------------------------- indic_num.py -----------------------------
-supported_lang = {"en", "hi", "gu", "mr", "bn", "te", "ta", "kn", "or", "pa"}
-# supported_lang = {'eng', 'hin', 'guj', 'mar', 'ben', 'tel', 'tam', 'kan', 'ori', 'pan'} # Three alphabet lang code
-
-all_num = {
- "en": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
- "hi": ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"],
- "gu": ["૦", "૧", "૨", "૩", "૪", "૫", "૬", "૭", "૮", "૯"],
- "mr": ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"],
- "bn": ["০", "১", "২", "৩", "৪", "৫", "৬", "৭", "৮", "৯"],
- "te": ["౦", "౧", "౨", "౩", "౪", "౫", "౬", "౭", "౮", "౯"],
- "ta": ["0", "௧", "௨", "௩", "௪", "௫", "௬", "௭", "௮", "௯", "௰"],
- "kn": ["೦", "೧", "೨", "೩", "೪", "೫", "೬", "೭", "೮", "೯"],
- "or": ["୦", "୧", "୨", "୩", "୪", "୫", "୬", "୭", "୮", "୯"],
- "pa": ["੦", "੧", "੨", "੩", "੪", "੫", "੬", "੭", "੮", "੯"],
-}
-
-num_dict = dict()
-num_dict["en"] = {
- "0": "zero",
- "1": "one",
- "2": "two",
- "3": "three",
- "4": "four",
- "5": "five",
- "6": "six",
- "7": "seven",
- "8": "eight",
- "9": "nine",
- "10": "ten",
- "11": "eleven",
- "12": "twelve",
- "13": "thirteen",
- "14": "fourteen",
- "15": "fifteen",
- "16": "sixteen",
- "17": "seventeen",
- "18": "eighteen",
- "19": "nineteen",
- "20": "twenty",
- "21": "twenty-one",
- "22": "twenty-two",
- "23": "twenty-three",
- "24": "twenty-four",
- "25": "twenty-five",
- "26": "twenty-six",
- "27": "twenty-seven",
- "28": "twenty-eight",
- "29": "twenty-nine",
- "30": "thirty",
- "31": "thirty-one",
- "32": "thirty-two",
- "33": "thirty-three",
- "34": "thirty-four",
- "35": "thirty-five",
- "36": "thirty-six",
- "37": "thirty-seven",
- "38": "thirty-eight",
- "39": "thirty-nine",
- "40": "forty",
- "41": "forty-one",
- "42": "forty-two",
- "43": "forty-three",
- "44": "forty-four",
- "45": "forty-five",
- "46": "forty-six",
- "47": "forty-seven",
- "48": "forty-eight",
- "49": "forty-nine",
- "50": "fifty",
- "51": "fifty-one",
- "52": "fifty-two",
- "53": "fifty-three",
- "54": "fifty-four",
- "55": "fifty-five",
- "56": "fifty-six",
- "57": "fifty-seven",
- "58": "fifty-eight",
- "59": "fifty-nine",
- "60": "sixty",
- "61": "sixty-one",
- "62": "sixty-two",
- "63": "sixty-three",
- "64": "sixty-four",
- "65": "sixty-five",
- "66": "sixty-six",
- "67": "sixty-seven",
- "68": "sixty-eight",
- "69": "sixty-nine",
- "70": "seventy",
- "71": "seventy-one",
- "72": "seventy-two",
- "73": "seventy-three",
- "74": "seventy-four",
- "75": "seventy-five",
- "76": "seventy-six",
- "77": "seventy-seven",
- "78": "seventy-eight",
- "79": "seventy-nine",
- "80": "eighty",
- "81": "eighty-one",
- "82": "eighty-two",
- "83": "eighty-three",
- "84": "eighty-four",
- "85": "eighty-five",
- "86": "eighty-six",
- "87": "eighty-seven",
- "88": "eighty-eight",
- "89": "eighty-nine",
- "90": "ninety",
- "91": "ninety-one",
- "92": "ninety-two",
- "93": "ninety-three",
- "94": "ninety-four",
- "95": "ninety-five",
- "96": "ninety-six",
- "97": "ninety-seven",
- "98": "ninety-eight",
- "99": "ninety-nine",
- "100": "hundred",
- "1000": "thousand",
- "100000": "lac",
- "10000000": "crore",
- "1000000000": "arab",
-} # English-India
-num_dict["hi"] = {
- "0": "शून्य",
- "1": "एक",
- "2": "दो",
- "3": "तीन",
- "4": "चार",
- "5": "पाँच",
- "6": "छः",
- "7": "सात",
- "8": "आठ",
- "9": "नौ",
- "10": "दस",
- "11": "ग्यारह",
- "12": "बारह",
- "13": "तेरह",
- "14": "चौदह",
- "15": "पंद्रह",
- "16": "सोलह",
- "17": "सत्रह",
- "18": "अट्ठारह",
- "19": "उन्नीस",
- "20": "बीस",
- "21": "इक्कीस",
- "22": "बाईस",
- "23": "तेईस",
- "24": "चौबिस",
- "25": "पच्चीस",
- "26": "छब्बीस",
- "27": "सत्ताईस",
- "28": "अट्ठाईस",
- "29": "उनतीस",
- "30": "तीस",
- "31": "इकतीस",
- "32": "बत्तीस",
- "33": "तैंतीस",
- "34": "चौंतीस",
- "35": "पैंतीस",
- "36": "छत्तीस",
- "37": "सैंतीस",
- "38": "अड़तीस",
- "39": "उनतालीस",
- "40": "चालीस",
- "41": "इकतालीस",
- "42": "बयालीस",
- "43": "तैंतालीस",
- "44": "चौंतालीस",
- "45": "पैंतालीस",
- "46": "छियालीस",
- "47": "सैंतालीस",
- "48": "अड़तालीस",
- "49": "उनचास",
- "50": "पचास",
- "51": "इक्यावन",
- "52": "बावन",
- "53": "तिरेपन",
- "54": "चौवन",
- "55": "पचपन",
- "56": "छप्पन",
- "57": "सत्तावन",
- "58": "अट्ठावन",
- "59": "उनसठ",
- "60": "साठ",
- "61": "इकसठ",
- "62": "बासठ",
- "63": "तिरेसठ",
- "64": "चौंसठ",
- "65": "पैंसठ",
- "66": "छयासठ",
- "67": "सरसठ",
- "68": "अड़सठ",
- "69": "उनहत्तर",
- "70": "सत्तर",
- "71": "इकहत्तर",
- "72": "बहत्तर",
- "73": "तिहत्तर",
- "74": "चौहत्तर",
- "75": "पचहत्तर",
- "76": "छिहत्तर",
- "77": "सतहत्तर",
- "78": "अठहत्तर",
- "79": "उन्यासी",
- "80": "अस्सी",
- "81": "इक्यासी",
- "82": "बयासी",
- "83": "तिरासी",
- "84": "चौरासी",
- "85": "पचासी",
- "86": "छियासी",
- "87": "सत्तासी",
- "88": "अठासी",
- "89": "नवासी",
- "90": "नब्बे",
- "91": "इक्यानवे",
- "92": "बानवे",
- "93": "तिरानवे",
- "94": "चौरानवे",
- "95": "पचानवे",
- "96": "छियानवे",
- "97": "सत्तानवे",
- "98": "अट्ठानवे",
- "99": "निन्यानवे",
- "100": "सौ",
- "1000": "हज़ार",
- "100000": "लाख",
- "10000000": "करोड़",
- "1000000000": "अरब",
-} # Hindi
-num_dict["gu"] = {
- "0": "શૂન્ય",
- "1": "એક",
- "2": "બે",
- "3": "ત્રણ",
- "4": "ચાર",
- "5": "પાંચ",
- "6": "છ",
- "7": "સાત",
- "8": "આઠ",
- "9": "નવ",
- "10": "દસ",
- "11": "અગિયાર",
- "12": "બાર",
- "13": "તેર",
- "14": "ચૌદ",
- "15": "પંદર",
- "16": "સોળ",
- "17": "સત્તર",
- "18": "અઢાર",
- "19": "ઓગણિસ",
- "20": "વીસ",
- "21": "એકવીસ",
- "22": "બાવીસ",
- "23": "તેવીસ",
- "24": "ચોવીસ",
- "25": "પચ્ચીસ",
- "26": "છવીસ",
- "27": "સત્તાવીસ",
- "28": "અઠ્ઠાવીસ",
- "29": "ઓગણત્રીસ",
- "30": "ત્રીસ",
- "31": "એકત્રીસ",
- "32": "બત્રીસ",
- "33": "તેત્રીસ",
- "34": "ચોત્રીસ",
- "35": "પાંત્રીસ",
- "36": "છત્રીસ",
- "37": "સડત્રીસ",
- "38": "અડત્રીસ",
- "39": "ઓગણચાલીસ",
- "40": "ચાલીસ",
- "41": "એકતાલીસ",
- "42": "બેતાલીસ",
- "43": "ત્રેતાલીસ",
- "44": "ચુંમાલીસ",
- "45": "પિસ્તાલીસ",
- "46": "છેતાલીસ",
- "47": "સુડતાલીસ",
- "48": "અડતાલીસ",
- "49": "ઓગણપચાસ",
- "50": "પચાસ",
- "51": "એકાવન",
- "52": "બાવન",
- "53": "ત્રેપન",
- "54": "ચોપન",
- "55": "પંચાવન",
- "56": "છપ્પન",
- "57": "સત્તાવન",
- "58": "અઠ્ઠાવન",
- "59": "ઓગણસાઠ",
- "60": "સાઈઠ",
- "61": "એકસઠ",
- "62": "બાસઠ",
- "63": "ત્રેસઠ",
- "64": "ચોસઠ",
- "65": "પાંસઠ",
- "66": "છાસઠ",
- "67": "સડસઠ",
- "68": "અડસઠ",
- "69": "અગણોસિત્તેર",
- "70": "સિત્તેર",
- "71": "એકોતેર",
- "72": "બોતેર",
- "73": "તોતેર",
- "74": "ચુમોતેર",
- "75": "પંચોતેર",
- "76": "છોતેર",
- "77": "સિત્યોતેર",
- "78": "ઇઠ્યોતેર",
- "79": "ઓગણાએંસી",
- "80": "એંસી",
- "81": "એક્યાસી",
- "82": "બ્યાસી",
- "83": "ત્યાસી",
- "84": "ચોર્યાસી",
- "85": "પંચાસી",
- "86": "છ્યાસી",
- "87": "સિત્યાસી",
- "88": "ઈઠ્યાસી",
- "89": "નેવ્યાસી",
- "90": "નેવું",
- "91": "એકાણું",
- "92": "બાણું",
- "93": "ત્રાણું",
- "94": "ચોરાણું",
- "95": "પંચાણું",
- "96": "છન્નું",
- "97": "સત્તાણું",
- "98": "અઠ્ઠાણું",
- "99": "નવ્વાણું",
- "100": "સો",
- "1000": "હજાર",
- "100000": "લાખ",
- "1000000": "દસ લાખ",
- "10000000": "કરોડ઼",
-} # Gujarati
-num_dict["mr"] = {
- "0": "शून्य",
- "1": "एक",
- "2": "दोन",
- "3": "तीन",
- "4": "चार",
- "5": "पाच",
- "6": "सहा",
- "7": "सात",
- "8": "आठ",
- "9": "नऊ",
- "10": "दहा",
- "11": "अकरा",
- "12": "बारा",
- "13": "तेरा",
- "14": "चौदा",
- "15": "पंधरा",
- "16": "सोळा",
- "17": "सतरा",
- "18": "अठरा",
- "19": "एकोणीस",
- "20": "वीस",
- "21": "एकवीस",
- "22": "बावीस",
- "23": "तेवीस",
- "24": "चोवीस",
- "25": "पंचवीस",
- "26": "सव्वीस",
- "27": "सत्तावीस",
- "28": "अठ्ठावीस",
- "29": "एकोणतीस",
- "30": "तीस",
- "31": "एकतीस",
- "32": "बत्तीस",
- "33": "तेहेतीस",
- "34": "चौतीस",
- "35": "पस्तीस",
- "36": "छत्तीस",
- "37": "सदतीस",
- "38": "अडतीस",
- "39": "एकोणचाळीस",
- "40": "चाळीस",
- "41": "एक्केचाळीस",
- "42": "बेचाळीस",
- "43": "त्रेचाळीस",
- "44": "चव्वेचाळीस",
- "45": "पंचेचाळीस",
- "46": "सेहेचाळीस",
- "47": "सत्तेचाळीस",
- "48": "अठ्ठेचाळीस",
- "49": "एकोणपन्नास",
- "50": "पन्नास",
- "51": "एक्कावन्न",
- "52": "बावन्न",
- "53": "त्रेपन्न",
- "54": "चोपन्न",
- "55": "पंचावन्न",
- "56": "छप्पन्न",
- "57": "सत्तावन्न",
- "58": "अठ्ठावन्न",
- "59": "एकोणसाठ",
- "60": "साठ",
- "61": "एकसष्ठ",
- "62": "बासष्ठ",
- "63": "त्रेसष्ठ",
- "64": "चौसष्ठ",
- "65": "पासष्ठ",
- "66": "सहासष्ठ",
- "67": "सदुसष्ठ",
- "68": "अडुसष्ठ",
- "69": "एकोणसत्तर",
- "70": "सत्तर",
- "71": "एक्काहत्तर",
- "72": "बाहत्तर",
- "73": "त्र्याहत्तर",
- "74": "चौर्याहत्तर",
- "75": "पंच्याहत्तर",
- "76": "शहात्तर",
- "77": "सत्याहत्तर",
- "78": "अठ्ठ्याहत्तर",
- "79": "एकोण ऐंशी",
- "80": "ऐंशी",
- "81": "एक्क्याऐंशी",
- "82": "ब्याऐंशी",
- "83": "त्र्याऐंशी",
- "84": "चौऱ्याऐंशी",
- "85": "पंच्याऐंशी",
- "86": "शहाऐंशी",
- "87": "सत्त्याऐंशी",
- "88": "अठ्ठ्याऐंशी",
- "89": "एकोणनव्वद",
- "90": "नव्वद",
- "91": "एक्क्याण्णव",
- "92": "ब्याण्णव",
- "93": "त्र्याण्णव",
- "94": "चौऱ्याण्णव",
- "95": "पंच्याण्णव",
- "96": "शहाण्णव",
- "97": "सत्त्याण्णव",
- "98": "अठ्ठ्याण्णव",
- "99": "नव्व्याण्णव",
- "100": "शे",
- "1000": "हजार",
- "100000": "लाख",
- "10000000": "कोटी",
- "1000000000": "अब्ज",
-} # Marathi
-num_dict["bn"] = {
- "0": "শূন্য",
- "1": "এক",
- "2": "দুই",
- "3": "তিন",
- "4": "চার",
- "5": "পাঁচ",
- "6": "ছয়",
- "7": "সাত",
- "8": "আট",
- "9": "নয়",
- "10": "দশ",
- "11": "এগার",
- "12": "বার",
- "13": "তের",
- "14": "চৌদ্দ",
- "15": "পনের",
- "16": "ষোল",
- "17": "সতের",
- "18": "আঠার",
- "19": "ঊনিশ",
- "20": "বিশ",
- "21": "একুশ",
- "22": "বাইশ",
- "23": "তেইশ",
- "24": "চব্বিশ",
- "25": "পঁচিশ",
- "26": "ছাব্বিশ",
- "27": "সাতাশ",
- "28": "আঠাশ",
- "29": "ঊনত্রিশ",
- "30": "ত্রিশ",
- "31": "একত্রিশ",
- "32": "বত্রিশ",
- "33": "তেত্রিশ",
- "34": "চৌত্রিশ",
- "35": "পঁয়ত্রিশ",
- "36": "ছত্রিশ",
- "37": "সাঁইত্রিশ",
- "38": "আটত্রিশ",
- "39": "ঊনচল্লিশ",
- "40": "চল্লিশ",
- "41": "একচল্লিশ",
- "42": "বিয়াল্লিশ",
- "43": "তেতাল্লিশ",
- "44": "চুয়াল্লিশ",
- "45": "পঁয়তাল্লিশ",
- "46": "ছেচল্লিশ",
- "47": "সাতচল্লিশ",
- "48": "আটচল্লিশ",
- "49": "ঊনপঞ্চাশ",
- "50": "পঞ্চাশ",
- "51": "একান্ন",
- "52": "বায়ান্ন",
- "53": "তিপ্পান্ন",
- "54": "চুয়ান্ন",
- "55": "পঞ্চান্ন",
- "56": "ছাপ্পান্ন",
- "57": "সাতান্ন",
- "58": "আটান্ন",
- "59": "ঊনষাট",
- "60": "ষাট",
- "61": "একষট্টি",
- "62": "বাষট্টি",
- "63": "তেষট্টি",
- "64": "চৌষট্টি",
- "65": "পঁয়ষট্টি",
- "66": "ছেষট্টি",
- "67": "সাতষট্টি",
- "68": "আটষট্টি",
- "69": "ঊনসত্তর",
- "70": "সত্তর",
- "71": "একাত্তর",
- "72": "বাহাত্তর",
- "73": "তিয়াত্তর",
- "74": "চুয়াত্তর",
- "75": "পঁচাত্তর",
- "76": "ছিয়াত্তর",
- "77": "সাতাত্তর",
- "78": "আটাত্তর",
- "79": "ঊনআশি",
- "80": "আশি",
- "81": "একাশি",
- "82": "বিরাশি",
- "83": "তিরাশি",
- "84": "চুরাশি",
- "85": "পঁচাশি",
- "86": "ছিয়াশি",
- "87": "সাতাশি",
- "88": "আটাশি",
- "89": "ঊননব্বই",
- "90": "নব্বই",
- "91": "একানব্বই",
- "92": "বিরানব্বই",
- "93": "তিরানব্বই",
- "94": "চুরানব্বই",
- "95": "পঁচানব্বই",
- "96": "ছিয়ানব্বই",
- "97": "সাতানব্বই",
- "98": "আটানব্বই",
- "99": "নিরানব্বই",
- "100": "শো",
- "1000": "হাজার",
- "100000": "লাখ",
- "10000000": "কোটি",
- "1000000000": "একশ’ কোটি",
-} # Bengali
-num_dict["te"] = {
- "0": "సున్నా",
- "1": "ఒకటి",
- "2": "రెండు",
- "3": "మూడు",
- "4": "నాలుగు",
- "5": "ఐదు",
- "6": "ఆరు",
- "7": "ఏడు",
- "8": "ఎనిమిది",
- "9": "తొమ్మిది",
- "10": "పది",
- "11": "పదకొండు",
- "12": "పన్నెండు",
- "13": "పదమూడు",
- "14": "పద్నాలుగు",
- "15": "పదిహేను",
- "16": "పదహారు",
- "17": "పదిహేడు",
- "18": "పద్దెనిమిది",
- "19": "పందొమ్మిది",
- "20": "ఇరవై",
- "21": "ఇరవై ఒకటి",
- "22": "ఇరవై రెండు",
- "23": "ఇరవై మూడు",
- "24": "ఇరవై నాలుగు",
- "25": "ఇరవై ఐదు",
- "26": "ఇరవై ఆరు",
- "27": "ఇరవై ఏడు",
- "28": "ఇరవై ఎనిమిది",
- "29": "ఇరవై తొమ్మిది",
- "30": "ముప్పై",
- "31": "ముప్పై ఒకటి",
- "32": "ముప్పై రెండు",
- "33": "ముప్పై మూడు",
- "34": "ముప్పై నాలుగు",
- "35": "ముప్పై ఐదు",
- "36": "ముప్పై ఆరు",
- "37": "ముప్పై ఏడు",
- "38": "ముప్పై ఎనిమిది",
- "39": "ముప్పై తొమ్మిది",
- "40": "నలభై",
- "41": "నలభై ఒకటి",
- "42": "నలభై రెండు",
- "43": "నలభై మూడు",
- "44": "నలభై నాలుగు",
- "45": "నలభై ఐదు",
- "46": "నలభై ఆరు",
- "47": "నలభై ఏడు",
- "48": "నలభై ఎనిమిది",
- "49": "నలభై తొమ్మిది",
- "50": "యాభై",
- "51": "యాభై ఒకటి",
- "52": "యాభై రెండు",
- "53": "యాభై మూడు",
- "54": "యాభై నాలుగు",
- "55": "యాభై ఐదు",
- "56": "యాభై ఆరు",
- "57": "యాభై ఏడు",
- "58": "యాభై ఎనిమిది",
- "59": "యాభై తొమ్మిది",
- "60": "అరవై",
- "61": "అరవై ఒకటి",
- "62": "అరవై రెండు",
- "63": "అరవై మూడు",
- "64": "అరవై నాలుగు",
- "65": "అరవై ఐదు",
- "66": "అరవై ఆరు",
- "67": "అరవై ఏడు",
- "68": "అరవై ఎనిమిది",
- "69": "అరవై తొమ్మిది",
- "70": "డెబ్బై",
- "71": "డెబ్బై ఒకటి",
- "72": "డెబ్బై రెండు",
- "73": "డెబ్బై మూడు",
- "74": "డెబ్బై నాలుగు",
- "75": "డెబ్బై ఐదు",
- "76": "డెబ్బై ఆరు",
- "77": "డెబ్బై ఏడు",
- "78": "డెబ్బై ఎనిమిది",
- "79": "డెబ్బై తొమ్మిది",
- "80": "ఎనభై",
- "81": "ఎనభై ఒకటి",
- "82": "ఎనభై రెండు",
- "83": "ఎనభై మూడు",
- "84": "ఎనభై నాలుగు",
- "85": "ఎనభై ఐదు",
- "86": "ఎనభై ఆరు",
- "87": "ఎనభై ఏడు",
- "88": "ఎనభై ఎనిమిది",
- "89": "ఎనభై తొమ్మిది",
- "90": "తొంభై",
- "91": "తొంభై ఒకటి",
- "92": "తొంభై రెండు",
- "93": "తొంభై మూడు",
- "94": "తొంభై నాలుగు",
- "95": "తొంభై ఐదు",
- "96": "తొంభై ఆరు",
- "97": "తొంభై ఏడు",
- "98": "తొంభై ఎనిమిది",
- "99": "తొంభై తొమ్మిది",
- "100": "వందల",
- "1000": "వేల",
- "100000": "లక్షల",
- "10000000": "కోట్ల",
- "1000000000": "బిలియన్",
-} # Telugu
-num_dict["ta"] = {
- "0": "பூஜ்ஜியம்",
- "1": "ஒன்று",
- "2": "இரண்டு",
- "3": "மூன்று",
- "4": "நான்கு",
- "5": "ஐந்து",
- "6": "ஆறு",
- "7": "ஏழு",
- "8": "எட்டு",
- "9": "ஒன்பது",
- "10": "பத்து",
- "11": "பதினொன்று",
- "12": "பன்னிரண்டு",
- "13": "பதிமூன்று",
- "14": "பதினான்கு",
- "15": "பதினைந்து",
- "16": "பதினாறு",
- "17": "பதினேழு",
- "18": "பதினெட்டு",
- "19": "பத்தொன்பது",
- "20": "இருபது",
- "21": "இருபது ஒன்று",
- "22": "இருபத்து இரண்டு",
- "23": "இருபத்து மூன்று",
- "24": "இருபத்து நான்கு",
- "25": "இருபத்து ஐந்து",
- "26": "இருபத்து ஆறு",
- "27": "இருபத்து ஏழு",
- "28": "இருபத்து எட்டு",
- "29": "இருபத்து ஒன்பது",
- "30": "முப்பது",
- "31": "முப்பத்து ஒன்று",
- "32": "முப்பத்து இரண்டு",
- "33": "முப்பத்து மூன்று",
- "34": "முப்பத்து நான்கு",
- "35": "முப்பத்து ஐந்து",
- "36": "முப்பத்து ஆறு",
- "37": "முப்பத்து ஏழு",
- "38": "முப்பத்து எட்டு",
- "39": "முப்பத்து ஒன்பது",
- "40": "நாற்பது",
- "41": "நாற்பத்து ஒன்று",
- "42": "நாற்பத்து இரண்டு",
- "43": "நாற்பத்து மூன்று",
- "44": "நாற்பத்து நான்கு",
- "45": "நாற்பத்து ஐந்து",
- "46": "நாற்பத்து ஆறு",
- "47": " நாற்பத்து ஏழு",
- "48": "நாற்பத்து எட்டு",
- "49": "நாற்பத்து ஒன்பது",
- "50": "ஐம்பது",
- "51": "ஐம்பத்து ஒன்று",
- "52": "ஐம்பத்து இரண்டு",
- "53": "ஐம்பத்து மூன்று",
- "54": "ஐம்பத்து நான்கு",
- "55": "ஐம்பத்து ஐந்து",
- "56": "ஐம்பத்து ஆறு",
- "57": "ஐம்பத்து ஏழு",
- "58": "ஐம்பத்து எட்டு",
- "59": "ஐம்பத்து ஒன்பது",
- "60": "அறுபது",
- "61": "அறுபத்து ஒன்று",
- "62": "அறுபத்து இரண்டு",
- "63": "அறுபத்து மூன்று",
- "64": "அறுபத்து நான்கு",
- "65": "அறுபத்து ஐந்து",
- "66": "அறுபத்து ஆறு",
- "67": "அறுபத்து ஏழு",
- "68": "அறுபத்து எட்டு",
- "69": "அறுபத்து ஒன்பது",
- "70": "எழுபது",
- "71": "எழுபத்தி ஒன்று",
- "72": "எழுபத்தி இரண்டு",
- "73": "எழுபத்தி முச்சக்கர",
- "74": "எழுபத்தி நான்கு",
- "75": "எழுபத்தி ஐந்து",
- "76": "எழுபத்தி ஆறு",
- "77": "எழுபத்தி ஏழு",
- "78": "எழுபத்தி எட்டு",
- "79": "எழுபத்தி ஒன்பது",
- "80": "எண்பது",
- "81": "எண்பத்தியொன்று",
- "82": "எண்பத்திரண்டு",
- "83": "எண்பத்திமூன்று",
- "84": "என்பதினான்கு",
- "85": "என்பதினைந்து",
- "86": "எண்பத்திஆறு",
- "87": "எண்பத்திஏழு",
- "88": "எண்பத்தியெட்டு",
- "89": "எண்பத்தியொன்பது",
- "90": "தொன்னூறு",
- "91": "தொண்ணூற்றியொன்று",
- "92": "தொண்ணூற்றிரண்டு",
- "93": "தொண்ணூற்றிமூன்று",
- "94": "தொண்ணூற்றிநான்கு",
- "95": "தொண்ணூற்றிஐந்து",
- "96": "தொண்ணூற்றியாறு",
- "97": "தொண்ணூற்றியேழு",
- "98": "தொண்ணூற்றியெட்டு",
- "99": "தொண்ணூற்றிஒன்பது",
- "100": "நூறு",
- "1000": "ஆயிரம்",
- "100000": "இலட்சம்",
- "10000000": "கோடி",
- "1000000000": "பில்லியன்",
-} # Tamil
-num_dict["kn"] = {
- "0": "ಸೊನ್ನೆ",
- "1": "ಒಂದು",
- "2": "ಎರಡು",
- "3": "ಮೂರು",
- "4": "ನಾಲ್ಕು",
- "5": "ಅಯ್ದು",
- "6": "ಆರು",
- "7": "ಏಳು",
- "8": "ಎಂಟು",
- "9": "ಒಂಬತ್ತು",
- "10": "ಹತ್ತು",
- "11": "ಹನ್ನೊಂದು",
- "12": "ಹನ್ನೆರಡು",
- "13": "ಹದಿಮೂರು",
- "14": "ಹದಿನಾಲ್ಕು",
- "15": "ಹದಿನೈದು",
- "16": "ಹದಿನಾರು",
- "17": "ಹದಿನೇಳು",
- "18": "ಹದಿನೆಂಟು",
- "19": "ಹತ್ತೊಂಬತ್ತು",
- "20": "ಇಪ್ಪತ್ತು",
- "21": "ಇಪ್ಪತ್ತ್’ಒಂದು",
- "22": "ಇಪ್ಪತ್ತ್’ಎರಡು",
- "23": "ಇಪ್ಪತ್ತ್’ಮೂರು",
- "24": "ಇಪ್ಪತ್ತ್’ನಾಲ್ಕು",
- "25": "ಇಪ್ಪತ್ತ್’ಐದು",
- "26": "ಇಪ್ಪತ್ತ್’ಆರು",
- "27": "ಇಪ್ಪತ್ತ್’ಏಳು",
- "28": "ಇಪ್ಪತ್ತ್’ಎಂಟು",
- "29": "ಇಪ್ಪತ್ತ್’ಒಂಬತ್ತು",
- "30": "ಮೂವತ್ತು",
- "31": "ಮುವತ್ತ್’ಒಂದು",
- "32": "ಮುವತ್ತ್’ಎರಡು",
- "33": "ಮುವತ್ತ್’ಮೂರು",
- "34": "ಮೂವತ್ತ್’ನಾಲ್ಕು",
- "35": "ಮೂವತ್ತ್’ಐದು",
- "36": "ಮೂವತ್ತ್’ಆರು",
- "37": "ಮೂವತ್ತ್’ಏಳು",
- "38": "ಮೂವತ್ತ್’ಎಂಟು",
- "39": "ಮೂವತ್ತ್’ಒಂಬತ್ತು",
- "40": "ನಲವತ್ತು",
- "41": "ನಲವತ್ತೊಂದು",
- "42": "ನಲವತ್ತ್ ಎರಡು",
- "43": "ನಲವತ್ತ್ ಮೂರು",
- "44": "ನಲವತ್ತ್ ನಾಲ್ಕು",
- "45": "ನಲವತ್ತೈದು",
- "46": "ನಲವತ್ತಾರು",
- "47": "ನಲವತ್ತೇಳು",
- "48": "ನಲವತ್ತೆಂಟು",
- "49": "ನಲವತ್ತೊಂಬತ್ತು",
- "50": "ಐವತ್ತು",
- "51": "ಐವತ್ತೊಂದು",
- "52": "ಐವತ್ತೆರಡು",
- "53": "ಐವತ್ತಮೂರು",
- "54": "ಐವತ್ತ್ನಾಲ್ಕು",
- "55": "ಐವತ್ತೈದು",
- "56": "ಐವತ್ತಾರು",
- "57": "ಐವತ್ತೇಳು",
- "58": "ಐವತ್ತೆಂಟು",
- "59": "ಐವತ್ತೊಂಬತ್ತು",
- "60": "ಅರವತ್ತು",
- "61": "ಅರವತ್ತೊಂದು",
- "62": "ಅರವತ್ತೆರಡು",
- "63": "ಅರವತ್ತ್ ಮೂರು",
- "64": "ಅರವತ್ತ್ ನಾಲ್ಕು",
- "65": "ಅರವತ್ತೈದು",
- "66": "ಅರವತ್ತಾರು",
- "67": "ಅರವತ್ತೇಳು",
- "68": "ಅರವತ್ತೆಂಟು",
- "69": "ಅರವತ್ತೊಂಬತ್ತು",
- "70": "ಎಪ್ಪತ್ತು",
- "71": "ಎಪ್ಪತ್ತೊಂದು",
- "72": "ಎಪ್ಪತ್ತೆರಡು",
- "73": "ಎಪ್ಪತ್ತ್ ಮೂರು",
- "74": "ಎಪ್ಪತ್ತ್ ನಾಲ್ಕು",
- "75": "ಎಪ್ಪತ್ತೈದು",
- "76": "ಎಪ್ಪತ್ತಾರು",
- "77": "ಎಪ್ಪತ್ತೇಳು",
- "78": "ಎಪ್ಪತ್ತೆಂಟು",
- "79": "ಎಪ್ಪತ್ತೊಂಬತ್ತು",
- "80": "ಎಂಬತ್ತು",
- "81": "ಎಂಬತ್ತೊಂದು",
- "82": "ಎಂಬತ್ತೆರಡು",
- "83": "ಎಂಬತ್ತ್ ಮೂರು",
- "84": "ಎಂಬತ್ತ್ ನಾಲ್ಕು",
- "85": "ಎಂಬತ್ತೈದು",
- "86": "ಎಂಬತ್ತಾರು",
- "87": "ಎಂಬತ್ತೇಳು",
- "88": "ಎಂಬತ್ತೆಂಟು",
- "89": "ಎಂಬತ್ತೊಂಬತ್ತು",
- "90": "ತೊಂಬತ್ತು",
- "91": "ತೊಂಬತ್ತೊಂದು",
- "92": "ತೊಂಬತ್ತೆರಡು",
- "93": "ತೊಂಬತ್ತ ಮೂರು",
- "94": "ತೊಂಬತ್ತ ನಾಲ್ಕು",
- "95": "ತೊಂಬತ್ತೈದು",
- "96": "ತೊಂಬತ್ತಾರು",
- "97": "ತೊಂಬತ್ತೇಳು",
- "98": "ತೊಂಬತ್ತೆಂಟು",
- "99": "ತೊಂಬತ್ತೊಂಬತ್ತು",
- "100": "ನೂರ",
- "1000": "ಸಾವಿರದ",
- "100000": "ಲಕ್ಷದ",
- "10000000": "ಕೋಟಿ",
- "1000000000": "ಶತಕೋಟಿ",
-} # Kannada
-num_dict["or"] = {
- "0": "ଶୁନ୍ୟ",
- "1": "ଏକ",
- "2": "ଦୁଇ",
- "3": "ତିନି",
- "4": "ଚାରି",
- "5": "ପାଞ୍ଚ",
- "6": "ଛଅ",
- "7": "ସାତ",
- "8": "ଆଠ",
- "9": "ନଅ",
- "10": "ନଅ",
- "11": "ଏଗାର",
- "12": "ବାର",
- "13": "ତେର",
- "14": "ଚଉଦ",
- "15": "ପନ୍ଦର",
- "16": "ଷୋହଳ",
- "17": "ସତର",
- "18": "ଅଠର",
- "19": "ଊଣାଇଶ",
- "20": "କୋଡିଏ",
- "21": "ଏକୋଇଶି",
- "22": "ବାଇଶି",
- "23": "ତେଇଶି",
- "24": "ଚବିଶି",
- "25": "ପଚିଶି",
- "26": "ଛବିଶି",
- "27": "ସତାଇଶି",
- "28": "ଅଠାଇଶି",
- "29": "ଅଣତିରିଶି",
- "30": "ତିରିଶି",
- "31": "ଏକତିରିଶି",
- "32": "ବତିଶି",
- "33": "ତେତିଶି",
- "34": "ଚଉତିରିଶି",
- "35": "ପଞ୍ଚତିରିଶି",
- "36": "ଛତିଶି",
- "37": "ସଂଇତିରିଶି",
- "38": "ଅଠତିରିଶି",
- "39": "ଅଣଚାଳିଶି",
- "40": "ଚାଳିଶି",
- "41": "ଏକଚାଳିଶି",
- "42": "ବୟାଳିଶି",
- "43": "ତେୟାଳିଶି",
- "44": "ଚଉରାଳିଶି",
- "45": "ପଞ୍ଚଚାଳିଶି",
- "46": "ଛୟାଳିଶି",
- "47": "ସତଚାଳିଶି",
- "48": "ଅଠଚାଳିଶି",
- "49": "ଅଣଚାଶ",
- "50": "ପଚାଶ",
- "51": "ଏକାବନ",
- "52": "ବାଉନ",
- "53": "ତେପନ",
- "54": "ଚଉବନ",
- "55": "ପଞ୍ଚାବନ",
- "56": "ଛପନ",
- "57": "ସତାବନ",
- "58": "ଅଠାବନ",
- "59": "ଅଣଷଠି",
- "60": "ଷାଠିଏ",
- "61": "ଏକଷଠି",
- "62": "ବାଷଠି",
- "63": "ତେଷଠି",
- "64": "ଚଉଷଠି",
- "65": "ପଞ୍ଚଷଠି",
- "66": "ଛଅଷଠି",
- "67": "ସତଷଠି",
- "68": "ଅଠଷଠି",
- "69": "ଅଣସ୍ତରୀ",
- "70": "ସତୂରୀ",
- "71": "ଏକସ୍ତରୀ",
- "72": "ବାସ୍ତରୀ",
- "73": "ତେସ୍ତରୀ",
- "74": "ଚଉସ୍ତରୀ",
- "75": "ପଞ୍ଚସ୍ତରୀ",
- "76": "ଛଅସ୍ତରୀ",
- "77": "ସତସ୍ତରୀ",
- "78": "ଅଠସ୍ତରୀ",
- "79": "ଅଣାଅଶୀ",
- "80": "ଅଶୀ",
- "81": "ଏକାଅଶୀ",
- "82": "ବୟାଅଶୀ",
- "83": "ତେୟାଅଶୀ",
- "84": "ଚଉରାଅଶୀ",
- "85": "ପଞ୍ଚାଅଶୀ",
- "86": "ଛୟାଅଶୀ",
- "87": "ସତାଅଶୀ",
- "88": "ଅଠାଅଶୀ",
- "89": "ଅଣାନବେ",
- "90": "ନବେ",
- "91": "ଏକାନବେ",
- "92": "ବୟାନବେ",
- "93": "ତେୟାନବେ",
- "94": "ଚଉରାନବେ",
- "95": "ପଞ୍ଚାନବେ",
- "96": "ଛୟାନବେ",
- "97": "ସତାନବେ",
- "98": "ଅଠାନବେ",
- "99": "ଅନେଶତ",
- "100": "ଶହେ",
- "1000": "ହଜାର",
- "100000": "ଲକ୍ଷ",
- "10000000": "କୋଟି",
- "1000000000": "କୋଟି",
-} # Oriya
-num_dict["pa"] = {
- "0": "ਸਿਫਰ ",
- "1": "ਇੱਕ",
- "2": "ਦੋ",
- "3": "ਤਿੰਨ",
- "4": "ਚਾਰ",
- "5": "ਪੰਜ",
- "6": "ਛੇ",
- "7": "ਸੱਤ",
- "8": "ਅੱਠ",
- "9": "ਨੌਂ",
- "10": "ਦੱਸ",
- "11": "ਗਿਆਰਾਂ",
- "12": "ਬਾਰਾਂ",
- "13": "ਤੇਰਾਂ",
- "14": "ਚੌਦਾਂ",
- "15": "ਪੰਦਰਾਂ",
- "16": "ਸੋਲ਼ਾਂ",
- "17": "ਸਤਾਰਾਂ",
- "18": "ਅਠਾਰਾਂ",
- "19": "ਉਨੀ",
- "20": "ਵੀਹ",
- "21": "ਇੱਕੀ",
- "22": "ਬਾਈ",
- "23": "ਤੇਈ",
- "24": "ਚੌਵੀ",
- "25": "ਪੰਝੀ",
- "26": "ਛੱਬੀ",
- "27": "ਸਤਾਈ",
- "28": "ਅਠਾਈ",
- "29": "ਉਨੱਤੀ",
- "30": "ਤੀਹ",
- "31": "ਇਕੱਤੀ",
- "32": "ਬੱਤੀ",
- "33": "ਤੇਤੀ",
- "34": "ਚੌਂਤੀ",
- "35": "ਪੈਂਤੀ",
- "36": "ਛੱਤੀ",
- "37": "ਸੈਂਤੀ",
- "38": "ਅਠੱਤੀ",
- "39": "ਉਨਤਾਲੀ",
- "40": "ਚਾਲੀ",
- "41": "ਇਕਤਾਲੀ",
- "42": "ਬਤਾਲੀ",
- "43": "ਤਰਤਾਲੀ",
- "44": "ਚੌਤਾਲੀ",
- "45": "ਪੰਜਤਾਲੀ",
- "46": "ਛਿਆਲੀ",
- "47": "ਸੰਤਾਲੀ",
- "48": "ਅੱਠਤਾਲੀ",
- "49": "ਉਣਿੰਜਾ",
- "50": "ਪੰਜਾਹ",
- "51": "ਇਕਵਿੰਜਾ",
- "52": "ਬਵਿੰਜਾ",
- "53": "ਤਰਵਿੰਜਾ",
- "54": "ਚਰਿੰਜਾ",
- "55": "ਪਚਵਿੰਜਾ",
- "56": "ਛਪਿੰਜਾ",
- "57": "ਸਤਵਿੰਜਾ",
- "58": "ਅੱਠਵਿੰਜਾ",
- "59": "ਉਣਾਠ",
- "60": "ਸੱਠ",
- "61": "ਇਕਾਠ",
- "62": "ਬਾਠ੍ਹ",
- "63": "ਤਰੇਠ੍ਹ",
- "64": "ਚੌਠ੍ਹ",
- "65": "ਪੈਂਠ",
- "66": "ਛਿਆਠ",
- "67": "ਸਤਾਹਠ",
- "68": "ਅੱਠਾਠ",
- "69": "ਉਣੱਤਰ",
- "70": "ਸੱਤਰ",
- "71": "ਇਕ੍ਹੱਤਰ",
- "72": "ਬਹੱਤਰ",
- "73": "ਤਹੱਤਰ",
- "74": "ਚੌਹੱਤਰ",
- "75": "ਪੰਜੱਤਰ",
- "76": "ਛਿਹੱਤਰ",
- "77": "ਸਤੱਤਰ",
- "78": "ਅਠੱਤਰ",
- "79": "ਉਣਾਸੀ",
- "80": "ਅੱਸੀ",
- "81": "ਇਕਾਸੀ",
- "82": "ਬਿਆਸੀ",
- "83": "ਤਰਾਸੀ",
- "84": "ਚਰਾਸੀ",
- "85": "ਪੰਜਾਸੀ",
- "86": "ਛਿਆਸੀ",
- "87": "ਸਤਾਸੀ",
- "88": "ਅਠਾਸੀ",
- "89": "ਉਣਾਨਵੇਂ",
- "90": "ਨੱਬੇ",
- "91": "ਇਕਾਨਵੇਂ",
- "92": "ਬਿਆਨਵੇਂ",
- "93": "ਤਰਾਨਵੇਂ",
- "94": "ਚਰਾਨਵੇਂ",
- "95": "ਪਚਾਨਵੇਂ",
- "96": "ਛਿਆਨਵੇਂ",
- "97": "ਸਤਾਨਵੇਂ",
- "98": "ਅਠਾਨਵੇਂ",
- "99": "ਨਿੜਾਨਵੇਂ",
- "100": "ਸੌ",
- "1000": "ਹਜਾਰ",
- "100000": "ਲੱਖ",
- "10000000": "ਕਰੋੜ",
- "1000000000": "ਅਰਬ",
-} # Punjabi
-
-# --------------------------- num_to_word.py ------------------------------
-"""
-Method to convert Numbers to Words
-for indian languages
-
-Use cases:-
-1) Speech recognition pre-processing
-2) Language modeling Data pre-processing
-
--------------------------
-check indic_numbers.py to add support
-for any indian language
-"""
-
-
-def language_specific_exception(words, lang, combiner):
- """
- Language Specific Exception will come here
- """
-
- def occurs_at_end(piece):
- return words[-len(piece) :] == piece
-
- if lang == "mr":
- words = words.replace("एक" + combiner + "शे", "शंभर")
- elif lang == "gu":
- words = words.replace("બે" + combiner + "સો", "બસ્સો")
- elif lang == "te":
- exception_dict = {
- "1": "ఒక",
- "100": "వంద",
- "100+": "వందలు",
- "1000": "వెయ్యి",
- "1000+": "వేలు",
- "100000": "లక్ష",
- "100000+": "లక్షలు",
- "10000000": "కోటి",
- "10000000+": "కోట్లు",
- }
-
- test_case = ["100", "1000", "100000", "10000000"]
- for test in test_case:
- test_word = num_dict["te"][test]
- match = num_dict["te"]["1"] + combiner + test_word
- # for numbers like : 100, 1000, 100000
- if words == match:
- return exception_dict[test]
- # for numbers like : 200, 4000, 800000
- elif occurs_at_end(test_word):
- words = words.replace(test_word, exception_dict[test + "+"])
- # for numbers like : 105, 1076, 123993
- elif not occurs_at_end(match):
- replacement = exception_dict["1"] + combiner + exception_dict[test]
- words = words.replace(match, replacement)
-
- # Exception case for 101...199
- special_case = "ఒక" + combiner + "వంద"
- words = words.replace(special_case, "నూట")
- elif lang == "kn":
- # special case for 100
- if words == ("ಒಂದು" + combiner + "ನೂರ"):
- return "ನೂರು"
- exception_dict = {
- "ನೂರ": "ನೂರು",
- "ಸಾವಿರದ": "ಸಾವಿರ",
- "ಲಕ್ಷದ": "ಲಕ್ಷ",
- "ಕೋಟಿಯ": "ಕೋಟಿ",
- }
- for expt in exception_dict:
- if occurs_at_end(expt):
- words = words.replace(expt, exception_dict[expt])
- return words
-
-
-def num_to_word(num, lang, separator=", ", combiner=" "):
- """
- Main Method
- :param num: Number digits from any indian language
- :param lang: Language Code from supported Language
- :param separator: Separator character i.e. separator = '-' --> 'two hundred-sixty'
- :param combiner: combine number with position i.e. combiner = '-' --> 'two-hundred sixty'
- :return: UTF-8 String of numbers in words
- """
- lang = lang.lower()
- num = str(num)
-
- # Load dictionary according to language code
- assert lang in supported_lang, "Language not supported"
- num_dic = num_dict[lang]
-
- # dash default combiner for english-india
- if (lang == "en") & (combiner == " "):
- combiner = "-"
-
- # Remove punctuations from numbers
- num = str(num).replace(",", "").replace(" ", "")
-
- # return word as it is if not number
- if not num.isdecimal():
- return num
-
- # Replace native language numbers with english digits
- for language in supported_lang:
- for num_index in range(10):
- num = num.replace(all_num[language][num_index], all_num["en"][num_index])
-
- # Assert that input contains only integer number
- for digit in num:
- assert digit in all_num["en"], "Give proper input"
-
- # Process
- # For Number longer than 9 digits
- def all_two_digit(digits_2):
- if len(digits_2) <= 1: # Provided only one/zero digit
- return num_dic.get(digits_2, "")
- elif digits_2 == "00": # Two Zero provided
- return num_dic["0"] + separator + num_dic["0"]
- elif digits_2[0] == "0": # First digit is zero
- return num_dic["0"] + separator + num_dic[digits_2[1]]
- else: # Both digit provided
- return num_dic[digits_2]
-
- # For Number less than 9 digits
- def two_digit(digits_2):
- digits_2 = digits_2.lstrip("0")
- if len(digits_2) != 0:
- return num_dic[digits_2]
- else:
- return ""
-
- def all_digit(digits):
- digits = digits.lstrip("0")
- digit_len = len(digits)
- if digit_len > 3:
- num_of_digits_to_process = (digit_len % 2) + 1
- process_digits = digits[:num_of_digits_to_process]
- base = str(10 ** (int(digit_len / 2) * 2 - 1))
- remain_digits = digits[num_of_digits_to_process:]
- return (
- num_dic[process_digits]
- + combiner
- + num_dic[base]
- + separator
- + all_digit(remain_digits)
- )
- elif len(digits) == 3:
- return (
- num_dic[digits[:1]]
- + combiner
- + num_dic["100"]
- + separator
- + two_digit(digits[1:])
- )
- else:
- return two_digit(digits)
-
- num = num.lstrip("0")
- full_digit_len = len(num)
-
- if full_digit_len == 0:
- output = num_dic["0"]
- elif full_digit_len <= 9:
- output = all_digit(num)
- else:
- iteration = round(full_digit_len / 2)
- output = all_two_digit(num[:2]) # First to digit
- for i in range(1, iteration):
- output = (
- output + separator + all_two_digit(num[i * 2 : (i + 1) * 2])
- ) # Next two digit pairs
- remaining_digits = num[iteration * 2 :]
- if not all_two_digit(remaining_digits) == "":
- output = (
- output + separator + all_two_digit(remaining_digits)
- ) # remaining Last one/two digits
-
- output = output.strip(separator)
-
- output = language_specific_exception(output, lang, combiner)
-
- return output
-
-
-# --------------------------------- num_to_word_on_a_sent ---------------------------------
-
-
-def is_digit(word, digit_pattern):
- return re.search(digit_pattern, word)
-
-
-def remove_punct(sent):
- clean = re.sub("[%s]" % re.escape(string.punctuation), " ", sent)
- return " ".join([word for word in clean.split() if word])
-
-
-def normalize_nums(text, lang):
- """
- text: str (eg)
- lang: lang code ['en', 'hi']
-
- returns: str
- (eg)
- """
-
- if lang in supported_lang:
- text = text.replace('-',' - ') # space separate hyphen
- words = text.split()
- lang_digits = [str(i) for i in range(0, 10)]
-
- digit_pattern = "[" + "".join(lang_digits) + "]"
- num_indices = [
- ind for ind, word in enumerate(words) if is_digit(word, digit_pattern)
- ]
-
- words_up = [
- num_to_word(word, lang, separator=" ", combiner=" ")
- if ind in num_indices
- else word
- for ind, word in enumerate(words)
- ]
- return " ".join(words_up)
- else:
- return text
-
-
-if __name__ == "__main__":
- print(normalize_nums("रीटा के पास 16 बिल्लियाँ हैं।", "hi"))
diff --git a/spaces/Harveenchadha/en_to_indic_translation/subword-nmt/subword_nmt/__init__.py b/spaces/Harveenchadha/en_to_indic_translation/subword-nmt/subword_nmt/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Harveenchadha/oiTrans/legacy/env.sh b/spaces/Harveenchadha/oiTrans/legacy/env.sh
deleted file mode 100644
index 9c9611b0d11e821bdb17b612b64c3d14e208cc74..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/oiTrans/legacy/env.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-
-export SRC=''
-
-## Python env directory where fairseq is installed
-export PYTHON_ENV=''
-
-export SUBWORD_NMT_DIR=''
-export INDIC_RESOURCES_PATH=''
-export INDIC_NLP_HOME=''
-
-export CUDA_HOME=''
-
-export PATH=$CUDA_HOME/bin:$INDIC_NLP_HOME:$PATH
-export LD_LIBRARY_PATH=$CUDA_HOME/lib64
-
-# set environment variable to control GPUS visible to the application
-#export CUDA_VISIBLE_DEVICES="'
diff --git a/spaces/Iceclear/StableSR/StableSR/basicsr/data/data_util.py b/spaces/Iceclear/StableSR/StableSR/basicsr/data/data_util.py
deleted file mode 100644
index dce2562fb9f99475c44e9185f50018a428859214..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/basicsr/data/data_util.py
+++ /dev/null
@@ -1,362 +0,0 @@
-import cv2
-import numpy as np
-import torch
-from os import path as osp
-from torch.nn import functional as F
-
-from basicsr.data.transforms import mod_crop
-from basicsr.utils import img2tensor, scandir
-
-
-def read_img_seq(path, require_mod_crop=False, scale=1, return_imgname=False):
- """Read a sequence of images from a given folder path.
-
- Args:
- path (list[str] | str): List of image paths or image folder path.
- require_mod_crop (bool): Require mod crop for each image.
- Default: False.
- scale (int): Scale factor for mod_crop. Default: 1.
- return_imgname(bool): Whether return image names. Default False.
-
- Returns:
- Tensor: size (t, c, h, w), RGB, [0, 1].
- list[str]: Returned image name list.
- """
- if isinstance(path, list):
- img_paths = path
- else:
- img_paths = sorted(list(scandir(path, full_path=True)))
- imgs = [cv2.imread(v).astype(np.float32) / 255. for v in img_paths]
-
- if require_mod_crop:
- imgs = [mod_crop(img, scale) for img in imgs]
- imgs = img2tensor(imgs, bgr2rgb=True, float32=True)
- imgs = torch.stack(imgs, dim=0)
-
- if return_imgname:
- imgnames = [osp.splitext(osp.basename(path))[0] for path in img_paths]
- return imgs, imgnames
- else:
- return imgs
-
-
-def generate_frame_indices(crt_idx, max_frame_num, num_frames, padding='reflection'):
- """Generate an index list for reading `num_frames` frames from a sequence
- of images.
-
- Args:
- crt_idx (int): Current center index.
- max_frame_num (int): Max number of the sequence of images (from 1).
- num_frames (int): Reading num_frames frames.
- padding (str): Padding mode, one of
- 'replicate' | 'reflection' | 'reflection_circle' | 'circle'
- Examples: current_idx = 0, num_frames = 5
- The generated frame indices under different padding mode:
- replicate: [0, 0, 0, 1, 2]
- reflection: [2, 1, 0, 1, 2]
- reflection_circle: [4, 3, 0, 1, 2]
- circle: [3, 4, 0, 1, 2]
-
- Returns:
- list[int]: A list of indices.
- """
- assert num_frames % 2 == 1, 'num_frames should be an odd number.'
- assert padding in ('replicate', 'reflection', 'reflection_circle', 'circle'), f'Wrong padding mode: {padding}.'
-
- max_frame_num = max_frame_num - 1 # start from 0
- num_pad = num_frames // 2
-
- indices = []
- for i in range(crt_idx - num_pad, crt_idx + num_pad + 1):
- if i < 0:
- if padding == 'replicate':
- pad_idx = 0
- elif padding == 'reflection':
- pad_idx = -i
- elif padding == 'reflection_circle':
- pad_idx = crt_idx + num_pad - i
- else:
- pad_idx = num_frames + i
- elif i > max_frame_num:
- if padding == 'replicate':
- pad_idx = max_frame_num
- elif padding == 'reflection':
- pad_idx = max_frame_num * 2 - i
- elif padding == 'reflection_circle':
- pad_idx = (crt_idx - num_pad) - (i - max_frame_num)
- else:
- pad_idx = i - num_frames
- else:
- pad_idx = i
- indices.append(pad_idx)
- return indices
-
-
-def paired_paths_from_lmdb(folders, keys):
- """Generate paired paths from lmdb files.
-
- Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is:
-
- ::
-
- lq.lmdb
- ├── data.mdb
- ├── lock.mdb
- ├── meta_info.txt
-
- The data.mdb and lock.mdb are standard lmdb files and you can refer to
- https://lmdb.readthedocs.io/en/release/ for more details.
-
- The meta_info.txt is a specified txt file to record the meta information
- of our datasets. It will be automatically created when preparing
- datasets by our provided dataset tools.
- Each line in the txt file records
- 1)image name (with extension),
- 2)image shape,
- 3)compression level, separated by a white space.
- Example: `baboon.png (120,125,3) 1`
-
- We use the image name without extension as the lmdb key.
- Note that we use the same key for the corresponding lq and gt images.
-
- Args:
- folders (list[str]): A list of folder path. The order of list should
- be [input_folder, gt_folder].
- keys (list[str]): A list of keys identifying folders. The order should
- be in consistent with folders, e.g., ['lq', 'gt'].
- Note that this key is different from lmdb keys.
-
- Returns:
- list[str]: Returned path list.
- """
- assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
- f'But got {len(folders)}')
- assert len(keys) == 2, f'The len of keys should be 2 with [input_key, gt_key]. But got {len(keys)}'
- input_folder, gt_folder = folders
- input_key, gt_key = keys
-
- if not (input_folder.endswith('.lmdb') and gt_folder.endswith('.lmdb')):
- raise ValueError(f'{input_key} folder and {gt_key} folder should both in lmdb '
- f'formats. But received {input_key}: {input_folder}; '
- f'{gt_key}: {gt_folder}')
- # ensure that the two meta_info files are the same
- with open(osp.join(input_folder, 'meta_info.txt')) as fin:
- input_lmdb_keys = [line.split('.')[0] for line in fin]
- with open(osp.join(gt_folder, 'meta_info.txt')) as fin:
- gt_lmdb_keys = [line.split('.')[0] for line in fin]
- if set(input_lmdb_keys) != set(gt_lmdb_keys):
- raise ValueError(f'Keys in {input_key}_folder and {gt_key}_folder are different.')
- else:
- paths = []
- for lmdb_key in sorted(input_lmdb_keys):
- paths.append(dict([(f'{input_key}_path', lmdb_key), (f'{gt_key}_path', lmdb_key)]))
- return paths
-
-
-def paired_paths_from_meta_info_file(folders, keys, meta_info_file, filename_tmpl):
- """Generate paired paths from an meta information file.
-
- Each line in the meta information file contains the image names and
- image shape (usually for gt), separated by a white space.
-
- Example of an meta information file:
- ```
- 0001_s001.png (480,480,3)
- 0001_s002.png (480,480,3)
- ```
-
- Args:
- folders (list[str]): A list of folder path. The order of list should
- be [input_folder, gt_folder].
- keys (list[str]): A list of keys identifying folders. The order should
- be in consistent with folders, e.g., ['lq', 'gt'].
- meta_info_file (str): Path to the meta information file.
- filename_tmpl (str): Template for each filename. Note that the
- template excludes the file extension. Usually the filename_tmpl is
- for files in the input folder.
-
- Returns:
- list[str]: Returned path list.
- """
- assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
- f'But got {len(folders)}')
- assert len(keys) == 2, f'The len of keys should be 2 with [input_key, gt_key]. But got {len(keys)}'
- input_folder, gt_folder = folders
- input_key, gt_key = keys
-
- with open(meta_info_file, 'r') as fin:
- gt_names = [line.strip().split(' ')[0] for line in fin]
-
- paths = []
- for gt_name in gt_names:
- basename, ext = osp.splitext(osp.basename(gt_name))
- input_name = f'{filename_tmpl.format(basename)}{ext}'
- input_path = osp.join(input_folder, input_name)
- gt_path = osp.join(gt_folder, gt_name)
- paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)]))
- return paths
-
-def paired_paths_from_meta_info_file_2(folders, keys, meta_info_file, filename_tmpl):
- """Generate paired paths from an meta information file.
-
- Each line in the meta information file contains the image names and
- image shape (usually for gt), separated by a white space.
-
- Example of an meta information file:
- ```
- 0001_s001.png (480,480,3)
- 0001_s002.png (480,480,3)
- ```
-
- Args:
- folders (list[str]): A list of folder path. The order of list should
- be [input_folder, gt_folder].
- keys (list[str]): A list of keys identifying folders. The order should
- be in consistent with folders, e.g., ['lq', 'gt'].
- meta_info_file (str): Path to the meta information file.
- filename_tmpl (str): Template for each filename. Note that the
- template excludes the file extension. Usually the filename_tmpl is
- for files in the input folder.
-
- Returns:
- list[str]: Returned path list.
- """
- assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
- f'But got {len(folders)}')
- assert len(keys) == 2, f'The len of keys should be 2 with [input_key, gt_key]. But got {len(keys)}'
- input_folder, gt_folder = folders
- input_key, gt_key = keys
-
- with open(meta_info_file, 'r') as fin:
- gt_names = [line.strip().split(' ')[0] for line in fin]
- with open(meta_info_file, 'r') as fin:
- input_names = [line.strip().split(' ')[1] for line in fin]
- paths = []
- for i in range(len(gt_names)):
- gt_name = gt_names[i]
- lq_name = input_names[i]
- basename, ext = osp.splitext(osp.basename(gt_name))
- basename = gt_name[:-len(ext)]
- gt_path = osp.join(gt_folder, gt_name)
- basename, ext = osp.splitext(osp.basename(lq_name))
- basename = lq_name[:-len(ext)]
- input_path = osp.join(input_folder, lq_name)
- paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)]))
- return paths
-
-def paired_paths_from_folder(folders, keys, filename_tmpl):
- """Generate paired paths from folders.
-
- Args:
- folders (list[str]): A list of folder path. The order of list should
- be [input_folder, gt_folder].
- keys (list[str]): A list of keys identifying folders. The order should
- be in consistent with folders, e.g., ['lq', 'gt'].
- filename_tmpl (str): Template for each filename. Note that the
- template excludes the file extension. Usually the filename_tmpl is
- for files in the input folder.
-
- Returns:
- list[str]: Returned path list.
- """
- assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
- f'But got {len(folders)}')
- assert len(keys) == 2, f'The len of keys should be 2 with [input_key, gt_key]. But got {len(keys)}'
- input_folder, gt_folder = folders
- input_key, gt_key = keys
-
- input_paths = list(scandir(input_folder))
- gt_paths = list(scandir(gt_folder))
- assert len(input_paths) == len(gt_paths), (f'{input_key} and {gt_key} datasets have different number of images: '
- f'{len(input_paths)}, {len(gt_paths)}.')
- paths = []
- for gt_path in gt_paths:
- basename, ext = osp.splitext(osp.basename(gt_path))
- input_name = f'{filename_tmpl.format(basename)}{ext}'
- input_path = osp.join(input_folder, input_name)
- assert input_name in input_paths, f'{input_name} is not in {input_key}_paths.'
- gt_path = osp.join(gt_folder, gt_path)
- paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)]))
- return paths
-
-
-def paths_from_folder(folder):
- """Generate paths from folder.
-
- Args:
- folder (str): Folder path.
-
- Returns:
- list[str]: Returned path list.
- """
-
- paths = list(scandir(folder))
- paths = [osp.join(folder, path) for path in paths]
- return paths
-
-
-def paths_from_lmdb(folder):
- """Generate paths from lmdb.
-
- Args:
- folder (str): Folder path.
-
- Returns:
- list[str]: Returned path list.
- """
- if not folder.endswith('.lmdb'):
- raise ValueError(f'Folder {folder}folder should in lmdb format.')
- with open(osp.join(folder, 'meta_info.txt')) as fin:
- paths = [line.split('.')[0] for line in fin]
- return paths
-
-
-def generate_gaussian_kernel(kernel_size=13, sigma=1.6):
- """Generate Gaussian kernel used in `duf_downsample`.
-
- Args:
- kernel_size (int): Kernel size. Default: 13.
- sigma (float): Sigma of the Gaussian kernel. Default: 1.6.
-
- Returns:
- np.array: The Gaussian kernel.
- """
- from scipy.ndimage import filters as filters
- kernel = np.zeros((kernel_size, kernel_size))
- # set element at the middle to one, a dirac delta
- kernel[kernel_size // 2, kernel_size // 2] = 1
- # gaussian-smooth the dirac, resulting in a gaussian filter
- return filters.gaussian_filter(kernel, sigma)
-
-
-def duf_downsample(x, kernel_size=13, scale=4):
- """Downsamping with Gaussian kernel used in the DUF official code.
-
- Args:
- x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w).
- kernel_size (int): Kernel size. Default: 13.
- scale (int): Downsampling factor. Supported scale: (2, 3, 4).
- Default: 4.
-
- Returns:
- Tensor: DUF downsampled frames.
- """
- assert scale in (2, 3, 4), f'Only support scale (2, 3, 4), but got {scale}.'
-
- squeeze_flag = False
- if x.ndim == 4:
- squeeze_flag = True
- x = x.unsqueeze(0)
- b, t, c, h, w = x.size()
- x = x.view(-1, 1, h, w)
- pad_w, pad_h = kernel_size // 2 + scale * 2, kernel_size // 2 + scale * 2
- x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), 'reflect')
-
- gaussian_filter = generate_gaussian_kernel(kernel_size, 0.4 * scale)
- gaussian_filter = torch.from_numpy(gaussian_filter).type_as(x).unsqueeze(0).unsqueeze(0)
- x = F.conv2d(x, gaussian_filter, stride=scale)
- x = x[:, :, 2:-2, 2:-2]
- x = x.view(b, t, c, x.size(2), x.size(3))
- if squeeze_flag:
- x = x.squeeze(0)
- return x
diff --git a/spaces/Illumotion/Koboldcpp/ggml-alloc.h b/spaces/Illumotion/Koboldcpp/ggml-alloc.h
deleted file mode 100644
index 0c224f174f396eb187ddc5a48f955f7c0ad4418e..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/ggml-alloc.h
+++ /dev/null
@@ -1,27 +0,0 @@
-#pragma once
-
-#include "ggml.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-
-GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);
-GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
-
-// tell the allocator to parse nodes following the order described in the list
-// you should call this if your graph are optimized to execute out-of-order
-GGML_API void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n);
-
-GGML_API void ggml_allocr_free(struct ggml_allocr * alloc);
-GGML_API bool ggml_allocr_is_measure(struct ggml_allocr * alloc);
-GGML_API void ggml_allocr_reset(struct ggml_allocr * alloc);
-GGML_API void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor);
-GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph);
-GGML_API size_t ggml_allocr_max_size(struct ggml_allocr * alloc);
-
-
-#ifdef __cplusplus
-}
-#endif
diff --git a/spaces/JUNGU/cartoonizer-demo-onnx-sota/README.md b/spaces/JUNGU/cartoonizer-demo-onnx-sota/README.md
deleted file mode 100644
index 983c02b6a0ddea2f7de8ec164715f580ccbe742e..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/cartoonizer-demo-onnx-sota/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Cartoonizer Demo ONNX
-emoji: 🗻
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: sayakpaul/cartoonizer-demo-onnx
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jamin252/Dog_Identifier/README.md b/spaces/Jamin252/Dog_Identifier/README.md
deleted file mode 100644
index 405c1ef012635e1ba31d813e105300eb242db567..0000000000000000000000000000000000000000
--- a/spaces/Jamin252/Dog_Identifier/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Dog Identifier
-emoji: 👀
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jamkonams/AutoGPT/autogpt/__init__.py b/spaces/Jamkonams/AutoGPT/autogpt/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Joabutt/waifugeneration/README.md b/spaces/Joabutt/waifugeneration/README.md
deleted file mode 100644
index abb538cdb369c075df029338100e8f12b0c40756..0000000000000000000000000000000000000000
--- a/spaces/Joabutt/waifugeneration/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Waifugeneration
-emoji: 🔥
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.3
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Joeythemonster/flax-midjourney-v4-diffusion/README.md b/spaces/Joeythemonster/flax-midjourney-v4-diffusion/README.md
deleted file mode 100644
index 2a2e9ca42e1295b4521a60a5a1daffc46ff96312..0000000000000000000000000000000000000000
--- a/spaces/Joeythemonster/flax-midjourney-v4-diffusion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Flax Midjourney V4 Diffusion
-emoji: 🏢
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-duplicated_from: marcoruizrueda/flax-midjourney-v4-diffusion
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Josh98/nl2bash_m/nl2bash_m.py b/spaces/Josh98/nl2bash_m/nl2bash_m.py
deleted file mode 100644
index 7669d37ca146917fbf1e2859de1b2fcb1d2c5a64..0000000000000000000000000000000000000000
--- a/spaces/Josh98/nl2bash_m/nl2bash_m.py
+++ /dev/null
@@ -1,146 +0,0 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# 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.
-"""nl2bash metric."""
-import re
-import string
-
-import datasets
-import numpy as np
-
-import evaluate
-
-
-_DESCRIPTION = """
-returns a score that indicates how close the bash command generated is to the actual command with a perfect score out of 1.0
-"""
-
-_KWARGS_DESCRIPTION = """
-Args:
- predictions: List of predicted texts.
- references: List of reference texts.
- cmd_weight: The weight you want to put on getting the command correct
- opt_weight: The weight you want to put on getting the option correct
- arg_weight: The weight you want to put on getting the arg correct
- ignore_case=False,
- ignore_numbers=False,
-Returns:
- nl2bash metric: Dictionary containing nl2bash score. Possible values are between 0.0 and 1.0, inclusive.
-Examples:
-
-
- >>> metric = evaluate.load("Josh98/nl2bash_m")
- >>> preds = ["ls -l /home/userr", "ls -l /home/josh", "lss /home/josh some argument"]
- >>> refs = [["ls -l /home/user"], ["ls -l --v /home/josh"], ["ls /home/josh"]]
- >>> results = exact_match.compute(references=refs, predictions=preds)
- >>> print(round(results["nl2bash"], 2))
- 0.708
-"""
-
-_CITATION = """
-"""
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class nl2bash_m(evaluate.Metric):
- def _info(self):
- return evaluate.MetricInfo(
- description=_DESCRIPTION,
- citation=_CITATION,
- inputs_description=_KWARGS_DESCRIPTION,
- features=[
- datasets.Features(
- {
- "predictions": datasets.Value("string", id="sequence"),
- "references": datasets.Sequence(datasets.Value("string", id="sequence"), id="references"),
- }
- ),
- datasets.Features(
- {
- "predictions": datasets.Value("string", id="sequence"),
- "references": datasets.Value("string", id="sequence"),
- }
- ),
- ],
- reference_urls=[],
- )
-
- def get_score(self, pred, ref):
- if not pred and not ref: return 1
- cor = 0
- for i in range(min(len(pred), len(ref))):
- if (pred[i] == ref[i]):
- cor += 1
-
- return cor/max(len(pred), len(ref))
-
- def _compute(
- self,
- predictions,
- references,
- cmd_weight = 0.65,
- opt_weight = 0.25,
- arg_weight = 0.15,
- ignore_case=True,
- ignore_numbers=True,
- ):
-
- predictions = np.asarray(predictions)
- references = np.asarray(references)
-
- if ignore_case:
- predictions = np.char.lower(predictions)
- references = np.char.lower(references)
-
- if ignore_numbers:
- repl_table = string.digits.maketrans("", "", string.digits)
- predictions = np.char.translate(predictions, table=repl_table)
- references = np.char.translate(references, table=repl_table)
-
-
- final_score = 0
- for pred, refs in zip(predictions, references):
- best_score = 0
- if len(pred) == 0 and min([len(ref) for ref in refs]) == 0:
- best_score = 1
- elif len(pred) == 0 or min([len(ref) for ref in refs]) == 0:
- best_score = 0
- else:
- for ref in refs:
- pred_words, ref_words = pred.split(), ref.split()
-
-
- # Get the cmd of predicted and ref
- cmd_corr = 1 if pred_words.pop(0)==ref_words.pop(0) else 0
-
- # Get the option of predicted and ref
- pred_option = [ x for x in pred_words if x[0] == '-']
- ref_option = [ x for x in ref_words if x[0] == '-']
-
- # Get the arguments of predicted and ref
- pred_args = [ x for x in pred_words if x[0] != '-']
- ref_args = [ x for x in ref_words if x[0] != '-']
-
- # Calculate scores
- cmd_score = cmd_weight * cmd_corr
- opt_score = opt_weight * self.get_score(pred_option, ref_option)
- arg_score = arg_weight * self.get_score(pred_args, ref_args)
-
- score = cmd_score + opt_score + arg_score
- best_score = max(best_score, score)
-
- final_score += best_score
-
- final_score = final_score/len(predictions)
-
- return {"nl2bash_m": (final_score)}
\ No newline at end of file
diff --git a/spaces/KPCGD/bingo/src/lib/bots/bing/index.ts b/spaces/KPCGD/bingo/src/lib/bots/bing/index.ts
deleted file mode 100644
index 2c4afae01a345b8415935228566cb30d695e768d..0000000000000000000000000000000000000000
--- a/spaces/KPCGD/bingo/src/lib/bots/bing/index.ts
+++ /dev/null
@@ -1,421 +0,0 @@
-import { fetch, WebSocket, debug } from '@/lib/isomorphic'
-import WebSocketAsPromised from 'websocket-as-promised'
-import {
- SendMessageParams,
- BingConversationStyle,
- ConversationResponse,
- ChatResponseMessage,
- ConversationInfo,
- InvocationEventType,
- ChatError,
- ErrorCode,
- ChatUpdateCompleteResponse,
- ImageInfo,
- KBlobResponse
-} from './types'
-
-import { convertMessageToMarkdown, websocketUtils, streamAsyncIterable } from './utils'
-import { WatchDog, createChunkDecoder } from '@/lib/utils'
-
-type Params = SendMessageParams<{ bingConversationStyle: BingConversationStyle }>
-
-const OPTIONS_SETS = [
- 'nlu_direct_response_filter',
- 'deepleo',
- 'disable_emoji_spoken_text',
- 'responsible_ai_policy_235',
- 'enablemm',
- 'iycapbing',
- 'iyxapbing',
- 'objopinion',
- 'rweasgv2',
- 'dagslnv1',
- 'dv3sugg',
- 'autosave',
- 'iyoloxap',
- 'iyoloneutral',
- 'clgalileo',
- 'gencontentv3',
-]
-
-export class BingWebBot {
- protected conversationContext?: ConversationInfo
- protected cookie: string
- protected ua: string
- protected endpoint = ''
- private lastText = ''
- private asyncTasks: Array> = []
-
- constructor(opts: {
- cookie: string
- ua: string
- bingConversationStyle?: BingConversationStyle
- conversationContext?: ConversationInfo
- }) {
- const { cookie, ua, conversationContext } = opts
- this.cookie = cookie?.includes(';') ? cookie : `_EDGE_V=1; _U=${cookie}`
- this.ua = ua
- this.conversationContext = conversationContext
- }
-
- static buildChatRequest(conversation: ConversationInfo) {
- const optionsSets = OPTIONS_SETS
- if (conversation.conversationStyle === BingConversationStyle.Precise) {
- optionsSets.push('h3precise')
- } else if (conversation.conversationStyle === BingConversationStyle.Creative) {
- optionsSets.push('h3imaginative')
- }
- return {
- arguments: [
- {
- source: 'cib',
- optionsSets,
- allowedMessageTypes: [
- 'Chat',
- 'InternalSearchQuery',
- 'Disengaged',
- 'InternalLoaderMessage',
- 'SemanticSerp',
- 'GenerateContentQuery',
- 'SearchQuery',
- ],
- sliceIds: [
- 'winmuid1tf',
- 'anssupfor_c',
- 'imgchatgptv2',
- 'tts2cf',
- 'contansperf',
- 'mlchatpc8500w',
- 'mlchatpc2',
- 'ctrlworkpay',
- 'winshortmsgtf',
- 'cibctrl',
- 'sydtransctrl',
- 'sydconfigoptc',
- '0705trt4',
- '517opinion',
- '628ajcopus0',
- '330uaugs0',
- '529rwea',
- '0626snptrcs0',
- '424dagslnv1',
- ],
- isStartOfSession: conversation.invocationId === 0,
- message: {
- author: 'user',
- inputMethod: 'Keyboard',
- text: conversation.prompt,
- imageUrl: conversation.imageUrl,
- messageType: 'Chat',
- },
- conversationId: conversation.conversationId,
- conversationSignature: conversation.conversationSignature,
- participant: { id: conversation.clientId },
- },
- ],
- invocationId: conversation.invocationId.toString(),
- target: 'chat',
- type: InvocationEventType.StreamInvocation,
- }
- }
-
- async createConversation(): Promise {
- const headers = {
- 'Accept-Encoding': 'gzip, deflate, br, zsdch',
- 'User-Agent': this.ua,
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- cookie: this.cookie,
- }
-
- let resp: ConversationResponse | undefined
- try {
- const response = await fetch(this.endpoint + '/api/create', { method: 'POST', headers, redirect: 'error', mode: 'cors', credentials: 'include' })
- if (response.status === 404) {
- throw new ChatError('Not Found', ErrorCode.NOTFOUND_ERROR)
- }
- resp = await response.json() as ConversationResponse
- } catch (err) {
- console.error('create conversation error', err)
- }
-
- if (!resp?.result) {
- throw new ChatError('Invalid response', ErrorCode.UNKOWN_ERROR)
- }
-
- const { value, message } = resp.result || {}
- if (value !== 'Success') {
- const errorMsg = `${value}: ${message}`
- if (value === 'UnauthorizedRequest') {
- throw new ChatError(errorMsg, ErrorCode.BING_UNAUTHORIZED)
- }
- if (value === 'Forbidden') {
- throw new ChatError(errorMsg, ErrorCode.BING_FORBIDDEN)
- }
- throw new ChatError(errorMsg, ErrorCode.UNKOWN_ERROR)
- }
- return resp
- }
-
- private async createContext(conversationStyle: BingConversationStyle) {
- if (!this.conversationContext) {
- const conversation = await this.createConversation()
- this.conversationContext = {
- conversationId: conversation.conversationId,
- conversationSignature: conversation.conversationSignature,
- clientId: conversation.clientId,
- invocationId: 0,
- conversationStyle,
- prompt: '',
- }
- }
- return this.conversationContext
- }
-
- async sendMessage(params: Params) {
- try {
- await this.createContext(params.options.bingConversationStyle)
- Object.assign(this.conversationContext!, { prompt: params.prompt, imageUrl: params.imageUrl })
- return this.sydneyProxy(params)
- } catch (error) {
- params.onEvent({
- type: 'ERROR',
- error: error instanceof ChatError ? error : new ChatError('Catch Error', ErrorCode.UNKOWN_ERROR),
- })
- }
- }
-
- private async sydneyProxy(params: Params) {
- const abortController = new AbortController()
- const response = await fetch(this.endpoint + '/api/sydney', {
- method: 'POST',
- headers: {
- 'Content-Type': 'application/json',
- },
- signal: abortController.signal,
- body: JSON.stringify(this.conversationContext!)
- })
- if (response.status !== 200) {
- params.onEvent({
- type: 'ERROR',
- error: new ChatError(
- 'Unknown error',
- ErrorCode.UNKOWN_ERROR,
- ),
- })
- }
- params.signal?.addEventListener('abort', () => {
- abortController.abort()
- })
-
- const textDecoder = createChunkDecoder()
- for await (const chunk of streamAsyncIterable(response.body!)) {
- this.parseEvents(params, websocketUtils.unpackMessage(textDecoder(chunk)))
- }
- }
-
- async sendWs() {
- const wsConfig: ConstructorParameters[1] = {
- packMessage: websocketUtils.packMessage,
- unpackMessage: websocketUtils.unpackMessage,
- createWebSocket: (url) => new WebSocket(url, {
- headers: {
- 'accept-language': 'zh-CN,zh;q=0.9',
- 'cache-control': 'no-cache',
- 'User-Agent': this.ua,
- pragma: 'no-cache',
- cookie: this.cookie,
- }
- })
- }
- const wsp = new WebSocketAsPromised('wss://sydney.bing.com/sydney/ChatHub', wsConfig)
-
- wsp.open().then(() => {
- wsp.sendPacked({ protocol: 'json', version: 1 })
- wsp.sendPacked({ type: 6 })
- wsp.sendPacked(BingWebBot.buildChatRequest(this.conversationContext!))
- })
-
- return wsp
- }
-
- private async useWs(params: Params) {
- const wsp = await this.sendWs()
- const watchDog = new WatchDog()
- wsp.onUnpackedMessage.addListener((events) => {
- watchDog.watch(() => {
- wsp.sendPacked({ type: 6 })
- })
- this.parseEvents(params, events)
- })
-
- wsp.onClose.addListener(() => {
- watchDog.reset()
- params.onEvent({ type: 'DONE' })
- wsp.removeAllListeners()
- })
-
- params.signal?.addEventListener('abort', () => {
- wsp.removeAllListeners()
- wsp.close()
- })
- }
-
- private async createImage(prompt: string, id: string) {
- try {
- const headers = {
- 'Accept-Encoding': 'gzip, deflate, br, zsdch',
- 'User-Agent': this.ua,
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- cookie: this.cookie,
- }
- const query = new URLSearchParams({
- prompt,
- id
- })
- const response = await fetch(this.endpoint + '/api/image?' + query.toString(),
- {
- method: 'POST',
- headers,
- mode: 'cors',
- credentials: 'include'
- })
- .then(res => res.text())
- if (response) {
- this.lastText += '\n' + response
- }
- } catch (err) {
- console.error('Create Image Error', err)
- }
- }
-
- private buildKnowledgeApiPayload(imageUrl: string, conversationStyle: BingConversationStyle) {
- const imageInfo: ImageInfo = {}
- let imageBase64: string | undefined = undefined
- const knowledgeRequest = {
- imageInfo,
- knowledgeRequest: {
- invokedSkills: [
- 'ImageById'
- ],
- subscriptionId: 'Bing.Chat.Multimodal',
- invokedSkillsRequestData: {
- enableFaceBlur: true
- },
- convoData: {
- convoid: this.conversationContext?.conversationId,
- convotone: conversationStyle,
- }
- },
- }
-
- if (imageUrl.startsWith('data:image/')) {
- imageBase64 = imageUrl.replace('data:image/', '');
- const partIndex = imageBase64.indexOf(',')
- if (partIndex) {
- imageBase64 = imageBase64.substring(partIndex + 1)
- }
- } else {
- imageInfo.url = imageUrl
- }
- return { knowledgeRequest, imageBase64 }
- }
-
- async uploadImage(imageUrl: string, conversationStyle: BingConversationStyle = BingConversationStyle.Creative): Promise {
- if (!imageUrl) {
- return
- }
- await this.createContext(conversationStyle)
- const payload = this.buildKnowledgeApiPayload(imageUrl, conversationStyle)
-
- const response = await fetch(this.endpoint + '/api/kblob',
- {
- headers: {
- 'Content-Type': 'application/json',
- },
- method: 'POST',
- mode: 'cors',
- credentials: 'include',
- body: JSON.stringify(payload),
- })
- .then(res => res.json())
- .catch(e => {
- console.log('Error', e)
- })
- return response
- }
-
- private async generateContent(message: ChatResponseMessage) {
- if (message.contentType === 'IMAGE') {
- this.asyncTasks.push(this.createImage(message.text, message.messageId))
- }
- }
-
- private async parseEvents(params: Params, events: any) {
- const conversation = this.conversationContext!
-
- events?.forEach(async (event: ChatUpdateCompleteResponse) => {
- debug('bing event', event)
- if (event.type === 3) {
- await Promise.all(this.asyncTasks)
- this.asyncTasks = []
- params.onEvent({ type: 'UPDATE_ANSWER', data: { text: this.lastText } })
- params.onEvent({ type: 'DONE' })
- conversation.invocationId = parseInt(event.invocationId, 10) + 1
- } else if (event.type === 1) {
- const messages = event.arguments[0].messages
- if (messages) {
- const text = convertMessageToMarkdown(messages[0])
- this.lastText = text
- params.onEvent({ type: 'UPDATE_ANSWER', data: { text, spokenText: messages[0].text, throttling: event.arguments[0].throttling } })
- }
- } else if (event.type === 2) {
- const messages = event.item.messages as ChatResponseMessage[] | undefined
- if (!messages) {
- params.onEvent({
- type: 'ERROR',
- error: new ChatError(
- event.item.result.error || 'Unknown error',
- event.item.result.value === 'Throttled' ? ErrorCode.THROTTLE_LIMIT
- : event.item.result.value === 'CaptchaChallenge' ? (this.conversationContext?.conversationId?.includes('BingProdUnAuthenticatedUsers') ? ErrorCode.BING_UNAUTHORIZED : ErrorCode.BING_CAPTCHA)
- : ErrorCode.UNKOWN_ERROR
- ),
- })
- return
- }
- const limited = messages.some((message) =>
- message.contentOrigin === 'TurnLimiter'
- || message.messageType === 'Disengaged'
- )
- if (limited) {
- params.onEvent({
- type: 'ERROR',
- error: new ChatError(
- 'Sorry, you have reached chat limit in this conversation.',
- ErrorCode.CONVERSATION_LIMIT,
- ),
- })
- return
- }
-
- const lastMessage = event.item.messages.at(-1) as ChatResponseMessage
- const specialMessage = event.item.messages.find(message => message.author === 'bot' && message.contentType === 'IMAGE')
- if (specialMessage) {
- this.generateContent(specialMessage)
- }
-
- if (lastMessage) {
- const text = convertMessageToMarkdown(lastMessage)
- this.lastText = text
- params.onEvent({
- type: 'UPDATE_ANSWER',
- data: { text, throttling: event.item.throttling, suggestedResponses: lastMessage.suggestedResponses, sourceAttributions: lastMessage.sourceAttributions },
- })
- }
- }
- })
- }
-
- resetConversation() {
- this.conversationContext = undefined
- }
-}
diff --git a/spaces/Kaixuanliu/textual-inversion-training/convertosd.py b/spaces/Kaixuanliu/textual-inversion-training/convertosd.py
deleted file mode 100644
index e4bec6cbe894dd74b24f633cc66346d687d3f802..0000000000000000000000000000000000000000
--- a/spaces/Kaixuanliu/textual-inversion-training/convertosd.py
+++ /dev/null
@@ -1,226 +0,0 @@
-# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
-# *Only* converts the UNet, VAE, and Text Encoder.
-# Does not convert optimizer state or any other thing.
-# Written by jachiam
-
-import argparse
-import os.path as osp
-
-import torch
-import gc
-
-# =================#
-# UNet Conversion #
-# =================#
-
-unet_conversion_map = [
- # (stable-diffusion, HF Diffusers)
- ("time_embed.0.weight", "time_embedding.linear_1.weight"),
- ("time_embed.0.bias", "time_embedding.linear_1.bias"),
- ("time_embed.2.weight", "time_embedding.linear_2.weight"),
- ("time_embed.2.bias", "time_embedding.linear_2.bias"),
- ("input_blocks.0.0.weight", "conv_in.weight"),
- ("input_blocks.0.0.bias", "conv_in.bias"),
- ("out.0.weight", "conv_norm_out.weight"),
- ("out.0.bias", "conv_norm_out.bias"),
- ("out.2.weight", "conv_out.weight"),
- ("out.2.bias", "conv_out.bias"),
-]
-
-unet_conversion_map_resnet = [
- # (stable-diffusion, HF Diffusers)
- ("in_layers.0", "norm1"),
- ("in_layers.2", "conv1"),
- ("out_layers.0", "norm2"),
- ("out_layers.3", "conv2"),
- ("emb_layers.1", "time_emb_proj"),
- ("skip_connection", "conv_shortcut"),
-]
-
-unet_conversion_map_layer = []
-# hardcoded number of downblocks and resnets/attentions...
-# would need smarter logic for other networks.
-for i in range(4):
- # loop over downblocks/upblocks
-
- for j in range(2):
- # loop over resnets/attentions for downblocks
- hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
- sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
- unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
-
- if i < 3:
- # no attention layers in down_blocks.3
- hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
- sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
- unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
-
- for j in range(3):
- # loop over resnets/attentions for upblocks
- hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
- sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
- unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
-
- if i > 0:
- # no attention layers in up_blocks.0
- hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
- sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
- unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
-
- if i < 3:
- # no downsample in down_blocks.3
- hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
- sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
- unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
-
- # no upsample in up_blocks.3
- hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
- sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
- unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
-
-hf_mid_atn_prefix = "mid_block.attentions.0."
-sd_mid_atn_prefix = "middle_block.1."
-unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
-
-for j in range(2):
- hf_mid_res_prefix = f"mid_block.resnets.{j}."
- sd_mid_res_prefix = f"middle_block.{2*j}."
- unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-def convert_unet_state_dict(unet_state_dict):
- # buyer beware: this is a *brittle* function,
- # and correct output requires that all of these pieces interact in
- # the exact order in which I have arranged them.
- mapping = {k: k for k in unet_state_dict.keys()}
- for sd_name, hf_name in unet_conversion_map:
- mapping[hf_name] = sd_name
- for k, v in mapping.items():
- if "resnets" in k:
- for sd_part, hf_part in unet_conversion_map_resnet:
- v = v.replace(hf_part, sd_part)
- mapping[k] = v
- for k, v in mapping.items():
- for sd_part, hf_part in unet_conversion_map_layer:
- v = v.replace(hf_part, sd_part)
- mapping[k] = v
- new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
- return new_state_dict
-
-
-# ================#
-# VAE Conversion #
-# ================#
-
-vae_conversion_map = [
- # (stable-diffusion, HF Diffusers)
- ("nin_shortcut", "conv_shortcut"),
- ("norm_out", "conv_norm_out"),
- ("mid.attn_1.", "mid_block.attentions.0."),
-]
-
-for i in range(4):
- # down_blocks have two resnets
- for j in range(2):
- hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
- sd_down_prefix = f"encoder.down.{i}.block.{j}."
- vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
-
- if i < 3:
- hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
- sd_downsample_prefix = f"down.{i}.downsample."
- vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
-
- hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
- sd_upsample_prefix = f"up.{3-i}.upsample."
- vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
-
- # up_blocks have three resnets
- # also, up blocks in hf are numbered in reverse from sd
- for j in range(3):
- hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
- sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
- vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
-
-# this part accounts for mid blocks in both the encoder and the decoder
-for i in range(2):
- hf_mid_res_prefix = f"mid_block.resnets.{i}."
- sd_mid_res_prefix = f"mid.block_{i+1}."
- vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-vae_conversion_map_attn = [
- # (stable-diffusion, HF Diffusers)
- ("norm.", "group_norm."),
- ("q.", "query."),
- ("k.", "key."),
- ("v.", "value."),
- ("proj_out.", "proj_attn."),
-]
-
-
-def reshape_weight_for_sd(w):
- # convert HF linear weights to SD conv2d weights
- return w.reshape(*w.shape, 1, 1)
-
-
-def convert_vae_state_dict(vae_state_dict):
- mapping = {k: k for k in vae_state_dict.keys()}
- for k, v in mapping.items():
- for sd_part, hf_part in vae_conversion_map:
- v = v.replace(hf_part, sd_part)
- mapping[k] = v
- for k, v in mapping.items():
- if "attentions" in k:
- for sd_part, hf_part in vae_conversion_map_attn:
- v = v.replace(hf_part, sd_part)
- mapping[k] = v
- new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
- weights_to_convert = ["q", "k", "v", "proj_out"]
- print("[1;32mConverting to CKPT ...")
- for k, v in new_state_dict.items():
- for weight_name in weights_to_convert:
- if f"mid.attn_1.{weight_name}.weight" in k:
- new_state_dict[k] = reshape_weight_for_sd(v)
- return new_state_dict
-
-
-# =========================#
-# Text Encoder Conversion #
-# =========================#
-# pretty much a no-op
-
-
-def convert_text_enc_state_dict(text_enc_dict):
- return text_enc_dict
-
-
-def convert(model_path, checkpoint_path):
- unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
- vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
- text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
-
- # Convert the UNet model
- unet_state_dict = torch.load(unet_path, map_location='cpu')
- unet_state_dict = convert_unet_state_dict(unet_state_dict)
- unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
-
- # Convert the VAE model
- vae_state_dict = torch.load(vae_path, map_location='cpu')
- vae_state_dict = convert_vae_state_dict(vae_state_dict)
- vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
-
- # Convert the text encoder model
- text_enc_dict = torch.load(text_enc_path, map_location='cpu')
- text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
- text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
-
- # Put together new checkpoint
- state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
-
- state_dict = {k:v.half() for k,v in state_dict.items()}
- state_dict = {"state_dict": state_dict}
- torch.save(state_dict, checkpoint_path)
- del state_dict, text_enc_dict, vae_state_dict, unet_state_dict
- torch.cuda.empty_cache()
- gc.collect()
diff --git a/spaces/Kaludi/VirtualBrainGPT/pages/Brain_Search.py b/spaces/Kaludi/VirtualBrainGPT/pages/Brain_Search.py
deleted file mode 100644
index 8152ac9f15fcd24f6e066bf6134de40edc52613f..0000000000000000000000000000000000000000
--- a/spaces/Kaludi/VirtualBrainGPT/pages/Brain_Search.py
+++ /dev/null
@@ -1,102 +0,0 @@
-from dotenv import load_dotenv
-import os
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chains.question_answering import load_qa_chain
-from langchain.chains.qa_with_sources import load_qa_with_sources_chain
-from langchain.llms import OpenAI
-from langchain.callbacks import get_openai_callback
-
-def extract_text_from_pdf(pdf):
- pdf_reader = PdfReader(pdf)
- text = ""
- for page in pdf_reader.pages:
- text += page.extract_text()
- return text
-
-
-def extract_text_from_txt(txt):
- text = txt.read().decode("utf-8")
- return text
-
-
-def extract_text_from_brain():
- with open('brain/brain_journal.txt', 'r', encoding='utf-8') as file:
- text = file.read()
- return text
-
-
-def main():
- load_dotenv()
- hide_streamlit_style = """
-
- """
- st.markdown(hide_streamlit_style, unsafe_allow_html=True)
- st.title("Digital Brain Journal Search 🔍")
- st.write("Ask any questions about any of your journal entries with OpenAI's Embeddings and Langchain. The virtual brain keeps track of everything in a user's life. If you have another TXT or PDF file you'd like to search for answers, click on the dropdown and select eithter TXT or PDF option in file type. Along with the response, you will also get information about the amount of tokens that were used and the Total Cost of the query.")
-
- # Add API key input
- api_key = st.text_input("Enter your OpenAI API key:", type="password")
- os.environ["OPENAI_API_KEY"] = api_key
-
- if not api_key:
- st.warning("Please enter your OpenAI API key to continue.")
- else:
- file_type = st.selectbox("Choose the file type", options=["Brain", "PDF", "TXT"])
-
- file = None
- text = None
-
- if file_type == "PDF":
- file = st.file_uploader("Upload your PDF", type="pdf")
- if file is not None:
- text = extract_text_from_pdf(file)
- elif file_type == "TXT":
- file = st.file_uploader("Upload your TXT", type="txt")
- if file is not None:
- text = extract_text_from_txt(file)
- elif file_type == "Brain":
- text = extract_text_from_brain()
-
- if file is not None or file_type == "Brain":
- # split into chunks
- text_splitter = CharacterTextSplitter(
- separator="\n",
- chunk_size=1000,
- chunk_overlap=200,
- length_function=len
- )
- chunks = text_splitter.split_text(text)
-
- # create embeddings
- embeddings = OpenAIEmbeddings()
- knowledge_base = FAISS.from_texts(chunks, embeddings)
-
- # show user input
- user_question = st.text_area("Ask a question about your document:")
-
- if st.button("Submit"):
- if user_question:
- docs = knowledge_base.similarity_search(user_question)
-
- llm = OpenAI()
- chain = load_qa_chain(llm, chain_type="stuff")
- with get_openai_callback() as cb:
- response = chain.run(input_documents=docs, question=user_question)
- print(cb)
-
- st.markdown("### Response:")
- st.write(response)
- st.write(cb)
- st.markdown("---")
- st.markdown("")
- st.markdown("
Blitz Diffusion - 540 Stable Diffusion models, but why? For your enjoyment!
-
2023.11.8 Toys to play with: The models OG, LeyLines, AM-mix1, Hassaku, MeinaMix v11, SCMix, AbsoluteReality v1, CrystalClear, CleanLinearMix and CrystalClearRemix have been added!
-
-
If a model is already loaded each new image takes less than 20 seconds to generate!
-
- If you get ERROR it's because that model ran out of memory, try again, or wait a minute and try again, have fun!
-
- """)
- with gr.Row():
- with gr.Column(scale=100):
- #Model selection dropdown
- model_name1 = gr.Dropdown(label="Select Model", choices=[m for m in models], type="index", value=current_model, interactive=True)
- with gr.Row():
- with gr.Column(scale=100):
- magic1=gr.Textbox(label="Your Prompt", lines=4) #Positive
- #with gr.Column(scale=100):
- #negative_prompt=gr.Textbox(label="Negative Prompt", lines=1)
- gr.HTML("""""")
- run=gr.Button("Generate Image")
- with gr.Row():
- with gr.Column(style="width=800px"):
- output1=gr.Image(label=(f"{current_model}"))
-
-
- with gr.Row():
- with gr.Column(scale=50):
- input_text=gr.Textbox(label="Use this box to extend an idea automagically, by typing some words and clicking Extend Idea",lines=2)
- see_prompts=gr.Button("Extend Idea -> overwrite the contents of the `Your Prompt´ box above")
- use_short=gr.Button("Copy the contents of this box to the `Your Prompt´ box above")
- def short_prompt(inputs):
- return(inputs)
-
- model_name1.change(set_model,inputs=model_name1,outputs=[output1])
-
- run.click(send_it1, inputs=[magic1, model_name1], outputs=[output1])
-
- use_short.click(short_prompt,inputs=[input_text],outputs=magic1)
-
- see_prompts.click(text_it1,inputs=[input_text],outputs=magic1)
-
-myface.queue(concurrency_count=200)
-myface.launch(inline=True, show_api=False, max_threads=400)
\ No newline at end of file
diff --git a/spaces/Yudha515/Rvc-Models/audiocraft/quantization/__init__.py b/spaces/Yudha515/Rvc-Models/audiocraft/quantization/__init__.py
deleted file mode 100644
index 836d6eb518978480c6b95d6f29ce4f84a9428793..0000000000000000000000000000000000000000
--- a/spaces/Yudha515/Rvc-Models/audiocraft/quantization/__init__.py
+++ /dev/null
@@ -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 .vq import ResidualVectorQuantizer
-from .base import BaseQuantizer, DummyQuantizer, QuantizedResult
diff --git a/spaces/Yunshansongbai/SVC-Nahida/vdecoder/vdecoder/hifigan/utils.py b/spaces/Yunshansongbai/SVC-Nahida/vdecoder/vdecoder/hifigan/utils.py
deleted file mode 100644
index 1063b035664d2c61bb03a607db7f0ef5e1b99e07..0000000000000000000000000000000000000000
--- a/spaces/Yunshansongbai/SVC-Nahida/vdecoder/vdecoder/hifigan/utils.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import glob
-import os
-import matplotlib
-import paddle
-from paddle.nn.utils import weight_norm
-# matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
-
- fig.canvas.draw()
- plt.close()
-
- return fig
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight = paddle.normal(mean, std, m.weight.shape)
-
-
-def apply_weight_norm(m):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size*dilation - dilation)/2)
-
-
-def load_checkpoint(filepath, device):
- assert os.path.isfile(filepath)
- print("加载'{}'".format(filepath))
- checkpoint_dict = paddle.load(filepath, map_location=device)
- print("完成。")
- return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
- print("保存检查点到{}".format(filepath))
- paddle.save(obj, filepath)
- print("完成。")
-
-
-def del_old_checkpoints(cp_dir, prefix, n_models=2):
- pattern = os.path.join(cp_dir, prefix + '????????')
- cp_list = glob.glob(pattern) # get checkpoint paths
- cp_list = sorted(cp_list)# sort by iter
- if len(cp_list) > n_models: # if more than n_models models are found
- for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
- open(cp, 'w').close()# empty file contents
- os.unlink(cp)# delete file (move to trash when using Colab)
-
-
-def scan_checkpoint(cp_dir, prefix):
- pattern = os.path.join(cp_dir, prefix + '????????')
- cp_list = glob.glob(pattern)
- if len(cp_list) == 0:
- return None
- return sorted(cp_list)[-1]
diff --git a/spaces/aadnk/whisper-webui/src/hooks/subTaskProgressListener.py b/spaces/aadnk/whisper-webui/src/hooks/subTaskProgressListener.py
deleted file mode 100644
index 9a8eaa876fcd18032875d67535e0558494842c60..0000000000000000000000000000000000000000
--- a/spaces/aadnk/whisper-webui/src/hooks/subTaskProgressListener.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from src.hooks.progressListener import ProgressListener
-
-from typing import Union
-
-class SubTaskProgressListener(ProgressListener):
- """
- A sub task listener that reports the progress of a sub task to a base task listener
- Parameters
- ----------
- base_task_listener : ProgressListener
- The base progress listener to accumulate overall progress in.
- base_task_total : float
- The maximum total progress that will be reported to the base progress listener.
- sub_task_start : float
- The starting progress of a sub task, in respect to the base progress listener.
- sub_task_total : float
- The total amount of progress a sub task will report to the base progress listener.
- """
- def __init__(
- self,
- base_task_listener: ProgressListener,
- base_task_total: float,
- sub_task_start: float,
- sub_task_total: float,
- ):
- self.base_task_listener = base_task_listener
- self.base_task_total = base_task_total
- self.sub_task_start = sub_task_start
- self.sub_task_total = sub_task_total
-
- def on_progress(self, current: Union[int, float], total: Union[int, float]):
- sub_task_progress_frac = current / total
- sub_task_progress = self.sub_task_start + self.sub_task_total * sub_task_progress_frac
- self.base_task_listener.on_progress(sub_task_progress, self.base_task_total)
-
- def on_finished(self):
- self.base_task_listener.on_progress(self.sub_task_start + self.sub_task_total, self.base_task_total)
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/datasets/wider_face.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/datasets/wider_face.py
deleted file mode 100644
index 3a13907db87a9986a7d701837259a0b712fc9dca..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/datasets/wider_face.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import os.path as osp
-import xml.etree.ElementTree as ET
-
-import mmcv
-
-from .builder import DATASETS
-from .xml_style import XMLDataset
-
-
-@DATASETS.register_module()
-class WIDERFaceDataset(XMLDataset):
- """Reader for the WIDER Face dataset in PASCAL VOC format.
-
- Conversion scripts can be found in
- https://github.com/sovrasov/wider-face-pascal-voc-annotations
- """
- CLASSES = ('face', )
-
- def __init__(self, **kwargs):
- super(WIDERFaceDataset, self).__init__(**kwargs)
-
- def load_annotations(self, ann_file):
- """Load annotation from WIDERFace XML style annotation file.
-
- Args:
- ann_file (str): Path of XML file.
-
- Returns:
- list[dict]: Annotation info from XML file.
- """
-
- data_infos = []
- img_ids = mmcv.list_from_file(ann_file)
- for img_id in img_ids:
- filename = f'{img_id}.jpg'
- xml_path = osp.join(self.img_prefix, 'Annotations',
- f'{img_id}.xml')
- tree = ET.parse(xml_path)
- root = tree.getroot()
- size = root.find('size')
- width = int(size.find('width').text)
- height = int(size.find('height').text)
- folder = root.find('folder').text
- data_infos.append(
- dict(
- id=img_id,
- filename=osp.join(folder, filename),
- width=width,
- height=height))
-
- return data_infos
diff --git a/spaces/abrar-lohia/text-2-character-anim/VQTrans/options/option_transformer.py b/spaces/abrar-lohia/text-2-character-anim/VQTrans/options/option_transformer.py
deleted file mode 100644
index cf48ce1fdac663ec44419d67721ac268806f8127..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/VQTrans/options/option_transformer.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import argparse
-
-def get_args_parser():
- parser = argparse.ArgumentParser(description='Optimal Transport AutoEncoder training for Amass',
- add_help=True,
- formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
- ## dataloader
-
- parser.add_argument('--dataname', type=str, default='kit', help='dataset directory')
- parser.add_argument('--batch-size', default=128, type=int, help='batch size')
- parser.add_argument('--fps', default=[20], nargs="+", type=int, help='frames per second')
- parser.add_argument('--seq-len', type=int, default=64, help='training motion length')
-
- ## optimization
- parser.add_argument('--total-iter', default=100000, type=int, help='number of total iterations to run')
- parser.add_argument('--warm-up-iter', default=1000, type=int, help='number of total iterations for warmup')
- parser.add_argument('--lr', default=2e-4, type=float, help='max learning rate')
- parser.add_argument('--lr-scheduler', default=[60000], nargs="+", type=int, help="learning rate schedule (iterations)")
- parser.add_argument('--gamma', default=0.05, type=float, help="learning rate decay")
-
- parser.add_argument('--weight-decay', default=1e-6, type=float, help='weight decay')
- parser.add_argument('--decay-option',default='all', type=str, choices=['all', 'noVQ'], help='disable weight decay on codebook')
- parser.add_argument('--optimizer',default='adamw', type=str, choices=['adam', 'adamw'], help='disable weight decay on codebook')
-
- ## vqvae arch
- parser.add_argument("--code-dim", type=int, default=512, help="embedding dimension")
- parser.add_argument("--nb-code", type=int, default=512, help="nb of embedding")
- parser.add_argument("--mu", type=float, default=0.99, help="exponential moving average to update the codebook")
- parser.add_argument("--down-t", type=int, default=3, help="downsampling rate")
- parser.add_argument("--stride-t", type=int, default=2, help="stride size")
- parser.add_argument("--width", type=int, default=512, help="width of the network")
- parser.add_argument("--depth", type=int, default=3, help="depth of the network")
- parser.add_argument("--dilation-growth-rate", type=int, default=3, help="dilation growth rate")
- parser.add_argument("--output-emb-width", type=int, default=512, help="output embedding width")
- parser.add_argument('--vq-act', type=str, default='relu', choices = ['relu', 'silu', 'gelu'], help='dataset directory')
-
- ## gpt arch
- parser.add_argument("--block-size", type=int, default=25, help="seq len")
- parser.add_argument("--embed-dim-gpt", type=int, default=512, help="embedding dimension")
- parser.add_argument("--clip-dim", type=int, default=512, help="latent dimension in the clip feature")
- parser.add_argument("--num-layers", type=int, default=2, help="nb of transformer layers")
- parser.add_argument("--n-head-gpt", type=int, default=8, help="nb of heads")
- parser.add_argument("--ff-rate", type=int, default=4, help="feedforward size")
- parser.add_argument("--drop-out-rate", type=float, default=0.1, help="dropout ratio in the pos encoding")
-
- ## quantizer
- parser.add_argument("--quantizer", type=str, default='ema_reset', choices = ['ema', 'orig', 'ema_reset', 'reset'], help="eps for optimal transport")
- parser.add_argument('--quantbeta', type=float, default=1.0, help='dataset directory')
-
- ## resume
- parser.add_argument("--resume-pth", type=str, default=None, help='resume vq pth')
- parser.add_argument("--resume-trans", type=str, default=None, help='resume gpt pth')
-
-
- ## output directory
- parser.add_argument('--out-dir', type=str, default='output_GPT_Final/', help='output directory')
- parser.add_argument('--exp-name', type=str, default='exp_debug', help='name of the experiment, will create a file inside out-dir')
- parser.add_argument('--vq-name', type=str, default='exp_debug', help='name of the generated dataset .npy, will create a file inside out-dir')
- ## other
- parser.add_argument('--print-iter', default=200, type=int, help='print frequency')
- parser.add_argument('--eval-iter', default=5000, type=int, help='evaluation frequency')
- parser.add_argument('--seed', default=123, type=int, help='seed for initializing training. ')
- parser.add_argument("--if-maxtest", action='store_true', help="test in max")
- parser.add_argument('--pkeep', type=float, default=1.0, help='keep rate for gpt training')
-
-
- return parser.parse_args()
\ No newline at end of file
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/app/__init__.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/app/__init__.py
deleted file mode 100644
index d15509415423ea283968d1ca2920bda4de77b035..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/app/__init__.py
+++ /dev/null
@@ -1,97 +0,0 @@
-"""Application-wide functionality.
-
-Applications
-------------
-
-Most applications need only call :func:`run` after creating one or more
-windows to begin processing events. For example, a simple application
-consisting of one window is::
-
- import pyglet
-
- win = pyglet.window.Window()
- pyglet.app.run()
-
-
-Events
-======
-
-To handle events on the main event loop, instantiate it manually. The
-following example exits the application as soon as any window is closed (the
-default policy is to wait until all windows are closed)::
-
- event_loop = pyglet.app.EventLoop()
-
- @event_loop.event
- def on_window_close(window):
- event_loop.exit()
-
-.. versionadded:: 1.1
-"""
-
-import sys
-import weakref
-
-from pyglet.app.base import EventLoop
-from pyglet import compat_platform
-
-_is_pyglet_doc_run = hasattr(sys, "is_pyglet_doc_run") and sys.is_pyglet_doc_run
-
-
-if _is_pyglet_doc_run:
- from pyglet.app.base import PlatformEventLoop
-else:
- if compat_platform == 'darwin':
- from pyglet.app.cocoa import CocoaEventLoop as PlatformEventLoop
- elif compat_platform in ('win32', 'cygwin'):
- from pyglet.app.win32 import Win32EventLoop as PlatformEventLoop
- else:
- from pyglet.app.xlib import XlibEventLoop as PlatformEventLoop
-
-
-class AppException(Exception):
- pass
-
-
-windows = weakref.WeakSet()
-"""Set of all open windows (including invisible windows). Instances of
-:class:`pyglet.window.Window` are automatically added to this set upon
-construction. The set uses weak references, so windows are removed from
-the set when they are no longer referenced or are closed explicitly.
-"""
-
-
-def run(interval=1/60):
- """Begin processing events, scheduled functions and window updates.
-
- This is a convenience function, equivalent to::
-
- pyglet.app.event_loop.run()
-
- """
- event_loop.run(interval)
-
-
-def exit():
- """Exit the application event loop.
-
- Causes the application event loop to finish, if an event loop is currently
- running. The application may not necessarily exit (for example, there may
- be additional code following the `run` invocation).
-
- This is a convenience function, equivalent to::
-
- event_loop.exit()
-
- """
- event_loop.exit()
-
-
-#: The global event loop. Applications can replace this
-#: with their own subclass of :class:`EventLoop` before calling
-#: :meth:`EventLoop.run`.
-event_loop = EventLoop()
-
-#: The platform-dependent event loop. Applications must not subclass
-# or replace this :class:`PlatformEventLoop` object.
-platform_event_loop = PlatformEventLoop()
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/_cell_widths.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/_cell_widths.py
deleted file mode 100644
index 36286df379e28ea997bea3ee1fd62cadebebbba9..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/_cell_widths.py
+++ /dev/null
@@ -1,451 +0,0 @@
-# Auto generated by make_terminal_widths.py
-
-CELL_WIDTHS = [
- (0, 0, 0),
- (1, 31, -1),
- (127, 159, -1),
- (768, 879, 0),
- (1155, 1161, 0),
- (1425, 1469, 0),
- (1471, 1471, 0),
- (1473, 1474, 0),
- (1476, 1477, 0),
- (1479, 1479, 0),
- (1552, 1562, 0),
- (1611, 1631, 0),
- (1648, 1648, 0),
- (1750, 1756, 0),
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- (1767, 1768, 0),
- (1770, 1773, 0),
- (1809, 1809, 0),
- (1840, 1866, 0),
- (1958, 1968, 0),
- (2027, 2035, 0),
- (2045, 2045, 0),
- (2070, 2073, 0),
- (2075, 2083, 0),
- (2085, 2087, 0),
- (2089, 2093, 0),
- (2137, 2139, 0),
- (2259, 2273, 0),
- (2275, 2306, 0),
- (2362, 2362, 0),
- (2364, 2364, 0),
- (2369, 2376, 0),
- (2381, 2381, 0),
- (2385, 2391, 0),
- (2402, 2403, 0),
- (2433, 2433, 0),
- (2492, 2492, 0),
- (2497, 2500, 0),
- (2509, 2509, 0),
- (2530, 2531, 0),
- (2558, 2558, 0),
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- (2620, 2620, 0),
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- (2635, 2637, 0),
- (2641, 2641, 0),
- (2672, 2673, 0),
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- (2817, 2817, 0),
- (2876, 2876, 0),
- (2879, 2879, 0),
- (2881, 2884, 0),
- (2893, 2893, 0),
- (2901, 2902, 0),
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- (3134, 3136, 0),
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- (70502, 70508, 0),
- (70512, 70516, 0),
- (70712, 70719, 0),
- (70722, 70724, 0),
- (70726, 70726, 0),
- (70750, 70750, 0),
- (70835, 70840, 0),
- (70842, 70842, 0),
- (70847, 70848, 0),
- (70850, 70851, 0),
- (71090, 71093, 0),
- (71100, 71101, 0),
- (71103, 71104, 0),
- (71132, 71133, 0),
- (71219, 71226, 0),
- (71229, 71229, 0),
- (71231, 71232, 0),
- (71339, 71339, 0),
- (71341, 71341, 0),
- (71344, 71349, 0),
- (71351, 71351, 0),
- (71453, 71455, 0),
- (71458, 71461, 0),
- (71463, 71467, 0),
- (71727, 71735, 0),
- (71737, 71738, 0),
- (71995, 71996, 0),
- (71998, 71998, 0),
- (72003, 72003, 0),
- (72148, 72151, 0),
- (72154, 72155, 0),
- (72160, 72160, 0),
- (72193, 72202, 0),
- (72243, 72248, 0),
- (72251, 72254, 0),
- (72263, 72263, 0),
- (72273, 72278, 0),
- (72281, 72283, 0),
- (72330, 72342, 0),
- (72344, 72345, 0),
- (72752, 72758, 0),
- (72760, 72765, 0),
- (72767, 72767, 0),
- (72850, 72871, 0),
- (72874, 72880, 0),
- (72882, 72883, 0),
- (72885, 72886, 0),
- (73009, 73014, 0),
- (73018, 73018, 0),
- (73020, 73021, 0),
- (73023, 73029, 0),
- (73031, 73031, 0),
- (73104, 73105, 0),
- (73109, 73109, 0),
- (73111, 73111, 0),
- (73459, 73460, 0),
- (92912, 92916, 0),
- (92976, 92982, 0),
- (94031, 94031, 0),
- (94095, 94098, 0),
- (94176, 94179, 2),
- (94180, 94180, 0),
- (94192, 94193, 2),
- (94208, 100343, 2),
- (100352, 101589, 2),
- (101632, 101640, 2),
- (110592, 110878, 2),
- (110928, 110930, 2),
- (110948, 110951, 2),
- (110960, 111355, 2),
- (113821, 113822, 0),
- (119143, 119145, 0),
- (119163, 119170, 0),
- (119173, 119179, 0),
- (119210, 119213, 0),
- (119362, 119364, 0),
- (121344, 121398, 0),
- (121403, 121452, 0),
- (121461, 121461, 0),
- (121476, 121476, 0),
- (121499, 121503, 0),
- (121505, 121519, 0),
- (122880, 122886, 0),
- (122888, 122904, 0),
- (122907, 122913, 0),
- (122915, 122916, 0),
- (122918, 122922, 0),
- (123184, 123190, 0),
- (123628, 123631, 0),
- (125136, 125142, 0),
- (125252, 125258, 0),
- (126980, 126980, 2),
- (127183, 127183, 2),
- (127374, 127374, 2),
- (127377, 127386, 2),
- (127488, 127490, 2),
- (127504, 127547, 2),
- (127552, 127560, 2),
- (127568, 127569, 2),
- (127584, 127589, 2),
- (127744, 127776, 2),
- (127789, 127797, 2),
- (127799, 127868, 2),
- (127870, 127891, 2),
- (127904, 127946, 2),
- (127951, 127955, 2),
- (127968, 127984, 2),
- (127988, 127988, 2),
- (127992, 128062, 2),
- (128064, 128064, 2),
- (128066, 128252, 2),
- (128255, 128317, 2),
- (128331, 128334, 2),
- (128336, 128359, 2),
- (128378, 128378, 2),
- (128405, 128406, 2),
- (128420, 128420, 2),
- (128507, 128591, 2),
- (128640, 128709, 2),
- (128716, 128716, 2),
- (128720, 128722, 2),
- (128725, 128727, 2),
- (128747, 128748, 2),
- (128756, 128764, 2),
- (128992, 129003, 2),
- (129292, 129338, 2),
- (129340, 129349, 2),
- (129351, 129400, 2),
- (129402, 129483, 2),
- (129485, 129535, 2),
- (129648, 129652, 2),
- (129656, 129658, 2),
- (129664, 129670, 2),
- (129680, 129704, 2),
- (129712, 129718, 2),
- (129728, 129730, 2),
- (129744, 129750, 2),
- (131072, 196605, 2),
- (196608, 262141, 2),
- (917760, 917999, 0),
-]
diff --git a/spaces/ali-ghamdan/deoldify/fastai/imports/core.py b/spaces/ali-ghamdan/deoldify/fastai/imports/core.py
deleted file mode 100644
index 51935a07ad61a5eda922941aa8869a2bcc5705c1..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/deoldify/fastai/imports/core.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import csv, gc, gzip, os, pickle, shutil, sys, warnings, yaml, io, subprocess
-import math, matplotlib.pyplot as plt, numpy as np, pandas as pd, random
-import scipy.stats, scipy.special
-import abc, collections, hashlib, itertools, json, operator, pathlib
-import mimetypes, inspect, typing, functools, importlib, weakref
-import html, re, requests, tarfile, numbers, tempfile, bz2
-
-from abc import abstractmethod, abstractproperty
-from collections import abc, Counter, defaultdict, namedtuple, OrderedDict
-from collections.abc import Iterable
-import concurrent
-from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
-from copy import copy, deepcopy
-from dataclasses import dataclass, field, InitVar
-from enum import Enum, IntEnum
-from functools import partial, reduce
-from pdb import set_trace
-from matplotlib import patches, patheffects
-from numpy import array, cos, exp, log, sin, tan, tanh
-from operator import attrgetter, itemgetter
-from pathlib import Path
-from warnings import warn
-from contextlib import contextmanager
-from fastprogress.fastprogress import MasterBar, ProgressBar
-from matplotlib.patches import Patch
-from pandas import Series, DataFrame
-from io import BufferedWriter, BytesIO
-
-import pkg_resources
-pkg_resources.require("fastprogress>=0.1.19")
-from fastprogress.fastprogress import master_bar, progress_bar
-
-#for type annotations
-from numbers import Number
-from typing import Any, AnyStr, Callable, Collection, Dict, Hashable, Iterator, List, Mapping, NewType, Optional
-from typing import Sequence, Tuple, TypeVar, Union
-from types import SimpleNamespace
-
-def try_import(module):
- "Try to import `module`. Returns module's object on success, None on failure"
- try: return importlib.import_module(module)
- except: return None
-
-def have_min_pkg_version(package, version):
- "Check whether we have at least `version` of `package`. Returns True on success, False otherwise."
- try:
- pkg_resources.require(f"{package}>={version}")
- return True
- except:
- return False
diff --git a/spaces/aliceoq/vozes-da-loirinha/lib/uvr5_pack/lib_v5/nets_123812KB.py b/spaces/aliceoq/vozes-da-loirinha/lib/uvr5_pack/lib_v5/nets_123812KB.py
deleted file mode 100644
index becbfae85683a13bbb19d3ea6c840da24e61e01e..0000000000000000000000000000000000000000
--- a/spaces/aliceoq/vozes-da-loirinha/lib/uvr5_pack/lib_v5/nets_123812KB.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import layers_123821KB as layers
-
-
-class BaseASPPNet(nn.Module):
- def __init__(self, nin, ch, dilations=(4, 8, 16)):
- super(BaseASPPNet, self).__init__()
- self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
- self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
- self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
- self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
- self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
- self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
- self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
- self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
- self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
- def __call__(self, x):
- h, e1 = self.enc1(x)
- h, e2 = self.enc2(h)
- h, e3 = self.enc3(h)
- h, e4 = self.enc4(h)
-
- h = self.aspp(h)
-
- h = self.dec4(h, e4)
- h = self.dec3(h, e3)
- h = self.dec2(h, e2)
- h = self.dec1(h, e1)
-
- return h
-
-
-class CascadedASPPNet(nn.Module):
- def __init__(self, n_fft):
- super(CascadedASPPNet, self).__init__()
- self.stg1_low_band_net = BaseASPPNet(2, 32)
- self.stg1_high_band_net = BaseASPPNet(2, 32)
-
- self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
- self.stg2_full_band_net = BaseASPPNet(16, 32)
-
- self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
- self.stg3_full_band_net = BaseASPPNet(32, 64)
-
- self.out = nn.Conv2d(64, 2, 1, bias=False)
- self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
- self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
-
- self.max_bin = n_fft // 2
- self.output_bin = n_fft // 2 + 1
-
- self.offset = 128
-
- def forward(self, x, aggressiveness=None):
- mix = x.detach()
- x = x.clone()
-
- x = x[:, :, : self.max_bin]
-
- bandw = x.size()[2] // 2
- aux1 = torch.cat(
- [
- self.stg1_low_band_net(x[:, :, :bandw]),
- self.stg1_high_band_net(x[:, :, bandw:]),
- ],
- dim=2,
- )
-
- h = torch.cat([x, aux1], dim=1)
- aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
- h = torch.cat([x, aux1, aux2], dim=1)
- h = self.stg3_full_band_net(self.stg3_bridge(h))
-
- mask = torch.sigmoid(self.out(h))
- mask = F.pad(
- input=mask,
- pad=(0, 0, 0, self.output_bin - mask.size()[2]),
- mode="replicate",
- )
-
- if self.training:
- aux1 = torch.sigmoid(self.aux1_out(aux1))
- aux1 = F.pad(
- input=aux1,
- pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
- mode="replicate",
- )
- aux2 = torch.sigmoid(self.aux2_out(aux2))
- aux2 = F.pad(
- input=aux2,
- pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
- mode="replicate",
- )
- return mask * mix, aux1 * mix, aux2 * mix
- else:
- if aggressiveness:
- mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
- mask[:, :, : aggressiveness["split_bin"]],
- 1 + aggressiveness["value"] / 3,
- )
- mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
- mask[:, :, aggressiveness["split_bin"] :],
- 1 + aggressiveness["value"],
- )
-
- return mask * mix
-
- def predict(self, x_mag, aggressiveness=None):
- h = self.forward(x_mag, aggressiveness)
-
- if self.offset > 0:
- h = h[:, :, :, self.offset : -self.offset]
- assert h.size()[3] > 0
-
- return h
diff --git a/spaces/alitrack/ChatPDF/README.md b/spaces/alitrack/ChatPDF/README.md
deleted file mode 100644
index 84a1fcadec658af6b4d056aecf1bd542bbc8ad3d..0000000000000000000000000000000000000000
--- a/spaces/alitrack/ChatPDF/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChatPDF
-emoji: 😁
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: shibing624/ChatPDF
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/pablio/test_rw.c b/spaces/amarchheda/ChordDuplicate/portaudio/pablio/test_rw.c
deleted file mode 100644
index 029240d4d2a6410172c1dfec7620ea74030ceaa9..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/pablio/test_rw.c
+++ /dev/null
@@ -1,105 +0,0 @@
-/*
- * $Id$
- * test_rw.c
- * Read input from one stream and write it to another.
- *
- * Author: Phil Burk, http://www.softsynth.com/portaudio/
- *
- * This program uses PABLIO, the Portable Audio Blocking I/O Library.
- * PABLIO is built on top of PortAudio, the Portable Audio Library.
- * For more information see: http://www.portaudio.com
- * Copyright (c) 1999-2000 Ross Bencina and Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-#include
-#include
-#include "pablio.h"
-
-/*
-** Note that many of the older ISA sound cards on PCs do NOT support
-** full duplex audio (simultaneous record and playback).
-** And some only support full duplex at lower sample rates.
-*/
-#define SAMPLE_RATE (44100)
-#define NUM_SECONDS (5)
-#define SAMPLES_PER_FRAME (2)
-#define FRAMES_PER_BLOCK (64)
-
-/* Select whether we will use floats or shorts. */
-#if 1
-#define SAMPLE_TYPE paFloat32
-typedef float SAMPLE;
-#else
-#define SAMPLE_TYPE paInt16
-typedef short SAMPLE;
-#endif
-
-/*******************************************************************/
-int main(void);
-int main(void)
-{
- int i;
- SAMPLE samples[SAMPLES_PER_FRAME * FRAMES_PER_BLOCK];
- PaError err;
- PABLIO_Stream *aStream;
-
- printf("Full duplex sound test using PortAudio and RingBuffers\n");
- fflush(stdout);
-
- /* Open simplified blocking I/O layer on top of PortAudio. */
- err = OpenAudioStream( &aStream, SAMPLE_RATE, SAMPLE_TYPE,
- (PABLIO_READ_WRITE | PABLIO_STEREO) );
- if( err != paNoError ) goto error;
-
- /* Process samples in the foreground. */
- for( i=0; i<(NUM_SECONDS * SAMPLE_RATE); i += FRAMES_PER_BLOCK )
- {
- /* Read one block of data into sample array from audio input. */
- ReadAudioStream( aStream, samples, FRAMES_PER_BLOCK );
- /* Write that same block of data to output. */
- WriteAudioStream( aStream, samples, FRAMES_PER_BLOCK );
- }
-
- CloseAudioStream( aStream );
-
- printf("Full duplex sound test complete.\n" );
- fflush(stdout);
- return 0;
-
-error:
- Pa_Terminate();
- fprintf( stderr, "An error occurred while using the portaudio stream\n" );
- fprintf( stderr, "Error number: %d\n", err );
- fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
- return -1;
-}
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/src/hostapi/wasapi/mingw-include/sal.h b/spaces/amarchheda/ChordDuplicate/portaudio/src/hostapi/wasapi/mingw-include/sal.h
deleted file mode 100644
index 3f99ab9a9227d04508b064d3ca7e305b80d2473e..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/src/hostapi/wasapi/mingw-include/sal.h
+++ /dev/null
@@ -1,252 +0,0 @@
-#pragma once
-
-#if __GNUC__ >=3
-#pragma GCC system_header
-#endif
-
-/*#define __null*/ // << Conflicts with GCC internal type __null
-#define __notnull
-#define __maybenull
-#define __readonly
-#define __notreadonly
-#define __maybereadonly
-#define __valid
-#define __notvalid
-#define __maybevalid
-#define __readableTo(extent)
-#define __elem_readableTo(size)
-#define __byte_readableTo(size)
-#define __writableTo(size)
-#define __elem_writableTo(size)
-#define __byte_writableTo(size)
-#define __deref
-#define __pre
-#define __post
-#define __precond(expr)
-#define __postcond(expr)
-#define __exceptthat
-#define __execeptthat
-#define __inner_success(expr)
-#define __inner_checkReturn
-#define __inner_typefix(ctype)
-#define __inner_override
-#define __inner_callback
-#define __inner_blocksOn(resource)
-#define __inner_fallthrough_dec
-#define __inner_fallthrough
-#define __refparam
-#define __inner_control_entrypoint(category)
-#define __inner_data_entrypoint(category)
-
-#define __ecount(size)
-#define __bcount(size)
-#define __in
-#define __in_ecount(size)
-#define __in_bcount(size)
-#define __in_z
-#define __in_ecount_z(size)
-#define __in_bcount_z(size)
-#define __in_nz
-#define __in_ecount_nz(size)
-#define __in_bcount_nz(size)
-#define __out
-#define __out_ecount(size)
-#define __out_bcount(size)
-#define __out_ecount_part(size,length)
-#define __out_bcount_part(size,length)
-#define __out_ecount_full(size)
-#define __out_bcount_full(size)
-#define __out_z
-#define __out_z_opt
-#define __out_ecount_z(size)
-#define __out_bcount_z(size)
-#define __out_ecount_part_z(size,length)
-#define __out_bcount_part_z(size,length)
-#define __out_ecount_full_z(size)
-#define __out_bcount_full_z(size)
-#define __out_nz
-#define __out_nz_opt
-#define __out_ecount_nz(size)
-#define __out_bcount_nz(size)
-#define __inout
-#define __inout_ecount(size)
-#define __inout_bcount(size)
-#define __inout_ecount_part(size,length)
-#define __inout_bcount_part(size,length)
-#define __inout_ecount_full(size)
-#define __inout_bcount_full(size)
-#define __inout_z
-#define __inout_ecount_z(size)
-#define __inout_bcount_z(size)
-#define __inout_nz
-#define __inout_ecount_nz(size)
-#define __inout_bcount_nz(size)
-#define __ecount_opt(size)
-#define __bcount_opt(size)
-#define __in_opt
-#define __in_ecount_opt(size)
-#define __in_bcount_opt(size)
-#define __in_z_opt
-#define __in_ecount_z_opt(size)
-#define __in_bcount_z_opt(size)
-#define __in_nz_opt
-#define __in_ecount_nz_opt(size)
-#define __in_bcount_nz_opt(size)
-#define __out_opt
-#define __out_ecount_opt(size)
-#define __out_bcount_opt(size)
-#define __out_ecount_part_opt(size,length)
-#define __out_bcount_part_opt(size,length)
-#define __out_ecount_full_opt(size)
-#define __out_bcount_full_opt(size)
-#define __out_ecount_z_opt(size)
-#define __out_bcount_z_opt(size)
-#define __out_ecount_part_z_opt(size,length)
-#define __out_bcount_part_z_opt(size,length)
-#define __out_ecount_full_z_opt(size)
-#define __out_bcount_full_z_opt(size)
-#define __out_ecount_nz_opt(size)
-#define __out_bcount_nz_opt(size)
-#define __inout_opt
-#define __inout_ecount_opt(size)
-#define __inout_bcount_opt(size)
-#define __inout_ecount_part_opt(size,length)
-#define __inout_bcount_part_opt(size,length)
-#define __inout_ecount_full_opt(size)
-#define __inout_bcount_full_opt(size)
-#define __inout_z_opt
-#define __inout_ecount_z_opt(size)
-#define __inout_ecount_z_opt(size)
-#define __inout_bcount_z_opt(size)
-#define __inout_nz_opt
-#define __inout_ecount_nz_opt(size)
-#define __inout_bcount_nz_opt(size)
-#define __deref_ecount(size)
-#define __deref_bcount(size)
-#define __deref_out
-#define __deref_out_ecount(size)
-#define __deref_out_bcount(size)
-#define __deref_out_ecount_part(size,length)
-#define __deref_out_bcount_part(size,length)
-#define __deref_out_ecount_full(size)
-#define __deref_out_bcount_full(size)
-#define __deref_out_z
-#define __deref_out_ecount_z(size)
-#define __deref_out_bcount_z(size)
-#define __deref_out_nz
-#define __deref_out_ecount_nz(size)
-#define __deref_out_bcount_nz(size)
-#define __deref_inout
-#define __deref_inout_z
-#define __deref_inout_ecount(size)
-#define __deref_inout_bcount(size)
-#define __deref_inout_ecount_part(size,length)
-#define __deref_inout_bcount_part(size,length)
-#define __deref_inout_ecount_full(size)
-#define __deref_inout_bcount_full(size)
-#define __deref_inout_z
-#define __deref_inout_ecount_z(size)
-#define __deref_inout_bcount_z(size)
-#define __deref_inout_nz
-#define __deref_inout_ecount_nz(size)
-#define __deref_inout_bcount_nz(size)
-#define __deref_ecount_opt(size)
-#define __deref_bcount_opt(size)
-#define __deref_out_opt
-#define __deref_out_ecount_opt(size)
-#define __deref_out_bcount_opt(size)
-#define __deref_out_ecount_part_opt(size,length)
-#define __deref_out_bcount_part_opt(size,length)
-#define __deref_out_ecount_full_opt(size)
-#define __deref_out_bcount_full_opt(size)
-#define __deref_out_z_opt
-#define __deref_out_ecount_z_opt(size)
-#define __deref_out_bcount_z_opt(size)
-#define __deref_out_nz_opt
-#define __deref_out_ecount_nz_opt(size)
-#define __deref_out_bcount_nz_opt(size)
-#define __deref_inout_opt
-#define __deref_inout_ecount_opt(size)
-#define __deref_inout_bcount_opt(size)
-#define __deref_inout_ecount_part_opt(size,length)
-#define __deref_inout_bcount_part_opt(size,length)
-#define __deref_inout_ecount_full_opt(size)
-#define __deref_inout_bcount_full_opt(size)
-#define __deref_inout_z_opt
-#define __deref_inout_ecount_z_opt(size)
-#define __deref_inout_bcount_z_opt(size)
-#define __deref_inout_nz_opt
-#define __deref_inout_ecount_nz_opt(size)
-#define __deref_inout_bcount_nz_opt(size)
-#define __deref_opt_ecount(size)
-#define __deref_opt_bcount(size)
-#define __deref_opt_out
-#define __deref_opt_out_z
-#define __deref_opt_out_ecount(size)
-#define __deref_opt_out_bcount(size)
-#define __deref_opt_out_ecount_part(size,length)
-#define __deref_opt_out_bcount_part(size,length)
-#define __deref_opt_out_ecount_full(size)
-#define __deref_opt_out_bcount_full(size)
-#define __deref_opt_inout
-#define __deref_opt_inout_ecount(size)
-#define __deref_opt_inout_bcount(size)
-#define __deref_opt_inout_ecount_part(size,length)
-#define __deref_opt_inout_bcount_part(size,length)
-#define __deref_opt_inout_ecount_full(size)
-#define __deref_opt_inout_bcount_full(size)
-#define __deref_opt_inout_z
-#define __deref_opt_inout_ecount_z(size)
-#define __deref_opt_inout_bcount_z(size)
-#define __deref_opt_inout_nz
-#define __deref_opt_inout_ecount_nz(size)
-#define __deref_opt_inout_bcount_nz(size)
-#define __deref_opt_ecount_opt(size)
-#define __deref_opt_bcount_opt(size)
-#define __deref_opt_out_opt
-#define __deref_opt_out_ecount_opt(size)
-#define __deref_opt_out_bcount_opt(size)
-#define __deref_opt_out_ecount_part_opt(size,length)
-#define __deref_opt_out_bcount_part_opt(size,length)
-#define __deref_opt_out_ecount_full_opt(size)
-#define __deref_opt_out_bcount_full_opt(size)
-#define __deref_opt_out_z_opt
-#define __deref_opt_out_ecount_z_opt(size)
-#define __deref_opt_out_bcount_z_opt(size)
-#define __deref_opt_out_nz_opt
-#define __deref_opt_out_ecount_nz_opt(size)
-#define __deref_opt_out_bcount_nz_opt(size)
-#define __deref_opt_inout_opt
-#define __deref_opt_inout_ecount_opt(size)
-#define __deref_opt_inout_bcount_opt(size)
-#define __deref_opt_inout_ecount_part_opt(size,length)
-#define __deref_opt_inout_bcount_part_opt(size,length)
-#define __deref_opt_inout_ecount_full_opt(size)
-#define __deref_opt_inout_bcount_full_opt(size)
-#define __deref_opt_inout_z_opt
-#define __deref_opt_inout_ecount_z_opt(size)
-#define __deref_opt_inout_bcount_z_opt(size)
-#define __deref_opt_inout_nz_opt
-#define __deref_opt_inout_ecount_nz_opt(size)
-#define __deref_opt_inout_bcount_nz_opt(size)
-
-#define __success(expr)
-#define __nullterminated
-#define __nullnullterminated
-#define __reserved
-#define __checkReturn
-#define __typefix(ctype)
-#define __override
-#define __callback
-#define __format_string
-#define __blocksOn(resource)
-#define __control_entrypoint(category)
-#define __data_entrypoint(category)
-
-#ifndef __fallthrough
- #define __fallthrough __inner_fallthrough
-#endif
-
-#ifndef __analysis_assume
- #define __analysis_assume(expr)
-#endif
diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/midas/midas_net.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/midas/midas_net.py
deleted file mode 100644
index 8a954977800b0a0f48807e80fa63041910e33c1f..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/midas/midas_net.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
-
-
-class MidasNet(BaseModel):
- """Network for monocular depth estimation.
- """
-
- def __init__(self, path=None, features=256, non_negative=True):
- """Init.
-
- Args:
- path (str, optional): Path to saved model. Defaults to None.
- features (int, optional): Number of features. Defaults to 256.
- backbone (str, optional): Backbone network for encoder. Defaults to resnet50
- """
- print("Loading weights: ", path)
-
- super(MidasNet, self).__init__()
-
- use_pretrained = False if path is None else True
-
- self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
-
- self.scratch.refinenet4 = FeatureFusionBlock(features)
- self.scratch.refinenet3 = FeatureFusionBlock(features)
- self.scratch.refinenet2 = FeatureFusionBlock(features)
- self.scratch.refinenet1 = FeatureFusionBlock(features)
-
- self.scratch.output_conv = nn.Sequential(
- nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
- Interpolate(scale_factor=2, mode="bilinear"),
- nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
- nn.ReLU(True),
- nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
- nn.ReLU(True) if non_negative else nn.Identity(),
- )
-
- if path:
- self.load(path)
-
- def forward(self, x):
- """Forward pass.
-
- Args:
- x (tensor): input data (image)
-
- Returns:
- tensor: depth
- """
-
- layer_1 = self.pretrained.layer1(x)
- layer_2 = self.pretrained.layer2(layer_1)
- layer_3 = self.pretrained.layer3(layer_2)
- layer_4 = self.pretrained.layer4(layer_3)
-
- layer_1_rn = self.scratch.layer1_rn(layer_1)
- layer_2_rn = self.scratch.layer2_rn(layer_2)
- layer_3_rn = self.scratch.layer3_rn(layer_3)
- layer_4_rn = self.scratch.layer4_rn(layer_4)
-
- path_4 = self.scratch.refinenet4(layer_4_rn)
- path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
- path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
- path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
- out = self.scratch.output_conv(path_1)
-
- return torch.squeeze(out, dim=1)
diff --git a/spaces/aphenx/bingo/src/lib/hooks/use-at-bottom.tsx b/spaces/aphenx/bingo/src/lib/hooks/use-at-bottom.tsx
deleted file mode 100644
index d37c8cf4162adcb0064e08ecec24eb731416b045..0000000000000000000000000000000000000000
--- a/spaces/aphenx/bingo/src/lib/hooks/use-at-bottom.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import * as React from 'react'
-
-export function useAtBottom(offset = 0) {
- const [isAtBottom, setIsAtBottom] = React.useState(false)
-
- React.useEffect(() => {
- const handleScroll = () => {
- setIsAtBottom(
- window.innerHeight + window.scrollY >=
- document.body.offsetHeight - offset
- )
- }
-
- window.addEventListener('scroll', handleScroll, { passive: true })
- handleScroll()
-
- return () => {
- window.removeEventListener('scroll', handleScroll)
- }
- }, [offset])
-
- return isAtBottom
-}
diff --git a/spaces/arnavkartikeya/SCRIPture-final/data/pretrain_dataset.py b/spaces/arnavkartikeya/SCRIPture-final/data/pretrain_dataset.py
deleted file mode 100644
index 703d543ab5267fdc6fe2b7c84ef6a631d8af90ad..0000000000000000000000000000000000000000
--- a/spaces/arnavkartikeya/SCRIPture-final/data/pretrain_dataset.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import json
-import os
-import random
-
-from torch.utils.data import Dataset
-
-from PIL import Image
-from PIL import ImageFile
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-Image.MAX_IMAGE_PIXELS = None
-
-from data.utils import pre_caption
-import os,glob
-
-class pretrain_dataset(Dataset):
- def __init__(self, ann_file, laion_path, transform):
-
- self.ann_pretrain = []
- for f in ann_file:
- print('loading '+f)
- ann = json.load(open(f,'r'))
- self.ann_pretrain += ann
-
- self.laion_path = laion_path
- if self.laion_path:
- self.laion_files = glob.glob(os.path.join(laion_path,'*.json'))
-
- print('loading '+self.laion_files[0])
- with open(self.laion_files[0],'r') as f:
- self.ann_laion = json.load(f)
-
- self.annotation = self.ann_pretrain + self.ann_laion
- else:
- self.annotation = self.ann_pretrain
-
- self.transform = transform
-
-
- def reload_laion(self, epoch):
- n = epoch%len(self.laion_files)
- print('loading '+self.laion_files[n])
- with open(self.laion_files[n],'r') as f:
- self.ann_laion = json.load(f)
-
- self.annotation = self.ann_pretrain + self.ann_laion
-
-
- def __len__(self):
- return len(self.annotation)
-
- def __getitem__(self, index):
-
- ann = self.annotation[index]
-
- image = Image.open(ann['image']).convert('RGB')
- image = self.transform(image)
- caption = pre_caption(ann['caption'],30)
-
- return image, caption
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vegalite/v3/schema/channels.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vegalite/v3/schema/channels.py
deleted file mode 100644
index 26432fa41aa87fe145344c88615c373457943521..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vegalite/v3/schema/channels.py
+++ /dev/null
@@ -1,5324 +0,0 @@
-# The contents of this file are automatically written by
-# tools/generate_schema_wrapper.py. Do not modify directly.
-
-from . import core
-import pandas as pd
-from altair.utils.schemapi import Undefined
-from altair.utils import parse_shorthand
-
-
-class FieldChannelMixin(object):
- def to_dict(self, validate=True, ignore=(), context=None):
- context = context or {}
- shorthand = self._get('shorthand')
- field = self._get('field')
-
- if shorthand is not Undefined and field is not Undefined:
- raise ValueError("{} specifies both shorthand={} and field={}. "
- "".format(self.__class__.__name__, shorthand, field))
-
- if isinstance(shorthand, (tuple, list)):
- # If given a list of shorthands, then transform it to a list of classes
- kwds = self._kwds.copy()
- kwds.pop('shorthand')
- return [self.__class__(sh, **kwds).to_dict(validate=validate, ignore=ignore, context=context)
- for sh in shorthand]
-
- if shorthand is Undefined:
- parsed = {}
- elif isinstance(shorthand, str):
- parsed = parse_shorthand(shorthand, data=context.get('data', None))
- type_required = 'type' in self._kwds
- type_in_shorthand = 'type' in parsed
- type_defined_explicitly = self._get('type') is not Undefined
- if not type_required:
- # Secondary field names don't require a type argument in VegaLite 3+.
- # We still parse it out of the shorthand, but drop it here.
- parsed.pop('type', None)
- elif not (type_in_shorthand or type_defined_explicitly):
- if isinstance(context.get('data', None), pd.DataFrame):
- raise ValueError("{} encoding field is specified without a type; "
- "the type cannot be inferred because it does not "
- "match any column in the data.".format(shorthand))
- else:
- raise ValueError("{} encoding field is specified without a type; "
- "the type cannot be automatically inferred because "
- "the data is not specified as a pandas.DataFrame."
- "".format(shorthand))
- else:
- # Shorthand is not a string; we pass the definition to field,
- # and do not do any parsing.
- parsed = {'field': shorthand}
-
- # Set shorthand to Undefined, because it's not part of the base schema.
- self.shorthand = Undefined
- self._kwds.update({k: v for k, v in parsed.items()
- if self._get(k) is Undefined})
- return super(FieldChannelMixin, self).to_dict(
- validate=validate,
- ignore=ignore,
- context=context
- )
-
-
-class ValueChannelMixin(object):
- def to_dict(self, validate=True, ignore=(), context=None):
- context = context or {}
- condition = getattr(self, 'condition', Undefined)
- copy = self # don't copy unless we need to
- if condition is not Undefined:
- if isinstance(condition, core.SchemaBase):
- pass
- elif 'field' in condition and 'type' not in condition:
- kwds = parse_shorthand(condition['field'], context.get('data', None))
- copy = self.copy(deep=['condition'])
- copy.condition.update(kwds)
- return super(ValueChannelMixin, copy).to_dict(validate=validate,
- ignore=ignore,
- context=context)
-
-
-class DatumChannelMixin(object):
- def to_dict(self, validate=True, ignore=(), context=None):
- context = context or {}
- datum = getattr(self, 'datum', Undefined)
- copy = self # don't copy unless we need to
- if datum is not Undefined:
- if isinstance(datum, core.SchemaBase):
- pass
- return super(DatumChannelMixin, copy).to_dict(validate=validate,
- ignore=ignore,
- context=context)
-
-
-class Color(FieldChannelMixin, core.StringFieldDefWithCondition):
- """Color schema wrapper
-
- Mapping(required=[shorthand])
- A FieldDef with Condition :raw-html:``
-
- Attributes
- ----------
-
- shorthand : string
- shorthand for field, aggregate, and type
- aggregate : :class:`Aggregate`
- Aggregation function for the field
- (e.g., ``mean``, ``sum``, ``median``, ``min``, ``max``, ``count`` ).
-
- **Default value:** ``undefined`` (None)
-
- **See also:** `aggregate `__
- documentation.
- bin : anyOf(boolean, :class:`BinParams`, None)
- A flag for binning a ``quantitative`` field, `an object defining binning parameters
- `__, or indicating that the
- data for ``x`` or ``y`` channel are binned before they are imported into Vega-Lite (
- ``"binned"`` ).
-
-
- If ``true``, default `binning parameters
- `__ will be applied.
-
- If ``"binned"``, this indicates that the data for the ``x`` (or ``y`` ) channel are
- already binned. You can map the bin-start field to ``x`` (or ``y`` ) and the bin-end
- field to ``x2`` (or ``y2`` ). The scale and axis will be formatted similar to
- binning in Vega-lite. To adjust the axis ticks based on the bin step, you can also
- set the axis's `tickMinStep
- `__ property.
-
- **Default value:** ``false``
-
- **See also:** `bin `__
- documentation.
- condition : anyOf(:class:`ConditionalStringValueDef`,
- List(:class:`ConditionalStringValueDef`))
- One or more value definition(s) with `a selection or a test predicate
- `__.
-
- **Note:** A field definition's ``condition`` property can only contain `conditional
- value definitions `__
- since Vega-Lite only allows at most one encoded field per encoding channel.
- field : :class:`Field`
- **Required.** A string defining the name of the field from which to pull a data
- value
- or an object defining iterated values from the `repeat
- `__ operator.
-
- **See also:** `field `__
- documentation.
-
- **Notes:**
- 1) Dots ( ``.`` ) and brackets ( ``[`` and ``]`` ) can be used to access nested
- objects (e.g., ``"field": "foo.bar"`` and ``"field": "foo['bar']"`` ).
- If field names contain dots or brackets but are not nested, you can use ``\\`` to
- escape dots and brackets (e.g., ``"a\\.b"`` and ``"a\\[0\\]"`` ).
- See more details about escaping in the `field documentation
- `__.
- 2) ``field`` is not required if ``aggregate`` is ``count``.
- legend : anyOf(:class:`Legend`, None)
- An object defining properties of the legend.
- If ``null``, the legend for the encoding channel will be removed.
-
- **Default value:** If undefined, default `legend properties
- `__ are applied.
-
- **See also:** `legend `__
- documentation.
- scale : anyOf(:class:`Scale`, None)
- An object defining properties of the channel's scale, which is the function that
- transforms values in the data domain (numbers, dates, strings, etc) to visual values
- (pixels, colors, sizes) of the encoding channels.
-
- If ``null``, the scale will be `disabled and the data value will be directly encoded
- `__.
-
- **Default value:** If undefined, default `scale properties
- `__ are applied.
-
- **See also:** `scale `__
- documentation.
- sort : :class:`Sort`
- Sort order for the encoded field.
-
- For continuous fields (quantitative or temporal), ``sort`` can be either
- ``"ascending"`` or ``"descending"``.
-
- For discrete fields, ``sort`` can be one of the following:
-
-
- * ``"ascending"`` or ``"descending"`` -- for sorting by the values' natural order in
- Javascript.
- * `A sort-by-encoding definition
- `__ for sorting
- by another encoding channel. (This type of sort definition is not available for
- ``row`` and ``column`` channels.)
- * `A sort field definition
- `__ for sorting by
- another field.
- * `An array specifying the field values in preferred order
- `__. In this case, the
- sort order will obey the values in the array, followed by any unspecified values
- in their original order. For discrete time field, values in the sort array can be
- `date-time definition objects `__. In addition, for time units
- ``"month"`` and ``"day"``, the values can be the month or day names (case
- insensitive) or their 3-letter initials (e.g., ``"Mon"``, ``"Tue"`` ).
- * ``null`` indicating no sort.
-
- **Default value:** ``"ascending"``
-
- **Note:** ``null`` is not supported for ``row`` and ``column``.
-
- **See also:** `sort `__
- documentation.
- timeUnit : :class:`TimeUnit`
- Time unit (e.g., ``year``, ``yearmonth``, ``month``, ``hours`` ) for a temporal
- field.
- or `a temporal field that gets casted as ordinal
- `__.
-
- **Default value:** ``undefined`` (None)
-
- **See also:** `timeUnit `__
- documentation.
- title : anyOf(string, None)
- A title for the field. If ``null``, the title will be removed.
-
- **Default value:** derived from the field's name and transformation function (
- ``aggregate``, ``bin`` and ``timeUnit`` ). If the field has an aggregate function,
- the function is displayed as part of the title (e.g., ``"Sum of Profit"`` ). If the
- field is binned or has a time unit applied, the applied function is shown in
- parentheses (e.g., ``"Profit (binned)"``, ``"Transaction Date (year-month)"`` ).
- Otherwise, the title is simply the field name.
-
- **Notes** :
-
- 1) You can customize the default field title format by providing the `fieldTitle
- `__ property in
- the `config `__ or `fieldTitle
- function via the compile function's options
- `__.
-
- 2) If both field definition's ``title`` and axis, header, or legend ``title`` are
- defined, axis/header/legend title will be used.
- type : :class:`StandardType`
- The encoded field's type of measurement ( ``"quantitative"``, ``"temporal"``,
- ``"ordinal"``, or ``"nominal"`` ).
- It can also be a ``"geojson"`` type for encoding `'geoshape'
- `__.
-
- **Note:**
-
-
- * Data values for a temporal field can be either a date-time string (e.g.,
- ``"2015-03-07 12:32:17"``, ``"17:01"``, ``"2015-03-16"``. ``"2015"`` ) or a
- timestamp number (e.g., ``1552199579097`` ).
- * Data ``type`` describes the semantics of the data rather than the primitive data
- types ( ``number``, ``string``, etc.). The same primitive data type can have
- different types of measurement. For example, numeric data can represent
- quantitative, ordinal, or nominal data.
- * When using with `bin `__, the
- ``type`` property can be either ``"quantitative"`` (for using a linear bin scale)
- or `"ordinal" (for using an ordinal bin scale)
- `__.
- * When using with `timeUnit
- `__, the ``type`` property
- can be either ``"temporal"`` (for using a temporal scale) or `"ordinal" (for using
- an ordinal scale) `__.
- * When using with `aggregate
- `__, the ``type`` property
- refers to the post-aggregation data type. For example, we can calculate count
- ``distinct`` of a categorical field ``"cat"`` using ``{"aggregate": "distinct",
- "field": "cat", "type": "quantitative"}``. The ``"type"`` of the aggregate output
- is ``"quantitative"``.
- * Secondary channels (e.g., ``x2``, ``y2``, ``xError``, ``yError`` ) do not have
- ``type`` as they have exactly the same type as their primary channels (e.g.,
- ``x``, ``y`` ).
-
- **See also:** `type `__
- documentation.
- """
- _class_is_valid_at_instantiation = False
- _encoding_name = "color"
-
- def __init__(self, shorthand=Undefined, aggregate=Undefined, bin=Undefined, condition=Undefined,
- field=Undefined, legend=Undefined, scale=Undefined, sort=Undefined, timeUnit=Undefined,
- title=Undefined, type=Undefined, **kwds):
- super(Color, self).__init__(shorthand=shorthand, aggregate=aggregate, bin=bin,
- condition=condition, field=field, legend=legend, scale=scale,
- sort=sort, timeUnit=timeUnit, title=title, type=type, **kwds)
-
-
-class ColorValue(ValueChannelMixin, core.StringValueDefWithCondition):
- """ColorValue schema wrapper
-
- Mapping(required=[])
- A ValueDef with Condition where either the condition or the value are
- optional.
-
- Attributes
- ----------
-
- condition : anyOf(:class:`ConditionalMarkPropFieldDef`, :class:`ConditionalStringValueDef`,
- List(:class:`ConditionalStringValueDef`))
- A field definition or one or more value definition(s) with a selection predicate.
- value : anyOf(string, None)
- A constant value in visual domain (e.g., ``"red"`` / "#0099ff" for color, values
- between ``0`` to ``1`` for opacity).
- """
- _class_is_valid_at_instantiation = False
- _encoding_name = "color"
-
- def __init__(self, value, condition=Undefined, **kwds):
- super(ColorValue, self).__init__(value=value, condition=condition, **kwds)
-
-
-class Column(FieldChannelMixin, core.FacetFieldDef):
- """Column schema wrapper
-
- Mapping(required=[shorthand])
-
- Attributes
- ----------
-
- shorthand : string
- shorthand for field, aggregate, and type
- aggregate : :class:`Aggregate`
- Aggregation function for the field
- (e.g., ``mean``, ``sum``, ``median``, ``min``, ``max``, ``count`` ).
-
- **Default value:** ``undefined`` (None)
-
- **See also:** `aggregate `__
- documentation.
- bin : anyOf(boolean, :class:`BinParams`, None)
- A flag for binning a ``quantitative`` field, `an object defining binning parameters
- `__, or indicating that the
- data for ``x`` or ``y`` channel are binned before they are imported into Vega-Lite (
- ``"binned"`` ).
-
-
- If ``true``, default `binning parameters
- `__ will be applied.
-
- If ``"binned"``, this indicates that the data for the ``x`` (or ``y`` ) channel are
- already binned. You can map the bin-start field to ``x`` (or ``y`` ) and the bin-end
- field to ``x2`` (or ``y2`` ). The scale and axis will be formatted similar to
- binning in Vega-lite. To adjust the axis ticks based on the bin step, you can also
- set the axis's `tickMinStep
- `__ property.
-
- **Default value:** ``false``
-
- **See also:** `bin `__
- documentation.
- field : :class:`Field`
- **Required.** A string defining the name of the field from which to pull a data
- value
- or an object defining iterated values from the `repeat
- `__ operator.
-
- **See also:** `field `__
- documentation.
-
- **Notes:**
- 1) Dots ( ``.`` ) and brackets ( ``[`` and ``]`` ) can be used to access nested
- objects (e.g., ``"field": "foo.bar"`` and ``"field": "foo['bar']"`` ).
- If field names contain dots or brackets but are not nested, you can use ``\\`` to
- escape dots and brackets (e.g., ``"a\\.b"`` and ``"a\\[0\\]"`` ).
- See more details about escaping in the `field documentation
- `__.
- 2) ``field`` is not required if ``aggregate`` is ``count``.
- header : :class:`Header`
- An object defining properties of a facet's header.
- sort : anyOf(:class:`SortArray`, :class:`SortOrder`, :class:`EncodingSortField`, None)
- Sort order for the encoded field.
-
- For continuous fields (quantitative or temporal), ``sort`` can be either
- ``"ascending"`` or ``"descending"``.
-
- For discrete fields, ``sort`` can be one of the following:
-
-
- * ``"ascending"`` or ``"descending"`` -- for sorting by the values' natural order in
- Javascript.
- * `A sort field definition
- `__ for sorting by
- another field.
- * `An array specifying the field values in preferred order
- `__. In this case, the
- sort order will obey the values in the array, followed by any unspecified values
- in their original order. For discrete time field, values in the sort array can be
- `date-time definition objects `__. In addition, for time units
- ``"month"`` and ``"day"``, the values can be the month or day names (case
- insensitive) or their 3-letter initials (e.g., ``"Mon"``, ``"Tue"`` ).
- * ``null`` indicating no sort.
-
- **Default value:** ``"ascending"``
-
- **Note:** ``null`` is not supported for ``row`` and ``column``.
- timeUnit : :class:`TimeUnit`
- Time unit (e.g., ``year``, ``yearmonth``, ``month``, ``hours`` ) for a temporal
- field.
- or `a temporal field that gets casted as ordinal
- `__.
-
- **Default value:** ``undefined`` (None)
-
- **See also:** `timeUnit `__
- documentation.
- title : anyOf(string, None)
- A title for the field. If ``null``, the title will be removed.
-
- **Default value:** derived from the field's name and transformation function (
- ``aggregate``, ``bin`` and ``timeUnit`` ). If the field has an aggregate function,
- the function is displayed as part of the title (e.g., ``"Sum of Profit"`` ). If the
- field is binned or has a time unit applied, the applied function is shown in
- parentheses (e.g., ``"Profit (binned)"``, ``"Transaction Date (year-month)"`` ).
- Otherwise, the title is simply the field name.
-
- **Notes** :
-
- 1) You can customize the default field title format by providing the `fieldTitle
- `__ property in
- the `config `__ or `fieldTitle
- function via the compile function's options
- `__.
-
- 2) If both field definition's ``title`` and axis, header, or legend ``title`` are
- defined, axis/header/legend title will be used.
- type : :class:`StandardType`
- The encoded field's type of measurement ( ``"quantitative"``, ``"temporal"``,
- ``"ordinal"``, or ``"nominal"`` ).
- It can also be a ``"geojson"`` type for encoding `'geoshape'
- `__.
-
- **Note:**
-
-
- * Data values for a temporal field can be either a date-time string (e.g.,
- ``"2015-03-07 12:32:17"``, ``"17:01"``, ``"2015-03-16"``. ``"2015"`` ) or a
- timestamp number (e.g., ``1552199579097`` ).
- * Data ``type`` describes the semantics of the data rather than the primitive data
- types ( ``number``, ``string``, etc.). The same primitive data type can have
- different types of measurement. For example, numeric data can represent
- quantitative, ordinal, or nominal data.
- * When using with `bin `__, the
- ``type`` property can be either ``"quantitative"`` (for using a linear bin scale)
- or `"ordinal" (for using an ordinal bin scale)
- `__.
- * When using with `timeUnit
- `__, the ``type`` property
- can be either ``"temporal"`` (for using a temporal scale) or `"ordinal" (for using
- an ordinal scale) `__.
- * When using with `aggregate
- `__, the ``type`` property
- refers to the post-aggregation data type. For example, we can calculate count
- ``distinct`` of a categorical field ``"cat"`` using ``{"aggregate": "distinct",
- "field": "cat", "type": "quantitative"}``. The ``"type"`` of the aggregate output
- is ``"quantitative"``.
- * Secondary channels (e.g., ``x2``, ``y2``, ``xError``, ``yError`` ) do not have
- ``type`` as they have exactly the same type as their primary channels (e.g.,
- ``x``, ``y`` ).
-
- **See also:** `type `__
- documentation.
- """
- _class_is_valid_at_instantiation = False
- _encoding_name = "column"
-
- def __init__(self, shorthand=Undefined, aggregate=Undefined, bin=Undefined, field=Undefined,
- header=Undefined, sort=Undefined, timeUnit=Undefined, title=Undefined, type=Undefined,
- **kwds):
- super(Column, self).__init__(shorthand=shorthand, aggregate=aggregate, bin=bin, field=field,
- header=header, sort=sort, timeUnit=timeUnit, title=title,
- type=type, **kwds)
-
-
-class Detail(FieldChannelMixin, core.FieldDefWithoutScale):
- """Detail schema wrapper
-
- Mapping(required=[shorthand])
- Definition object for a data field, its type and transformation of an encoding channel.
-
- Attributes
- ----------
-
- shorthand : string
- shorthand for field, aggregate, and type
- aggregate : :class:`Aggregate`
- Aggregation function for the field
- (e.g., ``mean``, ``sum``, ``median``, ``min``, ``max``, ``count`` ).
-
- **Default value:** ``undefined`` (None)
-
- **See also:** `aggregate `__
- documentation.
- bin : anyOf(boolean, :class:`BinParams`, enum('binned'), None)
- A flag for binning a ``quantitative`` field, `an object defining binning parameters
- `__, or indicating that the
- data for ``x`` or ``y`` channel are binned before they are imported into Vega-Lite (
- ``"binned"`` ).
-
-
- If ``true``, default `binning parameters
- `__ will be applied.
-
- If ``"binned"``, this indicates that the data for the ``x`` (or ``y`` ) channel are
- already binned. You can map the bin-start field to ``x`` (or ``y`` ) and the bin-end
- field to ``x2`` (or ``y2`` ). The scale and axis will be formatted similar to
- binning in Vega-lite. To adjust the axis ticks based on the bin step, you can also
- set the axis's `tickMinStep
- `__ property.
-
- **Default value:** ``false``
-
- **See also:** `bin `__
- documentation.
- field : :class:`Field`
- **Required.** A string defining the name of the field from which to pull a data
- value
- or an object defining iterated values from the `repeat
- `__ operator.
-
- **See also:** `field `__
- documentation.
-
- **Notes:**
- 1) Dots ( ``.`` ) and brackets ( ``[`` and ``]`` ) can be used to access nested
- objects (e.g., ``"field": "foo.bar"`` and ``"field": "foo['bar']"`` ).
- If field names contain dots or brackets but are not nested, you can use ``\\`` to
- escape dots and brackets (e.g., ``"a\\.b"`` and ``"a\\[0\\]"`` ).
- See more details about escaping in the `field documentation
- `__.
- 2) ``field`` is not required if ``aggregate`` is ``count``.
- timeUnit : :class:`TimeUnit`
- Time unit (e.g., ``year``, ``yearmonth``, ``month``, ``hours`` ) for a temporal
- field.
- or `a temporal field that gets casted as ordinal
- `__.
-
- **Default value:** ``undefined`` (None)
-
- **See also:** `timeUnit `__
- documentation.
- title : anyOf(string, None)
- A title for the field. If ``null``, the title will be removed.
-
- **Default value:** derived from the field's name and transformation function (
- ``aggregate``, ``bin`` and ``timeUnit`` ). If the field has an aggregate function,
- the function is displayed as part of the title (e.g., ``"Sum of Profit"`` ). If the
- field is binned or has a time unit applied, the applied function is shown in
- parentheses (e.g., ``"Profit (binned)"``, ``"Transaction Date (year-month)"`` ).
- Otherwise, the title is simply the field name.
-
- **Notes** :
-
- 1) You can customize the default field title format by providing the `fieldTitle
- `__ property in
- the `config `__ or `fieldTitle
- function via the compile function's options
- `__.
-
- 2) If both field definition's ``title`` and axis, header, or legend ``title`` are
- defined, axis/header/legend title will be used.
- type : :class:`StandardType`
- The encoded field's type of measurement ( ``"quantitative"``, ``"temporal"``,
- ``"ordinal"``, or ``"nominal"`` ).
- It can also be a ``"geojson"`` type for encoding `'geoshape'
- `__.
-
- **Note:**
-
-
- * Data values for a temporal field can be either a date-time string (e.g.,
- ``"2015-03-07 12:32:17"``, ``"17:01"``, ``"2015-03-16"``. ``"2015"`` ) or a
- timestamp number (e.g., ``1552199579097`` ).
- * Data ``type`` describes the semantics of the data rather than the primitive data
- types ( ``number``, ``string``, etc.). The same primitive data type can have
- different types of measurement. For example, numeric data can represent
- quantitative, ordinal, or nominal data.
- * When using with `bin `__, the
- ``type`` property can be either ``"quantitative"`` (for using a linear bin scale)
- or `"ordinal" (for using an ordinal bin scale)
- `__.
- * When using with `timeUnit
- `__, the ``type`` property
- can be either ``"temporal"`` (for using a temporal scale) or `"ordinal" (for using
- an ordinal scale) `__.
- * When using with `aggregate
- `__, the ``type`` property
- refers to the post-aggregation data type. For example, we can calculate count
- ``distinct`` of a categorical field ``"cat"`` using ``{"aggregate": "distinct",
- "field": "cat", "type": "quantitative"}``. The ``"type"`` of the aggregate output
- is ``"quantitative"``.
- * Secondary channels (e.g., ``x2``, ``y2``, ``xError``, ``yError`` ) do not have
- ``type`` as they have exactly the same type as their primary channels (e.g.,
- ``x``, ``y`` ).
-
- **See also:** `type `__
- documentation.
- """
- _class_is_valid_at_instantiation = False
- _encoding_name = "detail"
-
- def __init__(self, shorthand=Undefined, aggregate=Undefined, bin=Undefined, field=Undefined,
- timeUnit=Undefined, title=Undefined, type=Undefined, **kwds):
- super(Detail, self).__init__(shorthand=shorthand, aggregate=aggregate, bin=bin, field=field,
- timeUnit=timeUnit, title=title, type=type, **kwds)
-
-
-class Facet(FieldChannelMixin, core.FacetFieldDef):
- """Facet schema wrapper
-
- Mapping(required=[shorthand])
-
- Attributes
- ----------
-
- shorthand : string
- shorthand for field, aggregate, and type
- aggregate : :class:`Aggregate`
- Aggregation function for the field
- (e.g., ``mean``, ``sum``, ``median``, ``min``, ``max``, ``count`` ).
-
- **Default value:** ``undefined`` (None)
-
- **See also:** `aggregate `__
- documentation.
- bin : anyOf(boolean, :class:`BinParams`, None)
- A flag for binning a ``quantitative`` field, `an object defining binning parameters
- `__, or indicating that the
- data for ``x`` or ``y`` channel are binned before they are imported into Vega-Lite (
- ``"binned"`` ).
-
-
- If ``true``, default `binning parameters
- `__ will be applied.
-
- If ``"binned"``, this indicates that the data for the ``x`` (or ``y`` ) channel are
- already binned. You can map the bin-start field to ``x`` (or ``y`` ) and the bin-end
- field to ``x2`` (or ``y2`` ). The scale and axis will be formatted similar to
- binning in Vega-lite. To adjust the axis ticks based on the bin step, you can also
- set the axis's `tickMinStep
- `__ property.
-
- **Default value:** ``false``
-
- **See also:** `bin `__
- documentation.
- field : :class:`Field`
- **Required.** A string defining the name of the field from which to pull a data
- value
- or an object defining iterated values from the `repeat
- `__ operator.
-
- **See also:** `field `__
- documentation.
-
- **Notes:**
- 1) Dots ( ``.`` ) and brackets ( ``[`` and ``]`` ) can be used to access nested
- objects (e.g., ``"field": "foo.bar"`` and ``"field": "foo['bar']"`` ).
- If field names contain dots or brackets but are not nested, you can use ``\\`` to
- escape dots and brackets (e.g., ``"a\\.b"`` and ``"a\\[0\\]"`` ).
- See more details about escaping in the `field documentation
- `__.
- 2) ``field`` is not required if ``aggregate`` is ``count``.
- header : :class:`Header`
- An object defining properties of a facet's header.
- sort : anyOf(:class:`SortArray`, :class:`SortOrder`, :class:`EncodingSortField`, None)
- Sort order for the encoded field.
-
- For continuous fields (quantitative or temporal), ``sort`` can be either
- ``"ascending"`` or ``"descending"``.
-
- For discrete fields, ``sort`` can be one of the following:
-
-
- * ``"ascending"`` or ``"descending"`` -- for sorting by the values' natural order in
- Javascript.
- * `A sort field definition
- `__ for sorting by
- another field.
- * `An array specifying the field values in preferred order
- `__. In this case, the
- sort order will obey the values in the array, followed by any unspecified values
- in their original order. For discrete time field, values in the sort array can be
- `date-time definition objects `__. In addition, for time units
- ``"month"`` and ``"day"``, the values can be the month or day names (case
- insensitive) or their 3-letter initials (e.g., ``"Mon"``, ``"Tue"`` ).
- * ``null`` indicating no sort.
-
- **Default value:** ``"ascending"``
-
- **Note:** ``null`` is not supported for ``row`` and ``column``.
- timeUnit : :class:`TimeUnit`
- Time unit (e.g., ``year``, ``yearmonth``, ``month``, ``hours`` ) for a temporal
- field.
- or `a temporal field that gets casted as ordinal
- `__.
-
- **Default value:** ``undefined`` (None)
-
- **See also:** `timeUnit `__
- documentation.
- title : anyOf(string, None)
- A title for the field. If ``null``, the title will be removed.
-
- **Default value:** derived from the field's name and transformation function (
- ``aggregate``, ``bin`` and ``timeUnit`` ). If the field has an aggregate function,
- the function is displayed as part of the title (e.g., ``"Sum of Profit"`` ). If the
- field is binned or has a time unit applied, the applied function is shown in
- parentheses (e.g., ``"Profit (binned)"``, ``"Transaction Date (year-month)"`` ).
- Otherwise, the title is simply the field name.
-
- **Notes** :
-
- 1) You can customize the default field title format by providing the `fieldTitle
- `__ property in
- the `config `__ or `fieldTitle
- function via the compile function's options
- `__.
-
- 2) If both field definition's ``title`` and axis, header, or legend ``title`` are
- defined, axis/header/legend title will be used.
- type : :class:`StandardType`
- The encoded field's type of measurement ( ``"quantitative"``, ``"temporal"``,
- ``"ordinal"``, or ``"nominal"`` ).
- It can also be a ``"geojson"`` type for encoding `'geoshape'
- `__.
-
- **Note:**
-
-
- * Data values for a temporal field can be either a date-time string (e.g.,
- ``"2015-03-07 12:32:17"``, ``"17:01"``, ``"2015-03-16"``. ``"2015"`` ) or a
- timestamp number (e.g., ``1552199579097`` ).
- * Data ``type`` describes the semantics of the data rather than the primitive data
- types ( ``number``, ``string``, etc.). The same primitive data type can have
- different types of measurement. For example, numeric data can represent
- quantitative, ordinal, or nominal data.
- * When using with `bin `__, the
- ``type`` property can be either ``"quantitative"`` (for using a linear bin scale)
- or `"ordinal" (for using an ordinal bin scale)
- `__.
- * When using with `timeUnit
- `__, the ``type`` property
- can be either ``"temporal"`` (for using a temporal scale) or `"ordinal" (for using
- an ordinal scale) `__.
- * When using with `aggregate
- `__, the ``type`` property
- refers to the post-aggregation data type. For example, we can calculate count
- ``distinct`` of a categorical field ``"cat"`` using ``{"aggregate": "distinct",
- "field": "cat", "type": "quantitative"}``. The ``"type"`` of the aggregate output
- is ``"quantitative"``.
- * Secondary channels (e.g., ``x2``, ``y2``, ``xError``, ``yError`` ) do not have
- ``type`` as they have exactly the same type as their primary channels (e.g.,
- ``x``, ``y`` ).
-
- **See also:** `type `__
- documentation.
- """
- _class_is_valid_at_instantiation = False
- _encoding_name = "facet"
-
- def __init__(self, shorthand=Undefined, aggregate=Undefined, bin=Undefined, field=Undefined,
- header=Undefined, sort=Undefined, timeUnit=Undefined, title=Undefined, type=Undefined,
- **kwds):
- super(Facet, self).__init__(shorthand=shorthand, aggregate=aggregate, bin=bin, field=field,
- header=header, sort=sort, timeUnit=timeUnit, title=title, type=type,
- **kwds)
-
-
-class Fill(FieldChannelMixin, core.StringFieldDefWithCondition):
- """Fill schema wrapper
-
- Mapping(required=[shorthand])
- A FieldDef with Condition :raw-html:``
-
- Attributes
- ----------
-
- shorthand : string
- shorthand for field, aggregate, and type
- aggregate : :class:`Aggregate`
- Aggregation function for the field
- (e.g., ``mean``, ``sum``, ``median``, ``min``, ``max``, ``count`` ).
-
- **Default value:** ``undefined`` (None)
-
- **See also:** `aggregate `__
- documentation.
- bin : anyOf(boolean, :class:`BinParams`, None)
- A flag for binning a ``quantitative`` field, `an object defining binning parameters
- `__, or indicating that the
- data for ``x`` or ``y`` channel are binned before they are imported into Vega-Lite (
- ``"binned"`` ).
-
-
- If ``true``, default `binning parameters
- `__ will be applied.
-
- If ``"binned"``, this indicates that the data for the ``x`` (or ``y`` ) channel are
- already binned. You can map the bin-start field to ``x`` (or ``y`` ) and the bin-end
- field to ``x2`` (or ``y2`` ). The scale and axis will be formatted similar to
- binning in Vega-lite. To adjust the axis ticks based on the bin step, you can also
- set the axis's `tickMinStep
- `__ property.
-
- **Default value:** ``false``
-
- **See also:** `bin `__
- documentation.
- condition : anyOf(:class:`ConditionalStringValueDef`,
- List(:class:`ConditionalStringValueDef`))
- One or more value definition(s) with `a selection or a test predicate
- `__.
-
- **Note:** A field definition's ``condition`` property can only contain `conditional
- value definitions `__
- since Vega-Lite only allows at most one encoded field per encoding channel.
- field : :class:`Field`
- **Required.** A string defining the name of the field from which to pull a data
- value
- or an object defining iterated values from the `repeat
- `__ operator.
-
- **See also:** `field `__
- documentation.
-
- **Notes:**
- 1) Dots ( ``.`` ) and brackets ( ``[`` and ``]`` ) can be used to access nested
- objects (e.g., ``"field": "foo.bar"`` and ``"field": "foo['bar']"`` ).
- If field names contain dots or brackets but are not nested, you can use ``\\`` to
- escape dots and brackets (e.g., ``"a\\.b"`` and ``"a\\[0\\]"`` ).
- See more details about escaping in the `field documentation
- `__.
- 2) ``field`` is not required if ``aggregate`` is ``count``.
- legend : anyOf(:class:`Legend`, None)
- An object defining properties of the legend.
- If ``null``, the legend for the encoding channel will be removed.
-
- **Default value:** If undefined, default `legend properties
- `__ are applied.
-
- **See also:** `legend `__
- documentation.
- scale : anyOf(:class:`Scale`, None)
- An object defining properties of the channel's scale, which is the function that
- transforms values in the data domain (numbers, dates, strings, etc) to visual values
- (pixels, colors, sizes) of the encoding channels.
-
- If ``null``, the scale will be `disabled and the data value will be directly encoded
-
- 
- 
Toys to play with: The models OG, LeyLines, AM-mix1, Hassaku, MeinaMix v11, SCMix, AbsoluteReality v1, CrystalClear, CleanLinearMix and CrystalClearRemix have been added!