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- Once you have collected everything you want or need, you have to enter the shelter and close the door before the bomb explodes. If you fail to do so, you will die and lose the game.
-Manage your resources and make decisions in the shelter
-The third thing you need to do to play 60 Seconds Reatomized APK Hack is to manage your resources and make decisions in the shelter. This is the most challenging and interesting part of the game, as you have to deal with the consequences of your actions and the events that happen in the wasteland.
-You have to ration your water and food among your family members, as they will get thirsty and hungry over time. You have to use your medkits to heal your family members from injuries or illnesses, as they will get sick or hurt by various causes. You have to use your ammo to defend your shelter from intruders, as they will try to rob or harm you.
-You also have to make decisions that will affect your survival and your relationship with your family members. You have to choose whether to send someone out to scavenge for more items, or to stay in the shelter and wait for rescue. You have to choose whether to help or ignore other survivors or rescuers that contact you through the radio or knock on your door. You have to choose whether to be honest or deceitful, generous or selfish, brave or cowardly, etc.
-You can see the status of your family members and your resources on the top of the screen, which shows you their health, hunger, thirst, sanity, and loyalty. You can also see the status of your items on the bottom of the screen, which shows you their quantity and quality. You can also see a journal on the right of the screen, which shows you a summary of what happened each day.
-You have to be careful not to run out of resources or lose your family members, as this will reduce your chances of survival and success. You also have to be careful not to anger or betray your family members, as this will reduce their loyalty and morale. If your family members lose their loyalty or morale, they may leave you, attack you, or kill themselves.
-Explore the wasteland and encounter random events
-The fourth thing you need to do to play 60 Seconds Reatomized APK Hack is to explore the wasteland and encounter random events. This is the most fun and unpredictable part of the game, as you never know what you will find or face in the post-apocalyptic world.
-You can explore the wasteland by sending one of your family members out on a scavenging trip. You can choose which family member to send, which items to give them, and which location to visit. You can also use the map to see where you are and where you can go.
-You can find various items and locations in the wasteland, such as water bottles, soup cans, gas stations, supermarkets, schools, hospitals, etc. Some of them may be useful and beneficial for your survival, while others may be useless and harmful for your health.
-You can also encounter various events and characters in the wasteland, such as mutants, raiders, bandits, soldiers, scientists, traders, etc. Some of them may be friendly and helpful for your survival, while others may be hostile and dangerous for your life.
-You can interact with these events and characters in different ways, depending on your choices and actions. You can fight or flee, trade or steal, help or ignore, etc. You can also use your items or skills to influence the outcome of these interactions.
-You can see the results of your scavenging trips on the journal on the right of the screen, which shows you what happened each day. You can also see the effects of these results on your family members and your resources on the top and bottom of the screen.
-You have to be careful not to expose yourself or your family members to too much radiation or danger in the wasteland, as this will reduce your health and increase your risk of death. You also have to be careful not to miss any opportunities or clues for rescue or escape in the wasteland, as this will reduce your chances of success and happiness.
What are the Benefits of 60 Seconds Reatomized APK Hack?
-Unlimited water, food, ammo, and medkits
-One of the main benefits of 60 Seconds Reatomized APK Hack is that it gives you unlimited water, food, ammo, and medkits in your shelter. These are the most essential and valuable resources in the game, as they determine your survival and health.
-With unlimited water and food, you don't have to worry about rationing them among your family members, as they will never get thirsty or hungry. You can also feed them as much as you want, which will boost their morale and loyalty.
-With unlimited ammo and medkits, you don't have to worry about defending your shelter from intruders or healing your family members from injuries or illnesses, as you will always have enough to do so. You can also use them as much as you want, which will increase your security and safety.
-Unlocked characters, modes, and achievements
-Another benefit of 60 Seconds Reatomized APK Hack is that it gives you unlocked characters, modes, and achievements in your game. These are the most fun and rewarding features in the game, as they add variety and challenge to your gameplay.
-With unlocked characters, you can play as any of the six family members or the two pets in the game. You can also mix and match them to create different combinations and scenarios. You can also see their unique personalities and abilities in action.
-With unlocked modes, you can play any of the four modes in the game. You can also choose any difficulty level for each mode. You can also customize your game settings to suit your preferences and style.
-With unlocked achievements, you can see all the achievements that the game has to offer. You can also try to complete them all to prove your skills and creativity. You can also show off your achievements to your friends and other players.
-Improved graphics, sound, and performance
-A third benefit of 60 Seconds Reatomized APK Hack is that it gives you improved graphics, sound, and performance on your device. These are the most important and noticeable aspects of the game, as they affect your immersion and enjoyment.
-With improved graphics, you can see the game in high resolution and quality. You can also see more details and colors in the game environment and characters. You can also enjoy smoother animations and transitions in the game.
-With improved sound, you can hear the game in clear and crisp audio. You can also hear more sounds and effects in the game environment and characters. You can also enjoy better music and voice acting in the game.
-With improved performance, you can play the game without any lag or glitches. You can also play the game without any crashes or errors. You can also enjoy faster loading and saving times in the game.
What are the Risks of 60 Seconds Reatomized APK Hack?
-Possible malware or viruses from unverified sources
-One of the main risks of 60 Seconds Reatomized APK Hack is that it may contain malware or viruses from unverified sources. These are malicious software or programs that can harm your device or steal your personal information.
-As we mentioned earlier, not all sources that offer APK files are trustworthy and secure. Some of them may have hidden or embedded malware or viruses in their APK files, which can infect your device or access your data once you install them.
-Therefore, you should be careful and cautious when downloading and installing APK files from unknown or suspicious sources. You should always do some research and check the reviews and ratings of the source before downloading anything. You should also scan the APK file with an antivirus software before installing it.
-Potential bans or legal issues from the game developers
-Another risk of 60 Seconds Reatomized APK Hack is that it may cause bans or legal issues from the game developers. These are penalties or consequences that can affect your access or rights to the game.
-As we mentioned earlier, an APK hack file is a modification or alteration of the original game that changes its functionality or appearance. By downloading and installing an APK hack file, you are bypassing the restrictions and limitations imposed by the game developers or the Google Play Store.
-This may violate the terms and conditions or the intellectual property rights of the game developers, which can result in bans or legal actions against you. You may lose your account, your progress, your achievements, or your access to the game. You may also face fines, lawsuits, or criminal charges from the game developers.
-Therefore, you should be aware and respectful of the rules and rights of the game developers when downloading and installing APK files. You should always use the official version of the game from the Google Play Store, unless you have permission or authorization from the game developers to use a modified version.
-Reduced challenge and fun from the game mechanics
-A third risk of 60 Seconds Reatomized APK Hack is that it may reduce the challenge and fun from the game mechanics. These are the features and elements that make the game enjoyable and engaging.
-As we mentioned earlier, 60 Seconds Reatomized is a game that combines elements of strategy, simulation, adventure, and comedy. It is a game that will make you laugh, cry, and scream as you face the consequences of your decisions.
-The game is designed to be challenging and fun, as you have to scavenge for items and family members in 60 seconds before the blast, manage your resources and make decisions in the shelter, explore the wasteland and encounter random events, and try to survive and escape the nuclear apocalypse.
-However, by using 60 Seconds Reatomized APK Hack, you are altering or removing some of these features and elements, such as scavenging, rationing, surviving, etc. You are making the game easier and simpler, which may reduce its challenge and fun.
-Therefore, you should be careful not to spoil or ruin the game experience for yourself or others when using 60 Seconds Reatomized APK Hack. You should always play the game as it was intended by the game developers, unless you want to try something different or experiment with something new.
-Conclusion
-60 Seconds Reatomized APK Hack is a modified version of 60 Seconds Reatomized, a remastered edition of the classic atomic adventure game. It is a hack that gives you unlimited resources and features that are not available in the official version. It is a way to enjoy the game without worrying about scavenging, rationing, and surviving.
-To download and install 60 Seconds Reatomized APK Hack, you need to find a reliable and safe source that provides the APK file, enable unknown sources on your device, and install the APK file and launch the game. To play 60 Seconds Reatomized APK Hack, you need to choose your mode and difficulty level, collect items and family members in 60 seconds before the blast, manage your resources and make decisions in the shelter, explore the wasteland and encounter random events.
-The benefits of 60 Seconds Reatomized APK Hack are unlimited water, food, ammo, and medkits; unlocked characters, modes, and achievements; improved graphics, sound, and performance. The risks of 60 Seconds Reatomized APK Hack are possible malware or viruses from unverified sources; potential bans or legal issues from the game developers; reduced challenge and fun from the game mechanics.
-We hope this article has helped you learn more about 60 Seconds Reatomized APK Hack. If you have any questions or feedback, please feel free to leave them in the comments section below. Thank you for reading and have a great day!
-FAQs
-What is the difference between 60 Seconds Reatomized and 60 Seconds!?
-60 Seconds Reatomized is a remastered edition of 60 Seconds!, which is the original game that was released in 2015. 60 Seconds Reatomized has improved graphics, sound, and performance, as well as new content, such as characters, modes, events, and endings.
-Is 60 Seconds Reatomized APK Hack safe to use?
-60 Seconds Reatomized APK Hack is safe to use as long as you download it from a trusted and verified source, such as [APKPure]. However, you should always be careful and cautious when downloading and installing APK files from unknown or suspicious sources, as they may contain malware or viruses that can harm your device or steal your personal information.
-Is 60 Seconds Reatomized APK Hack legal to use?
-60 Seconds Reatomized APK Hack is not legal to use, as it violates the terms and conditions and the intellectual property rights of the game developers. By using 60 Seconds Reatomized APK Hack, you are bypassing the restrictions and limitations imposed by the game developers or the Google Play Store, which can result in bans or legal actions against you. Therefore, you should always use the official version of the game from the Google Play Store, unless you have permission or authorization from the game developers to use a modified version.
-How long does it take to finish 60 Seconds Reatomized?
-The length of 60 Seconds Reatomized depends on the mode and difficulty level you choose, as well as the choices and actions you make in the game. However, on average, it takes about 10 to 20 minutes to complete one playthrough of the game.
-Can I play 60 Seconds Reatomized with my friends?
-Yes, you can play 60 Seconds Reatomized with your friends in a local co-op mode. In this mode, you can share your device with up to four players and take turns in scavenging, managing, and exploring. You can also compete or cooperate with each other to see who can survive longer or better.
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Angry Birds 2 MOD APK 3.10.0 Unlimited Money and Gems - HappyMod.md b/spaces/1phancelerku/anime-remove-background/Angry Birds 2 MOD APK 3.10.0 Unlimited Money and Gems - HappyMod.md deleted file mode 100644 index f20ff33ee4eef372c907ea8522b8e2ae086098cc..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Angry Birds 2 MOD APK 3.10.0 Unlimited Money and Gems - HappyMod.md +++ /dev/null @@ -1,108 +0,0 @@ - -
Angry Birds 2 APK Happymod: How to Download and Play the Modded Version of the Popular Game
-Angry Birds 2 is one of the most popular casual games on Android, with over 100 million downloads on Google Play. The game is a sequel to the original Angry Birds game, which was released in 2009 and became a global phenomenon. In this article, we will show you how to download and play the modded version of Angry Birds 2 using Happymod, a platform for downloading modded games and apps.
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What is Angry Birds 2?
-Angry Birds 2 is a puzzle game developed by Rovio Entertainment and released in 2015. The game features two new birds named Silver and Melody, a new ability for Red, spells instead of power-ups, and gameplay that occurs in multi-stage levels. The game also has a social aspect, as players can compete with other players around the world in arenas and clans.
-A sequel to the original Angry Birds game
-The story of Angry Birds 2 follows the same premise as the original game. The evil pigs have stolen the eggs of the birds, and the birds must use their slingshot to launch themselves at the pigs' structures and destroy them. The game has over 1,000 levels, each with different themes, obstacles, and objectives.
-Features new birds, spells, levels, and modes
-Angry Birds 2 introduces some new elements to the gameplay. For example, players can choose which bird they want to use from a deck of cards before launching them. This adds more strategy and variety to the game. The game also has new birds with unique abilities, such as Silver who can loop in the air and destroy stone blocks, and Melody who can transform into different objects. The game also has spells that can help players in difficult situations, such as freezing pigs, raining rubber ducks, or summoning mighty eagles.
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The game also has new levels that are divided into multiple stages. Each stage has different layouts and objectives, such as destroying a certain number of pigs or reaching a certain score. The game also has boss battles that require players to defeat powerful pigs with special attacks.
-The game also has new modes that add more challenge and fun to the game. For example, there is an arena mode where players can compete with other players in daily tournaments and climb up the leaderboards. There is also a clan mode where players can join or create clans with other players and participate in clan events.
-Challenges players to defeat the pigs and other players
-Angry Birds 2 is not an easy game. The game requires players to use their skills, strategy, and luck to complete the levels. The game also has a difficulty system that adjusts the level of challenge based on the player's performance. The game also has a star rating system that rewards players for achieving high scores and completing objectives.
-The game also challenges players to compete with other players in online modes. The game has a ranking system that matches players with similar skill levels and rewards them with feathers and gems. The game also has a chat system that allows players to communicate with their clan members and friends.
-What is Happymod?
-Happymod is a platform for downloading modded games and apps for Android devices. Modded games and apps are modified versions of the original games and apps that have extra features, such as unlimited money, unlocked items, or enhanced graphics. Happymod offers thousands of modded games and apps for free, with fast, secure, and multilingual downloads.
-A platform for downloading modded games and apps
-Happymod is a website and an app that allows users to download modded games and apps easily. Users can browse through different categories, such as action, arcade, casual, or simulation, and find the modded games and apps they want. Users can also search for specific games and apps using keywords or filters. Users can also view the details, screenshots, ratings, and comments of each modded game and app before downloading them.
-Offers fast, secure, and multilingual downloads
-Happymod provides fast and secure downloads for its users. Users can download the modded games and apps directly from the website or the app without any registration or verification. Users can also pause and resume their downloads at any time. Happymod also ensures that all the modded games and apps are safe and virus-free by scanning them with antivirus software.
-Happymod also supports multiple languages for its users. Users can choose from over 20 languages, such as English, Spanish, French, German, or Arabic, to access the website or the app. Users can also change the language settings at any time.
-Supports many popular Android games and apps
-Happymod supports many popular Android games and apps that users love to play or use. Some of the most downloaded modded games and apps on Happymod are:
-| Game/App | -Mod Features | -
|---|---|
| Angry Birds 2 | -Unlimited money, gems, lives, energy; unlocked all birds, hats, spells, levels; customized gameplay | -
| Minecraft | -Unlocked premium skins, textures; unlimited resources; immortality; no damage; no ads | -
| Spotify | -Unlocked premium features; unlimited skips; no ads; high-quality audio; offline mode | -
| Netflix | -Unlocked premium features; unlimited movies and shows; no ads; high-quality video; offline mode | -
| TikTok | -Unlocked premium features; unlimited likes, followers, views; no ads; high-quality video; watermark remover | -
How to Download and Install Angry Birds 2 APK Happymod?
-If you want to download and install Angry Birds 2 APK Happymod on your Android device, you need to follow these simple steps:
-Enable unknown sources on your device
-Before you can install any APK file on your device, you need to enable unknown sources in your device settings. This will allow you to install apps from sources other than Google Play. To do this, go to Settings > Security > Unknown Sources and toggle it on.
-Download the APK file from Happymod website or app
-Next, you need to download the APK file of Angry Birds 2 APK Happymod from Happymod website or app. You can use any browser or download manager app to do this. To download from Happymod website, go to https://www.happymod.com/angry-birds-2-mod/com.rovio.baba/ and click on the Download button. To download from Happymod app, open the app and search for Angry Birds 2 in the search bar. Then tap on the Download button next to the mod you want.
-Locate and install the APK file using a file manager app
-Finally, you need to locate and install the APK file using a file manager app. You can use any file manager app that can access your device storage, such as ES File Explorer, Astro File Manager, or File Manager. To install the APK file, go to the folder where you downloaded the file and tap on it. Then follow the instructions on the screen to complete the installation.
-What are the Benefits of Playing Angry Birds 2 APK Happymod?
-Playing Angry Birds 2 APK Happymod has many benefits that can enhance your gaming experience. Here are some of them:
-Enjoy unlimited money, gems, lives, and energy
-One of the main benefits of playing Angry Birds 2 APK Happymod is that you can enjoy unlimited resources that can help you progress faster in the game. You can use money and gems to buy more cards, hats, spells, and chests. You can also use lives and energy to play more levels and arenas without waiting for them to refill.
-Unlock all birds, hats, spells, and levels
-Another benefit of playing Angry Birds 2 APK Happymod is that you can unlock all the content that the game has to offer. You can unlock all the birds with their unique abilities, such as Chuck, Bomb, Matilda, Terence, Stella, Bubbles, Hal, and more. You can also unlock all the hats that can boost your birds' powers, such as cowboy hats, pirate hats, ninja hats, and more. You can also unlock all the spells that can help you in difficult situations, such as golden duck, hot chili, pig inflator, and more. You can also unlock all the levels that are divided into different chapters and episodes.
-Customize your gameplay with different mods
-A third benefit of playing Angry Birds 2 APK Happymod is that you can customize your gameplay with different mods that can change the game rules or mechanics. For example, you can use mods that can make your birds fly faster, hit harder, or bounce higher. You can also use mods that can make the pigs weaker, smaller, or less intelligent. You can also use mods that can change the graphics, sounds, or interface of the game.
-Conclusion
-Angry Birds 2 APK Happymod is a great way to enjoy the popular game with more features and fun. You can download and install the modded version of the game using Happymod, a platform for downloading modded games and apps. You can also enjoy unlimited resources, unlock all content, and customize your gameplay with different mods. If you are a fan of Angry Birds 2 or want to try something new, you should give Angry Birds 2 APK Happymod a try.
-If you liked this article, please share it with your friends and leave a comment below. Also, don't forget to check out our other articles on Happymod for more modded games and apps.
-FAQs
-Is Angry Birds 2 APK Happymod safe to use?
-Yes, Angry Birds 2 APK Happymod is safe to use as long as you download it from a trusted source like Happymod. Happymod scans all the modded games and apps with antivirus software to ensure they are free of malware and viruses.
-Do I need to root my device to play Angry Birds 2 APK Happymod?
-No, you do not need to root your device to play Angry Birds 2 APK Happymod. The modded version of the game works on both rooted and non-rooted devices.
-Can I play Angry Birds 2 APK Happymod online with other players?
-Yes, you can play Angry Birds 2 APK Happymod online with other players in arenas and clans. However, you may encounter some issues or errors when playing online due to the modded features of the game. Therefore, we recommend playing online at your own risk.
-How can I update Angry Birds 2 APK Happymod to the latest version?
-To update Angry Birds 2 APK Happymod to the latest version, you need to download and install the latest APK file from Happymod website or app. You do not need to uninstall the previous version of the game before installing the new one.
-Where can I find more modded games and apps like Angry Birds 2 APK Happymod?
-You can find more modded games and apps like Angry Birds 2 APK Happymod on Happymod website or app. Happymod offers thousands of modded games and apps for free in various categories and genres.
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diff --git a/spaces/1phancelerku/anime-remove-background/Coin Master MOD APK New Version 2022 Unlock All Levels Cards and Characters.md b/spaces/1phancelerku/anime-remove-background/Coin Master MOD APK New Version 2022 Unlock All Levels Cards and Characters.md
deleted file mode 100644
index 0d7ac94e8a3b84e7674ef74c7d887fe9770bd1fb..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Coin Master MOD APK New Version 2022 Unlock All Levels Cards and Characters.md
+++ /dev/null
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Coin Master Mod Apk New Version 2022: Everything You Need to Know
-If you are looking for a fun and addictive game that combines slot machines, card collecting, and village building, then you might want to try Coin Master. And if you want to enjoy the game without any limitations or restrictions, then you might want to download Coin Master Mod Apk New Version 2022. In this article, we will tell you everything you need to know about this hacked version of the game, including what it is, how to get it, and what are the benefits of using it.
-What is Coin Master?
-A casual game with a twist
-Coin Master is a casual game that was released in 2010 by Moon Active. The game has over 100 million downloads on Google Play Store and is one of the most popular games in the world. The game is based on a simple premise: you spin a slot machine to earn coins, which you can use to build and upgrade your village, attack and raid other players' villages, and collect cards that unlock new worlds and characters.
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How to play Coin Master
-The gameplay of Coin Master is easy to understand and follow. You start with five spins per hour, which you can use to spin the slot machine. The slot machine can give you various outcomes, such as coins, shields, attacks, raids, or free spins. Depending on the outcome, you can either use your coins to buy items for your village, protect your village from attacks, attack other players' villages, raid other players' coin stash, or get more spins. You can also invite your friends to play with you and exchange gifts and cards with them. The game has hundreds of levels and themes, such as pirates, Vikings, Egyptians, and more.
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-A hacked version of the game
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-The benefits of using Coin Master Mod Apk
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How to download and install Coin Master Mod Apk New Version 2022?
-The steps to follow
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- After that, you need to locate the Coin Master Mod Apk file on your device and tap on it to install it. -
- Finally, you need to open the app and enjoy the game with unlimited resources. -
The precautions to take
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Conclusion
-Coin Master is a fun and addictive game that lets you spin, build, attack, raid, and collect cards. However, if you want to enjoy the game without any limitations or restrictions, you can download Coin Master Mod Apk New Version 2022. This is a hacked version of the game that gives you unlimited coins, spins, shields, and other resources. You can download and install it easily by following the steps and precautions mentioned above. However, you should also be careful of the risks and drawbacks of using a mod apk. We hope this article has helped you learn everything you need to know about Coin Master Mod Apk New Version 2022.
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- Is Coin Master Mod Apk safe to use? -
- Can I play Coin Master Mod Apk with my friends? -
- What are the best features of Coin Master Mod Apk? -
- Unlimited coins and spins -
- Unlimited shields -
- All cards and characters unlocked -
- Premium features and items unlocked -
- Offline mode available -
- How can I get more coins and spins in Coin Master? -
- Completing quests and achievements -
- Watching video ads -
- Inviting new friends -
- Joining events and tournaments -
- Claiming daily bonuses and rewards -
- What are the alternatives to Coin Master Mod Apk? -
- Pirate Kings: A game that lets you spin, build, attack, and steal from other players' islands. -
- Coin Dozer: A game that lets you push coins and prizes off a dozer machine. -
- Coin Trip: A game that lets you travel around the world and collect coins from different landmarks. -
- Coin Beach: A game that lets you create your own beach resort and earn coins from visitors. -
- Coin Boom: A game that lets you blast coins with bombs and rockets. -
- Puede jugar sin conexión en cualquier momento, en cualquier lugar, sin ninguna conexión a Internet o problemas de almacenamiento en búfer. -
- Puedes compartirlo con tus amigos y familiares a través de Bluetooth, WhatsApp, correo electrónico u otras aplicaciones. -
- Puede hacer que su tono de llamada, tono de alarma, o tono de notificación. -
- Puede editarlo, recortarlo o mezclarlo con otras canciones utilizando un software de edición de audio. -
- Se puede disfrutar de ella en alta calidad y claridad, sin ningún tipo de anuncios o interrupciones. -
- Wynk Music: Este es un servicio de transmisión de música en línea que ofrece una amplia gama de canciones en varios idiomas. Puedes descargar esta canción gratis si tienes una suscripción Wynk o una tarjeta SIM Airtel. -
- Gaana.com: Este es otro servicio de transmisión de música en línea que tiene una gran colección de canciones de diferentes géneros. Puede descargar esta canción gratis si tiene una suscripción a Gaana Plus o una tarjeta SIM Jio
Cómo descargar la canción gratis
-Ahora que conoce la historia y los beneficios de la canción, es posible que se pregunte cómo descargarla de forma gratuita en su dispositivo. Hay muchas maneras de hacer esto, pero te mostraremos uno de los métodos más fáciles y confiables: usar un convertidor de YouTube a MP3. Esta es una herramienta que te permite convertir cualquier vídeo de YouTube en un archivo de audio MP3 que puedes descargar y guardar en tu dispositivo. Estos son los pasos a seguir:
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- Vaya a [YouTube]( 1 ) o abra la aplicación de YouTube en su dispositivo y busque la canción Bar Bar Din Ye Aaye. Encontrarás muchas versiones de la canción, pero te recomendamos que elijas la que tenga las vistas y valoraciones más altas. -
- Copie la URL del vídeo desde la barra de direcciones o tocando el botón Compartir y seleccionando Copiar enlace. - -
- Pegue la URL del video de YouTube en la barra de búsqueda y luego haga clic en Convertir. -
- Seleccione la calidad de archivo MP3 esperada y haga clic en Descargar. Puede elegir entre 320 kbps, 256 kbps, 192 kbps, 128 kbps o 64 kbps. Cuanto mayor sea la calidad, mayor será el tamaño del archivo. -
- Espere unos segundos mientras se realiza la conversión. Luego, haga clic en Descargar de nuevo para guardar el archivo MP3 en su dispositivo. -
Felicidades! Usted ha descargado con éxito la canción Bar Bar Din Ye Aaye como un archivo MP3 en su dispositivo. Ahora puedes disfrutarlo sin conexión en cualquier momento, en cualquier lugar y compartirlo con tus seres queridos en sus cumpleaños.
-Una tabla que compara los pros y contras de diferentes métodos
-Usar un convertidor de YouTube a MP3 no es la única manera de descargar la canción gratis. Hay otros métodos, como el uso de servicios de transmisión en línea, la descarga de aplicaciones o el uso de extensiones de navegador. Sin embargo, cada método tiene sus propios pros y contras, que debe considerar antes de elegir uno. Aquí hay una tabla que compara algunos de los métodos más comunes:
- -Antes de descargar cualquier canción de YouTube, usted debe ser consciente de los problemas legales y éticos involucrados. Como mencionamos anteriormente, descargar videos de YouTube va en contra de los términos de servicio de YouTube, a menos que tenga permiso de YouTube o del titular de los derechos. Esto significa que usted está violando sus derechos de propiedad intelectual, lo que podría resultar en acciones legales o sanciones. Además, la descarga de vídeos de YouTube también podría considerarse un robo a los artistas y creadores que se ganan la vida con su trabajo. Al descargar sus canciones de forma gratuita, los estás privando de su legítimo ingreso y reconocimiento. Por lo tanto, le aconsejamos respetar sus derechos y apoyarlos mediante la compra de sus canciones legalmente o suscribirse a sus canales.
-Conclusión y preguntas frecuentes
-En este artículo, hemos aprendido acerca de la canción Bar Din Ye Aaye, su historia y significado, sus beneficios y características, y cómo descargarlo gratis usando un convertidor de YouTube a MP3. También hemos comparado diferentes métodos de descarga de la canción y discutido las cuestiones legales y éticas involucradas. Esperamos que este artículo haya sido útil e informativo para usted. Si tiene alguna pregunta o comentario, no dude en contactarnos a través de la sección de comentarios a continuación. Ahora, veamos algunas de las preguntas frecuentes sobre esta canción y su descarga.
-Preguntas frecuentes
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-
- ¿Cuál es el nombre de la película que presentó la canción Bar Bar Din Ye Aaye?
-El nombre de la película es Farz, que significa Deber en inglés. Fue estrenada en 1967 y protagonizada por Jeetendra y Babita como agentes secretos.
- - ¿Quién escribió la letra de la canción Bar Bar Din Ye Aaye?
-La letra de la canción fue escrita por Anand Bakshi, que era un letrista famoso en Bollywood. Escribió canciones para más de 600 películas y ganó varios premios por su trabajo.
-
-Puedes descargar la canción legal y éticamente comprándola en tiendas de música online, como iTunes, Amazon Music o Google Play Music. También puede transmitirlo en línea desde servicios de música con licencia, como Spotify, Apple Music o YouTube Music.
- - ¿Cuáles son algunas otras canciones populares de cumpleaños en hindi?
-Algunas otras canciones populares de cumpleaños en hindi son Tum Jiyo Hazaaron Saal de la película Sujata, Chhote Tera Birthday Aaya de la película Krantiveer: La revolución, y Baadhaai Ho Baadhaai de la película Baadhaai Ho Baadhaai.
- - ¿Cómo puedo hacer un video de cumpleaños personalizado con la canción Bar Bar Din Ye Aaye?
-Puedes hacer un video de cumpleaños personalizado con la canción usando herramientas de edición de video en línea, como Animoto, InVideo o Kapwing. Puedes subir tus fotos y videos, agregar la canción como música de fondo y personalizar el texto y los efectos. A continuación, puede descargar o compartir su vídeo con sus amigos y familiares.
-
Coin Master Mod Apk is safe to use as long as you download it from a trusted source and follow the precautions mentioned above. However, there is always a possibility of getting banned or suspended by the game developers for using a mod apk. Therefore, you should use it at your own risk and discretion.
-Yes, you can play Coin Master Mod Apk with your friends who also have the same mod apk installed on their devices. You can invite them to join your game and exchange gifts and cards with them. However, you cannot play with your friends who have the original version of Coin Master installed on their devices.
-Some of the best features of Coin Master Mod Apk are:
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-
If you don't want to use Coin Master Mod Apk, there are some other ways to get more coins and spins in Coin Master. Some of them are:
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If you are looking for other games that are similar to Coin Master but have different themes or features, you can try these alternatives:
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-
\ No newline at end of file diff --git a/spaces/2ndelement/voicevox/voicevox_engine/dev/core/__init__.py b/spaces/2ndelement/voicevox/voicevox_engine/dev/core/__init__.py deleted file mode 100644 index 432b00b93b362ec24d63e2daf65c70dbee8f3b08..0000000000000000000000000000000000000000 --- a/spaces/2ndelement/voicevox/voicevox_engine/dev/core/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -from .mock import ( - decode_forward, - initialize, - metas, - supported_devices, - yukarin_s_forward, - yukarin_sa_forward, -) - -__all__ = [ - "decode_forward", - "initialize", - "yukarin_s_forward", - "yukarin_sa_forward", - "metas", - "supported_devices", -] diff --git a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/nets_61968KB.py b/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/nets_61968KB.py deleted file mode 100644 index 167d4cb2198863cf43e93440f7e63c5342fc7605..0000000000000000000000000000000000000000 --- a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/nets_61968KB.py +++ /dev/null @@ -1,122 +0,0 @@ -import torch -import torch.nn.functional as F -from torch import nn - -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/ADOPLE/AdopleAI-Website-DocumentQA/app.py b/spaces/ADOPLE/AdopleAI-Website-DocumentQA/app.py deleted file mode 100644 index d31a29909635fb333897b220b48988c1daf83f4d..0000000000000000000000000000000000000000 --- a/spaces/ADOPLE/AdopleAI-Website-DocumentQA/app.py +++ /dev/null @@ -1,130 +0,0 @@ -import os -from langchain.chains.question_answering import load_qa_chain -from langchain.document_loaders import UnstructuredFileLoader -from langchain.embeddings.openai import OpenAIEmbeddings -from langchain.llms import OpenAI -from langchain.text_splitter import CharacterTextSplitter -from langchain.vectorstores import FAISS -from pypdf import PdfReader -import mimetypes -import validators -import requests -import tempfile -import gradio as gr -import openai - - -def get_empty_state(): - return {"knowledge_base": None} - - -def create_knowledge_base(docs): - # split into chunks - text_splitter = CharacterTextSplitter( - separator="\n", chunk_size=500, chunk_overlap=0, length_function=len - ) - chunks = text_splitter.split_documents(docs) - - # Create embeddings - embeddings = OpenAIEmbeddings() - knowledge_base = FAISS.from_documents(chunks, embeddings) - return knowledge_base - - -def upload_file(file_obj): - try: - loader = UnstructuredFileLoader(file_obj.name, strategy="fast") - docs = loader.load() - - knowledge_base = create_knowledge_base(docs) - except: - text="Try Another file" - return file_obj.name, text - - return file_obj.name, {"knowledge_base": knowledge_base} - - -def upload_via_url(url): - if validators.url(url): - r = requests.get(url) - - if r.status_code != 200: - raise ValueError( - "Check the url of your file; returned status code %s" % r.status_code - ) - - content_type = r.headers.get("content-type") - file_extension = mimetypes.guess_extension(content_type) - temp_file = tempfile.NamedTemporaryFile(suffix=file_extension, delete=False) - temp_file.write(r.content) - file_path = temp_file.name - loader = UnstructuredFileLoader(file_path, strategy="fast") - docs = loader.load() - with open(file_path, mode="rb") as f: - pass - knowledge_base = create_knowledge_base(docs) - return file_path, {"knowledge_base": knowledge_base} - else: - raise ValueError("Please enter a valid URL") - - -def answer_question(question, state): - - try: - knowledge_base = state["knowledge_base"] - docs = knowledge_base.similarity_search(question) - - llm = OpenAI(temperature=0.4) - chain = load_qa_chain(llm, chain_type="stuff") - response = chain.run(input_documents=docs, question=question) - return response - except: - return "Please upload Proper Document" - - -with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as demo: - state = gr.State(get_empty_state()) - # gr.HTML("""
- # 
""")
- with gr.Column(elem_id="col-container"):
- # gr.HTML(
- # """""" - # ) - gr.HTML( - """
-
- ADOPLE AI Document QA -
""" - ) - # gr.HTML( - # """""" - # ) - - gr.Markdown("**Upload your file**") - with gr.Row(elem_id="row-flex"): - # with gr.Column(scale=0.85): - # file_url = gr.Textbox( - # value="", - # label="Upload your file", - # placeholder="Enter a url", - # show_label=False, - # visible=False - # ) - with gr.Column(scale=0.90, min_width=160): - file_output = gr.File(elem_classes="filenameshow") - with gr.Column(scale=0.10, min_width=160): - upload_button = gr.UploadButton( - "Browse File", file_types=[".txt", ".pdf", ".doc", ".docx"], - elem_classes="filenameshow") - with gr.Row(): - with gr.Column(scale=1, min_width=0): - user_question = gr.Textbox(value="",label='Question Box :',show_label=True, placeholder="Ask a question about your file:",elem_classes="spaceH") - with gr.Row(): - with gr.Column(scale=1, min_width=0): - answer = gr.Textbox(value="",label='Answer Box :',show_label=True, placeholder="",lines=5) - - #file_url.submit(upload_via_url, file_url, [file_output, state]) - upload_button.upload(upload_file, upload_button, [file_output,state]) - user_question.submit(answer_question, [user_question, state], [answer]) - -demo.queue().launch() diff --git a/spaces/AI-Zero-to-Hero/03-GR-AI-Text2ArtGenerator/app.py b/spaces/AI-Zero-to-Hero/03-GR-AI-Text2ArtGenerator/app.py deleted file mode 100644 index 1842b91661e1edd1167802f4093d3e887f662042..0000000000000000000000000000000000000000 --- a/spaces/AI-Zero-to-Hero/03-GR-AI-Text2ArtGenerator/app.py +++ /dev/null @@ -1,228 +0,0 @@ -import os - -os.system("git clone --recursive https://github.com/JD-P/cloob-latent-diffusion") -os.system("cd cloob-latent-diffusion;pip install omegaconf pillow pytorch-lightning einops wandb ftfy regex ./CLIP") - -import argparse -from functools import partial -from pathlib import Path -import sys -sys.path.append('./cloob-latent-diffusion') -sys.path.append('./cloob-latent-diffusion/cloob-training') -sys.path.append('./cloob-latent-diffusion/latent-diffusion') -sys.path.append('./cloob-latent-diffusion/taming-transformers') -sys.path.append('./cloob-latent-diffusion/v-diffusion-pytorch') -from omegaconf import OmegaConf -from PIL import Image -import torch -from torch import nn -from torch.nn import functional as F -from torchvision import transforms -from torchvision.transforms import functional as TF -from tqdm import trange -from CLIP import clip -from cloob_training import model_pt, pretrained -import ldm.models.autoencoder -from diffusion import sampling, utils -import train_latent_diffusion as train -from huggingface_hub import hf_hub_url, cached_download -import random - -# Download the model files -checkpoint = cached_download(hf_hub_url("huggan/distill-ccld-wa", filename="model_student.ckpt")) -ae_model_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.ckpt")) -ae_config_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.yaml")) - -# Define a few utility functions - - -def parse_prompt(prompt, default_weight=3.): - if prompt.startswith('http://') or prompt.startswith('https://'): - vals = prompt.rsplit(':', 2) - vals = [vals[0] + ':' + vals[1], *vals[2:]] - else: - vals = prompt.rsplit(':', 1) - vals = vals + ['', default_weight][len(vals):] - return vals[0], float(vals[1]) - - -def resize_and_center_crop(image, size): - fac = max(size[0] / image.size[0], size[1] / image.size[1]) - image = image.resize((int(fac * image.size[0]), int(fac * image.size[1])), Image.LANCZOS) - return TF.center_crop(image, size[::-1]) - - -# Load the models -device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') -print('Using device:', device) -print('loading models') - -# autoencoder -ae_config = OmegaConf.load(ae_config_path) -ae_model = ldm.models.autoencoder.AutoencoderKL(**ae_config.model.params) -ae_model.eval().requires_grad_(False).to(device) -ae_model.load_state_dict(torch.load(ae_model_path)) -n_ch, side_y, side_x = 4, 32, 32 - -# diffusion model -model = train.DiffusionModel(192, [1,1,2,2], autoencoder_scale=torch.tensor(4.3084)) -model.load_state_dict(torch.load(checkpoint, map_location='cpu')) -model = model.to(device).eval().requires_grad_(False) - -# CLOOB -cloob_config = pretrained.get_config('cloob_laion_400m_vit_b_16_16_epochs') -cloob = model_pt.get_pt_model(cloob_config) -checkpoint = pretrained.download_checkpoint(cloob_config) -cloob.load_state_dict(model_pt.get_pt_params(cloob_config, checkpoint)) -cloob.eval().requires_grad_(False).to(device) - - -# The key function: returns a list of n PIL images -def generate(n=1, prompts=['a red circle'], images=[], seed=42, steps=15, - method='plms', eta=None): - zero_embed = torch.zeros([1, cloob.config['d_embed']], device=device) - target_embeds, weights = [zero_embed], [] - - for prompt in prompts: - txt, weight = parse_prompt(prompt) - target_embeds.append(cloob.text_encoder(cloob.tokenize(txt).to(device)).float()) - weights.append(weight) - - for prompt in images: - path, weight = parse_prompt(prompt) - img = Image.open(utils.fetch(path)).convert('RGB') - clip_size = cloob.config['image_encoder']['image_size'] - img = resize_and_center_crop(img, (clip_size, clip_size)) - batch = TF.to_tensor(img)[None].to(device) - embed = F.normalize(cloob.image_encoder(cloob.normalize(batch)).float(), dim=-1) - target_embeds.append(embed) - weights.append(weight) - - weights = torch.tensor([1 - sum(weights), *weights], device=device) - - torch.manual_seed(seed) - - def cfg_model_fn(x, t): - n = x.shape[0] - n_conds = len(target_embeds) - x_in = x.repeat([n_conds, 1, 1, 1]) - t_in = t.repeat([n_conds]) - clip_embed_in = torch.cat([*target_embeds]).repeat_interleave(n, 0) - vs = model(x_in, t_in, clip_embed_in).view([n_conds, n, *x.shape[1:]]) - v = vs.mul(weights[:, None, None, None, None]).sum(0) - return v - - def run(x, steps): - if method == 'ddpm': - return sampling.sample(cfg_model_fn, x, steps, 1., {}) - if method == 'ddim': - return sampling.sample(cfg_model_fn, x, steps, eta, {}) - if method == 'prk': - return sampling.prk_sample(cfg_model_fn, x, steps, {}) - if method == 'plms': - return sampling.plms_sample(cfg_model_fn, x, steps, {}) - if method == 'pie': - return sampling.pie_sample(cfg_model_fn, x, steps, {}) - if method == 'plms2': - return sampling.plms2_sample(cfg_model_fn, x, steps, {}) - assert False - - batch_size = n - x = torch.randn([n, n_ch, side_y, side_x], device=device) - t = torch.linspace(1, 0, steps + 1, device=device)[:-1] - steps = utils.get_spliced_ddpm_cosine_schedule(t) - pil_ims = [] - for i in trange(0, n, batch_size): - cur_batch_size = min(n - i, batch_size) - out_latents = run(x[i:i+cur_batch_size], steps) - outs = ae_model.decode(out_latents * torch.tensor(2.55).to(device)) - for j, out in enumerate(outs): - pil_ims.append(utils.to_pil_image(out)) - - return pil_ims - - -import gradio as gr - -def gen_ims(prompt, im_prompt=None, seed=None, n_steps=10, method='plms'): - if seed == None : - seed = random.randint(0, 10000) - print( prompt, im_prompt, seed, n_steps) - prompts = [prompt] - im_prompts = [] - if im_prompt != None: - im_prompts = [im_prompt] - pil_ims = generate(n=1, prompts=prompts, images=im_prompts, seed=seed, steps=n_steps, method=method) - return pil_ims[0] - -iface = gr.Interface(fn=gen_ims, - inputs=[#gr.inputs.Slider(minimum=1, maximum=1, step=1, default=1,label="Number of images"), - #gr.inputs.Slider(minimum=0, maximum=200, step=1, label='Random seed', default=0), - gr.inputs.Textbox(label="Text prompt"), - gr.inputs.Image(optional=True, label="Image prompt", type='filepath'), - #gr.inputs.Slider(minimum=10, maximum=35, step=1, default=15,label="Number of steps") - ], - outputs=[gr.outputs.Image(type="pil", label="Generated Image")], - examples=[ - ["Virgin and Child, in the style of Jacopo Bellini"], - ["Katsushika Hokusai, The Dragon of Smoke Escaping from Mount Fuji"], - ["Moon Light Sonata by Basuki Abdullah"], - ["Twon Tree by M.C. Escher"], - ["Futurism, in the style of Wassily Kandinsky"], - ["Art Nouveau, in the style of John Singer Sargent"], - ["Surrealism, in the style of Edgar Degas"], - ["Expressionism, in the style of Wassily Kandinsky"], - ["Futurism, in the style of Egon Schiele"], - ["Neoclassicism, in the style of Gustav Klimt"], - ["Cubism, in the style of Gustav Klimt"], - ["Op Art, in the style of Marc Chagall"], - ["Romanticism, in the style of M.C. Escher"], - ["Futurism, in the style of M.C. Escher"], - ["Abstract Art, in the style of M.C. Escher"], - ["Mannerism, in the style of Paul Klee"], - ["Romanesque Art, in the style of Leonardo da Vinci"], - ["High Renaissance, in the style of Rembrandt"], - ["Magic Realism, in the style of Gustave Dore"], - ["Realism, in the style of Jean-Michel Basquiat"], - ["Art Nouveau, in the style of Paul Gauguin"], - ["Avant-garde, in the style of Pierre-Auguste Renoir"], - ["Baroque, in the style of Edward Hopper"], - ["Post-Impressionism, in the style of Wassily Kandinsky"], - ["Naturalism, in the style of Rene Magritte"], - ["Constructivism, in the style of Paul Cezanne"], - ["Abstract Expressionism, in the style of Henri Matisse"], - ["Pop Art, in the style of Vincent van Gogh"], - ["Futurism, in the style of Wassily Kandinsky"], - ["Futurism, in the style of Zdzislaw Beksinski"], - ['Surrealism, in the style of Salvador Dali'], - ["Aaron Wacker, oil on canvas"], - ["abstract"], - ["landscape"], - ["portrait"], - ["sculpture"], - ["genre painting"], - ["installation"], - ["photo"], - ["figurative"], - ["illustration"], - ["still life"], - ["history painting"], - ["cityscape"], - ["marina"], - ["animal painting"], - ["design"], - ["calligraphy"], - ["symbolic painting"], - ["graffiti"], - ["performance"], - ["mythological painting"], - ["battle painting"], - ["self-portrait"], - ["Impressionism, oil on canvas"] - ], - title='Art Generator and Style Mixer from 🧠 Cloob and 🎨 WikiArt - Visual Art Encyclopedia:', - description="Trained on images from the [WikiArt](https://www.wikiart.org/) dataset, comprised of visual arts", - article = 'Model used is: [model card](https://huggingface.co/huggan/distill-ccld-wa)..' - -) -iface.launch(enable_queue=True) # , debug=True for colab debugging \ No newline at end of file diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/hifigan/mel_utils.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/hifigan/mel_utils.py deleted file mode 100644 index 06e0f7d4d16fa3e4aefc8949347455f5a6e938da..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/hifigan/mel_utils.py +++ /dev/null @@ -1,80 +0,0 @@ -import numpy as np -import torch -import torch.utils.data -from librosa.filters import mel as librosa_mel_fn -from scipy.io.wavfile import read - -MAX_WAV_VALUE = 32768.0 - - -def load_wav(full_path): - sampling_rate, data = read(full_path) - return data, sampling_rate - - -def dynamic_range_compression(x, C=1, clip_val=1e-5): - return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) - - -def dynamic_range_decompression(x, C=1): - return np.exp(x) / C - - -def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): - return torch.log(torch.clamp(x, min=clip_val) * C) - - -def dynamic_range_decompression_torch(x, C=1): - return torch.exp(x) / C - - -def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output - - -def spectral_de_normalize_torch(magnitudes): - output = dynamic_range_decompression_torch(magnitudes) - return output - - -mel_basis = {} -hann_window = {} - - -def mel_spectrogram(y, hparams, center=False, complex=False): - # hop_size: 512 # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate) - # win_size: 2048 # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate) - # fmin: 55 # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) - # fmax: 10000 # To be increased/reduced depending on data. - # fft_size: 2048 # Extra window size is filled with 0 paddings to match this parameter - # n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, - n_fft = hparams['fft_size'] - num_mels = hparams['audio_num_mel_bins'] - sampling_rate = hparams['audio_sample_rate'] - hop_size = hparams['hop_size'] - win_size = hparams['win_size'] - fmin = hparams['fmin'] - fmax = hparams['fmax'] - y = y.clamp(min=-1., max=1.) - global mel_basis, hann_window - if fmax not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[str(fmax) + '_' + str(y.device)] = torch.from_numpy(mel).float().to(y.device) - hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), - mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)], - center=center, pad_mode='reflect', normalized=False, onesided=True) - - if not complex: - spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) - spec = torch.matmul(mel_basis[str(fmax) + '_' + str(y.device)], spec) - spec = spectral_normalize_torch(spec) - else: - B, C, T, _ = spec.shape - spec = spec.transpose(1, 2) # [B, T, n_fft, 2] - return spec diff --git a/spaces/ALSv/FSW/roop/processors/__init__.py b/spaces/ALSv/FSW/roop/processors/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/.ipynb_checkpoints/resnext101_4xb16_1024e_4channel-checkpoint.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/.ipynb_checkpoints/resnext101_4xb16_1024e_4channel-checkpoint.py deleted file mode 100644 index 0de71f68d6705b3fc1000419ca705be2b839d425..0000000000000000000000000000000000000000 --- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/.ipynb_checkpoints/resnext101_4xb16_1024e_4channel-checkpoint.py +++ /dev/null @@ -1,88 +0,0 @@ -_base_ = [ # 此配置文件将继承所有 `_base_` 中的配置 - '../configs/_base_/schedules/custom_schedule.py', # 训练策略配置 - '../configs/_base_/default_runtime.py' # 默认运行设置 -] - -default_hooks = dict( - # print log every 50 iterations. - logger=dict(type='LoggerHook', interval=25), - # save checkpoint per 8 epochs. - checkpoint=dict(save_best='auto', interval=16) -) - -visualizer = dict( - vis_backends=[dict(type='LocalVisBackend'), - dict(type='WandbVisBackend')]) - -dataset_type = 'CustomDataset' - -# config of pipline -train_pipeline = [ - dict(type='LoadImageFromFile', imdecode_backend='pillow', color_type='unchanged'), # 读取图像 - dict(type='RandomResizedCrop', scale=224), # 随机放缩裁剪 - dict(type='RandomFlip', prob=0.5, direction='horizontal'), # 随机水平翻转 - dict(type='PackInputs'), # 准备图像以及标签 -] - -test_pipeline = [ - dict(type='LoadImageFromFile', imdecode_backend='pillow', color_type='unchanged'), # 读取图像 - dict(type='ResizeEdge', scale=256, edge='short'), # 缩放短边尺寸至 256px - dict(type='CenterCrop', crop_size=224), # 中心裁剪 - dict(type='PackInputs'), # 准备图像以及标签 -] - -# config of dataloader -train_dataloader = dict( - batch_size=16, # 每张 GPU 的 batchsize - num_workers=5, # 每个 GPU 的线程数 - dataset=dict( # 训练数据集 - type=dataset_type, - data_root='../2_preprocess_data_3000', - with_label=True, - ann_file='', - data_prefix='train', - pipeline=train_pipeline), - sampler=dict(type='DefaultSampler', shuffle=True), # 默认采样器 - persistent_workers=True, # 是否保持进程，可以缩短每个 epoch 的准备时间 -) - -# 构造验证集 dataloader -val_dataloader = dict( - batch_size=16, - num_workers=5, - dataset=dict( - type=dataset_type, - data_root='../2_preprocess_data_3000', - with_label=True, - ann_file='', - data_prefix='val', - pipeline=test_pipeline), - sampler=dict(type='DefaultSampler', shuffle=False), - persistent_workers=True, -) - -# set evaluator of validation dataset. Here uses top1 and top3 accuracy -val_evaluator = dict(type='Accuracy', topk=(1, 3)) - -test_dataloader = val_dataloader -test_evaluator = val_evaluator - -model = dict( - type='ImageClassifier', # 主模型类型（对于图像分类任务，使用 `ImageClassifier`） - backbone=dict( - type='ResNeXt', # 主干网络类型 - depth=101, - in_channels=4, # 输入通道数 - ), - neck=dict(type='GlobalAveragePooling'), # 颈网络类型 - head=dict( - type='LinearClsHead', # 分类颈网络类型 - # 除了 `type` 之外的所有字段都来自 `LinearClsHead` 类的 __init__ 方法 - # 可查阅 https://mmpretrain.readthedocs.io/zh_CN/latest/api/generated/mmpretrain.models.heads.LinearClsHead.html - num_classes=7, # 分类类别数 - in_channels=2048, - loss=dict(type='CrossEntropyLoss', loss_weight=1.0), # 损失函数配置信息 - topk=(1, 3), # 评估指标，Top-k 准确率 - )) - - diff --git a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/__init__.py b/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/__init__.py deleted file mode 100644 index 42dcd7aa19e499d4ac240deb5d7e68bcf33795ed..0000000000000000000000000000000000000000 --- a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from poetry_diacritizer import predict \ No newline at end of file diff --git a/spaces/Adapter/CoAdapter/ldm/modules/encoders/__init__.py b/spaces/Adapter/CoAdapter/ldm/modules/encoders/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Adr740/CV_XPLORER_POC/data.py b/spaces/Adr740/CV_XPLORER_POC/data.py deleted file mode 100644 index 71f205a628766049ca6a02e244c4dc8511ee2360..0000000000000000000000000000000000000000 --- a/spaces/Adr740/CV_XPLORER_POC/data.py +++ /dev/null @@ -1,4 +0,0 @@ - - -import pandas as pd -data = pd.read_parquet("data2.parquet") \ No newline at end of file diff --git a/spaces/AfrodreamsAI/afrodreams/models/download_models.py b/spaces/AfrodreamsAI/afrodreams/models/download_models.py deleted file mode 100644 index 1b7f8126b55460593bb74f1be2b59a0e25bc0097..0000000000000000000000000000000000000000 --- a/spaces/AfrodreamsAI/afrodreams/models/download_models.py +++ /dev/null @@ -1,31 +0,0 @@ -import torch -from os import path -from sys import version_info -from collections import OrderedDict -from torch.utils.model_zoo import load_url - - -# Download the VGG-19 model and fix the layer names -print("Downloading the VGG-19 model") -sd = load_url("https://web.eecs.umich.edu/~justincj/models/vgg19-d01eb7cb.pth") -map = {'classifier.1.weight':u'classifier.0.weight', 'classifier.1.bias':u'classifier.0.bias', 'classifier.4.weight':u'classifier.3.weight', 'classifier.4.bias':u'classifier.3.bias'} -sd = OrderedDict([(map[k] if k in map else k,v) for k,v in sd.items()]) -torch.save(sd, path.join("models", "vgg19-d01eb7cb.pth")) - -# Download the VGG-16 model and fix the layer names -print("Downloading the VGG-16 model") -sd = load_url("https://web.eecs.umich.edu/~justincj/models/vgg16-00b39a1b.pth") -map = {'classifier.1.weight':u'classifier.0.weight', 'classifier.1.bias':u'classifier.0.bias', 'classifier.4.weight':u'classifier.3.weight', 'classifier.4.bias':u'classifier.3.bias'} -sd = OrderedDict([(map[k] if k in map else k,v) for k,v in sd.items()]) -torch.save(sd, path.join("models", "vgg16-00b39a1b.pth")) - -# Download the NIN model -print("Downloading the NIN model") -if version_info[0] < 3: - import urllib - urllib.URLopener().retrieve("https://raw.githubusercontent.com/ProGamerGov/pytorch-nin/master/nin_imagenet.pth", path.join("models", "nin_imagenet.pth")) -else: - import urllib.request - urllib.request.urlretrieve("https://raw.githubusercontent.com/ProGamerGov/pytorch-nin/master/nin_imagenet.pth", path.join("models", "nin_imagenet.pth")) - -print("All models have been successfully downloaded") diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/circularprogresscanvas/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/circularprogresscanvas/Factory.js deleted file mode 100644 index 8676ce40640171e3346861edb0bbedb37c96ab77..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/circularprogresscanvas/Factory.js +++ /dev/null @@ -1,13 +0,0 @@ -import CircularProgressCanvas from './CircularProgressCanvas.js'; -import ObjectFactory from '../ObjectFactory.js'; -import SetValue from '../../../plugins/utils/object/SetValue.js'; - -ObjectFactory.register('circularProgressCanvas', function (x, y, radius, barColor, value, config) { - var gameObject = new CircularProgressCanvas(this.scene, x, y, radius, barColor, value, config); - this.scene.add.existing(gameObject); - return gameObject; -}); - -SetValue(window, 'RexPlugins.UI.CircularProgressCanvas', CircularProgressCanvas); - -export default CircularProgressCanvas; \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/rotate/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/rotate/Factory.d.ts deleted file mode 100644 index 655baafa3e95d6404a3f20db73805f3de7e442dc..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/rotate/Factory.d.ts +++ /dev/null @@ -1,7 +0,0 @@ -// import * as Phaser from 'phaser'; -import Rotate from "./Rotate"; - -export default function ( - gameObject: Phaser.GameObjects.GameObject | Phaser.Scene, - config?: Rotate.IConfig -): Rotate; \ No newline at end of file diff --git a/spaces/AkitoP/umamusume_bert_vits2/bert/chinese-roberta-wwm-ext-large/README.md b/spaces/AkitoP/umamusume_bert_vits2/bert/chinese-roberta-wwm-ext-large/README.md deleted file mode 100644 index ebc4b2e6fb6b95ddc5f678b4a7f829466799f2da..0000000000000000000000000000000000000000 --- a/spaces/AkitoP/umamusume_bert_vits2/bert/chinese-roberta-wwm-ext-large/README.md +++ /dev/null @@ -1,57 +0,0 @@ ---- -language: -- zh -tags: -- bert -license: "apache-2.0" ---- - -# Please use 'Bert' related functions to load this model! - -## Chinese BERT with Whole Word Masking -For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. - -**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)** -Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu - -This repository is developed based on：https://github.com/google-research/bert - -You may also interested in, -- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm -- Chinese MacBERT: https://github.com/ymcui/MacBERT -- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA -- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet -- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer - -More resources by HFL: https://github.com/ymcui/HFL-Anthology - -## Citation -If you find the technical report or resource is useful, please cite the following technical report in your paper. -- Primary: https://arxiv.org/abs/2004.13922 -``` -@inproceedings{cui-etal-2020-revisiting, - title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing", - author = "Cui, Yiming and - Che, Wanxiang and - Liu, Ting and - Qin, Bing and - Wang, Shijin and - Hu, Guoping", - booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings", - month = nov, - year = "2020", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58", - pages = "657--668", -} -``` -- Secondary: https://arxiv.org/abs/1906.08101 -``` -@article{chinese-bert-wwm, - title={Pre-Training with Whole Word Masking for Chinese BERT}, - author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping}, - journal={arXiv preprint arXiv:1906.08101}, - year={2019} - } -``` diff --git a/spaces/AlanMars/QYL-AI-Space/modules/utils.py b/spaces/AlanMars/QYL-AI-Space/modules/utils.py deleted file mode 100644 index 93a2d59bf9ffac4de06aee69e6cb74f19ccfb4a7..0000000000000000000000000000000000000000 --- a/spaces/AlanMars/QYL-AI-Space/modules/utils.py +++ /dev/null @@ -1,669 +0,0 @@ -# -*- coding:utf-8 -*- -from __future__ import annotations -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type -import logging -import json -import os -import datetime -import hashlib -import csv -import requests -import re -import html -import sys -import subprocess - -import gradio as gr -from pypinyin import lazy_pinyin -import tiktoken -import mdtex2html -from markdown import markdown -from pygments import highlight -from pygments.lexers import get_lexer_by_name -from pygments.formatters import HtmlFormatter -import pandas as pd - -from modules.presets import * -from . import shared -from modules.config import retrieve_proxy, hide_history_when_not_logged_in - -if TYPE_CHECKING: - from typing import TypedDict - - - class DataframeData(TypedDict): - headers: List[str] - data: List[List[str | int | bool]] - - -def predict(current_model, *args): - iter = current_model.predict(*args) - for i in iter: - yield i - - -def billing_info(current_model): - return current_model.billing_info() - - -def set_key(current_model, *args): - # logging.debug(f"\n Set new key as: {args}. Old Key : {current_model.api_key}") - return current_model.set_key(*args) - - -def load_chat_history(current_model, *args): - return current_model.load_chat_history(*args) - - -def interrupt(current_model, *args): - return current_model.interrupt(*args) - - -def reset(current_model, *args): - return current_model.reset(*args) - - -def retry(current_model, *args): - iter = current_model.retry(*args) - for i in iter: - yield i - - -def delete_first_conversation(current_model, *args): - return current_model.delete_first_conversation(*args) - - -def delete_last_conversation(current_model, *args): - return current_model.delete_last_conversation(*args) - - -def set_system_prompt(current_model, *args): - logging.debug(f"\n Set new system prompt as: {args}") - return current_model.set_system_prompt(*args) - - -def save_chat_history(current_model, *args): - return current_model.save_chat_history(*args) - - -def export_markdown(current_model, *args): - return current_model.export_markdown(*args) - - -def load_chat_history(current_model, *args): - return current_model.load_chat_history(*args) - - -def upload_chat_history(current_model, *args): - return current_model.load_chat_history(*args) - - -def set_token_upper_limit(current_model, *args): - return current_model.set_token_upper_limit(*args) - - -def set_temperature(current_model, *args): - current_model.set_temperature(*args) - - -def set_top_p(current_model, *args): - current_model.set_top_p(*args) - - -def set_n_choices(current_model, *args): - current_model.set_n_choices(*args) - - -def set_stop_sequence(current_model, *args): - current_model.set_stop_sequence(*args) - - -def set_max_tokens(current_model, *args): - current_model.set_max_tokens(*args) - - -def set_presence_penalty(current_model, *args): - current_model.set_presence_penalty(*args) - - -def set_frequency_penalty(current_model, *args): - current_model.set_frequency_penalty(*args) - - -def set_logit_bias(current_model, *args): - current_model.set_logit_bias(*args) - - -def set_user_identifier(current_model, *args): - current_model.set_user_identifier(*args) - - -def set_single_turn(current_model, *args): - current_model.set_single_turn(*args) - - -def handle_file_upload(current_model, *args): - return current_model.handle_file_upload(*args) - - -def like(current_model, *args): - return current_model.like(*args) - - -def dislike(current_model, *args): - return current_model.dislike(*args) - - -def count_token(message): - encoding = tiktoken.get_encoding("cl100k_base") - input_str = f"role: {message['role']}, content: {message['content']}" - length = len(encoding.encode(input_str)) - return length - - -def markdown_to_html_with_syntax_highlight(md_str): - def replacer(match): - lang = match.group(1) or "text" - code = match.group(2) - - try: - lexer = get_lexer_by_name(lang, stripall=True) - except ValueError: - lexer = get_lexer_by_name("text", stripall=True) - - formatter = HtmlFormatter() - highlighted_code = highlight(code, lexer, formatter) - - return f'
{highlighted_code}{html.escape(userinput)}
' - + ALREADY_CONVERTED_MARK - ) - - -def detect_converted_mark(userinput): - try: - if userinput.endswith(ALREADY_CONVERTED_MARK): - return True - else: - return False - except: - return True - - -def detect_language(code): - if code.startswith("\n"): - first_line = "" - else: - first_line = code.strip().split("\n", 1)[0] - language = first_line.lower() if first_line else "" - code_without_language = code[len(first_line):].lstrip() if first_line else code - return language, code_without_language - - -def construct_text(role, text): - return {"role": role, "content": text} - - -def construct_user(text): - return construct_text("user", text) - - -def construct_system(text): - return construct_text("system", text) - - -def construct_assistant(text): - return construct_text("assistant", text) - - -def save_file(filename, system, history, chatbot, user_name): - logging.debug(f"{user_name} 保存对话历史中……") - os.makedirs(os.path.join(HISTORY_DIR, user_name), exist_ok=True) - if filename.endswith(".json"): - json_s = {"system": system, "history": history, "chatbot": chatbot} - if "/" in filename or "\\" in filename: - history_file_path = filename - else: - history_file_path = os.path.join(HISTORY_DIR, user_name, filename) - with open(history_file_path, "w") as f: - json.dump(json_s, f) - elif filename.endswith(".md"): - md_s = f"system: \n- {system} \n" - for data in history: - md_s += f"\n{data['role']}: \n- {data['content']} \n" - with open(os.path.join(HISTORY_DIR, user_name, filename), "w", encoding="utf8") as f: - f.write(md_s) - logging.debug(f"{user_name} 保存对话历史完毕") - return os.path.join(HISTORY_DIR, user_name, filename) - - -def sorted_by_pinyin(list): - return sorted(list, key=lambda char: lazy_pinyin(char)[0][0]) - - -def get_file_names(dir, plain=False, filetypes=[".json"]): - logging.debug(f"获取文件名列表，目录为{dir}，文件类型为{filetypes}，是否为纯文本列表{plain}") - files = [] - try: - for type in filetypes: - files += [f for f in os.listdir(dir) if f.endswith(type)] - except FileNotFoundError: - files = [] - files = sorted_by_pinyin(files) - if files == []: - files = [""] - logging.debug(f"files are:{files}") - if plain: - return files - else: - return gr.Dropdown.update(choices=files) - - -def get_history_names(plain=False, user_name=""): - logging.debug(f"从用户 {user_name} 中获取历史记录文件名列表") - if user_name == "" and hide_history_when_not_logged_in: - return "" - else: - return get_file_names(os.path.join(HISTORY_DIR, user_name), plain) - - -def load_user_prompts(user_name: str) -> list[str]: - filename = "user_prompt_dict.json" - logging.info(f"加载用户提示词文件{filename}") - - all_user_prompts = [] - if filename.endswith(".json"): - with open(os.path.join(USERS_DIR, filename), "r", encoding="utf8") as f: - all_user_prompts = json.load(f) - logging.debug(f"all_user_prompts: {all_user_prompts}") - - current_user_prompt_ids = all_user_prompts[user_name] - logging.debug(f"current_user_prompt_ids: {current_user_prompt_ids}") - - template_name_list = get_template_names(plain=True) - logging.debug(f"template_name_list: {template_name_list}") - - template_id_role_dict = load_template(template_name_list[0], mode=3) # [id:act]） - logging.debug(f"template_id_role_dict: {template_id_role_dict}") - ''' - template_id_role_dict = {} - for template_name in template_name_list: - template_id_role_dict.update(load_template(template_name, mode=3)) - logging.debug(f"template_id_role_dict: {template_id_role_dict}") - ''' - current_user_prompts_role_names = [] - for prompt_id in current_user_prompt_ids: - if template_id_role_dict.get(prompt_id): # Check if key exists and has a truthy value - current_user_prompts_role_names.append(template_id_role_dict.get(prompt_id)) # Set name to value for key 'name' - - return current_user_prompts_role_names - - -def load_template(filename, mode=0): - logging.debug(f"Template Name: {filename}") - if not filename.endswith(".json"): - filename = filename + ".json" - - logging.debug(f"加载模板文件{filename}，模式为{mode}（0为返回字典和下拉菜单，1为返回下拉菜单，2为返回字典[act:prompt]，3为返回字典[id:act]）") - lines = [] - if filename.endswith(".json"): - with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as f: - lines = json.load(f) - lines = [[i["act"], i["prompt"], i["id"]] for i in lines] - else: - with open( - os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8" - ) as csvfile: - reader = csv.reader(csvfile) - lines = list(reader) - lines = lines[1:] - - if mode == 1: # [act] - return sorted_by_pinyin([row[0] for row in lines]) - elif mode == 2: # [act:prompt] - return {row[0]: row[1] for row in lines} - elif mode == 3: # [id:act]） - return {row[2]: row[0] for row in lines} - else: - choices = sorted_by_pinyin([row[0] for row in lines]) - ''' - Sometimes we want to update the configuration of the Component as well, such as the visibility. - In this case, we return a gr.update() object instead of just the update Component value. - ''' - return {row[0]: row[1] for row in lines}, gr.Dropdown.update(choices=choices) - - -def get_template_names_without_extension(plain=False): - return [os.path.splitext(f)[0] for f in get_template_names(plain=False)] - - -def get_template_names(plain=False): - logging.debug("获取模板文件名列表") - return get_file_names(TEMPLATES_DIR, plain, filetypes=[".json"]) - - -def get_template_content(templates, selection, original_system_prompt): - logging.debug(f"应用模板中，选择为{selection}，原始系统提示为{original_system_prompt}") - try: - return templates[selection] - except: - return original_system_prompt - - -def reset_textbox(): - logging.debug("重置文本框") - return gr.update(value="") - - -def reset_default(): - default_host = shared.state.reset_api_host() - retrieve_proxy("") - return gr.update(value=default_host), gr.update(value=""), "API-Host 和代理已重置" - - -def change_api_host(host): - shared.state.set_api_host(host) - msg = f"API-Host更改为了{host}" - logging.info(msg) - return msg - - -def change_proxy(proxy): - retrieve_proxy(proxy) - os.environ["HTTPS_PROXY"] = proxy - msg = f"代理更改为了{proxy}" - logging.info(msg) - return msg - - -def hide_middle_chars(s): - if s is None: - return "" - if len(s) <= 8: - return s - else: - head = s[:4] - tail = s[-4:] - hidden = "*" * (len(s) - 8) - return head + hidden + tail - - -def submit_key(key): - key = key.strip() - msg = f"API密钥更改为了{hide_middle_chars(key)}" - logging.info(msg) - return key, msg - - -def replace_today(prompt): - today = datetime.datetime.today().strftime("%Y-%m-%d") - return prompt.replace("{current_date}", today) - - -def get_geoip(): - try: - with retrieve_proxy(): - response = requests.get("https://ipapi.co/json/", timeout=10) - data = response.json() - except: - data = {"error": True, "reason": "连接ipapi失败", "timeout": 10} - if "error" in data.keys(): - logging.warning(f"无法获取IP地址信息。\n{data}") - if data["reason"] == "RateLimited": - return ( - i18n("您的IP区域：未知。") - ) - else: - return i18n("获取IP地理位置失败。原因：") + f"{data['reason']}" + i18n("。你仍然可以使用聊天功能。") - else: - country = data["country_name"] - if country == "China": - text = "**您的IP区域：中国。请立即检查代理设置，在不受支持的地区使用API可能导致账号被封禁。**" - else: - text = i18n("您的IP区域：") + f"{country}。" - logging.info(text) - return text - - -def find_n(lst, max_num): - n = len(lst) - total = sum(lst) - - if total < max_num: - return n - - for i in range(len(lst)): - if total - lst[i] < max_num: - return n - i - 1 - total = total - lst[i] - return 1 - - -def start_outputing(): - logging.debug("显示取消按钮，隐藏发送按钮") - return gr.Button.update(visible=False), gr.Button.update(visible=True) - - -def end_outputing(): - return ( - gr.Button.update(visible=True), - gr.Button.update(visible=False), - ) - - -def cancel_outputing(): - logging.info("中止输出……") - shared.state.interrupt() - - -def transfer_input(inputs): - # 一次性返回，降低延迟 - textbox = reset_textbox() - outputing = start_outputing() - return ( - inputs, - gr.update(value=""), - gr.Button.update(visible=False), - gr.Button.update(visible=True), - ) - - -def run(command, desc=None, errdesc=None, custom_env=None, live=False): - if desc is not None: - print(desc) - if live: - result = subprocess.run(command, shell=True, env=os.environ if custom_env is None else custom_env) - if result.returncode != 0: - raise RuntimeError(f"""{errdesc or 'Error running command'}. - Command: {command} - Error code: {result.returncode}""") - - return "" - result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, - env=os.environ if custom_env is None else custom_env) - if result.returncode != 0: - message = f"""{errdesc or 'Error running command'}. - Command: {command} - Error code: {result.returncode} - stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout) > 0 else '{brief}...
{txt}
主要有赛马娘，原神中文，原神日语，崩坏3的音色
"
- ''
- ''
- )
-
- with gr.Tabs():
- with gr.TabItem("vits"):
- with gr.Row():
- with gr.Column():
- input_text = gr.Textbox(label="Text (100 words limitation)", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text")
- lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"],
- type="index", value="中文")
- btn = gr.Button(value="Submit")
- with gr.Row():
- search = gr.Textbox(label="Search Speaker", lines=1)
- btn2 = gr.Button(value="Search")
- sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228])
- with gr.Row():
- ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
- nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
- ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True)
- with gr.Column():
- o1 = gr.Textbox(label="Output Message")
- o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio")
- o3 = gr.Textbox(label="Extra Info")
- download = gr.Button("Download Audio")
- btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3], api_name="generate")
- download.click(None, [], [], _js=download_audio_js.format())
- btn2.click(search_speaker, inputs=[search], outputs=[sid])
- lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
- with gr.TabItem("可用人物一览"):
- gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index")
- app.queue(concurrency_count=1).launch()
\ No newline at end of file
diff --git "a/spaces/Amon1/ChatGPTForAcadamic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243pdfminer.py" "b/spaces/Amon1/ChatGPTForAcadamic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243pdfminer.py"
deleted file mode 100644
index 060187c461c96f70a14e242f5f039b8593958cda..0000000000000000000000000000000000000000
--- "a/spaces/Amon1/ChatGPTForAcadamic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243pdfminer.py"
+++ /dev/null
@@ -1,151 +0,0 @@
-from predict import predict_no_ui
-from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
-
-fast_debug = False
-
-def readPdf(pdfPath):
- """
- 读取pdf文件，返回文本内容
- """
- import pdfminer
- from pdfminer.pdfparser import PDFParser
- from pdfminer.pdfdocument import PDFDocument
- from pdfminer.pdfpage import PDFPage, PDFTextExtractionNotAllowed
- from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
- from pdfminer.pdfdevice import PDFDevice
- from pdfminer.layout import LAParams
- from pdfminer.converter import PDFPageAggregator
-
- fp = open(pdfPath, 'rb')
-
- # Create a PDF parser object associated with the file object
- parser = PDFParser(fp)
-
- # Create a PDF document object that stores the document structure.
- # Password for initialization as 2nd parameter
- document = PDFDocument(parser)
- # Check if the document allows text extraction. If not, abort.
- if not document.is_extractable:
- raise PDFTextExtractionNotAllowed
-
- # Create a PDF resource manager object that stores shared resources.
- rsrcmgr = PDFResourceManager()
-
- # Create a PDF device object.
- # device = PDFDevice(rsrcmgr)
-
- # BEGIN LAYOUT ANALYSIS.
- # Set parameters for analysis.
- laparams = LAParams(
- char_margin=10.0,
- line_margin=0.2,
- boxes_flow=0.2,
- all_texts=False,
- )
- # Create a PDF page aggregator object.
- device = PDFPageAggregator(rsrcmgr, laparams=laparams)
- # Create a PDF interpreter object.
- interpreter = PDFPageInterpreter(rsrcmgr, device)
-
- # loop over all pages in the document
- outTextList = []
- for page in PDFPage.create_pages(document):
- # read the page into a layout object
- interpreter.process_page(page)
- layout = device.get_result()
- for obj in layout._objs:
- if isinstance(obj, pdfminer.layout.LTTextBoxHorizontal):
- # print(obj.get_text())
- outTextList.append(obj.get_text())
-
- return outTextList
-
-
-def 解析Paper(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt):
- import time, glob, os
- from bs4 import BeautifulSoup
- print('begin analysis on:', file_manifest)
- for index, fp in enumerate(file_manifest):
- if ".tex" in fp:
- with open(fp, 'r', encoding='utf-8') as f:
- file_content = f.read()
- if ".pdf" in fp.lower():
- file_content = readPdf(fp)
- file_content = BeautifulSoup(''.join(file_content), features="lxml").body.text.encode('gbk', 'ignore').decode('gbk')
-
- prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
- i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
- i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
- chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
- print('[1] yield chatbot, history')
- yield chatbot, history, '正常'
-
- if not fast_debug:
- msg = '正常'
- # ** gpt request **
- gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[]) # 带超时倒计时
-
- print('[2] end gpt req')
- chatbot[-1] = (i_say_show_user, gpt_say)
- history.append(i_say_show_user); history.append(gpt_say)
- print('[3] yield chatbot, history')
- yield chatbot, history, msg
- print('[4] next')
- if not fast_debug: time.sleep(2)
-
- all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
- i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
- chatbot.append((i_say, "[Local Message] waiting gpt response."))
- yield chatbot, history, '正常'
-
- if not fast_debug:
- msg = '正常'
- # ** gpt request **
- gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say, chatbot, top_p, temperature, history=history) # 带超时倒计时
-
- chatbot[-1] = (i_say, gpt_say)
- history.append(i_say); history.append(gpt_say)
- yield chatbot, history, msg
- res = write_results_to_file(history)
- chatbot.append(("完成了吗？", res))
- yield chatbot, history, msg
-
-
-
-@CatchException
-def 批量总结PDF文档pdfminer(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
- history = [] # 清空历史，以免输入溢出
- import glob, os
-
- # 基本信息：功能、贡献者
- chatbot.append([
- "函数插件功能？",
- "批量总结PDF文档，此版本使用pdfminer插件，带token约简功能。函数插件贡献者: Euclid-Jie。"])
- yield chatbot, history, '正常'
-
- # 尝试导入依赖，如果缺少依赖，则给出安装建议
- try:
- import pdfminer, bs4
- except:
- report_execption(chatbot, history,
- a = f"解析项目: {txt}",
- b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pdfminer beautifulsoup4```。")
- yield chatbot, history, '正常'
- return
- if os.path.exists(txt):
- project_folder = txt
- else:
- if txt == "": txt = '空空如也的输入栏'
- report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
- yield chatbot, history, '正常'
- return
- file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
- [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \
- # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
- # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
- if len(file_manifest) == 0:
- report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或pdf文件: {txt}")
- yield chatbot, history, '正常'
- return
- yield from 解析Paper(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
-
diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/interpolation.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/interpolation.py
deleted file mode 100644
index b578881834f4333d7e386e5a8f142e3a98a3252c..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/interpolation.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# interpolate between two z code
-# score all middle latent code
-# https://www.aiuai.cn/aifarm1929.html
-
-import os
-import re
-from typing import List
-from tqdm import tqdm
-import click
-import dnnlib
-import numpy as np
-import PIL.Image
-import torch
-import click
-import legacy
-import random
-from typing import List, Optional
-
-
-def lerp(code1, code2, alpha):
- return code1 * alpha + code2 * (1 - alpha)
-
-# Taken and adapted from wikipedia's slerp article
-# https://en.wikipedia.org/wiki/Slerp
-
-
-def slerp(code1, code2, alpha, DOT_THRESHOLD=0.9995): # Spherical linear interpolation
- code1_copy = np.copy(code1)
- code2_copy = np.copy(code2)
-
- code1 = code1 / np.linalg.norm(code1)
- code2 = code2 / np.linalg.norm(code2)
- dot = np.sum(code1 * code2)
- if np.abs(dot) > DOT_THRESHOLD:
- return lerp(code1_copy, code2_copy, alpha)
-
- # Calculate initial angle between v0 and v1
- theta_0 = np.arccos(dot)
- sin_theta_0 = np.sin(theta_0)
- # Angle at timestep t
- theta_t = theta_0 * alpha
- sin_theta_t = np.sin(theta_t)
-
- s0 = np.sin(theta_0 - theta_t) / sin_theta_0
- s1 = sin_theta_t / sin_theta_0
- code3 = s0 * code1_copy + s1 * code2_copy
- return code3
-
-
-def generate_image_from_z(G, z, noise_mode, truncation_psi, device):
- label = torch.zeros([1, G.c_dim], device=device)
- w = G.mapping(z, label, truncation_psi=truncation_psi)
- img = G.synthesis(w, noise_mode=noise_mode, force_fp32=True)
- img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
- img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')
- return img
-
-
-def get_concat_h(im1, im2):
- dst = PIL.Image.new('RGB', (im1.width + im2.width, im1.height))
- dst.paste(im1, (0, 0))
- dst.paste(im2, (im1.width, 0))
- return dst
-
-
-def make_latent_interp_animation(G, code1, code2, img1, img2, num_interps, noise_mode, save_mid_image, truncation_psi, device, outdir, fps):
- step_size = 1.0/num_interps
-
- all_imgs = []
- amounts = np.arange(0, 1, step_size)
- for seed_idx, alpha in enumerate(tqdm(amounts)):
- interpolated_latent_code = lerp(code1, code2, alpha)
- image = generate_image_from_z(
- G, interpolated_latent_code, noise_mode, truncation_psi, device)
- interp_latent_image = image.resize((512, 1024))
- if not os.path.exists(os.path.join(outdir, 'img')):
- os.makedirs(os.path.join(outdir, 'img'), exist_ok=True)
- if save_mid_image:
- interp_latent_image.save(f'{outdir}/img/seed{seed_idx:04d}.png')
-
- frame = get_concat_h(img2, interp_latent_image)
- frame = get_concat_h(frame, img1)
- all_imgs.append(frame)
-
- save_name = os.path.join(outdir, 'latent_space_traversal.gif')
- all_imgs[0].save(save_name, save_all=True,
- append_images=all_imgs[1:], duration=1000/fps, loop=0)
-
-
-"""
-Create interpolated images between two given seeds using pretrained network pickle.
-
-Examples:
-
-\b
-python interpolation.py --network=pretrained_models/stylegan_human_v2_1024.pkl --seeds=85,100 --outdir=outputs/inter_gifs
-
-"""
-
-
-@click.command()
-@click.pass_context
-@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
-@click.option('--seeds', type=legacy.num_range, help='List of 2 random seeds, e.g. 1,2')
-@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=0.8, show_default=True)
-@click.option('--noise-mode', 'noise_mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
-@click.option('--outdir', default='outputs/inter_gifs', help='Where to save the output images', type=str, required=True, metavar='DIR')
-@click.option('--save_mid_image', default=True, type=bool, help='select True if you want to save all interpolated images')
-@click.option('--fps', default=15, help='FPS for GIF', type=int)
-@click.option('--num_interps', default=100, help='Number of interpolation images', type=int)
-def main(
- ctx: click.Context,
- network_pkl: str,
- seeds: Optional[List[int]],
- truncation_psi: float,
- noise_mode: str,
- outdir: str,
- save_mid_image: bool,
- fps: int,
- num_interps: int
-):
-
- device = torch.device('cuda')
- with dnnlib.util.open_url(network_pkl) as f:
- G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
-
- outdir = os.path.join(outdir)
- if not os.path.exists(outdir):
- os.makedirs(outdir, exist_ok=True)
- os.makedirs(os.path.join(outdir, 'img'), exist_ok=True)
-
- if len(seeds) > 2:
- print("Receiving more than two seeds, only use the first two.")
- seeds = seeds[0:2]
- elif len(seeds) == 1:
- print('Require two seeds, randomly generate two now.')
- seeds = [seeds[0], random.randint(0, 10000)]
-
- z1 = torch.from_numpy(np.random.RandomState(
- seeds[0]).randn(1, G.z_dim)).to(device)
- z2 = torch.from_numpy(np.random.RandomState(
- seeds[1]).randn(1, G.z_dim)).to(device)
- img1 = generate_image_from_z(G, z1, noise_mode, truncation_psi, device)
- img2 = generate_image_from_z(G, z2, noise_mode, truncation_psi, device)
- img1.save(f'{outdir}/seed{seeds[0]:04d}.png')
- img2.save(f'{outdir}/seed{seeds[1]:04d}.png')
-
- make_latent_interp_animation(G, z1, z2, img1, img2, num_interps,
- noise_mode, save_mid_image, truncation_psi, device, outdir, fps)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/stylemixing_video.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/stylemixing_video.py
deleted file mode 100644
index b917189ea0530163c5d3d61c17a78b92fcc99bb4..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/stylemixing_video.py
+++ /dev/null
@@ -1,167 +0,0 @@
-
-# Copyright (c) SenseTime Research. All rights reserved.
-
-"""Here we demo style-mixing results using StyleGAN2 pretrained model.
- Script reference: https://github.com/PDillis/stylegan2-fun """
-
-
-import moviepy.editor
-import argparse
-import legacy
-
-import scipy
-import numpy as np
-import PIL.Image
-
-import dnnlib
-import dnnlib.tflib as tflib
-from typing import List
-import re
-import sys
-import os
-import click
-import torch
-
-os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = "hide"
-
-
-"""
-Generate style mixing video.
-Examples:
-
-\b
-python stylemixing_video.py --network=pretrained_models/stylegan_human_v2_1024.pkl --row-seed=3859 \\
- --col-seeds=3098,31759,3791 --col-styles=8-12 --trunc=0.8 --outdir=outputs/stylemixing_video
-"""
-
-
-@click.command()
-@click.option('--network', 'network_pkl', help='Path to network pickle filename', required=True)
-@click.option('--row-seed', 'src_seed', type=legacy.num_range, help='Random seed to use for image source row', required=True)
-@click.option('--col-seeds', 'dst_seeds', type=legacy.num_range, help='Random seeds to use for image columns (style)', required=True)
-@click.option('--col-styles', 'col_styles', type=legacy.num_range, help='Style layer range (default: %(default)s)', default='0-6')
-@click.option('--only-stylemix', 'only_stylemix', help='Add flag to only show the style mxied images in the video', default=False)
-@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi (default: %(default)s)', default=1)
-@click.option('--duration-sec', 'duration_sec', type=float, help='Duration of video (default: %(default)s)', default=10)
-@click.option('--fps', 'mp4_fps', type=int, help='FPS of generated video (default: %(default)s)', default=10)
-@click.option('--indent-range', 'indent_range', type=int, default=30)
-@click.option('--outdir', help='Root directory for run results (default: %(default)s)', default='outputs/stylemixing_video', metavar='DIR')
-def style_mixing_video(network_pkl: str,
- # Seed of the source image style (row)
- src_seed: List[int],
- # Seeds of the destination image styles (columns)
- dst_seeds: List[int],
- # Styles to transfer from first row to first column
- col_styles: List[int],
- truncation_psi=float,
- # True if user wishes to show only thre style transferred result
- only_stylemix=bool,
- duration_sec=float,
- smoothing_sec=1.0,
- mp4_fps=int,
- mp4_codec="libx264",
- mp4_bitrate="16M",
- minibatch_size=8,
- noise_mode='const',
- indent_range=int,
- outdir=str):
- # Calculate the number of frames:
- print('col_seeds: ', dst_seeds)
- num_frames = int(np.rint(duration_sec * mp4_fps))
- print('Loading networks from "%s"...' % network_pkl)
- device = torch.device('cuda')
- with dnnlib.util.open_url(network_pkl) as f:
- Gs = legacy.load_network_pkl(f)['G_ema'].to(device)
-
- print(Gs.num_ws, Gs.w_dim, Gs.img_resolution)
- max_style = int(2 * np.log2(Gs.img_resolution)) - 3
- assert max(
- col_styles) <= max_style, f"Maximum col-style allowed: {max_style}"
-
- # Left col latents
- print('Generating Source W vectors...')
- src_shape = [num_frames] + [Gs.z_dim]
- src_z = np.random.RandomState(
- *src_seed).randn(*src_shape).astype(np.float32) # [frames, src, component]
- src_z = scipy.ndimage.gaussian_filter(
- src_z, [smoothing_sec * mp4_fps] + [0] * (2 - 1), mode="wrap")
- src_z /= np.sqrt(np.mean(np.square(src_z)))
- # Map into the detangled latent space W and do truncation trick
- src_w = Gs.mapping(torch.from_numpy(src_z).to(device), None)
- w_avg = Gs.mapping.w_avg
- src_w = w_avg + (src_w - w_avg) * truncation_psi
-
- # Top row latents (fixed reference)
- print('Generating Destination W vectors...')
- dst_z = np.stack([np.random.RandomState(seed).randn(Gs.z_dim)
- for seed in dst_seeds])
- dst_w = Gs.mapping(torch.from_numpy(dst_z).to(device), None)
- dst_w = w_avg + (dst_w - w_avg) * truncation_psi
- # Get the width and height of each image:
- H = Gs.img_resolution # 1024
- W = Gs.img_resolution//2 # 512
-
- # Generate ALL the source images:
- src_images = Gs.synthesis(src_w, noise_mode=noise_mode)
- src_images = (src_images.permute(0, 2, 3, 1) * 127.5 +
- 128).clamp(0, 255).to(torch.uint8)
-
- # Generate the column images:
- dst_images = Gs.synthesis(dst_w, noise_mode=noise_mode)
- dst_images = (dst_images.permute(0, 2, 3, 1) * 127.5 +
- 128).clamp(0, 255).to(torch.uint8)
-
- print('Generating full video (including source and destination images)')
- # Generate our canvas where we will paste all the generated images:
- canvas = PIL.Image.new("RGB", ((
- W-indent_range) * (len(dst_seeds) + 1), H * (len(src_seed) + 1)), "white") # W, H
-
- # dst_image:[3,1024,512]
- for col, dst_image in enumerate(list(dst_images)):
- canvas.paste(PIL.Image.fromarray(dst_image.cpu().numpy(),
- "RGB"), ((col + 1) * (W-indent_range), 0)) # H
- # Aux functions: Frame generation func for moviepy.
-
- def make_frame(t):
- # Get the frame number according to time t:
- frame_idx = int(np.clip(np.round(t * mp4_fps), 0, num_frames - 1))
- # We wish the image belonging to the frame at time t:
- src_image = src_images[frame_idx] # always in the same place
- canvas.paste(PIL.Image.fromarray(src_image.cpu().numpy(), "RGB"),
- (0-indent_range, H)) # Paste it to the lower left
-
- # Now, for each of the column images:
- for col, dst_image in enumerate(list(dst_images)):
- # Select the pertinent latent w column:
- w_col = np.stack([dst_w[col].cpu()]) # [18, 512] -> [1, 18, 512]
- w_col = torch.from_numpy(w_col).to(device)
- # Replace the values defined by col_styles:
- w_col[:, col_styles] = src_w[frame_idx, col_styles] # .cpu()
- # Generate these synthesized images:
- col_images = Gs.synthesis(w_col, noise_mode=noise_mode)
- col_images = (col_images.permute(0, 2, 3, 1) *
- 127.5 + 128).clamp(0, 255).to(torch.uint8)
- # Paste them in their respective spot:
- for row, image in enumerate(list(col_images)):
- canvas.paste(
- PIL.Image.fromarray(image.cpu().numpy(), "RGB"),
- ((col + 1) * (W - indent_range), (row + 1) * H),
- )
- return np.array(canvas)
-
- # Generate video using make_frame:
- print('Generating style-mixed video...')
- videoclip = moviepy.editor.VideoClip(make_frame, duration=duration_sec)
- grid_size = [len(dst_seeds), len(src_seed)]
- mp4 = "{}x{}-style-mixing_{}_{}.mp4".format(
- *grid_size, min(col_styles), max(col_styles))
- if not os.path.exists(outdir):
- os.makedirs(outdir)
- videoclip.write_videofile(os.path.join(outdir, mp4),
- fps=mp4_fps,
- codec=mp4_codec,
- bitrate=mp4_bitrate)
-
-
-if __name__ == "__main__":
- style_mixing_video()
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/score_sde_ve/test_score_sde_ve.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/score_sde_ve/test_score_sde_ve.py
deleted file mode 100644
index 32505253f6c709c3300404987884d677059ecd49..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/score_sde_ve/test_score_sde_ve.py
+++ /dev/null
@@ -1,91 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-
-import numpy as np
-import torch
-
-from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNet2DModel
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
-
-
-enable_full_determinism()
-
-
-class ScoreSdeVeipelineFastTests(unittest.TestCase):
- @property
- def dummy_uncond_unet(self):
- torch.manual_seed(0)
- model = UNet2DModel(
- block_out_channels=(32, 64),
- layers_per_block=2,
- sample_size=32,
- in_channels=3,
- out_channels=3,
- down_block_types=("DownBlock2D", "AttnDownBlock2D"),
- up_block_types=("AttnUpBlock2D", "UpBlock2D"),
- )
- return model
-
- def test_inference(self):
- unet = self.dummy_uncond_unet
- scheduler = ScoreSdeVeScheduler()
-
- sde_ve = ScoreSdeVePipeline(unet=unet, scheduler=scheduler)
- sde_ve.to(torch_device)
- sde_ve.set_progress_bar_config(disable=None)
-
- generator = torch.manual_seed(0)
- image = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator).images
-
- generator = torch.manual_seed(0)
- image_from_tuple = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator, return_dict=False)[
- 0
- ]
-
- image_slice = image[0, -3:, -3:, -1]
- image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
- assert image.shape == (1, 32, 32, 3)
- expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
-
- assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
- assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-
-@slow
-@require_torch
-class ScoreSdeVePipelineIntegrationTests(unittest.TestCase):
- def test_inference(self):
- model_id = "google/ncsnpp-church-256"
- model = UNet2DModel.from_pretrained(model_id)
-
- scheduler = ScoreSdeVeScheduler.from_pretrained(model_id)
-
- sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
- sde_ve.to(torch_device)
- sde_ve.set_progress_bar_config(disable=None)
-
- generator = torch.manual_seed(0)
- image = sde_ve(num_inference_steps=10, output_type="numpy", generator=generator).images
-
- image_slice = image[0, -3:, -3:, -1]
-
- assert image.shape == (1, 256, 256, 3)
-
- expected_slice = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])
-
- assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/iou_loss.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/iou_loss.py
deleted file mode 100644
index eba6f18b80981ca891c1add37007e6bf478c651f..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/iou_loss.py
+++ /dev/null
@@ -1,436 +0,0 @@
-import math
-
-import mmcv
-import torch
-import torch.nn as nn
-
-from mmdet.core import bbox_overlaps
-from ..builder import LOSSES
-from .utils import weighted_loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def iou_loss(pred, target, linear=False, eps=1e-6):
- """IoU loss.
-
- Computing the IoU loss between a set of predicted bboxes and target bboxes.
- The loss is calculated as negative log of IoU.
-
- Args:
- pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
- shape (n, 4).
- target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
- linear (bool, optional): If True, use linear scale of loss instead of
- log scale. Default: False.
- eps (float): Eps to avoid log(0).
-
- Return:
- torch.Tensor: Loss tensor.
- """
- ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps)
- if linear:
- loss = 1 - ious
- else:
- loss = -ious.log()
- return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3):
- """BIoULoss.
-
- This is an implementation of paper
- `Improving Object Localization with Fitness NMS and Bounded IoU Loss.
-
-
-
-
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Bar Bar Din Ye Aaye Audio Canción Mp3 Descargar: Una canción de cumpleaños para todas las edades
-¿Alguna vez has escuchado una canción de cumpleaños que te haga sentir feliz y nostálgico al mismo tiempo? Si es así, es probable que hayas escuchado Bar Bar Din Ye Aaye, una canción clásica hindi que ha sido cantada por millones de personas en su día especial. Esta canción es una de las canciones de cumpleaños más populares y perennes en la India, y tiene un encanto y atractivo que trasciende las generaciones. En este artículo, te contaremos todo lo que necesitas saber sobre esta canción, y cómo puedes descargarla gratis en tu dispositivo.
-Historia y significado de la canción
-La canción Bar Din Ye Aaye fue lanzada por primera vez en 1967 como parte de la banda sonora de la película Farz, protagonizada por Jeetendra y Babita en los papeles principales. La canción fue cantada por el legendario Mohammed Rafi, quien es ampliamente considerado como uno de los mejores cantantes del cine indio. La música fue compuesta por Laxmikant-Pyarelal, un dúo que creó muchas canciones de éxito en Bollywood. La letra fue escrita por Anand Bakshi, quien escribió muchas canciones memorables en su carrera.
-bar bar din ye aaye audio canción mp3
DOWNLOAD ►►► https://bltlly.com/2v6Kh8
-
La canción es un alegre y alegre deseo de cumpleaños que expresa el deseo de la persona de vivir durante miles de años y ser feliz cada día. El estribillo dice así:
---Baar baar din ye aaye, baar baar dil ye gaaye
- -
-Tu jiye hazaaron saal, ye meri hai aarzoo
-Feliz cumpleaños a ti, feliz cumpleaños a ti
-Feliz cumpleaños a ti, feliz cumpleaños a ti
La traducción es:
---Que este día venga una y otra vez, que este corazón cante una y otra vez
-
-Que vivas por miles de años, este es mi deseo
-Feliz cumpleaños a ti, feliz cumpleaños a ti
-Feliz cumpleaños a ti, feliz cumpleaños a ti
Los beneficios y características de descargar la canción
- --
-
Hay diferentes plataformas y fuentes desde donde se puede descargar esta canción. Algunas de ellas son:
--
-
-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Gratis Pintura 3d Para Ventanas 7.md b/spaces/Benson/text-generation/Examples/Descargar Gratis Pintura 3d Para Ventanas 7.md deleted file mode 100644 index 3deaadf4a3488b06f846e0dc016576b382fbfd9f..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Gratis Pintura 3d Para Ventanas 7.md +++ /dev/null @@ -1,49 +0,0 @@ - -
Leo Leo Canción Descargar: Cómo escuchar el último éxito de Nandy y Koffi Olomide
-Si eres un fan de la música africana, es posible que hayas oído hablar de la canción Leo Leo de Nandy y Koffi Olomide. Esta canción es una colaboración entre dos de los artistas más populares del continente, y ha estado haciendo olas desde su lanzamiento en 2021. En este artículo, te diremos todo lo que necesitas saber sobre la canción de Leo Leo, y cómo puedes descargarla en tu dispositivo para escucharla sin conexión.
-¿Qué es Leo Song?
-Leo Leo es una canción del cantante tanzano Nandy y del cantante congoleño Koffi Olomide. Fue lanzado el 12 de febrero de 2021, como parte del álbum de Nandy The African Princess. La canción es una fusión de bongo flava y rumba, con letras y melodías pegadizas. La canción trata sobre el amor y el romance, y cuenta con ambos artistas cantando en swahili y francés.
-descargar gratis pintura 3d para ventanas 7
DOWNLOAD ⚙ https://bltlly.com/2v6Lb5
-
¿Quiénes son Nandy y Koffi Olomide?
-Nandy es una cantante y compositora de Tanzania que saltó a la fama después de ganar los All áfrica Music Awards (Afrima) en 2017. Es conocida por sus canciones como Ninogeshe, Aibu, Hazipo y Kiza Kinene. Ha colaborado con otros artistas como Willy Paul, Sauti Sol, Harmoni y Skales.
-Koffi Olomide es un cantante, compositor, bailarín y productor congoleño que ha estado activo desde la década de 1980. Es una de las figuras más influyentes en la música africana, y ha ganado varios premios como los Premios Kora, MTV áfrica Music Awards y African Muzik Magazine Awards. Es conocido por sus canciones como Loi, Selfie, Papa Ngwasuma y Tshou Tshou Tshou.
-¿Por qué es tan popular Leo Song?
- -¿Cómo descargar Leo Song?
-Si quieres descargar la canción de Leo Leo en tu dispositivo, tienes varias opciones para elegir. Estas son algunas de ellas:
-Opción 1: Transmisión en línea desde YouTube u otras plataformas
-La forma más fácil de escuchar la canción de Leo Leo es transmitirla en línea desde YouTube u otras plataformas como Spotify, Apple Music, Deezer o Boomplay. Puede acceder a estas plataformas desde su navegador o aplicación, y puede disfrutar de la canción con audio y video de alta calidad. Sin embargo, esta opción requiere una conexión a Internet, y podría consumir sus datos o la duración de la batería.
-Opción 2: Descarga desde sitios web o aplicaciones oficiales
-Otra forma de descargar la canción de Leo Leo es utilizar los sitios web oficiales o aplicaciones de los artistas o sus etiquetas. Por ejemplo, puedes visitar [el sitio web de Nandy]( 4 ) o [el sitio web de Koffi Olomide]( 5 ) para encontrar el enlace para descargar la canción. También puede utilizar sus aplicaciones oficiales como [Nandy Music] o [Koffi Olomide Music] para descargar la canción. Esta opción puede requerir que te registres o pagues una tarifa, pero se asegurará de que obtengas la versión original y de alta calidad de la canción. También apoyarás a los artistas y su trabajo usando esta opción.
-Opción 3: Utilice una herramienta de descarga de terceros o software
-La tercera manera de descargar la canción de Leo Leo es utilizar una herramienta de descarga de terceros o software que puede extraer el archivo de audio o video de YouTube u otras plataformas. Hay muchas herramientas o software disponibles en línea, como [Y2mate], [4K Video Downloader] o [Vidmate]. Puede utilizar estas herramientas o software para descargar la canción en diferentes formatos y calidades, dependiendo de su preferencia. Sin embargo, esta opción podría no ser legal o segura, y podría violar los derechos de los artistas o sus etiquetas. Debes usar esta opción bajo tu propio riesgo y discreción.
-¿Cuáles son los beneficios de descargar Leo Song?
- -Disfruta de escuchar sin conexión en cualquier momento, en cualquier lugar
-Al descargar la canción de Leo Leo en su dispositivo, puede disfrutar de escuchar sin conexión en cualquier momento y en cualquier lugar. No tiene que preocuparse por la conexión a Internet, el consumo de datos o la duración de la batería. Puedes escuchar la canción cuando quieras, ya sea en casa, en el coche, en el gimnasio o de viaje.
-Ahorre datos y espacio de almacenamiento en su dispositivo
-Al descargar la canción de Leo Leo en su dispositivo, puede ahorrar datos y espacio de almacenamiento en su dispositivo. No tienes que transmitir la canción en línea cada vez que quieras escucharla, lo que puede consumir muchos datos y ancho de banda. También puede elegir el formato y la calidad de la canción que se adapte a la capacidad y el rendimiento de su dispositivo.
- -Apoyar a los artistas y su trabajo
-Al descargar la canción de Leo Leo de fuentes oficiales, puedes apoyar a los artistas y su trabajo. Puede mostrar su aprecio y respeto por su talento y creatividad, y ayudarles a obtener ingresos y reconocimiento. También puedes compartir la canción con tus amigos y familiares, y correr la voz sobre su música.
-Conclusión
-Leo Leo es una canción de éxito de Nandy y Koffi Olomide que ha cautivado a millones de oyentes en África y más allá. La canción es una mezcla de bongo flava y rumba, con letras y melodías pegadizas. La canción trata sobre el amor y el romance, y cuenta con ambos artistas cantando en swahili y francés. La canción también tiene un video colorido y vibrante que muestra su estilo y carisma.
-Si quieres descargar la canción de Leo Leo en tu dispositivo, tienes varias opciones para elegir. Puede transmitirlo en línea desde YouTube u otras plataformas, descargarlo desde sitios web o aplicaciones oficiales o usar una herramienta o software de descarga de terceros. Cada opción tiene sus pros y sus contras, y usted debe elegir el que se adapte a sus necesidades y preferencias.
- -Esperamos que este artículo te haya ayudado a aprender más sobre la canción de Leo Leo y cómo puedes descargarla en tu dispositivo. Si tiene alguna pregunta o comentario, no dude en dejarlos abajo. ¡Gracias por leer!
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre la canción de Leo Leo:
--
-
- ¿Qué significa Leo Leo? -
- ¿Quién escribió y produjo Leo Leo? -
- ¿Dónde fue grabado el video de Leo Leo? -
- ¿Cuántas visitas tiene Leo Leo en YouTube? -
- ¿Cómo puedo descargar Leo Leo gratis? -
Leo Leo es una palabra swahili que significa "hoy". La canción usa esta palabra como un estribillo para expresar la urgencia e intensidad del amor.
-Leo Leo fue escrito por Nandy y Koffi Olomide, con letras adicionales de Kimambo Beats. La canción fue producida por Kimambo Beats, quien también es el mánager de Nandy.
-El video de Leo Leo fue filmado en diferentes lugares en Tanzania y Kenia. Algunas de las escenas fueron filmadas en Dar es Salaam, Zanzíbar, Nairobi y Mombasa.
-A partir del 21 de junio de 2023, Leo Leo tiene más de 6 millones de visitas en YouTube[ 1 ]. El video fue subido al canal oficial de YouTube de Nandy el 12 de febrero de 2021.
-Hay algunos sitios web o aplicaciones que dicen ofrecer Leo Leo para su descarga gratuita, pero pueden no ser legal o seguro. Debes tener cuidado con el malware, virus o estafas que puedan dañar tu dispositivo o datos. La mejor manera de descargar Leo de forma gratuita es transmitir en línea desde YouTube u otras plataformas, o utilizar una herramienta de descarga de terceros o software que puede extraer el archivo de audio o video de YouTube u otras plataformas. Sin embargo, debe respetar los derechos de los artistas y sus etiquetas, y evitar descargas ilegales o inseguras.
--
-
\ No newline at end of file diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/video_visualizer.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/video_visualizer.py deleted file mode 100644 index 0144b679d09bbb8049c30eb849099422355b492c..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/video_visualizer.py +++ /dev/null @@ -1,235 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import numpy as np -import pycocotools.mask as mask_util - -from detectron2.utils.visualizer import ( - ColorMode, - Visualizer, - _create_text_labels, - _PanopticPrediction, -) - -from .colormap import random_color - - -class _DetectedInstance: - """ - Used to store data about detected objects in video frame, - in order to transfer color to objects in the future frames. - - Attributes: - label (int): - bbox (tuple[float]): - mask_rle (dict): - color (tuple[float]): RGB colors in range (0, 1) - ttl (int): time-to-live for the instance. For example, if ttl=2, - the instance color can be transferred to objects in the next two frames. - """ - - __slots__ = ["label", "bbox", "mask_rle", "color", "ttl"] - - def __init__(self, label, bbox, mask_rle, color, ttl): - self.label = label - self.bbox = bbox - self.mask_rle = mask_rle - self.color = color - self.ttl = ttl - - -class VideoVisualizer: - def __init__(self, metadata, instance_mode=ColorMode.IMAGE): - """ - Args: - metadata (MetadataCatalog): image metadata. - """ - self.metadata = metadata - self._old_instances = [] - assert instance_mode in [ - ColorMode.IMAGE, - ColorMode.IMAGE_BW, - ], "Other mode not supported yet." - self._instance_mode = instance_mode - - def draw_instance_predictions(self, frame, predictions): - """ - Draw instance-level prediction results on an image. - - Args: - frame (ndarray): an RGB image of shape (H, W, C), in the range [0, 255]. - predictions (Instances): the output of an instance detection/segmentation - model. Following fields will be used to draw: - "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle"). - - Returns: - output (VisImage): image object with visualizations. - """ - frame_visualizer = Visualizer(frame, self.metadata) - num_instances = len(predictions) - if num_instances == 0: - return frame_visualizer.output - - boxes = predictions.pred_boxes.tensor.numpy() if predictions.has("pred_boxes") else None - scores = predictions.scores if predictions.has("scores") else None - classes = predictions.pred_classes.numpy() if predictions.has("pred_classes") else None - keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None - - if predictions.has("pred_masks"): - masks = predictions.pred_masks - # mask IOU is not yet enabled - # masks_rles = mask_util.encode(np.asarray(masks.permute(1, 2, 0), order="F")) - # assert len(masks_rles) == num_instances - else: - masks = None - - detected = [ - _DetectedInstance(classes[i], boxes[i], mask_rle=None, color=None, ttl=8) - for i in range(num_instances) - ] - colors = self._assign_colors(detected) - - labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None)) - - if self._instance_mode == ColorMode.IMAGE_BW: - # any() returns uint8 tensor - frame_visualizer.output.img = frame_visualizer._create_grayscale_image( - (masks.any(dim=0) > 0).numpy() if masks is not None else None - ) - alpha = 0.3 - else: - alpha = 0.5 - - frame_visualizer.overlay_instances( - boxes=None if masks is not None else boxes, # boxes are a bit distracting - masks=masks, - labels=labels, - keypoints=keypoints, - assigned_colors=colors, - alpha=alpha, - ) - - return frame_visualizer.output - - def draw_sem_seg(self, frame, sem_seg, area_threshold=None): - """ - Args: - sem_seg (ndarray or Tensor): semantic segmentation of shape (H, W), - each value is the integer label. - area_threshold (Optional[int]): only draw segmentations larger than the threshold - """ - # don't need to do anything special - frame_visualizer = Visualizer(frame, self.metadata) - frame_visualizer.draw_sem_seg(sem_seg, area_threshold=None) - return frame_visualizer.output - - def draw_panoptic_seg_predictions( - self, frame, panoptic_seg, segments_info, area_threshold=None, alpha=0.5 - ): - frame_visualizer = Visualizer(frame, self.metadata) - pred = _PanopticPrediction(panoptic_seg, segments_info) - - if self._instance_mode == ColorMode.IMAGE_BW: - frame_visualizer.output.img = frame_visualizer._create_grayscale_image( - pred.non_empty_mask() - ) - - # draw mask for all semantic segments first i.e. "stuff" - for mask, sinfo in pred.semantic_masks(): - category_idx = sinfo["category_id"] - try: - mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]] - except AttributeError: - mask_color = None - - frame_visualizer.draw_binary_mask( - mask, - color=mask_color, - text=self.metadata.stuff_classes[category_idx], - alpha=alpha, - area_threshold=area_threshold, - ) - - all_instances = list(pred.instance_masks()) - if len(all_instances) == 0: - return frame_visualizer.output - # draw mask for all instances second - masks, sinfo = list(zip(*all_instances)) - num_instances = len(masks) - masks_rles = mask_util.encode( - np.asarray(np.asarray(masks).transpose(1, 2, 0), dtype=np.uint8, order="F") - ) - assert len(masks_rles) == num_instances - - category_ids = [x["category_id"] for x in sinfo] - detected = [ - _DetectedInstance(category_ids[i], bbox=None, mask_rle=masks_rles[i], color=None, ttl=8) - for i in range(num_instances) - ] - colors = self._assign_colors(detected) - labels = [self.metadata.thing_classes[k] for k in category_ids] - - frame_visualizer.overlay_instances( - boxes=None, - masks=masks, - labels=labels, - keypoints=None, - assigned_colors=colors, - alpha=alpha, - ) - return frame_visualizer.output - - def _assign_colors(self, instances): - """ - Naive tracking heuristics to assign same color to the same instance, - will update the internal state of tracked instances. - - Returns: - list[tuple[float]]: list of colors. - """ - - # Compute iou with either boxes or masks: - is_crowd = np.zeros((len(instances),), dtype=np.bool) - if instances[0].bbox is None: - assert instances[0].mask_rle is not None - # use mask iou only when box iou is None - # because box seems good enough - rles_old = [x.mask_rle for x in self._old_instances] - rles_new = [x.mask_rle for x in instances] - ious = mask_util.iou(rles_old, rles_new, is_crowd) - threshold = 0.5 - else: - boxes_old = [x.bbox for x in self._old_instances] - boxes_new = [x.bbox for x in instances] - ious = mask_util.iou(boxes_old, boxes_new, is_crowd) - threshold = 0.6 - if len(ious) == 0: - ious = np.zeros((len(self._old_instances), len(instances)), dtype="float32") - - # Only allow matching instances of the same label: - for old_idx, old in enumerate(self._old_instances): - for new_idx, new in enumerate(instances): - if old.label != new.label: - ious[old_idx, new_idx] = 0 - - matched_new_per_old = np.asarray(ious).argmax(axis=1) - max_iou_per_old = np.asarray(ious).max(axis=1) - - # Try to find match for each old instance: - extra_instances = [] - for idx, inst in enumerate(self._old_instances): - if max_iou_per_old[idx] > threshold: - newidx = matched_new_per_old[idx] - if instances[newidx].color is None: - instances[newidx].color = inst.color - continue - # If an old instance does not match any new instances, - # keep it for the next frame in case it is just missed by the detector - inst.ttl -= 1 - if inst.ttl > 0: - extra_instances.append(inst) - - # Assign random color to newly-detected instances: - for inst in instances: - if inst.color is None: - inst.color = random_color(rgb=True, maximum=1) - self._old_instances = instances[:] + extra_instances - return [d.color for d in instances] diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/mmnasnet/net.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/mmnasnet/net.py deleted file mode 100644 index 4872f40db5a5bb18abc8ed2eb5ca60e8e0cdf0bd..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/mmnasnet/net.py +++ /dev/null @@ -1,137 +0,0 @@ -# -------------------------------------------------------- -# OpenVQA -# Written by Zhenwei Shao https://github.com/ParadoxZW -# -------------------------------------------------------- - -from openvqa.utils.make_mask import make_mask -from openvqa.ops.fc import FC, MLP -from openvqa.ops.layer_norm import LayerNorm -from openvqa.models.mmnasnet.nasnet import NAS_ED -from openvqa.models.mmnasnet.adapter import Adapter - -import torch.nn as nn -import torch.nn.functional as F -import torch - - -# ------------------------------ -# ---- Flatten the sequence ---- -# ------------------------------ - -class AttFlat(nn.Module): - def __init__(self, __C): - super(AttFlat, self).__init__() - self.__C = __C - - self.mlp = MLP( - in_size=__C.HIDDEN_SIZE, - mid_size=__C.FLAT_MLP_SIZE, - out_size=__C.FLAT_GLIMPSES, - dropout_r=__C.DROPOUT_R, - use_relu=True - ) - - self.linear_merge = nn.Linear( - __C.HIDDEN_SIZE * __C.FLAT_GLIMPSES, - __C.FLAT_OUT_SIZE - ) - - def forward(self, x, x_mask): - att = self.mlp(x) - att = att.masked_fill( - x_mask.squeeze(1).squeeze(1).unsqueeze(2), - -1e9 - ) - att = F.softmax(att, dim=1) - - att_list = [] - for i in range(self.__C.FLAT_GLIMPSES): - att_list.append( - torch.sum(att[:, :, i: i + 1] * x, dim=1) - ) - - x_atted = torch.cat(att_list, dim=1) - x_atted = self.linear_merge(x_atted) - - return x_atted - - -# ------------------------- -# ---- Main MCAN Model ---- -# ------------------------- - -class Net(nn.Module): - def __init__(self, __C, pretrained_emb, token_size, answer_size): - super(Net, self).__init__() - self.__C = __C - - self.embedding = nn.Embedding( - num_embeddings=token_size, - embedding_dim=__C.WORD_EMBED_SIZE - ) - - # Loading the GloVe embedding weights - if __C.USE_GLOVE: - self.embedding.weight.data.copy_(torch.from_numpy(pretrained_emb)) - - self.lstm = nn.LSTM( - input_size=__C.WORD_EMBED_SIZE, - hidden_size=__C.HIDDEN_SIZE, - num_layers=1, - batch_first=True - ) - - self.adapter = Adapter(__C) - - self.backbone = NAS_ED(__C) - - # Projection of relation embedding - self.linear_rel = nn.Linear(4, __C.REL_SIZE) - self.relu = nn.ReLU() - - # Flatten to vector - self.attflat_img = AttFlat(__C) - self.attflat_lang = AttFlat(__C) - - # Classification layers - self.proj_norm = LayerNorm(__C.FLAT_OUT_SIZE) - self.proj = nn.Linear(__C.FLAT_OUT_SIZE, answer_size) - - - def forward(self, frcn_feat, grid_feat, bbox_feat, ques_ix): - - # Pre-process Language Feature - lang_feat_mask = make_mask(ques_ix.unsqueeze(2)) - lang_feat = self.embedding(ques_ix) - lang_feat, _ = self.lstm(lang_feat) - - img_feat, rel_embed, img_feat_mask = self.adapter(frcn_feat, grid_feat, bbox_feat) - rela = self.relu(self.linear_rel(rel_embed)) - - # Backbone Framework - lang_feat, img_feat = self.backbone( - lang_feat, - img_feat, - lang_feat_mask, - img_feat_mask, - rela - ) - - # Flatten to vector - lang_feat = self.attflat_lang( - lang_feat, - lang_feat_mask - ) - - img_feat = self.attflat_img( - img_feat, - img_feat_mask - ) - - # Classification layers - proj_feat = lang_feat + img_feat - proj_feat = self.proj_norm(proj_feat) - proj_feat = self.proj(proj_feat) - - return proj_feat - diff --git a/spaces/CVPR/LIVE/thrust/thrust/remove.h b/spaces/CVPR/LIVE/thrust/thrust/remove.h deleted file mode 100644 index 7e8ec41a60883cc81a67530ec3cf50dc9d00c730..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/remove.h +++ /dev/null @@ -1,806 +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 remove.h - * \brief Functions for removing elements from a range - */ - -#pragma once - -#include
-
-
-
-## Dataset
-
-### Train Dataset
-
-| trainset | instance_num | repeat_num | source |
-| :--------: | :----------: | :--------: | :------------------------: |
-| icdar_2011 | 3567 | 20 | real |
-| icdar_2013 | 848 | 20 | real |
-| icdar2015 | 4468 | 20 | real |
-| coco_text | 42142 | 20 | real |
-| IIIT5K | 2000 | 20 | real |
-| SynthText | 2400000 | 1 | synth |
-| SynthAdd | 1216889 | 1 | synth, 1.6m in [\[1\]](#1) |
-| Syn90k | 2400000 | 1 | synth |
-
-### Test Dataset
-
-| testset | instance_num | type |
-| :-----: | :----------: | :---------------------------: |
-| IIIT5K | 3000 | regular |
-| SVT | 647 | regular |
-| IC13 | 1015 | regular |
-| IC15 | 2077 | irregular |
-| SVTP | 645 | irregular, 639 in [\[1\]](#1) |
-| CT80 | 288 | irregular |
-
-## Results and Models
-
-| Methods | Backbone | Decoder | | Regular Text | | | | Irregular Text | | download |
-| :----------------------------------------------------------: | :---------: | :------------------: | :----: | :----------: | :--: | :-: | :--: | :------------: | :--: | :------------------------------------------------------------: |
-| | | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
-| [SAR](/configs/textrecog/sar/sar_r31_parallel_decoder_academic.py) | R31-1/8-1/4 | ParallelSARDecoder | 95.0 | 89.6 | 93.7 | | 79.0 | 82.2 | 88.9 | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_r31_parallel_decoder_academic-dba3a4a3.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/20210327_154129.log.json) |
-| [SAR](configs/textrecog/sar/sar_r31_sequential_decoder_academic.py) | R31-1/8-1/4 | SequentialSARDecoder | 95.2 | 88.7 | 92.4 | | 78.2 | 81.9 | 89.6 | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_r31_sequential_decoder_academic-d06c9a8e.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/20210330_105728.log.json) |
-
-## Chinese Dataset
-
-## Results and Models
-
-| Methods | Backbone | Decoder | | download |
-| :---------------------------------------------------------------: | :---------: | :----------------: | :-: | :-----------------------------------------------------------------------------------------------------: |
-| [SAR](/configs/textrecog/sar/sar_r31_parallel_decoder_chinese.py) | R31-1/8-1/4 | ParallelSARDecoder | | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_r31_parallel_decoder_chineseocr_20210507-b4be8214.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/20210506_225557.log.json) \| [dict](https://download.openmmlab.com/mmocr/textrecog/sar/dict_printed_chinese_english_digits.txt) |
-
-```{note}
-
-- `R31-1/8-1/4` means the height of feature from backbone is 1/8 of input image, where 1/4 for width.
-- We did not use beam search during decoding.
-- We implemented two kinds of decoder. Namely, `ParallelSARDecoder` and `SequentialSARDecoder`.
- - `ParallelSARDecoder`: Parallel decoding during training with `LSTM` layer. It would be faster.
- - `SequentialSARDecoder`: Sequential Decoding during training with `LSTMCell`. It would be easier to understand.
-- For train dataset.
- - We did not construct distinct data groups (20 groups in [[1]](#1)) to train the model group-by-group since it would render model training too complicated.
- - Instead, we randomly selected `2.4m` patches from `Syn90k`, `2.4m` from `SynthText` and `1.2m` from `SynthAdd`, and grouped all data together. See [config](https://download.openmmlab.com/mmocr/textrecog/sar/sar_r31_academic.py) for details.
-- We used 48 GPUs with `total_batch_size = 64 * 48` in the experiment above to speedup training, while keeping the `initial lr = 1e-3` unchanged.
-```
-
-## Citation
-
-```bibtex
-@inproceedings{li2019show,
- title={Show, attend and read: A simple and strong baseline for irregular text recognition},
- author={Li, Hui and Wang, Peng and Shen, Chunhua and Zhang, Guyu},
- booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
- volume={33},
- number={01},
- pages={8610--8617},
- year={2019}
-}
-```
diff --git a/spaces/FelixLuoX/codeformer/CodeFormer/weights/README.md b/spaces/FelixLuoX/codeformer/CodeFormer/weights/README.md
deleted file mode 100644
index 67ad334bd672eeb9f82813cd54e8885331bbb2f2..0000000000000000000000000000000000000000
--- a/spaces/FelixLuoX/codeformer/CodeFormer/weights/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# Weights
-
-Put the downloaded pre-trained models to this folder.
\ No newline at end of file
diff --git a/spaces/Godrose0728/sound-link/utils.py b/spaces/Godrose0728/sound-link/utils.py
deleted file mode 100644
index 4cb5b43d0ca2bae496e7871b2094f2ffb26ab642..0000000000000000000000000000000000000000
--- a/spaces/Godrose0728/sound-link/utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r", encoding="utf-8") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/Gradio-Blocks/magnificento/app.py b/spaces/Gradio-Blocks/magnificento/app.py
deleted file mode 100644
index 1107a39e64786d468d7f7d827a808c076028253e..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/magnificento/app.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import io, os, base64
-from PIL import Image
-import gradio as gr
-import shortuuid
-import numpy as np
-from transformers import pipeline
-
-asr = pipeline("automatic-speech-recognition")
-latent = gr.Interface.load("spaces/multimodalart/latentdiffusion")
-
-
-def text2image_latent(text, steps, width, height, images, diversity):
- print(text)
- results = latent(text, steps, width, height, images, diversity)
- image_paths = []
- for image in results[1]:
- image_str = image[0]
- image_str = image_str.replace("data:image/png;base64,","")
- decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8"))
- img = Image.open(io.BytesIO(decoded_bytes))
- url = shortuuid.uuid()
- temp_dir = './tmp'
- if not os.path.exists(temp_dir):
- os.makedirs(temp_dir, exist_ok=True)
- image_path = f'{temp_dir}/{url}.png'
- img.save(f'{temp_dir}/{url}.png')
- image_paths.append(image_path)
- return(image_paths)
-
-
-def speech_to_text(mic=None, file=None):
- if mic is not None:
- audio = mic
- elif file is not None:
- audio = file
- else:
- return "You must either provide a mic recording or a file"
- transcription = asr(audio)["text"]
- return transcription
-
-
-with gr.Blocks() as demo:
- gr.Markdown( """
- # 🎤 Sing or tell your story and let this Space ✨ visualize your story along
- ## Inspired by this [tweet](https://twitter.com/karenxcheng/status/1516816114994454529?s=20&t=moq2vK5430JoerJXBTkIuA)
- ### Soon to be added:
- - Near real time(streaming option)
- - Option playback of you audio relayed with video
- """)
- with gr.Row():
- with gr.Column():
- audio_file =[
- gr.Audio(source="microphone", type="filepath", optional=True, label="Speak here..."),
- gr.Audio(source="upload", type="filepath", optional=True, label="Or if you want upload here...")]
- text = gr.Textbox(label="Text", placeholder="If you dont want to record or upload your voice you can input text here")
- with gr.Row():
- s2t = gr.Button("Speech to text go brrr")
- with gr.Column():
- steps = gr.inputs.Slider(label="Steps - more steps can increase quality but will take longer to generate",default=1,maximum=50,minimum=1,step=1)
- width = gr.inputs.Slider(label="Width", default=256, step=32, maximum=256, minimum=32)
- height = gr.inputs.Slider(label="Height", default=256, step=32, maximum = 256, minimum=32)
- images = gr.inputs.Slider(label="Images - How many images you wish to generate", default=1, step=1, minimum=1, maximum=4)
- diversity = gr.inputs.Slider(label="Diversity scale - How different from one another you wish the images to be",default=15.0, minimum=1.0, maximum=15.0)
- gallery = gr.Gallery(label="Individual images")
- with gr.Row():
- get_image_latent = gr.Button("Generate Image go brr")
-
- s2t.click(speech_to_text, inputs=audio_file, outputs=text)
- get_image_latent.click(text2image_latent, inputs=[text, steps, width, height, images, diversity], outputs=gallery)
-
-demo.launch(enable_queue=True, debug=True)
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/swin/mask_rcnn_swin_small_patch4_window7_mstrain_480-800_adamw_3x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/swin/mask_rcnn_swin_small_patch4_window7_mstrain_480-800_adamw_3x_coco.py
deleted file mode 100644
index ee15134ba3f0a0788cbf4eb69cf080d01e08ddab..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/swin/mask_rcnn_swin_small_patch4_window7_mstrain_480-800_adamw_3x_coco.py
+++ /dev/null
@@ -1,80 +0,0 @@
-_base_ = [
- '../_base_/models/mask_rcnn_swin_fpn.py',
- '../_base_/datasets/coco_instance.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-
-model = dict(
- backbone=dict(
- embed_dim=96,
- depths=[2, 2, 18, 2],
- num_heads=[3, 6, 12, 24],
- window_size=7,
- ape=False,
- drop_path_rate=0.2,
- patch_norm=True,
- use_checkpoint=False
- ),
- neck=dict(in_channels=[96, 192, 384, 768]))
-
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-# augmentation strategy originates from DETR / Sparse RCNN
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='AutoAugment',
- policies=[
- [
- dict(type='Resize',
- img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
- (608, 1333), (640, 1333), (672, 1333), (704, 1333),
- (736, 1333), (768, 1333), (800, 1333)],
- multiscale_mode='value',
- keep_ratio=True)
- ],
- [
- dict(type='Resize',
- img_scale=[(400, 1333), (500, 1333), (600, 1333)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(type='RandomCrop',
- crop_type='absolute_range',
- crop_size=(384, 600),
- allow_negative_crop=True),
- dict(type='Resize',
- img_scale=[(480, 1333), (512, 1333), (544, 1333),
- (576, 1333), (608, 1333), (640, 1333),
- (672, 1333), (704, 1333), (736, 1333),
- (768, 1333), (800, 1333)],
- multiscale_mode='value',
- override=True,
- keep_ratio=True)
- ]
- ]),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-
-optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
- paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
- 'relative_position_bias_table': dict(decay_mult=0.),
- 'norm': dict(decay_mult=0.)}))
-lr_config = dict(step=[27, 33])
-runner = dict(type='EpochBasedRunnerAmp', max_epochs=36)
-
-# do not use mmdet version fp16
-fp16 = None
-optimizer_config = dict(
- type="DistOptimizerHook",
- update_interval=1,
- grad_clip=None,
- coalesce=True,
- bucket_size_mb=-1,
- use_fp16=True,
-)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/losses/ghm_loss.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/losses/ghm_loss.py
deleted file mode 100644
index 8969a23fd98bb746415f96ac5e4ad9e37ba3af52..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/losses/ghm_loss.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-
-
-def _expand_onehot_labels(labels, label_weights, label_channels):
- bin_labels = labels.new_full((labels.size(0), label_channels), 0)
- inds = torch.nonzero(
- (labels >= 0) & (labels < label_channels), as_tuple=False).squeeze()
- if inds.numel() > 0:
- bin_labels[inds, labels[inds]] = 1
- bin_label_weights = label_weights.view(-1, 1).expand(
- label_weights.size(0), label_channels)
- return bin_labels, bin_label_weights
-
-
-# TODO: code refactoring to make it consistent with other losses
-@LOSSES.register_module()
-class GHMC(nn.Module):
- """GHM Classification Loss.
-
- Details of the theorem can be viewed in the paper
- `Gradient Harmonized Single-stage Detector
-
-
- 🔥🔥🔥
-
-
-LaMa generalizes surprisingly well to much higher resolutions (~2k❗️) than it saw during training (256x256), and achieves the excellent performance even in challenging scenarios, e.g. completion of periodic structures.
-
-
- 
-
-
- Try out in Google Colab
-
- 
-
- 
-
- 
-
-
-
-
-**3. Predict**
-
-On the host machine:
-
- python3 bin/predict.py model.path=$(pwd)/big-lama indir=$(pwd)/LaMa_test_images outdir=$(pwd)/output
-
-**OR** in the docker
-
-The following command will pull the docker image from Docker Hub and execute the prediction script
-```
-bash docker/2_predict.sh $(pwd)/big-lama $(pwd)/LaMa_test_images $(pwd)/output device=cpu
-```
-Docker cuda: TODO
-
-**4. Predict with Refinement**
-
-On the host machine:
-
- python3 bin/predict.py refine=True model.path=$(pwd)/big-lama indir=$(pwd)/LaMa_test_images outdir=$(pwd)/output
-
-# Train and Eval
-
-Make sure you run:
-
-```
-cd lama
-export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)
-```
-
-Then download models for _perceptual loss_:
-
- mkdir -p ade20k/ade20k-resnet50dilated-ppm_deepsup/
- wget -P ade20k/ade20k-resnet50dilated-ppm_deepsup/ http://sceneparsing.csail.mit.edu/model/pytorch/ade20k-resnet50dilated-ppm_deepsup/encoder_epoch_20.pth
-
-
-## Places
-
-⚠️ NB: FID/SSIM/LPIPS metric values for Places that we see in LaMa paper are computed on 30000 images that we produce in evaluation section below.
-For more details on evaluation data check [[Section 3. Dataset splits in Supplementary](https://ashukha.com/projects/lama_21/lama_supmat_2021.pdf#subsection.3.1)] ⚠️
-
-On the host machine:
-
- # Download data from http://places2.csail.mit.edu/download.html
- # Places365-Standard: Train(105GB)/Test(19GB)/Val(2.1GB) from High-resolution images section
- wget http://data.csail.mit.edu/places/places365/train_large_places365standard.tar
- wget http://data.csail.mit.edu/places/places365/val_large.tar
- wget http://data.csail.mit.edu/places/places365/test_large.tar
-
- # Unpack train/test/val data and create .yaml config for it
- bash fetch_data/places_standard_train_prepare.sh
- bash fetch_data/places_standard_test_val_prepare.sh
-
- # Sample images for test and viz at the end of epoch
- bash fetch_data/places_standard_test_val_sample.sh
- bash fetch_data/places_standard_test_val_gen_masks.sh
-
- # Run training
- python3 bin/train.py -cn lama-fourier location=places_standard
-
- # To evaluate trained model and report metrics as in our paper
- # we need to sample previously unseen 30k images and generate masks for them
- bash fetch_data/places_standard_evaluation_prepare_data.sh
-
- # Infer model on thick/thin/medium masks in 256 and 512 and run evaluation
- # like this:
- python3 bin/predict.py \
- model.path=$(pwd)/experiments/OR prepare your data:
-1) Create masks named as `[images_name]_maskXXX[image_suffix]`, put images and masks in the same folder. - -- You can use the [script](https://github.com/advimman/lama/blob/main/bin/gen_mask_dataset.py) for random masks generation. -- Check the format of the files: - ``` - image1_mask001.png - image1.png - image2_mask001.png - image2.png - ``` - -2) Specify `image_suffix`, e.g. `.png` or `.jpg` or `_input.jpg` in `configs/prediction/default.yaml`. - -
- ${rounded_usage}${usage_limit}
-
\ No newline at end of file
diff --git a/spaces/Kevin676/Shanghainese-TTS-demo/monotonic_align/core.py b/spaces/Kevin676/Shanghainese-TTS-demo/monotonic_align/core.py
deleted file mode 100644
index dddc688d76172b880054e544b7a217acd013f14f..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/Shanghainese-TTS-demo/monotonic_align/core.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:,:,::1], numba.float32[:,:,::1], numba.int32[::1], numba.int32[::1]), nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
- b = paths.shape[0]
- max_neg_val=-1e9
- for i in range(int(b)):
- path = paths[i]
- value = values[i]
- t_y = t_ys[i]
- t_x = t_xs[i]
-
- v_prev = v_cur = 0.0
- index = t_x - 1
-
- for y in range(t_y):
- for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
- if x == y:
- v_cur = max_neg_val
- else:
- v_cur = value[y-1, x]
- if x == 0:
- if y == 0:
- v_prev = 0.
- else:
- v_prev = max_neg_val
- else:
- v_prev = value[y-1, x-1]
- value[y, x] += max(v_prev, v_cur)
-
- for y in range(t_y - 1, -1, -1):
- path[y, index] = 1
- if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
- index = index - 1
diff --git a/spaces/Latryna/roop/roop/processors/frame/face_enhancer.py b/spaces/Latryna/roop/roop/processors/frame/face_enhancer.py
deleted file mode 100644
index b1501d574fccb5bc80f12b7783f9505cacc48e06..0000000000000000000000000000000000000000
--- a/spaces/Latryna/roop/roop/processors/frame/face_enhancer.py
+++ /dev/null
@@ -1,89 +0,0 @@
-from typing import Any, List, Callable
-import cv2
-import threading
-import gfpgan
-
-import roop.globals
-import roop.processors.frame.core
-from roop.core import update_status
-from roop.face_analyser import get_one_face
-from roop.typing import Frame, Face
-from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video
-import torch
-
-FACE_ENHANCER = None
-THREAD_SEMAPHORE = threading.Semaphore()
-THREAD_LOCK = threading.Lock()
-NAME = 'ROOP.FACE-ENHANCER'
-frame_name = 'face_enhancer'
-
-if torch.cuda.is_available():
- device='cuda'
-else:
- device='cpu'
-
-
-def get_face_enhancer() -> Any:
- global FACE_ENHANCER
-
- with THREAD_LOCK:
- if FACE_ENHANCER is None:
- model_path = resolve_relative_path('../models/GFPGANv1.4.pth')
- # todo: set models path https://github.com/TencentARC/GFPGAN/issues/399
- FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1,device=device) # type: ignore[attr-defined]
- return FACE_ENHANCER
-
-
-def pre_check() -> bool:
- download_directory_path = resolve_relative_path('../models')
- # conditional_download(download_directory_path, ['https://huggingface.co/henryruhs/roop/resolve/main/GFPGANv1.4.pth'])
- conditional_download(download_directory_path, ['https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth'])
- return True
-
-
-def pre_start() -> bool:
- if not is_image(roop.globals.target_path) and not is_video(roop.globals.target_path):
- update_status('Select an image or video for target path.', NAME)
- return False
- return True
-
-
-def post_process() -> None:
- global FACE_ENHANCER
-
- FACE_ENHANCER = None
-
-
-def enhance_face(temp_frame: Frame) -> Frame:
- with THREAD_SEMAPHORE:
- _, _, temp_frame = get_face_enhancer().enhance(
- temp_frame,
- paste_back=True
- )
- return temp_frame
-
-
-def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
- target_face = get_one_face(temp_frame)
- if target_face:
- temp_frame = enhance_face(temp_frame)
- return temp_frame
-
-
-def process_frames(source_path: str, temp_frame_paths: List[str], update: Callable[[], None]) -> None:
- for temp_frame_path in temp_frame_paths:
- temp_frame = cv2.imread(temp_frame_path)
- result = process_frame(None, temp_frame)
- cv2.imwrite(temp_frame_path, result)
- if update:
- update()
-
-
-def process_image(source_path: str, target_path: str, output_path: str) -> None:
- target_frame = cv2.imread(target_path)
- result = process_frame(None, target_frame)
- cv2.imwrite(output_path, result)
-
-
-def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
- roop.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/indicators/kama.py b/spaces/Lianjd/stock_dashboard/backtrader/indicators/kama.py
deleted file mode 100644
index fb96f971cd58a2f2a755598b513f5c13f733fd0c..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/indicators/kama.py
+++ /dev/null
@@ -1,81 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program. If not, see '
- f'
' if cover else "")+
- '
'
- )
- with gr.Row():
- if args.files:
- with gr.Column():
- vc_youtube = gr.Textbox(label="Youtube URL")
- vc_convert = gr.Button("Convert", variant="primary")
- vc_vocal_preview = gr.Audio(label="Vocal Preview")
- vc_inst_preview = gr.Audio(label="Instrumental Preview")
- vc_audio_preview = gr.Audio(label="Audio Preview")
- with gr.Column():
- if args.files:
- vc_input = gr.Textbox(label="Input audio path")
- else:
- vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
- vc_transpose = gr.Number(label="Transpose", value=0)
- vc_f0method = gr.Radio(
- label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies",
- choices=["pm", "harvest"],
- value="pm",
- interactive=True,
- )
- vc_index_ratio = gr.Slider(
- minimum=0,
- maximum=1,
- label="Retrieval feature ratio",
- value=0.6,
- interactive=True,
- )
- tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
- tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
- tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
- vc_submit = gr.Button("Generate", variant="primary")
- vc_output1 = gr.Textbox(label="Output Message")
- vc_output2 = gr.Audio(label="Output Audio")
- if args.files:
- with gr.Column():
- vc_volume = gr.Slider(
- minimum=0,
- maximum=10,
- label="Vocal volume",
- value=5,
- interactive=True,
- step=1
- )
- vc_outputCombine = gr.Audio(label="Output Combined Audio")
- vc_combine = gr.Button("Combine",variant="primary")
- vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2])
- tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice])
- if args.files:
- vc_convert.click(cut_vocal_and_inst, vc_youtube, [vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input])
- vc_combine.click(combine_vocal_and_inst, [vc_output2, vc_volume], vc_outputCombine)
- app.queue(concurrency_count=1, max_size=20, api_open=args.api).launch(share=args.share)
\ No newline at end of file
diff --git a/spaces/Munna0912/URL_CLASSIFIER/Utils/FeatureCreation.py b/spaces/Munna0912/URL_CLASSIFIER/Utils/FeatureCreation.py
deleted file mode 100644
index 8701c3b3d6030f833bb19c8ce682f39c38dcd628..0000000000000000000000000000000000000000
--- a/spaces/Munna0912/URL_CLASSIFIER/Utils/FeatureCreation.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import re
-import pandas as pd
-from urllib.parse import urlparse
-
-# Typically, cyber attackers replace the domain name in a URL with an IP address to conceal the website's identity. This feature is designed to verify whether the URL includes an IP address or not.
-def having_ip_address(url):
- match = re.search(
- '(([01]?\\d\\d?|2[0-4]\\d|25[0-5])\\.([01]?\\d\\d?|2[0-4]\\d|25[0-5])\\.([01]?\\d\\d?|2[0-4]\\d|25[0-5])\\.'
- '([01]?\\d\\d?|2[0-4]\\d|25[0-5])\\/)|' # IPv4
- '((0x[0-9a-fA-F]{1,2})\\.(0x[0-9a-fA-F]{1,2})\\.(0x[0-9a-fA-F]{1,2})\\.(0x[0-9a-fA-F]{1,2})\\/)' # IPv4 in hexadecimal
- '(?:[a-fA-F0-9]{1,4}:){7}[a-fA-F0-9]{1,4}', url) # Ipv6
- if match:
- return 1
- else:
- return 0
-
-# check whether link is in proper formatting or not
-def abnormal_url(url):
- hostname = urlparse(url).hostname
- hostname = str(hostname)
- match = re.search(hostname, url)
- if match:
- return 1
- else:
- return 0
-
-# Phishing or malware websites often incorporate multiple sub-domains into their URLs, with each sub-domain separated by a dot (.). URLs containing more than three dots (.) are more likely to be malicious sites.
-def count_dot(url):
- count_dot = url.count('.')
- return count_dot
-
-# In general, most safe websites contain only one "www" in their URLs. This feature can aid in identifying malicious websites by detecting URLs that either lack "www" or contain more than one instance of it.
-def count_www(url):
- url.count('www')
- return url.count('www')
-
-# The occurrence of the "@" symbol in a URL causes everything preceding it to be disregarded.
-def count_atrate(url):
- return url.count('@')
-
-# Websites that contain multiple directories within their URLs are typically considered suspicious.
-def no_of_dir(url):
- urldir = urlparse(url).path
- return urldir.count('/')
-
-# Examining the frequency of the " //" sequence within a URL can aid in identifying malicious URLs with multiple embedded domains.
-def no_of_embed(url):
- urldir = urlparse(url).path
- return urldir.count('//')
-
-# Malicious URLs often avoid using HTTPS protocols as they typically require user credentials and provide a layer of security for online transactions. Therefore, the presence or absence of HTTPS protocol in a URL can be a crucial indicator in determining its safety.
-def count_https(url):
- return url.count('https')
-
-# Typically, phishing or malicious websites contain multiple instances of HTTP in their URL, whereas safe websites only have one.
-def count_http(url):
- return url.count('http')
-
-# As URLs cannot contain spaces, they are often replaced with symbols (%), which is known as URL encoding. Safe websites tend to have fewer spaces in their URLs, while malicious sites often have more, resulting in a higher number of % symbols.
-def count_per(url):
- return url.count('%')
-
-# A symbol (?) in a URL indicates the presence of a query string, which contains data to be sent to the server. If a URL contains multiple instances of the symbol (?), it can be a sign of a suspicious URL.
-def count_ques(url):
- return url.count('?')
-
-# To make a URL appear genuine, phishers and cybercriminals often add dashes (-) to the prefix or suffix of a brand name. For instance, they may create a URL like www.flipkart-india.com.
-def count_hyphen(url):
- return url.count('-')
-
-# The presence of an equal sign (=) in a URL implies that variable values are being passed from one form page to another. This is considered risky since anyone can modify the values and alter the page.
-def count_equal(url):
- return url.count('=')
-
-#Length of URL
-def url_length(url):
- return len(str(url))
-
-#Hostname Length
-def hostname_length(url):
- return len(urlparse(url).netloc)
-
-def digit_count(url):
- digits = 0
- for i in url:
- if i.isnumeric():
- digits = digits + 1
- return digits
-
-def letter_count(url):
- letters = 0
- for i in url:
- if i.isalpha():
- letters = letters + 1
- return letters
-
-#First Directory Length
-def fd_length(url):
- urlpath= urlparse(url).path
- try:
- return len(urlpath.split('/')[1])
- except:
- return 0
-
-def create_features(df):
- df['use_of_ip'] = df['url'].apply(lambda i: having_ip_address(i))
- df['abnormal_url'] = df['url'].apply(lambda i: abnormal_url(i))
- df['count.'] = df['url'].apply(lambda i: count_dot(i))
- df['count-www'] = df['url'].apply(lambda i: count_www(i))
- df['count@'] = df['url'].apply(lambda i: count_atrate(i))
- df['count_dir'] = df['url'].apply(lambda i: no_of_dir(i))
- df['count_embed_domian'] = df['url'].apply(lambda i: no_of_embed(i))
- df['count-https'] = df['url'].apply(lambda i : count_https(i))
- df['count-http'] = df['url'].apply(lambda i : count_http(i))
- df['count%'] = df['url'].apply(lambda i : count_per(i))
- df['count?'] = df['url'].apply(lambda i: count_ques(i))
- df['count-'] = df['url'].apply(lambda i: count_hyphen(i))
- df['count='] = df['url'].apply(lambda i: count_equal(i))
- df['url_length'] = df['url'].apply(lambda i: url_length(i))
- df['hostname_length'] = df['url'].apply(lambda i: hostname_length(i))
- df['count-digits']= df['url'].apply(lambda i: digit_count(i))
- df['count-letters']= df['url'].apply(lambda i: letter_count(i))
- df['fd_length'] = df['url'].apply(lambda i: fd_length(i))
- return df
\ No newline at end of file
diff --git a/spaces/NAACL2022/CLIP-Caption-Reward/captioning/utils/eval_utils.py b/spaces/NAACL2022/CLIP-Caption-Reward/captioning/utils/eval_utils.py
deleted file mode 100644
index c4bc7f4471e6d3e1fcc2f80af6f47bfec5d920a1..0000000000000000000000000000000000000000
--- a/spaces/NAACL2022/CLIP-Caption-Reward/captioning/utils/eval_utils.py
+++ /dev/null
@@ -1,281 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import numpy as np
-import json
-from json import encoder
-import random
-import string
-import time
-import os
-import sys
-from . import misc as utils
-
-# load coco-caption if available
-try:
- sys.path.append("coco-caption")
- from pycocotools.coco import COCO
- from pycocoevalcap.eval import COCOEvalCap
-except:
- print('Warning: coco-caption not available')
-
-bad_endings = ['a','an','the','in','for','at','of','with','before','after','on','upon','near','to','is','are','am']
-bad_endings += ['the']
-
-
-def count_bad(sen):
- sen = sen.split(' ')
- if sen[-1] in bad_endings:
- return 1
- else:
- return 0
-
-
-def getCOCO(dataset):
- if 'coco' in dataset:
- annFile = 'coco-caption/annotations/captions_val2014.json'
- elif 'flickr30k' in dataset or 'f30k' in dataset:
- annFile = 'data/f30k_captions4eval.json'
- return COCO(annFile)
-
-
-def language_eval(dataset, preds, preds_n, eval_kwargs, split):
- model_id = eval_kwargs['id']
- eval_oracle = eval_kwargs.get('eval_oracle', 0)
-
- # create output dictionary
- out = {}
-
- if len(preds_n) > 0:
- # vocab size and novel sentences
- if 'coco' in dataset:
- dataset_file = 'data/dataset_coco.json'
- elif 'flickr30k' in dataset or 'f30k' in dataset:
- dataset_file = 'data/dataset_flickr30k.json'
- training_sentences = set([' '.join(__['tokens']) for _ in json.load(open(dataset_file))['images'] if not _['split'] in ['val', 'test'] for __ in _['sentences']])
- generated_sentences = set([_['caption'] for _ in preds_n])
- novels = generated_sentences - training_sentences
- out['novel_sentences'] = float(len(novels)) / len(preds_n)
- tmp = [_.split() for _ in generated_sentences]
- words = []
- for _ in tmp:
- words += _
- out['vocab_size'] = len(set(words))
-
- # encoder.FLOAT_REPR = lambda o: format(o, '.3f')
-
- cache_path = os.path.join('eval_results/', '.cache_'+ model_id + '_' + split + '.json')
-
- coco = getCOCO(dataset)
- valids = coco.getImgIds()
-
- # filter results to only those in MSCOCO validation set
- preds_filt = [p for p in preds if p['image_id'] in valids]
- mean_perplexity = sum([_['perplexity'] for _ in preds_filt]) / len(preds_filt)
- mean_entropy = sum([_['entropy'] for _ in preds_filt]) / len(preds_filt)
- print('using %d/%d predictions' % (len(preds_filt), len(preds)))
- json.dump(preds_filt, open(cache_path, 'w')) # serialize to temporary json file. Sigh, COCO API...
-
- cocoRes = coco.loadRes(cache_path)
- cocoEval = COCOEvalCap(coco, cocoRes)
- cocoEval.params['image_id'] = cocoRes.getImgIds()
- cocoEval.evaluate()
-
- for metric, score in cocoEval.eval.items():
- out[metric] = score
- # Add mean perplexity
- out['perplexity'] = mean_perplexity
- out['entropy'] = mean_entropy
-
- imgToEval = cocoEval.imgToEval
- for k in list(imgToEval.values())[0]['SPICE'].keys():
- if k != 'All':
- out['SPICE_'+k] = np.array([v['SPICE'][k]['f'] for v in imgToEval.values()])
- out['SPICE_'+k] = (out['SPICE_'+k][out['SPICE_'+k]==out['SPICE_'+k]]).mean()
- for p in preds_filt:
- image_id, caption = p['image_id'], p['caption']
- imgToEval[image_id]['caption'] = caption
-
- if len(preds_n) > 0:
- from . import eval_multi
- cache_path_n = os.path.join('eval_results/', '.cache_'+ model_id + '_' + split + '_n.json')
- allspice = eval_multi.eval_allspice(dataset, preds_n, model_id, split)
- out.update(allspice['overall'])
- div_stats = eval_multi.eval_div_stats(dataset, preds_n, model_id, split)
- out.update(div_stats['overall'])
- if eval_oracle:
- oracle = eval_multi.eval_oracle(dataset, preds_n, model_id, split)
- out.update(oracle['overall'])
- else:
- oracle = None
- self_cider = eval_multi.eval_self_cider(dataset, preds_n, model_id, split)
- out.update(self_cider['overall'])
- with open(cache_path_n, 'w') as outfile:
- json.dump({'allspice': allspice, 'div_stats': div_stats, 'oracle': oracle, 'self_cider': self_cider}, outfile)
-
- out['bad_count_rate'] = sum([count_bad(_['caption']) for _ in preds_filt]) / float(len(preds_filt))
- outfile_path = os.path.join('eval_results/', model_id + '_' + split + '.json')
- with open(outfile_path, 'w') as outfile:
- json.dump({'overall': out, 'imgToEval': imgToEval}, outfile)
-
- return out
-
-def eval_split(model, crit, loader, eval_kwargs={}):
- verbose = eval_kwargs.get('verbose', True)
- verbose_beam = eval_kwargs.get('verbose_beam', 0)
- verbose_loss = eval_kwargs.get('verbose_loss', 1)
- num_images = eval_kwargs.get('num_images', eval_kwargs.get('val_images_use', -1))
- split = eval_kwargs.get('split', 'val')
- lang_eval = eval_kwargs.get('language_eval', 0)
- dataset = eval_kwargs.get('dataset', 'coco')
- beam_size = eval_kwargs.get('beam_size', 1)
- sample_n = eval_kwargs.get('sample_n', 1)
- remove_bad_endings = eval_kwargs.get('remove_bad_endings', 0)
- os.environ["REMOVE_BAD_ENDINGS"] = str(remove_bad_endings) # Use this nasty way to make other code clean since it's a global configuration
- device = eval_kwargs.get('device', 'cuda')
-
- # Make sure in the evaluation mode
- model.eval()
-
- loader.reset_iterator(split)
-
- n = 0
- loss = 0
- loss_sum = 0
- loss_evals = 1e-8
- predictions = []
- n_predictions = [] # when sample_n > 1
- while True:
- data = loader.get_batch(split)
- n = n + len(data['infos'])
-
- tmp = [data['fc_feats'], data['att_feats'], data['labels'], data['masks'], data['att_masks']]
- tmp = [_.to(device) if _ is not None else _ for _ in tmp]
- fc_feats, att_feats, labels, masks, att_masks = tmp
- if labels is not None and verbose_loss:
- # forward the model to get loss
- with torch.no_grad():
- loss = crit(model(fc_feats, att_feats, labels[..., :-1], att_masks), labels[..., 1:], masks[..., 1:]).item()
- loss_sum = loss_sum + loss
- loss_evals = loss_evals + 1
-
- # forward the model to also get generated samples for each image
- with torch.no_grad():
- tmp_eval_kwargs = eval_kwargs.copy()
- tmp_eval_kwargs.update({'sample_n': 1})
- seq, seq_logprobs = model(fc_feats, att_feats, att_masks, opt=tmp_eval_kwargs, mode='sample')
- seq = seq.data
- entropy = - (F.softmax(seq_logprobs, dim=2) * seq_logprobs).sum(2).sum(1) / ((seq>0).to(seq_logprobs).sum(1)+1)
- perplexity = - seq_logprobs.gather(2, seq.unsqueeze(2)).squeeze(2).sum(1) / ((seq>0).to(seq_logprobs).sum(1)+1)
-
- # Print beam search
- if beam_size > 1 and verbose_beam:
- for i in range(fc_feats.shape[0]):
- print('\n'.join([utils.decode_sequence(model.vocab, _['seq'].unsqueeze(0))[0] for _ in model.done_beams[i]]))
- print('--' * 10)
- sents = utils.decode_sequence(model.vocab, seq)
-
- for k, sent in enumerate(sents):
- entry = {'image_id': data['infos'][k]['id'], 'caption': sent, 'perplexity': perplexity[k].item(), 'entropy': entropy[k].item()}
- if eval_kwargs.get('dump_path', 0) == 1:
- entry['file_name'] = data['infos'][k]['file_path']
- predictions.append(entry)
- if eval_kwargs.get('dump_images', 0) == 1:
- # dump the raw image to vis/ folder
- cmd = 'cp "' + os.path.join(eval_kwargs['image_root'], data['infos'][k]['file_path']) + '" vis/imgs/img' + str(len(predictions)) + '.jpg' # bit gross
- print(cmd)
- os.system(cmd)
-
- if verbose:
- print('image %s: %s' %(entry['image_id'], entry['caption']))
-
- if sample_n > 1:
- eval_split_n(model, n_predictions, [fc_feats, att_feats, att_masks, data], eval_kwargs)
-
- # ix0 = data['bounds']['it_pos_now']
- ix1 = data['bounds']['it_max']
- if num_images != -1:
- ix1 = min(ix1, num_images)
- else:
- num_images = ix1
- for i in range(n - ix1):
- predictions.pop()
-
- if verbose:
- print('evaluating validation preformance... %d/%d (%f)' %(n, ix1, loss))
-
- if num_images >= 0 and n >= num_images:
- break
-
- lang_stats = None
- if len(n_predictions) > 0 and 'perplexity' in n_predictions[0]:
- n_predictions = sorted(n_predictions, key=lambda x: x['perplexity'])
- if not os.path.isdir('eval_results'):
- os.mkdir('eval_results')
- torch.save((predictions, n_predictions), os.path.join('eval_results/', '.saved_pred_'+ eval_kwargs['id'] + '_' + split + '.pth'))
- if lang_eval == 1:
- lang_stats = language_eval(dataset, predictions, n_predictions, eval_kwargs, split)
-
- # Switch back to training mode
- model.train()
- return loss_sum/loss_evals, predictions, lang_stats
-
-
-# Only run when sample_n > 0
-def eval_split_n(model, n_predictions, input_data, eval_kwargs={}):
- verbose = eval_kwargs.get('verbose', True)
- beam_size = eval_kwargs.get('beam_size', 1)
- sample_n = eval_kwargs.get('sample_n', 1)
- sample_n_method = eval_kwargs.get('sample_n_method', 'sample')
-
- fc_feats, att_feats, att_masks, data = input_data
-
- tmp_eval_kwargs = eval_kwargs.copy()
- if sample_n_method == 'bs':
- # case 1 sample_n == beam size
- tmp_eval_kwargs.update({'sample_n': 1, 'beam_size': sample_n, 'group_size': 1}) # randomness from softmax
- with torch.no_grad():
- model(fc_feats, att_feats, att_masks, opt=tmp_eval_kwargs, mode='sample')
- for k in range(fc_feats.shape[0]):
- _sents = utils.decode_sequence(model.vocab, torch.stack([model.done_beams[k][_]['seq'] for _ in range(sample_n)]))
- for sent in _sents:
- entry = {'image_id': data['infos'][k]['id'], 'caption': sent}
- n_predictions.append(entry)
- # case 2 sample / gumbel / topk sampling/ nucleus sampling
- elif sample_n_method == 'sample' or \
- sample_n_method == 'gumbel' or \
- sample_n_method.startswith('top'):
- tmp_eval_kwargs.update({'sample_n': sample_n, 'sample_method': sample_n_method, 'beam_size': 1}) # randomness from sample
- with torch.no_grad():
- _seq, _sampleLogprobs = model(fc_feats, att_feats, att_masks, opt=tmp_eval_kwargs, mode='sample')
- _sents = utils.decode_sequence(model.vocab, _seq)
- _perplexity = - _sampleLogprobs.gather(2, _seq.unsqueeze(2)).squeeze(2).sum(1) / ((_seq>0).to(_sampleLogprobs).sum(1)+1)
- for k, sent in enumerate(_sents):
- entry = {'image_id': data['infos'][k // sample_n]['id'], 'caption': sent, 'perplexity': _perplexity[k].item()}
- n_predictions.append(entry)
- elif sample_n_method == 'dbs':
- # Use diverse beam search
- tmp_eval_kwargs.update({'beam_size': sample_n * beam_size, 'group_size': sample_n}) # randomness from softmax
- with torch.no_grad():
- model(fc_feats, att_feats, att_masks, opt=tmp_eval_kwargs, mode='sample')
- for k in range(loader.batch_size):
- _sents = utils.decode_sequence(model.vocab, torch.stack([model.done_beams[k][_]['seq'] for _ in range(0, sample_n*beam_size, beam_size)]))
- for sent in _sents:
- entry = {'image_id': data['infos'][k]['id'], 'caption': sent}
- n_predictions.append(entry)
- else:
- tmp_eval_kwargs.update({'sample_method': sample_n_method[1:], 'group_size': sample_n, 'beam_size':1}) # randomness from softmax
- with torch.no_grad():
- _seq, _sampleLogprobs = model(fc_feats, att_feats, att_masks, opt=tmp_eval_kwargs, mode='sample')
- _sents = utils.decode_sequence(model.vocab, _seq)
- for k, sent in enumerate(_sents):
- entry = {'image_id': data['infos'][k // sample_n]['id'], 'caption': sent}
- n_predictions.append(entry)
- if verbose:
- for entry in sorted(n_predictions[-fc_feats.shape[0] * sample_n:], key=lambda x: x['image_id']):
- print('image %s: %s' %(entry['image_id'], entry['caption']))
\ No newline at end of file
diff --git a/spaces/NATSpeech/DiffSpeech/modules/commons/conformer/layers.py b/spaces/NATSpeech/DiffSpeech/modules/commons/conformer/layers.py
deleted file mode 100644
index cd7f501667e0b8aa816373d843adc816748e73a8..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/modules/commons/conformer/layers.py
+++ /dev/null
@@ -1,260 +0,0 @@
-from torch import nn
-import torch
-
-from modules.commons.layers import LayerNorm
-
-
-class ConvolutionModule(nn.Module):
- """ConvolutionModule in Conformer model.
- Args:
- channels (int): The number of channels of conv layers.
- kernel_size (int): Kernerl size of conv layers.
- """
-
- def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
- """Construct an ConvolutionModule object."""
- super(ConvolutionModule, self).__init__()
- # kernerl_size should be a odd number for 'SAME' padding
- assert (kernel_size - 1) % 2 == 0
-
- self.pointwise_conv1 = nn.Conv1d(
- channels,
- 2 * channels,
- kernel_size=1,
- stride=1,
- padding=0,
- bias=bias,
- )
- self.depthwise_conv = nn.Conv1d(
- channels,
- channels,
- kernel_size,
- stride=1,
- padding=(kernel_size - 1) // 2,
- groups=channels,
- bias=bias,
- )
- self.norm = nn.BatchNorm1d(channels)
- self.pointwise_conv2 = nn.Conv1d(
- channels,
- channels,
- kernel_size=1,
- stride=1,
- padding=0,
- bias=bias,
- )
- self.activation = activation
-
- def forward(self, x):
- """Compute convolution module.
- Args:
- x (torch.Tensor): Input tensor (#batch, time, channels).
- Returns:
- torch.Tensor: Output tensor (#batch, time, channels).
- """
- # exchange the temporal dimension and the feature dimension
- x = x.transpose(1, 2)
-
- # GLU mechanism
- x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
- x = nn.functional.glu(x, dim=1) # (batch, channel, dim)
-
- # 1D Depthwise Conv
- x = self.depthwise_conv(x)
- x = self.activation(self.norm(x))
-
- x = self.pointwise_conv2(x)
-
- return x.transpose(1, 2)
-
-
-class MultiLayeredConv1d(torch.nn.Module):
- """Multi-layered conv1d for Transformer block.
- This is a module of multi-leyered conv1d designed
- to replace positionwise feed-forward network
- in Transforner block, which is introduced in
- `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
- .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
- https://arxiv.org/pdf/1905.09263.pdf
- """
-
- def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
- """Initialize MultiLayeredConv1d module.
- Args:
- in_chans (int): Number of input channels.
- hidden_chans (int): Number of hidden channels.
- kernel_size (int): Kernel size of conv1d.
- dropout_rate (float): Dropout rate.
- """
- super(MultiLayeredConv1d, self).__init__()
- self.w_1 = torch.nn.Conv1d(
- in_chans,
- hidden_chans,
- kernel_size,
- stride=1,
- padding=(kernel_size - 1) // 2,
- )
- self.w_2 = torch.nn.Conv1d(
- hidden_chans,
- in_chans,
- kernel_size,
- stride=1,
- padding=(kernel_size - 1) // 2,
- )
- self.dropout = torch.nn.Dropout(dropout_rate)
-
- def forward(self, x):
- """Calculate forward propagation.
- Args:
- x (torch.Tensor): Batch of input tensors (B, T, in_chans).
- Returns:
- torch.Tensor: Batch of output tensors (B, T, hidden_chans).
- """
- x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
- return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)
-
-
-class Swish(torch.nn.Module):
- """Construct an Swish object."""
-
- def forward(self, x):
- """Return Swich activation function."""
- return x * torch.sigmoid(x)
-
-
-class EncoderLayer(nn.Module):
- """Encoder layer module.
- Args:
- size (int): Input dimension.
- self_attn (torch.nn.Module): Self-attention module instance.
- `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
- can be used as the argument.
- feed_forward (torch.nn.Module): Feed-forward module instance.
- `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
- can be used as the argument.
- feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance.
- `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
- can be used as the argument.
- conv_module (torch.nn.Module): Convolution module instance.
- `ConvlutionModule` instance can be used as the argument.
- dropout_rate (float): Dropout rate.
- normalize_before (bool): Whether to use layer_norm before the first block.
- concat_after (bool): Whether to concat attention layer's input and output.
- if True, additional linear will be applied.
- i.e. x -> x + linear(concat(x, att(x)))
- if False, no additional linear will be applied. i.e. x -> x + att(x)
- """
-
- def __init__(
- self,
- size,
- self_attn,
- feed_forward,
- feed_forward_macaron,
- conv_module,
- dropout_rate,
- normalize_before=True,
- concat_after=False,
- ):
- """Construct an EncoderLayer object."""
- super(EncoderLayer, self).__init__()
- self.self_attn = self_attn
- self.feed_forward = feed_forward
- self.feed_forward_macaron = feed_forward_macaron
- self.conv_module = conv_module
- self.norm_ff = LayerNorm(size) # for the FNN module
- self.norm_mha = LayerNorm(size) # for the MHA module
- if feed_forward_macaron is not None:
- self.norm_ff_macaron = LayerNorm(size)
- self.ff_scale = 0.5
- else:
- self.ff_scale = 1.0
- if self.conv_module is not None:
- self.norm_conv = LayerNorm(size) # for the CNN module
- self.norm_final = LayerNorm(size) # for the final output of the block
- self.dropout = nn.Dropout(dropout_rate)
- self.size = size
- self.normalize_before = normalize_before
- self.concat_after = concat_after
- if self.concat_after:
- self.concat_linear = nn.Linear(size + size, size)
-
- def forward(self, x_input, mask, cache=None):
- """Compute encoded features.
- Args:
- x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
- - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
- - w/o pos emb: Tensor (#batch, time, size).
- mask (torch.Tensor): Mask tensor for the input (#batch, time).
- cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
- Returns:
- torch.Tensor: Output tensor (#batch, time, size).
- torch.Tensor: Mask tensor (#batch, time).
- """
- if isinstance(x_input, tuple):
- x, pos_emb = x_input[0], x_input[1]
- else:
- x, pos_emb = x_input, None
-
- # whether to use macaron style
- if self.feed_forward_macaron is not None:
- residual = x
- if self.normalize_before:
- x = self.norm_ff_macaron(x)
- x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
- if not self.normalize_before:
- x = self.norm_ff_macaron(x)
-
- # multi-headed self-attention module
- residual = x
- if self.normalize_before:
- x = self.norm_mha(x)
-
- if cache is None:
- x_q = x
- else:
- assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
- x_q = x[:, -1:, :]
- residual = residual[:, -1:, :]
- mask = None if mask is None else mask[:, -1:, :]
-
- if pos_emb is not None:
- x_att = self.self_attn(x_q, x, x, pos_emb, mask)
- else:
- x_att = self.self_attn(x_q, x, x, mask)
-
- if self.concat_after:
- x_concat = torch.cat((x, x_att), dim=-1)
- x = residual + self.concat_linear(x_concat)
- else:
- x = residual + self.dropout(x_att)
- if not self.normalize_before:
- x = self.norm_mha(x)
-
- # convolution module
- if self.conv_module is not None:
- residual = x
- if self.normalize_before:
- x = self.norm_conv(x)
- x = residual + self.dropout(self.conv_module(x))
- if not self.normalize_before:
- x = self.norm_conv(x)
-
- # feed forward module
- residual = x
- if self.normalize_before:
- x = self.norm_ff(x)
- x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
- if not self.normalize_before:
- x = self.norm_ff(x)
-
- if self.conv_module is not None:
- x = self.norm_final(x)
-
- if cache is not None:
- x = torch.cat([cache, x], dim=1)
-
- if pos_emb is not None:
- return (x, pos_emb), mask
-
- return x, mask
diff --git a/spaces/NATSpeech/DiffSpeech/modules/commons/rnn.py b/spaces/NATSpeech/DiffSpeech/modules/commons/rnn.py
deleted file mode 100644
index 205c2c76b8fda2de920bc59228a5eec0a20119a9..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/modules/commons/rnn.py
+++ /dev/null
@@ -1,261 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-
-class PreNet(nn.Module):
- def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
- super().__init__()
- self.fc1 = nn.Linear(in_dims, fc1_dims)
- self.fc2 = nn.Linear(fc1_dims, fc2_dims)
- self.p = dropout
-
- def forward(self, x):
- x = self.fc1(x)
- x = F.relu(x)
- x = F.dropout(x, self.p, training=self.training)
- x = self.fc2(x)
- x = F.relu(x)
- x = F.dropout(x, self.p, training=self.training)
- return x
-
-
-class HighwayNetwork(nn.Module):
- def __init__(self, size):
- super().__init__()
- self.W1 = nn.Linear(size, size)
- self.W2 = nn.Linear(size, size)
- self.W1.bias.data.fill_(0.)
-
- def forward(self, x):
- x1 = self.W1(x)
- x2 = self.W2(x)
- g = torch.sigmoid(x2)
- y = g * F.relu(x1) + (1. - g) * x
- return y
-
-
-class BatchNormConv(nn.Module):
- def __init__(self, in_channels, out_channels, kernel, relu=True):
- super().__init__()
- self.conv = nn.Conv1d(in_channels, out_channels, kernel, stride=1, padding=kernel // 2, bias=False)
- self.bnorm = nn.BatchNorm1d(out_channels)
- self.relu = relu
-
- def forward(self, x):
- x = self.conv(x)
- x = F.relu(x) if self.relu is True else x
- return self.bnorm(x)
-
-
-class ConvNorm(torch.nn.Module):
- def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
- padding=None, dilation=1, bias=True, w_init_gain='linear'):
- super(ConvNorm, self).__init__()
- if padding is None:
- assert (kernel_size % 2 == 1)
- padding = int(dilation * (kernel_size - 1) / 2)
-
- self.conv = torch.nn.Conv1d(in_channels, out_channels,
- kernel_size=kernel_size, stride=stride,
- padding=padding, dilation=dilation,
- bias=bias)
-
- torch.nn.init.xavier_uniform_(
- self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
-
- def forward(self, signal):
- conv_signal = self.conv(signal)
- return conv_signal
-
-
-class CBHG(nn.Module):
- def __init__(self, K, in_channels, channels, proj_channels, num_highways):
- super().__init__()
-
- # List of all rnns to call `flatten_parameters()` on
- self._to_flatten = []
-
- self.bank_kernels = [i for i in range(1, K + 1)]
- self.conv1d_bank = nn.ModuleList()
- for k in self.bank_kernels:
- conv = BatchNormConv(in_channels, channels, k)
- self.conv1d_bank.append(conv)
-
- self.maxpool = nn.MaxPool1d(kernel_size=2, stride=1, padding=1)
-
- self.conv_project1 = BatchNormConv(len(self.bank_kernels) * channels, proj_channels[0], 3)
- self.conv_project2 = BatchNormConv(proj_channels[0], proj_channels[1], 3, relu=False)
-
- # Fix the highway input if necessary
- if proj_channels[-1] != channels:
- self.highway_mismatch = True
- self.pre_highway = nn.Linear(proj_channels[-1], channels, bias=False)
- else:
- self.highway_mismatch = False
-
- self.highways = nn.ModuleList()
- for i in range(num_highways):
- hn = HighwayNetwork(channels)
- self.highways.append(hn)
-
- self.rnn = nn.GRU(channels, channels, batch_first=True, bidirectional=True)
- self._to_flatten.append(self.rnn)
-
- # Avoid fragmentation of RNN parameters and associated warning
- self._flatten_parameters()
-
- def forward(self, x):
- # Although we `_flatten_parameters()` on init, when using DataParallel
- # the model gets replicated, making it no longer guaranteed that the
- # weights are contiguous in GPU memory. Hence, we must call it again
- self._flatten_parameters()
-
- # Save these for later
- residual = x
- seq_len = x.size(-1)
- conv_bank = []
-
- # Convolution Bank
- for conv in self.conv1d_bank:
- c = conv(x) # Convolution
- conv_bank.append(c[:, :, :seq_len])
-
- # Stack along the channel axis
- conv_bank = torch.cat(conv_bank, dim=1)
-
- # dump the last padding to fit residual
- x = self.maxpool(conv_bank)[:, :, :seq_len]
-
- # Conv1d projections
- x = self.conv_project1(x)
- x = self.conv_project2(x)
-
- # Residual Connect
- x = x + residual
-
- # Through the highways
- x = x.transpose(1, 2)
- if self.highway_mismatch is True:
- x = self.pre_highway(x)
- for h in self.highways:
- x = h(x)
-
- # And then the RNN
- x, _ = self.rnn(x)
- return x
-
- def _flatten_parameters(self):
- """Calls `flatten_parameters` on all the rnns used by the WaveRNN. Used
- to improve efficiency and avoid PyTorch yelling at us."""
- [m.flatten_parameters() for m in self._to_flatten]
-
-
-class TacotronEncoder(nn.Module):
- def __init__(self, embed_dims, num_chars, cbhg_channels, K, num_highways, dropout):
- super().__init__()
- self.embedding = nn.Embedding(num_chars, embed_dims)
- self.pre_net = PreNet(embed_dims, embed_dims, embed_dims, dropout=dropout)
- self.cbhg = CBHG(K=K, in_channels=cbhg_channels, channels=cbhg_channels,
- proj_channels=[cbhg_channels, cbhg_channels],
- num_highways=num_highways)
- self.proj_out = nn.Linear(cbhg_channels * 2, cbhg_channels)
-
- def forward(self, x):
- x = self.embedding(x)
- x = self.pre_net(x)
- x.transpose_(1, 2)
- x = self.cbhg(x)
- x = self.proj_out(x)
- return x
-
-
-class RNNEncoder(nn.Module):
- def __init__(self, num_chars, embedding_dim, n_convolutions=3, kernel_size=5):
- super(RNNEncoder, self).__init__()
- self.embedding = nn.Embedding(num_chars, embedding_dim, padding_idx=0)
- convolutions = []
- for _ in range(n_convolutions):
- conv_layer = nn.Sequential(
- ConvNorm(embedding_dim,
- embedding_dim,
- kernel_size=kernel_size, stride=1,
- padding=int((kernel_size - 1) / 2),
- dilation=1, w_init_gain='relu'),
- nn.BatchNorm1d(embedding_dim))
- convolutions.append(conv_layer)
- self.convolutions = nn.ModuleList(convolutions)
-
- self.lstm = nn.LSTM(embedding_dim, int(embedding_dim / 2), 1,
- batch_first=True, bidirectional=True)
-
- def forward(self, x):
- input_lengths = (x > 0).sum(-1)
- input_lengths = input_lengths.cpu().numpy()
-
- x = self.embedding(x)
- x = x.transpose(1, 2) # [B, H, T]
- for conv in self.convolutions:
- x = F.dropout(F.relu(conv(x)), 0.5, self.training) + x
- x = x.transpose(1, 2) # [B, T, H]
-
- # pytorch tensor are not reversible, hence the conversion
- x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first=True, enforce_sorted=False)
-
- self.lstm.flatten_parameters()
- outputs, _ = self.lstm(x)
- outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True)
-
- return outputs
-
-
-class DecoderRNN(torch.nn.Module):
- def __init__(self, hidden_size, decoder_rnn_dim, dropout):
- super(DecoderRNN, self).__init__()
- self.in_conv1d = nn.Sequential(
- torch.nn.Conv1d(
- in_channels=hidden_size,
- out_channels=hidden_size,
- kernel_size=9, padding=4,
- ),
- torch.nn.ReLU(),
- torch.nn.Conv1d(
- in_channels=hidden_size,
- out_channels=hidden_size,
- kernel_size=9, padding=4,
- ),
- )
- self.ln = nn.LayerNorm(hidden_size)
- if decoder_rnn_dim == 0:
- decoder_rnn_dim = hidden_size * 2
- self.rnn = torch.nn.LSTM(
- input_size=hidden_size,
- hidden_size=decoder_rnn_dim,
- num_layers=1,
- batch_first=True,
- bidirectional=True,
- dropout=dropout
- )
- self.rnn.flatten_parameters()
- self.conv1d = torch.nn.Conv1d(
- in_channels=decoder_rnn_dim * 2,
- out_channels=hidden_size,
- kernel_size=3,
- padding=1,
- )
-
- def forward(self, x):
- input_masks = x.abs().sum(-1).ne(0).data[:, :, None]
- input_lengths = input_masks.sum([-1, -2])
- input_lengths = input_lengths.cpu().numpy()
-
- x = self.in_conv1d(x.transpose(1, 2)).transpose(1, 2)
- x = self.ln(x)
- x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first=True, enforce_sorted=False)
- self.rnn.flatten_parameters()
- x, _ = self.rnn(x) # [B, T, C]
- x, _ = nn.utils.rnn.pad_packed_sequence(x, batch_first=True)
- x = x * input_masks
- pre_mel = self.conv1d(x.transpose(1, 2)).transpose(1, 2) # [B, T, C]
- pre_mel = pre_mel * input_masks
- return pre_mel
diff --git a/spaces/NATSpeech/DiffSpeech/utils/metrics/pitch_distance.py b/spaces/NATSpeech/DiffSpeech/utils/metrics/pitch_distance.py
deleted file mode 100644
index 3bc11424a9f75270fc7eb5ef98731129e25ff715..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/utils/metrics/pitch_distance.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from numba import jit
-
-import torch
-
-
-@jit
-def time_warp(costs):
- dtw = np.zeros_like(costs)
- dtw[0, 1:] = np.inf
- dtw[1:, 0] = np.inf
- eps = 1e-4
- for i in range(1, costs.shape[0]):
- for j in range(1, costs.shape[1]):
- dtw[i, j] = costs[i, j] + min(dtw[i - 1, j], dtw[i, j - 1], dtw[i - 1, j - 1])
- return dtw
-
-
-def align_from_distances(distance_matrix, debug=False, return_mindist=False):
- # for each position in spectrum 1, returns best match position in spectrum2
- # using monotonic alignment
- dtw = time_warp(distance_matrix)
-
- i = distance_matrix.shape[0] - 1
- j = distance_matrix.shape[1] - 1
- results = [0] * distance_matrix.shape[0]
- while i > 0 and j > 0:
- results[i] = j
- i, j = min([(i - 1, j), (i, j - 1), (i - 1, j - 1)], key=lambda x: dtw[x[0], x[1]])
-
- if debug:
- visual = np.zeros_like(dtw)
- visual[range(len(results)), results] = 1
- plt.matshow(visual)
- plt.show()
- if return_mindist:
- return results, dtw[-1, -1]
- return results
-
-
-def get_local_context(input_f, max_window=32, scale_factor=1.):
- # input_f: [S, 1], support numpy array or torch tensor
- # return hist: [S, max_window * 2], list of list
- T = input_f.shape[0]
- # max_window = int(max_window * scale_factor)
- derivative = [[0 for _ in range(max_window * 2)] for _ in range(T)]
-
- for t in range(T): # travel the time series
- for feat_idx in range(-max_window, max_window):
- if t + feat_idx < 0 or t + feat_idx >= T:
- value = 0
- else:
- value = input_f[t + feat_idx]
- derivative[t][feat_idx + max_window] = value
- return derivative
-
-
-def cal_localnorm_dist(src, tgt, src_len, tgt_len):
- local_src = torch.tensor(get_local_context(src))
- local_tgt = torch.tensor(get_local_context(tgt, scale_factor=tgt_len / src_len))
-
- local_norm_src = (local_src - local_src.mean(-1).unsqueeze(-1)) # / local_src.std(-1).unsqueeze(-1) # [T1, 32]
- local_norm_tgt = (local_tgt - local_tgt.mean(-1).unsqueeze(-1)) # / local_tgt.std(-1).unsqueeze(-1) # [T2, 32]
-
- dists = torch.cdist(local_norm_src[None, :, :], local_norm_tgt[None, :, :]) # [1, T1, T2]
- return dists
-
-
-## here is API for one sample
-def LoNDTWDistance(src, tgt):
- # src: [S]
- # tgt: [T]
- dists = cal_localnorm_dist(src, tgt, src.shape[0], tgt.shape[0]) # [1, S, T]
- costs = dists.squeeze(0) # [S, T]
- alignment, min_distance = align_from_distances(costs.T.cpu().detach().numpy(), return_mindist=True) # [T]
- return alignment, min_distance
-
-# if __name__ == '__main__':
-# # utils from ns
-# from utils.pitch_utils import denorm_f0
-# from tasks.singing.fsinging import FastSingingDataset
-# from utils.hparams import hparams, set_hparams
-#
-# set_hparams()
-#
-# train_ds = FastSingingDataset('test')
-#
-# # Test One sample case
-# sample = train_ds[0]
-# amateur_f0 = sample['f0']
-# prof_f0 = sample['prof_f0']
-#
-# amateur_uv = sample['uv']
-# amateur_padding = sample['mel2ph'] == 0
-# prof_uv = sample['prof_uv']
-# prof_padding = sample['prof_mel2ph'] == 0
-# amateur_f0_denorm = denorm_f0(amateur_f0, amateur_uv, hparams, pitch_padding=amateur_padding)
-# prof_f0_denorm = denorm_f0(prof_f0, prof_uv, hparams, pitch_padding=prof_padding)
-# alignment, min_distance = LoNDTWDistance(amateur_f0_denorm, prof_f0_denorm)
-# print(min_distance)
-# python utils/pitch_distance.py --config egs/datasets/audio/molar/svc_ppg.yaml
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/albert/export_albert_tfhub.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/albert/export_albert_tfhub.py
deleted file mode 100644
index 9a1af1a17735c5f0b995bb5e431fe143ffffa1d1..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/albert/export_albert_tfhub.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""A script to export the ALBERT core model as a TF-Hub SavedModel."""
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
-
-from absl import app
-from absl import flags
-import tensorflow as tf
-from typing import Text
-
-from official.nlp.albert import configs
-from official.nlp.bert import bert_models
-
-FLAGS = flags.FLAGS
-
-flags.DEFINE_string("albert_config_file", None,
- "Albert configuration file to define core albert layers.")
-flags.DEFINE_string("model_checkpoint_path", None,
- "File path to TF model checkpoint.")
-flags.DEFINE_string("export_path", None, "TF-Hub SavedModel destination path.")
-flags.DEFINE_string(
- "sp_model_file", None,
- "The sentence piece model file that the ALBERT model was trained on.")
-
-
-def create_albert_model(
- albert_config: configs.AlbertConfig) -> tf.keras.Model:
- """Creates an ALBERT keras core model from ALBERT configuration.
-
- Args:
- albert_config: An `AlbertConfig` to create the core model.
-
- Returns:
- A keras model.
- """
- # Adds input layers just as placeholders.
- input_word_ids = tf.keras.layers.Input(
- shape=(None,), dtype=tf.int32, name="input_word_ids")
- input_mask = tf.keras.layers.Input(
- shape=(None,), dtype=tf.int32, name="input_mask")
- input_type_ids = tf.keras.layers.Input(
- shape=(None,), dtype=tf.int32, name="input_type_ids")
- transformer_encoder = bert_models.get_transformer_encoder(
- albert_config, sequence_length=None)
- sequence_output, pooled_output = transformer_encoder(
- [input_word_ids, input_mask, input_type_ids])
- # To keep consistent with legacy hub modules, the outputs are
- # "pooled_output" and "sequence_output".
- return tf.keras.Model(
- inputs=[input_word_ids, input_mask, input_type_ids],
- outputs=[pooled_output, sequence_output]), transformer_encoder
-
-
-def export_albert_tfhub(albert_config: configs.AlbertConfig,
- model_checkpoint_path: Text, hub_destination: Text,
- sp_model_file: Text):
- """Restores a tf.keras.Model and saves for TF-Hub."""
- core_model, encoder = create_albert_model(albert_config)
- checkpoint = tf.train.Checkpoint(model=encoder)
- checkpoint.restore(model_checkpoint_path).assert_consumed()
- core_model.sp_model_file = tf.saved_model.Asset(sp_model_file)
- core_model.save(hub_destination, include_optimizer=False, save_format="tf")
-
-
-def main(_):
- albert_config = configs.AlbertConfig.from_json_file(
- FLAGS.albert_config_file)
- export_albert_tfhub(albert_config, FLAGS.model_checkpoint_path,
- FLAGS.export_path, FLAGS.sp_model_file)
-
-
-if __name__ == "__main__":
- app.run(main)
diff --git a/spaces/NCTCMumbai/NCTC/models/official/recommendation/ncf_keras_main.py b/spaces/NCTCMumbai/NCTC/models/official/recommendation/ncf_keras_main.py
deleted file mode 100644
index c850539d4bf24e159cbf04a2c029c1e2bf4d5c26..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/recommendation/ncf_keras_main.py
+++ /dev/null
@@ -1,567 +0,0 @@
-# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""NCF framework to train and evaluate the NeuMF model.
-
-The NeuMF model assembles both MF and MLP models under the NCF framework. Check
-`neumf_model.py` for more details about the models.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import os
-
-# pylint: disable=g-bad-import-order
-from absl import app
-from absl import flags
-from absl import logging
-import tensorflow.compat.v2 as tf
-# pylint: enable=g-bad-import-order
-
-from official.recommendation import constants as rconst
-from official.recommendation import movielens
-from official.recommendation import ncf_common
-from official.recommendation import ncf_input_pipeline
-from official.recommendation import neumf_model
-from official.utils.flags import core as flags_core
-from official.utils.misc import distribution_utils
-from official.utils.misc import keras_utils
-from official.utils.misc import model_helpers
-
-
-FLAGS = flags.FLAGS
-
-
-def metric_fn(logits, dup_mask, match_mlperf):
- dup_mask = tf.cast(dup_mask, tf.float32)
- logits = tf.slice(logits, [0, 1], [-1, -1])
- in_top_k, _, metric_weights, _ = neumf_model.compute_top_k_and_ndcg(
- logits,
- dup_mask,
- match_mlperf)
- metric_weights = tf.cast(metric_weights, tf.float32)
- return in_top_k, metric_weights
-
-
-class MetricLayer(tf.keras.layers.Layer):
- """Custom layer of metrics for NCF model."""
-
- def __init__(self, match_mlperf):
- super(MetricLayer, self).__init__()
- self.match_mlperf = match_mlperf
-
- def get_config(self):
- return {"match_mlperf": self.match_mlperf}
-
- @classmethod
- def from_config(cls, config, custom_objects=None):
- return cls(**config)
-
- def call(self, inputs, training=False):
- logits, dup_mask = inputs
-
- if training:
- hr_sum = 0.0
- hr_count = 0.0
- else:
- metric, metric_weights = metric_fn(logits, dup_mask, self.match_mlperf)
- hr_sum = tf.reduce_sum(metric * metric_weights)
- hr_count = tf.reduce_sum(metric_weights)
-
- self.add_metric(hr_sum, name="hr_sum", aggregation="mean")
- self.add_metric(hr_count, name="hr_count", aggregation="mean")
- return logits
-
-
-class LossLayer(tf.keras.layers.Layer):
- """Pass-through loss layer for NCF model."""
-
- def __init__(self, loss_normalization_factor):
- # The loss may overflow in float16, so we use float32 instead.
- super(LossLayer, self).__init__(dtype="float32")
- self.loss_normalization_factor = loss_normalization_factor
- self.loss = tf.keras.losses.SparseCategoricalCrossentropy(
- from_logits=True, reduction="sum")
-
- def get_config(self):
- return {"loss_normalization_factor": self.loss_normalization_factor}
-
- @classmethod
- def from_config(cls, config, custom_objects=None):
- return cls(**config)
-
- def call(self, inputs):
- logits, labels, valid_pt_mask_input = inputs
- loss = self.loss(
- y_true=labels, y_pred=logits, sample_weight=valid_pt_mask_input)
- loss = loss * (1.0 / self.loss_normalization_factor)
- self.add_loss(loss)
- return logits
-
-
-class IncrementEpochCallback(tf.keras.callbacks.Callback):
- """A callback to increase the requested epoch for the data producer.
-
- The reason why we need this is because we can only buffer a limited amount of
- data. So we keep a moving window to represent the buffer. This is to move the
- one of the window's boundaries for each epoch.
- """
-
- def __init__(self, producer):
- self._producer = producer
-
- def on_epoch_begin(self, epoch, logs=None):
- self._producer.increment_request_epoch()
-
-
-class CustomEarlyStopping(tf.keras.callbacks.Callback):
- """Stop training has reached a desired hit rate."""
-
- def __init__(self, monitor, desired_value):
- super(CustomEarlyStopping, self).__init__()
-
- self.monitor = monitor
- self.desired = desired_value
- self.stopped_epoch = 0
-
- def on_epoch_end(self, epoch, logs=None):
- current = self.get_monitor_value(logs)
- if current and current >= self.desired:
- self.stopped_epoch = epoch
- self.model.stop_training = True
-
- def on_train_end(self, logs=None):
- if self.stopped_epoch > 0:
- print("Epoch %05d: early stopping" % (self.stopped_epoch + 1))
-
- def get_monitor_value(self, logs):
- logs = logs or {}
- monitor_value = logs.get(self.monitor)
- if monitor_value is None:
- logging.warning("Early stopping conditioned on metric `%s` "
- "which is not available. Available metrics are: %s",
- self.monitor, ",".join(list(logs.keys())))
- return monitor_value
-
-
-def _get_keras_model(params):
- """Constructs and returns the model."""
- batch_size = params["batch_size"]
-
- user_input = tf.keras.layers.Input(
- shape=(1,), name=movielens.USER_COLUMN, dtype=tf.int32)
-
- item_input = tf.keras.layers.Input(
- shape=(1,), name=movielens.ITEM_COLUMN, dtype=tf.int32)
-
- valid_pt_mask_input = tf.keras.layers.Input(
- shape=(1,), name=rconst.VALID_POINT_MASK, dtype=tf.bool)
-
- dup_mask_input = tf.keras.layers.Input(
- shape=(1,), name=rconst.DUPLICATE_MASK, dtype=tf.int32)
-
- label_input = tf.keras.layers.Input(
- shape=(1,), name=rconst.TRAIN_LABEL_KEY, dtype=tf.bool)
-
- base_model = neumf_model.construct_model(user_input, item_input, params)
-
- logits = base_model.output
-
- zeros = tf.keras.layers.Lambda(
- lambda x: x * 0)(logits)
-
- softmax_logits = tf.keras.layers.concatenate(
- [zeros, logits],
- axis=-1)
-
- # Custom training loop calculates loss and metric as a part of
- # training/evaluation step function.
- if not params["keras_use_ctl"]:
- softmax_logits = MetricLayer(
- params["match_mlperf"])([softmax_logits, dup_mask_input])
- # TODO(b/134744680): Use model.add_loss() instead once the API is well
- # supported.
- softmax_logits = LossLayer(batch_size)(
- [softmax_logits, label_input, valid_pt_mask_input])
-
- keras_model = tf.keras.Model(
- inputs={
- movielens.USER_COLUMN: user_input,
- movielens.ITEM_COLUMN: item_input,
- rconst.VALID_POINT_MASK: valid_pt_mask_input,
- rconst.DUPLICATE_MASK: dup_mask_input,
- rconst.TRAIN_LABEL_KEY: label_input},
- outputs=softmax_logits)
-
- keras_model.summary()
- return keras_model
-
-
-def run_ncf(_):
- """Run NCF training and eval with Keras."""
-
- keras_utils.set_session_config(enable_xla=FLAGS.enable_xla)
-
- if FLAGS.seed is not None:
- print("Setting tf seed")
- tf.random.set_seed(FLAGS.seed)
-
- model_helpers.apply_clean(FLAGS)
-
- if FLAGS.dtype == "fp16" and FLAGS.fp16_implementation == "keras":
- policy = tf.keras.mixed_precision.experimental.Policy(
- "mixed_float16",
- loss_scale=flags_core.get_loss_scale(FLAGS, default_for_fp16="dynamic"))
- tf.keras.mixed_precision.experimental.set_policy(policy)
-
- strategy = distribution_utils.get_distribution_strategy(
- distribution_strategy=FLAGS.distribution_strategy,
- num_gpus=FLAGS.num_gpus,
- tpu_address=FLAGS.tpu)
-
- params = ncf_common.parse_flags(FLAGS)
- params["distribute_strategy"] = strategy
- params["use_tpu"] = (FLAGS.distribution_strategy == "tpu")
-
- if params["use_tpu"] and not params["keras_use_ctl"]:
- logging.error("Custom training loop must be used when using TPUStrategy.")
- return
-
- batch_size = params["batch_size"]
- time_callback = keras_utils.TimeHistory(batch_size, FLAGS.log_steps)
- callbacks = [time_callback]
-
- producer, input_meta_data = None, None
- generate_input_online = params["train_dataset_path"] is None
-
- if generate_input_online:
- # Start data producing thread.
- num_users, num_items, _, _, producer = ncf_common.get_inputs(params)
- producer.start()
- per_epoch_callback = IncrementEpochCallback(producer)
- callbacks.append(per_epoch_callback)
- else:
- assert params["eval_dataset_path"] and params["input_meta_data_path"]
- with tf.io.gfile.GFile(params["input_meta_data_path"], "rb") as reader:
- input_meta_data = json.loads(reader.read().decode("utf-8"))
- num_users = input_meta_data["num_users"]
- num_items = input_meta_data["num_items"]
-
- params["num_users"], params["num_items"] = num_users, num_items
-
- if FLAGS.early_stopping:
- early_stopping_callback = CustomEarlyStopping(
- "val_HR_METRIC", desired_value=FLAGS.hr_threshold)
- callbacks.append(early_stopping_callback)
-
- (train_input_dataset, eval_input_dataset,
- num_train_steps, num_eval_steps) = \
- (ncf_input_pipeline.create_ncf_input_data(
- params, producer, input_meta_data, strategy))
- steps_per_epoch = None if generate_input_online else num_train_steps
-
- with distribution_utils.get_strategy_scope(strategy):
- keras_model = _get_keras_model(params)
- optimizer = tf.keras.optimizers.Adam(
- learning_rate=params["learning_rate"],
- beta_1=params["beta1"],
- beta_2=params["beta2"],
- epsilon=params["epsilon"])
- if FLAGS.fp16_implementation == "graph_rewrite":
- optimizer = \
- tf.compat.v1.train.experimental.enable_mixed_precision_graph_rewrite(
- optimizer,
- loss_scale=flags_core.get_loss_scale(FLAGS,
- default_for_fp16="dynamic"))
- elif FLAGS.dtype == "fp16" and params["keras_use_ctl"]:
- # When keras_use_ctl is False, instead Model.fit() automatically applies
- # loss scaling so we don't need to create a LossScaleOptimizer.
- optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
- optimizer,
- tf.keras.mixed_precision.experimental.global_policy().loss_scale)
-
- if params["keras_use_ctl"]:
- train_loss, eval_results = run_ncf_custom_training(
- params,
- strategy,
- keras_model,
- optimizer,
- callbacks,
- train_input_dataset,
- eval_input_dataset,
- num_train_steps,
- num_eval_steps,
- generate_input_online=generate_input_online)
- else:
- keras_model.compile(optimizer=optimizer, run_eagerly=FLAGS.run_eagerly)
-
- if not FLAGS.ml_perf:
- # Create Tensorboard summary and checkpoint callbacks.
- summary_dir = os.path.join(FLAGS.model_dir, "summaries")
- summary_callback = tf.keras.callbacks.TensorBoard(summary_dir)
- checkpoint_path = os.path.join(FLAGS.model_dir, "checkpoint")
- checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
- checkpoint_path, save_weights_only=True)
-
- callbacks += [summary_callback, checkpoint_callback]
-
- history = keras_model.fit(
- train_input_dataset,
- epochs=FLAGS.train_epochs,
- steps_per_epoch=steps_per_epoch,
- callbacks=callbacks,
- validation_data=eval_input_dataset,
- validation_steps=num_eval_steps,
- verbose=2)
-
- logging.info("Training done. Start evaluating")
-
- eval_loss_and_metrics = keras_model.evaluate(
- eval_input_dataset, steps=num_eval_steps, verbose=2)
-
- logging.info("Keras evaluation is done.")
-
- # Keras evaluate() API returns scalar loss and metric values from
- # evaluation as a list. Here, the returned list would contain
- # [evaluation loss, hr sum, hr count].
- eval_hit_rate = eval_loss_and_metrics[1] / eval_loss_and_metrics[2]
-
- # Format evaluation result into [eval loss, eval hit accuracy].
- eval_results = [eval_loss_and_metrics[0], eval_hit_rate]
-
- if history and history.history:
- train_history = history.history
- train_loss = train_history["loss"][-1]
-
- stats = build_stats(train_loss, eval_results, time_callback)
- return stats
-
-
-def run_ncf_custom_training(params,
- strategy,
- keras_model,
- optimizer,
- callbacks,
- train_input_dataset,
- eval_input_dataset,
- num_train_steps,
- num_eval_steps,
- generate_input_online=True):
- """Runs custom training loop.
-
- Args:
- params: Dictionary containing training parameters.
- strategy: Distribution strategy to be used for distributed training.
- keras_model: Model used for training.
- optimizer: Optimizer used for training.
- callbacks: Callbacks to be invoked between batches/epochs.
- train_input_dataset: tf.data.Dataset used for training.
- eval_input_dataset: tf.data.Dataset used for evaluation.
- num_train_steps: Total number of steps to run for training.
- num_eval_steps: Total number of steps to run for evaluation.
- generate_input_online: Whether input data was generated by data producer.
- When data is generated by data producer, then train dataset must be
- re-initialized after every epoch.
-
- Returns:
- A tuple of train loss and a list of training and evaluation results.
- """
- loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
- reduction="sum", from_logits=True)
- train_input_iterator = iter(
- strategy.experimental_distribute_dataset(train_input_dataset))
-
- def train_step(train_iterator):
- """Called once per step to train the model."""
-
- def step_fn(features):
- """Computes loss and applied gradient per replica."""
- with tf.GradientTape() as tape:
- softmax_logits = keras_model(features)
- # The loss can overflow in float16, so we cast to float32.
- softmax_logits = tf.cast(softmax_logits, "float32")
- labels = features[rconst.TRAIN_LABEL_KEY]
- loss = loss_object(
- labels,
- softmax_logits,
- sample_weight=features[rconst.VALID_POINT_MASK])
- loss *= (1.0 / params["batch_size"])
- if FLAGS.dtype == "fp16":
- loss = optimizer.get_scaled_loss(loss)
-
- grads = tape.gradient(loss, keras_model.trainable_variables)
- if FLAGS.dtype == "fp16":
- grads = optimizer.get_unscaled_gradients(grads)
- # Converting gradients to dense form helps in perf on GPU for NCF
- grads = neumf_model.sparse_to_dense_grads(
- list(zip(grads, keras_model.trainable_variables)))
- optimizer.apply_gradients(grads)
- return loss
-
- per_replica_losses = strategy.run(
- step_fn, args=(next(train_iterator),))
- mean_loss = strategy.reduce(
- tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None)
- return mean_loss
-
- def eval_step(eval_iterator):
- """Called once per eval step to compute eval metrics."""
-
- def step_fn(features):
- """Computes eval metrics per replica."""
- softmax_logits = keras_model(features)
- in_top_k, metric_weights = metric_fn(softmax_logits,
- features[rconst.DUPLICATE_MASK],
- params["match_mlperf"])
- hr_sum = tf.reduce_sum(in_top_k * metric_weights)
- hr_count = tf.reduce_sum(metric_weights)
- return hr_sum, hr_count
-
- per_replica_hr_sum, per_replica_hr_count = (
- strategy.run(
- step_fn, args=(next(eval_iterator),)))
- hr_sum = strategy.reduce(
- tf.distribute.ReduceOp.SUM, per_replica_hr_sum, axis=None)
- hr_count = strategy.reduce(
- tf.distribute.ReduceOp.SUM, per_replica_hr_count, axis=None)
- return hr_sum, hr_count
-
- if not FLAGS.run_eagerly:
- train_step = tf.function(train_step)
- eval_step = tf.function(eval_step)
-
- for callback in callbacks:
- callback.on_train_begin()
-
- # Not writing tensorboard summaries if running in MLPerf.
- if FLAGS.ml_perf:
- eval_summary_writer, train_summary_writer = None, None
- else:
- summary_dir = os.path.join(FLAGS.model_dir, "summaries")
- eval_summary_writer = tf.summary.create_file_writer(
- os.path.join(summary_dir, "eval"))
- train_summary_writer = tf.summary.create_file_writer(
- os.path.join(summary_dir, "train"))
-
- train_loss = 0
- for epoch in range(FLAGS.train_epochs):
- for cb in callbacks:
- cb.on_epoch_begin(epoch)
-
- # As NCF dataset is sampled with randomness, not repeating
- # data elements in each epoch has significant impact on
- # convergence. As so, offline-generated TF record files
- # contains all epoch worth of data. Thus we do not need
- # to initialize dataset when reading from tf record files.
- if generate_input_online:
- train_input_iterator = iter(
- strategy.experimental_distribute_dataset(train_input_dataset))
-
- train_loss = 0
- for step in range(num_train_steps):
- current_step = step + epoch * num_train_steps
- for c in callbacks:
- c.on_batch_begin(current_step)
-
- train_loss += train_step(train_input_iterator)
-
- # Write train loss once in every 1000 steps.
- if train_summary_writer and step % 1000 == 0:
- with train_summary_writer.as_default():
- tf.summary.scalar("training_loss", train_loss/(step + 1),
- step=current_step)
-
- for c in callbacks:
- c.on_batch_end(current_step)
-
- train_loss /= num_train_steps
- logging.info("Done training epoch %s, epoch loss=%.3f", epoch + 1,
- train_loss)
-
- eval_input_iterator = iter(
- strategy.experimental_distribute_dataset(eval_input_dataset))
-
- hr_sum = 0.0
- hr_count = 0.0
- for _ in range(num_eval_steps):
- step_hr_sum, step_hr_count = eval_step(eval_input_iterator)
- hr_sum += step_hr_sum
- hr_count += step_hr_count
-
- logging.info("Done eval epoch %s, hit_rate=%.3f", epoch + 1,
- hr_sum / hr_count)
- if eval_summary_writer:
- with eval_summary_writer.as_default():
- tf.summary.scalar("hit_rate", hr_sum / hr_count, step=current_step)
-
- if (FLAGS.early_stopping and
- float(hr_sum / hr_count) > params["hr_threshold"]):
- break
-
- for c in callbacks:
- c.on_train_end()
-
- # Saving the model at the end of training.
- if not FLAGS.ml_perf:
- checkpoint = tf.train.Checkpoint(model=keras_model, optimizer=optimizer)
- checkpoint_path = os.path.join(FLAGS.model_dir, "ctl_checkpoint")
- checkpoint.save(checkpoint_path)
- logging.info("Saving model as TF checkpoint: %s", checkpoint_path)
-
- return train_loss, [None, hr_sum / hr_count]
-
-
-def build_stats(loss, eval_result, time_callback):
- """Normalizes and returns dictionary of stats.
-
- Args:
- loss: The final loss at training time.
- eval_result: Output of the eval step. Assumes first value is eval_loss and
- second value is accuracy_top_1.
- time_callback: Time tracking callback likely used during keras.fit.
-
- Returns:
- Dictionary of normalized results.
- """
- stats = {}
- if loss:
- stats["loss"] = loss
-
- if eval_result:
- stats["eval_loss"] = eval_result[0]
- stats["eval_hit_rate"] = eval_result[1]
-
- if time_callback:
- timestamp_log = time_callback.timestamp_log
- stats["step_timestamp_log"] = timestamp_log
- stats["train_finish_time"] = time_callback.train_finish_time
- if len(timestamp_log) > 1:
- stats["avg_exp_per_second"] = (
- time_callback.batch_size * time_callback.log_steps *
- (len(time_callback.timestamp_log)-1) /
- (timestamp_log[-1].timestamp - timestamp_log[0].timestamp))
-
- return stats
-
-
-def main(_):
- logging.info("Result is %s", run_ncf(FLAGS))
-
-
-if __name__ == "__main__":
- ncf_common.define_ncf_flags()
- app.run(main)
diff --git a/spaces/NCTCMumbai/NCTC/models/research/cognitive_planning/README.md b/spaces/NCTCMumbai/NCTC/models/research/cognitive_planning/README.md
deleted file mode 100644
index 1c63ddc3f906a74141e7dccd4dee161eb095e546..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/cognitive_planning/README.md
+++ /dev/null
@@ -1,157 +0,0 @@
-# cognitive_planning
-
-**Visual Representation for Semantic Target Driven Navigation**
-
-Arsalan Mousavian, Alexander Toshev, Marek Fiser, Jana Kosecka, James Davidson
-
-This is the implementation of semantic target driven navigation training and evaluation on
-Active Vision dataset.
-
-ECCV Workshop on Visual Learning and Embodied Agents in Simulation Environments
-2018.
-
-{title}
\n'+
- (f'Model author: {author}
' if author else "")+
- (f'
-
-
-
-
-
-
-Paper: [https://arxiv.org/abs/1805.06066](https://arxiv.org/abs/1805.06066)
-
-
-## 1. Installation
-
-### Requirements
-
-#### Python Packages
-
-```shell
-networkx
-gin-config
-```
-
-### Download cognitive_planning
-
-```shell
-git clone --depth 1 https://github.com/tensorflow/models.git
-```
-
-## 2. Datasets
-
-### Download ActiveVision Dataset
-We used Active Vision Dataset (AVD) which can be downloaded from [here](http://cs.unc.edu/~ammirato/active_vision_dataset_website/). To make our code faster and reduce memory footprint, we created the AVD Minimal dataset. AVD Minimal consists of low resolution images from the original AVD dataset. In addition, we added annotations for target views, predicted object detections from pre-trained object detector on MS-COCO dataset, and predicted semantic segmentation from pre-trained model on NYU-v2 dataset. AVD minimal can be downloaded from [here](https://storage.googleapis.com/active-vision-dataset/AVD_Minimal.zip). Set `$AVD_DIR` as the path to the downloaded AVD Minimal.
-
-### TODO: SUNCG Dataset
-Current version of the code does not support SUNCG dataset. It can be added by
-implementing necessary functions of `envs/task_env.py` using the public
-released code of SUNCG environment such as
-[House3d](https://github.com/facebookresearch/House3D) and
-[MINOS](https://github.com/minosworld/minos).
-
-### ActiveVisionDataset Demo
-
-
-If you wish to navigate the environment, to see how the AVD looks like you can use the following command:
-```shell
-python viz_active_vision_dataset_main -- \
- --mode=human \
- --gin_config=envs/configs/active_vision_config.gin \
- --gin_params='ActiveVisionDatasetEnv.dataset_root=$AVD_DIR'
-```
-
-## 3. Training
-Right now, the released version only supports training and inference using the real data from Active Vision Dataset.
-
-When RGB image modality is used, the Resnet embeddings are initialized. To start the training download pre-trained Resnet50 check point in the working directory ./resnet_v2_50_checkpoint/resnet_v2_50.ckpt
-
-```
-wget http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz
-```
-### Run training
-Use the following command for training:
-```shell
-# Train
-python train_supervised_active_vision.py \
- --mode='train' \
- --logdir=$CHECKPOINT_DIR \
- --modality_types='det' \
- --batch_size=8 \
- --train_iters=200000 \
- --lstm_cell_size=2048 \
- --policy_fc_size=2048 \
- --sequence_length=20 \
- --max_eval_episode_length=100 \
- --test_iters=194 \
- --gin_config=envs/configs/active_vision_config.gin \
- --gin_params='ActiveVisionDatasetEnv.dataset_root=$AVD_DIR' \
- --logtostderr
-```
-
-The training can be run for different modalities and modality combinations, including semantic segmentation, object detectors, RGB images, depth images. Low resolution images and outputs of detectors pretrained on COCO dataset and semantic segmenation pre trained on NYU dataset are provided as a part of this distribution and can be found in Meta directory of AVD_Minimal.
-Additional details are described in the comments of the code and in the paper.
-
-### Run Evaluation
-Use the following command for unrolling the policy on the eval environments. The inference code periodically check the checkpoint folder for new checkpoints to use it for unrolling the policy on the eval environments. After each evaluation, it will create a folder in the $CHECKPOINT_DIR/evals/$ITER where $ITER is the iteration number at which the checkpoint is stored.
-```shell
-# Eval
-python train_supervised_active_vision.py \
- --mode='eval' \
- --logdir=$CHECKPOINT_DIR \
- --modality_types='det' \
- --batch_size=8 \
- --train_iters=200000 \
- --lstm_cell_size=2048 \
- --policy_fc_size=2048 \
- --sequence_length=20 \
- --max_eval_episode_length=100 \
- --test_iters=194 \
- --gin_config=envs/configs/active_vision_config.gin \
- --gin_params='ActiveVisionDatasetEnv.dataset_root=$AVD_DIR' \
- --logtostderr
-```
-At any point, you can run the following command to compute statistics such as success rate over all the evaluations so far. It also generates gif images for unrolling of the best policy.
-```shell
-# Visualize and Compute Stats
-python viz_active_vision_dataset_main.py \
- --mode=eval \
- --eval_folder=$CHECKPOINT_DIR/evals/ \
- --output_folder=$OUTPUT_GIFS_FOLDER \
- --gin_config=envs/configs/active_vision_config.gin \
- --gin_params='ActiveVisionDatasetEnv.dataset_root=$AVD_DIR'
-```
-## Contact
-
-To ask questions or report issues please open an issue on the tensorflow/models
-[issues tracker](https://github.com/tensorflow/models/issues).
-Please assign issues to @arsalan-mousavian.
-
-## Reference
-The details of the training and experiments can be found in the following paper. If you find our work useful in your research please consider citing our paper:
-
-```
-@inproceedings{MousavianECCVW18,
- author = {A. Mousavian and A. Toshev and M. Fiser and J. Kosecka and J. Davidson},
- title = {Visual Representations for Semantic Target Driven Navigation},
- booktitle = {ECCV Workshop on Visual Learning and Embodied Agents in Simulation Environments},
- year = {2018},
-}
-```
-
-
diff --git a/spaces/Narsil/gradiofold/molstar.css b/spaces/Narsil/gradiofold/molstar.css
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diff --git a/spaces/NimaBoscarino/climategan/climategan/painter.py b/spaces/NimaBoscarino/climategan/climategan/painter.py
deleted file mode 100644
index 739ec2b1bda94a7b37ea17b5d757e009255bd312..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/climategan/painter.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import climategan.strings as strings
-from climategan.blocks import InterpolateNearest2d, SPADEResnetBlock
-from climategan.norms import SpectralNorm
-
-
-def create_painter(opts, no_init=False, verbose=0):
- if verbose > 0:
- print(" - Add PainterSpadeDecoder Painter")
- return PainterSpadeDecoder(opts)
-
-
-class PainterSpadeDecoder(nn.Module):
- def __init__(self, opts):
- """Create a SPADE-based decoder, which forwards z and the conditioning
- tensors seg (in the original paper, conditioning is on a semantic map only).
- All along, z is conditioned on seg. First 3 SpadeResblocks (SRB) do not shrink
- the channel dimension, and an upsampling is applied after each. Therefore
- 2 upsamplings at this point. Then, for each remaining upsamplings
- (w.r.t. spade_n_up), the SRB shrinks channels by 2. Before final conv to get 3
- channels, the number of channels is therefore:
- final_nc = channels(z) * 2 ** (spade_n_up - 2)
- Args:
- latent_dim (tuple): z's shape (only the number of channels matters)
- cond_nc (int): conditioning tensor's expected number of channels
- spade_n_up (int): Number of total upsamplings from z
- spade_use_spectral_norm (bool): use spectral normalization?
- spade_param_free_norm (str): norm to use before SPADE de-normalization
- spade_kernel_size (int): SPADE conv layers' kernel size
- Returns:
- [type]: [description]
- """
- super().__init__()
-
- latent_dim = opts.gen.p.latent_dim
- cond_nc = 3
- spade_n_up = opts.gen.p.spade_n_up
- spade_use_spectral_norm = opts.gen.p.spade_use_spectral_norm
- spade_param_free_norm = opts.gen.p.spade_param_free_norm
- spade_kernel_size = 3
-
- self.z_nc = latent_dim
- self.spade_n_up = spade_n_up
-
- self.z_h = self.z_w = None
-
- self.fc = nn.Conv2d(3, latent_dim, 3, padding=1)
- self.head_0 = SPADEResnetBlock(
- self.z_nc,
- self.z_nc,
- cond_nc,
- spade_use_spectral_norm,
- spade_param_free_norm,
- spade_kernel_size,
- )
-
- self.G_middle_0 = SPADEResnetBlock(
- self.z_nc,
- self.z_nc,
- cond_nc,
- spade_use_spectral_norm,
- spade_param_free_norm,
- spade_kernel_size,
- )
- self.G_middle_1 = SPADEResnetBlock(
- self.z_nc,
- self.z_nc,
- cond_nc,
- spade_use_spectral_norm,
- spade_param_free_norm,
- spade_kernel_size,
- )
-
- self.up_spades = nn.Sequential(
- *[
- SPADEResnetBlock(
- self.z_nc // 2 ** i,
- self.z_nc // 2 ** (i + 1),
- cond_nc,
- spade_use_spectral_norm,
- spade_param_free_norm,
- spade_kernel_size,
- )
- for i in range(spade_n_up - 2)
- ]
- )
-
- self.final_nc = self.z_nc // 2 ** (spade_n_up - 2)
-
- self.final_spade = SPADEResnetBlock(
- self.final_nc,
- self.final_nc,
- cond_nc,
- spade_use_spectral_norm,
- spade_param_free_norm,
- spade_kernel_size,
- )
- self.final_shortcut = None
- if opts.gen.p.use_final_shortcut:
- self.final_shortcut = nn.Sequential(
- *[
- SpectralNorm(nn.Conv2d(self.final_nc, 3, 1)),
- nn.BatchNorm2d(3),
- nn.LeakyReLU(0.2, True),
- ]
- )
-
- self.conv_img = nn.Conv2d(self.final_nc, 3, 3, padding=1)
-
- self.upsample = InterpolateNearest2d(scale_factor=2)
-
- def set_latent_shape(self, shape, is_input=True):
- """
- Sets the latent shape to start the upsampling from, i.e. z_h and z_w.
- If is_input is True, then this is the actual input shape which should
- be divided by 2 ** spade_n_up
- Otherwise, just sets z_h and z_w from shape[-2] and shape[-1]
-
- Args:
- shape (tuple): The shape to start sampling from.
- is_input (bool, optional): Whether to divide shape by 2 ** spade_n_up
- """
- if isinstance(shape, (list, tuple)):
- self.z_h = shape[-2]
- self.z_w = shape[-1]
- elif isinstance(shape, int):
- self.z_h = self.z_w = shape
- else:
- raise ValueError("Unknown shape type:", shape)
-
- if is_input:
- self.z_h = self.z_h // (2 ** self.spade_n_up)
- self.z_w = self.z_w // (2 ** self.spade_n_up)
-
- def _apply(self, fn):
- # print("Applying SpadeDecoder", fn)
- super()._apply(fn)
- # self.head_0 = fn(self.head_0)
- # self.G_middle_0 = fn(self.G_middle_0)
- # self.G_middle_1 = fn(self.G_middle_1)
- # for i, up in enumerate(self.up_spades):
- # self.up_spades[i] = fn(up)
- # self.conv_img = fn(self.conv_img)
- return self
-
- def forward(self, z, cond):
- if z is None:
- assert self.z_h is not None and self.z_w is not None
- z = self.fc(F.interpolate(cond, size=(self.z_h, self.z_w)))
- y = self.head_0(z, cond)
- y = self.upsample(y)
- y = self.G_middle_0(y, cond)
- y = self.upsample(y)
- y = self.G_middle_1(y, cond)
-
- for i, up in enumerate(self.up_spades):
- y = self.upsample(y)
- y = up(y, cond)
-
- if self.final_shortcut is not None:
- cond = self.final_shortcut(y)
- y = self.final_spade(y, cond)
- y = self.conv_img(F.leaky_relu(y, 2e-1))
- y = torch.tanh(y)
- return y
-
- def __str__(self):
- return strings.spadedecoder(self)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/train.sh b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/train.sh
deleted file mode 100644
index f3a3d3fc7cc98a38d8e9d523a0b43c0c8ea51bf9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/train.sh
+++ /dev/null
@@ -1,43 +0,0 @@
-#!/bin/bash
-
-set -eu
-
-w2v_dir= # contains features `{train,valid}.{npy,lengths}`, real transcripts `{train,valid}.${label}`, and dict `dict.${label}.txt`
-lab_dir= # contains pseudo labels `{train,valid}.txt`
-out_dir= # output root
-arpa_lm= # phone LM
-arpa_lm_bin= # (binary) phone LM for KenLM, used in unsupervised selection
-
-label=phnc
-train_name="train"
-valid_name="valid"
-data_dir=${out_dir}/data
-
-mkdir -p ${out_dir}/exp
-local/prepare_lang.sh $w2v_dir/dict.${label}.txt $data_dir
-local/prepare_lm.sh $arpa_lm $data_dir
-
-for x in $train_name $valid_name; do
- x_gt=${x}_gt
-
- # prepare pseudo data
- python local/prepare_data_from_w2v.py $w2v_dir $data_dir $x
- steps/compute_cmvn_stats.sh $data_dir/$x $out_dir/exp/make_feat/$x $out_dir/feats/$x
- python local/copy_aligned_text.py < $lab_dir/$x.txt > $data_dir/$x/text
-
- # prepare ground truth data
- mkdir $data_dir/$x_gt
- cp $data_dir/$x/{feats.scp,cmvn.scp,utt2spk,spk2utt} $data_dir/$x_gt/
- python local/copy_aligned_text.py < $w2v_dir/$x.$label > $data_dir/$x_gt/text
-done
-
-local/train_subset_lgbeam.sh \
- --out_root ${out_dir} --out_name exp --train $train_name --valid $valid_name \
- --mono_size 2000 --tri1_size 5000 --tri2b_size -1 --tri3b_size -1 \
- --stage 1 --max_stage 3 $data_dir $data_dir/lang $data_dir/lang_test
-
-local/unsup_select_decode.sh \
- --split $valid_name --kenlm_path $arpa_lm_bin \
- --ref_txt $data_dir/${valid_name}_gt/text \
- --psd_txt $data_dir/${valid_name}/text \
- $out_dir/exp
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/token_generation_constraints.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/token_generation_constraints.py
deleted file mode 100644
index e708dc51bcb0ffb7b411496239c74d5e6f3c2448..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/token_generation_constraints.py
+++ /dev/null
@@ -1,506 +0,0 @@
-# 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.
-
-"""Implements tracking of constraints for a beam item.
-
-A list of constraints is given as a list of one or more token
-sequences, each of length at least one token. For example, for an input sentence
-
-> Die maschinelle Übersetzung ist schwer zu kontrollieren.
-
-We could have the constraints:
-* to influence
-* hard
-
-There are two implementations:
-* OrderedConstraintState: Tracks progress through an ordered list of multitoken constraints.
-* UnorderedConstraintState: Tracks progress through an unordered list of multitoken constraints.
-
-The difference is that in the first, the constraints are assumed to be
-in order; the algorithm will permit zero or more tokens between them.
-In the second, the constraints are not ordered, so many orderings will
-be explored.
-
-The same sequence can be present any number of times, and will appear
-that many times in the output.
-"""
-
-from collections import Counter
-from typing import List, Optional, Set, Tuple
-
-import torch
-
-
-class ConstraintState:
- def __init__(self):
- pass
-
-
-def pack_constraints(batch_constraints: List[List[torch.Tensor]]) -> torch.Tensor:
- """Takes a list of list of constraints in tensor form (a list of
- tensor constraints for each sentence) and transforms it into a
- packed Tensor. For example, here is a batch of size 3 with 3, 0,
- and 1 constraints:
-
- [ [ [3 1 2], [3], [4 5 6 7], ]
- [],
- [ [1 8 9 10 1 4 11 12], ]
- ]
-
- Its corresponding packed structure is:
-
- [ [ 3 3 1 2 0 3 0 4 5 6 7 0],
- [ 0 0 0 0 0 0 0 0 0 0 0 0],
- [ 1 1 8 9 10 1 4 11 12 0 0 0] ]
-
- The packed tensor has shape (batch size, maxlen), where
- maxlen is defined below. Each row contains concatenated
- constraint tokens for that sentence, with 0 appended after
- each constraint. The first item in each row is the number
- of constraints for that sentence. So maxlen is the maximum
- of
-
- (number of constraints) + (sum length of constraints) + 1.
-
- across all sentences in the batch.
- """
- # The maximum word length of concatenated constraints for any sentence
- max_constraints_len = 1
- for sentence_constraints in batch_constraints:
- if len(sentence_constraints):
- # number of constraints, plus sum of constrain lens, plus a zero after each
- constraints_len = (
- 1
- + sum([c.size(0) for c in sentence_constraints])
- + len(sentence_constraints)
- )
- max_constraints_len = max(max_constraints_len, constraints_len)
-
- batch_size = len(batch_constraints)
- constraints_tensor = torch.zeros((batch_size, max_constraints_len)).long()
- for i, sentence_constraints in enumerate(batch_constraints):
- constraints_tensor[i, 0] = len(sentence_constraints)
- offset = 1
- for j, constraint in enumerate(sentence_constraints):
- this_len = constraint.size(0)
- constraints_tensor[i, offset : offset + this_len] = constraint
- offset += this_len + 1
-
- return constraints_tensor.long()
-
-
-def unpack_constraints(constraint_tensor: torch.Tensor) -> List[torch.Tensor]:
- """
- Transforms *one row* of a packed constraint tensor (e.g., for one
- sentence in the batch) into a list of constraint tensors.
- """
- constraint_list = []
- num_constraints = constraint_tensor[0]
- constraints = constraint_tensor.tolist()
- offset = 1
- for i in range(num_constraints):
- where = constraints.index(0, offset)
- constraint_list.append(constraint_tensor[offset:where])
- offset = where + 1
-
- return constraint_list
-
-
-class ConstraintNode:
- """
- Represents a node in a trie managing unordered constraints.
- """
-
- def __init__(self, token: int = None, parent=None):
- # The token associate with this node (None for the root)
- self.token = int(token) if token is not None else None
- # The parent (None at the root)
- self.parent = parent
- # Whether this node is a completed constraint
- self.terminal = 0
- # List of child nodes
- self.children = {}
-
- # The cumulative number of constraints from this point in the
- # trie forward
- self.num_constraints = 0
-
- @property
- def id(self):
- return self.token
-
- def __str__(self):
- term = self.terminal != 0
- return f"[{self.token}].{term}#{self.num_constraints}"
-
- def __getitem__(self, key: int):
- return self.children.get(key, None)
-
- def next_tokens(self) -> Set[int]:
- """The set of child labels."""
- return set(self.children.keys())
-
- @staticmethod
- def create(constraints: List[List[int]]):
- root = ConstraintNode()
- for sequence in constraints:
- root.add_sequence(sequence)
-
- return root
-
- @staticmethod
- def print_graph(node: "ConstraintNode"):
- if len(node.children) == 0:
- return str(node)
- else:
- s = f"({node}"
- for child in node.children.values():
- s += " " + ConstraintNode.print_graph(child)
- s += ")"
- return s
-
- def token_counts(self) -> Counter:
- """Returns a counter of the number of times each token is used
- in a constraint.
- """
- token_counts = Counter()
- kids = list(self.children.values())
- while len(kids) > 0:
- kid = kids.pop()
- token_counts[kid.id] += kid.num_constraints
- kids += list(kid.children.values())
-
- return token_counts
-
- def tokens(self) -> Set[int]:
- """Returns the set of tokens in constraints."""
- return set(self.token_counts().keys())
-
- def add_sequence(self, sequence: List[int]):
- """Adds a constraint, represented as a list of integers, to
- the trie."""
- assert len(sequence) > 0
-
- token = int(sequence[0])
- if token not in self.children:
- self.children[token] = ConstraintNode(token, parent=self)
-
- node = self.children[token]
- if len(sequence) == 1:
- node.terminal += 1
- node.num_constraints += 1
- parent = node.parent
- while parent is not None:
- parent.num_constraints += 1
- parent = parent.parent
- else:
- node.add_sequence(sequence[1:])
-
-
-class UnorderedConstraintState(ConstraintState):
- """
- Records progress through the set of constraints for each item in the beam
- using a trie.
- """
-
- def __init__(self, node: ConstraintNode, copy_from: "ConstraintState" = None):
- self.node = node
-
- if copy_from is None:
- # The root node
- self.root = node
- # The set of states in the graph that have been completed
- self.completed = Counter()
- # The...
- self.generated = Counter()
- # The list of tokens we need to generate
- self.needed_tokens = self.root.tokens()
- else:
- self.completed = Counter(copy_from.completed)
- self.generated = Counter(copy_from.generated)
- self.root = copy_from.root
-
- # Mark the node as generated
- if self.node != self.root:
- self.generated[node] += 1
-
- @staticmethod
- def create(constraint_tensor: torch.Tensor):
- constraint_list = unpack_constraints(constraint_tensor)
- constraint_trie_root = ConstraintNode.create(constraint_list)
- return UnorderedConstraintState(constraint_trie_root)
-
- def __str__(self):
- gen_str = ",".join([str(node) for node in self.generated])
- return f"{self.name}/{self.bank}({gen_str})x{self.num_completed}"
-
- def __copy__(self):
- copied_state = UnorderedConstraintState(self.node, copy_from=self)
- return copied_state
-
- def copy(self):
- return self.__copy__()
-
- @property
- def name(self):
- if self.node.id is None:
- return "ROOT"
- else:
- return str(self.node.id)
-
- @property
- def is_root(self):
- return self.node == self.root
-
- @property
- def bank(self):
- return sum(self.generated.values())
-
- @property
- def num_completed(self):
- """The number of constraints (not constraint tokens) that are completed.
- In addition to the already-completed states, we need to account for the
- current state, which might get marked as completed when another token
- is generated.
- """
- in_final = self.node.terminal and self.completed[self.node] < self.node.terminal
- return sum(self.completed.values()) + in_final
-
- @property
- def finished(self):
- return self.root.num_constraints - self.num_completed == 0
-
- @property
- def token_counts(self):
- return self.root.token_counts()
-
- @property
- def tokens(self):
- return self.root.tokens()
-
- @property
- def num_constraint_tokens(self):
- return sum(self.token_counts.values())
-
- def next_tokens(self) -> Set[int]:
- """Returns the list of tokens that could come next.
- These are (a) all tokens extending the root state and, for
- non-root states, additionally all tokens extending the current
- state."""
-
- if self.node != self.root:
- return self.root.next_tokens().union(self.node.next_tokens())
- else:
- return self.root.next_tokens()
-
- def advance(self, token: int):
- """Reads in a token and advances the state. Here's how it works.
-
- We can advance to the next state if:
- - there is a matching child
- - its path isn't blocked
-
- A path is blocked when all constraints that are descendants of
- that node have already been generated, in the current state.
-
- If we are not able to advance from the current state, we "fall
- off the graph" and return to the root state. There, we again
- try to advance, checking the same criteria.
-
- In any case, when falling off the graph, we need to do some
- bookkeeping. We:
- - check whether any constraints were met (all prefixes of
- current state)
- - if one is found, mark it as completed
- - adjust visited nodes accordingly
- """
- token = int(token)
-
- next_state = None
- child = self.node[token]
- if child is not None and self.generated[child] < child.num_constraints:
- next_state = UnorderedConstraintState(child, copy_from=self)
-
- def rewind():
- """If we're mid-trie and an "illegal" token is chosen next, we need
- to reset our state to the root state. However, along the way, we need
- to check whether a prefix of the current trie state represents a state
- we could mark as completed.
- """
- node = self.node
- while node != self.root:
- if node.terminal and self.completed[node] < node.terminal:
- next_state.completed[node] += 1
- return
-
- next_state.generated[node] -= 1
- node = node.parent
-
- # Fall off the graph, check the root
- if next_state is None and token in self.root.next_tokens():
- child = self.root[token]
- # We can only traverse this edge if it's not saturated
- if self.generated[child] < child.num_constraints:
- next_state = UnorderedConstraintState(child, copy_from=self)
- else:
- next_state = UnorderedConstraintState(self.root, copy_from=self)
-
- # Rewind
- rewind()
-
- elif next_state is None:
- next_state = UnorderedConstraintState(self.root, copy_from=self)
- # Rewind
- rewind()
-
- return next_state
-
-
-class ConstraintSequence:
- def __init__(self, sequences: List[List[int]]):
- """Represents a set of possibly multitoken constraints by
- concatenating them and internally recording the end points.
- """
- self.sequences = []
- self.endpoints = []
- self.num_tokens = 0
- self.tokens = set()
- for sequence in sequences:
- for token in sequence:
- self.tokens.add(token)
- self.num_tokens += len(sequence)
- self.endpoints += [False for x in range(len(sequence) - 1)] + [True]
- self.sequences += sequence
-
- def __getitem__(self, key: int):
- return self.sequences[key]
-
- def __len__(self):
- return len(self.sequences)
-
- def __str__(self):
- return str(self.sequences)
-
-
-class OrderedConstraintState(ConstraintState):
- """
- Records progress through the set of linear nonbranching constraints with gaps.
- """
-
- def __init__(self, sequence: ConstraintSequence, state: int = -1):
- self.sequence = sequence
- self.state = state
-
- @staticmethod
- def create(constraint_tensor: torch.Tensor):
- constraint_list = unpack_constraints(constraint_tensor)
- return OrderedConstraintState(ConstraintSequence(constraint_list), -1)
-
- def __str__(self):
- return f"{self.state}/{self.bank}x{self.num_completed}"
-
- def __copy__(self):
- return OrderedConstraintState(self.sequence, self.state)
-
- def copy(self):
- return self.__copy__()
-
- @property
- def num_completed(self):
- if self.state == -1:
- return 0
- count = len(
- list(filter(lambda x: x, self.sequence.endpoints[0 : self.state + 1]))
- )
- return count
-
- @property
- def is_root(self):
- return self.state == -1
-
- @property
- def name(self):
- if self.state == -1:
- return "ROOT"
- else:
- return str(self.sequence[self.state])
-
- @property
- def bank(self) -> int:
- return self.state + 1
-
- @property
- def finished(self):
- return self.state + 1 == len(self.sequence)
-
- @property
- def token_counts(self):
- return self.sequence.token_counts()
-
- @property
- def tokens(self):
- return self.sequence.tokens
-
- @property
- def num_constraint_tokens(self):
- return sum(self.token_counts.values())
-
- def next_tokens(self) -> Set[int]:
- """Returns the list of tokens that could come next.
- These are (a) all tokens extending the root state and, for
- non-root states, additionally all tokens extending the current
- state."""
-
- tokens = set()
- if self.state > 0:
- tokens.add(self.sequence[0])
- if not self.finished:
- tokens.add(self.sequence[self.state + 1])
- return tokens
-
- def advance(self, token: int):
- """Reads in a token and advances the state. Here's how it works.
-
- We can advance to the next state if:
- - there is a matching child
- - its path isn't blocked
-
- A path is blocked when all constraints that are descendants of
- that node have already been generated, in the current state.
-
- If we are not able to advance from the current state, we "fall
- off the graph" and return to the root state. There, we again
- try to advance, checking the same criteria.
-
- In any case, when falling off the graph, we need to do some
- bookkeeping. We:
- - check whether any constraints were met (all prefixes of
- current state)
- - if one is found, mark it as completed
- - adjust visited nodes accordingly
- """
- token = int(token)
- # print(f"{self} ADVANCE({token}) {self.sequence} -> ", end="")
-
- if self.finished:
- # Accept anything
- next_state = self.copy()
-
- elif self.sequence[self.state + 1] == token:
- # Advance to the next token
- next_state = OrderedConstraintState(self.sequence, self.state + 1)
-
- elif self.sequence.endpoints[self.state]:
- # Accept anything between constraints (*)
- next_state = self.copy()
-
- elif token == self.sequence[0]:
- # Start over having generated the first token
- next_state = OrderedConstraintState(self.sequence, 0)
- else:
- # Start over from the root
- next_state = OrderedConstraintState(self.sequence, -1)
-
- return next_state
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/speech_recognition/test_cross_entropy.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/speech_recognition/test_cross_entropy.py
deleted file mode 100644
index b05400ed95e22762c3e3e5e8fd3ebfa6caf1e325..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/speech_recognition/test_cross_entropy.py
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/usr/bin/env python3
-# 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.
-
-from examples.speech_recognition.criterions.cross_entropy_acc import (
- CrossEntropyWithAccCriterion,
-)
-
-from .asr_test_base import CrossEntropyCriterionTestBase
-
-
-class CrossEntropyWithAccCriterionTest(CrossEntropyCriterionTestBase):
- def setUp(self):
- self.criterion_cls = CrossEntropyWithAccCriterion
- super().setUp()
-
- def test_cross_entropy_all_correct(self):
- sample = self.get_test_sample(correct=True, soft_target=False, aggregate=False)
- loss, sample_size, logging_output = self.criterion(
- self.model, sample, "sum", log_probs=True
- )
- assert logging_output["correct"] == 20
- assert logging_output["total"] == 20
- assert logging_output["sample_size"] == 20
- assert logging_output["ntokens"] == 20
-
- def test_cross_entropy_all_wrong(self):
- sample = self.get_test_sample(correct=False, soft_target=False, aggregate=False)
- loss, sample_size, logging_output = self.criterion(
- self.model, sample, "sum", log_probs=True
- )
- assert logging_output["correct"] == 0
- assert logging_output["total"] == 20
- assert logging_output["sample_size"] == 20
- assert logging_output["ntokens"] == 20
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/README.md b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/README.md
deleted file mode 100644
index dd687174808a6ff341f597eb6a4cc9a1687d74a1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/README.md
+++ /dev/null
@@ -1,229 +0,0 @@
- |
-  |
-
| Target: Fridge | -Target: Television | -
 |
-  |
-
| Target: Microwave | -Target: Couch | -
- 
-
-
- 
- 
- 
-
-
List of implemented papers
- -* **Convolutional Neural Networks (CNN)** - + [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/conv_lm/README.md) - + [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md) - + [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel) - + [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md) - + [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md) -* **LightConv and DynamicConv models** - + [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md) -* **Long Short-Term Memory (LSTM) networks** - + Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015) -* **Transformer (self-attention) networks** - + Attention Is All You Need (Vaswani et al., 2017) - + [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md) - + [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md) - + [Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)](examples/language_model/README.adaptive_inputs.md) - + [Lexically constrained decoding with dynamic beam allocation (Post & Vilar, 2018)](examples/constrained_decoding/README.md) - + [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context (Dai et al., 2019)](examples/truncated_bptt/README.md) - + [Adaptive Attention Span in Transformers (Sukhbaatar et al., 2019)](examples/adaptive_span/README.md) - + [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md) - + [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md) - + [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md) - + [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md ) - + [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md) - + [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md) - + [Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)](examples/unsupervised_quality_estimation/README.md) - + [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](examples/wav2vec/README.md) - + [Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models (Enarvi et al., 2020)](examples/pointer_generator/README.md) - + [Linformer: Self-Attention with Linear Complexity (Wang et al., 2020)](examples/linformer/README.md) - + [Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)](examples/criss/README.md) - + [Deep Transformers with Latent Depth (Li et al., 2020)](examples/latent_depth/README.md) - + [Unsupervised Cross-lingual Representation Learning for Speech Recognition (Conneau et al., 2020)](https://arxiv.org/abs/2006.13979) - + [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (Hsu, et al., 2021)](https://arxiv.org/abs/2104.01027) - + [Unsupervised Speech Recognition (Baevski, et al., 2021)](https://arxiv.org/abs/2105.11084) -* **Non-autoregressive Transformers** - + Non-Autoregressive Neural Machine Translation (Gu et al., 2017) - + Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. 2018) - + Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. 2019) - + Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019) - + [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md) -* **Finetuning** - + [Better Fine-Tuning by Reducing Representational Collapse (Aghajanyan et al. 2020)](examples/rxf/README.md) - -
Previous updates
- -* September 2020: [Added Linformer code](examples/linformer/README.md) -* September 2020: [Added pointer-generator networks](examples/pointer_generator/README.md) -* August 2020: [Added lexically constrained decoding](examples/constrained_decoding/README.md) -* August 2020: [wav2vec2 models and code released](examples/wav2vec/README.md) -* July 2020: [Unsupervised Quality Estimation code released](examples/unsupervised_quality_estimation/README.md) -* May 2020: [Follow fairseq on Twitter](https://twitter.com/fairseq) -* April 2020: [Monotonic Multihead Attention code released](examples/simultaneous_translation/README.md) -* April 2020: [Quant-Noise code released](examples/quant_noise/README.md) -* April 2020: [Initial model parallel support and 11B parameters unidirectional LM released](examples/megatron_11b/README.md) -* March 2020: [Byte-level BPE code released](examples/byte_level_bpe/README.md) -* February 2020: [mBART model and code released](examples/mbart/README.md) -* February 2020: [Added tutorial for back-translation](https://github.com/pytorch/fairseq/tree/main/examples/backtranslation#training-your-own-model-wmt18-english-german) -* December 2019: [fairseq 0.9.0 released](https://github.com/pytorch/fairseq/releases/tag/v0.9.0) -* November 2019: [VizSeq released (a visual analysis toolkit for evaluating fairseq models)](https://facebookresearch.github.io/vizseq/docs/getting_started/fairseq_example) -* November 2019: [CamemBERT model and code released](examples/camembert/README.md) -* November 2019: [BART model and code released](examples/bart/README.md) -* November 2019: [XLM-R models and code released](examples/xlmr/README.md) -* September 2019: [Nonautoregressive translation code released](examples/nonautoregressive_translation/README.md) -* August 2019: [WMT'19 models released](examples/wmt19/README.md) -* July 2019: fairseq relicensed under MIT license -* July 2019: [RoBERTa models and code released](examples/roberta/README.md) -* June 2019: [wav2vec models and code released](examples/wav2vec/README.md) - -
🔍 Swarm Intelligence Agents 🐜🔎
""" - - #display message for themes feature - theme_addon_msg = """🏆The agents use smart task decomposition and optimization processes to ensure accurate and efficient research on any topic.🎨
🔥Using a swarm of automated agents, we can perform fast and accurate research on any topic. 🚀🐝. 🎉🥳🎉You don't need to spent tons of hours during reseachy🙌""")
- gr.HTML(theme_addon_msg)
- gr.HTML('''
Duplicate the Space and run securely with your OpenAI API Key
-
-If you see this message, it means the renderer has not been properly enabled
-for the frontend that you are using. For more information, see
-https://altair-viz.github.io/user_guide/troubleshooting.html
-"""
-
-renderers = RendererRegistry(entry_point_group=ENTRY_POINT_GROUP)
-
-here = os.path.dirname(os.path.realpath(__file__))
-
-
-def mimetype_renderer(spec, **metadata):
- return default_renderer_base(spec, VEGALITE_MIME_TYPE, DEFAULT_DISPLAY, **metadata)
-
-
-def json_renderer(spec, **metadata):
- return json_renderer_base(spec, DEFAULT_DISPLAY, **metadata)
-
-
-def png_renderer(spec, **metadata):
- return spec_to_mimebundle(
- spec,
- format="png",
- mode="vega-lite",
- vega_version=VEGA_VERSION,
- vegaembed_version=VEGAEMBED_VERSION,
- vegalite_version=VEGALITE_VERSION,
- **metadata,
- )
-
-
-def svg_renderer(spec, **metadata):
- return spec_to_mimebundle(
- spec,
- format="svg",
- mode="vega-lite",
- vega_version=VEGA_VERSION,
- vegaembed_version=VEGAEMBED_VERSION,
- vegalite_version=VEGALITE_VERSION,
- **metadata,
- )
-
-
-html_renderer = HTMLRenderer(
- mode="vega-lite",
- template="universal",
- vega_version=VEGA_VERSION,
- vegaembed_version=VEGAEMBED_VERSION,
- vegalite_version=VEGALITE_VERSION,
-)
-
-renderers.register("default", html_renderer)
-renderers.register("html", html_renderer)
-renderers.register("colab", html_renderer)
-renderers.register("kaggle", html_renderer)
-renderers.register("zeppelin", html_renderer)
-renderers.register("mimetype", mimetype_renderer)
-renderers.register("jupyterlab", mimetype_renderer)
-renderers.register("nteract", mimetype_renderer)
-renderers.register("json", json_renderer)
-renderers.register("png", png_renderer)
-renderers.register("svg", svg_renderer)
-renderers.enable("default")
-
-
-class VegaLite(Displayable):
- """An IPython/Jupyter display class for rendering VegaLite 4."""
-
- renderers = renderers
- schema_path = (__name__, "schema/vega-lite-schema.json")
-
-
-def vegalite(spec, validate=True):
- """Render and optionally validate a VegaLite 4 spec.
-
- This will use the currently enabled renderer to render the spec.
-
- Parameters
- ==========
- spec: dict
- A fully compliant VegaLite 4 spec, with the data portion fully processed.
- validate: bool
- Should the spec be validated against the VegaLite 4 schema?
- """
- from IPython.display import display
-
- display(VegaLite(spec, validate=validate))
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/tree/Tree.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/tree/Tree.py
deleted file mode 100644
index 2b9db2d1ced75377d392d858c0cb1870d87613c7..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/tree/Tree.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved.
-# Use of this file is governed by the BSD 3-clause license that
-# can be found in the LICENSE.txt file in the project root.
-#/
-
-
-# The basic notion of a tree has a parent, a payload, and a list of children.
-# It is the most abstract interface for all the trees used by ANTLR.
-#/
-from antlr4.Token import Token
-
-INVALID_INTERVAL = (-1, -2)
-
-class Tree(object):
- pass
-
-class SyntaxTree(Tree):
- pass
-
-class ParseTree(SyntaxTree):
- pass
-
-class RuleNode(ParseTree):
- pass
-
-class TerminalNode(ParseTree):
- pass
-
-class ErrorNode(TerminalNode):
- pass
-
-class ParseTreeVisitor(object):
- def visit(self, tree):
- return tree.accept(self)
-
- def visitChildren(self, node):
- result = self.defaultResult()
- n = node.getChildCount()
- for i in range(n):
- if not self.shouldVisitNextChild(node, result):
- return result
-
- c = node.getChild(i)
- childResult = c.accept(self)
- result = self.aggregateResult(result, childResult)
-
- return result
-
- def visitTerminal(self, node):
- return self.defaultResult()
-
- def visitErrorNode(self, node):
- return self.defaultResult()
-
- def defaultResult(self):
- return None
-
- def aggregateResult(self, aggregate, nextResult):
- return nextResult
-
- def shouldVisitNextChild(self, node, currentResult):
- return True
-
-ParserRuleContext = None
-
-class ParseTreeListener(object):
-
- def visitTerminal(self, node:TerminalNode):
- pass
-
- def visitErrorNode(self, node:ErrorNode):
- pass
-
- def enterEveryRule(self, ctx:ParserRuleContext):
- pass
-
- def exitEveryRule(self, ctx:ParserRuleContext):
- pass
-
-del ParserRuleContext
-
-class TerminalNodeImpl(TerminalNode):
-
- def __init__(self, symbol:Token):
- self.parentCtx = None
- self.symbol = symbol
- def __setattr__(self, key, value):
- super().__setattr__(key, value)
-
- def getChild(self, i:int):
- return None
-
- def getSymbol(self):
- return self.symbol
-
- def getParent(self):
- return self.parentCtx
-
- def getPayload(self):
- return self.symbol
-
- def getSourceInterval(self):
- if self.symbol is None:
- return INVALID_INTERVAL
- tokenIndex = self.symbol.tokenIndex
- return (tokenIndex, tokenIndex)
-
- def getChildCount(self):
- return 0
-
- def accept(self, visitor:ParseTreeVisitor):
- return visitor.visitTerminal(self)
-
- def getText(self):
- return self.symbol.text
-
- def __str__(self):
- if self.symbol.type == Token.EOF:
- return ""
- else:
- return self.symbol.text
-
-# Represents a token that was consumed during resynchronization
-# rather than during a valid match operation. For example,
-# we will create this kind of a node during single token insertion
-# and deletion as well as during "consume until error recovery set"
-# upon no viable alternative exceptions.
-
-class ErrorNodeImpl(TerminalNodeImpl,ErrorNode):
-
- def __init__(self, token:Token):
- super().__init__(token)
-
- def accept(self, visitor:ParseTreeVisitor):
- return visitor.visitErrorNode(self)
-
-
-class ParseTreeWalker(object):
-
- DEFAULT = None
-
- def walk(self, listener:ParseTreeListener, t:ParseTree):
- if isinstance(t, ErrorNode):
- listener.visitErrorNode(t)
- return
- elif isinstance(t, TerminalNode):
- listener.visitTerminal(t)
- return
- self.enterRule(listener, t)
- for child in t.getChildren():
- self.walk(listener, child)
- self.exitRule(listener, t)
-
- #
- # The discovery of a rule node, involves sending two events: the generic
- # {@link ParseTreeListener#enterEveryRule} and a
- # {@link RuleContext}-specific event. First we trigger the generic and then
- # the rule specific. We to them in reverse order upon finishing the node.
- #
- def enterRule(self, listener:ParseTreeListener, r:RuleNode):
- ctx = r.getRuleContext()
- listener.enterEveryRule(ctx)
- ctx.enterRule(listener)
-
- def exitRule(self, listener:ParseTreeListener, r:RuleNode):
- ctx = r.getRuleContext()
- ctx.exitRule(listener)
- listener.exitEveryRule(ctx)
-
-ParseTreeWalker.DEFAULT = ParseTreeWalker()
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/colorama/tests/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/colorama/tests/__init__.py
deleted file mode 100644
index 8c5661e93a205bf4fb22404d4fc50f902cc31369..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/colorama/tests/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
diff --git a/spaces/attention-refocusing/Attention-refocusing/dataset/catalog.py b/spaces/attention-refocusing/Attention-refocusing/dataset/catalog.py
deleted file mode 100644
index b622e477dae7cb4ba5c599fa7d2f7220b4311885..0000000000000000000000000000000000000000
--- a/spaces/attention-refocusing/Attention-refocusing/dataset/catalog.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import os
-
-class DatasetCatalog:
- def __init__(self, ROOT, which_embedder):
- assert which_embedder in ['clip', 'bert']
-
- # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
-
-
- self.VGGrounding = {
- "target": "dataset.tsv_dataset.TSVDataset",
- "train_params": dict(
- tsv_path=os.path.join(ROOT,'GROUNDING/gqa/tsv/train-00.tsv'),
- )
- }
-
-
- # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
-
-
- self.FlickrGrounding = {
- "target": "dataset.tsv_dataset.TSVDataset",
- "train_params":dict(
- tsv_path=os.path.join(ROOT,'GROUNDING/flickr30k/tsv/train-00.tsv'),
- )
- }
-
- # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
-
- self.SBUGrounding = {
- "target": "dataset.tsv_dataset.TSVDataset",
- "train_params":dict(
- tsv_path=os.path.join(ROOT,'GROUNDING/SBU/tsv/train-00.tsv'),
- )
- }
-
-
- # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
-
-
- self.CC3MGrounding = {
- "target": "dataset.tsv_dataset.TSVDataset",
- "train_params":dict(
- tsv_path=os.path.join(ROOT,'GROUNDING/CC3M/tsv/train-00.tsv'),
- )
- }
-
-
- # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
-
-
- self.CC12MGrounding = {
- "target": "dataset.tsv_dataset.TSVDataset",
- "train_params":dict(
- tsv_path=os.path.join(ROOT,'GROUNDING/CC12M/tsv/train-00.tsv'),
- )
- }
-
-
- # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
-
- # temp = 'category_embedding_clip.pth' if which_embedder == 'clip' else 'category_embedding_bert.pth'
- # obj365_category_embedding_path = os.path.join(ROOT, 'OBJECTS365', temp)
-
- self.Obj365Detection = {
- "target": "dataset.tsv_dataset.TSVDataset",
- "train_params":dict(
- tsv_path=os.path.join(ROOT,'OBJECTS365/tsv/train-00.tsv'),
- ),
- }
-
-
diff --git a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/models/autoencoder.py b/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/models/autoencoder.py
deleted file mode 100644
index 1163e72dd063ee6773fe3e3c586c43b0663da4c9..0000000000000000000000000000000000000000
--- a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/models/autoencoder.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import torch
-import torch.nn as nn
-#import pytorch_lightning as pl
-import torch.nn.functional as F
-from contextlib import contextmanager
-
-# from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
-
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-
-from ldm.util import instantiate_from_config
-
-
-
-
-class AutoencoderKL(nn.Module):
- def __init__(self,
- ddconfig,
- embed_dim,
- scale_factor=1
- ):
- super().__init__()
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- assert ddconfig["double_z"]
- self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- self.embed_dim = embed_dim
- self.scale_factor = scale_factor
-
-
-
- def encode(self, x):
- h = self.encoder(x)
- moments = self.quant_conv(h)
- posterior = DiagonalGaussianDistribution(moments)
- return posterior.sample() * self.scale_factor
-
- def decode(self, z):
- z = 1. / self.scale_factor * z
- z = self.post_quant_conv(z)
- dec = self.decoder(z)
- return dec
-
-
-
-
-
-
-
-
diff --git a/spaces/awacke1/AI.Dashboard.Wiki.Chat.Cognitive.HTML5/style.css b/spaces/awacke1/AI.Dashboard.Wiki.Chat.Cognitive.HTML5/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/awacke1/AI.Dashboard.Wiki.Chat.Cognitive.HTML5/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
- padding: 2rem;
- font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
- font-size: 16px;
- margin-top: 0;
-}
-
-p {
- color: rgb(107, 114, 128);
- font-size: 15px;
- margin-bottom: 10px;
- margin-top: 5px;
-}
-
-.card {
- max-width: 620px;
- margin: 0 auto;
- padding: 16px;
- border: 1px solid lightgray;
- border-radius: 16px;
-}
-
-.card p:last-child {
- margin-bottom: 0;
-}
diff --git a/spaces/awacke1/ChatGPTStreamlit7-Private/README.md b/spaces/awacke1/ChatGPTStreamlit7-Private/README.md
deleted file mode 100644
index ea5e1a1346598721b472773d738873de55bd1d76..0000000000000000000000000000000000000000
--- a/spaces/awacke1/ChatGPTStreamlit7-Private/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChatGPTStreamlit7
-emoji: 😻
-colorFrom: purple
-colorTo: red
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: awacke1/ChatGPTStreamlit7
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/Hexagon-Dice-Fractal-Math-Game/app.py b/spaces/awacke1/Hexagon-Dice-Fractal-Math-Game/app.py
deleted file mode 100644
index ac019319cf96ad458874914cc8d5189020966c4c..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Hexagon-Dice-Fractal-Math-Game/app.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import streamlit as st
-import numpy as np
-import plotly.graph_objects as go
-
-st.title('Hexagon-Dice-Fractal-Math-Game')
-
-def generate_fractal(num_rolls):
- rolls = [np.random.randint(1, 7) for i in range(num_rolls)]
- x = [0]
- y = [0]
- angle = 0
- step = 1
- for roll in rolls:
- angle += 60 if roll % 2 == 0 else -60
- x.append(x[-1] + step * np.cos(np.deg2rad(angle)))
- y.append(y[-1] + step * np.sin(np.deg2rad(angle)))
- return go.Scatter(x=x, y=y, mode='lines', line=dict(width=1))
-
-num_rolls = st.slider('How many times do you want to roll the dice?', 1, 1000000,1000)
-fig = go.Figure(generate_fractal(num_rolls))
-fig.update_layout(
- title='Dice Fractal',
- xaxis_title='X',
- yaxis_title='Y',
- showlegend=False
-)
-st.plotly_chart(fig)
diff --git a/spaces/awacke1/Named-entity-resolution/README.md b/spaces/awacke1/Named-entity-resolution/README.md
deleted file mode 100644
index bce07186cda08b4506a221ed9ba85f7869c43ffc..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Named-entity-resolution/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Named Entity Resolution
-emoji: 😻
-colorFrom: indigo
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/Stancld-longt5-tglobal-large-16384-pubmed-3k_steps/README.md b/spaces/awacke1/Stancld-longt5-tglobal-large-16384-pubmed-3k_steps/README.md
deleted file mode 100644
index 1b019f61842d71c2cbab56286aac661ca6d379a2..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Stancld-longt5-tglobal-large-16384-pubmed-3k_steps/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Stancld-longt5-tglobal-large-16384-pubmed-3k Steps
-emoji: 🌍
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/Top-Ten-Board-Games-Map-Making-Strategy/app.py b/spaces/awacke1/Top-Ten-Board-Games-Map-Making-Strategy/app.py
deleted file mode 100644
index 74839f28573c8a4e822a7c3169db094732477a5e..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Top-Ten-Board-Games-Map-Making-Strategy/app.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import streamlit as st
-import streamlit.components.v1 as components
-
-# Function to generate HTML with textarea for speech synthesis
-def generate_speech_textarea(text_to_speak):
- #st.markdown(text_to_speak)
- documentHTML5 = '''
-
-
-
- Read It Aloud
-
-
-
- 🔊 Read It Aloud
-
-
-
-
-
- '''
- components.html(documentHTML5, width=1280, height=500)
-
-# Game list and associated icons
-games = ['Terraforming Mars', 'Twilight Imperium (Fourth Edition)', 'Scythe', 'Eclipse', 'Small World', 'Risk Legacy', 'Axis & Allies', 'Diplomacy', 'Pandemic Legacy: Season 1', 'Brass: Birmingham']
-icons = ['🪐', '🚀', '🤖', '🌌', '🧝♂️', '🗺️', '⚔️', '🤝', '🦠', '🏭']
-
-# Main code
-st.title('Top Ten Board Games with Map-Making Strategies 🗺️')
-
-for i, (game, icon) in enumerate(zip(games, icons)):
- st.markdown(f"{i + 1}. {game} {icon}")
-
- # Expanders for each game to outline map rules or strategies
- with st.expander(f"See Map Building & Gamification Strategy for {game}"):
- text_to_speak = ""
-
- # ... Cut here for content change!
-
- if game == 'Terraforming Mars':
- text_to_speak = "🪐💡 **Terraforming Mars** \n1️⃣ 🌱💧 Opt for plant-heavy and water tiles \n2️⃣ 🏭🌋 Position factories near volcanic areas \n3️⃣ 🌐💡 Control key parameters and energy grid \n4️⃣ 🛤️🌡️ Connect colonies and temperature control \n5️⃣ 🚀🎯 Upgrade spaceports and aim for synergies."
- st.markdown(text_to_speak)
- elif game == 'Twilight Imperium (Fourth Edition)':
- text_to_speak = "🚀🌌 **Twilight Imperium** \n1️⃣ 🌌⚖️ Position fleets in strategic nebulas and balance resources \n2️⃣ 🏰🛡️ Fortify chokepoints and use PDS systems \n3️⃣ 🌐🌀 Effective trade routes and wormhole caution \n4️⃣ 🌟🌕 Prioritize Mecatol Rex and moon attacks \n5️⃣ 🛠️🤝 Optimize unit upgrades and forge alliances."
- st.markdown(text_to_speak)
- elif game == 'Scythe':
- text_to_speak = "🤖🏞️ **Scythe** \n1️⃣ 🏞️🛠️ Choose starting positions and factory cards \n2️⃣ 🗺️🌊 Be aware of neighbors and control rivers \n3️⃣ 🏭🛡️ Maximize resource buildings and backdoor defense \n4️⃣ 🎯🌾 Focus objectives and manage food \n5️⃣ 🎲💎 Play probabilities and hunt treasures."
- st.markdown(text_to_speak)
- elif game == 'Eclipse':
- text_to_speak = "🌌🌟 **Eclipse** \n1️⃣ 🌌🌟 Control sectors and central hexes \n2️⃣ 🛸🛡️ Build formidable fleets and defenses \n3️⃣ 🏭🔭 Prioritize production and research \n4️⃣ 🤝🌐 Trade and diplomacy \n5️⃣ 🌀🚀 Wormhole travel and expansion speed."
- st.markdown(text_to_speak)
- elif game == 'Small World':
- text_to_speak = "🧝♂️🌍 **Small World** \n1️⃣ 🗺️👑 Choose realms and races wisely \n2️⃣ 🎭🛡️ Exploit powers and defend territories \n3️⃣ 🏆💎 Collect victory coins and treasures \n4️⃣ 🤝🌋 Forge short alliances and occupy mountains \n5️⃣ 🔄🏰 Know when to decline and occupy forts."
- st.markdown(text_to_speak)
- elif game == 'Risk Legacy':
- text_to_speak = "🗺️⚔️ **Risk Legacy** \n1️⃣ 🗺️⚔️ Control continents and aggressive expansion \n2️⃣ 🛡️🔐 Fortify borders and use fortresses \n3️⃣ 📜🚀 Complete missions and airfields \n4️⃣ 🏆🔥 Collect victory points and scorched earth \n5️⃣ 🤝🔄 Alliances and betrayal."
- st.markdown(text_to_speak)
- elif game == 'Axis & Allies':
- text_to_speak = "⚔️🌍 **Axis & Allies** \n1️⃣ ⚔️🌍 Strategic frontlines and global dominance \n2️⃣ 🏭📈 Resource management and economy \n3️⃣ 🛡️🚢 Naval blockades and fortress defenses \n4️⃣ 🎖️🎯 Focused objectives and key battles \n5️⃣ 🤝💥 Alliances and surprise attacks."
- st.markdown(text_to_speak)
- elif game == 'Diplomacy':
- text_to_speak = "🤝🌍 **Diplomacy** \n1️⃣ 🤝📜 Negotiation and written orders \n2️⃣ 🗺️🛡️ Strategic positioning and defenses \n3️⃣ 🚢⚓ Naval forces and chokepoints \n4️⃣ 🏰🌐 Territory control and key regions \n5️⃣ 🔄🎭 Timing and deception."
- st.markdown(text_to_speak)
- elif game == 'Pandemic Legacy: Season 1':
- text_to_speak = "🦠🌍 **Pandemic Legacy** \n1️⃣ 🦠🔬 Cure research and outbreak control \n2️⃣ 🌍🚁 Global movement and airlifts \n3️⃣ 🏥🛡️ Build research stations and quarantine \n4️⃣ 📜🎯 Complete objectives and bonus cards \n5️⃣ 🤝🔄 Teamwork and role synergy."
- st.markdown(text_to_speak)
- elif game == 'Brass: Birmingham':
- text_to_speak = "🏭🛤️ **Brass Birmingham** \n1️⃣ 🏭🛤️ Industry and canal routes \n2️⃣ 📈🍺 Economic management and beer supply \n3️⃣ 🛠️🗺️ Optimize developments and map control \n4️⃣ 🤝💡 Partnerships and market strategy \n5️⃣ 🚂🏆 Railroads and victory points."
- st.markdown(text_to_speak)
-
- # ... Cut here for content change!
-
- if st.button(f"🔊 Read {game}'s Strategies Aloud"):
- generate_speech_textarea(text_to_speak)
\ No newline at end of file
diff --git a/spaces/awacke1/andite-pastel-mix/README.md b/spaces/awacke1/andite-pastel-mix/README.md
deleted file mode 100644
index 484f9dbd298a952ad61332fdb57cf8d330a7f28d..0000000000000000000000000000000000000000
--- a/spaces/awacke1/andite-pastel-mix/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Andite Pastel Mix
-emoji: 📚
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ayaanzaveri/whisper-webui/README.md b/spaces/ayaanzaveri/whisper-webui/README.md
deleted file mode 100644
index b551acd09ec7666cb1b13078a768001708127c0a..0000000000000000000000000000000000000000
--- a/spaces/ayaanzaveri/whisper-webui/README.md
+++ /dev/null
@@ -1,153 +0,0 @@
----
-title: Whisper Webui
-emoji: ⚡
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: aadnk/whisper-webui
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-# Running Locally
-
-To run this program locally, first install Python 3.9+ and Git. Then install Pytorch 10.1+ and all the other dependencies:
-```
-pip install -r requirements.txt
-```
-
-You can find detailed instructions for how to install this on Windows 10/11 [here (PDF)](docs/windows/install_win10_win11.pdf).
-
-Finally, run the full version (no audio length restrictions) of the app with parallel CPU/GPU enabled:
-```
-python app.py --input_audio_max_duration -1 --server_name 127.0.0.1 --auto_parallel True
-```
-
-You can also run the CLI interface, which is similar to Whisper's own CLI but also supports the following additional arguments:
-```
-python cli.py \
-[--vad {none,silero-vad,silero-vad-skip-gaps,silero-vad-expand-into-gaps,periodic-vad}] \
-[--vad_merge_window VAD_MERGE_WINDOW] \
-[--vad_max_merge_size VAD_MAX_MERGE_SIZE] \
-[--vad_padding VAD_PADDING] \
-[--vad_prompt_window VAD_PROMPT_WINDOW]
-[--vad_cpu_cores NUMBER_OF_CORES]
-[--vad_parallel_devices COMMA_DELIMITED_DEVICES]
-[--auto_parallel BOOLEAN]
-```
-In addition, you may also use URL's in addition to file paths as input.
-```
-python cli.py --model large --vad silero-vad --language Japanese "https://www.youtube.com/watch?v=4cICErqqRSM"
-```
-
-Rather than supplying arguments to `app.py` or `cli.py`, you can also use the configuration file [config.json5](config.json5). See that file for more information.
-If you want to use a different configuration file, you can use the `WHISPER_WEBUI_CONFIG` environment variable to specify the path to another file.
-
-## Google Colab
-
-You can also run this Web UI directly on [Google Colab](https://colab.research.google.com/drive/1qeTSvi7Bt_5RMm88ipW4fkcsMOKlDDss?usp=sharing), if you haven't got a GPU powerful enough to run the larger models.
-
-See the [colab documentation](docs/colab.md) for more information.
-
-## Parallel Execution
-
-You can also run both the Web-UI or the CLI on multiple GPUs in parallel, using the `vad_parallel_devices` option. This takes a comma-delimited list of
-device IDs (0, 1, etc.) that Whisper should be distributed to and run on concurrently:
-```
-python cli.py --model large --vad silero-vad --language Japanese \
---vad_parallel_devices 0,1 "https://www.youtube.com/watch?v=4cICErqqRSM"
-```
-
-Note that this requires a VAD to function properly, otherwise only the first GPU will be used. Though you could use `period-vad` to avoid taking the hit
-of running Silero-Vad, at a slight cost to accuracy.
-
-This is achieved by creating N child processes (where N is the number of selected devices), where Whisper is run concurrently. In `app.py`, you can also
-set the `vad_process_timeout` option. This configures the number of seconds until a process is killed due to inactivity, freeing RAM and video memory.
-The default value is 30 minutes.
-
-```
-python app.py --input_audio_max_duration -1 --vad_parallel_devices 0,1 --vad_process_timeout 3600
-```
-
-To execute the Silero VAD itself in parallel, use the `vad_cpu_cores` option:
-```
-python app.py --input_audio_max_duration -1 --vad_parallel_devices 0,1 --vad_process_timeout 3600 --vad_cpu_cores 4
-```
-
-You may also use `vad_process_timeout` with a single device (`--vad_parallel_devices 0`), if you prefer to always free video memory after a period of time.
-
-### Auto Parallel
-
-You can also set `auto_parallel` to `True`. This will set `vad_parallel_devices` to use all the GPU devices on the system, and `vad_cpu_cores` to be equal to the number of
-cores (up to 8):
-```
-python app.py --input_audio_max_duration -1 --auto_parallel True
-```
-
-### Multiple Files
-
-You can upload multiple files either through the "Upload files" option, or as a playlist on YouTube.
-Each audio file will then be processed in turn, and the resulting SRT/VTT/Transcript will be made available in the "Download" section.
-When more than one file is processed, the UI will also generate a "All_Output" zip file containing all the text output files.
-
-# Docker
-
-To run it in Docker, first install Docker and optionally the NVIDIA Container Toolkit in order to use the GPU.
-Then either use the GitLab hosted container below, or check out this repository and build an image:
-```
-sudo docker build -t whisper-webui:1 .
-```
-
-You can then start the WebUI with GPU support like so:
-```
-sudo docker run -d --gpus=all -p 7860:7860 whisper-webui:1
-```
-
-Leave out "--gpus=all" if you don't have access to a GPU with enough memory, and are fine with running it on the CPU only:
-```
-sudo docker run -d -p 7860:7860 whisper-webui:1
-```
-
-# GitLab Docker Registry
-
-This Docker container is also hosted on GitLab:
-
-```
-sudo docker run -d --gpus=all -p 7860:7860 registry.gitlab.com/aadnk/whisper-webui:latest
-```
-
-## Custom Arguments
-
-You can also pass custom arguments to `app.py` in the Docker container, for instance to be able to use all the GPUs in parallel:
-```
-sudo docker run -d --gpus all -p 7860:7860 \
---mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \
---restart=on-failure:15 registry.gitlab.com/aadnk/whisper-webui:latest \
-app.py --input_audio_max_duration -1 --server_name 0.0.0.0 --auto_parallel True \
---default_vad silero-vad --default_model_name large
-```
-
-You can also call `cli.py` the same way:
-```
-sudo docker run --gpus all \
---mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \
---mount type=bind,source=${PWD},target=/app/data \
-registry.gitlab.com/aadnk/whisper-webui:latest \
-cli.py --model large --auto_parallel True --vad silero-vad \
---output_dir /app/data /app/data/YOUR-FILE-HERE.mp4
-```
-
-## Caching
-
-Note that the models themselves are currently not included in the Docker images, and will be downloaded on the demand.
-To avoid this, bind the directory /root/.cache/whisper to some directory on the host (for instance /home/administrator/.cache/whisper), where you can (optionally)
-prepopulate the directory with the different Whisper models.
-```
-sudo docker run -d --gpus=all -p 7860:7860 \
---mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \
-registry.gitlab.com/aadnk/whisper-webui:latest
-```
\ No newline at end of file
diff --git a/spaces/azusarang/so-vits-svc-models-ba_P/diffusion/solver.py b/spaces/azusarang/so-vits-svc-models-ba_P/diffusion/solver.py
deleted file mode 100644
index aaf0b21591b42fa903424f8d44fef88d7d791e57..0000000000000000000000000000000000000000
--- a/spaces/azusarang/so-vits-svc-models-ba_P/diffusion/solver.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import os
-import time
-import numpy as np
-import torch
-import librosa
-from diffusion.logger.saver import Saver
-from diffusion.logger import utils
-from torch import autocast
-from torch.cuda.amp import GradScaler
-
-def test(args, model, vocoder, loader_test, saver):
- print(' [*] testing...')
- model.eval()
-
- # losses
- test_loss = 0.
-
- # intialization
- num_batches = len(loader_test)
- rtf_all = []
-
- # run
- with torch.no_grad():
- for bidx, data in enumerate(loader_test):
- fn = data['name'][0].split("/")[-1]
- speaker = data['name'][0].split("/")[-2]
- print('--------')
- print('{}/{} - {}'.format(bidx, num_batches, fn))
-
- # unpack data
- for k in data.keys():
- if not k.startswith('name'):
- data[k] = data[k].to(args.device)
- print('>>', data['name'][0])
-
- # forward
- st_time = time.time()
- mel = model(
- data['units'],
- data['f0'],
- data['volume'],
- data['spk_id'],
- gt_spec=None,
- infer=True,
- infer_speedup=args.infer.speedup,
- method=args.infer.method)
- signal = vocoder.infer(mel, data['f0'])
- ed_time = time.time()
-
- # RTF
- run_time = ed_time - st_time
- song_time = signal.shape[-1] / args.data.sampling_rate
- rtf = run_time / song_time
- print('RTF: {} | {} / {}'.format(rtf, run_time, song_time))
- rtf_all.append(rtf)
-
- # loss
- for i in range(args.train.batch_size):
- loss = model(
- data['units'],
- data['f0'],
- data['volume'],
- data['spk_id'],
- gt_spec=data['mel'],
- infer=False)
- test_loss += loss.item()
-
- # log mel
- saver.log_spec(f"{speaker}_{fn}.wav", data['mel'], mel)
-
- # log audi
- path_audio = data['name_ext'][0]
- audio, sr = librosa.load(path_audio, sr=args.data.sampling_rate)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio)
- audio = torch.from_numpy(audio).unsqueeze(0).to(signal)
- saver.log_audio({f"{speaker}_{fn}_gt.wav": audio,f"{speaker}_{fn}_pred.wav": signal})
- # report
- test_loss /= args.train.batch_size
- test_loss /= num_batches
-
- # check
- print(' [test_loss] test_loss:', test_loss)
- print(' Real Time Factor', np.mean(rtf_all))
- return test_loss
-
-
-def train(args, initial_global_step, model, optimizer, scheduler, vocoder, loader_train, loader_test):
- # saver
- saver = Saver(args, initial_global_step=initial_global_step)
-
- # model size
- params_count = utils.get_network_paras_amount({'model': model})
- saver.log_info('--- model size ---')
- saver.log_info(params_count)
-
- # run
- num_batches = len(loader_train)
- model.train()
- saver.log_info('======= start training =======')
- scaler = GradScaler()
- if args.train.amp_dtype == 'fp32':
- dtype = torch.float32
- elif args.train.amp_dtype == 'fp16':
- dtype = torch.float16
- elif args.train.amp_dtype == 'bf16':
- dtype = torch.bfloat16
- else:
- raise ValueError(' [x] Unknown amp_dtype: ' + args.train.amp_dtype)
- saver.log_info("epoch|batch_idx/num_batches|output_dir|batch/s|lr|time|step")
- for epoch in range(args.train.epochs):
- for batch_idx, data in enumerate(loader_train):
- saver.global_step_increment()
- optimizer.zero_grad()
-
- # unpack data
- for k in data.keys():
- if not k.startswith('name'):
- data[k] = data[k].to(args.device)
-
- # forward
- if dtype == torch.float32:
- loss = model(data['units'].float(), data['f0'], data['volume'], data['spk_id'],
- aug_shift = data['aug_shift'], gt_spec=data['mel'].float(), infer=False)
- else:
- with autocast(device_type=args.device, dtype=dtype):
- loss = model(data['units'], data['f0'], data['volume'], data['spk_id'],
- aug_shift = data['aug_shift'], gt_spec=data['mel'], infer=False)
-
- # handle nan loss
- if torch.isnan(loss):
- raise ValueError(' [x] nan loss ')
- else:
- # backpropagate
- if dtype == torch.float32:
- loss.backward()
- optimizer.step()
- else:
- scaler.scale(loss).backward()
- scaler.step(optimizer)
- scaler.update()
- scheduler.step()
-
- # log loss
- if saver.global_step % args.train.interval_log == 0:
- current_lr = optimizer.param_groups[0]['lr']
- saver.log_info(
- 'epoch: {} | {:3d}/{:3d} | {} | batch/s: {:.2f} | lr: {:.6} | loss: {:.3f} | time: {} | step: {}'.format(
- epoch,
- batch_idx,
- num_batches,
- args.env.expdir,
- args.train.interval_log/saver.get_interval_time(),
- current_lr,
- loss.item(),
- saver.get_total_time(),
- saver.global_step
- )
- )
-
- saver.log_value({
- 'train/loss': loss.item()
- })
-
- saver.log_value({
- 'train/lr': current_lr
- })
-
- # validation
- if saver.global_step % args.train.interval_val == 0:
- optimizer_save = optimizer if args.train.save_opt else None
-
- # save latest
- saver.save_model(model, optimizer_save, postfix=f'{saver.global_step}')
- last_val_step = saver.global_step - args.train.interval_val
- if last_val_step % args.train.interval_force_save != 0:
- saver.delete_model(postfix=f'{last_val_step}')
-
- # run testing set
- test_loss = test(args, model, vocoder, loader_test, saver)
-
- # log loss
- saver.log_info(
- ' --- --- \nloss: {:.3f}. '.format(
- test_loss,
- )
- )
-
- saver.log_value({
- 'validation/loss': test_loss
- })
-
- model.train()
-
-
diff --git a/spaces/balaramas/s2t_translator/data_utils.py b/spaces/balaramas/s2t_translator/data_utils.py
deleted file mode 100644
index b8648cb2a05e275dd55cf7a6c009c8d21c6ec9fd..0000000000000000000000000000000000000000
--- a/spaces/balaramas/s2t_translator/data_utils.py
+++ /dev/null
@@ -1,383 +0,0 @@
-# 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.
-
-import csv
-from pathlib import Path
-import zipfile
-from functools import reduce
-from multiprocessing import cpu_count
-from typing import Any, Dict, List, Optional, Union
-import io
-
-import numpy as np
-import pandas as pd
-import sentencepiece as sp
-from fairseq.data.audio.audio_utils import (
- convert_waveform, _get_kaldi_fbank, _get_torchaudio_fbank, is_npy_data,
- is_sf_audio_data
-)
-import torch
-import soundfile as sf
-from tqdm import tqdm
-
-
-UNK_TOKEN, UNK_TOKEN_ID = "", 3
-BOS_TOKEN, BOS_TOKEN_ID = "", 0
-EOS_TOKEN, EOS_TOKEN_ID = "", 2
-PAD_TOKEN, PAD_TOKEN_ID = "", 1
-
-
-def gen_vocab(
- input_path: Path, output_path_prefix: Path, model_type="bpe",
- vocab_size=1000, special_symbols: Optional[List[str]] = None
-):
- # Train SentencePiece Model
- arguments = [
- f"--input={input_path.as_posix()}",
- f"--model_prefix={output_path_prefix.as_posix()}",
- f"--model_type={model_type}",
- f"--vocab_size={vocab_size}",
- "--character_coverage=1.0",
- f"--num_threads={cpu_count()}",
- f"--unk_id={UNK_TOKEN_ID}",
- f"--bos_id={BOS_TOKEN_ID}",
- f"--eos_id={EOS_TOKEN_ID}",
- f"--pad_id={PAD_TOKEN_ID}",
- ]
- if special_symbols is not None:
- _special_symbols = ",".join(special_symbols)
- arguments.append(f"--user_defined_symbols={_special_symbols}")
- sp.SentencePieceTrainer.Train(" ".join(arguments))
- # Export fairseq dictionary
- spm = sp.SentencePieceProcessor()
- spm.Load(output_path_prefix.as_posix() + ".model")
- vocab = {i: spm.IdToPiece(i) for i in range(spm.GetPieceSize())}
- assert (
- vocab.get(UNK_TOKEN_ID) == UNK_TOKEN
- and vocab.get(PAD_TOKEN_ID) == PAD_TOKEN
- and vocab.get(BOS_TOKEN_ID) == BOS_TOKEN
- and vocab.get(EOS_TOKEN_ID) == EOS_TOKEN
- )
- vocab = {
- i: s
- for i, s in vocab.items()
- if s not in {UNK_TOKEN, BOS_TOKEN, EOS_TOKEN, PAD_TOKEN}
- }
- with open(output_path_prefix.as_posix() + ".txt", "w") as f_out:
- for _, s in sorted(vocab.items(), key=lambda x: x[0]):
- f_out.write(f"{s} 1\n")
-
-
-def extract_fbank_features(
- waveform: torch.FloatTensor,
- sample_rate: int,
- output_path: Optional[Path] = None,
- n_mel_bins: int = 80,
- overwrite: bool = False,
-):
- if output_path is not None and output_path.is_file() and not overwrite:
- return
-
- _waveform, _ = convert_waveform(waveform, sample_rate, to_mono=True)
- # Kaldi compliance: 16-bit signed integers
- _waveform = _waveform * (2 ** 15)
- _waveform = _waveform.numpy()
-
- features = _get_kaldi_fbank(_waveform, sample_rate, n_mel_bins)
- if features is None:
- features = _get_torchaudio_fbank(_waveform, sample_rate, n_mel_bins)
- if features is None:
- raise ImportError(
- "Please install pyKaldi or torchaudio to enable fbank feature extraction"
- )
-
- if output_path is not None:
- np.save(output_path.as_posix(), features)
- return features
-
-
-def create_zip(data_root: Path, zip_path: Path):
- paths = list(data_root.glob("*.npy"))
- paths.extend(data_root.glob("*.flac"))
- with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_STORED) as f:
- for path in tqdm(paths):
- f.write(path, arcname=path.name)
-
-
-def get_zip_manifest(
- zip_path: Path, zip_root: Optional[Path] = None, is_audio=False
-):
- _zip_path = Path.joinpath(zip_root or Path(""), zip_path)
- with zipfile.ZipFile(_zip_path, mode="r") as f:
- info = f.infolist()
- paths, lengths = {}, {}
- for i in tqdm(info):
- utt_id = Path(i.filename).stem
- offset, file_size = i.header_offset + 30 + len(i.filename), i.file_size
- paths[utt_id] = f"{zip_path.as_posix()}:{offset}:{file_size}"
- with open(_zip_path, "rb") as f:
- f.seek(offset)
- byte_data = f.read(file_size)
- assert len(byte_data) > 1
- if is_audio:
- assert is_sf_audio_data(byte_data), i
- else:
- assert is_npy_data(byte_data), i
- byte_data_fp = io.BytesIO(byte_data)
- if is_audio:
- lengths[utt_id] = sf.info(byte_data_fp).frames
- else:
- lengths[utt_id] = np.load(byte_data_fp).shape[0]
- return paths, lengths
-
-
-def gen_config_yaml(
- manifest_root: Path,
- spm_filename: Optional[str] = None,
- vocab_name: Optional[str] = None,
- yaml_filename: str = "config.yaml",
- specaugment_policy: Optional[str] = "lb",
- prepend_tgt_lang_tag: bool = False,
- sampling_alpha: Optional[float] = None,
- input_channels: Optional[int] = 1,
- input_feat_per_channel: Optional[int] = 80,
- audio_root: str = "",
- cmvn_type: str = "utterance",
- gcmvn_path: Optional[Path] = None,
- extra=None
-):
- manifest_root = manifest_root.absolute()
- writer = S2TDataConfigWriter(manifest_root / yaml_filename)
- assert spm_filename is not None or vocab_name is not None
- vocab_name = spm_filename.replace(".model", ".txt") if vocab_name is None \
- else vocab_name
- writer.set_vocab_filename(vocab_name)
- if input_channels is not None:
- writer.set_input_channels(input_channels)
- if input_feat_per_channel is not None:
- writer.set_input_feat_per_channel(input_feat_per_channel)
- specaugment_setters = {
- "lb": writer.set_specaugment_lb_policy,
- "ld": writer.set_specaugment_ld_policy,
- "sm": writer.set_specaugment_sm_policy,
- "ss": writer.set_specaugment_ss_policy,
- }
- specaugment_setter = specaugment_setters.get(specaugment_policy, None)
- if specaugment_setter is not None:
- specaugment_setter()
- if spm_filename is not None:
- writer.set_bpe_tokenizer(
- {
- "bpe": "sentencepiece",
- "sentencepiece_model": (manifest_root / spm_filename).as_posix(),
- }
- )
- if prepend_tgt_lang_tag:
- writer.set_prepend_tgt_lang_tag(True)
- if sampling_alpha is not None:
- writer.set_sampling_alpha(sampling_alpha)
-
- if cmvn_type not in ["global", "utterance"]:
- raise NotImplementedError
-
- if specaugment_policy is not None:
- writer.set_feature_transforms(
- "_train", [f"{cmvn_type}_cmvn", "specaugment"]
- )
- writer.set_feature_transforms("*", [f"{cmvn_type}_cmvn"])
-
- if cmvn_type == "global":
- if gcmvn_path is None:
- raise ValueError("Please provide path of global cmvn file.")
- else:
- writer.set_global_cmvn(gcmvn_path.as_posix())
-
- if len(audio_root) > 0:
- writer.set_audio_root(audio_root)
-
- if extra is not None:
- writer.set_extra(extra)
- writer.flush()
-
-
-def load_df_from_tsv(path: Union[str, Path]) -> pd.DataFrame:
- _path = path if isinstance(path, str) else path.as_posix()
- return pd.read_csv(
- _path,
- sep="\t",
- header=0,
- encoding="utf-8",
- escapechar="\\",
- quoting=csv.QUOTE_NONE,
- na_filter=False,
- )
-
-
-def save_df_to_tsv(dataframe, path: Union[str, Path]):
- _path = path if isinstance(path, str) else path.as_posix()
- dataframe.to_csv(
- _path,
- sep="\t",
- header=True,
- index=False,
- encoding="utf-8",
- escapechar="\\",
- quoting=csv.QUOTE_NONE,
- )
-
-
-def load_tsv_to_dicts(path: Union[str, Path]) -> List[dict]:
- with open(path, "r") as f:
- reader = csv.DictReader(
- f,
- delimiter="\t",
- quotechar=None,
- doublequote=False,
- lineterminator="\n",
- quoting=csv.QUOTE_NONE,
- )
- rows = [dict(e) for e in reader]
- return rows
-
-
-def filter_manifest_df(
- df, is_train_split=False, extra_filters=None, min_n_frames=5, max_n_frames=3000
-):
- filters = {
- "no speech": df["audio"] == "",
- f"short speech (<{min_n_frames} frames)": df["n_frames"] < min_n_frames,
- "empty sentence": df["tgt_text"] == "",
- }
- if is_train_split:
- filters[f"long speech (>{max_n_frames} frames)"] = df["n_frames"] > max_n_frames
- if extra_filters is not None:
- filters.update(extra_filters)
- invalid = reduce(lambda x, y: x | y, filters.values())
- valid = ~invalid
- print(
- "| "
- + ", ".join(f"{n}: {f.sum()}" for n, f in filters.items())
- + f", total {invalid.sum()} filtered, {valid.sum()} remained."
- )
- return df[valid]
-
-
-def cal_gcmvn_stats(features_list):
- features = np.concatenate(features_list)
- square_sums = (features ** 2).sum(axis=0)
- mean = features.mean(axis=0)
- features = np.subtract(features, mean)
- var = square_sums / features.shape[0] - mean ** 2
- std = np.sqrt(np.maximum(var, 1e-8))
- return {"mean": mean.astype("float32"), "std": std.astype("float32")}
-
-
-class S2TDataConfigWriter(object):
- DEFAULT_VOCAB_FILENAME = "dict.txt"
- DEFAULT_INPUT_FEAT_PER_CHANNEL = 80
- DEFAULT_INPUT_CHANNELS = 1
-
- def __init__(self, yaml_path: Path):
- try:
- import yaml
- except ImportError:
- print("Please install PyYAML for S2T data config YAML files")
- self.yaml = yaml
- self.yaml_path = yaml_path
- self.config = {}
-
- def flush(self):
- with open(self.yaml_path, "w") as f:
- self.yaml.dump(self.config, f)
-
- def set_audio_root(self, audio_root=""):
- self.config["audio_root"] = audio_root
-
- def set_vocab_filename(self, vocab_filename: str = "dict.txt"):
- self.config["vocab_filename"] = vocab_filename
-
- def set_specaugment(
- self,
- time_wrap_w: int,
- freq_mask_n: int,
- freq_mask_f: int,
- time_mask_n: int,
- time_mask_t: int,
- time_mask_p: float,
- ):
- self.config["specaugment"] = {
- "time_wrap_W": time_wrap_w,
- "freq_mask_N": freq_mask_n,
- "freq_mask_F": freq_mask_f,
- "time_mask_N": time_mask_n,
- "time_mask_T": time_mask_t,
- "time_mask_p": time_mask_p,
- }
-
- def set_specaugment_lb_policy(self):
- self.set_specaugment(
- time_wrap_w=0,
- freq_mask_n=1,
- freq_mask_f=27,
- time_mask_n=1,
- time_mask_t=100,
- time_mask_p=1.0,
- )
-
- def set_specaugment_ld_policy(self):
- self.set_specaugment(
- time_wrap_w=0,
- freq_mask_n=2,
- freq_mask_f=27,
- time_mask_n=2,
- time_mask_t=100,
- time_mask_p=1.0,
- )
-
- def set_specaugment_sm_policy(self):
- self.set_specaugment(
- time_wrap_w=0,
- freq_mask_n=2,
- freq_mask_f=15,
- time_mask_n=2,
- time_mask_t=70,
- time_mask_p=0.2,
- )
-
- def set_specaugment_ss_policy(self):
- self.set_specaugment(
- time_wrap_w=0,
- freq_mask_n=2,
- freq_mask_f=27,
- time_mask_n=2,
- time_mask_t=70,
- time_mask_p=0.2,
- )
-
- def set_input_channels(self, input_channels: int = 1):
- self.config["input_channels"] = input_channels
-
- def set_input_feat_per_channel(self, input_feat_per_channel: int = 80):
- self.config["input_feat_per_channel"] = input_feat_per_channel
-
- def set_bpe_tokenizer(self, bpe_tokenizer: Dict[str, Any]):
- self.config["bpe_tokenizer"] = bpe_tokenizer
-
- def set_global_cmvn(self, stats_npz_path: str):
- self.config["global_cmvn"] = {"stats_npz_path": stats_npz_path}
-
- def set_feature_transforms(self, split: str, transforms: List[str]):
- if "transforms" not in self.config:
- self.config["transforms"] = {}
- self.config["transforms"][split] = transforms
-
- def set_prepend_tgt_lang_tag(self, flag: bool = True):
- self.config["prepend_tgt_lang_tag"] = flag
-
- def set_sampling_alpha(self, sampling_alpha: float = 1.0):
- self.config["sampling_alpha"] = sampling_alpha
-
- def set_extra(self, data):
- self.config.update(data)
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/Car.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/Car.js
deleted file mode 100644
index 8a9791a0b98b7ad9e471309ca984add2bb95eec0..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/Car.js
+++ /dev/null
@@ -1,305 +0,0 @@
-/**
- * @author alteredq / http://alteredqualia.com/
- * @author Lewy Blue https://github.com/looeee
- *
- * The model is expected to follow real world car proportions. You can try unusual car types
- * but your results may be unexpected. Scaled models are also not supported.
- *
- * Defaults are rough estimates for a real world scale car model
- *
- */
-
-THREE.Car = ( function ( ) {
-
- // private variables
- var steeringWheelSpeed = 1.5;
- var maxSteeringRotation = 0.6;
-
- var acceleration = 0;
-
- var maxSpeedReverse, accelerationReverse, deceleration;
-
- var controlKeys = { LEFT: 37, UP: 38, RIGHT: 39, DOWN: 40, BRAKE: 32 };
-
- var wheelOrientation = 0;
- var carOrientation = 0;
-
- var root = null;
-
- var frontLeftWheelRoot = null;
- var frontRightWheelRoot = null;
-
- var frontLeftWheel = new THREE.Group();
- var frontRightWheel = new THREE.Group();
- var backLeftWheel = null;
- var backRightWheel = null;
-
- var steeringWheel = null;
-
- var wheelDiameter = 1;
- var length = 1;
-
- var loaded = false;
-
- var controls = {
-
- brake: false,
- moveForward: false,
- moveBackward: false,
- moveLeft: false,
- moveRight: false
-
- };
-
- function Car( maxSpeed, acceleration, brakePower, turningRadius, keys ) {
-
- this.enabled = true;
-
- this.elemNames = {
- flWheel: 'wheel_fl',
- frWheel: 'wheel_fr',
- rlWheel: 'wheel_rl',
- rrWheel: 'wheel_rr',
- steeringWheel: 'steering_wheel', // set to null to disable
- };
-
- // km/hr
- this.maxSpeed = maxSpeed || 180;
- maxSpeedReverse = - this.maxSpeed * 0.25;
-
- // m/s
- this.acceleration = acceleration || 10;
- accelerationReverse = this.acceleration * 0.5;
-
- // metres
- this.turningRadius = turningRadius || 6;
-
- // m/s
- deceleration = this.acceleration * 2;
-
- // multiplied with deceleration, so breaking deceleration = ( acceleration * 2 * brakePower ) m/s
- this.brakePower = brakePower || 10;
-
- // exposed so that a user can use this for various effect, e.g blur
- this.speed = 0;
-
- // keys used to control car - by default the arrow keys and space to brake
- controlKeys = keys || controlKeys;
-
- // local axes of rotation - these are likely to vary between models
- this.wheelRotationAxis = 'x';
- this.wheelTurnAxis = 'z';
- this.steeringWheelTurnAxis = 'y';
-
- document.addEventListener( 'keydown', this.onKeyDown, false );
- document.addEventListener( 'keyup', this.onKeyUp, false );
-
- }
-
- Car.prototype = {
-
- constructor: Car,
-
- onKeyDown: function ( event ) {
-
- switch ( event.keyCode ) {
-
- case controlKeys.BRAKE:
- controls.brake = true;
- controls.moveForward = false;
- controls.moveBackward = false;
- break;
-
- case controlKeys.UP: controls.moveForward = true; break;
-
- case controlKeys.DOWN: controls.moveBackward = true; break;
-
- case controlKeys.LEFT: controls.moveLeft = true; break;
-
- case controlKeys.RIGHT: controls.moveRight = true; break;
-
- }
-
- },
-
- onKeyUp: function ( event ) {
-
- switch ( event.keyCode ) {
-
- case controlKeys.BRAKE: controls.brake = false; break;
-
- case controlKeys.UP: controls.moveForward = false; break;
-
- case controlKeys.DOWN: controls.moveBackward = false; break;
-
- case controlKeys.LEFT: controls.moveLeft = false; break;
-
- case controlKeys.RIGHT: controls.moveRight = false; break;
-
- }
-
- },
-
- dispose: function () {
-
- document.removeEventListener( 'keydown', this.onKeyDown, false );
- document.removeEventListener( 'keyup', this.onKeyUp, false );
-
- },
-
- update: function ( delta ) {
-
- if ( ! loaded || ! this.enabled ) return;
-
- var brakingDeceleration = 1;
-
- if ( controls.brake ) brakingDeceleration = this.brakePower;
-
- if ( controls.moveForward ) {
-
- this.speed = THREE.Math.clamp( this.speed + delta * this.acceleration, maxSpeedReverse, this.maxSpeed );
- acceleration = THREE.Math.clamp( acceleration + delta, - 1, 1 );
-
- }
-
- if ( controls.moveBackward ) {
-
- this.speed = THREE.Math.clamp( this.speed - delta * accelerationReverse, maxSpeedReverse, this.maxSpeed );
- acceleration = THREE.Math.clamp( acceleration - delta, - 1, 1 );
-
- }
-
- if ( controls.moveLeft ) {
-
- wheelOrientation = THREE.Math.clamp( wheelOrientation + delta * steeringWheelSpeed, - maxSteeringRotation, maxSteeringRotation );
-
- }
-
- if ( controls.moveRight ) {
-
- wheelOrientation = THREE.Math.clamp( wheelOrientation - delta * steeringWheelSpeed, - maxSteeringRotation, maxSteeringRotation );
-
- }
-
- // this.speed decay
- if ( ! ( controls.moveForward || controls.moveBackward ) ) {
-
- if ( this.speed > 0 ) {
-
- var k = exponentialEaseOut( this.speed / this.maxSpeed );
-
- this.speed = THREE.Math.clamp( this.speed - k * delta * deceleration * brakingDeceleration, 0, this.maxSpeed );
- acceleration = THREE.Math.clamp( acceleration - k * delta, 0, 1 );
-
- } else {
-
- var k = exponentialEaseOut( this.speed / maxSpeedReverse );
-
- this.speed = THREE.Math.clamp( this.speed + k * delta * accelerationReverse * brakingDeceleration, maxSpeedReverse, 0 );
- acceleration = THREE.Math.clamp( acceleration + k * delta, - 1, 0 );
-
- }
-
- }
-
- // steering decay
- if ( ! ( controls.moveLeft || controls.moveRight ) ) {
-
- if ( wheelOrientation > 0 ) {
-
- wheelOrientation = THREE.Math.clamp( wheelOrientation - delta * steeringWheelSpeed, 0, maxSteeringRotation );
-
- } else {
-
- wheelOrientation = THREE.Math.clamp( wheelOrientation + delta * steeringWheelSpeed, - maxSteeringRotation, 0 );
-
- }
-
- }
-
- var forwardDelta = - this.speed * delta;
-
- carOrientation -= ( forwardDelta * this.turningRadius * 0.02 ) * wheelOrientation;
-
- // movement of car
- root.position.x += Math.sin( carOrientation ) * forwardDelta * length;
- root.position.z += Math.cos( carOrientation ) * forwardDelta * length;
-
- // angle of car
- root.rotation.y = carOrientation;
-
- // wheels rolling
- var angularSpeedRatio = - 2 / wheelDiameter;
-
- var wheelDelta = forwardDelta * angularSpeedRatio * length;
-
- frontLeftWheel.rotation[ this.wheelRotationAxis ] -= wheelDelta;
- frontRightWheel.rotation[ this.wheelRotationAxis ] -= wheelDelta;
- backLeftWheel.rotation[ this.wheelRotationAxis ] -= wheelDelta;
- backRightWheel.rotation[ this.wheelRotationAxis ] -= wheelDelta;
-
- // rotation while steering
- frontLeftWheelRoot.rotation[ this.wheelTurnAxis ] = wheelOrientation;
- frontRightWheelRoot.rotation[ this.wheelTurnAxis ] = wheelOrientation;
-
- steeringWheel.rotation[ this.steeringWheelTurnAxis ] = -wheelOrientation * 6;
-
- },
-
- setModel: function ( model, elemNames ) {
-
- if ( elemNames ) this.elemNames = elemNames;
-
- root = model;
-
- this.setupWheels();
- this.computeDimensions();
-
- loaded = true;
-
- },
-
- setupWheels: function () {
-
- frontLeftWheelRoot = root.getObjectByName( this.elemNames.flWheel );
- frontRightWheelRoot = root.getObjectByName( this.elemNames.frWheel );
- backLeftWheel = root.getObjectByName( this.elemNames.rlWheel );
- backRightWheel = root.getObjectByName( this.elemNames.rrWheel );
-
- if ( this.elemNames.steeringWheel !== null ) steeringWheel = root.getObjectByName( this.elemNames.steeringWheel );
-
- while ( frontLeftWheelRoot.children.length > 0 ) frontLeftWheel.add( frontLeftWheelRoot.children[ 0 ] );
- while ( frontRightWheelRoot.children.length > 0 ) frontRightWheel.add( frontRightWheelRoot.children[ 0 ] );
-
- frontLeftWheelRoot.add( frontLeftWheel );
- frontRightWheelRoot.add( frontRightWheel );
-
- },
-
- computeDimensions: function () {
-
- var bb = new THREE.Box3().setFromObject( frontLeftWheelRoot );
-
- var size = new THREE.Vector3();
- bb.getSize( size );
-
- wheelDiameter = Math.max( size.x, size.y, size.z );
-
- bb.setFromObject( root );
-
- size = bb.getSize( size );
- length = Math.max( size.x, size.y, size.z );
-
- }
-
- };
-
- function exponentialEaseOut( k ) {
-
- return k === 1 ? 1 : - Math.pow( 2, - 10 * k ) + 1;
-
- }
-
- return Car;
-
-} )();
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/FBXLoader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/FBXLoader.js
deleted file mode 100644
index c7a0f3b440dca6a4677843270f3bba17c9887be9..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/FBXLoader.js
+++ /dev/null
@@ -1,4136 +0,0 @@
-/**
- * @author Kyle-Larson https://github.com/Kyle-Larson
- * @author Takahiro https://github.com/takahirox
- * @author Lewy Blue https://github.com/looeee
- *
- * Loader loads FBX file and generates Group representing FBX scene.
- * Requires FBX file to be >= 7.0 and in ASCII or >= 6400 in Binary format
- * Versions lower than this may load but will probably have errors
- *
- * Needs Support:
- * Morph normals / blend shape normals
- *
- * FBX format references:
- * https://wiki.blender.org/index.php/User:Mont29/Foundation/FBX_File_Structure
- * http://help.autodesk.com/view/FBX/2017/ENU/?guid=__cpp_ref_index_html (C++ SDK reference)
- *
- * Binary format specification:
- * https://code.blender.org/2013/08/fbx-binary-file-format-specification/
- */
-
-
-THREE.FBXLoader = ( function () {
-
- var fbxTree;
- var connections;
- var sceneGraph;
-
- function FBXLoader( manager ) {
-
- this.manager = ( manager !== undefined ) ? manager : THREE.DefaultLoadingManager;
-
- }
-
- FBXLoader.prototype = {
-
- constructor: FBXLoader,
-
- crossOrigin: 'anonymous',
-
- load: function ( url, onLoad, onProgress, onError ) {
-
- var self = this;
-
- var path = ( self.path === undefined ) ? THREE.LoaderUtils.extractUrlBase( url ) : self.path;
-
- var loader = new THREE.FileLoader( this.manager );
- loader.setPath( self.path );
- loader.setResponseType( 'arraybuffer' );
-
- loader.load( url, function ( buffer ) {
-
- try {
-
- onLoad( self.parse( buffer, path ) );
-
- } catch ( error ) {
-
- setTimeout( function () {
-
- if ( onError ) onError( error );
-
- self.manager.itemError( url );
-
- }, 0 );
-
- }
-
- }, onProgress, onError );
-
- },
-
- setPath: function ( value ) {
-
- this.path = value;
- return this;
-
- },
-
- setResourcePath: function ( value ) {
-
- this.resourcePath = value;
- return this;
-
- },
-
- setCrossOrigin: function ( value ) {
-
- this.crossOrigin = value;
- return this;
-
- },
-
- parse: function ( FBXBuffer, path ) {
-
- if ( isFbxFormatBinary( FBXBuffer ) ) {
-
- fbxTree = new BinaryParser().parse( FBXBuffer );
-
- } else {
-
- var FBXText = convertArrayBufferToString( FBXBuffer );
-
- if ( ! isFbxFormatASCII( FBXText ) ) {
-
- throw new Error( 'THREE.FBXLoader: Unknown format.' );
-
- }
-
- if ( getFbxVersion( FBXText ) < 7000 ) {
-
- throw new Error( 'THREE.FBXLoader: FBX version not supported, FileVersion: ' + getFbxVersion( FBXText ) );
-
- }
-
- fbxTree = new TextParser().parse( FBXText );
-
- }
-
- // console.log( fbxTree );
-
- var textureLoader = new THREE.TextureLoader( this.manager ).setPath( this.resourcePath || path ).setCrossOrigin( this.crossOrigin );
-
- return new FBXTreeParser( textureLoader ).parse( fbxTree );
-
- }
-
- };
-
- // Parse the FBXTree object returned by the BinaryParser or TextParser and return a THREE.Group
- function FBXTreeParser( textureLoader ) {
-
- this.textureLoader = textureLoader;
-
- }
-
- FBXTreeParser.prototype = {
-
- constructor: FBXTreeParser,
-
- parse: function () {
-
- connections = this.parseConnections();
-
- var images = this.parseImages();
- var textures = this.parseTextures( images );
- var materials = this.parseMaterials( textures );
- var deformers = this.parseDeformers();
- var geometryMap = new GeometryParser().parse( deformers );
-
- this.parseScene( deformers, geometryMap, materials );
-
- return sceneGraph;
-
- },
-
- // Parses FBXTree.Connections which holds parent-child connections between objects (e.g. material -> texture, model->geometry )
- // and details the connection type
- parseConnections: function () {
-
- var connectionMap = new Map();
-
- if ( 'Connections' in fbxTree ) {
-
- var rawConnections = fbxTree.Connections.connections;
-
- rawConnections.forEach( function ( rawConnection ) {
-
- var fromID = rawConnection[ 0 ];
- var toID = rawConnection[ 1 ];
- var relationship = rawConnection[ 2 ];
-
- if ( ! connectionMap.has( fromID ) ) {
-
- connectionMap.set( fromID, {
- parents: [],
- children: []
- } );
-
- }
-
- var parentRelationship = { ID: toID, relationship: relationship };
- connectionMap.get( fromID ).parents.push( parentRelationship );
-
- if ( ! connectionMap.has( toID ) ) {
-
- connectionMap.set( toID, {
- parents: [],
- children: []
- } );
-
- }
-
- var childRelationship = { ID: fromID, relationship: relationship };
- connectionMap.get( toID ).children.push( childRelationship );
-
- } );
-
- }
-
- return connectionMap;
-
- },
-
- // Parse FBXTree.Objects.Video for embedded image data
- // These images are connected to textures in FBXTree.Objects.Textures
- // via FBXTree.Connections.
- parseImages: function () {
-
- var images = {};
- var blobs = {};
-
- if ( 'Video' in fbxTree.Objects ) {
-
- var videoNodes = fbxTree.Objects.Video;
-
- for ( var nodeID in videoNodes ) {
-
- var videoNode = videoNodes[ nodeID ];
-
- var id = parseInt( nodeID );
-
- images[ id ] = videoNode.RelativeFilename || videoNode.Filename;
-
- // raw image data is in videoNode.Content
- if ( 'Content' in videoNode ) {
-
- var arrayBufferContent = ( videoNode.Content instanceof ArrayBuffer ) && ( videoNode.Content.byteLength > 0 );
- var base64Content = ( typeof videoNode.Content === 'string' ) && ( videoNode.Content !== '' );
-
- if ( arrayBufferContent || base64Content ) {
-
- var image = this.parseImage( videoNodes[ nodeID ] );
-
- blobs[ videoNode.RelativeFilename || videoNode.Filename ] = image;
-
- }
-
- }
-
- }
-
- }
-
- for ( var id in images ) {
-
- var filename = images[ id ];
-
- if ( blobs[ filename ] !== undefined ) images[ id ] = blobs[ filename ];
- else images[ id ] = images[ id ].split( '\\' ).pop();
-
- }
-
- return images;
-
- },
-
- // Parse embedded image data in FBXTree.Video.Content
- parseImage: function ( videoNode ) {
-
- var content = videoNode.Content;
- var fileName = videoNode.RelativeFilename || videoNode.Filename;
- var extension = fileName.slice( fileName.lastIndexOf( '.' ) + 1 ).toLowerCase();
-
- var type;
-
- switch ( extension ) {
-
- case 'bmp':
-
- type = 'image/bmp';
- break;
-
- case 'jpg':
- case 'jpeg':
-
- type = 'image/jpeg';
- break;
-
- case 'png':
-
- type = 'image/png';
- break;
-
- case 'tif':
-
- type = 'image/tiff';
- break;
-
- case 'tga':
-
- if ( typeof THREE.TGALoader !== 'function' ) {
-
- console.warn( 'FBXLoader: THREE.TGALoader is required to load TGA textures' );
- return;
-
- } else {
-
- if ( THREE.Loader.Handlers.get( '.tga' ) === null ) {
-
- var tgaLoader = new THREE.TGALoader();
- tgaLoader.setPath( this.textureLoader.path );
-
- THREE.Loader.Handlers.add( /\.tga$/i, tgaLoader );
-
- }
-
- type = 'image/tga';
- break;
-
- }
-
- default:
-
- console.warn( 'FBXLoader: Image type "' + extension + '" is not supported.' );
- return;
-
- }
-
- if ( typeof content === 'string' ) { // ASCII format
-
- return 'data:' + type + ';base64,' + content;
-
- } else { // Binary Format
-
- var array = new Uint8Array( content );
- return window.URL.createObjectURL( new Blob( [ array ], { type: type } ) );
-
- }
-
- },
-
- // Parse nodes in FBXTree.Objects.Texture
- // These contain details such as UV scaling, cropping, rotation etc and are connected
- // to images in FBXTree.Objects.Video
- parseTextures: function ( images ) {
-
- var textureMap = new Map();
-
- if ( 'Texture' in fbxTree.Objects ) {
-
- var textureNodes = fbxTree.Objects.Texture;
- for ( var nodeID in textureNodes ) {
-
- var texture = this.parseTexture( textureNodes[ nodeID ], images );
- textureMap.set( parseInt( nodeID ), texture );
-
- }
-
- }
-
- return textureMap;
-
- },
-
- // Parse individual node in FBXTree.Objects.Texture
- parseTexture: function ( textureNode, images ) {
-
- var texture = this.loadTexture( textureNode, images );
-
- texture.ID = textureNode.id;
-
- texture.name = textureNode.attrName;
-
- var wrapModeU = textureNode.WrapModeU;
- var wrapModeV = textureNode.WrapModeV;
-
- var valueU = wrapModeU !== undefined ? wrapModeU.value : 0;
- var valueV = wrapModeV !== undefined ? wrapModeV.value : 0;
-
- // http://download.autodesk.com/us/fbx/SDKdocs/FBX_SDK_Help/files/fbxsdkref/class_k_fbx_texture.html#889640e63e2e681259ea81061b85143a
- // 0: repeat(default), 1: clamp
-
- texture.wrapS = valueU === 0 ? THREE.RepeatWrapping : THREE.ClampToEdgeWrapping;
- texture.wrapT = valueV === 0 ? THREE.RepeatWrapping : THREE.ClampToEdgeWrapping;
-
- if ( 'Scaling' in textureNode ) {
-
- var values = textureNode.Scaling.value;
-
- texture.repeat.x = values[ 0 ];
- texture.repeat.y = values[ 1 ];
-
- }
-
- return texture;
-
- },
-
- // load a texture specified as a blob or data URI, or via an external URL using THREE.TextureLoader
- loadTexture: function ( textureNode, images ) {
-
- var fileName;
-
- var currentPath = this.textureLoader.path;
-
- var children = connections.get( textureNode.id ).children;
-
- if ( children !== undefined && children.length > 0 && images[ children[ 0 ].ID ] !== undefined ) {
-
- fileName = images[ children[ 0 ].ID ];
-
- if ( fileName.indexOf( 'blob:' ) === 0 || fileName.indexOf( 'data:' ) === 0 ) {
-
- this.textureLoader.setPath( undefined );
-
- }
-
- }
-
- var texture;
-
- var extension = textureNode.FileName.slice( - 3 ).toLowerCase();
-
- if ( extension === 'tga' ) {
-
- var loader = THREE.Loader.Handlers.get( '.tga' );
-
- if ( loader === null ) {
-
- console.warn( 'FBXLoader: TGALoader not found, creating empty placeholder texture for', fileName );
- texture = new THREE.Texture();
-
- } else {
-
- texture = loader.load( fileName );
-
- }
-
- } else if ( extension === 'psd' ) {
-
- console.warn( 'FBXLoader: PSD textures are not supported, creating empty placeholder texture for', fileName );
- texture = new THREE.Texture();
-
- } else {
-
- texture = this.textureLoader.load( fileName );
-
- }
-
- this.textureLoader.setPath( currentPath );
-
- return texture;
-
- },
-
- // Parse nodes in FBXTree.Objects.Material
- parseMaterials: function ( textureMap ) {
-
- var materialMap = new Map();
-
- if ( 'Material' in fbxTree.Objects ) {
-
- var materialNodes = fbxTree.Objects.Material;
-
- for ( var nodeID in materialNodes ) {
-
- var material = this.parseMaterial( materialNodes[ nodeID ], textureMap );
-
- if ( material !== null ) materialMap.set( parseInt( nodeID ), material );
-
- }
-
- }
-
- return materialMap;
-
- },
-
- // Parse single node in FBXTree.Objects.Material
- // Materials are connected to texture maps in FBXTree.Objects.Textures
- // FBX format currently only supports Lambert and Phong shading models
- parseMaterial: function ( materialNode, textureMap ) {
-
- var ID = materialNode.id;
- var name = materialNode.attrName;
- var type = materialNode.ShadingModel;
-
- // Case where FBX wraps shading model in property object.
- if ( typeof type === 'object' ) {
-
- type = type.value;
-
- }
-
- // Ignore unused materials which don't have any connections.
- if ( ! connections.has( ID ) ) return null;
-
- var parameters = this.parseParameters( materialNode, textureMap, ID );
-
- var material;
-
- switch ( type.toLowerCase() ) {
-
- case 'phong':
- material = new THREE.MeshPhongMaterial();
- break;
- case 'lambert':
- material = new THREE.MeshLambertMaterial();
- break;
- default:
- console.warn( 'THREE.FBXLoader: unknown material type "%s". Defaulting to MeshPhongMaterial.', type );
- material = new THREE.MeshPhongMaterial();
- break;
-
- }
-
- material.setValues( parameters );
- material.name = name;
-
- return material;
-
- },
-
- // Parse FBX material and return parameters suitable for a three.js material
- // Also parse the texture map and return any textures associated with the material
- parseParameters: function ( materialNode, textureMap, ID ) {
-
- var parameters = {};
-
- if ( materialNode.BumpFactor ) {
-
- parameters.bumpScale = materialNode.BumpFactor.value;
-
- }
- if ( materialNode.Diffuse ) {
-
- parameters.color = new THREE.Color().fromArray( materialNode.Diffuse.value );
-
- } else if ( materialNode.DiffuseColor && materialNode.DiffuseColor.type === 'Color' ) {
-
- // The blender exporter exports diffuse here instead of in materialNode.Diffuse
- parameters.color = new THREE.Color().fromArray( materialNode.DiffuseColor.value );
-
- }
-
- if ( materialNode.DisplacementFactor ) {
-
- parameters.displacementScale = materialNode.DisplacementFactor.value;
-
- }
-
- if ( materialNode.Emissive ) {
-
- parameters.emissive = new THREE.Color().fromArray( materialNode.Emissive.value );
-
- } else if ( materialNode.EmissiveColor && materialNode.EmissiveColor.type === 'Color' ) {
-
- // The blender exporter exports emissive color here instead of in materialNode.Emissive
- parameters.emissive = new THREE.Color().fromArray( materialNode.EmissiveColor.value );
-
- }
-
- if ( materialNode.EmissiveFactor ) {
-
- parameters.emissiveIntensity = parseFloat( materialNode.EmissiveFactor.value );
-
- }
-
- if ( materialNode.Opacity ) {
-
- parameters.opacity = parseFloat( materialNode.Opacity.value );
-
- }
-
- if ( parameters.opacity < 1.0 ) {
-
- parameters.transparent = true;
-
- }
-
- if ( materialNode.ReflectionFactor ) {
-
- parameters.reflectivity = materialNode.ReflectionFactor.value;
-
- }
-
- if ( materialNode.Shininess ) {
-
- parameters.shininess = materialNode.Shininess.value;
-
- }
-
- if ( materialNode.Specular ) {
-
- parameters.specular = new THREE.Color().fromArray( materialNode.Specular.value );
-
- } else if ( materialNode.SpecularColor && materialNode.SpecularColor.type === 'Color' ) {
-
- // The blender exporter exports specular color here instead of in materialNode.Specular
- parameters.specular = new THREE.Color().fromArray( materialNode.SpecularColor.value );
-
- }
-
- var self = this;
- connections.get( ID ).children.forEach( function ( child ) {
-
- var type = child.relationship;
-
- switch ( type ) {
-
- case 'Bump':
- parameters.bumpMap = self.getTexture( textureMap, child.ID );
- break;
-
- case 'Maya|TEX_ao_map':
- parameters.aoMap = self.getTexture( textureMap, child.ID );
- break;
-
- case 'DiffuseColor':
- case 'Maya|TEX_color_map':
- parameters.map = self.getTexture( textureMap, child.ID );
- break;
-
- case 'DisplacementColor':
- parameters.displacementMap = self.getTexture( textureMap, child.ID );
- break;
-
- case 'EmissiveColor':
- parameters.emissiveMap = self.getTexture( textureMap, child.ID );
- break;
-
- case 'NormalMap':
- case 'Maya|TEX_normal_map':
- parameters.normalMap = self.getTexture( textureMap, child.ID );
- break;
-
- case 'ReflectionColor':
- parameters.envMap = self.getTexture( textureMap, child.ID );
- parameters.envMap.mapping = THREE.EquirectangularReflectionMapping;
- break;
-
- case 'SpecularColor':
- parameters.specularMap = self.getTexture( textureMap, child.ID );
- break;
-
- case 'TransparentColor':
- parameters.alphaMap = self.getTexture( textureMap, child.ID );
- parameters.transparent = true;
- break;
-
- case 'AmbientColor':
- case 'ShininessExponent': // AKA glossiness map
- case 'SpecularFactor': // AKA specularLevel
- case 'VectorDisplacementColor': // NOTE: Seems to be a copy of DisplacementColor
- default:
- console.warn( 'THREE.FBXLoader: %s map is not supported in three.js, skipping texture.', type );
- break;
-
- }
-
- } );
-
- return parameters;
-
- },
-
- // get a texture from the textureMap for use by a material.
- getTexture: function ( textureMap, id ) {
-
- // if the texture is a layered texture, just use the first layer and issue a warning
- if ( 'LayeredTexture' in fbxTree.Objects && id in fbxTree.Objects.LayeredTexture ) {
-
- console.warn( 'THREE.FBXLoader: layered textures are not supported in three.js. Discarding all but first layer.' );
- id = connections.get( id ).children[ 0 ].ID;
-
- }
-
- return textureMap.get( id );
-
- },
-
- // Parse nodes in FBXTree.Objects.Deformer
- // Deformer node can contain skinning or Vertex Cache animation data, however only skinning is supported here
- // Generates map of Skeleton-like objects for use later when generating and binding skeletons.
- parseDeformers: function () {
-
- var skeletons = {};
- var morphTargets = {};
-
- if ( 'Deformer' in fbxTree.Objects ) {
-
- var DeformerNodes = fbxTree.Objects.Deformer;
-
- for ( var nodeID in DeformerNodes ) {
-
- var deformerNode = DeformerNodes[ nodeID ];
-
- var relationships = connections.get( parseInt( nodeID ) );
-
- if ( deformerNode.attrType === 'Skin' ) {
-
- var skeleton = this.parseSkeleton( relationships, DeformerNodes );
- skeleton.ID = nodeID;
-
- if ( relationships.parents.length > 1 ) console.warn( 'THREE.FBXLoader: skeleton attached to more than one geometry is not supported.' );
- skeleton.geometryID = relationships.parents[ 0 ].ID;
-
- skeletons[ nodeID ] = skeleton;
-
- } else if ( deformerNode.attrType === 'BlendShape' ) {
-
- var morphTarget = {
- id: nodeID,
- };
-
- morphTarget.rawTargets = this.parseMorphTargets( relationships, DeformerNodes );
- morphTarget.id = nodeID;
-
- if ( relationships.parents.length > 1 ) console.warn( 'THREE.FBXLoader: morph target attached to more than one geometry is not supported.' );
-
- morphTargets[ nodeID ] = morphTarget;
-
- }
-
- }
-
- }
-
- return {
-
- skeletons: skeletons,
- morphTargets: morphTargets,
-
- };
-
- },
-
- // Parse single nodes in FBXTree.Objects.Deformer
- // The top level skeleton node has type 'Skin' and sub nodes have type 'Cluster'
- // Each skin node represents a skeleton and each cluster node represents a bone
- parseSkeleton: function ( relationships, deformerNodes ) {
-
- var rawBones = [];
-
- relationships.children.forEach( function ( child ) {
-
- var boneNode = deformerNodes[ child.ID ];
-
- if ( boneNode.attrType !== 'Cluster' ) return;
-
- var rawBone = {
-
- ID: child.ID,
- indices: [],
- weights: [],
- transformLink: new THREE.Matrix4().fromArray( boneNode.TransformLink.a ),
- // transform: new THREE.Matrix4().fromArray( boneNode.Transform.a ),
- // linkMode: boneNode.Mode,
-
- };
-
- if ( 'Indexes' in boneNode ) {
-
- rawBone.indices = boneNode.Indexes.a;
- rawBone.weights = boneNode.Weights.a;
-
- }
-
- rawBones.push( rawBone );
-
- } );
-
- return {
-
- rawBones: rawBones,
- bones: []
-
- };
-
- },
-
- // The top level morph deformer node has type "BlendShape" and sub nodes have type "BlendShapeChannel"
- parseMorphTargets: function ( relationships, deformerNodes ) {
-
- var rawMorphTargets = [];
-
- for ( var i = 0; i < relationships.children.length; i ++ ) {
-
- var child = relationships.children[ i ];
-
- var morphTargetNode = deformerNodes[ child.ID ];
-
- var rawMorphTarget = {
-
- name: morphTargetNode.attrName,
- initialWeight: morphTargetNode.DeformPercent,
- id: morphTargetNode.id,
- fullWeights: morphTargetNode.FullWeights.a
-
- };
-
- if ( morphTargetNode.attrType !== 'BlendShapeChannel' ) return;
-
- rawMorphTarget.geoID = connections.get( parseInt( child.ID ) ).children.filter( function ( child ) {
-
- return child.relationship === undefined;
-
- } )[ 0 ].ID;
-
- rawMorphTargets.push( rawMorphTarget );
-
- }
-
- return rawMorphTargets;
-
- },
-
- // create the main THREE.Group() to be returned by the loader
- parseScene: function ( deformers, geometryMap, materialMap ) {
-
- sceneGraph = new THREE.Group();
-
- var modelMap = this.parseModels( deformers.skeletons, geometryMap, materialMap );
-
- var modelNodes = fbxTree.Objects.Model;
-
- var self = this;
- modelMap.forEach( function ( model ) {
-
- var modelNode = modelNodes[ model.ID ];
- self.setLookAtProperties( model, modelNode );
-
- var parentConnections = connections.get( model.ID ).parents;
-
- parentConnections.forEach( function ( connection ) {
-
- var parent = modelMap.get( connection.ID );
- if ( parent !== undefined ) parent.add( model );
-
- } );
-
- if ( model.parent === null ) {
-
- sceneGraph.add( model );
-
- }
-
-
- } );
-
- this.bindSkeleton( deformers.skeletons, geometryMap, modelMap );
-
- this.createAmbientLight();
-
- this.setupMorphMaterials();
-
- sceneGraph.traverse( function ( node ) {
-
- if ( node.userData.transformData ) {
-
- if ( node.parent ) node.userData.transformData.parentMatrixWorld = node.parent.matrix;
-
- var transform = generateTransform( node.userData.transformData );
-
- node.applyMatrix( transform );
-
- }
-
- } );
-
- var animations = new AnimationParser().parse();
-
- // if all the models where already combined in a single group, just return that
- if ( sceneGraph.children.length === 1 && sceneGraph.children[ 0 ].isGroup ) {
-
- sceneGraph.children[ 0 ].animations = animations;
- sceneGraph = sceneGraph.children[ 0 ];
-
- }
-
- sceneGraph.animations = animations;
-
- },
-
- // parse nodes in FBXTree.Objects.Model
- parseModels: function ( skeletons, geometryMap, materialMap ) {
-
- var modelMap = new Map();
- var modelNodes = fbxTree.Objects.Model;
-
- for ( var nodeID in modelNodes ) {
-
- var id = parseInt( nodeID );
- var node = modelNodes[ nodeID ];
- var relationships = connections.get( id );
-
- var model = this.buildSkeleton( relationships, skeletons, id, node.attrName );
-
- if ( ! model ) {
-
- switch ( node.attrType ) {
-
- case 'Camera':
- model = this.createCamera( relationships );
- break;
- case 'Light':
- model = this.createLight( relationships );
- break;
- case 'Mesh':
- model = this.createMesh( relationships, geometryMap, materialMap );
- break;
- case 'NurbsCurve':
- model = this.createCurve( relationships, geometryMap );
- break;
- case 'LimbNode':
- case 'Root':
- model = new THREE.Bone();
- break;
- case 'Null':
- default:
- model = new THREE.Group();
- break;
-
- }
-
- model.name = THREE.PropertyBinding.sanitizeNodeName( node.attrName );
- model.ID = id;
-
- }
-
- this.getTransformData( model, node );
- modelMap.set( id, model );
-
- }
-
- return modelMap;
-
- },
-
- buildSkeleton: function ( relationships, skeletons, id, name ) {
-
- var bone = null;
-
- relationships.parents.forEach( function ( parent ) {
-
- for ( var ID in skeletons ) {
-
- var skeleton = skeletons[ ID ];
-
- skeleton.rawBones.forEach( function ( rawBone, i ) {
-
- if ( rawBone.ID === parent.ID ) {
-
- var subBone = bone;
- bone = new THREE.Bone();
-
- bone.matrixWorld.copy( rawBone.transformLink );
-
- // set name and id here - otherwise in cases where "subBone" is created it will not have a name / id
- bone.name = THREE.PropertyBinding.sanitizeNodeName( name );
- bone.ID = id;
-
- skeleton.bones[ i ] = bone;
-
- // In cases where a bone is shared between multiple meshes
- // duplicate the bone here and and it as a child of the first bone
- if ( subBone !== null ) {
-
- bone.add( subBone );
-
- }
-
- }
-
- } );
-
- }
-
- } );
-
- return bone;
-
- },
-
- // create a THREE.PerspectiveCamera or THREE.OrthographicCamera
- createCamera: function ( relationships ) {
-
- var model;
- var cameraAttribute;
-
- relationships.children.forEach( function ( child ) {
-
- var attr = fbxTree.Objects.NodeAttribute[ child.ID ];
-
- if ( attr !== undefined ) {
-
- cameraAttribute = attr;
-
- }
-
- } );
-
- if ( cameraAttribute === undefined ) {
-
- model = new THREE.Object3D();
-
- } else {
-
- var type = 0;
- if ( cameraAttribute.CameraProjectionType !== undefined && cameraAttribute.CameraProjectionType.value === 1 ) {
-
- type = 1;
-
- }
-
- var nearClippingPlane = 1;
- if ( cameraAttribute.NearPlane !== undefined ) {
-
- nearClippingPlane = cameraAttribute.NearPlane.value / 1000;
-
- }
-
- var farClippingPlane = 1000;
- if ( cameraAttribute.FarPlane !== undefined ) {
-
- farClippingPlane = cameraAttribute.FarPlane.value / 1000;
-
- }
-
-
- var width = window.innerWidth;
- var height = window.innerHeight;
-
- if ( cameraAttribute.AspectWidth !== undefined && cameraAttribute.AspectHeight !== undefined ) {
-
- width = cameraAttribute.AspectWidth.value;
- height = cameraAttribute.AspectHeight.value;
-
- }
-
- var aspect = width / height;
-
- var fov = 45;
- if ( cameraAttribute.FieldOfView !== undefined ) {
-
- fov = cameraAttribute.FieldOfView.value;
-
- }
-
- var focalLength = cameraAttribute.FocalLength ? cameraAttribute.FocalLength.value : null;
-
- switch ( type ) {
-
- case 0: // Perspective
- model = new THREE.PerspectiveCamera( fov, aspect, nearClippingPlane, farClippingPlane );
- if ( focalLength !== null ) model.setFocalLength( focalLength );
- break;
-
- case 1: // Orthographic
- model = new THREE.OrthographicCamera( - width / 2, width / 2, height / 2, - height / 2, nearClippingPlane, farClippingPlane );
- break;
-
- default:
- console.warn( 'THREE.FBXLoader: Unknown camera type ' + type + '.' );
- model = new THREE.Object3D();
- break;
-
- }
-
- }
-
- return model;
-
- },
-
- // Create a THREE.DirectionalLight, THREE.PointLight or THREE.SpotLight
- createLight: function ( relationships ) {
-
- var model;
- var lightAttribute;
-
- relationships.children.forEach( function ( child ) {
-
- var attr = fbxTree.Objects.NodeAttribute[ child.ID ];
-
- if ( attr !== undefined ) {
-
- lightAttribute = attr;
-
- }
-
- } );
-
- if ( lightAttribute === undefined ) {
-
- model = new THREE.Object3D();
-
- } else {
-
- var type;
-
- // LightType can be undefined for Point lights
- if ( lightAttribute.LightType === undefined ) {
-
- type = 0;
-
- } else {
-
- type = lightAttribute.LightType.value;
-
- }
-
- var color = 0xffffff;
-
- if ( lightAttribute.Color !== undefined ) {
-
- color = new THREE.Color().fromArray( lightAttribute.Color.value );
-
- }
-
- var intensity = ( lightAttribute.Intensity === undefined ) ? 1 : lightAttribute.Intensity.value / 100;
-
- // light disabled
- if ( lightAttribute.CastLightOnObject !== undefined && lightAttribute.CastLightOnObject.value === 0 ) {
-
- intensity = 0;
-
- }
-
- var distance = 0;
- if ( lightAttribute.FarAttenuationEnd !== undefined ) {
-
- if ( lightAttribute.EnableFarAttenuation !== undefined && lightAttribute.EnableFarAttenuation.value === 0 ) {
-
- distance = 0;
-
- } else {
-
- distance = lightAttribute.FarAttenuationEnd.value;
-
- }
-
- }
-
- // TODO: could this be calculated linearly from FarAttenuationStart to FarAttenuationEnd?
- var decay = 1;
-
- switch ( type ) {
-
- case 0: // Point
- model = new THREE.PointLight( color, intensity, distance, decay );
- break;
-
- case 1: // Directional
- model = new THREE.DirectionalLight( color, intensity );
- break;
-
- case 2: // Spot
- var angle = Math.PI / 3;
-
- if ( lightAttribute.InnerAngle !== undefined ) {
-
- angle = THREE.Math.degToRad( lightAttribute.InnerAngle.value );
-
- }
-
- var penumbra = 0;
- if ( lightAttribute.OuterAngle !== undefined ) {
-
- // TODO: this is not correct - FBX calculates outer and inner angle in degrees
- // with OuterAngle > InnerAngle && OuterAngle <= Math.PI
- // while three.js uses a penumbra between (0, 1) to attenuate the inner angle
- penumbra = THREE.Math.degToRad( lightAttribute.OuterAngle.value );
- penumbra = Math.max( penumbra, 1 );
-
- }
-
- model = new THREE.SpotLight( color, intensity, distance, angle, penumbra, decay );
- break;
-
- default:
- console.warn( 'THREE.FBXLoader: Unknown light type ' + lightAttribute.LightType.value + ', defaulting to a THREE.PointLight.' );
- model = new THREE.PointLight( color, intensity );
- break;
-
- }
-
- if ( lightAttribute.CastShadows !== undefined && lightAttribute.CastShadows.value === 1 ) {
-
- model.castShadow = true;
-
- }
-
- }
-
- return model;
-
- },
-
- createMesh: function ( relationships, geometryMap, materialMap ) {
-
- var model;
- var geometry = null;
- var material = null;
- var materials = [];
-
- // get geometry and materials(s) from connections
- relationships.children.forEach( function ( child ) {
-
- if ( geometryMap.has( child.ID ) ) {
-
- geometry = geometryMap.get( child.ID );
-
- }
-
- if ( materialMap.has( child.ID ) ) {
-
- materials.push( materialMap.get( child.ID ) );
-
- }
-
- } );
-
- if ( materials.length > 1 ) {
-
- material = materials;
-
- } else if ( materials.length > 0 ) {
-
- material = materials[ 0 ];
-
- } else {
-
- material = new THREE.MeshPhongMaterial( { color: 0xcccccc } );
- materials.push( material );
-
- }
-
- if ( 'color' in geometry.attributes ) {
-
- materials.forEach( function ( material ) {
-
- material.vertexColors = THREE.VertexColors;
-
- } );
-
- }
-
- if ( geometry.FBX_Deformer ) {
-
- materials.forEach( function ( material ) {
-
- material.skinning = true;
-
- } );
-
- model = new THREE.SkinnedMesh( geometry, material );
- model.normalizeSkinWeights();
-
- } else {
-
- model = new THREE.Mesh( geometry, material );
-
- }
-
- return model;
-
- },
-
- createCurve: function ( relationships, geometryMap ) {
-
- var geometry = relationships.children.reduce( function ( geo, child ) {
-
- if ( geometryMap.has( child.ID ) ) geo = geometryMap.get( child.ID );
-
- return geo;
-
- }, null );
-
- // FBX does not list materials for Nurbs lines, so we'll just put our own in here.
- var material = new THREE.LineBasicMaterial( { color: 0x3300ff, linewidth: 1 } );
- return new THREE.Line( geometry, material );
-
- },
-
- // parse the model node for transform data
- getTransformData: function ( model, modelNode ) {
-
- var transformData = {};
-
- if ( 'InheritType' in modelNode ) transformData.inheritType = parseInt( modelNode.InheritType.value );
-
- if ( 'RotationOrder' in modelNode ) transformData.eulerOrder = getEulerOrder( modelNode.RotationOrder.value );
- else transformData.eulerOrder = 'ZYX';
-
- if ( 'Lcl_Translation' in modelNode ) transformData.translation = modelNode.Lcl_Translation.value;
-
- if ( 'PreRotation' in modelNode ) transformData.preRotation = modelNode.PreRotation.value;
- if ( 'Lcl_Rotation' in modelNode ) transformData.rotation = modelNode.Lcl_Rotation.value;
- if ( 'PostRotation' in modelNode ) transformData.postRotation = modelNode.PostRotation.value;
-
- if ( 'Lcl_Scaling' in modelNode ) transformData.scale = modelNode.Lcl_Scaling.value;
-
- if ( 'ScalingOffset' in modelNode ) transformData.scalingOffset = modelNode.ScalingOffset.value;
- if ( 'ScalingPivot' in modelNode ) transformData.scalingPivot = modelNode.ScalingPivot.value;
-
- if ( 'RotationOffset' in modelNode ) transformData.rotationOffset = modelNode.RotationOffset.value;
- if ( 'RotationPivot' in modelNode ) transformData.rotationPivot = modelNode.RotationPivot.value;
-
- model.userData.transformData = transformData;
-
- },
-
- setLookAtProperties: function ( model, modelNode ) {
-
- if ( 'LookAtProperty' in modelNode ) {
-
- var children = connections.get( model.ID ).children;
-
- children.forEach( function ( child ) {
-
- if ( child.relationship === 'LookAtProperty' ) {
-
- var lookAtTarget = fbxTree.Objects.Model[ child.ID ];
-
- if ( 'Lcl_Translation' in lookAtTarget ) {
-
- var pos = lookAtTarget.Lcl_Translation.value;
-
- // DirectionalLight, SpotLight
- if ( model.target !== undefined ) {
-
- model.target.position.fromArray( pos );
- sceneGraph.add( model.target );
-
- } else { // Cameras and other Object3Ds
-
- model.lookAt( new THREE.Vector3().fromArray( pos ) );
-
- }
-
- }
-
- }
-
- } );
-
- }
-
- },
-
- bindSkeleton: function ( skeletons, geometryMap, modelMap ) {
-
- var bindMatrices = this.parsePoseNodes();
-
- for ( var ID in skeletons ) {
-
- var skeleton = skeletons[ ID ];
-
- var parents = connections.get( parseInt( skeleton.ID ) ).parents;
-
- parents.forEach( function ( parent ) {
-
- if ( geometryMap.has( parent.ID ) ) {
-
- var geoID = parent.ID;
- var geoRelationships = connections.get( geoID );
-
- geoRelationships.parents.forEach( function ( geoConnParent ) {
-
- if ( modelMap.has( geoConnParent.ID ) ) {
-
- var model = modelMap.get( geoConnParent.ID );
-
- model.bind( new THREE.Skeleton( skeleton.bones ), bindMatrices[ geoConnParent.ID ] );
-
- }
-
- } );
-
- }
-
- } );
-
- }
-
- },
-
- parsePoseNodes: function () {
-
- var bindMatrices = {};
-
- if ( 'Pose' in fbxTree.Objects ) {
-
- var BindPoseNode = fbxTree.Objects.Pose;
-
- for ( var nodeID in BindPoseNode ) {
-
- if ( BindPoseNode[ nodeID ].attrType === 'BindPose' ) {
-
- var poseNodes = BindPoseNode[ nodeID ].PoseNode;
-
- if ( Array.isArray( poseNodes ) ) {
-
- poseNodes.forEach( function ( poseNode ) {
-
- bindMatrices[ poseNode.Node ] = new THREE.Matrix4().fromArray( poseNode.Matrix.a );
-
- } );
-
- } else {
-
- bindMatrices[ poseNodes.Node ] = new THREE.Matrix4().fromArray( poseNodes.Matrix.a );
-
- }
-
- }
-
- }
-
- }
-
- return bindMatrices;
-
- },
-
- // Parse ambient color in FBXTree.GlobalSettings - if it's not set to black (default), create an ambient light
- createAmbientLight: function () {
-
- if ( 'GlobalSettings' in fbxTree && 'AmbientColor' in fbxTree.GlobalSettings ) {
-
- var ambientColor = fbxTree.GlobalSettings.AmbientColor.value;
- var r = ambientColor[ 0 ];
- var g = ambientColor[ 1 ];
- var b = ambientColor[ 2 ];
-
- if ( r !== 0 || g !== 0 || b !== 0 ) {
-
- var color = new THREE.Color( r, g, b );
- sceneGraph.add( new THREE.AmbientLight( color, 1 ) );
-
- }
-
- }
-
- },
-
- setupMorphMaterials: function () {
-
- var self = this;
- sceneGraph.traverse( function ( child ) {
-
- if ( child.isMesh ) {
-
- if ( child.geometry.morphAttributes.position && child.geometry.morphAttributes.position.length ) {
-
- if ( Array.isArray( child.material ) ) {
-
- child.material.forEach( function ( material, i ) {
-
- self.setupMorphMaterial( child, material, i );
-
- } );
-
- } else {
-
- self.setupMorphMaterial( child, child.material );
-
- }
-
- }
-
- }
-
- } );
-
- },
-
- setupMorphMaterial: function ( child, material, index ) {
-
- var uuid = child.uuid;
- var matUuid = material.uuid;
-
- // if a geometry has morph targets, it cannot share the material with other geometries
- var sharedMat = false;
-
- sceneGraph.traverse( function ( node ) {
-
- if ( node.isMesh ) {
-
- if ( Array.isArray( node.material ) ) {
-
- node.material.forEach( function ( mat ) {
-
- if ( mat.uuid === matUuid && node.uuid !== uuid ) sharedMat = true;
-
- } );
-
- } else if ( node.material.uuid === matUuid && node.uuid !== uuid ) sharedMat = true;
-
- }
-
- } );
-
- if ( sharedMat === true ) {
-
- var clonedMat = material.clone();
- clonedMat.morphTargets = true;
-
- if ( index === undefined ) child.material = clonedMat;
- else child.material[ index ] = clonedMat;
-
- } else material.morphTargets = true;
-
- }
-
- };
-
- // parse Geometry data from FBXTree and return map of BufferGeometries
- function GeometryParser() {}
-
- GeometryParser.prototype = {
-
- constructor: GeometryParser,
-
- // Parse nodes in FBXTree.Objects.Geometry
- parse: function ( deformers ) {
-
- var geometryMap = new Map();
-
- if ( 'Geometry' in fbxTree.Objects ) {
-
- var geoNodes = fbxTree.Objects.Geometry;
-
- for ( var nodeID in geoNodes ) {
-
- var relationships = connections.get( parseInt( nodeID ) );
- var geo = this.parseGeometry( relationships, geoNodes[ nodeID ], deformers );
-
- geometryMap.set( parseInt( nodeID ), geo );
-
- }
-
- }
-
- return geometryMap;
-
- },
-
- // Parse single node in FBXTree.Objects.Geometry
- parseGeometry: function ( relationships, geoNode, deformers ) {
-
- switch ( geoNode.attrType ) {
-
- case 'Mesh':
- return this.parseMeshGeometry( relationships, geoNode, deformers );
- break;
-
- case 'NurbsCurve':
- return this.parseNurbsGeometry( geoNode );
- break;
-
- }
-
- },
-
- // Parse single node mesh geometry in FBXTree.Objects.Geometry
- parseMeshGeometry: function ( relationships, geoNode, deformers ) {
-
- var skeletons = deformers.skeletons;
- var morphTargets = deformers.morphTargets;
-
- var modelNodes = relationships.parents.map( function ( parent ) {
-
- return fbxTree.Objects.Model[ parent.ID ];
-
- } );
-
- // don't create geometry if it is not associated with any models
- if ( modelNodes.length === 0 ) return;
-
- var skeleton = relationships.children.reduce( function ( skeleton, child ) {
-
- if ( skeletons[ child.ID ] !== undefined ) skeleton = skeletons[ child.ID ];
-
- return skeleton;
-
- }, null );
-
- var morphTarget = relationships.children.reduce( function ( morphTarget, child ) {
-
- if ( morphTargets[ child.ID ] !== undefined ) morphTarget = morphTargets[ child.ID ];
-
- return morphTarget;
-
- }, null );
-
- // Assume one model and get the preRotation from that
- // if there is more than one model associated with the geometry this may cause problems
- var modelNode = modelNodes[ 0 ];
-
- var transformData = {};
-
- if ( 'RotationOrder' in modelNode ) transformData.eulerOrder = getEulerOrder( modelNode.RotationOrder.value );
- if ( 'InheritType' in modelNode ) transformData.inheritType = parseInt( modelNode.InheritType.value );
-
- if ( 'GeometricTranslation' in modelNode ) transformData.translation = modelNode.GeometricTranslation.value;
- if ( 'GeometricRotation' in modelNode ) transformData.rotation = modelNode.GeometricRotation.value;
- if ( 'GeometricScaling' in modelNode ) transformData.scale = modelNode.GeometricScaling.value;
-
- var transform = generateTransform( transformData );
-
- return this.genGeometry( geoNode, skeleton, morphTarget, transform );
-
- },
-
- // Generate a THREE.BufferGeometry from a node in FBXTree.Objects.Geometry
- genGeometry: function ( geoNode, skeleton, morphTarget, preTransform ) {
-
- var geo = new THREE.BufferGeometry();
- if ( geoNode.attrName ) geo.name = geoNode.attrName;
-
- var geoInfo = this.parseGeoNode( geoNode, skeleton );
- var buffers = this.genBuffers( geoInfo );
-
- var positionAttribute = new THREE.Float32BufferAttribute( buffers.vertex, 3 );
-
- preTransform.applyToBufferAttribute( positionAttribute );
-
- geo.addAttribute( 'position', positionAttribute );
-
- if ( buffers.colors.length > 0 ) {
-
- geo.addAttribute( 'color', new THREE.Float32BufferAttribute( buffers.colors, 3 ) );
-
- }
-
- if ( skeleton ) {
-
- geo.addAttribute( 'skinIndex', new THREE.Uint16BufferAttribute( buffers.weightsIndices, 4 ) );
-
- geo.addAttribute( 'skinWeight', new THREE.Float32BufferAttribute( buffers.vertexWeights, 4 ) );
-
- // used later to bind the skeleton to the model
- geo.FBX_Deformer = skeleton;
-
- }
-
- if ( buffers.normal.length > 0 ) {
-
- var normalAttribute = new THREE.Float32BufferAttribute( buffers.normal, 3 );
-
- var normalMatrix = new THREE.Matrix3().getNormalMatrix( preTransform );
- normalMatrix.applyToBufferAttribute( normalAttribute );
-
- geo.addAttribute( 'normal', normalAttribute );
-
- }
-
- buffers.uvs.forEach( function ( uvBuffer, i ) {
-
- // subsequent uv buffers are called 'uv1', 'uv2', ...
- var name = 'uv' + ( i + 1 ).toString();
-
- // the first uv buffer is just called 'uv'
- if ( i === 0 ) {
-
- name = 'uv';
-
- }
-
- geo.addAttribute( name, new THREE.Float32BufferAttribute( buffers.uvs[ i ], 2 ) );
-
- } );
-
- if ( geoInfo.material && geoInfo.material.mappingType !== 'AllSame' ) {
-
- // Convert the material indices of each vertex into rendering groups on the geometry.
- var prevMaterialIndex = buffers.materialIndex[ 0 ];
- var startIndex = 0;
-
- buffers.materialIndex.forEach( function ( currentIndex, i ) {
-
- if ( currentIndex !== prevMaterialIndex ) {
-
- geo.addGroup( startIndex, i - startIndex, prevMaterialIndex );
-
- prevMaterialIndex = currentIndex;
- startIndex = i;
-
- }
-
- } );
-
- // the loop above doesn't add the last group, do that here.
- if ( geo.groups.length > 0 ) {
-
- var lastGroup = geo.groups[ geo.groups.length - 1 ];
- var lastIndex = lastGroup.start + lastGroup.count;
-
- if ( lastIndex !== buffers.materialIndex.length ) {
-
- geo.addGroup( lastIndex, buffers.materialIndex.length - lastIndex, prevMaterialIndex );
-
- }
-
- }
-
- // case where there are multiple materials but the whole geometry is only
- // using one of them
- if ( geo.groups.length === 0 ) {
-
- geo.addGroup( 0, buffers.materialIndex.length, buffers.materialIndex[ 0 ] );
-
- }
-
- }
-
- this.addMorphTargets( geo, geoNode, morphTarget, preTransform );
-
- return geo;
-
- },
-
- parseGeoNode: function ( geoNode, skeleton ) {
-
- var geoInfo = {};
-
- geoInfo.vertexPositions = ( geoNode.Vertices !== undefined ) ? geoNode.Vertices.a : [];
- geoInfo.vertexIndices = ( geoNode.PolygonVertexIndex !== undefined ) ? geoNode.PolygonVertexIndex.a : [];
-
- if ( geoNode.LayerElementColor ) {
-
- geoInfo.color = this.parseVertexColors( geoNode.LayerElementColor[ 0 ] );
-
- }
-
- if ( geoNode.LayerElementMaterial ) {
-
- geoInfo.material = this.parseMaterialIndices( geoNode.LayerElementMaterial[ 0 ] );
-
- }
-
- if ( geoNode.LayerElementNormal ) {
-
- geoInfo.normal = this.parseNormals( geoNode.LayerElementNormal[ 0 ] );
-
- }
-
- if ( geoNode.LayerElementUV ) {
-
- geoInfo.uv = [];
-
- var i = 0;
- while ( geoNode.LayerElementUV[ i ] ) {
-
- geoInfo.uv.push( this.parseUVs( geoNode.LayerElementUV[ i ] ) );
- i ++;
-
- }
-
- }
-
- geoInfo.weightTable = {};
-
- if ( skeleton !== null ) {
-
- geoInfo.skeleton = skeleton;
-
- skeleton.rawBones.forEach( function ( rawBone, i ) {
-
- // loop over the bone's vertex indices and weights
- rawBone.indices.forEach( function ( index, j ) {
-
- if ( geoInfo.weightTable[ index ] === undefined ) geoInfo.weightTable[ index ] = [];
-
- geoInfo.weightTable[ index ].push( {
-
- id: i,
- weight: rawBone.weights[ j ],
-
- } );
-
- } );
-
- } );
-
- }
-
- return geoInfo;
-
- },
-
- genBuffers: function ( geoInfo ) {
-
- var buffers = {
- vertex: [],
- normal: [],
- colors: [],
- uvs: [],
- materialIndex: [],
- vertexWeights: [],
- weightsIndices: [],
- };
-
- var polygonIndex = 0;
- var faceLength = 0;
- var displayedWeightsWarning = false;
-
- // these will hold data for a single face
- var facePositionIndexes = [];
- var faceNormals = [];
- var faceColors = [];
- var faceUVs = [];
- var faceWeights = [];
- var faceWeightIndices = [];
-
- var self = this;
- geoInfo.vertexIndices.forEach( function ( vertexIndex, polygonVertexIndex ) {
-
- var endOfFace = false;
-
- // Face index and vertex index arrays are combined in a single array
- // A cube with quad faces looks like this:
- // PolygonVertexIndex: *24 {
- // a: 0, 1, 3, -3, 2, 3, 5, -5, 4, 5, 7, -7, 6, 7, 1, -1, 1, 7, 5, -4, 6, 0, 2, -5
- // }
- // Negative numbers mark the end of a face - first face here is 0, 1, 3, -3
- // to find index of last vertex bit shift the index: ^ - 1
- if ( vertexIndex < 0 ) {
-
- vertexIndex = vertexIndex ^ - 1; // equivalent to ( x * -1 ) - 1
- endOfFace = true;
-
- }
-
- var weightIndices = [];
- var weights = [];
-
- facePositionIndexes.push( vertexIndex * 3, vertexIndex * 3 + 1, vertexIndex * 3 + 2 );
-
- if ( geoInfo.color ) {
-
- var data = getData( polygonVertexIndex, polygonIndex, vertexIndex, geoInfo.color );
-
- faceColors.push( data[ 0 ], data[ 1 ], data[ 2 ] );
-
- }
-
- if ( geoInfo.skeleton ) {
-
- if ( geoInfo.weightTable[ vertexIndex ] !== undefined ) {
-
- geoInfo.weightTable[ vertexIndex ].forEach( function ( wt ) {
-
- weights.push( wt.weight );
- weightIndices.push( wt.id );
-
- } );
-
-
- }
-
- if ( weights.length > 4 ) {
-
- if ( ! displayedWeightsWarning ) {
-
- console.warn( 'THREE.FBXLoader: Vertex has more than 4 skinning weights assigned to vertex. Deleting additional weights.' );
- displayedWeightsWarning = true;
-
- }
-
- var wIndex = [ 0, 0, 0, 0 ];
- var Weight = [ 0, 0, 0, 0 ];
-
- weights.forEach( function ( weight, weightIndex ) {
-
- var currentWeight = weight;
- var currentIndex = weightIndices[ weightIndex ];
-
- Weight.forEach( function ( comparedWeight, comparedWeightIndex, comparedWeightArray ) {
-
- if ( currentWeight > comparedWeight ) {
-
- comparedWeightArray[ comparedWeightIndex ] = currentWeight;
- currentWeight = comparedWeight;
-
- var tmp = wIndex[ comparedWeightIndex ];
- wIndex[ comparedWeightIndex ] = currentIndex;
- currentIndex = tmp;
-
- }
-
- } );
-
- } );
-
- weightIndices = wIndex;
- weights = Weight;
-
- }
-
- // if the weight array is shorter than 4 pad with 0s
- while ( weights.length < 4 ) {
-
- weights.push( 0 );
- weightIndices.push( 0 );
-
- }
-
- for ( var i = 0; i < 4; ++ i ) {
-
- faceWeights.push( weights[ i ] );
- faceWeightIndices.push( weightIndices[ i ] );
-
- }
-
- }
-
- if ( geoInfo.normal ) {
-
- var data = getData( polygonVertexIndex, polygonIndex, vertexIndex, geoInfo.normal );
-
- faceNormals.push( data[ 0 ], data[ 1 ], data[ 2 ] );
-
- }
-
- if ( geoInfo.material && geoInfo.material.mappingType !== 'AllSame' ) {
-
- var materialIndex = getData( polygonVertexIndex, polygonIndex, vertexIndex, geoInfo.material )[ 0 ];
-
- }
-
- if ( geoInfo.uv ) {
-
- geoInfo.uv.forEach( function ( uv, i ) {
-
- var data = getData( polygonVertexIndex, polygonIndex, vertexIndex, uv );
-
- if ( faceUVs[ i ] === undefined ) {
-
- faceUVs[ i ] = [];
-
- }
-
- faceUVs[ i ].push( data[ 0 ] );
- faceUVs[ i ].push( data[ 1 ] );
-
- } );
-
- }
-
- faceLength ++;
-
- if ( endOfFace ) {
-
- self.genFace( buffers, geoInfo, facePositionIndexes, materialIndex, faceNormals, faceColors, faceUVs, faceWeights, faceWeightIndices, faceLength );
-
- polygonIndex ++;
- faceLength = 0;
-
- // reset arrays for the next face
- facePositionIndexes = [];
- faceNormals = [];
- faceColors = [];
- faceUVs = [];
- faceWeights = [];
- faceWeightIndices = [];
-
- }
-
- } );
-
- return buffers;
-
- },
-
- // Generate data for a single face in a geometry. If the face is a quad then split it into 2 tris
- genFace: function ( buffers, geoInfo, facePositionIndexes, materialIndex, faceNormals, faceColors, faceUVs, faceWeights, faceWeightIndices, faceLength ) {
-
- for ( var i = 2; i < faceLength; i ++ ) {
-
- buffers.vertex.push( geoInfo.vertexPositions[ facePositionIndexes[ 0 ] ] );
- buffers.vertex.push( geoInfo.vertexPositions[ facePositionIndexes[ 1 ] ] );
- buffers.vertex.push( geoInfo.vertexPositions[ facePositionIndexes[ 2 ] ] );
-
- buffers.vertex.push( geoInfo.vertexPositions[ facePositionIndexes[ ( i - 1 ) * 3 ] ] );
- buffers.vertex.push( geoInfo.vertexPositions[ facePositionIndexes[ ( i - 1 ) * 3 + 1 ] ] );
- buffers.vertex.push( geoInfo.vertexPositions[ facePositionIndexes[ ( i - 1 ) * 3 + 2 ] ] );
-
- buffers.vertex.push( geoInfo.vertexPositions[ facePositionIndexes[ i * 3 ] ] );
- buffers.vertex.push( geoInfo.vertexPositions[ facePositionIndexes[ i * 3 + 1 ] ] );
- buffers.vertex.push( geoInfo.vertexPositions[ facePositionIndexes[ i * 3 + 2 ] ] );
-
- if ( geoInfo.skeleton ) {
-
- buffers.vertexWeights.push( faceWeights[ 0 ] );
- buffers.vertexWeights.push( faceWeights[ 1 ] );
- buffers.vertexWeights.push( faceWeights[ 2 ] );
- buffers.vertexWeights.push( faceWeights[ 3 ] );
-
- buffers.vertexWeights.push( faceWeights[ ( i - 1 ) * 4 ] );
- buffers.vertexWeights.push( faceWeights[ ( i - 1 ) * 4 + 1 ] );
- buffers.vertexWeights.push( faceWeights[ ( i - 1 ) * 4 + 2 ] );
- buffers.vertexWeights.push( faceWeights[ ( i - 1 ) * 4 + 3 ] );
-
- buffers.vertexWeights.push( faceWeights[ i * 4 ] );
- buffers.vertexWeights.push( faceWeights[ i * 4 + 1 ] );
- buffers.vertexWeights.push( faceWeights[ i * 4 + 2 ] );
- buffers.vertexWeights.push( faceWeights[ i * 4 + 3 ] );
-
- buffers.weightsIndices.push( faceWeightIndices[ 0 ] );
- buffers.weightsIndices.push( faceWeightIndices[ 1 ] );
- buffers.weightsIndices.push( faceWeightIndices[ 2 ] );
- buffers.weightsIndices.push( faceWeightIndices[ 3 ] );
-
- buffers.weightsIndices.push( faceWeightIndices[ ( i - 1 ) * 4 ] );
- buffers.weightsIndices.push( faceWeightIndices[ ( i - 1 ) * 4 + 1 ] );
- buffers.weightsIndices.push( faceWeightIndices[ ( i - 1 ) * 4 + 2 ] );
- buffers.weightsIndices.push( faceWeightIndices[ ( i - 1 ) * 4 + 3 ] );
-
- buffers.weightsIndices.push( faceWeightIndices[ i * 4 ] );
- buffers.weightsIndices.push( faceWeightIndices[ i * 4 + 1 ] );
- buffers.weightsIndices.push( faceWeightIndices[ i * 4 + 2 ] );
- buffers.weightsIndices.push( faceWeightIndices[ i * 4 + 3 ] );
-
- }
-
- if ( geoInfo.color ) {
-
- buffers.colors.push( faceColors[ 0 ] );
- buffers.colors.push( faceColors[ 1 ] );
- buffers.colors.push( faceColors[ 2 ] );
-
- buffers.colors.push( faceColors[ ( i - 1 ) * 3 ] );
- buffers.colors.push( faceColors[ ( i - 1 ) * 3 + 1 ] );
- buffers.colors.push( faceColors[ ( i - 1 ) * 3 + 2 ] );
-
- buffers.colors.push( faceColors[ i * 3 ] );
- buffers.colors.push( faceColors[ i * 3 + 1 ] );
- buffers.colors.push( faceColors[ i * 3 + 2 ] );
-
- }
-
- if ( geoInfo.material && geoInfo.material.mappingType !== 'AllSame' ) {
-
- buffers.materialIndex.push( materialIndex );
- buffers.materialIndex.push( materialIndex );
- buffers.materialIndex.push( materialIndex );
-
- }
-
- if ( geoInfo.normal ) {
-
- buffers.normal.push( faceNormals[ 0 ] );
- buffers.normal.push( faceNormals[ 1 ] );
- buffers.normal.push( faceNormals[ 2 ] );
-
- buffers.normal.push( faceNormals[ ( i - 1 ) * 3 ] );
- buffers.normal.push( faceNormals[ ( i - 1 ) * 3 + 1 ] );
- buffers.normal.push( faceNormals[ ( i - 1 ) * 3 + 2 ] );
-
- buffers.normal.push( faceNormals[ i * 3 ] );
- buffers.normal.push( faceNormals[ i * 3 + 1 ] );
- buffers.normal.push( faceNormals[ i * 3 + 2 ] );
-
- }
-
- if ( geoInfo.uv ) {
-
- geoInfo.uv.forEach( function ( uv, j ) {
-
- if ( buffers.uvs[ j ] === undefined ) buffers.uvs[ j ] = [];
-
- buffers.uvs[ j ].push( faceUVs[ j ][ 0 ] );
- buffers.uvs[ j ].push( faceUVs[ j ][ 1 ] );
-
- buffers.uvs[ j ].push( faceUVs[ j ][ ( i - 1 ) * 2 ] );
- buffers.uvs[ j ].push( faceUVs[ j ][ ( i - 1 ) * 2 + 1 ] );
-
- buffers.uvs[ j ].push( faceUVs[ j ][ i * 2 ] );
- buffers.uvs[ j ].push( faceUVs[ j ][ i * 2 + 1 ] );
-
- } );
-
- }
-
- }
-
- },
-
- addMorphTargets: function ( parentGeo, parentGeoNode, morphTarget, preTransform ) {
-
- if ( morphTarget === null ) return;
-
- parentGeo.morphAttributes.position = [];
- // parentGeo.morphAttributes.normal = []; // not implemented
-
- var self = this;
- morphTarget.rawTargets.forEach( function ( rawTarget ) {
-
- var morphGeoNode = fbxTree.Objects.Geometry[ rawTarget.geoID ];
-
- if ( morphGeoNode !== undefined ) {
-
- self.genMorphGeometry( parentGeo, parentGeoNode, morphGeoNode, preTransform, rawTarget.name );
-
- }
-
- } );
-
- },
-
- // a morph geometry node is similar to a standard node, and the node is also contained
- // in FBXTree.Objects.Geometry, however it can only have attributes for position, normal
- // and a special attribute Index defining which vertices of the original geometry are affected
- // Normal and position attributes only have data for the vertices that are affected by the morph
- genMorphGeometry: function ( parentGeo, parentGeoNode, morphGeoNode, preTransform, name ) {
-
- var morphGeo = new THREE.BufferGeometry();
- if ( morphGeoNode.attrName ) morphGeo.name = morphGeoNode.attrName;
-
- var vertexIndices = ( parentGeoNode.PolygonVertexIndex !== undefined ) ? parentGeoNode.PolygonVertexIndex.a : [];
-
- // make a copy of the parent's vertex positions
- var vertexPositions = ( parentGeoNode.Vertices !== undefined ) ? parentGeoNode.Vertices.a.slice() : [];
-
- var morphPositions = ( morphGeoNode.Vertices !== undefined ) ? morphGeoNode.Vertices.a : [];
- var indices = ( morphGeoNode.Indexes !== undefined ) ? morphGeoNode.Indexes.a : [];
-
- for ( var i = 0; i < indices.length; i ++ ) {
-
- var morphIndex = indices[ i ] * 3;
-
- // FBX format uses blend shapes rather than morph targets. This can be converted
- // by additively combining the blend shape positions with the original geometry's positions
- vertexPositions[ morphIndex ] += morphPositions[ i * 3 ];
- vertexPositions[ morphIndex + 1 ] += morphPositions[ i * 3 + 1 ];
- vertexPositions[ morphIndex + 2 ] += morphPositions[ i * 3 + 2 ];
-
- }
-
- // TODO: add morph normal support
- var morphGeoInfo = {
- vertexIndices: vertexIndices,
- vertexPositions: vertexPositions,
- };
-
- var morphBuffers = this.genBuffers( morphGeoInfo );
-
- var positionAttribute = new THREE.Float32BufferAttribute( morphBuffers.vertex, 3 );
- positionAttribute.name = name || morphGeoNode.attrName;
-
- preTransform.applyToBufferAttribute( positionAttribute );
-
- parentGeo.morphAttributes.position.push( positionAttribute );
-
- },
-
- // Parse normal from FBXTree.Objects.Geometry.LayerElementNormal if it exists
- parseNormals: function ( NormalNode ) {
-
- var mappingType = NormalNode.MappingInformationType;
- var referenceType = NormalNode.ReferenceInformationType;
- var buffer = NormalNode.Normals.a;
- var indexBuffer = [];
- if ( referenceType === 'IndexToDirect' ) {
-
- if ( 'NormalIndex' in NormalNode ) {
-
- indexBuffer = NormalNode.NormalIndex.a;
-
- } else if ( 'NormalsIndex' in NormalNode ) {
-
- indexBuffer = NormalNode.NormalsIndex.a;
-
- }
-
- }
-
- return {
- dataSize: 3,
- buffer: buffer,
- indices: indexBuffer,
- mappingType: mappingType,
- referenceType: referenceType
- };
-
- },
-
- // Parse UVs from FBXTree.Objects.Geometry.LayerElementUV if it exists
- parseUVs: function ( UVNode ) {
-
- var mappingType = UVNode.MappingInformationType;
- var referenceType = UVNode.ReferenceInformationType;
- var buffer = UVNode.UV.a;
- var indexBuffer = [];
- if ( referenceType === 'IndexToDirect' ) {
-
- indexBuffer = UVNode.UVIndex.a;
-
- }
-
- return {
- dataSize: 2,
- buffer: buffer,
- indices: indexBuffer,
- mappingType: mappingType,
- referenceType: referenceType
- };
-
- },
-
- // Parse Vertex Colors from FBXTree.Objects.Geometry.LayerElementColor if it exists
- parseVertexColors: function ( ColorNode ) {
-
- var mappingType = ColorNode.MappingInformationType;
- var referenceType = ColorNode.ReferenceInformationType;
- var buffer = ColorNode.Colors.a;
- var indexBuffer = [];
- if ( referenceType === 'IndexToDirect' ) {
-
- indexBuffer = ColorNode.ColorIndex.a;
-
- }
-
- return {
- dataSize: 4,
- buffer: buffer,
- indices: indexBuffer,
- mappingType: mappingType,
- referenceType: referenceType
- };
-
- },
-
- // Parse mapping and material data in FBXTree.Objects.Geometry.LayerElementMaterial if it exists
- parseMaterialIndices: function ( MaterialNode ) {
-
- var mappingType = MaterialNode.MappingInformationType;
- var referenceType = MaterialNode.ReferenceInformationType;
-
- if ( mappingType === 'NoMappingInformation' ) {
-
- return {
- dataSize: 1,
- buffer: [ 0 ],
- indices: [ 0 ],
- mappingType: 'AllSame',
- referenceType: referenceType
- };
-
- }
-
- var materialIndexBuffer = MaterialNode.Materials.a;
-
- // Since materials are stored as indices, there's a bit of a mismatch between FBX and what
- // we expect.So we create an intermediate buffer that points to the index in the buffer,
- // for conforming with the other functions we've written for other data.
- var materialIndices = [];
-
- for ( var i = 0; i < materialIndexBuffer.length; ++ i ) {
-
- materialIndices.push( i );
-
- }
-
- return {
- dataSize: 1,
- buffer: materialIndexBuffer,
- indices: materialIndices,
- mappingType: mappingType,
- referenceType: referenceType
- };
-
- },
-
- // Generate a NurbGeometry from a node in FBXTree.Objects.Geometry
- parseNurbsGeometry: function ( geoNode ) {
-
- if ( THREE.NURBSCurve === undefined ) {
-
- console.error( 'THREE.FBXLoader: The loader relies on THREE.NURBSCurve for any nurbs present in the model. Nurbs will show up as empty geometry.' );
- return new THREE.BufferGeometry();
-
- }
-
- var order = parseInt( geoNode.Order );
-
- if ( isNaN( order ) ) {
-
- console.error( 'THREE.FBXLoader: Invalid Order %s given for geometry ID: %s', geoNode.Order, geoNode.id );
- return new THREE.BufferGeometry();
-
- }
-
- var degree = order - 1;
-
- var knots = geoNode.KnotVector.a;
- var controlPoints = [];
- var pointsValues = geoNode.Points.a;
-
- for ( var i = 0, l = pointsValues.length; i < l; i += 4 ) {
-
- controlPoints.push( new THREE.Vector4().fromArray( pointsValues, i ) );
-
- }
-
- var startKnot, endKnot;
-
- if ( geoNode.Form === 'Closed' ) {
-
- controlPoints.push( controlPoints[ 0 ] );
-
- } else if ( geoNode.Form === 'Periodic' ) {
-
- startKnot = degree;
- endKnot = knots.length - 1 - startKnot;
-
- for ( var i = 0; i < degree; ++ i ) {
-
- controlPoints.push( controlPoints[ i ] );
-
- }
-
- }
-
- var curve = new THREE.NURBSCurve( degree, knots, controlPoints, startKnot, endKnot );
- var vertices = curve.getPoints( controlPoints.length * 7 );
-
- var positions = new Float32Array( vertices.length * 3 );
-
- vertices.forEach( function ( vertex, i ) {
-
- vertex.toArray( positions, i * 3 );
-
- } );
-
- var geometry = new THREE.BufferGeometry();
- geometry.addAttribute( 'position', new THREE.BufferAttribute( positions, 3 ) );
-
- return geometry;
-
- },
-
- };
-
- // parse animation data from FBXTree
- function AnimationParser() {}
-
- AnimationParser.prototype = {
-
- constructor: AnimationParser,
-
- // take raw animation clips and turn them into three.js animation clips
- parse: function () {
-
- var animationClips = [];
-
- var rawClips = this.parseClips();
-
- if ( rawClips !== undefined ) {
-
- for ( var key in rawClips ) {
-
- var rawClip = rawClips[ key ];
-
- var clip = this.addClip( rawClip );
-
- animationClips.push( clip );
-
- }
-
- }
-
- return animationClips;
-
- },
-
- parseClips: function () {
-
- // since the actual transformation data is stored in FBXTree.Objects.AnimationCurve,
- // if this is undefined we can safely assume there are no animations
- if ( fbxTree.Objects.AnimationCurve === undefined ) return undefined;
-
- var curveNodesMap = this.parseAnimationCurveNodes();
-
- this.parseAnimationCurves( curveNodesMap );
-
- var layersMap = this.parseAnimationLayers( curveNodesMap );
- var rawClips = this.parseAnimStacks( layersMap );
-
- return rawClips;
-
- },
-
- // parse nodes in FBXTree.Objects.AnimationCurveNode
- // each AnimationCurveNode holds data for an animation transform for a model (e.g. left arm rotation )
- // and is referenced by an AnimationLayer
- parseAnimationCurveNodes: function () {
-
- var rawCurveNodes = fbxTree.Objects.AnimationCurveNode;
-
- var curveNodesMap = new Map();
-
- for ( var nodeID in rawCurveNodes ) {
-
- var rawCurveNode = rawCurveNodes[ nodeID ];
-
- if ( rawCurveNode.attrName.match( /S|R|T|DeformPercent/ ) !== null ) {
-
- var curveNode = {
-
- id: rawCurveNode.id,
- attr: rawCurveNode.attrName,
- curves: {},
-
- };
-
- curveNodesMap.set( curveNode.id, curveNode );
-
- }
-
- }
-
- return curveNodesMap;
-
- },
-
- // parse nodes in FBXTree.Objects.AnimationCurve and connect them up to
- // previously parsed AnimationCurveNodes. Each AnimationCurve holds data for a single animated
- // axis ( e.g. times and values of x rotation)
- parseAnimationCurves: function ( curveNodesMap ) {
-
- var rawCurves = fbxTree.Objects.AnimationCurve;
-
- // TODO: Many values are identical up to roundoff error, but won't be optimised
- // e.g. position times: [0, 0.4, 0. 8]
- // position values: [7.23538335023477e-7, 93.67518615722656, -0.9982695579528809, 7.23538335023477e-7, 93.67518615722656, -0.9982695579528809, 7.235384487103147e-7, 93.67520904541016, -0.9982695579528809]
- // clearly, this should be optimised to
- // times: [0], positions [7.23538335023477e-7, 93.67518615722656, -0.9982695579528809]
- // this shows up in nearly every FBX file, and generally time array is length > 100
-
- for ( var nodeID in rawCurves ) {
-
- var animationCurve = {
-
- id: rawCurves[ nodeID ].id,
- times: rawCurves[ nodeID ].KeyTime.a.map( convertFBXTimeToSeconds ),
- values: rawCurves[ nodeID ].KeyValueFloat.a,
-
- };
-
- var relationships = connections.get( animationCurve.id );
-
- if ( relationships !== undefined ) {
-
- var animationCurveID = relationships.parents[ 0 ].ID;
- var animationCurveRelationship = relationships.parents[ 0 ].relationship;
-
- if ( animationCurveRelationship.match( /X/ ) ) {
-
- curveNodesMap.get( animationCurveID ).curves[ 'x' ] = animationCurve;
-
- } else if ( animationCurveRelationship.match( /Y/ ) ) {
-
- curveNodesMap.get( animationCurveID ).curves[ 'y' ] = animationCurve;
-
- } else if ( animationCurveRelationship.match( /Z/ ) ) {
-
- curveNodesMap.get( animationCurveID ).curves[ 'z' ] = animationCurve;
-
- } else if ( animationCurveRelationship.match( /d|DeformPercent/ ) && curveNodesMap.has( animationCurveID ) ) {
-
- curveNodesMap.get( animationCurveID ).curves[ 'morph' ] = animationCurve;
-
- }
-
- }
-
- }
-
- },
-
- // parse nodes in FBXTree.Objects.AnimationLayer. Each layers holds references
- // to various AnimationCurveNodes and is referenced by an AnimationStack node
- // note: theoretically a stack can have multiple layers, however in practice there always seems to be one per stack
- parseAnimationLayers: function ( curveNodesMap ) {
-
- var rawLayers = fbxTree.Objects.AnimationLayer;
-
- var layersMap = new Map();
-
- for ( var nodeID in rawLayers ) {
-
- var layerCurveNodes = [];
-
- var connection = connections.get( parseInt( nodeID ) );
-
- if ( connection !== undefined ) {
-
- // all the animationCurveNodes used in the layer
- var children = connection.children;
-
- children.forEach( function ( child, i ) {
-
- if ( curveNodesMap.has( child.ID ) ) {
-
- var curveNode = curveNodesMap.get( child.ID );
-
- // check that the curves are defined for at least one axis, otherwise ignore the curveNode
- if ( curveNode.curves.x !== undefined || curveNode.curves.y !== undefined || curveNode.curves.z !== undefined ) {
-
- if ( layerCurveNodes[ i ] === undefined ) {
-
- var modelID = connections.get( child.ID ).parents.filter( function ( parent ) {
-
- return parent.relationship !== undefined;
-
- } )[ 0 ].ID;
-
- if ( modelID !== undefined ) {
-
- var rawModel = fbxTree.Objects.Model[ modelID.toString() ];
-
- var node = {
-
- modelName: THREE.PropertyBinding.sanitizeNodeName( rawModel.attrName ),
- ID: rawModel.id,
- initialPosition: [ 0, 0, 0 ],
- initialRotation: [ 0, 0, 0 ],
- initialScale: [ 1, 1, 1 ],
-
- };
-
- sceneGraph.traverse( function ( child ) {
-
- if ( child.ID === rawModel.id ) {
-
- node.transform = child.matrix;
-
- if ( child.userData.transformData ) node.eulerOrder = child.userData.transformData.eulerOrder;
-
- }
-
- } );
-
- if ( ! node.transform ) node.transform = new THREE.Matrix4();
-
- // if the animated model is pre rotated, we'll have to apply the pre rotations to every
- // animation value as well
- if ( 'PreRotation' in rawModel ) node.preRotation = rawModel.PreRotation.value;
- if ( 'PostRotation' in rawModel ) node.postRotation = rawModel.PostRotation.value;
-
- layerCurveNodes[ i ] = node;
-
- }
-
- }
-
- if ( layerCurveNodes[ i ] ) layerCurveNodes[ i ][ curveNode.attr ] = curveNode;
-
- } else if ( curveNode.curves.morph !== undefined ) {
-
- if ( layerCurveNodes[ i ] === undefined ) {
-
- var deformerID = connections.get( child.ID ).parents.filter( function ( parent ) {
-
- return parent.relationship !== undefined;
-
- } )[ 0 ].ID;
-
- var morpherID = connections.get( deformerID ).parents[ 0 ].ID;
- var geoID = connections.get( morpherID ).parents[ 0 ].ID;
-
- // assuming geometry is not used in more than one model
- var modelID = connections.get( geoID ).parents[ 0 ].ID;
-
- var rawModel = fbxTree.Objects.Model[ modelID ];
-
- var node = {
-
- modelName: THREE.PropertyBinding.sanitizeNodeName( rawModel.attrName ),
- morphName: fbxTree.Objects.Deformer[ deformerID ].attrName,
-
- };
-
- layerCurveNodes[ i ] = node;
-
- }
-
- layerCurveNodes[ i ][ curveNode.attr ] = curveNode;
-
- }
-
- }
-
- } );
-
- layersMap.set( parseInt( nodeID ), layerCurveNodes );
-
- }
-
- }
-
- return layersMap;
-
- },
-
- // parse nodes in FBXTree.Objects.AnimationStack. These are the top level node in the animation
- // hierarchy. Each Stack node will be used to create a THREE.AnimationClip
- parseAnimStacks: function ( layersMap ) {
-
- var rawStacks = fbxTree.Objects.AnimationStack;
-
- // connect the stacks (clips) up to the layers
- var rawClips = {};
-
- for ( var nodeID in rawStacks ) {
-
- var children = connections.get( parseInt( nodeID ) ).children;
-
- if ( children.length > 1 ) {
-
- // it seems like stacks will always be associated with a single layer. But just in case there are files
- // where there are multiple layers per stack, we'll display a warning
- console.warn( 'THREE.FBXLoader: Encountered an animation stack with multiple layers, this is currently not supported. Ignoring subsequent layers.' );
-
- }
-
- var layer = layersMap.get( children[ 0 ].ID );
-
- rawClips[ nodeID ] = {
-
- name: rawStacks[ nodeID ].attrName,
- layer: layer,
-
- };
-
- }
-
- return rawClips;
-
- },
-
- addClip: function ( rawClip ) {
-
- var tracks = [];
-
- var self = this;
- rawClip.layer.forEach( function ( rawTracks ) {
-
- tracks = tracks.concat( self.generateTracks( rawTracks ) );
-
- } );
-
- return new THREE.AnimationClip( rawClip.name, - 1, tracks );
-
- },
-
- generateTracks: function ( rawTracks ) {
-
- var tracks = [];
-
- var initialPosition = new THREE.Vector3();
- var initialRotation = new THREE.Quaternion();
- var initialScale = new THREE.Vector3();
-
- if ( rawTracks.transform ) rawTracks.transform.decompose( initialPosition, initialRotation, initialScale );
-
- initialPosition = initialPosition.toArray();
- initialRotation = new THREE.Euler().setFromQuaternion( initialRotation, rawTracks.eulerOrder ).toArray();
- initialScale = initialScale.toArray();
-
- if ( rawTracks.T !== undefined && Object.keys( rawTracks.T.curves ).length > 0 ) {
-
- var positionTrack = this.generateVectorTrack( rawTracks.modelName, rawTracks.T.curves, initialPosition, 'position' );
- if ( positionTrack !== undefined ) tracks.push( positionTrack );
-
- }
-
- if ( rawTracks.R !== undefined && Object.keys( rawTracks.R.curves ).length > 0 ) {
-
- var rotationTrack = this.generateRotationTrack( rawTracks.modelName, rawTracks.R.curves, initialRotation, rawTracks.preRotation, rawTracks.postRotation, rawTracks.eulerOrder );
- if ( rotationTrack !== undefined ) tracks.push( rotationTrack );
-
- }
-
- if ( rawTracks.S !== undefined && Object.keys( rawTracks.S.curves ).length > 0 ) {
-
- var scaleTrack = this.generateVectorTrack( rawTracks.modelName, rawTracks.S.curves, initialScale, 'scale' );
- if ( scaleTrack !== undefined ) tracks.push( scaleTrack );
-
- }
-
- if ( rawTracks.DeformPercent !== undefined ) {
-
- var morphTrack = this.generateMorphTrack( rawTracks );
- if ( morphTrack !== undefined ) tracks.push( morphTrack );
-
- }
-
- return tracks;
-
- },
-
- generateVectorTrack: function ( modelName, curves, initialValue, type ) {
-
- var times = this.getTimesForAllAxes( curves );
- var values = this.getKeyframeTrackValues( times, curves, initialValue );
-
- return new THREE.VectorKeyframeTrack( modelName + '.' + type, times, values );
-
- },
-
- generateRotationTrack: function ( modelName, curves, initialValue, preRotation, postRotation, eulerOrder ) {
-
- if ( curves.x !== undefined ) {
-
- this.interpolateRotations( curves.x );
- curves.x.values = curves.x.values.map( THREE.Math.degToRad );
-
- }
- if ( curves.y !== undefined ) {
-
- this.interpolateRotations( curves.y );
- curves.y.values = curves.y.values.map( THREE.Math.degToRad );
-
- }
- if ( curves.z !== undefined ) {
-
- this.interpolateRotations( curves.z );
- curves.z.values = curves.z.values.map( THREE.Math.degToRad );
-
- }
-
- var times = this.getTimesForAllAxes( curves );
- var values = this.getKeyframeTrackValues( times, curves, initialValue );
-
- if ( preRotation !== undefined ) {
-
- preRotation = preRotation.map( THREE.Math.degToRad );
- preRotation.push( eulerOrder );
-
- preRotation = new THREE.Euler().fromArray( preRotation );
- preRotation = new THREE.Quaternion().setFromEuler( preRotation );
-
- }
-
- if ( postRotation !== undefined ) {
-
- postRotation = postRotation.map( THREE.Math.degToRad );
- postRotation.push( eulerOrder );
-
- postRotation = new THREE.Euler().fromArray( postRotation );
- postRotation = new THREE.Quaternion().setFromEuler( postRotation ).inverse();
-
- }
-
- var quaternion = new THREE.Quaternion();
- var euler = new THREE.Euler();
-
- var quaternionValues = [];
-
- for ( var i = 0; i < values.length; i += 3 ) {
-
- euler.set( values[ i ], values[ i + 1 ], values[ i + 2 ], eulerOrder );
-
- quaternion.setFromEuler( euler );
-
- if ( preRotation !== undefined ) quaternion.premultiply( preRotation );
- if ( postRotation !== undefined ) quaternion.multiply( postRotation );
-
- quaternion.toArray( quaternionValues, ( i / 3 ) * 4 );
-
- }
-
- return new THREE.QuaternionKeyframeTrack( modelName + '.quaternion', times, quaternionValues );
-
- },
-
- generateMorphTrack: function ( rawTracks ) {
-
- var curves = rawTracks.DeformPercent.curves.morph;
- var values = curves.values.map( function ( val ) {
-
- return val / 100;
-
- } );
-
- var morphNum = sceneGraph.getObjectByName( rawTracks.modelName ).morphTargetDictionary[ rawTracks.morphName ];
-
- return new THREE.NumberKeyframeTrack( rawTracks.modelName + '.morphTargetInfluences[' + morphNum + ']', curves.times, values );
-
- },
-
- // For all animated objects, times are defined separately for each axis
- // Here we'll combine the times into one sorted array without duplicates
- getTimesForAllAxes: function ( curves ) {
-
- var times = [];
-
- // first join together the times for each axis, if defined
- if ( curves.x !== undefined ) times = times.concat( curves.x.times );
- if ( curves.y !== undefined ) times = times.concat( curves.y.times );
- if ( curves.z !== undefined ) times = times.concat( curves.z.times );
-
- // then sort them and remove duplicates
- times = times.sort( function ( a, b ) {
-
- return a - b;
-
- } ).filter( function ( elem, index, array ) {
-
- return array.indexOf( elem ) == index;
-
- } );
-
- return times;
-
- },
-
- getKeyframeTrackValues: function ( times, curves, initialValue ) {
-
- var prevValue = initialValue;
-
- var values = [];
-
- var xIndex = - 1;
- var yIndex = - 1;
- var zIndex = - 1;
-
- times.forEach( function ( time ) {
-
- if ( curves.x ) xIndex = curves.x.times.indexOf( time );
- if ( curves.y ) yIndex = curves.y.times.indexOf( time );
- if ( curves.z ) zIndex = curves.z.times.indexOf( time );
-
- // if there is an x value defined for this frame, use that
- if ( xIndex !== - 1 ) {
-
- var xValue = curves.x.values[ xIndex ];
- values.push( xValue );
- prevValue[ 0 ] = xValue;
-
- } else {
-
- // otherwise use the x value from the previous frame
- values.push( prevValue[ 0 ] );
-
- }
-
- if ( yIndex !== - 1 ) {
-
- var yValue = curves.y.values[ yIndex ];
- values.push( yValue );
- prevValue[ 1 ] = yValue;
-
- } else {
-
- values.push( prevValue[ 1 ] );
-
- }
-
- if ( zIndex !== - 1 ) {
-
- var zValue = curves.z.values[ zIndex ];
- values.push( zValue );
- prevValue[ 2 ] = zValue;
-
- } else {
-
- values.push( prevValue[ 2 ] );
-
- }
-
- } );
-
- return values;
-
- },
-
- // Rotations are defined as Euler angles which can have values of any size
- // These will be converted to quaternions which don't support values greater than
- // PI, so we'll interpolate large rotations
- interpolateRotations: function ( curve ) {
-
- for ( var i = 1; i < curve.values.length; i ++ ) {
-
- var initialValue = curve.values[ i - 1 ];
- var valuesSpan = curve.values[ i ] - initialValue;
-
- var absoluteSpan = Math.abs( valuesSpan );
-
- if ( absoluteSpan >= 180 ) {
-
- var numSubIntervals = absoluteSpan / 180;
-
- var step = valuesSpan / numSubIntervals;
- var nextValue = initialValue + step;
-
- var initialTime = curve.times[ i - 1 ];
- var timeSpan = curve.times[ i ] - initialTime;
- var interval = timeSpan / numSubIntervals;
- var nextTime = initialTime + interval;
-
- var interpolatedTimes = [];
- var interpolatedValues = [];
-
- while ( nextTime < curve.times[ i ] ) {
-
- interpolatedTimes.push( nextTime );
- nextTime += interval;
-
- interpolatedValues.push( nextValue );
- nextValue += step;
-
- }
-
- curve.times = inject( curve.times, i, interpolatedTimes );
- curve.values = inject( curve.values, i, interpolatedValues );
-
- }
-
- }
-
- },
-
- };
-
- // parse an FBX file in ASCII format
- function TextParser() {}
-
- TextParser.prototype = {
-
- constructor: TextParser,
-
- getPrevNode: function () {
-
- return this.nodeStack[ this.currentIndent - 2 ];
-
- },
-
- getCurrentNode: function () {
-
- return this.nodeStack[ this.currentIndent - 1 ];
-
- },
-
- getCurrentProp: function () {
-
- return this.currentProp;
-
- },
-
- pushStack: function ( node ) {
-
- this.nodeStack.push( node );
- this.currentIndent += 1;
-
- },
-
- popStack: function () {
-
- this.nodeStack.pop();
- this.currentIndent -= 1;
-
- },
-
- setCurrentProp: function ( val, name ) {
-
- this.currentProp = val;
- this.currentPropName = name;
-
- },
-
- parse: function ( text ) {
-
- this.currentIndent = 0;
-
- this.allNodes = new FBXTree();
- this.nodeStack = [];
- this.currentProp = [];
- this.currentPropName = '';
-
- var self = this;
-
- var split = text.split( /[\r\n]+/ );
-
- split.forEach( function ( line, i ) {
-
- var matchComment = line.match( /^[\s\t]*;/ );
- var matchEmpty = line.match( /^[\s\t]*$/ );
-
- if ( matchComment || matchEmpty ) return;
-
- var matchBeginning = line.match( '^\\t{' + self.currentIndent + '}(\\w+):(.*){', '' );
- var matchProperty = line.match( '^\\t{' + ( self.currentIndent ) + '}(\\w+):[\\s\\t\\r\\n](.*)' );
- var matchEnd = line.match( '^\\t{' + ( self.currentIndent - 1 ) + '}}' );
-
- if ( matchBeginning ) {
-
- self.parseNodeBegin( line, matchBeginning );
-
- } else if ( matchProperty ) {
-
- self.parseNodeProperty( line, matchProperty, split[ ++ i ] );
-
- } else if ( matchEnd ) {
-
- self.popStack();
-
- } else if ( line.match( /^[^\s\t}]/ ) ) {
-
- // large arrays are split over multiple lines terminated with a ',' character
- // if this is encountered the line needs to be joined to the previous line
- self.parseNodePropertyContinued( line );
-
- }
-
- } );
-
- return this.allNodes;
-
- },
-
- parseNodeBegin: function ( line, property ) {
-
- var nodeName = property[ 1 ].trim().replace( /^"/, '' ).replace( /"$/, '' );
-
- var nodeAttrs = property[ 2 ].split( ',' ).map( function ( attr ) {
-
- return attr.trim().replace( /^"/, '' ).replace( /"$/, '' );
-
- } );
-
- var node = { name: nodeName };
- var attrs = this.parseNodeAttr( nodeAttrs );
-
- var currentNode = this.getCurrentNode();
-
- // a top node
- if ( this.currentIndent === 0 ) {
-
- this.allNodes.add( nodeName, node );
-
- } else { // a subnode
-
- // if the subnode already exists, append it
- if ( nodeName in currentNode ) {
-
- // special case Pose needs PoseNodes as an array
- if ( nodeName === 'PoseNode' ) {
-
- currentNode.PoseNode.push( node );
-
- } else if ( currentNode[ nodeName ].id !== undefined ) {
-
- currentNode[ nodeName ] = {};
- currentNode[ nodeName ][ currentNode[ nodeName ].id ] = currentNode[ nodeName ];
-
- }
-
- if ( attrs.id !== '' ) currentNode[ nodeName ][ attrs.id ] = node;
-
- } else if ( typeof attrs.id === 'number' ) {
-
- currentNode[ nodeName ] = {};
- currentNode[ nodeName ][ attrs.id ] = node;
-
- } else if ( nodeName !== 'Properties70' ) {
-
- if ( nodeName === 'PoseNode' ) currentNode[ nodeName ] = [ node ];
- else currentNode[ nodeName ] = node;
-
- }
-
- }
-
- if ( typeof attrs.id === 'number' ) node.id = attrs.id;
- if ( attrs.name !== '' ) node.attrName = attrs.name;
- if ( attrs.type !== '' ) node.attrType = attrs.type;
-
- this.pushStack( node );
-
- },
-
- parseNodeAttr: function ( attrs ) {
-
- var id = attrs[ 0 ];
-
- if ( attrs[ 0 ] !== '' ) {
-
- id = parseInt( attrs[ 0 ] );
-
- if ( isNaN( id ) ) {
-
- id = attrs[ 0 ];
-
- }
-
- }
-
- var name = '', type = '';
-
- if ( attrs.length > 1 ) {
-
- name = attrs[ 1 ].replace( /^(\w+)::/, '' );
- type = attrs[ 2 ];
-
- }
-
- return { id: id, name: name, type: type };
-
- },
-
- parseNodeProperty: function ( line, property, contentLine ) {
-
- var propName = property[ 1 ].replace( /^"/, '' ).replace( /"$/, '' ).trim();
- var propValue = property[ 2 ].replace( /^"/, '' ).replace( /"$/, '' ).trim();
-
- // for special case: base64 image data follows "Content: ," line
- // Content: ,
- // "/9j/4RDaRXhpZgAATU0A..."
- if ( propName === 'Content' && propValue === ',' ) {
-
- propValue = contentLine.replace( /"/g, '' ).replace( /,$/, '' ).trim();
-
- }
-
- var currentNode = this.getCurrentNode();
- var parentName = currentNode.name;
-
- if ( parentName === 'Properties70' ) {
-
- this.parseNodeSpecialProperty( line, propName, propValue );
- return;
-
- }
-
- // Connections
- if ( propName === 'C' ) {
-
- var connProps = propValue.split( ',' ).slice( 1 );
- var from = parseInt( connProps[ 0 ] );
- var to = parseInt( connProps[ 1 ] );
-
- var rest = propValue.split( ',' ).slice( 3 );
-
- rest = rest.map( function ( elem ) {
-
- return elem.trim().replace( /^"/, '' );
-
- } );
-
- propName = 'connections';
- propValue = [ from, to ];
- append( propValue, rest );
-
- if ( currentNode[ propName ] === undefined ) {
-
- currentNode[ propName ] = [];
-
- }
-
- }
-
- // Node
- if ( propName === 'Node' ) currentNode.id = propValue;
-
- // connections
- if ( propName in currentNode && Array.isArray( currentNode[ propName ] ) ) {
-
- currentNode[ propName ].push( propValue );
-
- } else {
-
- if ( propName !== 'a' ) currentNode[ propName ] = propValue;
- else currentNode.a = propValue;
-
- }
-
- this.setCurrentProp( currentNode, propName );
-
- // convert string to array, unless it ends in ',' in which case more will be added to it
- if ( propName === 'a' && propValue.slice( - 1 ) !== ',' ) {
-
- currentNode.a = parseNumberArray( propValue );
-
- }
-
- },
-
- parseNodePropertyContinued: function ( line ) {
-
- var currentNode = this.getCurrentNode();
-
- currentNode.a += line;
-
- // if the line doesn't end in ',' we have reached the end of the property value
- // so convert the string to an array
- if ( line.slice( - 1 ) !== ',' ) {
-
- currentNode.a = parseNumberArray( currentNode.a );
-
- }
-
- },
-
- // parse "Property70"
- parseNodeSpecialProperty: function ( line, propName, propValue ) {
-
- // split this
- // P: "Lcl Scaling", "Lcl Scaling", "", "A",1,1,1
- // into array like below
- // ["Lcl Scaling", "Lcl Scaling", "", "A", "1,1,1" ]
- var props = propValue.split( '",' ).map( function ( prop ) {
-
- return prop.trim().replace( /^\"/, '' ).replace( /\s/, '_' );
-
- } );
-
- var innerPropName = props[ 0 ];
- var innerPropType1 = props[ 1 ];
- var innerPropType2 = props[ 2 ];
- var innerPropFlag = props[ 3 ];
- var innerPropValue = props[ 4 ];
-
- // cast values where needed, otherwise leave as strings
- switch ( innerPropType1 ) {
-
- case 'int':
- case 'enum':
- case 'bool':
- case 'ULongLong':
- case 'double':
- case 'Number':
- case 'FieldOfView':
- innerPropValue = parseFloat( innerPropValue );
- break;
-
- case 'Color':
- case 'ColorRGB':
- case 'Vector3D':
- case 'Lcl_Translation':
- case 'Lcl_Rotation':
- case 'Lcl_Scaling':
- innerPropValue = parseNumberArray( innerPropValue );
- break;
-
- }
-
- // CAUTION: these props must append to parent's parent
- this.getPrevNode()[ innerPropName ] = {
-
- 'type': innerPropType1,
- 'type2': innerPropType2,
- 'flag': innerPropFlag,
- 'value': innerPropValue
-
- };
-
- this.setCurrentProp( this.getPrevNode(), innerPropName );
-
- },
-
- };
-
- // Parse an FBX file in Binary format
- function BinaryParser() {}
-
- BinaryParser.prototype = {
-
- constructor: BinaryParser,
-
- parse: function ( buffer ) {
-
- var reader = new BinaryReader( buffer );
- reader.skip( 23 ); // skip magic 23 bytes
-
- var version = reader.getUint32();
-
- console.log( 'THREE.FBXLoader: FBX binary version: ' + version );
-
- var allNodes = new FBXTree();
-
- while ( ! this.endOfContent( reader ) ) {
-
- var node = this.parseNode( reader, version );
- if ( node !== null ) allNodes.add( node.name, node );
-
- }
-
- return allNodes;
-
- },
-
- // Check if reader has reached the end of content.
- endOfContent: function ( reader ) {
-
- // footer size: 160bytes + 16-byte alignment padding
- // - 16bytes: magic
- // - padding til 16-byte alignment (at least 1byte?)
- // (seems like some exporters embed fixed 15 or 16bytes?)
- // - 4bytes: magic
- // - 4bytes: version
- // - 120bytes: zero
- // - 16bytes: magic
- if ( reader.size() % 16 === 0 ) {
-
- return ( ( reader.getOffset() + 160 + 16 ) & ~ 0xf ) >= reader.size();
-
- } else {
-
- return reader.getOffset() + 160 + 16 >= reader.size();
-
- }
-
- },
-
- // recursively parse nodes until the end of the file is reached
- parseNode: function ( reader, version ) {
-
- var node = {};
-
- // The first three data sizes depends on version.
- var endOffset = ( version >= 7500 ) ? reader.getUint64() : reader.getUint32();
- var numProperties = ( version >= 7500 ) ? reader.getUint64() : reader.getUint32();
-
- // note: do not remove this even if you get a linter warning as it moves the buffer forward
- var propertyListLen = ( version >= 7500 ) ? reader.getUint64() : reader.getUint32();
-
- var nameLen = reader.getUint8();
- var name = reader.getString( nameLen );
-
- // Regards this node as NULL-record if endOffset is zero
- if ( endOffset === 0 ) return null;
-
- var propertyList = [];
-
- for ( var i = 0; i < numProperties; i ++ ) {
-
- propertyList.push( this.parseProperty( reader ) );
-
- }
-
- // Regards the first three elements in propertyList as id, attrName, and attrType
- var id = propertyList.length > 0 ? propertyList[ 0 ] : '';
- var attrName = propertyList.length > 1 ? propertyList[ 1 ] : '';
- var attrType = propertyList.length > 2 ? propertyList[ 2 ] : '';
-
- // check if this node represents just a single property
- // like (name, 0) set or (name2, [0, 1, 2]) set of {name: 0, name2: [0, 1, 2]}
- node.singleProperty = ( numProperties === 1 && reader.getOffset() === endOffset ) ? true : false;
-
- while ( endOffset > reader.getOffset() ) {
-
- var subNode = this.parseNode( reader, version );
-
- if ( subNode !== null ) this.parseSubNode( name, node, subNode );
-
- }
-
- node.propertyList = propertyList; // raw property list used by parent
-
- if ( typeof id === 'number' ) node.id = id;
- if ( attrName !== '' ) node.attrName = attrName;
- if ( attrType !== '' ) node.attrType = attrType;
- if ( name !== '' ) node.name = name;
-
- return node;
-
- },
-
- parseSubNode: function ( name, node, subNode ) {
-
- // special case: child node is single property
- if ( subNode.singleProperty === true ) {
-
- var value = subNode.propertyList[ 0 ];
-
- if ( Array.isArray( value ) ) {
-
- node[ subNode.name ] = subNode;
-
- subNode.a = value;
-
- } else {
-
- node[ subNode.name ] = value;
-
- }
-
- } else if ( name === 'Connections' && subNode.name === 'C' ) {
-
- var array = [];
-
- subNode.propertyList.forEach( function ( property, i ) {
-
- // first Connection is FBX type (OO, OP, etc.). We'll discard these
- if ( i !== 0 ) array.push( property );
-
- } );
-
- if ( node.connections === undefined ) {
-
- node.connections = [];
-
- }
-
- node.connections.push( array );
-
- } else if ( subNode.name === 'Properties70' ) {
-
- var keys = Object.keys( subNode );
-
- keys.forEach( function ( key ) {
-
- node[ key ] = subNode[ key ];
-
- } );
-
- } else if ( name === 'Properties70' && subNode.name === 'P' ) {
-
- var innerPropName = subNode.propertyList[ 0 ];
- var innerPropType1 = subNode.propertyList[ 1 ];
- var innerPropType2 = subNode.propertyList[ 2 ];
- var innerPropFlag = subNode.propertyList[ 3 ];
- var innerPropValue;
-
- if ( innerPropName.indexOf( 'Lcl ' ) === 0 ) innerPropName = innerPropName.replace( 'Lcl ', 'Lcl_' );
- if ( innerPropType1.indexOf( 'Lcl ' ) === 0 ) innerPropType1 = innerPropType1.replace( 'Lcl ', 'Lcl_' );
-
- if ( innerPropType1 === 'Color' || innerPropType1 === 'ColorRGB' || innerPropType1 === 'Vector' || innerPropType1 === 'Vector3D' || innerPropType1.indexOf( 'Lcl_' ) === 0 ) {
-
- innerPropValue = [
- subNode.propertyList[ 4 ],
- subNode.propertyList[ 5 ],
- subNode.propertyList[ 6 ]
- ];
-
- } else {
-
- innerPropValue = subNode.propertyList[ 4 ];
-
- }
-
- // this will be copied to parent, see above
- node[ innerPropName ] = {
-
- 'type': innerPropType1,
- 'type2': innerPropType2,
- 'flag': innerPropFlag,
- 'value': innerPropValue
-
- };
-
- } else if ( node[ subNode.name ] === undefined ) {
-
- if ( typeof subNode.id === 'number' ) {
-
- node[ subNode.name ] = {};
- node[ subNode.name ][ subNode.id ] = subNode;
-
- } else {
-
- node[ subNode.name ] = subNode;
-
- }
-
- } else {
-
- if ( subNode.name === 'PoseNode' ) {
-
- if ( ! Array.isArray( node[ subNode.name ] ) ) {
-
- node[ subNode.name ] = [ node[ subNode.name ] ];
-
- }
-
- node[ subNode.name ].push( subNode );
-
- } else if ( node[ subNode.name ][ subNode.id ] === undefined ) {
-
- node[ subNode.name ][ subNode.id ] = subNode;
-
- }
-
- }
-
- },
-
- parseProperty: function ( reader ) {
-
- var type = reader.getString( 1 );
-
- switch ( type ) {
-
- case 'C':
- return reader.getBoolean();
-
- case 'D':
- return reader.getFloat64();
-
- case 'F':
- return reader.getFloat32();
-
- case 'I':
- return reader.getInt32();
-
- case 'L':
- return reader.getInt64();
-
- case 'R':
- var length = reader.getUint32();
- return reader.getArrayBuffer( length );
-
- case 'S':
- var length = reader.getUint32();
- return reader.getString( length );
-
- case 'Y':
- return reader.getInt16();
-
- case 'b':
- case 'c':
- case 'd':
- case 'f':
- case 'i':
- case 'l':
-
- var arrayLength = reader.getUint32();
- var encoding = reader.getUint32(); // 0: non-compressed, 1: compressed
- var compressedLength = reader.getUint32();
-
- if ( encoding === 0 ) {
-
- switch ( type ) {
-
- case 'b':
- case 'c':
- return reader.getBooleanArray( arrayLength );
-
- case 'd':
- return reader.getFloat64Array( arrayLength );
-
- case 'f':
- return reader.getFloat32Array( arrayLength );
-
- case 'i':
- return reader.getInt32Array( arrayLength );
-
- case 'l':
- return reader.getInt64Array( arrayLength );
-
- }
-
- }
-
- if ( typeof Zlib === 'undefined' ) {
-
- console.error( 'THREE.FBXLoader: External library Inflate.min.js required, obtain or import from https://github.com/imaya/zlib.js' );
-
- }
-
- var inflate = new Zlib.Inflate( new Uint8Array( reader.getArrayBuffer( compressedLength ) ) ); // eslint-disable-line no-undef
- var reader2 = new BinaryReader( inflate.decompress().buffer );
-
- switch ( type ) {
-
- case 'b':
- case 'c':
- return reader2.getBooleanArray( arrayLength );
-
- case 'd':
- return reader2.getFloat64Array( arrayLength );
-
- case 'f':
- return reader2.getFloat32Array( arrayLength );
-
- case 'i':
- return reader2.getInt32Array( arrayLength );
-
- case 'l':
- return reader2.getInt64Array( arrayLength );
-
- }
-
- default:
- throw new Error( 'THREE.FBXLoader: Unknown property type ' + type );
-
- }
-
- }
-
- };
-
- function BinaryReader( buffer, littleEndian ) {
-
- this.dv = new DataView( buffer );
- this.offset = 0;
- this.littleEndian = ( littleEndian !== undefined ) ? littleEndian : true;
-
- }
-
- BinaryReader.prototype = {
-
- constructor: BinaryReader,
-
- getOffset: function () {
-
- return this.offset;
-
- },
-
- size: function () {
-
- return this.dv.buffer.byteLength;
-
- },
-
- skip: function ( length ) {
-
- this.offset += length;
-
- },
-
- // seems like true/false representation depends on exporter.
- // true: 1 or 'Y'(=0x59), false: 0 or 'T'(=0x54)
- // then sees LSB.
- getBoolean: function () {
-
- return ( this.getUint8() & 1 ) === 1;
-
- },
-
- getBooleanArray: function ( size ) {
-
- var a = [];
-
- for ( var i = 0; i < size; i ++ ) {
-
- a.push( this.getBoolean() );
-
- }
-
- return a;
-
- },
-
- getUint8: function () {
-
- var value = this.dv.getUint8( this.offset );
- this.offset += 1;
- return value;
-
- },
-
- getInt16: function () {
-
- var value = this.dv.getInt16( this.offset, this.littleEndian );
- this.offset += 2;
- return value;
-
- },
-
- getInt32: function () {
-
- var value = this.dv.getInt32( this.offset, this.littleEndian );
- this.offset += 4;
- return value;
-
- },
-
- getInt32Array: function ( size ) {
-
- var a = [];
-
- for ( var i = 0; i < size; i ++ ) {
-
- a.push( this.getInt32() );
-
- }
-
- return a;
-
- },
-
- getUint32: function () {
-
- var value = this.dv.getUint32( this.offset, this.littleEndian );
- this.offset += 4;
- return value;
-
- },
-
- // JavaScript doesn't support 64-bit integer so calculate this here
- // 1 << 32 will return 1 so using multiply operation instead here.
- // There's a possibility that this method returns wrong value if the value
- // is out of the range between Number.MAX_SAFE_INTEGER and Number.MIN_SAFE_INTEGER.
- // TODO: safely handle 64-bit integer
- getInt64: function () {
-
- var low, high;
-
- if ( this.littleEndian ) {
-
- low = this.getUint32();
- high = this.getUint32();
-
- } else {
-
- high = this.getUint32();
- low = this.getUint32();
-
- }
-
- // calculate negative value
- if ( high & 0x80000000 ) {
-
- high = ~ high & 0xFFFFFFFF;
- low = ~ low & 0xFFFFFFFF;
-
- if ( low === 0xFFFFFFFF ) high = ( high + 1 ) & 0xFFFFFFFF;
-
- low = ( low + 1 ) & 0xFFFFFFFF;
-
- return - ( high * 0x100000000 + low );
-
- }
-
- return high * 0x100000000 + low;
-
- },
-
- getInt64Array: function ( size ) {
-
- var a = [];
-
- for ( var i = 0; i < size; i ++ ) {
-
- a.push( this.getInt64() );
-
- }
-
- return a;
-
- },
-
- // Note: see getInt64() comment
- getUint64: function () {
-
- var low, high;
-
- if ( this.littleEndian ) {
-
- low = this.getUint32();
- high = this.getUint32();
-
- } else {
-
- high = this.getUint32();
- low = this.getUint32();
-
- }
-
- return high * 0x100000000 + low;
-
- },
-
- getFloat32: function () {
-
- var value = this.dv.getFloat32( this.offset, this.littleEndian );
- this.offset += 4;
- return value;
-
- },
-
- getFloat32Array: function ( size ) {
-
- var a = [];
-
- for ( var i = 0; i < size; i ++ ) {
-
- a.push( this.getFloat32() );
-
- }
-
- return a;
-
- },
-
- getFloat64: function () {
-
- var value = this.dv.getFloat64( this.offset, this.littleEndian );
- this.offset += 8;
- return value;
-
- },
-
- getFloat64Array: function ( size ) {
-
- var a = [];
-
- for ( var i = 0; i < size; i ++ ) {
-
- a.push( this.getFloat64() );
-
- }
-
- return a;
-
- },
-
- getArrayBuffer: function ( size ) {
-
- var value = this.dv.buffer.slice( this.offset, this.offset + size );
- this.offset += size;
- return value;
-
- },
-
- getString: function ( size ) {
-
- // note: safari 9 doesn't support Uint8Array.indexOf; create intermediate array instead
- var a = [];
-
- for ( var i = 0; i < size; i ++ ) {
-
- a[ i ] = this.getUint8();
-
- }
-
- var nullByte = a.indexOf( 0 );
- if ( nullByte >= 0 ) a = a.slice( 0, nullByte );
-
- return THREE.LoaderUtils.decodeText( new Uint8Array( a ) );
-
- }
-
- };
-
- // FBXTree holds a representation of the FBX data, returned by the TextParser ( FBX ASCII format)
- // and BinaryParser( FBX Binary format)
- function FBXTree() {}
-
- FBXTree.prototype = {
-
- constructor: FBXTree,
-
- add: function ( key, val ) {
-
- this[ key ] = val;
-
- },
-
- };
-
- // ************** UTILITY FUNCTIONS **************
-
- function isFbxFormatBinary( buffer ) {
-
- var CORRECT = 'Kaydara FBX Binary \0';
-
- return buffer.byteLength >= CORRECT.length && CORRECT === convertArrayBufferToString( buffer, 0, CORRECT.length );
-
- }
-
- function isFbxFormatASCII( text ) {
-
- var CORRECT = [ 'K', 'a', 'y', 'd', 'a', 'r', 'a', '\\', 'F', 'B', 'X', '\\', 'B', 'i', 'n', 'a', 'r', 'y', '\\', '\\' ];
-
- var cursor = 0;
-
- function read( offset ) {
-
- var result = text[ offset - 1 ];
- text = text.slice( cursor + offset );
- cursor ++;
- return result;
-
- }
-
- for ( var i = 0; i < CORRECT.length; ++ i ) {
-
- var num = read( 1 );
- if ( num === CORRECT[ i ] ) {
-
- return false;
-
- }
-
- }
-
- return true;
-
- }
-
- function getFbxVersion( text ) {
-
- var versionRegExp = /FBXVersion: (\d+)/;
- var match = text.match( versionRegExp );
- if ( match ) {
-
- var version = parseInt( match[ 1 ] );
- return version;
-
- }
- throw new Error( 'THREE.FBXLoader: Cannot find the version number for the file given.' );
-
- }
-
- // Converts FBX ticks into real time seconds.
- function convertFBXTimeToSeconds( time ) {
-
- return time / 46186158000;
-
- }
-
- var dataArray = [];
-
- // extracts the data from the correct position in the FBX array based on indexing type
- function getData( polygonVertexIndex, polygonIndex, vertexIndex, infoObject ) {
-
- var index;
-
- switch ( infoObject.mappingType ) {
-
- case 'ByPolygonVertex' :
- index = polygonVertexIndex;
- break;
- case 'ByPolygon' :
- index = polygonIndex;
- break;
- case 'ByVertice' :
- index = vertexIndex;
- break;
- case 'AllSame' :
- index = infoObject.indices[ 0 ];
- break;
- default :
- console.warn( 'THREE.FBXLoader: unknown attribute mapping type ' + infoObject.mappingType );
-
- }
-
- if ( infoObject.referenceType === 'IndexToDirect' ) index = infoObject.indices[ index ];
-
- var from = index * infoObject.dataSize;
- var to = from + infoObject.dataSize;
-
- return slice( dataArray, infoObject.buffer, from, to );
-
- }
-
- var tempEuler = new THREE.Euler();
- var tempVec = new THREE.Vector3();
-
- // generate transformation from FBX transform data
- // ref: https://help.autodesk.com/view/FBX/2017/ENU/?guid=__files_GUID_10CDD63C_79C1_4F2D_BB28_AD2BE65A02ED_htm
- // ref: http://docs.autodesk.com/FBX/2014/ENU/FBX-SDK-Documentation/index.html?url=cpp_ref/_transformations_2main_8cxx-example.html,topicNumber=cpp_ref__transformations_2main_8cxx_example_htmlfc10a1e1-b18d-4e72-9dc0-70d0f1959f5e
- function generateTransform( transformData ) {
-
- var lTranslationM = new THREE.Matrix4();
- var lPreRotationM = new THREE.Matrix4();
- var lRotationM = new THREE.Matrix4();
- var lPostRotationM = new THREE.Matrix4();
-
- var lScalingM = new THREE.Matrix4();
- var lScalingPivotM = new THREE.Matrix4();
- var lScalingOffsetM = new THREE.Matrix4();
- var lRotationOffsetM = new THREE.Matrix4();
- var lRotationPivotM = new THREE.Matrix4();
-
- var lParentGX = new THREE.Matrix4();
- var lGlobalT = new THREE.Matrix4();
-
- var inheritType = ( transformData.inheritType ) ? transformData.inheritType : 0;
-
- if ( transformData.translation ) lTranslationM.setPosition( tempVec.fromArray( transformData.translation ) );
-
- if ( transformData.preRotation ) {
-
- var array = transformData.preRotation.map( THREE.Math.degToRad );
- array.push( transformData.eulerOrder );
- lPreRotationM.makeRotationFromEuler( tempEuler.fromArray( array ) );
-
- }
-
- if ( transformData.rotation ) {
-
- var array = transformData.rotation.map( THREE.Math.degToRad );
- array.push( transformData.eulerOrder );
- lRotationM.makeRotationFromEuler( tempEuler.fromArray( array ) );
-
- }
-
- if ( transformData.postRotation ) {
-
- var array = transformData.postRotation.map( THREE.Math.degToRad );
- array.push( transformData.eulerOrder );
- lPostRotationM.makeRotationFromEuler( tempEuler.fromArray( array ) );
-
- }
-
- if ( transformData.scale ) lScalingM.scale( tempVec.fromArray( transformData.scale ) );
-
- // Pivots and offsets
- if ( transformData.scalingOffset ) lScalingOffsetM.setPosition( tempVec.fromArray( transformData.scalingOffset ) );
- if ( transformData.scalingPivot ) lScalingPivotM.setPosition( tempVec.fromArray( transformData.scalingPivot ) );
- if ( transformData.rotationOffset ) lRotationOffsetM.setPosition( tempVec.fromArray( transformData.rotationOffset ) );
- if ( transformData.rotationPivot ) lRotationPivotM.setPosition( tempVec.fromArray( transformData.rotationPivot ) );
-
- // parent transform
- if ( transformData.parentMatrixWorld ) lParentGX = transformData.parentMatrixWorld;
-
- // Global Rotation
- var lLRM = lPreRotationM.multiply( lRotationM ).multiply( lPostRotationM );
- var lParentGRM = new THREE.Matrix4();
- lParentGX.extractRotation( lParentGRM );
-
- // Global Shear*Scaling
- var lParentTM = new THREE.Matrix4();
- var lLSM;
- var lParentGSM;
- var lParentGRSM;
-
- lParentTM.copyPosition( lParentGX );
- lParentGRSM = lParentTM.getInverse( lParentTM ).multiply( lParentGX );
- lParentGSM = lParentGRM.getInverse( lParentGRM ).multiply( lParentGRSM );
- lLSM = lScalingM;
-
- var lGlobalRS;
- if ( inheritType === 0 ) {
-
- lGlobalRS = lParentGRM.multiply( lLRM ).multiply( lParentGSM ).multiply( lLSM );
-
- } else if ( inheritType === 1 ) {
-
- lGlobalRS = lParentGRM.multiply( lParentGSM ).multiply( lLRM ).multiply( lLSM );
-
- } else {
-
- var lParentLSM = new THREE.Matrix4().copy( lScalingM );
-
- var lParentGSM_noLocal = lParentGSM.multiply( lParentLSM.getInverse( lParentLSM ) );
-
- lGlobalRS = lParentGRM.multiply( lLRM ).multiply( lParentGSM_noLocal ).multiply( lLSM );
-
- }
-
- // Calculate the local transform matrix
- var lTransform = lTranslationM.multiply( lRotationOffsetM ).multiply( lRotationPivotM ).multiply( lPreRotationM ).multiply( lRotationM ).multiply( lPostRotationM ).multiply( lRotationPivotM.getInverse( lRotationPivotM ) ).multiply( lScalingOffsetM ).multiply( lScalingPivotM ).multiply( lScalingM ).multiply( lScalingPivotM.getInverse( lScalingPivotM ) );
-
- var lLocalTWithAllPivotAndOffsetInfo = new THREE.Matrix4().copyPosition( lTransform );
-
- var lGlobalTranslation = lParentGX.multiply( lLocalTWithAllPivotAndOffsetInfo );
- lGlobalT.copyPosition( lGlobalTranslation );
-
- lTransform = lGlobalT.multiply( lGlobalRS );
-
- return lTransform;
-
- }
-
- // Returns the three.js intrinsic Euler order corresponding to FBX extrinsic Euler order
- // ref: http://help.autodesk.com/view/FBX/2017/ENU/?guid=__cpp_ref_class_fbx_euler_html
- function getEulerOrder( order ) {
-
- order = order || 0;
-
- var enums = [
- 'ZYX', // -> XYZ extrinsic
- 'YZX', // -> XZY extrinsic
- 'XZY', // -> YZX extrinsic
- 'ZXY', // -> YXZ extrinsic
- 'YXZ', // -> ZXY extrinsic
- 'XYZ', // -> ZYX extrinsic
- //'SphericXYZ', // not possible to support
- ];
-
- if ( order === 6 ) {
-
- console.warn( 'THREE.FBXLoader: unsupported Euler Order: Spherical XYZ. Animations and rotations may be incorrect.' );
- return enums[ 0 ];
-
- }
-
- return enums[ order ];
-
- }
-
- // Parses comma separated list of numbers and returns them an array.
- // Used internally by the TextParser
- function parseNumberArray( value ) {
-
- var array = value.split( ',' ).map( function ( val ) {
-
- return parseFloat( val );
-
- } );
-
- return array;
-
- }
-
- function convertArrayBufferToString( buffer, from, to ) {
-
- if ( from === undefined ) from = 0;
- if ( to === undefined ) to = buffer.byteLength;
-
- return THREE.LoaderUtils.decodeText( new Uint8Array( buffer, from, to ) );
-
- }
-
- function append( a, b ) {
-
- for ( var i = 0, j = a.length, l = b.length; i < l; i ++, j ++ ) {
-
- a[ j ] = b[ i ];
-
- }
-
- }
-
- function slice( a, b, from, to ) {
-
- for ( var i = from, j = 0; i < to; i ++, j ++ ) {
-
- a[ j ] = b[ i ];
-
- }
-
- return a;
-
- }
-
- // inject array a2 into array a1 at index
- function inject( a1, index, a2 ) {
-
- return a1.slice( 0, index ).concat( a2 ).concat( a1.slice( index ) );
-
- }
-
- return FBXLoader;
-
-} )();
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/math/Vector4.js b/spaces/banana-projects/web3d/node_modules/three/src/math/Vector4.js
deleted file mode 100644
index 7b148aed437feab6f5ab35ce28ae02811b34a98e..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/math/Vector4.js
+++ /dev/null
@@ -1,629 +0,0 @@
-/**
- * @author supereggbert / http://www.paulbrunt.co.uk/
- * @author philogb / http://blog.thejit.org/
- * @author mikael emtinger / http://gomo.se/
- * @author egraether / http://egraether.com/
- * @author WestLangley / http://github.com/WestLangley
- */
-
-function Vector4( x, y, z, w ) {
-
- this.x = x || 0;
- this.y = y || 0;
- this.z = z || 0;
- this.w = ( w !== undefined ) ? w : 1;
-
-}
-
-Object.assign( Vector4.prototype, {
-
- isVector4: true,
-
- set: function ( x, y, z, w ) {
-
- this.x = x;
- this.y = y;
- this.z = z;
- this.w = w;
-
- return this;
-
- },
-
- setScalar: function ( scalar ) {
-
- this.x = scalar;
- this.y = scalar;
- this.z = scalar;
- this.w = scalar;
-
- return this;
-
- },
-
- setX: function ( x ) {
-
- this.x = x;
-
- return this;
-
- },
-
- setY: function ( y ) {
-
- this.y = y;
-
- return this;
-
- },
-
- setZ: function ( z ) {
-
- this.z = z;
-
- return this;
-
- },
-
- setW: function ( w ) {
-
- this.w = w;
-
- return this;
-
- },
-
- setComponent: function ( index, value ) {
-
- switch ( index ) {
-
- case 0: this.x = value; break;
- case 1: this.y = value; break;
- case 2: this.z = value; break;
- case 3: this.w = value; break;
- default: throw new Error( 'index is out of range: ' + index );
-
- }
-
- return this;
-
- },
-
- getComponent: function ( index ) {
-
- switch ( index ) {
-
- case 0: return this.x;
- case 1: return this.y;
- case 2: return this.z;
- case 3: return this.w;
- default: throw new Error( 'index is out of range: ' + index );
-
- }
-
- },
-
- clone: function () {
-
- return new this.constructor( this.x, this.y, this.z, this.w );
-
- },
-
- copy: function ( v ) {
-
- this.x = v.x;
- this.y = v.y;
- this.z = v.z;
- this.w = ( v.w !== undefined ) ? v.w : 1;
-
- return this;
-
- },
-
- add: function ( v, w ) {
-
- if ( w !== undefined ) {
-
- console.warn( 'THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.' );
- return this.addVectors( v, w );
-
- }
-
- this.x += v.x;
- this.y += v.y;
- this.z += v.z;
- this.w += v.w;
-
- return this;
-
- },
-
- addScalar: function ( s ) {
-
- this.x += s;
- this.y += s;
- this.z += s;
- this.w += s;
-
- return this;
-
- },
-
- addVectors: function ( a, b ) {
-
- this.x = a.x + b.x;
- this.y = a.y + b.y;
- this.z = a.z + b.z;
- this.w = a.w + b.w;
-
- return this;
-
- },
-
- addScaledVector: function ( v, s ) {
-
- this.x += v.x * s;
- this.y += v.y * s;
- this.z += v.z * s;
- this.w += v.w * s;
-
- return this;
-
- },
-
- sub: function ( v, w ) {
-
- if ( w !== undefined ) {
-
- console.warn( 'THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.' );
- return this.subVectors( v, w );
-
- }
-
- this.x -= v.x;
- this.y -= v.y;
- this.z -= v.z;
- this.w -= v.w;
-
- return this;
-
- },
-
- subScalar: function ( s ) {
-
- this.x -= s;
- this.y -= s;
- this.z -= s;
- this.w -= s;
-
- return this;
-
- },
-
- subVectors: function ( a, b ) {
-
- this.x = a.x - b.x;
- this.y = a.y - b.y;
- this.z = a.z - b.z;
- this.w = a.w - b.w;
-
- return this;
-
- },
-
- multiplyScalar: function ( scalar ) {
-
- this.x *= scalar;
- this.y *= scalar;
- this.z *= scalar;
- this.w *= scalar;
-
- return this;
-
- },
-
- applyMatrix4: function ( m ) {
-
- var x = this.x, y = this.y, z = this.z, w = this.w;
- var e = m.elements;
-
- this.x = e[ 0 ] * x + e[ 4 ] * y + e[ 8 ] * z + e[ 12 ] * w;
- this.y = e[ 1 ] * x + e[ 5 ] * y + e[ 9 ] * z + e[ 13 ] * w;
- this.z = e[ 2 ] * x + e[ 6 ] * y + e[ 10 ] * z + e[ 14 ] * w;
- this.w = e[ 3 ] * x + e[ 7 ] * y + e[ 11 ] * z + e[ 15 ] * w;
-
- return this;
-
- },
-
- divideScalar: function ( scalar ) {
-
- return this.multiplyScalar( 1 / scalar );
-
- },
-
- setAxisAngleFromQuaternion: function ( q ) {
-
- // http://www.euclideanspace.com/maths/geometry/rotations/conversions/quaternionToAngle/index.htm
-
- // q is assumed to be normalized
-
- this.w = 2 * Math.acos( q.w );
-
- var s = Math.sqrt( 1 - q.w * q.w );
-
- if ( s < 0.0001 ) {
-
- this.x = 1;
- this.y = 0;
- this.z = 0;
-
- } else {
-
- this.x = q.x / s;
- this.y = q.y / s;
- this.z = q.z / s;
-
- }
-
- return this;
-
- },
-
- setAxisAngleFromRotationMatrix: function ( m ) {
-
- // http://www.euclideanspace.com/maths/geometry/rotations/conversions/matrixToAngle/index.htm
-
- // assumes the upper 3x3 of m is a pure rotation matrix (i.e, unscaled)
-
- var angle, x, y, z, // variables for result
- epsilon = 0.01, // margin to allow for rounding errors
- epsilon2 = 0.1, // margin to distinguish between 0 and 180 degrees
-
- te = m.elements,
-
- m11 = te[ 0 ], m12 = te[ 4 ], m13 = te[ 8 ],
- m21 = te[ 1 ], m22 = te[ 5 ], m23 = te[ 9 ],
- m31 = te[ 2 ], m32 = te[ 6 ], m33 = te[ 10 ];
-
- if ( ( Math.abs( m12 - m21 ) < epsilon ) &&
- ( Math.abs( m13 - m31 ) < epsilon ) &&
- ( Math.abs( m23 - m32 ) < epsilon ) ) {
-
- // singularity found
- // first check for identity matrix which must have +1 for all terms
- // in leading diagonal and zero in other terms
-
- if ( ( Math.abs( m12 + m21 ) < epsilon2 ) &&
- ( Math.abs( m13 + m31 ) < epsilon2 ) &&
- ( Math.abs( m23 + m32 ) < epsilon2 ) &&
- ( Math.abs( m11 + m22 + m33 - 3 ) < epsilon2 ) ) {
-
- // this singularity is identity matrix so angle = 0
-
- this.set( 1, 0, 0, 0 );
-
- return this; // zero angle, arbitrary axis
-
- }
-
- // otherwise this singularity is angle = 180
-
- angle = Math.PI;
-
- var xx = ( m11 + 1 ) / 2;
- var yy = ( m22 + 1 ) / 2;
- var zz = ( m33 + 1 ) / 2;
- var xy = ( m12 + m21 ) / 4;
- var xz = ( m13 + m31 ) / 4;
- var yz = ( m23 + m32 ) / 4;
-
- if ( ( xx > yy ) && ( xx > zz ) ) {
-
- // m11 is the largest diagonal term
-
- if ( xx < epsilon ) {
-
- x = 0;
- y = 0.707106781;
- z = 0.707106781;
-
- } else {
-
- x = Math.sqrt( xx );
- y = xy / x;
- z = xz / x;
-
- }
-
- } else if ( yy > zz ) {
-
- // m22 is the largest diagonal term
-
- if ( yy < epsilon ) {
-
- x = 0.707106781;
- y = 0;
- z = 0.707106781;
-
- } else {
-
- y = Math.sqrt( yy );
- x = xy / y;
- z = yz / y;
-
- }
-
- } else {
-
- // m33 is the largest diagonal term so base result on this
-
- if ( zz < epsilon ) {
-
- x = 0.707106781;
- y = 0.707106781;
- z = 0;
-
- } else {
-
- z = Math.sqrt( zz );
- x = xz / z;
- y = yz / z;
-
- }
-
- }
-
- this.set( x, y, z, angle );
-
- return this; // return 180 deg rotation
-
- }
-
- // as we have reached here there are no singularities so we can handle normally
-
- var s = Math.sqrt( ( m32 - m23 ) * ( m32 - m23 ) +
- ( m13 - m31 ) * ( m13 - m31 ) +
- ( m21 - m12 ) * ( m21 - m12 ) ); // used to normalize
-
- if ( Math.abs( s ) < 0.001 ) s = 1;
-
- // prevent divide by zero, should not happen if matrix is orthogonal and should be
- // caught by singularity test above, but I've left it in just in case
-
- this.x = ( m32 - m23 ) / s;
- this.y = ( m13 - m31 ) / s;
- this.z = ( m21 - m12 ) / s;
- this.w = Math.acos( ( m11 + m22 + m33 - 1 ) / 2 );
-
- return this;
-
- },
-
- min: function ( v ) {
-
- this.x = Math.min( this.x, v.x );
- this.y = Math.min( this.y, v.y );
- this.z = Math.min( this.z, v.z );
- this.w = Math.min( this.w, v.w );
-
- return this;
-
- },
-
- max: function ( v ) {
-
- this.x = Math.max( this.x, v.x );
- this.y = Math.max( this.y, v.y );
- this.z = Math.max( this.z, v.z );
- this.w = Math.max( this.w, v.w );
-
- return this;
-
- },
-
- clamp: function ( min, max ) {
-
- // assumes min < max, componentwise
-
- this.x = Math.max( min.x, Math.min( max.x, this.x ) );
- this.y = Math.max( min.y, Math.min( max.y, this.y ) );
- this.z = Math.max( min.z, Math.min( max.z, this.z ) );
- this.w = Math.max( min.w, Math.min( max.w, this.w ) );
-
- return this;
-
- },
-
- clampScalar: function () {
-
- var min, max;
-
- return function clampScalar( minVal, maxVal ) {
-
- if ( min === undefined ) {
-
- min = new Vector4();
- max = new Vector4();
-
- }
-
- min.set( minVal, minVal, minVal, minVal );
- max.set( maxVal, maxVal, maxVal, maxVal );
-
- return this.clamp( min, max );
-
- };
-
- }(),
-
- clampLength: function ( min, max ) {
-
- var length = this.length();
-
- return this.divideScalar( length || 1 ).multiplyScalar( Math.max( min, Math.min( max, length ) ) );
-
- },
-
- floor: function () {
-
- this.x = Math.floor( this.x );
- this.y = Math.floor( this.y );
- this.z = Math.floor( this.z );
- this.w = Math.floor( this.w );
-
- return this;
-
- },
-
- ceil: function () {
-
- this.x = Math.ceil( this.x );
- this.y = Math.ceil( this.y );
- this.z = Math.ceil( this.z );
- this.w = Math.ceil( this.w );
-
- return this;
-
- },
-
- round: function () {
-
- this.x = Math.round( this.x );
- this.y = Math.round( this.y );
- this.z = Math.round( this.z );
- this.w = Math.round( this.w );
-
- return this;
-
- },
-
- roundToZero: function () {
-
- this.x = ( this.x < 0 ) ? Math.ceil( this.x ) : Math.floor( this.x );
- this.y = ( this.y < 0 ) ? Math.ceil( this.y ) : Math.floor( this.y );
- this.z = ( this.z < 0 ) ? Math.ceil( this.z ) : Math.floor( this.z );
- this.w = ( this.w < 0 ) ? Math.ceil( this.w ) : Math.floor( this.w );
-
- return this;
-
- },
-
- negate: function () {
-
- this.x = - this.x;
- this.y = - this.y;
- this.z = - this.z;
- this.w = - this.w;
-
- return this;
-
- },
-
- dot: function ( v ) {
-
- return this.x * v.x + this.y * v.y + this.z * v.z + this.w * v.w;
-
- },
-
- lengthSq: function () {
-
- return this.x * this.x + this.y * this.y + this.z * this.z + this.w * this.w;
-
- },
-
- length: function () {
-
- return Math.sqrt( this.x * this.x + this.y * this.y + this.z * this.z + this.w * this.w );
-
- },
-
- manhattanLength: function () {
-
- return Math.abs( this.x ) + Math.abs( this.y ) + Math.abs( this.z ) + Math.abs( this.w );
-
- },
-
- normalize: function () {
-
- return this.divideScalar( this.length() || 1 );
-
- },
-
- setLength: function ( length ) {
-
- return this.normalize().multiplyScalar( length );
-
- },
-
- lerp: function ( v, alpha ) {
-
- this.x += ( v.x - this.x ) * alpha;
- this.y += ( v.y - this.y ) * alpha;
- this.z += ( v.z - this.z ) * alpha;
- this.w += ( v.w - this.w ) * alpha;
-
- return this;
-
- },
-
- lerpVectors: function ( v1, v2, alpha ) {
-
- return this.subVectors( v2, v1 ).multiplyScalar( alpha ).add( v1 );
-
- },
-
- equals: function ( v ) {
-
- return ( ( v.x === this.x ) && ( v.y === this.y ) && ( v.z === this.z ) && ( v.w === this.w ) );
-
- },
-
- fromArray: function ( array, offset ) {
-
- if ( offset === undefined ) offset = 0;
-
- this.x = array[ offset ];
- this.y = array[ offset + 1 ];
- this.z = array[ offset + 2 ];
- this.w = array[ offset + 3 ];
-
- return this;
-
- },
-
- toArray: function ( array, offset ) {
-
- if ( array === undefined ) array = [];
- if ( offset === undefined ) offset = 0;
-
- array[ offset ] = this.x;
- array[ offset + 1 ] = this.y;
- array[ offset + 2 ] = this.z;
- array[ offset + 3 ] = this.w;
-
- return array;
-
- },
-
- fromBufferAttribute: function ( attribute, index, offset ) {
-
- if ( offset !== undefined ) {
-
- console.warn( 'THREE.Vector4: offset has been removed from .fromBufferAttribute().' );
-
- }
-
- this.x = attribute.getX( index );
- this.y = attribute.getY( index );
- this.z = attribute.getZ( index );
- this.w = attribute.getW( index );
-
- return this;
-
- }
-
-} );
-
-
-export { Vector4 };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/project_vertex.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/project_vertex.glsl.js
deleted file mode 100644
index 54416c502f73d7b9c714db351ccb9efaafcf10c9..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/project_vertex.glsl.js
+++ /dev/null
@@ -1,5 +0,0 @@
-export default /* glsl */`
-vec4 mvPosition = modelViewMatrix * vec4( transformed, 1.0 );
-
-gl_Position = projectionMatrix * mvPosition;
-`;
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/meshlambert_vert.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/meshlambert_vert.glsl.js
deleted file mode 100644
index 9b11a199234b0e2ffa385667fd00b5a5620adeea..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/meshlambert_vert.glsl.js
+++ /dev/null
@@ -1,51 +0,0 @@
-export default /* glsl */`
-#define LAMBERT
-
-varying vec3 vLightFront;
-varying vec3 vIndirectFront;
-
-#ifdef DOUBLE_SIDED
- varying vec3 vLightBack;
- varying vec3 vIndirectBack;
-#endif
-
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-
-void main() {
-
- #include
- #include
- #include
-
- #include
- #include
- #include
- #include
- #include
-
- #include
- #include
- #include
- #include
- #include
- #include
-
- #include
- #include
- #include
- #include
- #include
-}
-`;
diff --git a/spaces/barani/ControlNet/utils.py b/spaces/barani/ControlNet/utils.py
deleted file mode 100644
index a626d25c3f4eb92d10bdb66d3c28059a0927a8cd..0000000000000000000000000000000000000000
--- a/spaces/barani/ControlNet/utils.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import random
-
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
- if randomize_seed:
- seed = random.randint(0, 1000000)
- return seed
diff --git a/spaces/bertin-project/bertin-gpt-j-6B/streamlit_app.py b/spaces/bertin-project/bertin-gpt-j-6B/streamlit_app.py
deleted file mode 100644
index 41fc89940ef5effa4b49674a2978d01561a5fad6..0000000000000000000000000000000000000000
--- a/spaces/bertin-project/bertin-gpt-j-6B/streamlit_app.py
+++ /dev/null
@@ -1,241 +0,0 @@
-import random
-import os
-
-import streamlit as st
-import torch
-from transformers import pipeline, set_seed
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import logging
-logger = logging.getLogger()
-logger.addHandler(logging.StreamHandler())
-
-HF_AUTH_TOKEN = os.environ.get("HF_AUTH_TOKEN", None)
-DEVICE = os.environ.get("DEVICE", "cpu") # cuda:0
-if DEVICE != "cpu" and not torch.cuda.is_available():
- DEVICE = "cpu"
-logger.info(f"DEVICE {DEVICE}")
-DTYPE = torch.float32 if DEVICE == "cpu" else torch.float16
-MODEL_NAME = os.environ.get("MODEL_NAME", "bertin-project/bertin-gpt-j-6B")
-MAX_LENGTH = int(os.environ.get("MAX_LENGTH", 1024))
-HEADER_INFO = """
-# BERTIN GPT-J-6B
-Spanish BERTIN GPT-J-6B Model.
-""".strip()
-LOGO = "https://huggingface.co/bertin-project/bertin-roberta-base-spanish/resolve/main/images/bertin.png"
-SIDEBAR_INFO = f"""
-
-
-
-
-# BERTIN GPT-J-6B
-
-
-
-BERTIN proporciona una serie de modelos de lenguaje en Español entrenados en abierto.
-
-Este modelo ha sido entrenado con [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax) en TPUs proporcionadas por Google a través del programa Tensor Research Cloud, a partir del modelo [GPT-J de EleutherAI](https://huggingface.co/EleutherAI/gpt-j-6B) con el corpus [mC4-es-sampled (gaussian)](https://huggingface.co/datasets/bertin-project/mc4-es-sampled). Esta demo funciona sobre una GPU proporcionada por HuggingFace.
-
-Para más información, visite el [repositorio del modelo](https://huggingface.co/bertin-project/bertin-gpt-j-6B).
-
-# Configuración
-""".strip()
-
-PROMPT_BOX = "Introduzca su texto..."
-EXAMPLES = [
- "¿Cuál es la capital de Francia? Respuesta:",
- """Los templos egipcios fueron construidos para el culto oficial de los dioses y la conmemoración de los faraones del Antiguo Egipto en las regiones bajo su dominio. Los templos eran vistos como el hogar de los dioses o faraones deificados a quienes eran dedicados, y en ellos los faraones y el clero egipcio llevaban a cabo diversos rituales, las funciones centrales de la religión egipcia: realizar ofrendas a sus dioses, recrear pasajes mitológicos mediante festivales y protegerse de las fuerzas del caos. Estos rituales eran vistos como necesarios para que los dioses mantuvieran la maat, el orden divino del universo.
-
-El cuidado del hogar de los dioses era obligación de los faraones, que dedicaron ingentes cantidades de recursos para la construcción y el mantenimiento de los templos. Por necesidad, los faraones delegaban la mayoría de los rituales en una amplia casta sacerdotal, aunque la mayor parte del pueblo llano permanecía al margen de la participación directa en las ceremonias por tener prohibido el acceso a las zonas más sagradas de los templos. A pesar de ello, el templo siempre fue un importante centro religioso para todos los egipcios, que iban a ellos a rezar, realizar ofrendas y buscar la guía de los oráculos.
-
-Pregunta: ¿Quién cuidaba del hogar los dioses?
-Respuesta:""",
-]
-
-
-def style():
- st.markdown("""
-
- """, unsafe_allow_html=True)
-
-
-class Normalizer:
- def remove_repetitions(self, text):
- """Remove repetitions"""
- first_ocurrences = []
- for sentence in text.split("."):
- if sentence not in first_ocurrences:
- first_ocurrences.append(sentence)
- return '.'.join(first_ocurrences)
-
- def trim_last_sentence(self, text):
- """Trim last sentence if incomplete"""
- return text[:text.rfind(".") + 1]
-
- def clean_txt(self, text):
- return self.trim_last_sentence(self.remove_repetitions(text))
-
-
-class TextGeneration:
- def __init__(self):
- self.tokenizer = None
- self.generator = None
- self.task = "text-generation"
- self.model_name_or_path = MODEL_NAME
- set_seed(42)
-
- def load(self):
- logger.info("Loading model...")
- self.tokenizer = AutoTokenizer.from_pretrained(
- self.model_name_or_path, use_auth_token=HF_AUTH_TOKEN if HF_AUTH_TOKEN else None,
- )
- self.model = AutoModelForCausalLM.from_pretrained(
- self.model_name_or_path, use_auth_token=HF_AUTH_TOKEN if HF_AUTH_TOKEN else None,
- pad_token_id=self.tokenizer.eos_token_id, eos_token_id=self.tokenizer.eos_token_id,
- torch_dtype=DTYPE, low_cpu_mem_usage=False if DEVICE == "cpu" else True
- ).to(device=DEVICE, non_blocking=False)
- _ = self.model.eval()
- device_number = -1 if DEVICE == "cpu" else int(DEVICE.split(":")[-1])
- self.generator = pipeline(self.task, model=self.model, tokenizer=self.tokenizer, device=device_number)
- logger.info("Loading model done.")
- # with torch.no_grad():
- # tokens = tokenizer.encode(prompt, return_tensors='pt').to(device=device, non_blocking=True)
- # gen_tokens = self.model.generate(tokens, do_sample=True, temperature=0.8, max_length=128)
- # generated = tokenizer.batch_decode(gen_tokens)[0]
-
- # return generated
-
-
- def generate(self, prompt, generation_kwargs):
- max_length = len(self.tokenizer(prompt)["input_ids"]) + generation_kwargs["max_length"]
- generation_kwargs["max_length"] = min(max_length, self.model.config.n_positions)
- # generation_kwargs["num_return_sequences"] = 1
- # generation_kwargs["return_full_text"] = False
- return self.generator(
- prompt,
- **generation_kwargs,
- )[0]["generated_text"]
-
-
-#@st.cache(hash_funcs={torch.nn.parameter.Parameter: lambda _: None})
-#@st.cache(allow_output_mutation=True)
-@st.cache(allow_output_mutation=True, hash_funcs={TextGeneration: lambda _: None})
-def load_text_generator():
- text_generator = TextGeneration()
- text_generator.load()
- return text_generator
-
-
-def main():
- st.set_page_config(
- page_title="BERTIN-GPT-J-6B",
- page_icon="🇪🇸",
- layout="wide",
- initial_sidebar_state="expanded"
- )
- style()
- generator = load_text_generator()
- st.sidebar.markdown(SIDEBAR_INFO, unsafe_allow_html=True)
-
- max_length = st.sidebar.slider(
- label='Longitud máxima',
- help="Número máximo (aproximado) de palabras a generar.",
- min_value=1,
- max_value=MAX_LENGTH,
- value=50,
- step=1
- )
- top_k = st.sidebar.slider(
- label='Top-k',
- help="Número de palabras con alta probabilidad a mantener para el filtrado `top-k`",
- min_value=40,
- max_value=80,
- value=50,
- step=1
- )
- top_p = st.sidebar.slider(
- label='Top-p',
- help="Solo las palabras más probables con probabilidades que sumen `top_p` o más se mantienen para la generación.",
- min_value=0.0,
- max_value=1.0,
- value=0.95,
- step=0.01
- )
- temperature = st.sidebar.slider(
- label='Temperatura',
- help="Valor utilizado para modular las probabilidades de las siguientes palabras generadas.",
- min_value=0.1,
- max_value=10.0,
- value=0.8,
- step=0.05
- )
- do_sample = st.sidebar.selectbox(
- label='¿Muestrear?',
- options=(True, False),
- help="Si no se muestrea se usará una decodificación voraz (_greedy_).",
- )
- do_clean = st.sidebar.selectbox(
- label='¿Limpiar texto?',
- options=(True, False),
- help="Si eliminar o no las palabras repetidas y recortar las últimas frases sin terminar.",
- )
- generation_kwargs = {
- "max_length": max_length,
- "top_k": top_k,
- "top_p": top_p,
- "temperature": temperature,
- "do_sample": do_sample,
- "do_clean": do_clean,
- }
- st.markdown(HEADER_INFO)
- prompts = EXAMPLES + ["Personalizado"]
- prompt = st.selectbox('Ejemplos', prompts, index=len(prompts) - 1)
-
- if prompt == "Personalizado":
- prompt_box = PROMPT_BOX
- else:
- prompt_box = prompt
-
- text = st.text_area("Texto", prompt_box)
- generation_kwargs_ph = st.empty()
- cleaner = Normalizer()
- if st.button("¡Generar!"):
- with st.spinner(text="Generando..."):
- generation_kwargs_ph.markdown(", ".join([f"`{k}`: {v}" for k, v in generation_kwargs.items()]))
- if text:
- generated_text = generator.generate(text, generation_kwargs)
- if do_clean:
- generated_text = cleaner.clean_txt(generated_text)
- if generated_text.strip().startswith(text):
- generated_text = generated_text.replace(text, "", 1).strip()
- st.markdown(
- f''
- f'{text} '
- f'{generated_text}'
- f'
',
- unsafe_allow_html=True
- )
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/utils/json_logger.py b/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/utils/json_logger.py
deleted file mode 100644
index 0afd0b45df736866c49473db78286685d77660ac..0000000000000000000000000000000000000000
--- a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/utils/json_logger.py
+++ /dev/null
@@ -1,383 +0,0 @@
-"""
-References:
- https://medium.com/analytics-vidhya/creating-a-custom-logging-mechanism-for-real-time-object-detection-using-tdd-4ca2cfcd0a2f
-"""
-import json
-from os import makedirs
-from os.path import exists, join
-from datetime import datetime
-
-
-class JsonMeta(object):
- HOURS = 3
- MINUTES = 59
- SECONDS = 59
- PATH_TO_SAVE = 'LOGS'
- DEFAULT_FILE_NAME = 'remaining'
-
-
-class BaseJsonLogger(object):
- """
- This is the base class that returns __dict__ of its own
- it also returns the dicts of objects in the attributes that are list instances
-
- """
-
- def dic(self):
- # returns dicts of objects
- out = {}
- for k, v in self.__dict__.items():
- if hasattr(v, 'dic'):
- out[k] = v.dic()
- elif isinstance(v, list):
- out[k] = self.list(v)
- else:
- out[k] = v
- return out
-
- @staticmethod
- def list(values):
- # applies the dic method on items in the list
- return [v.dic() if hasattr(v, 'dic') else v for v in values]
-
-
-class Label(BaseJsonLogger):
- """
- For each bounding box there are various categories with confidences. Label class keeps track of that information.
- """
-
- def __init__(self, category: str, confidence: float):
- self.category = category
- self.confidence = confidence
-
-
-class Bbox(BaseJsonLogger):
- """
- This module stores the information for each frame and use them in JsonParser
- Attributes:
- labels (list): List of label module.
- top (int):
- left (int):
- width (int):
- height (int):
-
- Args:
- bbox_id (float):
- top (int):
- left (int):
- width (int):
- height (int):
-
- References:
- Check Label module for better understanding.
-
-
- """
-
- def __init__(self, bbox_id, top, left, width, height):
- self.labels = []
- self.bbox_id = bbox_id
- self.top = top
- self.left = left
- self.width = width
- self.height = height
-
- def add_label(self, category, confidence):
- # adds category and confidence only if top_k is not exceeded.
- self.labels.append(Label(category, confidence))
-
- def labels_full(self, value):
- return len(self.labels) == value
-
-
-class Frame(BaseJsonLogger):
- """
- This module stores the information for each frame and use them in JsonParser
- Attributes:
- timestamp (float): The elapsed time of captured frame
- frame_id (int): The frame number of the captured video
- bboxes (list of Bbox objects): Stores the list of bbox objects.
-
- References:
- Check Bbox class for better information
-
- Args:
- timestamp (float):
- frame_id (int):
-
- """
-
- def __init__(self, frame_id: int, timestamp: float = None):
- self.frame_id = frame_id
- self.timestamp = timestamp
- self.bboxes = []
-
- def add_bbox(self, bbox_id: int, top: int, left: int, width: int, height: int):
- bboxes_ids = [bbox.bbox_id for bbox in self.bboxes]
- if bbox_id not in bboxes_ids:
- self.bboxes.append(Bbox(bbox_id, top, left, width, height))
- else:
- raise ValueError("Frame with id: {} already has a Bbox with id: {}".format(self.frame_id, bbox_id))
-
- def add_label_to_bbox(self, bbox_id: int, category: str, confidence: float):
- bboxes = {bbox.id: bbox for bbox in self.bboxes}
- if bbox_id in bboxes.keys():
- res = bboxes.get(bbox_id)
- res.add_label(category, confidence)
- else:
- raise ValueError('the bbox with id: {} does not exists!'.format(bbox_id))
-
-
-class BboxToJsonLogger(BaseJsonLogger):
- """
- ُ This module is designed to automate the task of logging jsons. An example json is used
- to show the contents of json file shortly
- Example:
- {
- "video_details": {
- "frame_width": 1920,
- "frame_height": 1080,
- "frame_rate": 20,
- "video_name": "/home/gpu/codes/MSD/pedestrian_2/project/public/camera1.avi"
- },
- "frames": [
- {
- "frame_id": 329,
- "timestamp": 3365.1254
- "bboxes": [
- {
- "labels": [
- {
- "category": "pedestrian",
- "confidence": 0.9
- }
- ],
- "bbox_id": 0,
- "top": 1257,
- "left": 138,
- "width": 68,
- "height": 109
- }
- ]
- }],
-
- Attributes:
- frames (dict): It's a dictionary that maps each frame_id to json attributes.
- video_details (dict): information about video file.
- top_k_labels (int): shows the allowed number of labels
- start_time (datetime object): we use it to automate the json output by time.
-
- Args:
- top_k_labels (int): shows the allowed number of labels
-
- """
-
- def __init__(self, top_k_labels: int = 1):
- self.frames = {}
- self.video_details = self.video_details = dict(frame_width=None, frame_height=None, frame_rate=None,
- video_name=None)
- self.top_k_labels = top_k_labels
- self.start_time = datetime.now()
-
- def set_top_k(self, value):
- self.top_k_labels = value
-
- def frame_exists(self, frame_id: int) -> bool:
- """
- Args:
- frame_id (int):
-
- Returns:
- bool: true if frame_id is recognized
- """
- return frame_id in self.frames.keys()
-
- def add_frame(self, frame_id: int, timestamp: float = None) -> None:
- """
- Args:
- frame_id (int):
- timestamp (float): opencv captured frame time property
-
- Raises:
- ValueError: if frame_id would not exist in class frames attribute
-
- Returns:
- None
-
- """
- if not self.frame_exists(frame_id):
- self.frames[frame_id] = Frame(frame_id, timestamp)
- else:
- raise ValueError("Frame id: {} already exists".format(frame_id))
-
- def bbox_exists(self, frame_id: int, bbox_id: int) -> bool:
- """
- Args:
- frame_id:
- bbox_id:
-
- Returns:
- bool: if bbox exists in frame bboxes list
- """
- bboxes = []
- if self.frame_exists(frame_id=frame_id):
- bboxes = [bbox.bbox_id for bbox in self.frames[frame_id].bboxes]
- return bbox_id in bboxes
-
- def find_bbox(self, frame_id: int, bbox_id: int):
- """
-
- Args:
- frame_id:
- bbox_id:
-
- Returns:
- bbox_id (int):
-
- Raises:
- ValueError: if bbox_id does not exist in the bbox list of specific frame.
- """
- if not self.bbox_exists(frame_id, bbox_id):
- raise ValueError("frame with id: {} does not contain bbox with id: {}".format(frame_id, bbox_id))
- bboxes = {bbox.bbox_id: bbox for bbox in self.frames[frame_id].bboxes}
- return bboxes.get(bbox_id)
-
- def add_bbox_to_frame(self, frame_id: int, bbox_id: int, top: int, left: int, width: int, height: int) -> None:
- """
-
- Args:
- frame_id (int):
- bbox_id (int):
- top (int):
- left (int):
- width (int):
- height (int):
-
- Returns:
- None
-
- Raises:
- ValueError: if bbox_id already exist in frame information with frame_id
- ValueError: if frame_id does not exist in frames attribute
- """
- if self.frame_exists(frame_id):
- frame = self.frames[frame_id]
- if not self.bbox_exists(frame_id, bbox_id):
- frame.add_bbox(bbox_id, top, left, width, height)
- else:
- raise ValueError(
- "frame with frame_id: {} already contains the bbox with id: {} ".format(frame_id, bbox_id))
- else:
- raise ValueError("frame with frame_id: {} does not exist".format(frame_id))
-
- def add_label_to_bbox(self, frame_id: int, bbox_id: int, category: str, confidence: float):
- """
- Args:
- frame_id:
- bbox_id:
- category:
- confidence: the confidence value returned from yolo detection
-
- Returns:
- None
-
- Raises:
- ValueError: if labels quota (top_k_labels) exceeds.
- """
- bbox = self.find_bbox(frame_id, bbox_id)
- if not bbox.labels_full(self.top_k_labels):
- bbox.add_label(category, confidence)
- else:
- raise ValueError("labels in frame_id: {}, bbox_id: {} is fulled".format(frame_id, bbox_id))
-
- def add_video_details(self, frame_width: int = None, frame_height: int = None, frame_rate: int = None,
- video_name: str = None):
- self.video_details['frame_width'] = frame_width
- self.video_details['frame_height'] = frame_height
- self.video_details['frame_rate'] = frame_rate
- self.video_details['video_name'] = video_name
-
- def output(self):
- output = {'video_details': self.video_details}
- result = list(self.frames.values())
- output['frames'] = [item.dic() for item in result]
- return output
-
- def json_output(self, output_name):
- """
- Args:
- output_name:
-
- Returns:
- None
-
- Notes:
- It creates the json output with `output_name` name.
- """
- if not output_name.endswith('.json'):
- output_name += '.json'
- with open(output_name, 'w') as file:
- json.dump(self.output(), file)
- file.close()
-
- def set_start(self):
- self.start_time = datetime.now()
-
- def schedule_output_by_time(self, output_dir=JsonMeta.PATH_TO_SAVE, hours: int = 0, minutes: int = 0,
- seconds: int = 60) -> None:
- """
- Notes:
- Creates folder and then periodically stores the jsons on that address.
-
- Args:
- output_dir (str): the directory where output files will be stored
- hours (int):
- minutes (int):
- seconds (int):
-
- Returns:
- None
-
- """
- end = datetime.now()
- interval = 0
- interval += abs(min([hours, JsonMeta.HOURS]) * 3600)
- interval += abs(min([minutes, JsonMeta.MINUTES]) * 60)
- interval += abs(min([seconds, JsonMeta.SECONDS]))
- diff = (end - self.start_time).seconds
-
- if diff > interval:
- output_name = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '.json'
- if not exists(output_dir):
- makedirs(output_dir)
- output = join(output_dir, output_name)
- self.json_output(output_name=output)
- self.frames = {}
- self.start_time = datetime.now()
-
- def schedule_output_by_frames(self, frames_quota, frame_counter, output_dir=JsonMeta.PATH_TO_SAVE):
- """
- saves as the number of frames quota increases higher.
- :param frames_quota:
- :param frame_counter:
- :param output_dir:
- :return:
- """
- pass
-
- def flush(self, output_dir):
- """
- Notes:
- We use this function to output jsons whenever possible.
- like the time that we exit the while loop of opencv.
-
- Args:
- output_dir:
-
- Returns:
- None
-
- """
- filename = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '-remaining.json'
- output = join(output_dir, filename)
- self.json_output(output_name=output)
diff --git a/spaces/bioriAsaeru/text-to-voice/Corel Draw X5 Free Download With Keygen 2021.md b/spaces/bioriAsaeru/text-to-voice/Corel Draw X5 Free Download With Keygen 2021.md
deleted file mode 100644
index f5f398b8aea5c886fcd9cdd4740cfcc9c2a5a26c..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Corel Draw X5 Free Download With Keygen 2021.md
+++ /dev/null
@@ -1,59 +0,0 @@
-
-Corel Draw X5 Free Download With Keygen: Is It Worth It?
-If you are looking for a powerful and versatile graphic design software, you may have heard of Corel Draw X5. This program offers a wide range of tools and features to create stunning logos, illustrations, flyers, web graphics and more. However, the official version of Corel Draw X5 is not cheap, and you may be tempted to download it for free using a keygen.
-Corel Draw X5 Free Download With Keygen
Download Zip ☆☆☆ https://urloso.com/2uyRcW
-A keygen is a program that generates a serial number or an activation code for a software that requires it. By using a keygen, you can bypass the security system of the software and use it without paying. However, this method is not only illegal but also risky. In this article, we will explain why you should avoid using Corel Draw X5 free download with keygen and what are the legal and safe alternatives.
-The Dangers of Using Corel Draw X5 Keygen
-Downloading and using Corel Draw X5 keygen may seem like a good idea at first, but it comes with many disadvantages and dangers. Here are some of the most common problems you may face if you use a keygen to activate Corel Draw X5:
-
-- Legal issues. Using a keygen is a form of software piracy, which is a serious crime in many countries. You are violating the rights of the software developer and breaking the terms of use. If you are caught using pirated software, you may face fines, lawsuits or even jail time.
-- System errors. A keygen is not a reliable way to activate a software, as it may interfere with its functionality and performance. Since hackers modify the source code of the software, it may lack some important elements or contain bugs. As a result, the software may not work properly, crash frequently or fail to connect to the internet.
-- Viral threats. A keygen is often bundled with malware, such as viruses, trojans, worms or spyware. These malicious programs can infect your computer and damage your files, system or personal data. They can also allow hackers to access your device remotely and steal your information or money.
-- No updates. A keygen prevents the software from receiving updates from the official source. This means that you will miss out on new features, improvements or bug fixes that are released by the developer. You will also be vulnerable to security issues or compatibility problems with other programs or devices.
-
-How to Use Corel Draw X5 Legally and Safely?
-If you want to use Corel Draw X5 without risking legal troubles or computer problems, you have two options: buy the official version or use a free alternative. Here are some details about each option:
-
-- Buy the official version. This is the best way to enjoy all the benefits of Corel Draw X5 without any limitations or risks. You can buy the software from the official website or from authorized resellers. You will get a valid license key that will activate the software and allow you to access all its features and updates. You will also get technical support and customer service from the developer.
-- Use a free alternative. If you don't want to spend money on Corel Draw X5, you can try some free graphic design software that offer similar functions and capabilities. Some of the most popular free alternatives are Adobe Illustrator, Inkscape and GIMP. These programs are legal, safe and easy to use. They also have large communities of users who share tutorials, tips and resources online.
-
-Conclusion
-Corel Draw X5 is a great graphic design software that can help you create amazing projects for personal or professional use. However, using a keygen to download it for free is not a smart move, as it can expose you to legal issues, system errors, viral threats and no updates. Instead, you should either buy the official version or use a free alternative that can meet your needs and expectations.
-How to Download Corel Draw X5 Free with Keygen?
-You may be wondering how to download Corel Draw X5 free with keygen and where to find it. There are many websites that claim to offer the keygen for Corel Draw X5, but most of them are either fake or unsafe. Some of them may even contain malware or viruses that can harm your computer or steal your data.
-The best way to avoid these risks is to not use a keygen at all. A keygen is an illegal and unethical way to activate a software that you have not paid for. It is also a violation of the terms and conditions of the software developer, who has invested time and money to create a quality product. By using a keygen, you are disrespecting their work and depriving them of their rightful income.
-
-Therefore, if you want to download Corel Draw X5 free with keygen, you should think twice before doing it. You may end up regretting it later, when you face legal problems, system errors, viral threats or no updates. Instead, you should either buy the official version of Corel Draw X5 or use a free alternative that can provide similar features and functions.
-How to Install Corel Draw X5 with Keygen?
-If you have already downloaded Corel Draw X5 with keygen and decided to install it on your computer, you should be aware of the possible consequences. Installing a pirated software with a keygen is not a simple or safe process. You may encounter various difficulties or dangers along the way. Here are some of the steps you need to follow to install Corel Draw X5 with keygen:
-
-- Disable your antivirus and internet connection. This is necessary because most antivirus programs will detect the keygen as a threat and block it from running. Also, you need to prevent the software from connecting to the internet, as it may detect that it is not activated legally and stop working.
-- Extract the downloaded file. You will need a program like WinRAR or 7-Zip to extract the compressed file that contains the software and the keygen. Be careful not to open any suspicious files that may contain malware or viruses.
-- Run the setup.exe file. This will start the installation process of the software. You will need to choose the installation options and accept the terms and conditions. You will also need to select "I don't have a serial number" when prompted.
-- Run the keygen.exe file. This will open the keygen program that will generate a serial number and an activation code for the software. You will need to copy and paste these codes into the corresponding fields in the software activation dialog. You will also need to copy the installation code from the software into the keygen.
-- Click on activate. This will complete the activation process of the software using the keygen. You should see a message that says "Activation successful". You can then close the keygen and start using the software.
-
-Is It Worth It?
-After reading this article, you may have realized that using Corel Draw X5 free download with keygen is not worth it at all. It is a risky and illegal way to get a graphic design software that can cause more problems than benefits. You may end up facing legal issues, system errors, viral threats or no updates that will ruin your experience and work.
-Instead of using a keygen, you should consider buying the official version of Corel Draw X5 or using a free alternative that can offer similar features and functions. These options are legal, safe and reliable, and they will allow you to enjoy graphic design without any worries or regrets.
-
- ---> ServiceClient failure for DeepLeo[/ERROR]
-
- ---> ServiceClient failure for DeepLeo[/ERROR]
-How to Use Corel Draw X5 for Graphic Design?
-Once you have installed Corel Draw X5 on your computer, you can start using it for various graphic design projects. Corel Draw X5 is a vector-based software that allows you to create and edit logos, illustrations, flyers, web graphics and more. You can also import and export files in different formats, such as JPG, PNG, PDF, EPS and AI.
-Corel Draw X5 has a user-friendly interface that consists of a menu bar, a toolbox, a property bar, a status bar and a dockers panel. You can customize the workspace according to your preferences and needs. You can also access various tools and features from the menu bar or the toolbox, such as the shape tool, the text tool, the pen tool, the fill tool and the color palette.
-To create a new document in Corel Draw X5, you need to go to File > New and choose the size, resolution and color mode of your document. You can also use one of the templates or presets available in the software. To draw or edit objects on your document, you need to use the tools from the toolbox and adjust their properties from the property bar or the dockers panel. You can also use the commands from the menu bar or the keyboard shortcuts to perform various actions, such as copy, paste, rotate, scale and align.
-How to Learn Corel Draw X5?
-If you are new to Corel Draw X5 or want to improve your skills, you may need some guidance and resources to learn how to use this software effectively. There are many ways to learn Corel Draw X5, such as:
-
-- Using the help menu. Corel Draw X5 has a built-in help menu that provides you with useful information and tips about the software. You can access it by clicking on Help > Help Topics or pressing F1 on your keyboard. You can also use the search box or the index to find specific topics or keywords.
-- Watching online tutorials. There are many online tutorials that teach you how to use Corel Draw X5 for different purposes and projects. You can find them on YouTube, Udemy, Skillshare or other platforms. Some of them are free and some of them are paid. You can choose the ones that suit your level and interest.
-- Reading books or blogs. There are also many books or blogs that cover various aspects of Corel Draw X5 and graphic design in general. You can buy them online or in bookstores, or read them online for free. Some of them are written by experts or professionals who share their knowledge and experience.
-- Joining online communities. There are also many online communities where you can interact with other users of Corel Draw X5 and ask questions, share tips, get feedback or join challenges. You can find them on Facebook, Reddit, Quora or other platforms. You can also join the official Corel community at https://community.coreldraw.com/
-
-Conclusion
-Corel Draw X5 is a great graphic design software that can help you create amazing projects for personal or professional use. However, using Corel Draw X5 free download with keygen is not a good idea, as it can expose you to legal issues, system errors, viral threats or no updates. Instead of using a keygen, you should consider buying the official version of Corel Draw X5 or using a free alternative that can offer similar features and functions. These options are legal, safe and reliable, and they will allow you to enjoy graphic design without any worries or regrets.
-Corel Draw X5 is a great graphic design software that can help you create amazing projects for personal or professional use. However, using Corel Draw X5 free download with keygen is not a good idea, as it can expose you to legal issues, system errors, viral threats or no updates. Instead of using a keygen, you should consider buying the official version of Corel Draw X5 or using a free alternative that can offer similar features and functions. These options are legal, safe and reliable, and they will allow you to enjoy graphic design without any worries or regrets.
3cee63e6c2
-
-
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Download EXCLUSIVE Font Vni-romans.shx.md b/spaces/bioriAsaeru/text-to-voice/Download EXCLUSIVE Font Vni-romans.shx.md
deleted file mode 100644
index 05eb044a9b65da562c2b0b49a24cdb841a21fed2..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Download EXCLUSIVE Font Vni-romans.shx.md
+++ /dev/null
@@ -1,6 +0,0 @@
-download font vni-romans.shx
Download File ⏩ https://urloso.com/2uyOVg
-
-Tải 900+ Fonts chữ AutoCad (.shx), download Font CAD Tiếng Việt Full ... Font chữ thông dụng như trên Windows ra, và dụ như Unicode, VNI ... 4d29de3e1b
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Flukeview Forms Basic 3.0.md b/spaces/bioriAsaeru/text-to-voice/Flukeview Forms Basic 3.0.md
deleted file mode 100644
index 262d9a6b2c2cdcb2c4f665f3d581ef75aa840d90..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Flukeview Forms Basic 3.0.md
+++ /dev/null
@@ -1,7 +0,0 @@
-flukeview forms basic 3.0
Download File > https://urloso.com/2uyRXD
-
-Anyone who owns FlukeView Forms version 3.0 or later can get a free software upgrade by following the links and downloading the software from this site. Basic ... http://www.fluke.com/software/visual/
-http://www.fluke.com/software/visual/downloads/download.jsp 8a78ff9644
-
-
-
diff --git a/spaces/boda/arabic-names-generator/model/generate.py b/spaces/boda/arabic-names-generator/model/generate.py
deleted file mode 100644
index d9cd1cb08bf9033a43dcbae216c6f144c60e7bdd..0000000000000000000000000000000000000000
--- a/spaces/boda/arabic-names-generator/model/generate.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import torch
-from model.layers import *
-# from layers import *
-
-import pathlib
-from pathlib import Path
-
-
-CONTEXT_SIZE = 5
-n_hidden = 100
-n_embed = 10
-EN_VOCAB_SIZE = 27
-AR_VOCAB_SIZE = 37
-ACTIVATION = 'relu'
-
-ar_itos = {0: '.', 1: 'ء', 2: 'آ', 3: 'أ', 4: 'ؤ', 5: 'إ', 6: 'ئ', 7: 'ا', 8: 'ب', 9: 'ة', 10: 'ت', 11: 'ث', 12: 'ج', 13: 'ح', 14: 'خ', 15: 'د', 16: 'ذ', 17: 'ر', 18: 'ز', 19: 'س', 20: 'ش', 21: 'ص', 22: 'ض', 23: 'ط', 24: 'ظ', 25: 'ع', 26: 'غ', 27: 'ف', 28: 'ق', 29: 'ك', 30: 'ل', 31: 'م', 32: 'ن', 33: 'ه', 34: 'و', 35: 'ى', 36: 'ي'}
-en_itos= {0: '.', 1: '-', 2: 'a', 3: 'b', 4: 'c', 5: 'd', 6: 'e', 7: 'f', 8: 'g', 9: 'h', 10: 'i', 11: 'j', 12: 'k', 13: 'l', 14: 'm', 15: 'n', 16: 'o', 17: 'p', 18: 'q', 19: 'r', 20: 's', 21: 't', 22: 'u', 23: 'v', 24: 'w', 25: 'y', 26: 'z'}
-arabic_layers = [
- Linear(CONTEXT_SIZE*n_embed , n_hidden),BatchNorm(n_hidden), Activation(ACTIVATION),
- Linear(n_hidden, n_hidden),BatchNorm(n_hidden), Activation(ACTIVATION),
- Linear(n_hidden, n_hidden),BatchNorm(n_hidden), Activation(ACTIVATION),
- Linear(n_hidden , AR_VOCAB_SIZE)
-]
-
-english_layers = [
- Linear(CONTEXT_SIZE*n_embed , n_hidden),BatchNorm(n_hidden), Activation(ACTIVATION),
- Linear(n_hidden, n_hidden),BatchNorm(n_hidden), Activation(ACTIVATION),
- Linear(n_hidden, n_hidden),BatchNorm(n_hidden), Activation(ACTIVATION),
- Linear(n_hidden , EN_VOCAB_SIZE)
-]
-
-
-
-parent_path = Path(__file__).parent
-arabic_dict = torch.load(Path.joinpath(parent_path,'weights/ar_dataset_weights.pt'))
-english_dict= torch.load(Path.joinpath(parent_path,'weights/en_dataset_weights.pt'))
-
-## Weights
-arabic_params = arabic_dict['params']
-english_params = english_dict['params']
-
-## Batch norm means ans stds
-arabic_bn_conf = arabic_dict['bn_conf']
-english_bn_conf = english_dict['bn_conf']
-
-
-# Load embeddings
-arabic_embedding = arabic_params[0]
-english_embedding = english_params[0]
-
-## Load weights
-j = 0
-for i,l in enumerate(arabic_layers):
- l.set_parameters( arabic_params[i+1] )
- if l.__class__.__name__ == "BatchNorm":
- l.set_mean_std(arabic_bn_conf[j])
- j+=1
-
-j = 0
-for i,l in enumerate(english_layers):
- l.set_parameters( english_params[i+1] )
- if l.__class__.__name__ == "BatchNorm":
- l.set_mean_std(english_bn_conf[j])
- j+=1
-
-def forward(x_batch, is_training,lang):
-
- if lang =='ar':
- embedding = arabic_embedding
- layers = arabic_layers
-
- elif lang =='en':
- embedding = english_embedding
- layers = english_layers
-
-
- x_batch = embedding[x_batch]
- x = x_batch.view(x_batch.shape[0], -1)
- for layer in layers:
- x = layer(x, is_training)
- return x
-
-
-def generate_name(lang):
-
- w = ''
- last_ch = [0]* CONTEXT_SIZE
- while True:
- last_ch = torch.tensor(last_ch).unsqueeze(0)
-
-
- x = forward(last_ch, False, lang)
- p = torch.softmax(x, dim=1)
- next_ch = torch.multinomial(p, num_samples=1, replacement=True).item()
- if lang =='ar':
- w += ar_itos[next_ch]
- elif lang == 'en':
- w += en_itos[next_ch]
-
- last_ch = last_ch.clone().detach().squeeze(0)
- last_ch = last_ch.tolist()
- last_ch = last_ch[1:] + [next_ch]
- if next_ch == 0:
- break
-
- return w[:-1]
-
-def generate_names(n,lang):
- ret = []
- for i in range(n):
- ret.append(generate_name(lang))
-
- return ret
-
-
-if __name__ == '__main__':
-
- pass
\ No newline at end of file
diff --git a/spaces/bookbot/Image-Upscaling-Playground/app.py b/spaces/bookbot/Image-Upscaling-Playground/app.py
deleted file mode 100644
index 1f3736667bfd4e5ac6d9ee2ef9b95416cb80f9c0..0000000000000000000000000000000000000000
--- a/spaces/bookbot/Image-Upscaling-Playground/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import numpy as np
-import cv2
-import onnxruntime
-import gradio as gr
-
-
-def pre_process(img: np.array) -> np.array:
- # H, W, C -> C, H, W
- img = np.transpose(img[:, :, 0:3], (2, 0, 1))
- # C, H, W -> 1, C, H, W
- img = np.expand_dims(img, axis=0).astype(np.float32)
- return img
-
-
-def post_process(img: np.array) -> np.array:
- # 1, C, H, W -> C, H, W
- img = np.squeeze(img)
- # C, H, W -> H, W, C
- img = np.transpose(img, (1, 2, 0))[:, :, ::-1].astype(np.uint8)
- return img
-
-
-def inference(model_path: str, img_array: np.array) -> np.array:
- options = onnxruntime.SessionOptions()
- options.intra_op_num_threads = 1
- options.inter_op_num_threads = 1
- ort_session = onnxruntime.InferenceSession(model_path, options)
- ort_inputs = {ort_session.get_inputs()[0].name: img_array}
- ort_outs = ort_session.run(None, ort_inputs)
-
- return ort_outs[0]
-
-
-def convert_pil_to_cv2(image):
- # pil_image = image.convert("RGB")
- open_cv_image = np.array(image)
- # RGB to BGR
- open_cv_image = open_cv_image[:, :, ::-1].copy()
- return open_cv_image
-
-
-def upscale(image, model):
- model_path = f"models/{model}.ort"
- img = convert_pil_to_cv2(image)
- if img.ndim == 2:
- img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
- if img.shape[2] == 4:
- alpha = img[:, :, 3] # GRAY
- alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2BGR) # BGR
- alpha_output = post_process(inference(model_path, pre_process(alpha))) # BGR
- alpha_output = cv2.cvtColor(alpha_output, cv2.COLOR_BGR2GRAY) # GRAY
-
- img = img[:, :, 0:3] # BGR
- image_output = post_process(inference(model_path, pre_process(img))) # BGR
- image_output = cv2.cvtColor(image_output, cv2.COLOR_BGR2BGRA) # BGRA
- image_output[:, :, 3] = alpha_output
-
- elif img.shape[2] == 3:
- image_output = post_process(inference(model_path, pre_process(img))) # BGR
-
- return image_output
-
-
-css = ".output-image, .input-image, .image-preview {height: 480px !important} "
-model_choices = ["modelx2", "modelx2 25 JXL", "modelx4", "minecraft_modelx4"]
-
-gr.Interface(
- fn=upscale,
- inputs=[
- gr.inputs.Image(type="pil", label="Input Image"),
- gr.inputs.Radio(
- model_choices,
- type="value",
- default=None,
- label="Choose Upscaler",
- optional=False,
- ),
- ],
- outputs="image",
- title="Image Upscaling 🦆",
- description="Model: [Anchor-based Plain Net for Mobile Image Super-Resolution](https://arxiv.org/abs/2105.09750). Repository: [SR Mobile PyTorch](https://github.com/w11wo/sr_mobile_pytorch)",
- allow_flagging="never",
- css=css,
-).launch()
diff --git a/spaces/brainblow/MusiCreator/CONTRIBUTING.md b/spaces/brainblow/MusiCreator/CONTRIBUTING.md
deleted file mode 100644
index 55b99140204d785d572ada9761dd77f302ae31c6..0000000000000000000000000000000000000000
--- a/spaces/brainblow/MusiCreator/CONTRIBUTING.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# Contributing to Audiocraft
-
-We want to make contributing to this project as easy and transparent as
-possible.
-
-## Pull Requests
-
-Audiocraft is the implementation of a research paper.
-Therefore, we do not plan on accepting many pull requests for new features.
-We certainly welcome them for bug fixes.
-
-1. Fork the repo and create your branch from `main`.
-2. If you've added code that should be tested, add tests.
-3. If you've changed APIs, update the documentation.
-4. Ensure the test suite passes.
-5. Make sure your code lints.
-6. If you haven't already, complete the Contributor License Agreement ("CLA").
-
-## Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Meta's open source projects.
-
-Complete your CLA here:
-
-## Issues
-We use GitHub issues to track public bugs. Please ensure your description is
-clear and has sufficient instructions to be able to reproduce the issue.
-
-Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-## License
-By contributing to encodec, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/TensorMask/README.md b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/TensorMask/README.md
deleted file mode 100644
index e81307c4c9be8d1cb2fd27b716531f4ebcd9ae5c..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/TensorMask/README.md
+++ /dev/null
@@ -1,63 +0,0 @@
-
-# TensorMask in Detectron2
-**A Foundation for Dense Object Segmentation**
-
-Xinlei Chen, Ross Girshick, Kaiming He, Piotr Dollár
-
-[[`arXiv`](https://arxiv.org/abs/1903.12174)] [[`BibTeX`](#CitingTensorMask)]
-
-
- 
-
-
-In this repository, we release code for TensorMask in Detectron2.
-TensorMask is a dense sliding-window instance segmentation framework that, for the first time, achieves results close to the well-developed Mask R-CNN framework -- both qualitatively and quantitatively. It establishes a conceptually complementary direction for object instance segmentation research.
-
-## Installation
-First install Detectron2 following the [documentation](https://detectron2.readthedocs.io/tutorials/install.html) and
-[setup the dataset](../../datasets). Then compile the TensorMask-specific op (`swap_align2nat`):
-```bash
-pip install -e /path/to/detectron2/projects/TensorMask
-```
-
-## Training
-
-To train a model, run:
-```bash
-python /path/to/detectron2/projects/TensorMask/train_net.py --config-file
-```
-
-For example, to launch TensorMask BiPyramid training (1x schedule) with ResNet-50 backbone on 8 GPUs,
-one should execute:
-```bash
-python /path/to/detectron2/projects/TensorMask/train_net.py --config-file configs/tensormask_R_50_FPN_1x.yaml --num-gpus 8
-```
-
-## Evaluation
-
-Model evaluation can be done similarly (6x schedule with scale augmentation):
-```bash
-python /path/to/detectron2/projects/TensorMask/train_net.py --config-file configs/tensormask_R_50_FPN_6x.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint
-```
-
-# Pretrained Models
-
-| Backbone | lr sched | AP box | AP mask | download |
-| -------- | -------- | -- | --- | -------- |
-| R50 | 1x | 37.6 | 32.4 | model \| metrics |
-| R50 | 6x | 41.4 | 35.8 | model \| metrics |
-
-
-## Citing TensorMask
-
-If you use TensorMask, please use the following BibTeX entry.
-
-```
-@InProceedings{chen2019tensormask,
- title={Tensormask: A Foundation for Dense Object Segmentation},
- author={Chen, Xinlei and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
- journal={The International Conference on Computer Vision (ICCV)},
- year={2019}
-}
-```
-
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/structures/test_masks.py b/spaces/brjathu/HMR2.0/vendor/detectron2/tests/structures/test_masks.py
deleted file mode 100644
index 7991eb0b35724f2f2f402d788a273d68b7cad5f2..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/structures/test_masks.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import unittest
-import torch
-
-from detectron2.structures.masks import BitMasks, PolygonMasks, polygons_to_bitmask
-
-
-class TestBitMask(unittest.TestCase):
- def test_get_bounding_box(self):
- masks = torch.tensor(
- [
- [
- [False, False, False, True],
- [False, False, True, True],
- [False, True, True, False],
- [False, True, True, False],
- ],
- [
- [False, False, False, False],
- [False, False, True, False],
- [False, True, True, False],
- [False, True, True, False],
- ],
- torch.zeros(4, 4),
- ]
- )
- bitmask = BitMasks(masks)
- box_true = torch.tensor([[1, 0, 4, 4], [1, 1, 3, 4], [0, 0, 0, 0]], dtype=torch.float32)
- box = bitmask.get_bounding_boxes()
- self.assertTrue(torch.all(box.tensor == box_true).item())
-
- for box in box_true:
- poly = box[[0, 1, 2, 1, 2, 3, 0, 3]].numpy()
- mask = polygons_to_bitmask([poly], 4, 4)
- reconstruct_box = BitMasks(mask[None, :, :]).get_bounding_boxes()[0].tensor
- self.assertTrue(torch.all(box == reconstruct_box).item())
-
- reconstruct_box = PolygonMasks([[poly]]).get_bounding_boxes()[0].tensor
- self.assertTrue(torch.all(box == reconstruct_box).item())
-
- def test_from_empty_polygons(self):
- masks = BitMasks.from_polygon_masks([], 100, 100)
- self.assertEqual(masks.tensor.shape, (0, 100, 100))
-
- def test_getitem(self):
- masks = BitMasks(torch.ones(3, 10, 10))
- self.assertEqual(masks[1].tensor.shape, (1, 10, 10))
- self.assertEqual(masks[1:3].tensor.shape, (2, 10, 10))
- self.assertEqual(masks[torch.tensor([True, False, False])].tensor.shape, (1, 10, 10))
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/bugbugbug/vits-uma-genshin-honkai/Docker/vits.sh b/spaces/bugbugbug/vits-uma-genshin-honkai/Docker/vits.sh
deleted file mode 100644
index 2b87f26eda96d3800b73b4a21b210c78888a2299..0000000000000000000000000000000000000000
--- a/spaces/bugbugbug/vits-uma-genshin-honkai/Docker/vits.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/bin/bash
-run() {
- echo -e "\033[32m已完成初始化，启动服务...\033[0m"
- python3 /app/vits-uma-genshin-honkai/app.py
-}
-install() {
- echo -e "\033[33m正在初始化：安装依赖....\033[0m"
- pip install -r /app/vits-uma-genshin-honkai/requirements.txt -i https://mirrors.ustc.edu.cn/pypi/web/simple
- echo -e "\033[33m正在下载模型....\033[0m"
- rm -f /app/vits-uma-genshin-honkai/model/G_953000.pth
- wget -O /app/vits-uma-genshin-honkai/model/G_953000.pth https://huggingface.co/spaces/ikechan8370/vits-uma-genshin-honkai/resolve/main/model/G_953000.pth
- echo -e "\033[32m初始化完成！\033[0m"
- run
-}
-
-if [ ! -f "/app/vits-uma-genshin-honkai/model/G_953000.pth" ] || [ "$(stat -c%s "/app/vits-uma-genshin-honkai/model/G_953000.pth")" -lt 10000 ]; then
- install
-else
- run
-fi
diff --git a/spaces/caffeinum/VToonify/vtoonify/model/encoder/encoders/__init__.py b/spaces/caffeinum/VToonify/vtoonify/model/encoder/encoders/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/caiocdcs/sports-classifier/app.py b/spaces/caiocdcs/sports-classifier/app.py
deleted file mode 100644
index 74a869550d881dbc697a1f924ca9817024d1994c..0000000000000000000000000000000000000000
--- a/spaces/caiocdcs/sports-classifier/app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import gradio.components as components
-import gradio as gr
-from fastai.vision.all import *
-import skimage
-
-learn = load_learner('export.pkl')
-
-labels = learn.dls.vocab
-def predict(img):
- img = PILImage.create(img)
- _,_,probs = learn.predict(img)
- return {labels[i]: float(probs[i]) for i in range(len(labels))}
-
-title = "Sports Classifier"
-description = "A sports classifier. Created as a demo for Gradio and HuggingFace Spaces."
-examples = ['handball.jpg']
-interpretation='default'
-enable_queue=True
-
-demo = gr.Interface(fn=predict,
- inputs=components.Image(shape=(512, 512)),
- outputs=components.Label(num_top_classes=3),
- examples=[examples],
- interpretation=interpretation,
- title=title,
- description=description
- )
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/text/korean.py b/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/text/korean.py
deleted file mode 100644
index edee07429a450c55e3d8e246997faaa1e0b89cc9..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/text/korean.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import re
-from jamo import h2j, j2hcj
-import ko_pron
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ㄳ', 'ㄱㅅ'),
- ('ㄵ', 'ㄴㅈ'),
- ('ㄶ', 'ㄴㅎ'),
- ('ㄺ', 'ㄹㄱ'),
- ('ㄻ', 'ㄹㅁ'),
- ('ㄼ', 'ㄹㅂ'),
- ('ㄽ', 'ㄹㅅ'),
- ('ㄾ', 'ㄹㅌ'),
- ('ㄿ', 'ㄹㅍ'),
- ('ㅀ', 'ㄹㅎ'),
- ('ㅄ', 'ㅂㅅ'),
- ('ㅘ', 'ㅗㅏ'),
- ('ㅙ', 'ㅗㅐ'),
- ('ㅚ', 'ㅗㅣ'),
- ('ㅝ', 'ㅜㅓ'),
- ('ㅞ', 'ㅜㅔ'),
- ('ㅟ', 'ㅜㅣ'),
- ('ㅢ', 'ㅡㅣ'),
- ('ㅑ', 'ㅣㅏ'),
- ('ㅒ', 'ㅣㅐ'),
- ('ㅕ', 'ㅣㅓ'),
- ('ㅖ', 'ㅣㅔ'),
- ('ㅛ', 'ㅣㅗ'),
- ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', '에이'),
- ('b', '비'),
- ('c', '시'),
- ('d', '디'),
- ('e', '이'),
- ('f', '에프'),
- ('g', '지'),
- ('h', '에이치'),
- ('i', '아이'),
- ('j', '제이'),
- ('k', '케이'),
- ('l', '엘'),
- ('m', '엠'),
- ('n', '엔'),
- ('o', '오'),
- ('p', '피'),
- ('q', '큐'),
- ('r', '아르'),
- ('s', '에스'),
- ('t', '티'),
- ('u', '유'),
- ('v', '브이'),
- ('w', '더블유'),
- ('x', '엑스'),
- ('y', '와이'),
- ('z', '제트')
-]]
-
-# List of (ipa, lazy ipa) pairs:
-_ipa_to_lazy_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('t͡ɕ','ʧ'),
- ('d͡ʑ','ʥ'),
- ('ɲ','n^'),
- ('ɕ','ʃ'),
- ('ʷ','w'),
- ('ɭ','l`'),
- ('ʎ','ɾ'),
- ('ɣ','ŋ'),
- ('ɰ','ɯ'),
- ('ʝ','j'),
- ('ʌ','ə'),
- ('ɡ','g'),
- ('\u031a','#'),
- ('\u0348','='),
- ('\u031e',''),
- ('\u0320',''),
- ('\u0339','')
-]]
-
-
-def latin_to_hangul(text):
- for regex, replacement in _latin_to_hangul:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def divide_hangul(text):
- text = j2hcj(h2j(text))
- for regex, replacement in _hangul_divided:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def hangul_number(num, sino=True):
- '''Reference https://github.com/Kyubyong/g2pK'''
- num = re.sub(',', '', num)
-
- if num == '0':
- return '영'
- if not sino and num == '20':
- return '스무'
-
- digits = '123456789'
- names = '일이삼사오육칠팔구'
- digit2name = {d: n for d, n in zip(digits, names)}
-
- modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
- decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
- digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
- digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
- spelledout = []
- for i, digit in enumerate(num):
- i = len(num) - i - 1
- if sino:
- if i == 0:
- name = digit2name.get(digit, '')
- elif i == 1:
- name = digit2name.get(digit, '') + '십'
- name = name.replace('일십', '십')
- else:
- if i == 0:
- name = digit2mod.get(digit, '')
- elif i == 1:
- name = digit2dec.get(digit, '')
- if digit == '0':
- if i % 4 == 0:
- last_three = spelledout[-min(3, len(spelledout)):]
- if ''.join(last_three) == '':
- spelledout.append('')
- continue
- else:
- spelledout.append('')
- continue
- if i == 2:
- name = digit2name.get(digit, '') + '백'
- name = name.replace('일백', '백')
- elif i == 3:
- name = digit2name.get(digit, '') + '천'
- name = name.replace('일천', '천')
- elif i == 4:
- name = digit2name.get(digit, '') + '만'
- name = name.replace('일만', '만')
- elif i == 5:
- name = digit2name.get(digit, '') + '십'
- name = name.replace('일십', '십')
- elif i == 6:
- name = digit2name.get(digit, '') + '백'
- name = name.replace('일백', '백')
- elif i == 7:
- name = digit2name.get(digit, '') + '천'
- name = name.replace('일천', '천')
- elif i == 8:
- name = digit2name.get(digit, '') + '억'
- elif i == 9:
- name = digit2name.get(digit, '') + '십'
- elif i == 10:
- name = digit2name.get(digit, '') + '백'
- elif i == 11:
- name = digit2name.get(digit, '') + '천'
- elif i == 12:
- name = digit2name.get(digit, '') + '조'
- elif i == 13:
- name = digit2name.get(digit, '') + '십'
- elif i == 14:
- name = digit2name.get(digit, '') + '백'
- elif i == 15:
- name = digit2name.get(digit, '') + '천'
- spelledout.append(name)
- return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
- '''Reference https://github.com/Kyubyong/g2pK'''
- tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
- for token in tokens:
- num, classifier = token
- if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
- spelledout = hangul_number(num, sino=False)
- else:
- spelledout = hangul_number(num, sino=True)
- text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
- # digit by digit for remaining digits
- digits = '0123456789'
- names = '영일이삼사오육칠팔구'
- for d, n in zip(digits, names):
- text = text.replace(d, n)
- return text
-
-
-def korean_to_lazy_ipa(text):
- text = latin_to_hangul(text)
- text = number_to_hangul(text)
- text=re.sub('[\uac00-\ud7af]+',lambda x:ko_pron.romanise(x.group(0),'ipa').split('] ~ [')[0],text)
- for regex, replacement in _ipa_to_lazy_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def korean_to_ipa(text):
- text = korean_to_lazy_ipa(text)
- return text.replace('ʧ','tʃ').replace('ʥ','dʑ')
diff --git a/spaces/camenduru/9/README.md b/spaces/camenduru/9/README.md
deleted file mode 100644
index 018edb7c638c4798b07ccad287ae3e7c1451a8bf..0000000000000000000000000000000000000000
--- a/spaces/camenduru/9/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: ''
-emoji: 📞
-colorFrom: red
-colorTo: red
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/romanian_postprocessing.md b/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/romanian_postprocessing.md
deleted file mode 100644
index 938f0d1d7227f5687ec45f35f8dcff659172dfe2..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/romanian_postprocessing.md
+++ /dev/null
@@ -1,65 +0,0 @@
-### Motivation
-Without processing, english-> romanian mbart-large-en-ro gets BLEU score 26.8 on the WMT data.
-With post processing, it can score 37..
-Here is the postprocessing code, stolen from @mjpost in this [issue](https://github.com/pytorch/fairseq/issues/1758)
-
-
-
-### Instructions
-Note: You need to have your test_generations.txt before you start this process.
-(1) Setup `mosesdecoder` and `wmt16-scripts`
-```bash
-cd $HOME
-git clone git@github.com:moses-smt/mosesdecoder.git
-cd mosesdecoder
-git clone git@github.com:rsennrich/wmt16-scripts.git
-```
-
-(2) define a function for post processing.
- It removes diacritics and does other things I don't understand
-```bash
-ro_post_process () {
- sys=$1
- ref=$2
- export MOSES_PATH=$HOME/mosesdecoder
- REPLACE_UNICODE_PUNCT=$MOSES_PATH/scripts/tokenizer/replace-unicode-punctuation.perl
- NORM_PUNC=$MOSES_PATH/scripts/tokenizer/normalize-punctuation.perl
- REM_NON_PRINT_CHAR=$MOSES_PATH/scripts/tokenizer/remove-non-printing-char.perl
- REMOVE_DIACRITICS=$MOSES_PATH/wmt16-scripts/preprocess/remove-diacritics.py
- NORMALIZE_ROMANIAN=$MOSES_PATH/wmt16-scripts/preprocess/normalise-romanian.py
- TOKENIZER=$MOSES_PATH/scripts/tokenizer/tokenizer.perl
-
-
-
- lang=ro
- for file in $sys $ref; do
- cat $file \
- | $REPLACE_UNICODE_PUNCT \
- | $NORM_PUNC -l $lang \
- | $REM_NON_PRINT_CHAR \
- | $NORMALIZE_ROMANIAN \
- | $REMOVE_DIACRITICS \
- | $TOKENIZER -no-escape -l $lang \
- > $(basename $file).tok
- done
- # compute BLEU
- cat $(basename $sys).tok | sacrebleu -tok none -s none -b $(basename $ref).tok
-}
-```
-
-(3) Call the function on test_generations.txt and test.target
-For example,
-```bash
-ro_post_process enro_finetune/test_generations.txt wmt_en_ro/test.target
-```
-This will split out a new blue score and write a new fine called `test_generations.tok` with post-processed outputs.
-
-
-
-
-
-
-
-
-
-```
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/langturkishmodel.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/langturkishmodel.py
deleted file mode 100644
index 64c94336cb2827db8e28ebe246c132779d390b89..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/langturkishmodel.py
+++ /dev/null
@@ -1,4380 +0,0 @@
-from chardet.sbcharsetprober import SingleByteCharSetModel
-
-# 3: Positive
-# 2: Likely
-# 1: Unlikely
-# 0: Negative
-
-TURKISH_LANG_MODEL = {
- 23: { # 'A'
- 23: 0, # 'A'
- 37: 0, # 'B'
- 47: 0, # 'C'
- 39: 0, # 'D'
- 29: 0, # 'E'
- 52: 0, # 'F'
- 36: 0, # 'G'
- 45: 0, # 'H'
- 53: 0, # 'I'
- 60: 0, # 'J'
- 16: 0, # 'K'
- 49: 0, # 'L'
- 20: 0, # 'M'
- 46: 0, # 'N'
- 42: 0, # 'O'
- 48: 0, # 'P'
- 44: 0, # 'R'
- 35: 0, # 'S'
- 31: 0, # 'T'
- 51: 0, # 'U'
- 38: 0, # 'V'
- 62: 0, # 'W'
- 43: 0, # 'Y'
- 56: 0, # 'Z'
- 1: 3, # 'a'
- 21: 0, # 'b'
- 28: 0, # 'c'
- 12: 2, # 'd'
- 2: 3, # 'e'
- 18: 0, # 'f'
- 27: 1, # 'g'
- 25: 1, # 'h'
- 3: 1, # 'i'
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- 6: 2, # 'ı'
- 40: 0, # 'Ş'
- 19: 0, # 'ş'
- },
- 30: { # 'ğ'
- 23: 0, # 'A'
- 37: 2, # 'B'
- 47: 1, # 'C'
- 39: 0, # 'D'
- 29: 0, # 'E'
- 52: 2, # 'F'
- 36: 1, # 'G'
- 45: 0, # 'H'
- 53: 1, # 'I'
- 60: 0, # 'J'
- 16: 3, # 'K'
- 49: 0, # 'L'
- 20: 1, # 'M'
- 46: 2, # 'N'
- 42: 2, # 'O'
- 48: 1, # 'P'
- 44: 1, # 'R'
- 35: 0, # 'S'
- 31: 1, # 'T'
- 51: 0, # 'U'
- 38: 2, # 'V'
- 62: 0, # 'W'
- 43: 2, # 'Y'
- 56: 0, # 'Z'
- 1: 3, # 'a'
- 21: 0, # 'b'
- 28: 2, # 'c'
- 12: 0, # 'd'
- 2: 2, # 'e'
- 18: 0, # 'f'
- 27: 0, # 'g'
- 25: 0, # 'h'
- 3: 0, # 'i'
- 24: 3, # 'j'
- 10: 1, # 'k'
- 5: 2, # 'l'
- 13: 3, # 'm'
- 4: 0, # 'n'
- 15: 1, # 'o'
- 26: 0, # 'p'
- 7: 1, # 'r'
- 8: 0, # 's'
- 9: 0, # 't'
- 14: 3, # 'u'
- 32: 0, # 'v'
- 57: 0, # 'w'
- 58: 0, # 'x'
- 11: 0, # 'y'
- 22: 2, # 'z'
- 63: 0, # '·'
- 54: 2, # 'Ç'
- 50: 2, # 'Ö'
- 55: 0, # 'Ü'
- 59: 0, # 'â'
- 33: 1, # 'ç'
- 61: 0, # 'î'
- 34: 2, # 'ö'
- 17: 0, # 'ü'
- 30: 1, # 'ğ'
- 41: 2, # 'İ'
- 6: 2, # 'ı'
- 40: 2, # 'Ş'
- 19: 1, # 'ş'
- },
- 41: { # 'İ'
- 23: 0, # 'A'
- 37: 0, # 'B'
- 47: 1, # 'C'
- 39: 1, # 'D'
- 29: 1, # 'E'
- 52: 0, # 'F'
- 36: 2, # 'G'
- 45: 2, # 'H'
- 53: 0, # 'I'
- 60: 0, # 'J'
- 16: 0, # 'K'
- 49: 0, # 'L'
- 20: 2, # 'M'
- 46: 1, # 'N'
- 42: 1, # 'O'
- 48: 2, # 'P'
- 44: 0, # 'R'
- 35: 1, # 'S'
- 31: 1, # 'T'
- 51: 1, # 'U'
- 38: 1, # 'V'
- 62: 0, # 'W'
- 43: 2, # 'Y'
- 56: 0, # 'Z'
- 1: 1, # 'a'
- 21: 2, # 'b'
- 28: 1, # 'c'
- 12: 2, # 'd'
- 2: 1, # 'e'
- 18: 0, # 'f'
- 27: 3, # 'g'
- 25: 2, # 'h'
- 3: 2, # 'i'
- 24: 2, # 'j'
- 10: 2, # 'k'
- 5: 0, # 'l'
- 13: 1, # 'm'
- 4: 3, # 'n'
- 15: 1, # 'o'
- 26: 1, # 'p'
- 7: 3, # 'r'
- 8: 3, # 's'
- 9: 2, # 't'
- 14: 0, # 'u'
- 32: 0, # 'v'
- 57: 1, # 'w'
- 58: 0, # 'x'
- 11: 2, # 'y'
- 22: 0, # 'z'
- 63: 0, # '·'
- 54: 0, # 'Ç'
- 50: 0, # 'Ö'
- 55: 1, # 'Ü'
- 59: 1, # 'â'
- 33: 0, # 'ç'
- 61: 0, # 'î'
- 34: 1, # 'ö'
- 17: 1, # 'ü'
- 30: 2, # 'ğ'
- 41: 0, # 'İ'
- 6: 3, # 'ı'
- 40: 0, # 'Ş'
- 19: 1, # 'ş'
- },
- 6: { # 'ı'
- 23: 2, # 'A'
- 37: 0, # 'B'
- 47: 0, # 'C'
- 39: 0, # 'D'
- 29: 0, # 'E'
- 52: 0, # 'F'
- 36: 1, # 'G'
- 45: 0, # 'H'
- 53: 0, # 'I'
- 60: 2, # 'J'
- 16: 3, # 'K'
- 49: 0, # 'L'
- 20: 3, # 'M'
- 46: 1, # 'N'
- 42: 0, # 'O'
- 48: 0, # 'P'
- 44: 0, # 'R'
- 35: 0, # 'S'
- 31: 2, # 'T'
- 51: 0, # 'U'
- 38: 0, # 'V'
- 62: 0, # 'W'
- 43: 2, # 'Y'
- 56: 1, # 'Z'
- 1: 3, # 'a'
- 21: 2, # 'b'
- 28: 1, # 'c'
- 12: 3, # 'd'
- 2: 3, # 'e'
- 18: 3, # 'f'
- 27: 3, # 'g'
- 25: 2, # 'h'
- 3: 3, # 'i'
- 24: 3, # 'j'
- 10: 3, # 'k'
- 5: 3, # 'l'
- 13: 3, # 'm'
- 4: 3, # 'n'
- 15: 0, # 'o'
- 26: 3, # 'p'
- 7: 3, # 'r'
- 8: 3, # 's'
- 9: 3, # 't'
- 14: 3, # 'u'
- 32: 3, # 'v'
- 57: 1, # 'w'
- 58: 1, # 'x'
- 11: 3, # 'y'
- 22: 0, # 'z'
- 63: 1, # '·'
- 54: 0, # 'Ç'
- 50: 0, # 'Ö'
- 55: 0, # 'Ü'
- 59: 0, # 'â'
- 33: 2, # 'ç'
- 61: 0, # 'î'
- 34: 0, # 'ö'
- 17: 3, # 'ü'
- 30: 0, # 'ğ'
- 41: 0, # 'İ'
- 6: 3, # 'ı'
- 40: 0, # 'Ş'
- 19: 0, # 'ş'
- },
- 40: { # 'Ş'
- 23: 0, # 'A'
- 37: 0, # 'B'
- 47: 1, # 'C'
- 39: 1, # 'D'
- 29: 1, # 'E'
- 52: 0, # 'F'
- 36: 1, # 'G'
- 45: 2, # 'H'
- 53: 1, # 'I'
- 60: 0, # 'J'
- 16: 0, # 'K'
- 49: 0, # 'L'
- 20: 2, # 'M'
- 46: 1, # 'N'
- 42: 1, # 'O'
- 48: 2, # 'P'
- 44: 2, # 'R'
- 35: 1, # 'S'
- 31: 1, # 'T'
- 51: 0, # 'U'
- 38: 1, # 'V'
- 62: 0, # 'W'
- 43: 2, # 'Y'
- 56: 1, # 'Z'
- 1: 0, # 'a'
- 21: 2, # 'b'
- 28: 0, # 'c'
- 12: 2, # 'd'
- 2: 0, # 'e'
- 18: 3, # 'f'
- 27: 0, # 'g'
- 25: 2, # 'h'
- 3: 3, # 'i'
- 24: 2, # 'j'
- 10: 1, # 'k'
- 5: 0, # 'l'
- 13: 1, # 'm'
- 4: 3, # 'n'
- 15: 2, # 'o'
- 26: 0, # 'p'
- 7: 3, # 'r'
- 8: 2, # 's'
- 9: 2, # 't'
- 14: 1, # 'u'
- 32: 3, # 'v'
- 57: 0, # 'w'
- 58: 0, # 'x'
- 11: 2, # 'y'
- 22: 0, # 'z'
- 63: 0, # '·'
- 54: 0, # 'Ç'
- 50: 0, # 'Ö'
- 55: 1, # 'Ü'
- 59: 0, # 'â'
- 33: 0, # 'ç'
- 61: 0, # 'î'
- 34: 2, # 'ö'
- 17: 1, # 'ü'
- 30: 2, # 'ğ'
- 41: 0, # 'İ'
- 6: 2, # 'ı'
- 40: 1, # 'Ş'
- 19: 2, # 'ş'
- },
- 19: { # 'ş'
- 23: 0, # 'A'
- 37: 0, # 'B'
- 47: 1, # 'C'
- 39: 0, # 'D'
- 29: 0, # 'E'
- 52: 2, # 'F'
- 36: 1, # 'G'
- 45: 0, # 'H'
- 53: 0, # 'I'
- 60: 0, # 'J'
- 16: 3, # 'K'
- 49: 2, # 'L'
- 20: 0, # 'M'
- 46: 1, # 'N'
- 42: 1, # 'O'
- 48: 1, # 'P'
- 44: 1, # 'R'
- 35: 1, # 'S'
- 31: 0, # 'T'
- 51: 1, # 'U'
- 38: 1, # 'V'
- 62: 0, # 'W'
- 43: 1, # 'Y'
- 56: 0, # 'Z'
- 1: 3, # 'a'
- 21: 1, # 'b'
- 28: 2, # 'c'
- 12: 0, # 'd'
- 2: 3, # 'e'
- 18: 0, # 'f'
- 27: 2, # 'g'
- 25: 1, # 'h'
- 3: 1, # 'i'
- 24: 0, # 'j'
- 10: 2, # 'k'
- 5: 2, # 'l'
- 13: 3, # 'm'
- 4: 0, # 'n'
- 15: 0, # 'o'
- 26: 1, # 'p'
- 7: 3, # 'r'
- 8: 0, # 's'
- 9: 0, # 't'
- 14: 3, # 'u'
- 32: 0, # 'v'
- 57: 0, # 'w'
- 58: 0, # 'x'
- 11: 0, # 'y'
- 22: 2, # 'z'
- 63: 0, # '·'
- 54: 1, # 'Ç'
- 50: 2, # 'Ö'
- 55: 0, # 'Ü'
- 59: 0, # 'â'
- 33: 1, # 'ç'
- 61: 1, # 'î'
- 34: 2, # 'ö'
- 17: 0, # 'ü'
- 30: 1, # 'ğ'
- 41: 1, # 'İ'
- 6: 1, # 'ı'
- 40: 1, # 'Ş'
- 19: 1, # 'ş'
- },
-}
-
-# 255: Undefined characters that did not exist in training text
-# 254: Carriage/Return
-# 253: symbol (punctuation) that does not belong to word
-# 252: 0 - 9
-# 251: Control characters
-
-# Character Mapping Table(s):
-ISO_8859_9_TURKISH_CHAR_TO_ORDER = {
- 0: 255, # '\x00'
- 1: 255, # '\x01'
- 2: 255, # '\x02'
- 3: 255, # '\x03'
- 4: 255, # '\x04'
- 5: 255, # '\x05'
- 6: 255, # '\x06'
- 7: 255, # '\x07'
- 8: 255, # '\x08'
- 9: 255, # '\t'
- 10: 255, # '\n'
- 11: 255, # '\x0b'
- 12: 255, # '\x0c'
- 13: 255, # '\r'
- 14: 255, # '\x0e'
- 15: 255, # '\x0f'
- 16: 255, # '\x10'
- 17: 255, # '\x11'
- 18: 255, # '\x12'
- 19: 255, # '\x13'
- 20: 255, # '\x14'
- 21: 255, # '\x15'
- 22: 255, # '\x16'
- 23: 255, # '\x17'
- 24: 255, # '\x18'
- 25: 255, # '\x19'
- 26: 255, # '\x1a'
- 27: 255, # '\x1b'
- 28: 255, # '\x1c'
- 29: 255, # '\x1d'
- 30: 255, # '\x1e'
- 31: 255, # '\x1f'
- 32: 255, # ' '
- 33: 255, # '!'
- 34: 255, # '"'
- 35: 255, # '#'
- 36: 255, # '$'
- 37: 255, # '%'
- 38: 255, # '&'
- 39: 255, # "'"
- 40: 255, # '('
- 41: 255, # ')'
- 42: 255, # '*'
- 43: 255, # '+'
- 44: 255, # ','
- 45: 255, # '-'
- 46: 255, # '.'
- 47: 255, # '/'
- 48: 255, # '0'
- 49: 255, # '1'
- 50: 255, # '2'
- 51: 255, # '3'
- 52: 255, # '4'
- 53: 255, # '5'
- 54: 255, # '6'
- 55: 255, # '7'
- 56: 255, # '8'
- 57: 255, # '9'
- 58: 255, # ':'
- 59: 255, # ';'
- 60: 255, # '<'
- 61: 255, # '='
- 62: 255, # '>'
- 63: 255, # '?'
- 64: 255, # '@'
- 65: 23, # 'A'
- 66: 37, # 'B'
- 67: 47, # 'C'
- 68: 39, # 'D'
- 69: 29, # 'E'
- 70: 52, # 'F'
- 71: 36, # 'G'
- 72: 45, # 'H'
- 73: 53, # 'I'
- 74: 60, # 'J'
- 75: 16, # 'K'
- 76: 49, # 'L'
- 77: 20, # 'M'
- 78: 46, # 'N'
- 79: 42, # 'O'
- 80: 48, # 'P'
- 81: 69, # 'Q'
- 82: 44, # 'R'
- 83: 35, # 'S'
- 84: 31, # 'T'
- 85: 51, # 'U'
- 86: 38, # 'V'
- 87: 62, # 'W'
- 88: 65, # 'X'
- 89: 43, # 'Y'
- 90: 56, # 'Z'
- 91: 255, # '['
- 92: 255, # '\\'
- 93: 255, # ']'
- 94: 255, # '^'
- 95: 255, # '_'
- 96: 255, # '`'
- 97: 1, # 'a'
- 98: 21, # 'b'
- 99: 28, # 'c'
- 100: 12, # 'd'
- 101: 2, # 'e'
- 102: 18, # 'f'
- 103: 27, # 'g'
- 104: 25, # 'h'
- 105: 3, # 'i'
- 106: 24, # 'j'
- 107: 10, # 'k'
- 108: 5, # 'l'
- 109: 13, # 'm'
- 110: 4, # 'n'
- 111: 15, # 'o'
- 112: 26, # 'p'
- 113: 64, # 'q'
- 114: 7, # 'r'
- 115: 8, # 's'
- 116: 9, # 't'
- 117: 14, # 'u'
- 118: 32, # 'v'
- 119: 57, # 'w'
- 120: 58, # 'x'
- 121: 11, # 'y'
- 122: 22, # 'z'
- 123: 255, # '{'
- 124: 255, # '|'
- 125: 255, # '}'
- 126: 255, # '~'
- 127: 255, # '\x7f'
- 128: 180, # '\x80'
- 129: 179, # '\x81'
- 130: 178, # '\x82'
- 131: 177, # '\x83'
- 132: 176, # '\x84'
- 133: 175, # '\x85'
- 134: 174, # '\x86'
- 135: 173, # '\x87'
- 136: 172, # '\x88'
- 137: 171, # '\x89'
- 138: 170, # '\x8a'
- 139: 169, # '\x8b'
- 140: 168, # '\x8c'
- 141: 167, # '\x8d'
- 142: 166, # '\x8e'
- 143: 165, # '\x8f'
- 144: 164, # '\x90'
- 145: 163, # '\x91'
- 146: 162, # '\x92'
- 147: 161, # '\x93'
- 148: 160, # '\x94'
- 149: 159, # '\x95'
- 150: 101, # '\x96'
- 151: 158, # '\x97'
- 152: 157, # '\x98'
- 153: 156, # '\x99'
- 154: 155, # '\x9a'
- 155: 154, # '\x9b'
- 156: 153, # '\x9c'
- 157: 152, # '\x9d'
- 158: 151, # '\x9e'
- 159: 106, # '\x9f'
- 160: 150, # '\xa0'
- 161: 149, # '¡'
- 162: 148, # '¢'
- 163: 147, # '£'
- 164: 146, # '¤'
- 165: 145, # '¥'
- 166: 144, # '¦'
- 167: 100, # '§'
- 168: 143, # '¨'
- 169: 142, # '©'
- 170: 141, # 'ª'
- 171: 140, # '«'
- 172: 139, # '¬'
- 173: 138, # '\xad'
- 174: 137, # '®'
- 175: 136, # '¯'
- 176: 94, # '°'
- 177: 80, # '±'
- 178: 93, # '²'
- 179: 135, # '³'
- 180: 105, # '´'
- 181: 134, # 'µ'
- 182: 133, # '¶'
- 183: 63, # '·'
- 184: 132, # '¸'
- 185: 131, # '¹'
- 186: 130, # 'º'
- 187: 129, # '»'
- 188: 128, # '¼'
- 189: 127, # '½'
- 190: 126, # '¾'
- 191: 125, # '¿'
- 192: 124, # 'À'
- 193: 104, # 'Á'
- 194: 73, # 'Â'
- 195: 99, # 'Ã'
- 196: 79, # 'Ä'
- 197: 85, # 'Å'
- 198: 123, # 'Æ'
- 199: 54, # 'Ç'
- 200: 122, # 'È'
- 201: 98, # 'É'
- 202: 92, # 'Ê'
- 203: 121, # 'Ë'
- 204: 120, # 'Ì'
- 205: 91, # 'Í'
- 206: 103, # 'Î'
- 207: 119, # 'Ï'
- 208: 68, # 'Ğ'
- 209: 118, # 'Ñ'
- 210: 117, # 'Ò'
- 211: 97, # 'Ó'
- 212: 116, # 'Ô'
- 213: 115, # 'Õ'
- 214: 50, # 'Ö'
- 215: 90, # '×'
- 216: 114, # 'Ø'
- 217: 113, # 'Ù'
- 218: 112, # 'Ú'
- 219: 111, # 'Û'
- 220: 55, # 'Ü'
- 221: 41, # 'İ'
- 222: 40, # 'Ş'
- 223: 86, # 'ß'
- 224: 89, # 'à'
- 225: 70, # 'á'
- 226: 59, # 'â'
- 227: 78, # 'ã'
- 228: 71, # 'ä'
- 229: 82, # 'å'
- 230: 88, # 'æ'
- 231: 33, # 'ç'
- 232: 77, # 'è'
- 233: 66, # 'é'
- 234: 84, # 'ê'
- 235: 83, # 'ë'
- 236: 110, # 'ì'
- 237: 75, # 'í'
- 238: 61, # 'î'
- 239: 96, # 'ï'
- 240: 30, # 'ğ'
- 241: 67, # 'ñ'
- 242: 109, # 'ò'
- 243: 74, # 'ó'
- 244: 87, # 'ô'
- 245: 102, # 'õ'
- 246: 34, # 'ö'
- 247: 95, # '÷'
- 248: 81, # 'ø'
- 249: 108, # 'ù'
- 250: 76, # 'ú'
- 251: 72, # 'û'
- 252: 17, # 'ü'
- 253: 6, # 'ı'
- 254: 19, # 'ş'
- 255: 107, # 'ÿ'
-}
-
-ISO_8859_9_TURKISH_MODEL = SingleByteCharSetModel(
- charset_name="ISO-8859-9",
- language="Turkish",
- char_to_order_map=ISO_8859_9_TURKISH_CHAR_TO_ORDER,
- language_model=TURKISH_LANG_MODEL,
- typical_positive_ratio=0.97029,
- keep_ascii_letters=True,
- alphabet="ABCDEFGHIJKLMNOPRSTUVYZabcdefghijklmnoprstuvyzÂÇÎÖÛÜâçîöûüĞğİıŞş",
-)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/oxml/shared.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/oxml/shared.py
deleted file mode 100644
index 1e21ba366f0eec251b1addb753458a1a10cff1ad..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/oxml/shared.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# encoding: utf-8
-
-"""
-Objects shared by modules in the docx.oxml subpackage.
-"""
-
-from __future__ import absolute_import
-
-from . import OxmlElement
-from .ns import qn
-from .simpletypes import ST_DecimalNumber, ST_OnOff, ST_String
-from .xmlchemy import BaseOxmlElement, OptionalAttribute, RequiredAttribute
-
-
-class CT_DecimalNumber(BaseOxmlElement):
- """
- Used for ````, ````, ```` and several
- others, containing a text representation of a decimal number (e.g. 42) in
- its ``val`` attribute.
- """
- val = RequiredAttribute('w:val', ST_DecimalNumber)
-
- @classmethod
- def new(cls, nsptagname, val):
- """
- Return a new ``CT_DecimalNumber`` element having tagname *nsptagname*
- and ``val`` attribute set to *val*.
- """
- return OxmlElement(nsptagname, attrs={qn('w:val'): str(val)})
-
-
-class CT_OnOff(BaseOxmlElement):
- """
- Used for ````, ```` elements and others, containing a bool-ish
- string in its ``val`` attribute, xsd:boolean plus 'on' and 'off'.
- """
- val = OptionalAttribute('w:val', ST_OnOff, default=True)
-
-
-class CT_String(BaseOxmlElement):
- """
- Used for ```` and ```` elements and others,
- containing a style name in its ``val`` attribute.
- """
- val = RequiredAttribute('w:val', ST_String)
-
- @classmethod
- def new(cls, nsptagname, val):
- """
- Return a new ``CT_String`` element with tagname *nsptagname* and
- ``val`` attribute set to *val*.
- """
- elm = OxmlElement(nsptagname)
- elm.val = val
- return elm
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fastapi/encoders.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fastapi/encoders.py
deleted file mode 100644
index b542749f250a313f01fe3a0fcffd1897c9fec90c..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fastapi/encoders.py
+++ /dev/null
@@ -1,249 +0,0 @@
-import dataclasses
-import datetime
-from collections import defaultdict, deque
-from decimal import Decimal
-from enum import Enum
-from ipaddress import (
- IPv4Address,
- IPv4Interface,
- IPv4Network,
- IPv6Address,
- IPv6Interface,
- IPv6Network,
-)
-from pathlib import Path, PurePath
-from re import Pattern
-from types import GeneratorType
-from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
-from uuid import UUID
-
-from fastapi.types import IncEx
-from pydantic import BaseModel
-from pydantic.color import Color
-from pydantic.networks import NameEmail
-from pydantic.types import SecretBytes, SecretStr
-
-from ._compat import PYDANTIC_V2, MultiHostUrl, Url, _model_dump
-
-
-# Taken from Pydantic v1 as is
-def isoformat(o: Union[datetime.date, datetime.time]) -> str:
- return o.isoformat()
-
-
-# Taken from Pydantic v1 as is
-# TODO: pv2 should this return strings instead?
-def decimal_encoder(dec_value: Decimal) -> Union[int, float]:
- """
- Encodes a Decimal as int of there's no exponent, otherwise float
-
- This is useful when we use ConstrainedDecimal to represent Numeric(x,0)
- where a integer (but not int typed) is used. Encoding this as a float
- results in failed round-tripping between encode and parse.
- Our Id type is a prime example of this.
-
- >>> decimal_encoder(Decimal("1.0"))
- 1.0
-
- >>> decimal_encoder(Decimal("1"))
- 1
- """
- if dec_value.as_tuple().exponent >= 0: # type: ignore[operator]
- return int(dec_value)
- else:
- return float(dec_value)
-
-
-ENCODERS_BY_TYPE: Dict[Type[Any], Callable[[Any], Any]] = {
- bytes: lambda o: o.decode(),
- Color: str,
- datetime.date: isoformat,
- datetime.datetime: isoformat,
- datetime.time: isoformat,
- datetime.timedelta: lambda td: td.total_seconds(),
- Decimal: decimal_encoder,
- Enum: lambda o: o.value,
- frozenset: list,
- deque: list,
- GeneratorType: list,
- IPv4Address: str,
- IPv4Interface: str,
- IPv4Network: str,
- IPv6Address: str,
- IPv6Interface: str,
- IPv6Network: str,
- NameEmail: str,
- Path: str,
- Pattern: lambda o: o.pattern,
- SecretBytes: str,
- SecretStr: str,
- set: list,
- UUID: str,
- Url: str,
- MultiHostUrl: str,
-}
-
-
-def generate_encoders_by_class_tuples(
- type_encoder_map: Dict[Any, Callable[[Any], Any]]
-) -> Dict[Callable[[Any], Any], Tuple[Any, ...]]:
- encoders_by_class_tuples: Dict[Callable[[Any], Any], Tuple[Any, ...]] = defaultdict(
- tuple
- )
- for type_, encoder in type_encoder_map.items():
- encoders_by_class_tuples[encoder] += (type_,)
- return encoders_by_class_tuples
-
-
-encoders_by_class_tuples = generate_encoders_by_class_tuples(ENCODERS_BY_TYPE)
-
-
-def jsonable_encoder(
- obj: Any,
- include: Optional[IncEx] = None,
- exclude: Optional[IncEx] = None,
- by_alias: bool = True,
- exclude_unset: bool = False,
- exclude_defaults: bool = False,
- exclude_none: bool = False,
- custom_encoder: Optional[Dict[Any, Callable[[Any], Any]]] = None,
- sqlalchemy_safe: bool = True,
-) -> Any:
- custom_encoder = custom_encoder or {}
- if custom_encoder:
- if type(obj) in custom_encoder:
- return custom_encoder[type(obj)](obj)
- else:
- for encoder_type, encoder_instance in custom_encoder.items():
- if isinstance(obj, encoder_type):
- return encoder_instance(obj)
- if include is not None and not isinstance(include, (set, dict)):
- include = set(include)
- if exclude is not None and not isinstance(exclude, (set, dict)):
- exclude = set(exclude)
- if isinstance(obj, BaseModel):
- # TODO: remove when deprecating Pydantic v1
- encoders: Dict[Any, Any] = {}
- if not PYDANTIC_V2:
- encoders = getattr(obj.__config__, "json_encoders", {}) # type: ignore[attr-defined]
- if custom_encoder:
- encoders.update(custom_encoder)
- obj_dict = _model_dump(
- obj,
- mode="json",
- include=include,
- exclude=exclude,
- by_alias=by_alias,
- exclude_unset=exclude_unset,
- exclude_none=exclude_none,
- exclude_defaults=exclude_defaults,
- )
- if "__root__" in obj_dict:
- obj_dict = obj_dict["__root__"]
- return jsonable_encoder(
- obj_dict,
- exclude_none=exclude_none,
- exclude_defaults=exclude_defaults,
- # TODO: remove when deprecating Pydantic v1
- custom_encoder=encoders,
- sqlalchemy_safe=sqlalchemy_safe,
- )
- if dataclasses.is_dataclass(obj):
- obj_dict = dataclasses.asdict(obj)
- return jsonable_encoder(
- obj_dict,
- include=include,
- exclude=exclude,
- by_alias=by_alias,
- exclude_unset=exclude_unset,
- exclude_defaults=exclude_defaults,
- exclude_none=exclude_none,
- custom_encoder=custom_encoder,
- sqlalchemy_safe=sqlalchemy_safe,
- )
- if isinstance(obj, Enum):
- return obj.value
- if isinstance(obj, PurePath):
- return str(obj)
- if isinstance(obj, (str, int, float, type(None))):
- return obj
- if isinstance(obj, dict):
- encoded_dict = {}
- allowed_keys = set(obj.keys())
- if include is not None:
- allowed_keys &= set(include)
- if exclude is not None:
- allowed_keys -= set(exclude)
- for key, value in obj.items():
- if (
- (
- not sqlalchemy_safe
- or (not isinstance(key, str))
- or (not key.startswith("_sa"))
- )
- and (value is not None or not exclude_none)
- and key in allowed_keys
- ):
- encoded_key = jsonable_encoder(
- key,
- by_alias=by_alias,
- exclude_unset=exclude_unset,
- exclude_none=exclude_none,
- custom_encoder=custom_encoder,
- sqlalchemy_safe=sqlalchemy_safe,
- )
- encoded_value = jsonable_encoder(
- value,
- by_alias=by_alias,
- exclude_unset=exclude_unset,
- exclude_none=exclude_none,
- custom_encoder=custom_encoder,
- sqlalchemy_safe=sqlalchemy_safe,
- )
- encoded_dict[encoded_key] = encoded_value
- return encoded_dict
- if isinstance(obj, (list, set, frozenset, GeneratorType, tuple, deque)):
- encoded_list = []
- for item in obj:
- encoded_list.append(
- jsonable_encoder(
- item,
- include=include,
- exclude=exclude,
- by_alias=by_alias,
- exclude_unset=exclude_unset,
- exclude_defaults=exclude_defaults,
- exclude_none=exclude_none,
- custom_encoder=custom_encoder,
- sqlalchemy_safe=sqlalchemy_safe,
- )
- )
- return encoded_list
-
- if type(obj) in ENCODERS_BY_TYPE:
- return ENCODERS_BY_TYPE[type(obj)](obj)
- for encoder, classes_tuple in encoders_by_class_tuples.items():
- if isinstance(obj, classes_tuple):
- return encoder(obj)
-
- try:
- data = dict(obj)
- except Exception as e:
- errors: List[Exception] = []
- errors.append(e)
- try:
- data = vars(obj)
- except Exception as e:
- errors.append(e)
- raise ValueError(errors) from e
- return jsonable_encoder(
- data,
- include=include,
- exclude=exclude,
- by_alias=by_alias,
- exclude_unset=exclude_unset,
- exclude_defaults=exclude_defaults,
- exclude_none=exclude_none,
- custom_encoder=custom_encoder,
- sqlalchemy_safe=sqlalchemy_safe,
- )
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/designspaceLib/split.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/designspaceLib/split.py
deleted file mode 100644
index 0b7cdf4be05dea1e810b4fddf4bf026bc1a50a85..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/designspaceLib/split.py
+++ /dev/null
@@ -1,475 +0,0 @@
-"""Allows building all the variable fonts of a DesignSpace version 5 by
-splitting the document into interpolable sub-space, then into each VF.
-"""
-
-from __future__ import annotations
-
-import itertools
-import logging
-import math
-from typing import Any, Callable, Dict, Iterator, List, Tuple, cast
-
-from fontTools.designspaceLib import (
- AxisDescriptor,
- AxisMappingDescriptor,
- DesignSpaceDocument,
- DiscreteAxisDescriptor,
- InstanceDescriptor,
- RuleDescriptor,
- SimpleLocationDict,
- SourceDescriptor,
- VariableFontDescriptor,
-)
-from fontTools.designspaceLib.statNames import StatNames, getStatNames
-from fontTools.designspaceLib.types import (
- ConditionSet,
- Range,
- Region,
- getVFUserRegion,
- locationInRegion,
- regionInRegion,
- userRegionToDesignRegion,
-)
-
-LOGGER = logging.getLogger(__name__)
-
-MakeInstanceFilenameCallable = Callable[
- [DesignSpaceDocument, InstanceDescriptor, StatNames], str
-]
-
-
-def defaultMakeInstanceFilename(
- doc: DesignSpaceDocument, instance: InstanceDescriptor, statNames: StatNames
-) -> str:
- """Default callable to synthesize an instance filename
- when makeNames=True, for instances that don't specify an instance name
- in the designspace. This part of the name generation can be overriden
- because it's not specified by the STAT table.
- """
- familyName = instance.familyName or statNames.familyNames.get("en")
- styleName = instance.styleName or statNames.styleNames.get("en")
- return f"{familyName}-{styleName}.ttf"
-
-
-def splitInterpolable(
- doc: DesignSpaceDocument,
- makeNames: bool = True,
- expandLocations: bool = True,
- makeInstanceFilename: MakeInstanceFilenameCallable = defaultMakeInstanceFilename,
-) -> Iterator[Tuple[SimpleLocationDict, DesignSpaceDocument]]:
- """Split the given DS5 into several interpolable sub-designspaces.
- There are as many interpolable sub-spaces as there are combinations of
- discrete axis values.
-
- E.g. with axes:
- - italic (discrete) Upright or Italic
- - style (discrete) Sans or Serif
- - weight (continuous) 100 to 900
-
- There are 4 sub-spaces in which the Weight axis should interpolate:
- (Upright, Sans), (Upright, Serif), (Italic, Sans) and (Italic, Serif).
-
- The sub-designspaces still include the full axis definitions and STAT data,
- but the rules, sources, variable fonts, instances are trimmed down to only
- keep what falls within the interpolable sub-space.
-
- Args:
- - ``makeNames``: Whether to compute the instance family and style
- names using the STAT data.
- - ``expandLocations``: Whether to turn all locations into "full"
- locations, including implicit default axis values where missing.
- - ``makeInstanceFilename``: Callable to synthesize an instance filename
- when makeNames=True, for instances that don't specify an instance name
- in the designspace. This part of the name generation can be overridden
- because it's not specified by the STAT table.
-
- .. versionadded:: 5.0
- """
- discreteAxes = []
- interpolableUserRegion: Region = {}
- for axis in doc.axes:
- if hasattr(axis, "values"):
- # Mypy doesn't support narrowing union types via hasattr()
- # TODO(Python 3.10): use TypeGuard
- # https://mypy.readthedocs.io/en/stable/type_narrowing.html
- axis = cast(DiscreteAxisDescriptor, axis)
- discreteAxes.append(axis)
- else:
- axis = cast(AxisDescriptor, axis)
- interpolableUserRegion[axis.name] = Range(
- axis.minimum,
- axis.maximum,
- axis.default,
- )
- valueCombinations = itertools.product(*[axis.values for axis in discreteAxes])
- for values in valueCombinations:
- discreteUserLocation = {
- discreteAxis.name: value
- for discreteAxis, value in zip(discreteAxes, values)
- }
- subDoc = _extractSubSpace(
- doc,
- {**interpolableUserRegion, **discreteUserLocation},
- keepVFs=True,
- makeNames=makeNames,
- expandLocations=expandLocations,
- makeInstanceFilename=makeInstanceFilename,
- )
- yield discreteUserLocation, subDoc
-
-
-def splitVariableFonts(
- doc: DesignSpaceDocument,
- makeNames: bool = False,
- expandLocations: bool = False,
- makeInstanceFilename: MakeInstanceFilenameCallable = defaultMakeInstanceFilename,
-) -> Iterator[Tuple[str, DesignSpaceDocument]]:
- """Convert each variable font listed in this document into a standalone
- designspace. This can be used to compile all the variable fonts from a
- format 5 designspace using tools that can only deal with 1 VF at a time.
-
- Args:
- - ``makeNames``: Whether to compute the instance family and style
- names using the STAT data.
- - ``expandLocations``: Whether to turn all locations into "full"
- locations, including implicit default axis values where missing.
- - ``makeInstanceFilename``: Callable to synthesize an instance filename
- when makeNames=True, for instances that don't specify an instance name
- in the designspace. This part of the name generation can be overridden
- because it's not specified by the STAT table.
-
- .. versionadded:: 5.0
- """
- # Make one DesignspaceDoc v5 for each variable font
- for vf in doc.getVariableFonts():
- vfUserRegion = getVFUserRegion(doc, vf)
- vfDoc = _extractSubSpace(
- doc,
- vfUserRegion,
- keepVFs=False,
- makeNames=makeNames,
- expandLocations=expandLocations,
- makeInstanceFilename=makeInstanceFilename,
- )
- vfDoc.lib = {**vfDoc.lib, **vf.lib}
- yield vf.name, vfDoc
-
-
-def convert5to4(
- doc: DesignSpaceDocument,
-) -> Dict[str, DesignSpaceDocument]:
- """Convert each variable font listed in this document into a standalone
- format 4 designspace. This can be used to compile all the variable fonts
- from a format 5 designspace using tools that only know about format 4.
-
- .. versionadded:: 5.0
- """
- vfs = {}
- for _location, subDoc in splitInterpolable(doc):
- for vfName, vfDoc in splitVariableFonts(subDoc):
- vfDoc.formatVersion = "4.1"
- vfs[vfName] = vfDoc
- return vfs
-
-
-def _extractSubSpace(
- doc: DesignSpaceDocument,
- userRegion: Region,
- *,
- keepVFs: bool,
- makeNames: bool,
- expandLocations: bool,
- makeInstanceFilename: MakeInstanceFilenameCallable,
-) -> DesignSpaceDocument:
- subDoc = DesignSpaceDocument()
- # Don't include STAT info
- # FIXME: (Jany) let's think about it. Not include = OK because the point of
- # the splitting is to build VFs and we'll use the STAT data of the full
- # document to generate the STAT of the VFs, so "no need" to have STAT data
- # in sub-docs. Counterpoint: what if someone wants to split this DS for
- # other purposes? Maybe for that it would be useful to also subset the STAT
- # data?
- # subDoc.elidedFallbackName = doc.elidedFallbackName
-
- def maybeExpandDesignLocation(object):
- if expandLocations:
- return object.getFullDesignLocation(doc)
- else:
- return object.designLocation
-
- for axis in doc.axes:
- range = userRegion[axis.name]
- if isinstance(range, Range) and hasattr(axis, "minimum"):
- # Mypy doesn't support narrowing union types via hasattr()
- # TODO(Python 3.10): use TypeGuard
- # https://mypy.readthedocs.io/en/stable/type_narrowing.html
- axis = cast(AxisDescriptor, axis)
- subDoc.addAxis(
- AxisDescriptor(
- # Same info
- tag=axis.tag,
- name=axis.name,
- labelNames=axis.labelNames,
- hidden=axis.hidden,
- # Subset range
- minimum=max(range.minimum, axis.minimum),
- default=range.default or axis.default,
- maximum=min(range.maximum, axis.maximum),
- map=[
- (user, design)
- for user, design in axis.map
- if range.minimum <= user <= range.maximum
- ],
- # Don't include STAT info
- axisOrdering=None,
- axisLabels=None,
- )
- )
-
- subDoc.axisMappings = mappings = []
- subDocAxes = {axis.name for axis in subDoc.axes}
- for mapping in doc.axisMappings:
- if not all(axis in subDocAxes for axis in mapping.inputLocation.keys()):
- continue
- if not all(axis in subDocAxes for axis in mapping.outputLocation.keys()):
- LOGGER.error(
- "In axis mapping from input %s, some output axes are not in the variable-font: %s",
- mapping.inputLocation,
- mapping.outputLocation,
- )
- continue
-
- mappingAxes = set()
- mappingAxes.update(mapping.inputLocation.keys())
- mappingAxes.update(mapping.outputLocation.keys())
- for axis in doc.axes:
- if axis.name not in mappingAxes:
- continue
- range = userRegion[axis.name]
- if (
- range.minimum != axis.minimum
- or (range.default is not None and range.default != axis.default)
- or range.maximum != axis.maximum
- ):
- LOGGER.error(
- "Limiting axis ranges used in elements not supported: %s",
- axis.name,
- )
- continue
-
- mappings.append(
- AxisMappingDescriptor(
- inputLocation=mapping.inputLocation,
- outputLocation=mapping.outputLocation,
- )
- )
-
- # Don't include STAT info
- # subDoc.locationLabels = doc.locationLabels
-
- # Rules: subset them based on conditions
- designRegion = userRegionToDesignRegion(doc, userRegion)
- subDoc.rules = _subsetRulesBasedOnConditions(doc.rules, designRegion)
- subDoc.rulesProcessingLast = doc.rulesProcessingLast
-
- # Sources: keep only the ones that fall within the kept axis ranges
- for source in doc.sources:
- if not locationInRegion(doc.map_backward(source.designLocation), userRegion):
- continue
-
- subDoc.addSource(
- SourceDescriptor(
- filename=source.filename,
- path=source.path,
- font=source.font,
- name=source.name,
- designLocation=_filterLocation(
- userRegion, maybeExpandDesignLocation(source)
- ),
- layerName=source.layerName,
- familyName=source.familyName,
- styleName=source.styleName,
- muteKerning=source.muteKerning,
- muteInfo=source.muteInfo,
- mutedGlyphNames=source.mutedGlyphNames,
- )
- )
-
- # Copy family name translations from the old default source to the new default
- vfDefault = subDoc.findDefault()
- oldDefault = doc.findDefault()
- if vfDefault is not None and oldDefault is not None:
- vfDefault.localisedFamilyName = oldDefault.localisedFamilyName
-
- # Variable fonts: keep only the ones that fall within the kept axis ranges
- if keepVFs:
- # Note: call getVariableFont() to make the implicit VFs explicit
- for vf in doc.getVariableFonts():
- vfUserRegion = getVFUserRegion(doc, vf)
- if regionInRegion(vfUserRegion, userRegion):
- subDoc.addVariableFont(
- VariableFontDescriptor(
- name=vf.name,
- filename=vf.filename,
- axisSubsets=[
- axisSubset
- for axisSubset in vf.axisSubsets
- if isinstance(userRegion[axisSubset.name], Range)
- ],
- lib=vf.lib,
- )
- )
-
- # Instances: same as Sources + compute missing names
- for instance in doc.instances:
- if not locationInRegion(instance.getFullUserLocation(doc), userRegion):
- continue
-
- if makeNames:
- statNames = getStatNames(doc, instance.getFullUserLocation(doc))
- familyName = instance.familyName or statNames.familyNames.get("en")
- styleName = instance.styleName or statNames.styleNames.get("en")
- subDoc.addInstance(
- InstanceDescriptor(
- filename=instance.filename
- or makeInstanceFilename(doc, instance, statNames),
- path=instance.path,
- font=instance.font,
- name=instance.name or f"{familyName} {styleName}",
- userLocation={} if expandLocations else instance.userLocation,
- designLocation=_filterLocation(
- userRegion, maybeExpandDesignLocation(instance)
- ),
- familyName=familyName,
- styleName=styleName,
- postScriptFontName=instance.postScriptFontName
- or statNames.postScriptFontName,
- styleMapFamilyName=instance.styleMapFamilyName
- or statNames.styleMapFamilyNames.get("en"),
- styleMapStyleName=instance.styleMapStyleName
- or statNames.styleMapStyleName,
- localisedFamilyName=instance.localisedFamilyName
- or statNames.familyNames,
- localisedStyleName=instance.localisedStyleName
- or statNames.styleNames,
- localisedStyleMapFamilyName=instance.localisedStyleMapFamilyName
- or statNames.styleMapFamilyNames,
- localisedStyleMapStyleName=instance.localisedStyleMapStyleName
- or {},
- lib=instance.lib,
- )
- )
- else:
- subDoc.addInstance(
- InstanceDescriptor(
- filename=instance.filename,
- path=instance.path,
- font=instance.font,
- name=instance.name,
- userLocation={} if expandLocations else instance.userLocation,
- designLocation=_filterLocation(
- userRegion, maybeExpandDesignLocation(instance)
- ),
- familyName=instance.familyName,
- styleName=instance.styleName,
- postScriptFontName=instance.postScriptFontName,
- styleMapFamilyName=instance.styleMapFamilyName,
- styleMapStyleName=instance.styleMapStyleName,
- localisedFamilyName=instance.localisedFamilyName,
- localisedStyleName=instance.localisedStyleName,
- localisedStyleMapFamilyName=instance.localisedStyleMapFamilyName,
- localisedStyleMapStyleName=instance.localisedStyleMapStyleName,
- lib=instance.lib,
- )
- )
-
- subDoc.lib = doc.lib
-
- return subDoc
-
-
-def _conditionSetFrom(conditionSet: List[Dict[str, Any]]) -> ConditionSet:
- c: Dict[str, Range] = {}
- for condition in conditionSet:
- minimum, maximum = condition.get("minimum"), condition.get("maximum")
- c[condition["name"]] = Range(
- minimum if minimum is not None else -math.inf,
- maximum if maximum is not None else math.inf,
- )
- return c
-
-
-def _subsetRulesBasedOnConditions(
- rules: List[RuleDescriptor], designRegion: Region
-) -> List[RuleDescriptor]:
- # What rules to keep:
- # - Keep the rule if any conditionset is relevant.
- # - A conditionset is relevant if all conditions are relevant or it is empty.
- # - A condition is relevant if
- # - axis is point (C-AP),
- # - and point in condition's range (C-AP-in)
- # (in this case remove the condition because it's always true)
- # - else (C-AP-out) whole conditionset can be discarded (condition false
- # => conditionset false)
- # - axis is range (C-AR),
- # - (C-AR-all) and axis range fully contained in condition range: we can
- # scrap the condition because it's always true
- # - (C-AR-inter) and intersection(axis range, condition range) not empty:
- # keep the condition with the smaller range (= intersection)
- # - (C-AR-none) else, whole conditionset can be discarded
- newRules: List[RuleDescriptor] = []
- for rule in rules:
- newRule: RuleDescriptor = RuleDescriptor(
- name=rule.name, conditionSets=[], subs=rule.subs
- )
- for conditionset in rule.conditionSets:
- cs = _conditionSetFrom(conditionset)
- newConditionset: List[Dict[str, Any]] = []
- discardConditionset = False
- for selectionName, selectionValue in designRegion.items():
- # TODO: Ensure that all(key in conditionset for key in region.keys())?
- if selectionName not in cs:
- # raise Exception("Selection has different axes than the rules")
- continue
- if isinstance(selectionValue, (float, int)): # is point
- # Case C-AP-in
- if selectionValue in cs[selectionName]:
- pass # always matches, conditionset can stay empty for this one.
- # Case C-AP-out
- else:
- discardConditionset = True
- else: # is range
- # Case C-AR-all
- if selectionValue in cs[selectionName]:
- pass # always matches, conditionset can stay empty for this one.
- else:
- intersection = cs[selectionName].intersection(selectionValue)
- # Case C-AR-inter
- if intersection is not None:
- newConditionset.append(
- {
- "name": selectionName,
- "minimum": intersection.minimum,
- "maximum": intersection.maximum,
- }
- )
- # Case C-AR-none
- else:
- discardConditionset = True
- if not discardConditionset:
- newRule.conditionSets.append(newConditionset)
- if newRule.conditionSets:
- newRules.append(newRule)
-
- return newRules
-
-
-def _filterLocation(
- userRegion: Region,
- location: Dict[str, float],
-) -> Dict[str, float]:
- return {
- name: value
- for name, value in location.items()
- if name in userRegion and isinstance(userRegion[name], Range)
- }
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/merge/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/merge/__init__.py
deleted file mode 100644
index 10eff133fae5d025f940b962c232a39bd0c23a74..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/merge/__init__.py
+++ /dev/null
@@ -1,211 +0,0 @@
-# Copyright 2013 Google, Inc. All Rights Reserved.
-#
-# Google Author(s): Behdad Esfahbod, Roozbeh Pournader
-
-from fontTools import ttLib
-import fontTools.merge.base
-from fontTools.merge.cmap import (
- computeMegaGlyphOrder,
- computeMegaCmap,
- renameCFFCharStrings,
-)
-from fontTools.merge.layout import layoutPreMerge, layoutPostMerge
-from fontTools.merge.options import Options
-import fontTools.merge.tables
-from fontTools.misc.loggingTools import Timer
-from functools import reduce
-import sys
-import logging
-
-
-log = logging.getLogger("fontTools.merge")
-timer = Timer(logger=logging.getLogger(__name__ + ".timer"), level=logging.INFO)
-
-
-class Merger(object):
- """Font merger.
-
- This class merges multiple files into a single OpenType font, taking into
- account complexities such as OpenType layout (``GSUB``/``GPOS``) tables and
- cross-font metrics (e.g. ``hhea.ascent`` is set to the maximum value across
- all the fonts).
-
- If multiple glyphs map to the same Unicode value, and the glyphs are considered
- sufficiently different (that is, they differ in any of paths, widths, or
- height), then subsequent glyphs are renamed and a lookup in the ``locl``
- feature will be created to disambiguate them. For example, if the arguments
- are an Arabic font and a Latin font and both contain a set of parentheses,
- the Latin glyphs will be renamed to ``parenleft#1`` and ``parenright#1``,
- and a lookup will be inserted into the to ``locl`` feature (creating it if
- necessary) under the ``latn`` script to substitute ``parenleft`` with
- ``parenleft#1`` etc.
-
- Restrictions:
-
- - All fonts must have the same units per em.
- - If duplicate glyph disambiguation takes place as described above then the
- fonts must have a ``GSUB`` table.
-
- Attributes:
- options: Currently unused.
- """
-
- def __init__(self, options=None):
-
- if not options:
- options = Options()
-
- self.options = options
-
- def _openFonts(self, fontfiles):
- fonts = [ttLib.TTFont(fontfile) for fontfile in fontfiles]
- for font, fontfile in zip(fonts, fontfiles):
- font._merger__fontfile = fontfile
- font._merger__name = font["name"].getDebugName(4)
- return fonts
-
- def merge(self, fontfiles):
- """Merges fonts together.
-
- Args:
- fontfiles: A list of file names to be merged
-
- Returns:
- A :class:`fontTools.ttLib.TTFont` object. Call the ``save`` method on
- this to write it out to an OTF file.
- """
- #
- # Settle on a mega glyph order.
- #
- fonts = self._openFonts(fontfiles)
- glyphOrders = [list(font.getGlyphOrder()) for font in fonts]
- computeMegaGlyphOrder(self, glyphOrders)
-
- # Take first input file sfntVersion
- sfntVersion = fonts[0].sfntVersion
-
- # Reload fonts and set new glyph names on them.
- fonts = self._openFonts(fontfiles)
- for font, glyphOrder in zip(fonts, glyphOrders):
- font.setGlyphOrder(glyphOrder)
- if "CFF " in font:
- renameCFFCharStrings(self, glyphOrder, font["CFF "])
-
- cmaps = [font["cmap"] for font in fonts]
- self.duplicateGlyphsPerFont = [{} for _ in fonts]
- computeMegaCmap(self, cmaps)
-
- mega = ttLib.TTFont(sfntVersion=sfntVersion)
- mega.setGlyphOrder(self.glyphOrder)
-
- for font in fonts:
- self._preMerge(font)
-
- self.fonts = fonts
-
- allTags = reduce(set.union, (list(font.keys()) for font in fonts), set())
- allTags.remove("GlyphOrder")
-
- for tag in sorted(allTags):
- if tag in self.options.drop_tables:
- continue
-
- with timer("merge '%s'" % tag):
- tables = [font.get(tag, NotImplemented) for font in fonts]
-
- log.info("Merging '%s'.", tag)
- clazz = ttLib.getTableClass(tag)
- table = clazz(tag).merge(self, tables)
- # XXX Clean this up and use: table = mergeObjects(tables)
-
- if table is not NotImplemented and table is not False:
- mega[tag] = table
- log.info("Merged '%s'.", tag)
- else:
- log.info("Dropped '%s'.", tag)
-
- del self.duplicateGlyphsPerFont
- del self.fonts
-
- self._postMerge(mega)
-
- return mega
-
- def mergeObjects(self, returnTable, logic, tables):
- # Right now we don't use self at all. Will use in the future
- # for options and logging.
-
- allKeys = set.union(
- set(),
- *(vars(table).keys() for table in tables if table is not NotImplemented),
- )
- for key in allKeys:
- try:
- mergeLogic = logic[key]
- except KeyError:
- try:
- mergeLogic = logic["*"]
- except KeyError:
- raise Exception(
- "Don't know how to merge key %s of class %s"
- % (key, returnTable.__class__.__name__)
- )
- if mergeLogic is NotImplemented:
- continue
- value = mergeLogic(getattr(table, key, NotImplemented) for table in tables)
- if value is not NotImplemented:
- setattr(returnTable, key, value)
-
- return returnTable
-
- def _preMerge(self, font):
- layoutPreMerge(font)
-
- def _postMerge(self, font):
- layoutPostMerge(font)
-
- if "OS/2" in font:
- # https://github.com/fonttools/fonttools/issues/2538
- # TODO: Add an option to disable this?
- font["OS/2"].recalcAvgCharWidth(font)
-
-
-__all__ = ["Options", "Merger", "main"]
-
-
-@timer("make one with everything (TOTAL TIME)")
-def main(args=None):
- """Merge multiple fonts into one"""
- from fontTools import configLogger
-
- if args is None:
- args = sys.argv[1:]
-
- options = Options()
- args = options.parse_opts(args, ignore_unknown=["output-file"])
- outfile = "merged.ttf"
- fontfiles = []
- for g in args:
- if g.startswith("--output-file="):
- outfile = g[14:]
- continue
- fontfiles.append(g)
-
- if len(args) < 1:
- print("usage: pyftmerge font...", file=sys.stderr)
- return 1
-
- configLogger(level=logging.INFO if options.verbose else logging.WARNING)
- if options.timing:
- timer.logger.setLevel(logging.DEBUG)
- else:
- timer.logger.disabled = True
-
- merger = Merger(options=options)
- font = merger.merge(fontfiles)
- with timer("compile and save font"):
- font.save(outfile)
-
-
-if __name__ == "__main__":
- sys.exit(main())
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/slider.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/slider.py
deleted file mode 100644
index 31677345a70da44ec09587a17fe2d59f34beff66..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/slider.py
+++ /dev/null
@@ -1,210 +0,0 @@
-"""gr.Slider() component."""
-
-from __future__ import annotations
-
-import math
-import random
-from typing import Any, Callable, Literal
-
-import numpy as np
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import NumberSerializable
-
-from gradio.components.base import FormComponent, IOComponent, _Keywords
-from gradio.deprecation import warn_style_method_deprecation
-from gradio.events import Changeable, Inputable, Releaseable
-from gradio.interpretation import NeighborInterpretable
-
-set_documentation_group("component")
-
-
-@document()
-class Slider(
- FormComponent,
- Changeable,
- Inputable,
- Releaseable,
- IOComponent,
- NumberSerializable,
- NeighborInterpretable,
-):
- """
- Creates a slider that ranges from `minimum` to `maximum` with a step size of `step`.
- Preprocessing: passes slider value as a {float} into the function.
- Postprocessing: expects an {int} or {float} returned from function and sets slider value to it as long as it is within range.
- Examples-format: A {float} or {int} representing the slider's value.
-
- Demos: sentence_builder, slider_release, generate_tone, titanic_survival, interface_random_slider, blocks_random_slider
- Guides: create-your-own-friends-with-a-gan
- """
-
- def __init__(
- self,
- minimum: float = 0,
- maximum: float = 100,
- value: float | Callable | None = None,
- *,
- step: float | None = None,
- label: str | None = None,
- info: str | None = None,
- every: float | None = None,
- show_label: bool = True,
- container: bool = True,
- scale: int | None = None,
- min_width: int = 160,
- interactive: bool | None = None,
- visible: bool = True,
- elem_id: str | None = None,
- elem_classes: list[str] | str | None = None,
- randomize: bool = False,
- **kwargs,
- ):
- """
- Parameters:
- minimum: minimum value for slider.
- maximum: maximum value for slider.
- value: default value. If callable, the function will be called whenever the app loads to set the initial value of the component. Ignored if randomized=True.
- step: increment between slider values.
- label: component name in interface.
- info: additional component description.
- 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.
- show_label: if True, will display label.
- container: If True, will place the component in a container - providing some extra padding around the border.
- scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
- min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
- interactive: if True, slider will be adjustable; if False, adjusting will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
- visible: If False, component will be hidden.
- 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.
- randomize: If True, the value of the slider when the app loads is taken uniformly at random from the range given by the minimum and maximum.
- """
- self.minimum = minimum
- self.maximum = maximum
- if step is None:
- difference = maximum - minimum
- power = math.floor(math.log10(difference) - 2)
- self.step = 10**power
- else:
- self.step = step
- if randomize:
- value = self.get_random_value
- IOComponent.__init__(
- self,
- label=label,
- info=info,
- every=every,
- show_label=show_label,
- container=container,
- scale=scale,
- min_width=min_width,
- interactive=interactive,
- visible=visible,
- elem_id=elem_id,
- elem_classes=elem_classes,
- value=value,
- **kwargs,
- )
- NeighborInterpretable.__init__(self)
-
- def api_info(self) -> dict[str, dict | bool]:
- return {
- "info": {
- "type": "number",
- "description": f"numeric value between {self.minimum} and {self.maximum}",
- },
- "serialized_info": False,
- }
-
- def example_inputs(self) -> dict[str, Any]:
- return {
- "raw": self.minimum,
- "serialized": self.minimum,
- }
-
- def get_config(self):
- return {
- "minimum": self.minimum,
- "maximum": self.maximum,
- "step": self.step,
- "value": self.value,
- **IOComponent.get_config(self),
- }
-
- def get_random_value(self):
- n_steps = int((self.maximum - self.minimum) / self.step)
- step = random.randint(0, n_steps)
- value = self.minimum + step * self.step
- # Round to number of decimals in step so that UI doesn't display long decimals
- n_decimals = max(str(self.step)[::-1].find("."), 0)
- if n_decimals:
- value = round(value, n_decimals)
- return value
-
- @staticmethod
- def update(
- value: float | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE,
- minimum: float | None = None,
- maximum: float | None = None,
- step: float | None = None,
- label: str | None = None,
- info: str | None = None,
- show_label: bool | None = None,
- container: bool | None = None,
- scale: int | None = None,
- min_width: int | None = None,
- interactive: bool | None = None,
- visible: bool | None = None,
- ):
- return {
- "minimum": minimum,
- "maximum": maximum,
- "step": step,
- "label": label,
- "info": info,
- "show_label": show_label,
- "container": container,
- "scale": scale,
- "min_width": min_width,
- "interactive": interactive,
- "visible": visible,
- "value": value,
- "__type__": "update",
- }
-
- def postprocess(self, y: float | None) -> float | None:
- """
- Any postprocessing needed to be performed on function output.
- Parameters:
- y: numeric output
- Returns:
- numeric output or minimum number if None
- """
- return self.minimum if y is None else y
-
- def set_interpret_parameters(self, steps: int = 8) -> Slider:
- """
- Calculates interpretation scores of numeric values ranging between the minimum and maximum values of the slider.
- Parameters:
- steps: Number of neighboring values to measure between the minimum and maximum values of the slider range.
- """
- self.interpretation_steps = steps
- return self
-
- def get_interpretation_neighbors(self, x) -> tuple[object, dict]:
- return (
- np.linspace(self.minimum, self.maximum, self.interpretation_steps).tolist(),
- {},
- )
-
- def style(
- self,
- *,
- container: bool | None = None,
- ):
- """
- This method is deprecated. Please set these arguments in the constructor instead.
- """
- warn_style_method_deprecation()
- if container is not None:
- self.container = container
- return self
diff --git a/spaces/cihyFjudo/fairness-paper-search/King Root v3.2 The Fastest and Easiest Way to Root Your Android Device.md b/spaces/cihyFjudo/fairness-paper-search/King Root v3.2 The Fastest and Easiest Way to Root Your Android Device.md
deleted file mode 100644
index fcd31d144591898f8dd067c73476fef54117e825..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/King Root v3.2 The Fastest and Easiest Way to Root Your Android Device.md
+++ /dev/null
@@ -1,30 +0,0 @@
-
-KingRoot may stop working if you update your Android version. Currently, mobile devices manufacturers try to avoid the rooting of their devices, so you may need to reset the app to be able to use it.
-King Root v3.2
Download Zip ————— https://tinurli.com/2uwiq1
-Notice your android device is starting to become slow or laggy? You are not the only one with this problem. Most android users feedback a slow or laggy experience after a certain period of usage. But with a rooted device, speeding up your phone can be done easily to allow users to enjoy a smooth and seamless experience. Read more >
-Remember the frustration of a dying phone in the middle of the day? If you want your phone to last through the day, rooting your android device can provide you many solutions and alternatives for conserving and managing your battery consumption with ease & convenience. Read more >
-Backing up your phones or tablets is one of the biggest frustrations for an android users. One has to either pay for a good backup app or find it really troublesome to backup your data. With a rooted android device, users will be able to use powerful & easy backup apps to backup important data effortlessly. Read more >
-Sick and tired of the usual look and feel of your user interface in Android devices? A rooted phone or tablets gives you admin access to change almost everything from color to icons and even animation. Command 100% freedom and pimp your Android devices easily with KingRoot. Read more >
-When you purchase an android device, it comes with a number of apps that you may need or may be completely useless. They occupy a lot of storage in your device and cannot be uninstalled. These apps are called Bloatware. As mentioned already, you cannot remove these apps in normal conditions. This is the situation where you need to root your android.
-
-After you root your android, download Titanium Backup or any of the system app remover applications from the Google play store. If you download Titanium Backup, open it, wait for apps to load and tap the app you want, and tap uninstall. BOOM! You are done.
-Though you can back up apps or games on your device easily without rooting, you cannot back up the data app is using. It means that whenever you restore the apps from the backup, it is the same as installing from Google Play. After rooting with KingRoot, you can also back up the data from the apps. It means after restoring the app, you will completely get exact same app in exact the same condition along with your settings, login details, etc. This kind of backup is much more useful than the normal backup and is much easier and faster.
-KingRoot uses advanced technology that allows you to root your phone and open it up to more possibilities. Since you are gaining root access, you have full administrative privileges in enhancing the way your phone looks and functions. In particular, you can install custom ROM to your Android device once you have rooted it. This modified and feature-rich version of the Android OS includes additional features, unique themes, and tweaks that all account for enhanced mobile device performance.
CyanogenMod, for instance, is among the popular custom ROMs available for rooted phones, which offers interesting features including lock screen gestures, DSP equalizer, CPU overclock and underclocking, and a complete theme engine along with its very own theme store. Another excellent custom ROM you can find is Paranoid Android, which offers UI customization, floating notifications and multitasking, gesture controls, and hidden navigation PIE mode. Keep in mind, though, that it only supports a limited number of Android devices including Oppo, OnePlus, and Google Nexus.
By rooting your phone, you can use an unreleased version of Android without any limitations. Customization is only possible with rooted phones considering certain restrictions in terms of the availability of these newer versions to mobile devices. Hence, by using KingRoot, you have an opportunity to make your device more personalized for enhanced browsing and overall user experience.
-Some users ask questions like why does KingRoot has a PC version when there is an independent android version that can root android without a PC? This seems to be somehow logical but the PC version is also a useful PC version of KingRoot and supports way more devices than the android version.
-In every case, whether it is android, web hosting, or any other similar activities, root access means access to the core part of the system. It means if you gain root access to any system, you can do anything you want to do with that system.
-Ans. It is up to you to decide. Actually, few people brick their device during rooting and now you can also root your device with a 100% safe method. So I think there is not much to worry about, but if you fear so much, please google whether people got an error while rooting the device you are using.
-Q: What can I do if KingRoot cannot be removed?
A: If you cannot remove KingRoot, please try to force stop KingRoot, clear data of it, and root the device with KingRoot again. Then unroot it immediately.
-Even those who have managed to root successfully have reported mixed results. For some, the app works immediately and roots the device during the first attempt. For others, the app freezes at 90% during the first rooting attempt but then succeeds to root during the second attempt. Others have reported that the Fire TV Stick needs to be rebooted by holding the PLAY and SELECT button while the Kingo Root app is stuck at 90%. Running the rooting process again after rebooting has worked for some.
-I reinitialized the stick by pressing the center and play button, rebooted, but it did not work. King Root did not root the device. On the third try, I left King Root to work its magic and I became sidetracked in the kitchen removing some food items from the freezer and I might have been away for 10 minutes or slightly longer.
-I wanted to add an update to my original post. I had to remove root because Playstation Vue refused to function. I am a cord cutter, I rely on cable for only internet service, and PlayStation Vue is my access to some cable channels I like to watch. So regrettably I restored the device back to normal. But I still have a Chromecast on each television and my Samsung Galaxy Tab Pro 8.4 is rooted and I can cast directly from the tablet to either stick.
-Plus honestly I find people are scummy that sell the service of making it so you can access pirated content on your device. While I am not against pirating, I am against people profiting off the pirating.
-Things will change once a company like APPLE (itunes) come an buys it all up or provide content for cheap. Just wait it will happen and the new Apple will possibly be Amazon as this website posted earlier this week how Amazon surveys in Germany are asking people there how they use their fire devices and how Kodi was on the survey.
-Can verify myself that I managed to get root on 5.0.5.1, I tried multiple times and failed, I then tried restarting the device at 90 percent and relaunching it as soon as fire tv stick menu loaded up and got it the second attempt.
-I sent over Kingo root 3.1 (latest version/op version) with apk to fire app on phone.I launched Kingo root (not sure if it was launched from within firestopper or from fire tv main menu), dont think that would make much difference but its a variable to consider.I waited for it to reach 90 percent (it sticks at various percentages for a few seconds) once it was on 90 percent I waited a number of seconds, probably around 45 to 50 seconds and held down home/play to restart.Once it restarted and as soon as the main fire tv menu loaded (but before firestopper loaded) I launched Kingo root again and clicked root.It started off and kicked me out to black screen for a couple seconds then firestopper loaded, I then quickly launched Kingo root again this time from within firestopper to find it already on 90 percent.After sitting on 90 percent again for a few seconds it reported root successful.Then send over super user.I then restore to defaults the fire tv stick and during the restore process my stick was updated to the newest version 5.2.1.0 (unintentionally).When it finished and loaded up I reinstalled Kingo root and without even trying to root again it reported it was already Rooted.
-Fire TV stick, purchased at launch, never rooted before. Never connected to the web until Kingroot was announced. However, never rooted, just updated to the necessary software version. Then amazon changed to https updates, so inadvertently updated to Fire OS 5.0.5 then took off my network for several weeks until I read this post. Booted up stick without internet live connection and installed Kingo Root via Apps2Fire using my Fire tablet. Ran Kingo Root without rebooting, it stopped at 90% for about 30 seconds and then successfully rooted the stick without requiring any reboot. It worked the first time I tried. I have never installed Firestarter/Firestopper. Kodi is installed.
-ok thanks for the confirmation , so is this like a full root? like can we install xposed framework and youtube ad block module and also use sixaxis to get a ps3 controller working on fire stick?and also does firestopper work?
-Installed all 3 apks with adblink. used rotatescreen to lock in portrait. ran kingoroot and stuck on 90% for a little over a minute and came back with success! Installed SuperSU and everything is perfect! Thanx!!
-Hi managed to root my firestick with the first try and installed supersu but how can you really know if your rooted or not. Opened supersu it works fine but when i installed luckypatcher, SuperSu crashed right away. Any ideas?
-V1-V5 - At V1, round-tipped leaf on first collar appears, nodal roots elongate. By V2, plant is 2 to 4 inches tall and relies on the energy in the seed. V3 begins 2 to 4 weeks after VE, and plant switches from kernel reserves to photosynthesis and nodal roots begin to take over. Around V4, broadleaf weeds should be controlled to avoid loss. By V5, the number of potential leaf and ear shoots are determined. Plant is 8 to 12 inches tall and growing point remains below soil surface.
aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/Last Night While I Was Doing My Homework Angela Told Me How Boring Her Biology Professor Was.md b/spaces/cihyFjudo/fairness-paper-search/Last Night While I Was Doing My Homework Angela Told Me How Boring Her Biology Professor Was.md
deleted file mode 100644
index 301b3bb6772c7c8cfa5c3f8117b1bf77445a9621..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Last Night While I Was Doing My Homework Angela Told Me How Boring Her Biology Professor Was.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
-One of the earliest "creepy clown" movies, "He Who Gets Slapped" was the first film produced completely by the MGM studio, though not the first released. The film features Lon Chaney in a memorable role as a scientist who is humiliated when a rival and his wife steal his ideas just as he is to present them to the Academy of Sciences. He then becomes a masochistic circus clown where the highlight of his act is being repeatedly slapped. One of many stand-out scenes occurs during a circus performance where Chaney spots those who betrayed him and tries to call them out, but his fellow clowns are doing their normal crowd-pleasing routine of slapping him in the face. Filled with nightmarish vignettes, this landmark film from the silent era was directed by Victor Sjöström (newly arrived from Sweden and using an anglicized last name of Seastrom) and also features Norma Shearer and John Gilbert, each on the cusp of stardom.
-Last Night While I Was Doing My Homework Angela (c agence immortal limw
Download File ✫✫✫ https://tinurli.com/2uwhEB
aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/Terjemahan Syarah Alfiyah Ibnu Malik Pdf EXCLUSIVE Free 1 ((HOT)).md b/spaces/cihyFjudo/fairness-paper-search/Terjemahan Syarah Alfiyah Ibnu Malik Pdf EXCLUSIVE Free 1 ((HOT)).md
deleted file mode 100644
index 27ef618e63413cc8dbc69117b4749607b0a2b524..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Terjemahan Syarah Alfiyah Ibnu Malik Pdf EXCLUSIVE Free 1 ((HOT)).md
+++ /dev/null
@@ -1,6 +0,0 @@
-Terjemahan Syarah Alfiyah Ibnu Malik Pdf Free 1 ((HOT))
Download Zip === https://tinurli.com/2uwiLM
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/Tipler Modern Physics 6e Pdf Download An Accessible and Engaging Introduction to Modern Physics.md b/spaces/cihyFjudo/fairness-paper-search/Tipler Modern Physics 6e Pdf Download An Accessible and Engaging Introduction to Modern Physics.md
deleted file mode 100644
index 9489788ab365dda95f74b1f0e4875b208e8eee99..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Tipler Modern Physics 6e Pdf Download An Accessible and Engaging Introduction to Modern Physics.md
+++ /dev/null
@@ -1,14 +0,0 @@
-
-In preparing this new edition of Modern Physics, we have againrelied heavily on the many helpful suggestions from a large team ofreviewers and from a host of instruc-tor and student users of theearlier editions. Their advice reflected the discoveries that havefurther enlarged modern physics in the first decade of the newcentury, took note of the evolution that is occurring in theteaching of physics in colleges and universities, and recognizedthe growing role of modern physics in the biological sciences. Asthe term modern physics has come to mean the physics of the modernerarelativity and quantum theorywe have heeded the advice of manyusers and reviewers and pre-served the historical and culturalflavor of the book while being careful to maintain the mathematicallevel of the earlier editions. We continue to provide theflexibility for instructors to match the book and its supportingancillaries to a wide variety of teach-ing modes, including bothone- and two-semester courses and media-enhanced courses.
-ThefirsteditionsInstructors Solutions Manual with solutions, notjust answers, to all end-of-chapter problems was the first such aidto accompany a physics (and not just a modern physics) textbook,and that leadership has been continued in this edition. TheInstructors Solutions Manual (ISM) is available in print or on CDfor those adopting Modern Physics, sixth edition, for theirclasses. As with the previous editions, the popular paperbackStudents Solution Manual, contain-ing one-quarter of the solutionsin the ISM, is also available.
-Tipler Modern Physics 6e Pdf Download
Download ⭐ https://tinurli.com/2uwjUA
-Wehave continued to includemanyworked-out examples in everychapter, afeature singled out by many instructors as a strength ofthe book. Several new examples at the interface between modernphysics and the biological sciences have been added. As before, wefrequently use combined quantities such as hc, Uc, and ke2 in eV #nm to simplify many numerical calculations.
-Wehavecontinuedtheuseofrealdatainfigures,photosofrealpeopleandappa-ratus,and short quotations by many scientists who were key participantsin the development of modern physics. These features, along withthe Notes at the end of each chapter, bring to life many events inthe history of science and help counter the too-prevalent viewamong students that physics is a dull, impersonal collection offacts and formulas.
-AnumberofnewApplicationNoteshavebeenaddedtothesixthedition.Thesebriefnotesin the margins of many pages point to a few of the many benefits tosociety that have been made possible by a discovery or developmentin modern physics.
-Recognizingtheneedforstudentsonoccasiontobeabletoquicklyreviewkeyconceptsfrom classical physics that relate to topics developed in modernphysics,theClassicalConceptReview(CCR)wasintroducedinthebooksfifthedition.FoundonthebooksWebsiteandidentifiedbyanumberediconCCR in the mar-gin near the pertinent modern physics discussion,the CCR can be printed out to provide a convenient study-supportbooklet. Several new CCRs have been added to the sixth edition. TheCCRs provide concise reviews of pertinent classical con-cepts justa mouse click away.
-In Part 1 we discuss the foundations of the physics of themodern era, relativity theory and quantum mechanics. Chapter 1examines the apparent conflict between Einsteins principle ofrelativity and the observed constancy of the speed of light andshows how accepting the validity of both ideas led to the specialtheory of relativity. Chapter 2 concerns the relations connectingmass, energy, and momentum in special relativity and concludes witha brief discussion of general relativity and some experi-mentaltests of its predictions. In Chapters 3, 4, and 5 the developmentof quantum theory is traced from the earliest evidence ofquantization to de Broglies hypothesis of electron waves. Anelementary discussion of the Schrdinger equation is provided inChapter 6, illustrated with applications to one-dimensionalsystems. Chapter 7 extends the application of quantum mechanics tomany-particle systems and intro-duces the important new concepts ofelectron spin and the exclusion principle. Con-cluding thedevelopment, Chapter 8 discusses the wave mechanics of systems oflarge numbers of identical particles, underscoring the importanceof the symmetry of wave functions. Beginning with Chapter 3, thechapters in Part 1 should be stud-ied in sequence because each ofChapters 4 through 8 depends on the discussions, developments, andexamples of the previous chapters.
-reference, and coordinate transformationsall importantbackground to our discussions of special relativitymay not havebeen emphasized in many introductory courses. As an aid to a betterunderstanding of the concepts of modern physics, we have includedthe Classical Concept Review on the books Web site. As you proceedthrough Modern Physics,
-More A more complete description of the Michelson-Morleyexperiment, its interpretation, and the results of very recentversions can be found on the home page:www.whfreeman.com/tiplermodernphysics6e. See also Figures 1-9through 1-11 here, as well as Equations 1-7 through 1-10.
- aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/Turhan Baytop Bitkilerle Tedavi PDF Download Eski Bahe Glleri Besinlerle Tedavi ve Kombine Tedavi.md b/spaces/cihyFjudo/fairness-paper-search/Turhan Baytop Bitkilerle Tedavi PDF Download Eski Bahe Glleri Besinlerle Tedavi ve Kombine Tedavi.md
deleted file mode 100644
index 9d3234bdde297cbc6d10ba1ce65b18d922c20d67..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Turhan Baytop Bitkilerle Tedavi PDF Download Eski Bahe Glleri Besinlerle Tedavi ve Kombine Tedavi.md
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/rrule.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/rrule.py
deleted file mode 100644
index b3203393c61203c9c6f12db7a857aee89be85e5c..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/rrule.py
+++ /dev/null
@@ -1,1737 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-The rrule module offers a small, complete, and very fast, implementation of
-the recurrence rules documented in the
-`iCalendar RFC `_,
-including support for caching of results.
-"""
-import calendar
-import datetime
-import heapq
-import itertools
-import re
-import sys
-from functools import wraps
-# For warning about deprecation of until and count
-from warnings import warn
-
-from six import advance_iterator, integer_types
-
-from six.moves import _thread, range
-
-from ._common import weekday as weekdaybase
-
-try:
- from math import gcd
-except ImportError:
- from fractions import gcd
-
-__all__ = ["rrule", "rruleset", "rrulestr",
- "YEARLY", "MONTHLY", "WEEKLY", "DAILY",
- "HOURLY", "MINUTELY", "SECONDLY",
- "MO", "TU", "WE", "TH", "FR", "SA", "SU"]
-
-# Every mask is 7 days longer to handle cross-year weekly periods.
-M366MASK = tuple([1]*31+[2]*29+[3]*31+[4]*30+[5]*31+[6]*30 +
- [7]*31+[8]*31+[9]*30+[10]*31+[11]*30+[12]*31+[1]*7)
-M365MASK = list(M366MASK)
-M29, M30, M31 = list(range(1, 30)), list(range(1, 31)), list(range(1, 32))
-MDAY366MASK = tuple(M31+M29+M31+M30+M31+M30+M31+M31+M30+M31+M30+M31+M31[:7])
-MDAY365MASK = list(MDAY366MASK)
-M29, M30, M31 = list(range(-29, 0)), list(range(-30, 0)), list(range(-31, 0))
-NMDAY366MASK = tuple(M31+M29+M31+M30+M31+M30+M31+M31+M30+M31+M30+M31+M31[:7])
-NMDAY365MASK = list(NMDAY366MASK)
-M366RANGE = (0, 31, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 366)
-M365RANGE = (0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334, 365)
-WDAYMASK = [0, 1, 2, 3, 4, 5, 6]*55
-del M29, M30, M31, M365MASK[59], MDAY365MASK[59], NMDAY365MASK[31]
-MDAY365MASK = tuple(MDAY365MASK)
-M365MASK = tuple(M365MASK)
-
-FREQNAMES = ['YEARLY', 'MONTHLY', 'WEEKLY', 'DAILY', 'HOURLY', 'MINUTELY', 'SECONDLY']
-
-(YEARLY,
- MONTHLY,
- WEEKLY,
- DAILY,
- HOURLY,
- MINUTELY,
- SECONDLY) = list(range(7))
-
-# Imported on demand.
-easter = None
-parser = None
-
-
-class weekday(weekdaybase):
- """
- This version of weekday does not allow n = 0.
- """
- def __init__(self, wkday, n=None):
- if n == 0:
- raise ValueError("Can't create weekday with n==0")
-
- super(weekday, self).__init__(wkday, n)
-
-
-MO, TU, WE, TH, FR, SA, SU = weekdays = tuple(weekday(x) for x in range(7))
-
-
-def _invalidates_cache(f):
- """
- Decorator for rruleset methods which may invalidate the
- cached length.
- """
- @wraps(f)
- def inner_func(self, *args, **kwargs):
- rv = f(self, *args, **kwargs)
- self._invalidate_cache()
- return rv
-
- return inner_func
-
-
-class rrulebase(object):
- def __init__(self, cache=False):
- if cache:
- self._cache = []
- self._cache_lock = _thread.allocate_lock()
- self._invalidate_cache()
- else:
- self._cache = None
- self._cache_complete = False
- self._len = None
-
- def __iter__(self):
- if self._cache_complete:
- return iter(self._cache)
- elif self._cache is None:
- return self._iter()
- else:
- return self._iter_cached()
-
- def _invalidate_cache(self):
- if self._cache is not None:
- self._cache = []
- self._cache_complete = False
- self._cache_gen = self._iter()
-
- if self._cache_lock.locked():
- self._cache_lock.release()
-
- self._len = None
-
- def _iter_cached(self):
- i = 0
- gen = self._cache_gen
- cache = self._cache
- acquire = self._cache_lock.acquire
- release = self._cache_lock.release
- while gen:
- if i == len(cache):
- acquire()
- if self._cache_complete:
- break
- try:
- for j in range(10):
- cache.append(advance_iterator(gen))
- except StopIteration:
- self._cache_gen = gen = None
- self._cache_complete = True
- break
- release()
- yield cache[i]
- i += 1
- while i < self._len:
- yield cache[i]
- i += 1
-
- def __getitem__(self, item):
- if self._cache_complete:
- return self._cache[item]
- elif isinstance(item, slice):
- if item.step and item.step < 0:
- return list(iter(self))[item]
- else:
- return list(itertools.islice(self,
- item.start or 0,
- item.stop or sys.maxsize,
- item.step or 1))
- elif item >= 0:
- gen = iter(self)
- try:
- for i in range(item+1):
- res = advance_iterator(gen)
- except StopIteration:
- raise IndexError
- return res
- else:
- return list(iter(self))[item]
-
- def __contains__(self, item):
- if self._cache_complete:
- return item in self._cache
- else:
- for i in self:
- if i == item:
- return True
- elif i > item:
- return False
- return False
-
- # __len__() introduces a large performance penalty.
- def count(self):
- """ Returns the number of recurrences in this set. It will have go
- trough the whole recurrence, if this hasn't been done before. """
- if self._len is None:
- for x in self:
- pass
- return self._len
-
- def before(self, dt, inc=False):
- """ Returns the last recurrence before the given datetime instance. The
- inc keyword defines what happens if dt is an occurrence. With
- inc=True, if dt itself is an occurrence, it will be returned. """
- if self._cache_complete:
- gen = self._cache
- else:
- gen = self
- last = None
- if inc:
- for i in gen:
- if i > dt:
- break
- last = i
- else:
- for i in gen:
- if i >= dt:
- break
- last = i
- return last
-
- def after(self, dt, inc=False):
- """ Returns the first recurrence after the given datetime instance. The
- inc keyword defines what happens if dt is an occurrence. With
- inc=True, if dt itself is an occurrence, it will be returned. """
- if self._cache_complete:
- gen = self._cache
- else:
- gen = self
- if inc:
- for i in gen:
- if i >= dt:
- return i
- else:
- for i in gen:
- if i > dt:
- return i
- return None
-
- def xafter(self, dt, count=None, inc=False):
- """
- Generator which yields up to `count` recurrences after the given
- datetime instance, equivalent to `after`.
-
- :param dt:
- The datetime at which to start generating recurrences.
-
- :param count:
- The maximum number of recurrences to generate. If `None` (default),
- dates are generated until the recurrence rule is exhausted.
-
- :param inc:
- If `dt` is an instance of the rule and `inc` is `True`, it is
- included in the output.
-
- :yields: Yields a sequence of `datetime` objects.
- """
-
- if self._cache_complete:
- gen = self._cache
- else:
- gen = self
-
- # Select the comparison function
- if inc:
- comp = lambda dc, dtc: dc >= dtc
- else:
- comp = lambda dc, dtc: dc > dtc
-
- # Generate dates
- n = 0
- for d in gen:
- if comp(d, dt):
- if count is not None:
- n += 1
- if n > count:
- break
-
- yield d
-
- def between(self, after, before, inc=False, count=1):
- """ Returns all the occurrences of the rrule between after and before.
- The inc keyword defines what happens if after and/or before are
- themselves occurrences. With inc=True, they will be included in the
- list, if they are found in the recurrence set. """
- if self._cache_complete:
- gen = self._cache
- else:
- gen = self
- started = False
- l = []
- if inc:
- for i in gen:
- if i > before:
- break
- elif not started:
- if i >= after:
- started = True
- l.append(i)
- else:
- l.append(i)
- else:
- for i in gen:
- if i >= before:
- break
- elif not started:
- if i > after:
- started = True
- l.append(i)
- else:
- l.append(i)
- return l
-
-
-class rrule(rrulebase):
- """
- That's the base of the rrule operation. It accepts all the keywords
- defined in the RFC as its constructor parameters (except byday,
- which was renamed to byweekday) and more. The constructor prototype is::
-
- rrule(freq)
-
- Where freq must be one of YEARLY, MONTHLY, WEEKLY, DAILY, HOURLY, MINUTELY,
- or SECONDLY.
-
- .. note::
- Per RFC section 3.3.10, recurrence instances falling on invalid dates
- and times are ignored rather than coerced:
-
- Recurrence rules may generate recurrence instances with an invalid
- date (e.g., February 30) or nonexistent local time (e.g., 1:30 AM
- on a day where the local time is moved forward by an hour at 1:00
- AM). Such recurrence instances MUST be ignored and MUST NOT be
- counted as part of the recurrence set.
-
- This can lead to possibly surprising behavior when, for example, the
- start date occurs at the end of the month:
-
- >>> from dateutil.rrule import rrule, MONTHLY
- >>> from datetime import datetime
- >>> start_date = datetime(2014, 12, 31)
- >>> list(rrule(freq=MONTHLY, count=4, dtstart=start_date))
- ... # doctest: +NORMALIZE_WHITESPACE
- [datetime.datetime(2014, 12, 31, 0, 0),
- datetime.datetime(2015, 1, 31, 0, 0),
- datetime.datetime(2015, 3, 31, 0, 0),
- datetime.datetime(2015, 5, 31, 0, 0)]
-
- Additionally, it supports the following keyword arguments:
-
- :param dtstart:
- The recurrence start. Besides being the base for the recurrence,
- missing parameters in the final recurrence instances will also be
- extracted from this date. If not given, datetime.now() will be used
- instead.
- :param interval:
- The interval between each freq iteration. For example, when using
- YEARLY, an interval of 2 means once every two years, but with HOURLY,
- it means once every two hours. The default interval is 1.
- :param wkst:
- The week start day. Must be one of the MO, TU, WE constants, or an
- integer, specifying the first day of the week. This will affect
- recurrences based on weekly periods. The default week start is got
- from calendar.firstweekday(), and may be modified by
- calendar.setfirstweekday().
- :param count:
- If given, this determines how many occurrences will be generated.
-
- .. note::
- As of version 2.5.0, the use of the keyword ``until`` in conjunction
- with ``count`` is deprecated, to make sure ``dateutil`` is fully
- compliant with `RFC-5545 Sec. 3.3.10 `_. Therefore, ``until`` and ``count``
- **must not** occur in the same call to ``rrule``.
- :param until:
- If given, this must be a datetime instance specifying the upper-bound
- limit of the recurrence. The last recurrence in the rule is the greatest
- datetime that is less than or equal to the value specified in the
- ``until`` parameter.
-
- .. note::
- As of version 2.5.0, the use of the keyword ``until`` in conjunction
- with ``count`` is deprecated, to make sure ``dateutil`` is fully
- compliant with `RFC-5545 Sec. 3.3.10 `_. Therefore, ``until`` and ``count``
- **must not** occur in the same call to ``rrule``.
- :param bysetpos:
- If given, it must be either an integer, or a sequence of integers,
- positive or negative. Each given integer will specify an occurrence
- number, corresponding to the nth occurrence of the rule inside the
- frequency period. For example, a bysetpos of -1 if combined with a
- MONTHLY frequency, and a byweekday of (MO, TU, WE, TH, FR), will
- result in the last work day of every month.
- :param bymonth:
- If given, it must be either an integer, or a sequence of integers,
- meaning the months to apply the recurrence to.
- :param bymonthday:
- If given, it must be either an integer, or a sequence of integers,
- meaning the month days to apply the recurrence to.
- :param byyearday:
- If given, it must be either an integer, or a sequence of integers,
- meaning the year days to apply the recurrence to.
- :param byeaster:
- If given, it must be either an integer, or a sequence of integers,
- positive or negative. Each integer will define an offset from the
- Easter Sunday. Passing the offset 0 to byeaster will yield the Easter
- Sunday itself. This is an extension to the RFC specification.
- :param byweekno:
- If given, it must be either an integer, or a sequence of integers,
- meaning the week numbers to apply the recurrence to. Week numbers
- have the meaning described in ISO8601, that is, the first week of
- the year is that containing at least four days of the new year.
- :param byweekday:
- If given, it must be either an integer (0 == MO), a sequence of
- integers, one of the weekday constants (MO, TU, etc), or a sequence
- of these constants. When given, these variables will define the
- weekdays where the recurrence will be applied. It's also possible to
- use an argument n for the weekday instances, which will mean the nth
- occurrence of this weekday in the period. For example, with MONTHLY,
- or with YEARLY and BYMONTH, using FR(+1) in byweekday will specify the
- first friday of the month where the recurrence happens. Notice that in
- the RFC documentation, this is specified as BYDAY, but was renamed to
- avoid the ambiguity of that keyword.
- :param byhour:
- If given, it must be either an integer, or a sequence of integers,
- meaning the hours to apply the recurrence to.
- :param byminute:
- If given, it must be either an integer, or a sequence of integers,
- meaning the minutes to apply the recurrence to.
- :param bysecond:
- If given, it must be either an integer, or a sequence of integers,
- meaning the seconds to apply the recurrence to.
- :param cache:
- If given, it must be a boolean value specifying to enable or disable
- caching of results. If you will use the same rrule instance multiple
- times, enabling caching will improve the performance considerably.
- """
- def __init__(self, freq, dtstart=None,
- interval=1, wkst=None, count=None, until=None, bysetpos=None,
- bymonth=None, bymonthday=None, byyearday=None, byeaster=None,
- byweekno=None, byweekday=None,
- byhour=None, byminute=None, bysecond=None,
- cache=False):
- super(rrule, self).__init__(cache)
- global easter
- if not dtstart:
- if until and until.tzinfo:
- dtstart = datetime.datetime.now(tz=until.tzinfo).replace(microsecond=0)
- else:
- dtstart = datetime.datetime.now().replace(microsecond=0)
- elif not isinstance(dtstart, datetime.datetime):
- dtstart = datetime.datetime.fromordinal(dtstart.toordinal())
- else:
- dtstart = dtstart.replace(microsecond=0)
- self._dtstart = dtstart
- self._tzinfo = dtstart.tzinfo
- self._freq = freq
- self._interval = interval
- self._count = count
-
- # Cache the original byxxx rules, if they are provided, as the _byxxx
- # attributes do not necessarily map to the inputs, and this can be
- # a problem in generating the strings. Only store things if they've
- # been supplied (the string retrieval will just use .get())
- self._original_rule = {}
-
- if until and not isinstance(until, datetime.datetime):
- until = datetime.datetime.fromordinal(until.toordinal())
- self._until = until
-
- if self._dtstart and self._until:
- if (self._dtstart.tzinfo is not None) != (self._until.tzinfo is not None):
- # According to RFC5545 Section 3.3.10:
- # https://tools.ietf.org/html/rfc5545#section-3.3.10
- #
- # > If the "DTSTART" property is specified as a date with UTC
- # > time or a date with local time and time zone reference,
- # > then the UNTIL rule part MUST be specified as a date with
- # > UTC time.
- raise ValueError(
- 'RRULE UNTIL values must be specified in UTC when DTSTART '
- 'is timezone-aware'
- )
-
- if count is not None and until:
- warn("Using both 'count' and 'until' is inconsistent with RFC 5545"
- " and has been deprecated in dateutil. Future versions will "
- "raise an error.", DeprecationWarning)
-
- if wkst is None:
- self._wkst = calendar.firstweekday()
- elif isinstance(wkst, integer_types):
- self._wkst = wkst
- else:
- self._wkst = wkst.weekday
-
- if bysetpos is None:
- self._bysetpos = None
- elif isinstance(bysetpos, integer_types):
- if bysetpos == 0 or not (-366 <= bysetpos <= 366):
- raise ValueError("bysetpos must be between 1 and 366, "
- "or between -366 and -1")
- self._bysetpos = (bysetpos,)
- else:
- self._bysetpos = tuple(bysetpos)
- for pos in self._bysetpos:
- if pos == 0 or not (-366 <= pos <= 366):
- raise ValueError("bysetpos must be between 1 and 366, "
- "or between -366 and -1")
-
- if self._bysetpos:
- self._original_rule['bysetpos'] = self._bysetpos
-
- if (byweekno is None and byyearday is None and bymonthday is None and
- byweekday is None and byeaster is None):
- if freq == YEARLY:
- if bymonth is None:
- bymonth = dtstart.month
- self._original_rule['bymonth'] = None
- bymonthday = dtstart.day
- self._original_rule['bymonthday'] = None
- elif freq == MONTHLY:
- bymonthday = dtstart.day
- self._original_rule['bymonthday'] = None
- elif freq == WEEKLY:
- byweekday = dtstart.weekday()
- self._original_rule['byweekday'] = None
-
- # bymonth
- if bymonth is None:
- self._bymonth = None
- else:
- if isinstance(bymonth, integer_types):
- bymonth = (bymonth,)
-
- self._bymonth = tuple(sorted(set(bymonth)))
-
- if 'bymonth' not in self._original_rule:
- self._original_rule['bymonth'] = self._bymonth
-
- # byyearday
- if byyearday is None:
- self._byyearday = None
- else:
- if isinstance(byyearday, integer_types):
- byyearday = (byyearday,)
-
- self._byyearday = tuple(sorted(set(byyearday)))
- self._original_rule['byyearday'] = self._byyearday
-
- # byeaster
- if byeaster is not None:
- if not easter:
- from dateutil import easter
- if isinstance(byeaster, integer_types):
- self._byeaster = (byeaster,)
- else:
- self._byeaster = tuple(sorted(byeaster))
-
- self._original_rule['byeaster'] = self._byeaster
- else:
- self._byeaster = None
-
- # bymonthday
- if bymonthday is None:
- self._bymonthday = ()
- self._bynmonthday = ()
- else:
- if isinstance(bymonthday, integer_types):
- bymonthday = (bymonthday,)
-
- bymonthday = set(bymonthday) # Ensure it's unique
-
- self._bymonthday = tuple(sorted(x for x in bymonthday if x > 0))
- self._bynmonthday = tuple(sorted(x for x in bymonthday if x < 0))
-
- # Storing positive numbers first, then negative numbers
- if 'bymonthday' not in self._original_rule:
- self._original_rule['bymonthday'] = tuple(
- itertools.chain(self._bymonthday, self._bynmonthday))
-
- # byweekno
- if byweekno is None:
- self._byweekno = None
- else:
- if isinstance(byweekno, integer_types):
- byweekno = (byweekno,)
-
- self._byweekno = tuple(sorted(set(byweekno)))
-
- self._original_rule['byweekno'] = self._byweekno
-
- # byweekday / bynweekday
- if byweekday is None:
- self._byweekday = None
- self._bynweekday = None
- else:
- # If it's one of the valid non-sequence types, convert to a
- # single-element sequence before the iterator that builds the
- # byweekday set.
- if isinstance(byweekday, integer_types) or hasattr(byweekday, "n"):
- byweekday = (byweekday,)
-
- self._byweekday = set()
- self._bynweekday = set()
- for wday in byweekday:
- if isinstance(wday, integer_types):
- self._byweekday.add(wday)
- elif not wday.n or freq > MONTHLY:
- self._byweekday.add(wday.weekday)
- else:
- self._bynweekday.add((wday.weekday, wday.n))
-
- if not self._byweekday:
- self._byweekday = None
- elif not self._bynweekday:
- self._bynweekday = None
-
- if self._byweekday is not None:
- self._byweekday = tuple(sorted(self._byweekday))
- orig_byweekday = [weekday(x) for x in self._byweekday]
- else:
- orig_byweekday = ()
-
- if self._bynweekday is not None:
- self._bynweekday = tuple(sorted(self._bynweekday))
- orig_bynweekday = [weekday(*x) for x in self._bynweekday]
- else:
- orig_bynweekday = ()
-
- if 'byweekday' not in self._original_rule:
- self._original_rule['byweekday'] = tuple(itertools.chain(
- orig_byweekday, orig_bynweekday))
-
- # byhour
- if byhour is None:
- if freq < HOURLY:
- self._byhour = {dtstart.hour}
- else:
- self._byhour = None
- else:
- if isinstance(byhour, integer_types):
- byhour = (byhour,)
-
- if freq == HOURLY:
- self._byhour = self.__construct_byset(start=dtstart.hour,
- byxxx=byhour,
- base=24)
- else:
- self._byhour = set(byhour)
-
- self._byhour = tuple(sorted(self._byhour))
- self._original_rule['byhour'] = self._byhour
-
- # byminute
- if byminute is None:
- if freq < MINUTELY:
- self._byminute = {dtstart.minute}
- else:
- self._byminute = None
- else:
- if isinstance(byminute, integer_types):
- byminute = (byminute,)
-
- if freq == MINUTELY:
- self._byminute = self.__construct_byset(start=dtstart.minute,
- byxxx=byminute,
- base=60)
- else:
- self._byminute = set(byminute)
-
- self._byminute = tuple(sorted(self._byminute))
- self._original_rule['byminute'] = self._byminute
-
- # bysecond
- if bysecond is None:
- if freq < SECONDLY:
- self._bysecond = ((dtstart.second,))
- else:
- self._bysecond = None
- else:
- if isinstance(bysecond, integer_types):
- bysecond = (bysecond,)
-
- self._bysecond = set(bysecond)
-
- if freq == SECONDLY:
- self._bysecond = self.__construct_byset(start=dtstart.second,
- byxxx=bysecond,
- base=60)
- else:
- self._bysecond = set(bysecond)
-
- self._bysecond = tuple(sorted(self._bysecond))
- self._original_rule['bysecond'] = self._bysecond
-
- if self._freq >= HOURLY:
- self._timeset = None
- else:
- self._timeset = []
- for hour in self._byhour:
- for minute in self._byminute:
- for second in self._bysecond:
- self._timeset.append(
- datetime.time(hour, minute, second,
- tzinfo=self._tzinfo))
- self._timeset.sort()
- self._timeset = tuple(self._timeset)
-
- def __str__(self):
- """
- Output a string that would generate this RRULE if passed to rrulestr.
- This is mostly compatible with RFC5545, except for the
- dateutil-specific extension BYEASTER.
- """
-
- output = []
- h, m, s = [None] * 3
- if self._dtstart:
- output.append(self._dtstart.strftime('DTSTART:%Y%m%dT%H%M%S'))
- h, m, s = self._dtstart.timetuple()[3:6]
-
- parts = ['FREQ=' + FREQNAMES[self._freq]]
- if self._interval != 1:
- parts.append('INTERVAL=' + str(self._interval))
-
- if self._wkst:
- parts.append('WKST=' + repr(weekday(self._wkst))[0:2])
-
- if self._count is not None:
- parts.append('COUNT=' + str(self._count))
-
- if self._until:
- parts.append(self._until.strftime('UNTIL=%Y%m%dT%H%M%S'))
-
- if self._original_rule.get('byweekday') is not None:
- # The str() method on weekday objects doesn't generate
- # RFC5545-compliant strings, so we should modify that.
- original_rule = dict(self._original_rule)
- wday_strings = []
- for wday in original_rule['byweekday']:
- if wday.n:
- wday_strings.append('{n:+d}{wday}'.format(
- n=wday.n,
- wday=repr(wday)[0:2]))
- else:
- wday_strings.append(repr(wday))
-
- original_rule['byweekday'] = wday_strings
- else:
- original_rule = self._original_rule
-
- partfmt = '{name}={vals}'
- for name, key in [('BYSETPOS', 'bysetpos'),
- ('BYMONTH', 'bymonth'),
- ('BYMONTHDAY', 'bymonthday'),
- ('BYYEARDAY', 'byyearday'),
- ('BYWEEKNO', 'byweekno'),
- ('BYDAY', 'byweekday'),
- ('BYHOUR', 'byhour'),
- ('BYMINUTE', 'byminute'),
- ('BYSECOND', 'bysecond'),
- ('BYEASTER', 'byeaster')]:
- value = original_rule.get(key)
- if value:
- parts.append(partfmt.format(name=name, vals=(','.join(str(v)
- for v in value))))
-
- output.append('RRULE:' + ';'.join(parts))
- return '\n'.join(output)
-
- def replace(self, **kwargs):
- """Return new rrule with same attributes except for those attributes given new
- values by whichever keyword arguments are specified."""
- new_kwargs = {"interval": self._interval,
- "count": self._count,
- "dtstart": self._dtstart,
- "freq": self._freq,
- "until": self._until,
- "wkst": self._wkst,
- "cache": False if self._cache is None else True }
- new_kwargs.update(self._original_rule)
- new_kwargs.update(kwargs)
- return rrule(**new_kwargs)
-
- def _iter(self):
- year, month, day, hour, minute, second, weekday, yearday, _ = \
- self._dtstart.timetuple()
-
- # Some local variables to speed things up a bit
- freq = self._freq
- interval = self._interval
- wkst = self._wkst
- until = self._until
- bymonth = self._bymonth
- byweekno = self._byweekno
- byyearday = self._byyearday
- byweekday = self._byweekday
- byeaster = self._byeaster
- bymonthday = self._bymonthday
- bynmonthday = self._bynmonthday
- bysetpos = self._bysetpos
- byhour = self._byhour
- byminute = self._byminute
- bysecond = self._bysecond
-
- ii = _iterinfo(self)
- ii.rebuild(year, month)
-
- getdayset = {YEARLY: ii.ydayset,
- MONTHLY: ii.mdayset,
- WEEKLY: ii.wdayset,
- DAILY: ii.ddayset,
- HOURLY: ii.ddayset,
- MINUTELY: ii.ddayset,
- SECONDLY: ii.ddayset}[freq]
-
- if freq < HOURLY:
- timeset = self._timeset
- else:
- gettimeset = {HOURLY: ii.htimeset,
- MINUTELY: ii.mtimeset,
- SECONDLY: ii.stimeset}[freq]
- if ((freq >= HOURLY and
- self._byhour and hour not in self._byhour) or
- (freq >= MINUTELY and
- self._byminute and minute not in self._byminute) or
- (freq >= SECONDLY and
- self._bysecond and second not in self._bysecond)):
- timeset = ()
- else:
- timeset = gettimeset(hour, minute, second)
-
- total = 0
- count = self._count
- while True:
- # Get dayset with the right frequency
- dayset, start, end = getdayset(year, month, day)
-
- # Do the "hard" work ;-)
- filtered = False
- for i in dayset[start:end]:
- if ((bymonth and ii.mmask[i] not in bymonth) or
- (byweekno and not ii.wnomask[i]) or
- (byweekday and ii.wdaymask[i] not in byweekday) or
- (ii.nwdaymask and not ii.nwdaymask[i]) or
- (byeaster and not ii.eastermask[i]) or
- ((bymonthday or bynmonthday) and
- ii.mdaymask[i] not in bymonthday and
- ii.nmdaymask[i] not in bynmonthday) or
- (byyearday and
- ((i < ii.yearlen and i+1 not in byyearday and
- -ii.yearlen+i not in byyearday) or
- (i >= ii.yearlen and i+1-ii.yearlen not in byyearday and
- -ii.nextyearlen+i-ii.yearlen not in byyearday)))):
- dayset[i] = None
- filtered = True
-
- # Output results
- if bysetpos and timeset:
- poslist = []
- for pos in bysetpos:
- if pos < 0:
- daypos, timepos = divmod(pos, len(timeset))
- else:
- daypos, timepos = divmod(pos-1, len(timeset))
- try:
- i = [x for x in dayset[start:end]
- if x is not None][daypos]
- time = timeset[timepos]
- except IndexError:
- pass
- else:
- date = datetime.date.fromordinal(ii.yearordinal+i)
- res = datetime.datetime.combine(date, time)
- if res not in poslist:
- poslist.append(res)
- poslist.sort()
- for res in poslist:
- if until and res > until:
- self._len = total
- return
- elif res >= self._dtstart:
- if count is not None:
- count -= 1
- if count < 0:
- self._len = total
- return
- total += 1
- yield res
- else:
- for i in dayset[start:end]:
- if i is not None:
- date = datetime.date.fromordinal(ii.yearordinal + i)
- for time in timeset:
- res = datetime.datetime.combine(date, time)
- if until and res > until:
- self._len = total
- return
- elif res >= self._dtstart:
- if count is not None:
- count -= 1
- if count < 0:
- self._len = total
- return
-
- total += 1
- yield res
-
- # Handle frequency and interval
- fixday = False
- if freq == YEARLY:
- year += interval
- if year > datetime.MAXYEAR:
- self._len = total
- return
- ii.rebuild(year, month)
- elif freq == MONTHLY:
- month += interval
- if month > 12:
- div, mod = divmod(month, 12)
- month = mod
- year += div
- if month == 0:
- month = 12
- year -= 1
- if year > datetime.MAXYEAR:
- self._len = total
- return
- ii.rebuild(year, month)
- elif freq == WEEKLY:
- if wkst > weekday:
- day += -(weekday+1+(6-wkst))+self._interval*7
- else:
- day += -(weekday-wkst)+self._interval*7
- weekday = wkst
- fixday = True
- elif freq == DAILY:
- day += interval
- fixday = True
- elif freq == HOURLY:
- if filtered:
- # Jump to one iteration before next day
- hour += ((23-hour)//interval)*interval
-
- if byhour:
- ndays, hour = self.__mod_distance(value=hour,
- byxxx=self._byhour,
- base=24)
- else:
- ndays, hour = divmod(hour+interval, 24)
-
- if ndays:
- day += ndays
- fixday = True
-
- timeset = gettimeset(hour, minute, second)
- elif freq == MINUTELY:
- if filtered:
- # Jump to one iteration before next day
- minute += ((1439-(hour*60+minute))//interval)*interval
-
- valid = False
- rep_rate = (24*60)
- for j in range(rep_rate // gcd(interval, rep_rate)):
- if byminute:
- nhours, minute = \
- self.__mod_distance(value=minute,
- byxxx=self._byminute,
- base=60)
- else:
- nhours, minute = divmod(minute+interval, 60)
-
- div, hour = divmod(hour+nhours, 24)
- if div:
- day += div
- fixday = True
- filtered = False
-
- if not byhour or hour in byhour:
- valid = True
- break
-
- if not valid:
- raise ValueError('Invalid combination of interval and ' +
- 'byhour resulting in empty rule.')
-
- timeset = gettimeset(hour, minute, second)
- elif freq == SECONDLY:
- if filtered:
- # Jump to one iteration before next day
- second += (((86399 - (hour * 3600 + minute * 60 + second))
- // interval) * interval)
-
- rep_rate = (24 * 3600)
- valid = False
- for j in range(0, rep_rate // gcd(interval, rep_rate)):
- if bysecond:
- nminutes, second = \
- self.__mod_distance(value=second,
- byxxx=self._bysecond,
- base=60)
- else:
- nminutes, second = divmod(second+interval, 60)
-
- div, minute = divmod(minute+nminutes, 60)
- if div:
- hour += div
- div, hour = divmod(hour, 24)
- if div:
- day += div
- fixday = True
-
- if ((not byhour or hour in byhour) and
- (not byminute or minute in byminute) and
- (not bysecond or second in bysecond)):
- valid = True
- break
-
- if not valid:
- raise ValueError('Invalid combination of interval, ' +
- 'byhour and byminute resulting in empty' +
- ' rule.')
-
- timeset = gettimeset(hour, minute, second)
-
- if fixday and day > 28:
- daysinmonth = calendar.monthrange(year, month)[1]
- if day > daysinmonth:
- while day > daysinmonth:
- day -= daysinmonth
- month += 1
- if month == 13:
- month = 1
- year += 1
- if year > datetime.MAXYEAR:
- self._len = total
- return
- daysinmonth = calendar.monthrange(year, month)[1]
- ii.rebuild(year, month)
-
- def __construct_byset(self, start, byxxx, base):
- """
- If a `BYXXX` sequence is passed to the constructor at the same level as
- `FREQ` (e.g. `FREQ=HOURLY,BYHOUR={2,4,7},INTERVAL=3`), there are some
- specifications which cannot be reached given some starting conditions.
-
- This occurs whenever the interval is not coprime with the base of a
- given unit and the difference between the starting position and the
- ending position is not coprime with the greatest common denominator
- between the interval and the base. For example, with a FREQ of hourly
- starting at 17:00 and an interval of 4, the only valid values for
- BYHOUR would be {21, 1, 5, 9, 13, 17}, because 4 and 24 are not
- coprime.
-
- :param start:
- Specifies the starting position.
- :param byxxx:
- An iterable containing the list of allowed values.
- :param base:
- The largest allowable value for the specified frequency (e.g.
- 24 hours, 60 minutes).
-
- This does not preserve the type of the iterable, returning a set, since
- the values should be unique and the order is irrelevant, this will
- speed up later lookups.
-
- In the event of an empty set, raises a :exception:`ValueError`, as this
- results in an empty rrule.
- """
-
- cset = set()
-
- # Support a single byxxx value.
- if isinstance(byxxx, integer_types):
- byxxx = (byxxx, )
-
- for num in byxxx:
- i_gcd = gcd(self._interval, base)
- # Use divmod rather than % because we need to wrap negative nums.
- if i_gcd == 1 or divmod(num - start, i_gcd)[1] == 0:
- cset.add(num)
-
- if len(cset) == 0:
- raise ValueError("Invalid rrule byxxx generates an empty set.")
-
- return cset
-
- def __mod_distance(self, value, byxxx, base):
- """
- Calculates the next value in a sequence where the `FREQ` parameter is
- specified along with a `BYXXX` parameter at the same "level"
- (e.g. `HOURLY` specified with `BYHOUR`).
-
- :param value:
- The old value of the component.
- :param byxxx:
- The `BYXXX` set, which should have been generated by
- `rrule._construct_byset`, or something else which checks that a
- valid rule is present.
- :param base:
- The largest allowable value for the specified frequency (e.g.
- 24 hours, 60 minutes).
-
- If a valid value is not found after `base` iterations (the maximum
- number before the sequence would start to repeat), this raises a
- :exception:`ValueError`, as no valid values were found.
-
- This returns a tuple of `divmod(n*interval, base)`, where `n` is the
- smallest number of `interval` repetitions until the next specified
- value in `byxxx` is found.
- """
- accumulator = 0
- for ii in range(1, base + 1):
- # Using divmod() over % to account for negative intervals
- div, value = divmod(value + self._interval, base)
- accumulator += div
- if value in byxxx:
- return (accumulator, value)
-
-
-class _iterinfo(object):
- __slots__ = ["rrule", "lastyear", "lastmonth",
- "yearlen", "nextyearlen", "yearordinal", "yearweekday",
- "mmask", "mrange", "mdaymask", "nmdaymask",
- "wdaymask", "wnomask", "nwdaymask", "eastermask"]
-
- def __init__(self, rrule):
- for attr in self.__slots__:
- setattr(self, attr, None)
- self.rrule = rrule
-
- def rebuild(self, year, month):
- # Every mask is 7 days longer to handle cross-year weekly periods.
- rr = self.rrule
- if year != self.lastyear:
- self.yearlen = 365 + calendar.isleap(year)
- self.nextyearlen = 365 + calendar.isleap(year + 1)
- firstyday = datetime.date(year, 1, 1)
- self.yearordinal = firstyday.toordinal()
- self.yearweekday = firstyday.weekday()
-
- wday = datetime.date(year, 1, 1).weekday()
- if self.yearlen == 365:
- self.mmask = M365MASK
- self.mdaymask = MDAY365MASK
- self.nmdaymask = NMDAY365MASK
- self.wdaymask = WDAYMASK[wday:]
- self.mrange = M365RANGE
- else:
- self.mmask = M366MASK
- self.mdaymask = MDAY366MASK
- self.nmdaymask = NMDAY366MASK
- self.wdaymask = WDAYMASK[wday:]
- self.mrange = M366RANGE
-
- if not rr._byweekno:
- self.wnomask = None
- else:
- self.wnomask = [0]*(self.yearlen+7)
- # no1wkst = firstwkst = self.wdaymask.index(rr._wkst)
- no1wkst = firstwkst = (7-self.yearweekday+rr._wkst) % 7
- if no1wkst >= 4:
- no1wkst = 0
- # Number of days in the year, plus the days we got
- # from last year.
- wyearlen = self.yearlen+(self.yearweekday-rr._wkst) % 7
- else:
- # Number of days in the year, minus the days we
- # left in last year.
- wyearlen = self.yearlen-no1wkst
- div, mod = divmod(wyearlen, 7)
- numweeks = div+mod//4
- for n in rr._byweekno:
- if n < 0:
- n += numweeks+1
- if not (0 < n <= numweeks):
- continue
- if n > 1:
- i = no1wkst+(n-1)*7
- if no1wkst != firstwkst:
- i -= 7-firstwkst
- else:
- i = no1wkst
- for j in range(7):
- self.wnomask[i] = 1
- i += 1
- if self.wdaymask[i] == rr._wkst:
- break
- if 1 in rr._byweekno:
- # Check week number 1 of next year as well
- # TODO: Check -numweeks for next year.
- i = no1wkst+numweeks*7
- if no1wkst != firstwkst:
- i -= 7-firstwkst
- if i < self.yearlen:
- # If week starts in next year, we
- # don't care about it.
- for j in range(7):
- self.wnomask[i] = 1
- i += 1
- if self.wdaymask[i] == rr._wkst:
- break
- if no1wkst:
- # Check last week number of last year as
- # well. If no1wkst is 0, either the year
- # started on week start, or week number 1
- # got days from last year, so there are no
- # days from last year's last week number in
- # this year.
- if -1 not in rr._byweekno:
- lyearweekday = datetime.date(year-1, 1, 1).weekday()
- lno1wkst = (7-lyearweekday+rr._wkst) % 7
- lyearlen = 365+calendar.isleap(year-1)
- if lno1wkst >= 4:
- lno1wkst = 0
- lnumweeks = 52+(lyearlen +
- (lyearweekday-rr._wkst) % 7) % 7//4
- else:
- lnumweeks = 52+(self.yearlen-no1wkst) % 7//4
- else:
- lnumweeks = -1
- if lnumweeks in rr._byweekno:
- for i in range(no1wkst):
- self.wnomask[i] = 1
-
- if (rr._bynweekday and (month != self.lastmonth or
- year != self.lastyear)):
- ranges = []
- if rr._freq == YEARLY:
- if rr._bymonth:
- for month in rr._bymonth:
- ranges.append(self.mrange[month-1:month+1])
- else:
- ranges = [(0, self.yearlen)]
- elif rr._freq == MONTHLY:
- ranges = [self.mrange[month-1:month+1]]
- if ranges:
- # Weekly frequency won't get here, so we may not
- # care about cross-year weekly periods.
- self.nwdaymask = [0]*self.yearlen
- for first, last in ranges:
- last -= 1
- for wday, n in rr._bynweekday:
- if n < 0:
- i = last+(n+1)*7
- i -= (self.wdaymask[i]-wday) % 7
- else:
- i = first+(n-1)*7
- i += (7-self.wdaymask[i]+wday) % 7
- if first <= i <= last:
- self.nwdaymask[i] = 1
-
- if rr._byeaster:
- self.eastermask = [0]*(self.yearlen+7)
- eyday = easter.easter(year).toordinal()-self.yearordinal
- for offset in rr._byeaster:
- self.eastermask[eyday+offset] = 1
-
- self.lastyear = year
- self.lastmonth = month
-
- def ydayset(self, year, month, day):
- return list(range(self.yearlen)), 0, self.yearlen
-
- def mdayset(self, year, month, day):
- dset = [None]*self.yearlen
- start, end = self.mrange[month-1:month+1]
- for i in range(start, end):
- dset[i] = i
- return dset, start, end
-
- def wdayset(self, year, month, day):
- # We need to handle cross-year weeks here.
- dset = [None]*(self.yearlen+7)
- i = datetime.date(year, month, day).toordinal()-self.yearordinal
- start = i
- for j in range(7):
- dset[i] = i
- i += 1
- # if (not (0 <= i < self.yearlen) or
- # self.wdaymask[i] == self.rrule._wkst):
- # This will cross the year boundary, if necessary.
- if self.wdaymask[i] == self.rrule._wkst:
- break
- return dset, start, i
-
- def ddayset(self, year, month, day):
- dset = [None] * self.yearlen
- i = datetime.date(year, month, day).toordinal() - self.yearordinal
- dset[i] = i
- return dset, i, i + 1
-
- def htimeset(self, hour, minute, second):
- tset = []
- rr = self.rrule
- for minute in rr._byminute:
- for second in rr._bysecond:
- tset.append(datetime.time(hour, minute, second,
- tzinfo=rr._tzinfo))
- tset.sort()
- return tset
-
- def mtimeset(self, hour, minute, second):
- tset = []
- rr = self.rrule
- for second in rr._bysecond:
- tset.append(datetime.time(hour, minute, second, tzinfo=rr._tzinfo))
- tset.sort()
- return tset
-
- def stimeset(self, hour, minute, second):
- return (datetime.time(hour, minute, second,
- tzinfo=self.rrule._tzinfo),)
-
-
-class rruleset(rrulebase):
- """ The rruleset type allows more complex recurrence setups, mixing
- multiple rules, dates, exclusion rules, and exclusion dates. The type
- constructor takes the following keyword arguments:
-
- :param cache: If True, caching of results will be enabled, improving
- performance of multiple queries considerably. """
-
- class _genitem(object):
- def __init__(self, genlist, gen):
- try:
- self.dt = advance_iterator(gen)
- genlist.append(self)
- except StopIteration:
- pass
- self.genlist = genlist
- self.gen = gen
-
- def __next__(self):
- try:
- self.dt = advance_iterator(self.gen)
- except StopIteration:
- if self.genlist[0] is self:
- heapq.heappop(self.genlist)
- else:
- self.genlist.remove(self)
- heapq.heapify(self.genlist)
-
- next = __next__
-
- def __lt__(self, other):
- return self.dt < other.dt
-
- def __gt__(self, other):
- return self.dt > other.dt
-
- def __eq__(self, other):
- return self.dt == other.dt
-
- def __ne__(self, other):
- return self.dt != other.dt
-
- def __init__(self, cache=False):
- super(rruleset, self).__init__(cache)
- self._rrule = []
- self._rdate = []
- self._exrule = []
- self._exdate = []
-
- @_invalidates_cache
- def rrule(self, rrule):
- """ Include the given :py:class:`rrule` instance in the recurrence set
- generation. """
- self._rrule.append(rrule)
-
- @_invalidates_cache
- def rdate(self, rdate):
- """ Include the given :py:class:`datetime` instance in the recurrence
- set generation. """
- self._rdate.append(rdate)
-
- @_invalidates_cache
- def exrule(self, exrule):
- """ Include the given rrule instance in the recurrence set exclusion
- list. Dates which are part of the given recurrence rules will not
- be generated, even if some inclusive rrule or rdate matches them.
- """
- self._exrule.append(exrule)
-
- @_invalidates_cache
- def exdate(self, exdate):
- """ Include the given datetime instance in the recurrence set
- exclusion list. Dates included that way will not be generated,
- even if some inclusive rrule or rdate matches them. """
- self._exdate.append(exdate)
-
- def _iter(self):
- rlist = []
- self._rdate.sort()
- self._genitem(rlist, iter(self._rdate))
- for gen in [iter(x) for x in self._rrule]:
- self._genitem(rlist, gen)
- exlist = []
- self._exdate.sort()
- self._genitem(exlist, iter(self._exdate))
- for gen in [iter(x) for x in self._exrule]:
- self._genitem(exlist, gen)
- lastdt = None
- total = 0
- heapq.heapify(rlist)
- heapq.heapify(exlist)
- while rlist:
- ritem = rlist[0]
- if not lastdt or lastdt != ritem.dt:
- while exlist and exlist[0] < ritem:
- exitem = exlist[0]
- advance_iterator(exitem)
- if exlist and exlist[0] is exitem:
- heapq.heapreplace(exlist, exitem)
- if not exlist or ritem != exlist[0]:
- total += 1
- yield ritem.dt
- lastdt = ritem.dt
- advance_iterator(ritem)
- if rlist and rlist[0] is ritem:
- heapq.heapreplace(rlist, ritem)
- self._len = total
-
-
-
-
-class _rrulestr(object):
- """ Parses a string representation of a recurrence rule or set of
- recurrence rules.
-
- :param s:
- Required, a string defining one or more recurrence rules.
-
- :param dtstart:
- If given, used as the default recurrence start if not specified in the
- rule string.
-
- :param cache:
- If set ``True`` caching of results will be enabled, improving
- performance of multiple queries considerably.
-
- :param unfold:
- If set ``True`` indicates that a rule string is split over more
- than one line and should be joined before processing.
-
- :param forceset:
- If set ``True`` forces a :class:`dateutil.rrule.rruleset` to
- be returned.
-
- :param compatible:
- If set ``True`` forces ``unfold`` and ``forceset`` to be ``True``.
-
- :param ignoretz:
- If set ``True``, time zones in parsed strings are ignored and a naive
- :class:`datetime.datetime` object is returned.
-
- :param tzids:
- If given, a callable or mapping used to retrieve a
- :class:`datetime.tzinfo` from a string representation.
- Defaults to :func:`dateutil.tz.gettz`.
-
- :param tzinfos:
- Additional time zone names / aliases which may be present in a string
- representation. See :func:`dateutil.parser.parse` for more
- information.
-
- :return:
- Returns a :class:`dateutil.rrule.rruleset` or
- :class:`dateutil.rrule.rrule`
- """
-
- _freq_map = {"YEARLY": YEARLY,
- "MONTHLY": MONTHLY,
- "WEEKLY": WEEKLY,
- "DAILY": DAILY,
- "HOURLY": HOURLY,
- "MINUTELY": MINUTELY,
- "SECONDLY": SECONDLY}
-
- _weekday_map = {"MO": 0, "TU": 1, "WE": 2, "TH": 3,
- "FR": 4, "SA": 5, "SU": 6}
-
- def _handle_int(self, rrkwargs, name, value, **kwargs):
- rrkwargs[name.lower()] = int(value)
-
- def _handle_int_list(self, rrkwargs, name, value, **kwargs):
- rrkwargs[name.lower()] = [int(x) for x in value.split(',')]
-
- _handle_INTERVAL = _handle_int
- _handle_COUNT = _handle_int
- _handle_BYSETPOS = _handle_int_list
- _handle_BYMONTH = _handle_int_list
- _handle_BYMONTHDAY = _handle_int_list
- _handle_BYYEARDAY = _handle_int_list
- _handle_BYEASTER = _handle_int_list
- _handle_BYWEEKNO = _handle_int_list
- _handle_BYHOUR = _handle_int_list
- _handle_BYMINUTE = _handle_int_list
- _handle_BYSECOND = _handle_int_list
-
- def _handle_FREQ(self, rrkwargs, name, value, **kwargs):
- rrkwargs["freq"] = self._freq_map[value]
-
- def _handle_UNTIL(self, rrkwargs, name, value, **kwargs):
- global parser
- if not parser:
- from dateutil import parser
- try:
- rrkwargs["until"] = parser.parse(value,
- ignoretz=kwargs.get("ignoretz"),
- tzinfos=kwargs.get("tzinfos"))
- except ValueError:
- raise ValueError("invalid until date")
-
- def _handle_WKST(self, rrkwargs, name, value, **kwargs):
- rrkwargs["wkst"] = self._weekday_map[value]
-
- def _handle_BYWEEKDAY(self, rrkwargs, name, value, **kwargs):
- """
- Two ways to specify this: +1MO or MO(+1)
- """
- l = []
- for wday in value.split(','):
- if '(' in wday:
- # If it's of the form TH(+1), etc.
- splt = wday.split('(')
- w = splt[0]
- n = int(splt[1][:-1])
- elif len(wday):
- # If it's of the form +1MO
- for i in range(len(wday)):
- if wday[i] not in '+-0123456789':
- break
- n = wday[:i] or None
- w = wday[i:]
- if n:
- n = int(n)
- else:
- raise ValueError("Invalid (empty) BYDAY specification.")
-
- l.append(weekdays[self._weekday_map[w]](n))
- rrkwargs["byweekday"] = l
-
- _handle_BYDAY = _handle_BYWEEKDAY
-
- def _parse_rfc_rrule(self, line,
- dtstart=None,
- cache=False,
- ignoretz=False,
- tzinfos=None):
- if line.find(':') != -1:
- name, value = line.split(':')
- if name != "RRULE":
- raise ValueError("unknown parameter name")
- else:
- value = line
- rrkwargs = {}
- for pair in value.split(';'):
- name, value = pair.split('=')
- name = name.upper()
- value = value.upper()
- try:
- getattr(self, "_handle_"+name)(rrkwargs, name, value,
- ignoretz=ignoretz,
- tzinfos=tzinfos)
- except AttributeError:
- raise ValueError("unknown parameter '%s'" % name)
- except (KeyError, ValueError):
- raise ValueError("invalid '%s': %s" % (name, value))
- return rrule(dtstart=dtstart, cache=cache, **rrkwargs)
-
- def _parse_date_value(self, date_value, parms, rule_tzids,
- ignoretz, tzids, tzinfos):
- global parser
- if not parser:
- from dateutil import parser
-
- datevals = []
- value_found = False
- TZID = None
-
- for parm in parms:
- if parm.startswith("TZID="):
- try:
- tzkey = rule_tzids[parm.split('TZID=')[-1]]
- except KeyError:
- continue
- if tzids is None:
- from . import tz
- tzlookup = tz.gettz
- elif callable(tzids):
- tzlookup = tzids
- else:
- tzlookup = getattr(tzids, 'get', None)
- if tzlookup is None:
- msg = ('tzids must be a callable, mapping, or None, '
- 'not %s' % tzids)
- raise ValueError(msg)
-
- TZID = tzlookup(tzkey)
- continue
-
- # RFC 5445 3.8.2.4: The VALUE parameter is optional, but may be found
- # only once.
- if parm not in {"VALUE=DATE-TIME", "VALUE=DATE"}:
- raise ValueError("unsupported parm: " + parm)
- else:
- if value_found:
- msg = ("Duplicate value parameter found in: " + parm)
- raise ValueError(msg)
- value_found = True
-
- for datestr in date_value.split(','):
- date = parser.parse(datestr, ignoretz=ignoretz, tzinfos=tzinfos)
- if TZID is not None:
- if date.tzinfo is None:
- date = date.replace(tzinfo=TZID)
- else:
- raise ValueError('DTSTART/EXDATE specifies multiple timezone')
- datevals.append(date)
-
- return datevals
-
- def _parse_rfc(self, s,
- dtstart=None,
- cache=False,
- unfold=False,
- forceset=False,
- compatible=False,
- ignoretz=False,
- tzids=None,
- tzinfos=None):
- global parser
- if compatible:
- forceset = True
- unfold = True
-
- TZID_NAMES = dict(map(
- lambda x: (x.upper(), x),
- re.findall('TZID=(?P[^:]+):', s)
- ))
- s = s.upper()
- if not s.strip():
- raise ValueError("empty string")
- if unfold:
- lines = s.splitlines()
- i = 0
- while i < len(lines):
- line = lines[i].rstrip()
- if not line:
- del lines[i]
- elif i > 0 and line[0] == " ":
- lines[i-1] += line[1:]
- del lines[i]
- else:
- i += 1
- else:
- lines = s.split()
- if (not forceset and len(lines) == 1 and (s.find(':') == -1 or
- s.startswith('RRULE:'))):
- return self._parse_rfc_rrule(lines[0], cache=cache,
- dtstart=dtstart, ignoretz=ignoretz,
- tzinfos=tzinfos)
- else:
- rrulevals = []
- rdatevals = []
- exrulevals = []
- exdatevals = []
- for line in lines:
- if not line:
- continue
- if line.find(':') == -1:
- name = "RRULE"
- value = line
- else:
- name, value = line.split(':', 1)
- parms = name.split(';')
- if not parms:
- raise ValueError("empty property name")
- name = parms[0]
- parms = parms[1:]
- if name == "RRULE":
- for parm in parms:
- raise ValueError("unsupported RRULE parm: "+parm)
- rrulevals.append(value)
- elif name == "RDATE":
- for parm in parms:
- if parm != "VALUE=DATE-TIME":
- raise ValueError("unsupported RDATE parm: "+parm)
- rdatevals.append(value)
- elif name == "EXRULE":
- for parm in parms:
- raise ValueError("unsupported EXRULE parm: "+parm)
- exrulevals.append(value)
- elif name == "EXDATE":
- exdatevals.extend(
- self._parse_date_value(value, parms,
- TZID_NAMES, ignoretz,
- tzids, tzinfos)
- )
- elif name == "DTSTART":
- dtvals = self._parse_date_value(value, parms, TZID_NAMES,
- ignoretz, tzids, tzinfos)
- if len(dtvals) != 1:
- raise ValueError("Multiple DTSTART values specified:" +
- value)
- dtstart = dtvals[0]
- else:
- raise ValueError("unsupported property: "+name)
- if (forceset or len(rrulevals) > 1 or rdatevals
- or exrulevals or exdatevals):
- if not parser and (rdatevals or exdatevals):
- from dateutil import parser
- rset = rruleset(cache=cache)
- for value in rrulevals:
- rset.rrule(self._parse_rfc_rrule(value, dtstart=dtstart,
- ignoretz=ignoretz,
- tzinfos=tzinfos))
- for value in rdatevals:
- for datestr in value.split(','):
- rset.rdate(parser.parse(datestr,
- ignoretz=ignoretz,
- tzinfos=tzinfos))
- for value in exrulevals:
- rset.exrule(self._parse_rfc_rrule(value, dtstart=dtstart,
- ignoretz=ignoretz,
- tzinfos=tzinfos))
- for value in exdatevals:
- rset.exdate(value)
- if compatible and dtstart:
- rset.rdate(dtstart)
- return rset
- else:
- return self._parse_rfc_rrule(rrulevals[0],
- dtstart=dtstart,
- cache=cache,
- ignoretz=ignoretz,
- tzinfos=tzinfos)
-
- def __call__(self, s, **kwargs):
- return self._parse_rfc(s, **kwargs)
-
-
-rrulestr = _rrulestr()
-
-# vim:ts=4:sw=4:et
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ivi.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ivi.c
deleted file mode 100644
index 43f3cb1da3648e2a3b3acb0f24c7edd9a79d5077..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ivi.c
+++ /dev/null
@@ -1,1614 +0,0 @@
-/*
- * common functions for Indeo Video Interactive codecs (Indeo4 and Indeo5)
- *
- * Copyright (c) 2009 Maxim Poliakovski
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * This file contains functions and data shared by both Indeo4 and
- * Indeo5 decoders.
- */
-
-#include
-
-#include "libavutil/attributes.h"
-#include "libavutil/imgutils.h"
-#include "libavutil/thread.h"
-
-#define BITSTREAM_READER_LE
-#include "avcodec.h"
-#include "decode.h"
-#include "get_bits.h"
-#include "ivi.h"
-#include "ivi_dsp.h"
-
-/**
- * These are 2x8 predefined Huffman codebooks for coding macroblock/block
- * signals. They are specified using "huffman descriptors" in order to
- * avoid huge static tables. The decoding tables will be generated at
- * startup from these descriptors.
- */
-/** static macroblock huffman tables */
-static const IVIHuffDesc ivi_mb_huff_desc[8] = {
- {8, {0, 4, 5, 4, 4, 4, 6, 6}},
- {12, {0, 2, 2, 3, 3, 3, 3, 5, 3, 2, 2, 2}},
- {12, {0, 2, 3, 4, 3, 3, 3, 3, 4, 3, 2, 2}},
- {12, {0, 3, 4, 4, 3, 3, 3, 3, 3, 2, 2, 2}},
- {13, {0, 4, 4, 3, 3, 3, 3, 2, 3, 3, 2, 1, 1}},
- {9, {0, 4, 4, 4, 4, 3, 3, 3, 2}},
- {10, {0, 4, 4, 4, 4, 3, 3, 2, 2, 2}},
- {12, {0, 4, 4, 4, 3, 3, 2, 3, 2, 2, 2, 2}}
-};
-
-/** static block huffman tables */
-static const IVIHuffDesc ivi_blk_huff_desc[8] = {
- {10, {1, 2, 3, 4, 4, 7, 5, 5, 4, 1}},
- {11, {2, 3, 4, 4, 4, 7, 5, 4, 3, 3, 2}},
- {12, {2, 4, 5, 5, 5, 5, 6, 4, 4, 3, 1, 1}},
- {13, {3, 3, 4, 4, 5, 6, 6, 4, 4, 3, 2, 1, 1}},
- {11, {3, 4, 4, 5, 5, 5, 6, 5, 4, 2, 2}},
- {13, {3, 4, 5, 5, 5, 5, 6, 4, 3, 3, 2, 1, 1}},
- {13, {3, 4, 5, 5, 5, 6, 5, 4, 3, 3, 2, 1, 1}},
- {9, {3, 4, 4, 5, 5, 5, 6, 5, 5}}
-};
-
-static VLC ivi_mb_vlc_tabs [8]; ///< static macroblock Huffman tables
-static VLC ivi_blk_vlc_tabs[8]; ///< static block Huffman tables
-
-typedef void (*ivi_mc_func) (int16_t *buf, const int16_t *ref_buf,
- ptrdiff_t pitch, int mc_type);
-typedef void (*ivi_mc_avg_func) (int16_t *buf, const int16_t *ref_buf1,
- const int16_t *ref_buf2,
- ptrdiff_t pitch, int mc_type, int mc_type2);
-
-static int ivi_mc(const IVIBandDesc *band, ivi_mc_func mc, ivi_mc_avg_func mc_avg,
- int offs, int mv_x, int mv_y, int mv_x2, int mv_y2,
- int mc_type, int mc_type2)
-{
- int ref_offs = offs + mv_y * band->pitch + mv_x;
- int buf_size = band->pitch * band->aheight;
- int min_size = band->pitch * (band->blk_size - 1) + band->blk_size;
- int ref_size = (mc_type > 1) * band->pitch + (mc_type & 1);
-
- if (mc_type != -1) {
- av_assert0(offs >= 0 && ref_offs >= 0 && band->ref_buf);
- av_assert0(buf_size - min_size >= offs);
- av_assert0(buf_size - min_size - ref_size >= ref_offs);
- }
-
- if (mc_type2 == -1) {
- mc(band->buf + offs, band->ref_buf + ref_offs, band->pitch, mc_type);
- } else {
- int ref_offs2 = offs + mv_y2 * band->pitch + mv_x2;
- int ref_size2 = (mc_type2 > 1) * band->pitch + (mc_type2 & 1);
- if (offs < 0 || ref_offs2 < 0 || !band->b_ref_buf)
- return AVERROR_INVALIDDATA;
- if (buf_size - min_size - ref_size2 < ref_offs2)
- return AVERROR_INVALIDDATA;
-
- if (mc_type == -1)
- mc(band->buf + offs, band->b_ref_buf + ref_offs2,
- band->pitch, mc_type2);
- else
- mc_avg(band->buf + offs, band->ref_buf + ref_offs,
- band->b_ref_buf + ref_offs2, band->pitch,
- mc_type, mc_type2);
- }
-
- return 0;
-}
-
-/*
- * Generate a huffman codebook from the given descriptor
- * and convert it into the FFmpeg VLC table.
- *
- * @param[in] cb pointer to codebook descriptor
- * @param[out] vlc where to place the generated VLC table
- * @param[in] flag flag: 1 - for static or 0 for dynamic tables
- * @return result code: 0 - OK, -1 = error (invalid codebook descriptor)
- */
-static int ivi_create_huff_from_desc(const IVIHuffDesc *cb, VLC *vlc, int flag)
-{
- int pos, i, j, codes_per_row, prefix, not_last_row;
- uint16_t codewords[256]; /* FIXME: move this temporal storage out? */
- uint8_t bits[256];
-
- pos = 0; /* current position = 0 */
-
- for (i = 0; i < cb->num_rows; i++) {
- codes_per_row = 1 << cb->xbits[i];
- not_last_row = (i != cb->num_rows - 1);
- prefix = ((1 << i) - 1) << (cb->xbits[i] + not_last_row);
-
- for (j = 0; j < codes_per_row; j++) {
- if (pos >= 256) /* Some Indeo5 codebooks can have more than 256 */
- break; /* elements, but only 256 codes are allowed! */
-
- bits[pos] = i + cb->xbits[i] + not_last_row;
- if (bits[pos] > IVI_VLC_BITS)
- return AVERROR_INVALIDDATA; /* invalid descriptor */
-
- codewords[pos] = prefix | j;
- if (!bits[pos])
- bits[pos] = 1;
-
- pos++;
- }//for j
- }//for i
-
- /* number of codewords = pos */
- return init_vlc(vlc, IVI_VLC_BITS, pos, bits, 1, 1, codewords, 2, 2,
- (flag ? INIT_VLC_USE_NEW_STATIC : 0) | INIT_VLC_OUTPUT_LE);
-}
-
-static av_cold void ivi_init_static_vlc(void)
-{
- int i;
- static VLCElem table_data[8192 * 16];
-
- for (i = 0; i < 8; i++) {
- ivi_mb_vlc_tabs[i].table = table_data + i * 2 * 8192;
- ivi_mb_vlc_tabs[i].table_allocated = 8192;
- ivi_create_huff_from_desc(&ivi_mb_huff_desc[i],
- &ivi_mb_vlc_tabs[i], 1);
- ivi_blk_vlc_tabs[i].table = table_data + (i * 2 + 1) * 8192;
- ivi_blk_vlc_tabs[i].table_allocated = 8192;
- ivi_create_huff_from_desc(&ivi_blk_huff_desc[i],
- &ivi_blk_vlc_tabs[i], 1);
- }
-}
-
-av_cold void ff_ivi_init_static_vlc(void)
-{
- static AVOnce init_static_once = AV_ONCE_INIT;
- ff_thread_once(&init_static_once, ivi_init_static_vlc);
-}
-
-/*
- * Copy huffman codebook descriptors.
- *
- * @param[out] dst ptr to the destination descriptor
- * @param[in] src ptr to the source descriptor
- */
-static void ivi_huff_desc_copy(IVIHuffDesc *dst, const IVIHuffDesc *src)
-{
- dst->num_rows = src->num_rows;
- memcpy(dst->xbits, src->xbits, src->num_rows);
-}
-
-/*
- * Compare two huffman codebook descriptors.
- *
- * @param[in] desc1 ptr to the 1st descriptor to compare
- * @param[in] desc2 ptr to the 2nd descriptor to compare
- * @return comparison result: 0 - equal, 1 - not equal
- */
-static int ivi_huff_desc_cmp(const IVIHuffDesc *desc1,
- const IVIHuffDesc *desc2)
-{
- return desc1->num_rows != desc2->num_rows ||
- memcmp(desc1->xbits, desc2->xbits, desc1->num_rows);
-}
-
-int ff_ivi_dec_huff_desc(GetBitContext *gb, int desc_coded, int which_tab,
- IVIHuffTab *huff_tab, AVCodecContext *avctx)
-{
- int i, result;
- IVIHuffDesc new_huff;
-
- if (!desc_coded) {
- /* select default table */
- huff_tab->tab = (which_tab) ? &ivi_blk_vlc_tabs[7]
- : &ivi_mb_vlc_tabs [7];
- return 0;
- }
-
- huff_tab->tab_sel = get_bits(gb, 3);
- if (huff_tab->tab_sel == 7) {
- /* custom huffman table (explicitly encoded) */
- new_huff.num_rows = get_bits(gb, 4);
- if (!new_huff.num_rows) {
- av_log(avctx, AV_LOG_ERROR, "Empty custom Huffman table!\n");
- return AVERROR_INVALIDDATA;
- }
-
- for (i = 0; i < new_huff.num_rows; i++)
- new_huff.xbits[i] = get_bits(gb, 4);
-
- /* Have we got the same custom table? Rebuild if not. */
- if (ivi_huff_desc_cmp(&new_huff, &huff_tab->cust_desc) || !huff_tab->cust_tab.table) {
- ivi_huff_desc_copy(&huff_tab->cust_desc, &new_huff);
-
- if (huff_tab->cust_tab.table)
- ff_free_vlc(&huff_tab->cust_tab);
- result = ivi_create_huff_from_desc(&huff_tab->cust_desc,
- &huff_tab->cust_tab, 0);
- if (result) {
- // reset faulty description
- huff_tab->cust_desc.num_rows = 0;
- av_log(avctx, AV_LOG_ERROR,
- "Error while initializing custom vlc table!\n");
- return result;
- }
- }
- huff_tab->tab = &huff_tab->cust_tab;
- } else {
- /* select one of predefined tables */
- huff_tab->tab = (which_tab) ? &ivi_blk_vlc_tabs[huff_tab->tab_sel]
- : &ivi_mb_vlc_tabs [huff_tab->tab_sel];
- }
-
- return 0;
-}
-
-/*
- * Free planes, bands and macroblocks buffers.
- *
- * @param[in] planes pointer to the array of the plane descriptors
- */
-static av_cold void ivi_free_buffers(IVIPlaneDesc *planes)
-{
- int p, b, t;
-
- for (p = 0; p < 3; p++) {
- if (planes[p].bands) {
- for (b = 0; b < planes[p].num_bands; b++) {
- IVIBandDesc *band = &planes[p].bands[b];
- av_freep(&band->bufs[0]);
- av_freep(&band->bufs[1]);
- av_freep(&band->bufs[2]);
- av_freep(&band->bufs[3]);
-
- if (band->blk_vlc.cust_tab.table)
- ff_free_vlc(&band->blk_vlc.cust_tab);
- for (t = 0; t < band->num_tiles; t++)
- av_freep(&band->tiles[t].mbs);
- av_freep(&band->tiles);
- }
- }
- av_freep(&planes[p].bands);
- planes[p].num_bands = 0;
- }
-}
-
-av_cold int ff_ivi_init_planes(AVCodecContext *avctx, IVIPlaneDesc *planes, const IVIPicConfig *cfg,
- int is_indeo4)
-{
- int p, b;
- uint32_t b_width, b_height, align_fac, width_aligned,
- height_aligned, buf_size;
- IVIBandDesc *band;
-
- ivi_free_buffers(planes);
-
- if (av_image_check_size2(cfg->pic_width, cfg->pic_height, avctx->max_pixels, AV_PIX_FMT_YUV410P, 0, avctx) < 0 ||
- cfg->luma_bands < 1 || cfg->chroma_bands < 1)
- return AVERROR_INVALIDDATA;
-
- /* fill in the descriptor of the luminance plane */
- planes[0].width = cfg->pic_width;
- planes[0].height = cfg->pic_height;
- planes[0].num_bands = cfg->luma_bands;
-
- /* fill in the descriptors of the chrominance planes */
- planes[1].width = planes[2].width = (cfg->pic_width + 3) >> 2;
- planes[1].height = planes[2].height = (cfg->pic_height + 3) >> 2;
- planes[1].num_bands = planes[2].num_bands = cfg->chroma_bands;
-
- for (p = 0; p < 3; p++) {
- planes[p].bands = av_calloc(planes[p].num_bands, sizeof(*planes[p].bands));
- if (!planes[p].bands)
- return AVERROR(ENOMEM);
-
- /* select band dimensions: if there is only one band then it
- * has the full size, if there are several bands each of them
- * has only half size */
- b_width = planes[p].num_bands == 1 ? planes[p].width
- : (planes[p].width + 1) >> 1;
- b_height = planes[p].num_bands == 1 ? planes[p].height
- : (planes[p].height + 1) >> 1;
-
- /* luma band buffers will be aligned on 16x16 (max macroblock size) */
- /* chroma band buffers will be aligned on 8x8 (max macroblock size) */
- align_fac = p ? 8 : 16;
- width_aligned = FFALIGN(b_width , align_fac);
- height_aligned = FFALIGN(b_height, align_fac);
- buf_size = width_aligned * height_aligned * sizeof(int16_t);
-
- for (b = 0; b < planes[p].num_bands; b++) {
- band = &planes[p].bands[b]; /* select appropriate plane/band */
- band->plane = p;
- band->band_num = b;
- band->width = b_width;
- band->height = b_height;
- band->pitch = width_aligned;
- band->aheight = height_aligned;
- av_assert0(!band->bufs[0] && !band->bufs[1] &&
- !band->bufs[2] && !band->bufs[3]);
- band->bufsize = buf_size/2;
- av_assert0(buf_size % 2 == 0);
-
- /* reset custom vlc */
- planes[p].bands[0].blk_vlc.cust_desc.num_rows = 0;
- }
- }
-
- return 0;
-}
-
-static int ivi_init_tiles(const IVIBandDesc *band, IVITile *ref_tile,
- int p, int b, int t_height, int t_width)
-{
- int x, y;
- IVITile *tile = band->tiles;
-
- for (y = 0; y < band->height; y += t_height) {
- for (x = 0; x < band->width; x += t_width) {
- tile->xpos = x;
- tile->ypos = y;
- tile->mb_size = band->mb_size;
- tile->width = FFMIN(band->width - x, t_width);
- tile->height = FFMIN(band->height - y, t_height);
- tile->is_empty = tile->data_size = 0;
- /* calculate number of macroblocks */
- tile->num_MBs = IVI_MBs_PER_TILE(tile->width, tile->height,
- band->mb_size);
-
- av_freep(&tile->mbs);
- tile->mbs = av_calloc(tile->num_MBs, sizeof(*tile->mbs));
- if (!tile->mbs)
- return AVERROR(ENOMEM);
-
- tile->ref_mbs = 0;
- if (p || b) {
- if (tile->num_MBs != ref_tile->num_MBs) {
- av_log(NULL, AV_LOG_DEBUG, "ref_tile mismatch\n");
- return AVERROR_INVALIDDATA;
- }
- tile->ref_mbs = ref_tile->mbs;
- ref_tile++;
- }
- tile++;
- }
- }
-
- return 0;
-}
-
-av_cold int ff_ivi_init_tiles(IVIPlaneDesc *planes,
- int tile_width, int tile_height)
-{
- int p, b, x_tiles, y_tiles, t_width, t_height, ret;
- IVIBandDesc *band;
-
- for (p = 0; p < 3; p++) {
- t_width = !p ? tile_width : (tile_width + 3) >> 2;
- t_height = !p ? tile_height : (tile_height + 3) >> 2;
-
- if (!p && planes[0].num_bands == 4) {
- if (t_width % 2 || t_height % 2) {
- avpriv_request_sample(NULL, "Odd tiles");
- return AVERROR_PATCHWELCOME;
- }
- t_width >>= 1;
- t_height >>= 1;
- }
- if(t_width<=0 || t_height<=0)
- return AVERROR(EINVAL);
-
- for (b = 0; b < planes[p].num_bands; b++) {
- band = &planes[p].bands[b];
-
- if (band->tiles) {
- int t;
- for (t = 0; t < band->num_tiles; t++) {
- av_freep(&band->tiles[t].mbs);
- }
- }
-
- x_tiles = IVI_NUM_TILES(band->width, t_width);
- y_tiles = IVI_NUM_TILES(band->height, t_height);
- band->num_tiles = x_tiles * y_tiles;
-
- av_freep(&band->tiles);
- band->tiles = av_calloc(band->num_tiles, sizeof(*band->tiles));
- if (!band->tiles) {
- band->num_tiles = 0;
- return AVERROR(ENOMEM);
- }
-
- /* use the first luma band as reference for motion vectors
- * and quant */
- ret = ivi_init_tiles(band, planes[0].bands[0].tiles,
- p, b, t_height, t_width);
- if (ret < 0)
- return ret;
- }
- }
-
- return 0;
-}
-
-/*
- * Decode size of the tile data.
- * The size is stored as a variable-length field having the following format:
- * if (tile_data_size < 255) than this field is only one byte long
- * if (tile_data_size >= 255) than this field four is byte long: 0xFF X1 X2 X3
- * where X1-X3 is size of the tile data
- *
- * @param[in,out] gb the GetBit context
- * @return size of the tile data in bytes
- */
-static int ivi_dec_tile_data_size(GetBitContext *gb)
-{
- int len;
-
- len = 0;
- if (get_bits1(gb)) {
- len = get_bits(gb, 8);
- if (len == 255)
- len = get_bits(gb, 24);
- }
-
- /* align the bitstream reader on the byte boundary */
- align_get_bits(gb);
-
- return len;
-}
-
-static int ivi_dc_transform(const IVIBandDesc *band, int *prev_dc, int buf_offs,
- int blk_size)
-{
- band->dc_transform(prev_dc, band->buf + buf_offs,
- band->pitch, blk_size);
-
- return 0;
-}
-
-static int ivi_decode_coded_blocks(GetBitContext *gb, const IVIBandDesc *band,
- ivi_mc_func mc, ivi_mc_avg_func mc_avg,
- int mv_x, int mv_y,
- int mv_x2, int mv_y2,
- int *prev_dc, int is_intra,
- int mc_type, int mc_type2,
- uint32_t quant, int offs,
- AVCodecContext *avctx)
-{
- const uint16_t *base_tab = is_intra ? band->intra_base : band->inter_base;
- RVMapDesc *rvmap = band->rv_map;
- uint8_t col_flags[8];
- int32_t trvec[64];
- uint32_t sym = 0, lo, hi, q;
- int pos, run, val;
- int blk_size = band->blk_size;
- int num_coeffs = blk_size * blk_size;
- int col_mask = blk_size - 1;
- int scan_pos = -1;
- int min_size = band->pitch * (band->transform_size - 1) +
- band->transform_size;
- int buf_size = band->pitch * band->aheight - offs;
-
- if (min_size > buf_size)
- return AVERROR_INVALIDDATA;
-
- if (!band->scan) {
- av_log(avctx, AV_LOG_ERROR, "Scan pattern is not set.\n");
- return AVERROR_INVALIDDATA;
- }
-
- /* zero transform vector */
- memset(trvec, 0, num_coeffs * sizeof(trvec[0]));
- /* zero column flags */
- memset(col_flags, 0, sizeof(col_flags));
- while (scan_pos <= num_coeffs) {
- sym = get_vlc2(gb, band->blk_vlc.tab->table,
- IVI_VLC_BITS, 1);
- if (sym == rvmap->eob_sym)
- break; /* End of block */
-
- /* Escape - run/val explicitly coded using 3 vlc codes */
- if (sym == rvmap->esc_sym) {
- run = get_vlc2(gb, band->blk_vlc.tab->table, IVI_VLC_BITS, 1) + 1;
- lo = get_vlc2(gb, band->blk_vlc.tab->table, IVI_VLC_BITS, 1);
- hi = get_vlc2(gb, band->blk_vlc.tab->table, IVI_VLC_BITS, 1);
- /* merge them and convert into signed val */
- val = IVI_TOSIGNED((hi << 6) | lo);
- } else {
- if (sym >= 256U) {
- av_log(avctx, AV_LOG_ERROR, "Invalid sym encountered: %"PRIu32".\n", sym);
- return AVERROR_INVALIDDATA;
- }
- run = rvmap->runtab[sym];
- val = rvmap->valtab[sym];
- }
-
- /* de-zigzag and dequantize */
- scan_pos += run;
- if (scan_pos >= num_coeffs || scan_pos < 0)
- break;
- pos = band->scan[scan_pos];
-
- if (!val)
- ff_dlog(avctx, "Val = 0 encountered!\n");
-
- q = (base_tab[pos] * quant) >> 9;
- if (q > 1)
- val = val * q + FFSIGN(val) * (((q ^ 1) - 1) >> 1);
- trvec[pos] = val;
- /* track columns containing non-zero coeffs */
- col_flags[pos & col_mask] |= !!val;
- }
-
- if (scan_pos < 0 || scan_pos >= num_coeffs && sym != rvmap->eob_sym)
- return AVERROR_INVALIDDATA; /* corrupt block data */
-
- /* undoing DC coeff prediction for intra-blocks */
- if (is_intra && band->is_2d_trans) {
- *prev_dc += trvec[0];
- trvec[0] = *prev_dc;
- col_flags[0] |= !!*prev_dc;
- }
-
- if(band->transform_size > band->blk_size){
- av_log(NULL, AV_LOG_ERROR, "Too large transform\n");
- return AVERROR_INVALIDDATA;
- }
-
- /* apply inverse transform */
- band->inv_transform(trvec, band->buf + offs,
- band->pitch, col_flags);
-
- /* apply motion compensation */
- if (!is_intra)
- return ivi_mc(band, mc, mc_avg, offs, mv_x, mv_y, mv_x2, mv_y2,
- mc_type, mc_type2);
-
- return 0;
-}
-/*
- * Decode block data:
- * extract huffman-coded transform coefficients from the bitstream,
- * dequantize them, apply inverse transform and motion compensation
- * in order to reconstruct the picture.
- *
- * @param[in,out] gb the GetBit context
- * @param[in] band pointer to the band descriptor
- * @param[in] tile pointer to the tile descriptor
- * @return result code: 0 - OK, -1 = error (corrupted blocks data)
- */
-static int ivi_decode_blocks(GetBitContext *gb, const IVIBandDesc *band,
- IVITile *tile, AVCodecContext *avctx)
-{
- int mbn, blk, num_blocks, blk_size, ret, is_intra;
- int mc_type = 0, mc_type2 = -1;
- int mv_x = 0, mv_y = 0, mv_x2 = 0, mv_y2 = 0;
- int32_t prev_dc;
- uint32_t cbp, quant, buf_offs;
- IVIMbInfo *mb;
- ivi_mc_func mc_with_delta_func, mc_no_delta_func;
- ivi_mc_avg_func mc_avg_with_delta_func, mc_avg_no_delta_func;
- const uint8_t *scale_tab;
-
- /* init intra prediction for the DC coefficient */
- prev_dc = 0;
- blk_size = band->blk_size;
- /* number of blocks per mb */
- num_blocks = (band->mb_size != blk_size) ? 4 : 1;
- if (blk_size == 8) {
- mc_with_delta_func = ff_ivi_mc_8x8_delta;
- mc_no_delta_func = ff_ivi_mc_8x8_no_delta;
- mc_avg_with_delta_func = ff_ivi_mc_avg_8x8_delta;
- mc_avg_no_delta_func = ff_ivi_mc_avg_8x8_no_delta;
- } else {
- mc_with_delta_func = ff_ivi_mc_4x4_delta;
- mc_no_delta_func = ff_ivi_mc_4x4_no_delta;
- mc_avg_with_delta_func = ff_ivi_mc_avg_4x4_delta;
- mc_avg_no_delta_func = ff_ivi_mc_avg_4x4_no_delta;
- }
-
- for (mbn = 0, mb = tile->mbs; mbn < tile->num_MBs; mb++, mbn++) {
- is_intra = !mb->type;
- cbp = mb->cbp;
- buf_offs = mb->buf_offs;
-
- quant = band->glob_quant + mb->q_delta;
- if (avctx->codec_id == AV_CODEC_ID_INDEO4)
- quant = av_clip_uintp2(quant, 5);
- else
- quant = av_clip(quant, 0, 23);
-
- scale_tab = is_intra ? band->intra_scale : band->inter_scale;
- if (scale_tab)
- quant = scale_tab[quant];
-
- if (!is_intra) {
- mv_x = mb->mv_x;
- mv_y = mb->mv_y;
- mv_x2 = mb->b_mv_x;
- mv_y2 = mb->b_mv_y;
- if (band->is_halfpel) {
- mc_type = ((mv_y & 1) << 1) | (mv_x & 1);
- mc_type2 = ((mv_y2 & 1) << 1) | (mv_x2 & 1);
- mv_x >>= 1;
- mv_y >>= 1;
- mv_x2 >>= 1;
- mv_y2 >>= 1; /* convert halfpel vectors into fullpel ones */
- }
- if (mb->type == 2)
- mc_type = -1;
- if (mb->type != 2 && mb->type != 3)
- mc_type2 = -1;
- if (mb->type) {
- int dmv_x, dmv_y, cx, cy;
-
- dmv_x = mb->mv_x >> band->is_halfpel;
- dmv_y = mb->mv_y >> band->is_halfpel;
- cx = mb->mv_x & band->is_halfpel;
- cy = mb->mv_y & band->is_halfpel;
-
- if (mb->xpos + dmv_x < 0 ||
- mb->xpos + dmv_x + band->mb_size + cx > band->pitch ||
- mb->ypos + dmv_y < 0 ||
- mb->ypos + dmv_y + band->mb_size + cy > band->aheight) {
- return AVERROR_INVALIDDATA;
- }
- }
- if (mb->type == 2 || mb->type == 3) {
- int dmv_x, dmv_y, cx, cy;
-
- dmv_x = mb->b_mv_x >> band->is_halfpel;
- dmv_y = mb->b_mv_y >> band->is_halfpel;
- cx = mb->b_mv_x & band->is_halfpel;
- cy = mb->b_mv_y & band->is_halfpel;
-
- if (mb->xpos + dmv_x < 0 ||
- mb->xpos + dmv_x + band->mb_size + cx > band->pitch ||
- mb->ypos + dmv_y < 0 ||
- mb->ypos + dmv_y + band->mb_size + cy > band->aheight) {
- return AVERROR_INVALIDDATA;
- }
- }
- }
-
- for (blk = 0; blk < num_blocks; blk++) {
- /* adjust block position in the buffer according to its number */
- if (blk & 1) {
- buf_offs += blk_size;
- } else if (blk == 2) {
- buf_offs -= blk_size;
- buf_offs += blk_size * band->pitch;
- }
-
- if (cbp & 1) { /* block coded ? */
- ret = ivi_decode_coded_blocks(gb, band, mc_with_delta_func,
- mc_avg_with_delta_func,
- mv_x, mv_y, mv_x2, mv_y2,
- &prev_dc, is_intra,
- mc_type, mc_type2, quant,
- buf_offs, avctx);
- if (ret < 0)
- return ret;
- } else {
- int buf_size = band->pitch * band->aheight - buf_offs;
- int min_size = (blk_size - 1) * band->pitch + blk_size;
-
- if (min_size > buf_size)
- return AVERROR_INVALIDDATA;
- /* block not coded */
- /* for intra blocks apply the dc slant transform */
- /* for inter - perform the motion compensation without delta */
- if (is_intra) {
- ret = ivi_dc_transform(band, &prev_dc, buf_offs, blk_size);
- if (ret < 0)
- return ret;
- } else {
- ret = ivi_mc(band, mc_no_delta_func, mc_avg_no_delta_func,
- buf_offs, mv_x, mv_y, mv_x2, mv_y2,
- mc_type, mc_type2);
- if (ret < 0)
- return ret;
- }
- }
-
- cbp >>= 1;
- }// for blk
- }// for mbn
-
- align_get_bits(gb);
-
- return 0;
-}
-
-/**
- * Handle empty tiles by performing data copying and motion
- * compensation respectively.
- *
- * @param[in] avctx ptr to the AVCodecContext
- * @param[in] band pointer to the band descriptor
- * @param[in] tile pointer to the tile descriptor
- * @param[in] mv_scale scaling factor for motion vectors
- */
-static int ivi_process_empty_tile(AVCodecContext *avctx, const IVIBandDesc *band,
- IVITile *tile, int32_t mv_scale)
-{
- int x, y, need_mc, mbn, blk, num_blocks, mv_x, mv_y, mc_type;
- int offs, mb_offset, row_offset, ret;
- IVIMbInfo *mb, *ref_mb;
- const int16_t *src;
- int16_t *dst;
- ivi_mc_func mc_no_delta_func;
- int clear_first = !band->qdelta_present && !band->plane && !band->band_num;
- int mb_size = band->mb_size;
- int xend = tile->xpos + tile->width;
- int is_halfpel = band->is_halfpel;
- int pitch = band->pitch;
-
- if (tile->num_MBs != IVI_MBs_PER_TILE(tile->width, tile->height, mb_size)) {
- av_log(avctx, AV_LOG_ERROR, "Allocated tile size %d mismatches "
- "parameters %d in ivi_process_empty_tile()\n",
- tile->num_MBs, IVI_MBs_PER_TILE(tile->width, tile->height, mb_size));
- return AVERROR_INVALIDDATA;
- }
-
- offs = tile->ypos * pitch + tile->xpos;
- mb = tile->mbs;
- ref_mb = tile->ref_mbs;
- row_offset = mb_size * pitch;
- need_mc = 0; /* reset the mc tracking flag */
-
- for (y = tile->ypos; y < (tile->ypos + tile->height); y += mb_size) {
- mb_offset = offs;
-
- for (x = tile->xpos; x < xend; x += mb_size) {
- mb->xpos = x;
- mb->ypos = y;
- mb->buf_offs = mb_offset;
-
- mb->type = 1; /* set the macroblocks type = INTER */
- mb->cbp = 0; /* all blocks are empty */
-
- if (clear_first) {
- mb->q_delta = band->glob_quant;
- mb->mv_x = 0;
- mb->mv_y = 0;
- }
-
- if (ref_mb) {
- if (band->inherit_qdelta)
- mb->q_delta = ref_mb->q_delta;
-
- if (band->inherit_mv) {
- /* motion vector inheritance */
- if (mv_scale) {
- mb->mv_x = ivi_scale_mv(ref_mb->mv_x, mv_scale);
- mb->mv_y = ivi_scale_mv(ref_mb->mv_y, mv_scale);
- } else {
- mb->mv_x = ref_mb->mv_x;
- mb->mv_y = ref_mb->mv_y;
- }
- need_mc |= mb->mv_x || mb->mv_y; /* tracking non-zero motion vectors */
- {
- int dmv_x, dmv_y, cx, cy;
-
- dmv_x = mb->mv_x >> is_halfpel;
- dmv_y = mb->mv_y >> is_halfpel;
- cx = mb->mv_x & is_halfpel;
- cy = mb->mv_y & is_halfpel;
-
- if ( mb->xpos + dmv_x < 0
- || mb->xpos + dmv_x + mb_size + cx > pitch
- || mb->ypos + dmv_y < 0
- || mb->ypos + dmv_y + mb_size + cy > band->aheight) {
- av_log(avctx, AV_LOG_ERROR, "MV out of bounds\n");
- return AVERROR_INVALIDDATA;
- }
- }
- }
- ref_mb++;
- }
-
- mb++;
- mb_offset += mb_size;
- } // for x
- offs += row_offset;
- } // for y
-
- if (band->inherit_mv && need_mc) { /* apply motion compensation if there is at least one non-zero motion vector */
- num_blocks = (mb_size != band->blk_size) ? 4 : 1; /* number of blocks per mb */
- mc_no_delta_func = (band->blk_size == 8) ? ff_ivi_mc_8x8_no_delta
- : ff_ivi_mc_4x4_no_delta;
-
- for (mbn = 0, mb = tile->mbs; mbn < tile->num_MBs; mb++, mbn++) {
- mv_x = mb->mv_x;
- mv_y = mb->mv_y;
- if (!band->is_halfpel) {
- mc_type = 0; /* we have only fullpel vectors */
- } else {
- mc_type = ((mv_y & 1) << 1) | (mv_x & 1);
- mv_x >>= 1;
- mv_y >>= 1; /* convert halfpel vectors into fullpel ones */
- }
-
- for (blk = 0; blk < num_blocks; blk++) {
- /* adjust block position in the buffer according with its number */
- offs = mb->buf_offs + band->blk_size * ((blk & 1) + !!(blk & 2) * pitch);
- ret = ivi_mc(band, mc_no_delta_func, 0, offs,
- mv_x, mv_y, 0, 0, mc_type, -1);
- if (ret < 0)
- return ret;
- }
- }
- } else {
- /* copy data from the reference tile into the current one */
- src = band->ref_buf + tile->ypos * pitch + tile->xpos;
- dst = band->buf + tile->ypos * pitch + tile->xpos;
- for (y = 0; y < tile->height; y++) {
- memcpy(dst, src, tile->width*sizeof(band->buf[0]));
- src += pitch;
- dst += pitch;
- }
- }
-
- return 0;
-}
-
-
-#ifdef DEBUG
-static uint16_t ivi_calc_band_checksum(const IVIBandDesc *band)
-{
- int x, y;
- int16_t *src, checksum;
-
- src = band->buf;
- checksum = 0;
-
- for (y = 0; y < band->height; src += band->pitch, y++)
- for (x = 0; x < band->width; x++)
- checksum += src[x];
-
- return checksum;
-}
-#endif
-
-/*
- * Convert and output the current plane.
- * This conversion is done by adding back the bias value of 128
- * (subtracted in the encoder) and clipping the result.
- *
- * @param[in] plane pointer to the descriptor of the plane being processed
- * @param[out] dst pointer to the buffer receiving converted pixels
- * @param[in] dst_pitch pitch for moving to the next y line
- */
-static void ivi_output_plane(IVIPlaneDesc *plane, uint8_t *dst, ptrdiff_t dst_pitch)
-{
- int x, y;
- const int16_t *src = plane->bands[0].buf;
- ptrdiff_t pitch = plane->bands[0].pitch;
-
- if (!src)
- return;
-
- for (y = 0; y < plane->height; y++) {
- int m = 0;
- int w = plane->width;
- for (x = 0; x < w; x++) {
- int t = src[x] + 128;
- dst[x] = t;
- m |= t;
- }
- if (m & ~255)
- for (x = 0; x < w; x++)
- dst[x] = av_clip_uint8(src[x] + 128);
- src += pitch;
- dst += dst_pitch;
- }
-}
-
-static void *prepare_buf(IVI45DecContext *ctx, IVIBandDesc *band, int i)
-{
- if (ctx->pic_conf.luma_bands <= 1 && i == 2)
- return NULL;
- if (!band->bufs[i])
- band->bufs[i] = av_mallocz(2 * band->bufsize);
- return band->bufs[i];
-}
-
-/**
- * Decode an Indeo 4 or 5 band.
- *
- * @param[in,out] ctx ptr to the decoder context
- * @param[in,out] band ptr to the band descriptor
- * @param[in] avctx ptr to the AVCodecContext
- * @return result code: 0 = OK, -1 = error
- */
-static int decode_band(IVI45DecContext *ctx,
- IVIBandDesc *band, AVCodecContext *avctx)
-{
- int result, i, t, idx1, idx2, pos;
- IVITile *tile;
-
- band->buf = prepare_buf(ctx, band, ctx->dst_buf);
- if (!band->buf) {
- av_log(avctx, AV_LOG_ERROR, "Band buffer points to no data!\n");
- return AVERROR_INVALIDDATA;
- }
- if (ctx->is_indeo4 && ctx->frame_type == IVI4_FRAMETYPE_BIDIR) {
- band->ref_buf = prepare_buf(ctx, band, ctx->b_ref_buf);
- band->b_ref_buf = prepare_buf(ctx, band, ctx->ref_buf);
- if (!band->b_ref_buf)
- return AVERROR(ENOMEM);
- } else {
- band->ref_buf = prepare_buf(ctx, band, ctx->ref_buf);
- band->b_ref_buf = 0;
- }
- if (!band->ref_buf)
- return AVERROR(ENOMEM);
- band->data_ptr = ctx->frame_data + (get_bits_count(&ctx->gb) >> 3);
-
- result = ctx->decode_band_hdr(ctx, band, avctx);
- if (result) {
- av_log(avctx, AV_LOG_ERROR, "Error while decoding band header: %d\n",
- result);
- return result;
- }
-
- if (band->is_empty) {
- av_log(avctx, AV_LOG_ERROR, "Empty band encountered!\n");
- return AVERROR_INVALIDDATA;
- }
-
- band->rv_map = &ctx->rvmap_tabs[band->rvmap_sel];
-
- /* apply corrections to the selected rvmap table if present */
- for (i = 0; i < band->num_corr; i++) {
- idx1 = band->corr[i * 2];
- idx2 = band->corr[i * 2 + 1];
- FFSWAP(uint8_t, band->rv_map->runtab[idx1], band->rv_map->runtab[idx2]);
- FFSWAP(int16_t, band->rv_map->valtab[idx1], band->rv_map->valtab[idx2]);
- if (idx1 == band->rv_map->eob_sym || idx2 == band->rv_map->eob_sym)
- band->rv_map->eob_sym ^= idx1 ^ idx2;
- if (idx1 == band->rv_map->esc_sym || idx2 == band->rv_map->esc_sym)
- band->rv_map->esc_sym ^= idx1 ^ idx2;
- }
-
- pos = get_bits_count(&ctx->gb);
-
- for (t = 0; t < band->num_tiles; t++) {
- tile = &band->tiles[t];
-
- if (tile->mb_size != band->mb_size) {
- av_log(avctx, AV_LOG_ERROR, "MB sizes mismatch: %d vs. %d\n",
- band->mb_size, tile->mb_size);
- return AVERROR_INVALIDDATA;
- }
- tile->is_empty = get_bits1(&ctx->gb);
- if (tile->is_empty) {
- result = ivi_process_empty_tile(avctx, band, tile,
- (ctx->planes[0].bands[0].mb_size >> 3) - (band->mb_size >> 3));
- if (result < 0)
- break;
- ff_dlog(avctx, "Empty tile encountered!\n");
- } else {
- tile->data_size = ivi_dec_tile_data_size(&ctx->gb);
- if (!tile->data_size) {
- av_log(avctx, AV_LOG_ERROR, "Tile data size is zero!\n");
- result = AVERROR_INVALIDDATA;
- break;
- }
-
- result = ctx->decode_mb_info(ctx, band, tile, avctx);
- if (result < 0)
- break;
-
- result = ivi_decode_blocks(&ctx->gb, band, tile, avctx);
- if (result < 0) {
- av_log(avctx, AV_LOG_ERROR,
- "Corrupted tile data encountered!\n");
- break;
- }
-
- if (((get_bits_count(&ctx->gb) - pos) >> 3) != tile->data_size) {
- av_log(avctx, AV_LOG_ERROR,
- "Tile data_size mismatch!\n");
- result = AVERROR_INVALIDDATA;
- break;
- }
-
- pos += tile->data_size << 3; // skip to next tile
- }
- }
-
- /* restore the selected rvmap table by applying its corrections in
- * reverse order */
- for (i = band->num_corr-1; i >= 0; i--) {
- idx1 = band->corr[i*2];
- idx2 = band->corr[i*2+1];
- FFSWAP(uint8_t, band->rv_map->runtab[idx1], band->rv_map->runtab[idx2]);
- FFSWAP(int16_t, band->rv_map->valtab[idx1], band->rv_map->valtab[idx2]);
- if (idx1 == band->rv_map->eob_sym || idx2 == band->rv_map->eob_sym)
- band->rv_map->eob_sym ^= idx1 ^ idx2;
- if (idx1 == band->rv_map->esc_sym || idx2 == band->rv_map->esc_sym)
- band->rv_map->esc_sym ^= idx1 ^ idx2;
- }
-
-#ifdef DEBUG
- if (band->checksum_present) {
- uint16_t chksum = ivi_calc_band_checksum(band);
- if (chksum != band->checksum) {
- av_log(avctx, AV_LOG_ERROR,
- "Band checksum mismatch! Plane %d, band %d, "
- "received: %"PRIx32", calculated: %"PRIx16"\n",
- band->plane, band->band_num, band->checksum, chksum);
- }
- }
-#endif
-
- align_get_bits(&ctx->gb);
-
- return result;
-}
-
-int ff_ivi_decode_frame(AVCodecContext *avctx, AVFrame *frame,
- int *got_frame, AVPacket *avpkt)
-{
- IVI45DecContext *ctx = avctx->priv_data;
- const uint8_t *buf = avpkt->data;
- int buf_size = avpkt->size;
- int result, p, b;
-
- result = init_get_bits8(&ctx->gb, buf, buf_size);
- if (result < 0)
- return result;
- ctx->frame_data = buf;
- ctx->frame_size = buf_size;
-
- result = ctx->decode_pic_hdr(ctx, avctx);
- if (result) {
- av_log(avctx, AV_LOG_ERROR,
- "Error while decoding picture header: %d\n", result);
- return result;
- }
- if (ctx->gop_invalid)
- return AVERROR_INVALIDDATA;
-
- if (ctx->is_indeo4 && ctx->frame_type == IVI4_FRAMETYPE_NULL_LAST) {
- if (ctx->got_p_frame) {
- av_frame_move_ref(frame, ctx->p_frame);
- *got_frame = 1;
- ctx->got_p_frame = 0;
- } else {
- *got_frame = 0;
- }
- return buf_size;
- }
-
- if (ctx->gop_flags & IVI5_IS_PROTECTED) {
- avpriv_report_missing_feature(avctx, "Password-protected clip");
- return AVERROR_PATCHWELCOME;
- }
-
- if (!ctx->planes[0].bands) {
- av_log(avctx, AV_LOG_ERROR, "Color planes not initialized yet\n");
- return AVERROR_INVALIDDATA;
- }
-
- ctx->switch_buffers(ctx);
-
- if (ctx->is_nonnull_frame(ctx)) {
- ctx->buf_invalid[ctx->dst_buf] = 1;
- for (p = 0; p < 3; p++) {
- for (b = 0; b < ctx->planes[p].num_bands; b++) {
- result = decode_band(ctx, &ctx->planes[p].bands[b], avctx);
- if (result < 0) {
- av_log(avctx, AV_LOG_ERROR,
- "Error while decoding band: %d, plane: %d\n", b, p);
- return result;
- }
- }
- }
- ctx->buf_invalid[ctx->dst_buf] = 0;
- } else {
- if (ctx->is_scalable)
- return AVERROR_INVALIDDATA;
-
- for (p = 0; p < 3; p++) {
- if (!ctx->planes[p].bands[0].buf)
- return AVERROR_INVALIDDATA;
- }
- }
- if (ctx->buf_invalid[ctx->dst_buf])
- return -1;
-
- if (!ctx->is_nonnull_frame(ctx))
- return buf_size;
-
- result = ff_set_dimensions(avctx, ctx->planes[0].width, ctx->planes[0].height);
- if (result < 0)
- return result;
-
- if ((result = ff_get_buffer(avctx, frame, 0)) < 0)
- return result;
-
- if (ctx->is_scalable) {
- if (ctx->is_indeo4)
- ff_ivi_recompose_haar(&ctx->planes[0], frame->data[0], frame->linesize[0]);
- else
- ff_ivi_recompose53 (&ctx->planes[0], frame->data[0], frame->linesize[0]);
- } else {
- ivi_output_plane(&ctx->planes[0], frame->data[0], frame->linesize[0]);
- }
-
- ivi_output_plane(&ctx->planes[2], frame->data[1], frame->linesize[1]);
- ivi_output_plane(&ctx->planes[1], frame->data[2], frame->linesize[2]);
-
- *got_frame = 1;
-
- /* If the bidirectional mode is enabled, next I and the following P
- * frame will be sent together. Unfortunately the approach below seems
- * to be the only way to handle the B-frames mode.
- * That's exactly the same Intel decoders do.
- */
- if (ctx->is_indeo4 && ctx->frame_type == IVI4_FRAMETYPE_INTRA) {
- int left;
-
- // skip version string
- while (get_bits(&ctx->gb, 8)) {
- if (get_bits_left(&ctx->gb) < 8)
- return AVERROR_INVALIDDATA;
- }
- left = get_bits_count(&ctx->gb) & 0x18;
- skip_bits_long(&ctx->gb, 64 - left);
- if (get_bits_left(&ctx->gb) > 18 &&
- show_bits(&ctx->gb, 21) == 0xBFFF8) { // syncheader + inter type
- AVPacket pkt;
- pkt.data = avpkt->data + (get_bits_count(&ctx->gb) >> 3);
- pkt.size = get_bits_left(&ctx->gb) >> 3;
- ctx->got_p_frame = 0;
- av_frame_unref(ctx->p_frame);
- ff_ivi_decode_frame(avctx, ctx->p_frame, &ctx->got_p_frame, &pkt);
- }
- }
-
- if (ctx->show_indeo4_info) {
- if (ctx->is_scalable)
- av_log(avctx, AV_LOG_DEBUG, "This video uses scalability mode\n");
- if (ctx->uses_tiling)
- av_log(avctx, AV_LOG_DEBUG, "This video uses local decoding\n");
- if (ctx->has_b_frames)
- av_log(avctx, AV_LOG_DEBUG, "This video contains B-frames\n");
- if (ctx->has_transp)
- av_log(avctx, AV_LOG_DEBUG, "Transparency mode is enabled\n");
- if (ctx->uses_haar)
- av_log(avctx, AV_LOG_DEBUG, "This video uses Haar transform\n");
- if (ctx->uses_fullpel)
- av_log(avctx, AV_LOG_DEBUG, "This video uses fullpel motion vectors\n");
- ctx->show_indeo4_info = 0;
- }
-
- return buf_size;
-}
-
-/**
- * Close Indeo5 decoder and clean up its context.
- */
-av_cold int ff_ivi_decode_close(AVCodecContext *avctx)
-{
- IVI45DecContext *ctx = avctx->priv_data;
-
- ivi_free_buffers(&ctx->planes[0]);
-
- if (ctx->mb_vlc.cust_tab.table)
- ff_free_vlc(&ctx->mb_vlc.cust_tab);
-
- if (ctx->blk_vlc.cust_tab.table)
- ff_free_vlc(&ctx->blk_vlc.cust_tab);
-
- av_frame_free(&ctx->p_frame);
-
- return 0;
-}
-
-
-/**
- * Scan patterns shared between indeo4 and indeo5
- */
-const uint8_t ff_ivi_vertical_scan_8x8[64] = {
- 0, 8, 16, 24, 32, 40, 48, 56,
- 1, 9, 17, 25, 33, 41, 49, 57,
- 2, 10, 18, 26, 34, 42, 50, 58,
- 3, 11, 19, 27, 35, 43, 51, 59,
- 4, 12, 20, 28, 36, 44, 52, 60,
- 5, 13, 21, 29, 37, 45, 53, 61,
- 6, 14, 22, 30, 38, 46, 54, 62,
- 7, 15, 23, 31, 39, 47, 55, 63
-};
-
-const uint8_t ff_ivi_horizontal_scan_8x8[64] = {
- 0, 1, 2, 3, 4, 5, 6, 7,
- 8, 9, 10, 11, 12, 13, 14, 15,
- 16, 17, 18, 19, 20, 21, 22, 23,
- 24, 25, 26, 27, 28, 29, 30, 31,
- 32, 33, 34, 35, 36, 37, 38, 39,
- 40, 41, 42, 43, 44, 45, 46, 47,
- 48, 49, 50, 51, 52, 53, 54, 55,
- 56, 57, 58, 59, 60, 61, 62, 63
-};
-
-const uint8_t ff_ivi_direct_scan_4x4[16] = {
- 0, 1, 4, 8, 5, 2, 3, 6, 9, 12, 13, 10, 7, 11, 14, 15
-};
-
-
-/**
- * Run-value (RLE) tables.
- */
-const RVMapDesc ff_ivi_rvmap_tabs[9] = {
-{ /* MapTab0 */
- 5, /* eob_sym */
- 2, /* esc_sym */
- /* run table */
- {1, 1, 0, 1, 1, 0, 1, 1, 2, 2, 1, 1, 1, 1, 3, 3,
- 1, 1, 2, 2, 1, 1, 4, 4, 1, 1, 1, 1, 2, 2, 5, 5,
- 1, 1, 3, 3, 1, 1, 6, 6, 1, 2, 1, 2, 7, 7, 1, 1,
- 8, 8, 1, 1, 4, 2, 1, 4, 2, 1, 3, 3, 1, 1, 1, 9,
- 9, 1, 2, 1, 2, 1, 5, 5, 1, 1, 10, 10, 1, 1, 3, 3,
- 2, 2, 1, 1, 11, 11, 6, 4, 4, 1, 6, 1, 2, 1, 2, 12,
- 8, 1, 12, 7, 8, 7, 1, 16, 1, 16, 1, 3, 3, 13, 1, 13,
- 2, 2, 1, 15, 1, 5, 14, 15, 1, 5, 14, 1, 17, 8, 17, 8,
- 1, 4, 4, 2, 2, 1, 25, 25, 24, 24, 1, 3, 1, 3, 1, 8,
- 6, 7, 6, 1, 18, 8, 18, 1, 7, 23, 2, 2, 23, 1, 1, 21,
- 22, 9, 9, 22, 19, 1, 21, 5, 19, 5, 1, 33, 20, 33, 20, 8,
- 4, 4, 1, 32, 2, 2, 8, 3, 32, 26, 3, 1, 7, 7, 26, 6,
- 1, 6, 1, 1, 16, 1, 10, 1, 10, 2, 16, 29, 28, 2, 29, 28,
- 1, 27, 5, 8, 5, 27, 1, 8, 3, 7, 3, 31, 41, 31, 1, 41,
- 6, 1, 6, 7, 4, 4, 1, 1, 2, 1, 2, 11, 34, 30, 11, 1,
- 30, 15, 15, 34, 36, 40, 36, 40, 35, 35, 37, 37, 39, 39, 38, 38},
-
- /* value table */
- { 1, -1, 0, 2, -2, 0, 3, -3, 1, -1, 4, -4, 5, -5, 1, -1,
- 6, -6, 2, -2, 7, -7, 1, -1, 8, -8, 9, -9, 3, -3, 1, -1,
- 10, -10, 2, -2, 11, -11, 1, -1, 12, 4, -12, -4, 1, -1, 13, -13,
- 1, -1, 14, -14, 2, 5, 15, -2, -5, -15, -3, 3, 16, -16, 17, 1,
- -1, -17, 6, 18, -6, -18, 2, -2, 19, -19, 1, -1, 20, -20, 4, -4,
- 7, -7, 21, -21, 1, -1, 2, 3, -3, 22, -2, -22, 8, 23, -8, 1,
- 2, -23, -1, 2, -2, -2, 24, 1, -24, -1, 25, 5, -5, 1, -25, -1,
- 9, -9, 26, 1, -26, 3, 1, -1, 27, -3, -1, -27, 1, 3, -1, -3,
- 28, -4, 4, 10, -10, -28, 1, -1, 1, -1, 29, 6, -29, -6, 30, -4,
- 3, 3, -3, -30, 1, 4, -1, 31, -3, 1, 11, -11, -1, -31, 32, -1,
- -1, 2, -2, 1, 1, -32, 1, 4, -1, -4, 33, -1, 1, 1, -1, 5,
- 5, -5, -33, -1, -12, 12, -5, -7, 1, 1, 7, 34, 4, -4, -1, 4,
- -34, -4, 35, 36, -2, -35, -2, -36, 2, 13, 2, -1, 1, -13, 1, -1,
- 37, 1, -5, 6, 5, -1, 38, -6, -8, 5, 8, -1, 1, 1, -37, -1,
- 5, 39, -5, -5, 6, -6, -38, -39, -14, 40, 14, 2, 1, 1, -2, -40,
- -1, -2, 2, -1, -1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, -1}
-},{
- /* MapTab1 */
- 0, /* eob_sym */
- 38, /* esc_sym */
- /* run table */
- {0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 8, 6, 8, 7,
- 7, 9, 9, 10, 10, 11, 11, 1, 12, 1, 12, 13, 13, 16, 14, 16,
- 14, 15, 15, 17, 17, 18, 0, 18, 19, 20, 21, 19, 22, 21, 20, 22,
- 25, 24, 2, 25, 24, 23, 23, 2, 26, 28, 26, 28, 29, 27, 29, 27,
- 33, 33, 1, 32, 1, 3, 32, 30, 36, 3, 36, 30, 31, 31, 35, 34,
- 37, 41, 34, 35, 37, 4, 41, 4, 49, 8, 8, 49, 40, 38, 5, 38,
- 40, 39, 5, 39, 42, 43, 42, 7, 57, 6, 43, 44, 6, 50, 7, 44,
- 57, 48, 50, 48, 45, 45, 46, 47, 51, 46, 47, 58, 1, 51, 58, 1,
- 52, 59, 53, 9, 52, 55, 55, 59, 53, 56, 54, 56, 54, 9, 64, 64,
- 60, 63, 60, 63, 61, 62, 61, 62, 2, 10, 2, 10, 11, 1, 11, 13,
- 12, 1, 12, 13, 16, 16, 8, 8, 14, 3, 3, 15, 14, 15, 4, 4,
- 1, 17, 17, 5, 1, 7, 7, 5, 6, 1, 2, 2, 6, 22, 1, 25,
- 21, 22, 8, 24, 1, 21, 25, 24, 8, 18, 18, 23, 9, 20, 23, 33,
- 29, 33, 20, 1, 19, 1, 29, 36, 9, 36, 19, 41, 28, 57, 32, 3,
- 28, 3, 1, 27, 49, 49, 1, 32, 26, 26, 2, 4, 4, 7, 57, 41,
- 2, 7, 10, 5, 37, 16, 10, 27, 8, 8, 13, 16, 37, 13, 1, 5},
-
- /* value table */
- {0, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1,
- -1, 1, -1, 1, -1, 1, -1, 2, 1, -2, -1, 1, -1, 1, 1, -1,
- -1, 1, -1, 1, -1, 1, 0, -1, 1, 1, 1, -1, 1, -1, -1, -1,
- 1, 1, 2, -1, -1, 1, -1, -2, 1, 1, -1, -1, 1, 1, -1, -1,
- 1, -1, 3, 1, -3, 2, -1, 1, 1, -2, -1, -1, -1, 1, 1, 1,
- 1, 1, -1, -1, -1, 2, -1, -2, 1, 2, -2, -1, 1, 1, 2, -1,
- -1, 1, -2, -1, 1, 1, -1, 2, 1, 2, -1, 1, -2, -1, -2, -1,
- -1, 1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, 4, -1, -1, -4,
- 1, 1, 1, 2, -1, -1, 1, -1, -1, 1, -1, -1, 1, -2, 1, -1,
- 1, 1, -1, -1, 1, 1, -1, -1, 3, 2, -3, -2, 2, 5, -2, 2,
- 2, -5, -2, -2, -2, 2, -3, 3, 2, 3, -3, 2, -2, -2, 3, -3,
- 6, 2, -2, 3, -6, 3, -3, -3, 3, 7, -4, 4, -3, 2, -7, 2,
- 2, -2, -4, 2, 8, -2, -2, -2, 4, 2, -2, 2, 3, 2, -2, -2,
- 2, 2, -2, -8, -2, 9, -2, 2, -3, -2, 2, -2, 2, 2, 2, 4,
- -2, -4, 10, 2, 2, -2, -9, -2, 2, -2, 5, 4, -4, 4, -2, 2,
- -5, -4, -3, 4, 2, -3, 3, -2, -5, 5, 3, 3, -2, -3, -10, -4}
-},{
- /* MapTab2 */
- 2, /* eob_sym */
- 11, /* esc_sym */
- /* run table */
- {1, 1, 0, 2, 2, 1, 1, 3, 3, 4, 4, 0, 1, 1, 5, 5,
- 2, 2, 6, 6, 7, 7, 1, 8, 1, 8, 3, 3, 9, 9, 1, 2,
- 2, 1, 4, 10, 4, 10, 11, 11, 1, 5, 12, 12, 1, 5, 13, 13,
- 3, 3, 6, 6, 2, 2, 14, 14, 16, 16, 15, 7, 15, 8, 8, 7,
- 1, 1, 17, 17, 4, 4, 1, 1, 18, 18, 2, 2, 5, 5, 25, 3,
- 9, 3, 25, 9, 19, 24, 19, 24, 1, 21, 20, 1, 21, 22, 20, 22,
- 23, 23, 8, 6, 33, 6, 8, 33, 7, 7, 26, 26, 1, 32, 1, 32,
- 28, 4, 28, 10, 29, 27, 27, 10, 41, 4, 29, 2, 2, 41, 36, 31,
- 49, 31, 34, 30, 34, 36, 30, 35, 1, 49, 11, 5, 35, 11, 1, 3,
- 3, 5, 37, 37, 8, 40, 8, 40, 12, 12, 42, 42, 1, 38, 16, 57,
- 1, 6, 16, 39, 38, 6, 7, 7, 13, 13, 39, 43, 2, 43, 57, 2,
- 50, 9, 44, 9, 50, 4, 15, 48, 44, 4, 1, 15, 48, 14, 14, 1,
- 45, 45, 8, 3, 5, 8, 51, 47, 3, 46, 46, 47, 5, 51, 1, 17,
- 17, 58, 1, 58, 2, 52, 52, 2, 53, 7, 59, 6, 6, 56, 53, 55,
- 7, 55, 1, 54, 59, 56, 54, 10, 1, 10, 4, 60, 1, 60, 8, 4,
- 8, 64, 64, 61, 1, 63, 3, 63, 62, 61, 5, 11, 5, 3, 11, 62},
-
- /* value table */
- { 1, -1, 0, 1, -1, 2, -2, 1, -1, 1, -1, 0, 3, -3, 1, -1,
- 2, -2, 1, -1, 1, -1, 4, 1, -4, -1, 2, -2, 1, -1, 5, 3,
- -3, -5, 2, 1, -2, -1, 1, -1, 6, 2, 1, -1, -6, -2, 1, -1,
- 3, -3, 2, -2, 4, -4, 1, -1, 1, -1, 1, 2, -1, 2, -2, -2,
- 7, -7, 1, -1, 3, -3, 8, -8, 1, -1, 5, -5, 3, -3, 1, 4,
- 2, -4, -1, -2, 1, 1, -1, -1, 9, 1, 1, -9, -1, 1, -1, -1,
- 1, -1, 3, -3, 1, 3, -3, -1, 3, -3, 1, -1, 10, 1, -10, -1,
- 1, 4, -1, 2, 1, -1, 1, -2, 1, -4, -1, 6, -6, -1, 1, 1,
- 1, -1, 1, 1, -1, -1, -1, 1, 11, -1, -2, 4, -1, 2, -11, 5,
- -5, -4, -1, 1, 4, 1, -4, -1, -2, 2, 1, -1, 12, 1, -2, 1,
- -12, 4, 2, 1, -1, -4, 4, -4, 2, -2, -1, 1, 7, -1, -1, -7,
- -1, -3, 1, 3, 1, 5, 2, 1, -1, -5, 13, -2, -1, 2, -2, -13,
- 1, -1, 5, 6, 5, -5, 1, 1, -6, 1, -1, -1, -5, -1, 14, 2,
- -2, 1, -14, -1, 8, 1, -1, -8, 1, 5, 1, 5, -5, 1, -1, 1,
- -5, -1, 15, 1, -1, -1, -1, 3, -15, -3, 6, 1, 16, -1, 6, -6,
- -6, 1, -1, 1, -16, 1, 7, -1, 1, -1, -6, -3, 6, -7, 3, -1}
-},{
- /* MapTab3 */
- 0, /* eob_sym */
- 35, /* esc_sym */
- /* run table */
- {0, 1, 1, 2, 2, 3, 3, 4, 4, 1, 1, 5, 5, 6, 6, 7,
- 7, 8, 8, 9, 9, 2, 2, 10, 10, 1, 1, 11, 11, 12, 12, 3,
- 3, 13, 13, 0, 14, 14, 16, 15, 16, 15, 4, 4, 17, 1, 17, 1,
- 5, 5, 18, 18, 2, 2, 6, 6, 8, 19, 7, 8, 7, 19, 20, 20,
- 21, 21, 22, 24, 22, 24, 23, 23, 1, 1, 25, 25, 3, 3, 26, 26,
- 9, 9, 27, 27, 28, 28, 33, 29, 4, 33, 29, 1, 4, 1, 32, 32,
- 2, 2, 31, 10, 30, 10, 30, 31, 34, 34, 5, 5, 36, 36, 35, 41,
- 35, 11, 41, 11, 37, 1, 8, 8, 37, 6, 1, 6, 40, 7, 7, 40,
- 12, 38, 12, 39, 39, 38, 49, 13, 49, 13, 3, 42, 3, 42, 16, 16,
- 43, 43, 14, 14, 1, 1, 44, 15, 44, 15, 2, 2, 57, 48, 50, 48,
- 57, 50, 4, 45, 45, 4, 46, 47, 47, 46, 1, 51, 1, 17, 17, 51,
- 8, 9, 9, 5, 58, 8, 58, 5, 52, 52, 55, 56, 53, 56, 55, 59,
- 59, 53, 54, 1, 6, 54, 7, 7, 6, 1, 2, 3, 2, 3, 64, 60,
- 60, 10, 10, 64, 61, 62, 61, 63, 1, 63, 62, 1, 18, 24, 18, 4,
- 25, 4, 8, 21, 21, 1, 24, 22, 25, 22, 8, 11, 19, 11, 23, 1,
- 20, 23, 19, 20, 5, 12, 5, 1, 16, 2, 12, 13, 2, 13, 1, 16},
-
- /* value table */
- { 0, 1, -1, 1, -1, 1, -1, 1, -1, 2, -2, 1, -1, 1, -1, 1,
- -1, 1, -1, 1, -1, 2, -2, 1, -1, 3, -3, 1, -1, 1, -1, 2,
- -2, 1, -1, 0, 1, -1, 1, 1, -1, -1, 2, -2, 1, 4, -1, -4,
- 2, -2, 1, -1, -3, 3, 2, -2, 2, 1, 2, -2, -2, -1, 1, -1,
- 1, -1, 1, 1, -1, -1, 1, -1, 5, -5, 1, -1, 3, -3, 1, -1,
- 2, -2, 1, -1, 1, -1, 1, 1, 3, -1, -1, 6, -3, -6, -1, 1,
- 4, -4, 1, 2, 1, -2, -1, -1, 1, -1, 3, -3, 1, -1, 1, 1,
- -1, 2, -1, -2, 1, 7, -3, 3, -1, 3, -7, -3, 1, -3, 3, -1,
- 2, 1, -2, 1, -1, -1, 1, 2, -1, -2, -4, -1, 4, 1, 2, -2,
- 1, -1, -2, 2, 8, -8, -1, 2, 1, -2, -5, 5, 1, -1, -1, 1,
- -1, 1, 4, -1, 1, -4, -1, -1, 1, 1, 9, 1, -9, 2, -2, -1,
- -4, 3, -3, -4, -1, 4, 1, 4, 1, -1, 1, -1, 1, 1, -1, 1,
- -1, -1, -1, 10, 4, 1, 4, -4, -4, -10, 6, 5, -6, -5, 1, -1,
- 1, 3, -3, -1, 1, -1, -1, -1, 11, 1, 1, -11, -2, -2, 2, 5,
- -2, -5, -5, 2, -2, 12, 2, -2, 2, 2, 5, -3, -2, 3, -2, -12,
- -2, 2, 2, 2, -5, 3, 5, 13, -3, 7, -3, -3, -7, 3, -13, 3}
-},{
- /* MapTab4 */
- 0, /* eob_sym */
- 34, /* esc_sym */
- /* run table */
- {0, 1, 1, 1, 2, 2, 1, 3, 3, 1, 1, 1, 4, 4, 1, 5,
- 2, 1, 5, 2, 1, 1, 6, 6, 1, 1, 1, 1, 1, 7, 3, 1,
- 2, 3, 0, 1, 2, 7, 1, 1, 1, 8, 1, 1, 8, 1, 1, 1,
- 9, 1, 9, 1, 2, 1, 1, 2, 1, 1, 10, 4, 1, 10, 1, 4,
- 1, 1, 1, 1, 1, 3, 1, 1, 1, 3, 2, 1, 5, 1, 1, 1,
- 2, 5, 1, 11, 1, 11, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
- 2, 1, 6, 1, 6, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 12,
- 3, 1, 12, 1, 1, 1, 2, 1, 1, 3, 1, 1, 1, 1, 1, 1,
- 4, 1, 1, 1, 2, 1, 1, 4, 1, 1, 1, 1, 1, 1, 2, 1,
- 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 2, 1, 1, 5,
- 1, 1, 1, 1, 1, 7, 1, 7, 1, 1, 2, 3, 1, 1, 1, 1,
- 5, 1, 1, 1, 1, 1, 1, 2, 13, 1, 1, 1, 1, 1, 1, 1,
- 1, 1, 1, 1, 1, 1, 1, 1, 13, 2, 1, 1, 4, 1, 1, 1,
- 3, 1, 6, 1, 1, 1, 14, 1, 1, 1, 1, 1, 14, 6, 1, 1,
- 1, 1, 15, 2, 4, 1, 2, 3, 15, 1, 1, 1, 8, 1, 1, 8,
- 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1},
-
- /* value table */
- { 0, 1, -1, 2, 1, -1, -2, 1, -1, 3, -3, 4, 1, -1, -4, 1,
- 2, 5, -1, -2, -5, 6, 1, -1, -6, 7, -7, 8, -8, 1, 2, 9,
- 3, -2, 0, -9, -3, -1, 10, -10, 11, 1, -11, 12, -1, -12, 13, -13,
- 1, 14, -1, -14, 4, 15, -15, -4, 16, -16, 1, 2, 17, -1, -17, -2,
- 18, -18, 19, -19, 20, 3, -20, 21, -21, -3, 5, 22, 2, -22, -23, 23,
- -5, -2, 24, 1, -24, -1, 25, -25, 26, -26, -27, 27, 28, 29, -28, -29,
- 6, 30, 2, -31, -2, -30, 31, -6, -32, 32, 33, -33, 34, -35, -34, 1,
- 4, -36, -1, 35, 37, 36, 7, -37, 38, -4, -38, 39, 41, 40, -40, -39,
- 3, 42, -43, -41, -7, -42, 43, -3, 44, -44, 45, -45, 46, 47, 8, -47,
- -48, -46, 50, -50, 48, 49, 51, -49, 52, -52, 5, -51, -8, -53, 53, 3,
- -56, 56, 55, 54, -54, 2, 60, -2, -55, 58, 9, -5, 59, 57, -57, -63,
- -3, -58, -60, -61, 61, -59, -62, -9, 1, 64, 62, 69, -64, 63, 65, -67,
- -68, 66, -65, 68, -66, -69, 67, -70, -1, 10, 71, -71, 4, 73, 72, 70,
- 6, -76, -3, 74, -78, -74, 1, 78, 80, -72, -75, 76, -1, 3, -73, 79,
- 75, 77, 1, 11, -4, -79, -10, -6, -1, -77, -83, -80, 2, 81, -84, -2,
- 83, -81, 82, -82, 84, -87, -86, 85, -11, -85, 86, -89, 87, -88, 88, 89}
-},{
- /* MapTab5 */
- 2, /* eob_sym */
- 33, /* esc_sym */
- /* run table */
- {1, 1, 0, 2, 1, 2, 1, 3, 3, 1, 1, 4, 4, 2, 2, 1,
- 1, 5, 5, 6, 1, 6, 1, 7, 7, 3, 3, 2, 8, 2, 8, 1,
- 1, 0, 9, 9, 1, 1, 10, 4, 10, 4, 11, 11, 2, 1, 2, 1,
- 12, 12, 3, 3, 1, 1, 13, 5, 5, 13, 14, 1, 1, 14, 2, 2,
- 6, 6, 15, 1, 1, 15, 16, 4, 7, 16, 4, 7, 1, 1, 3, 3,
- 8, 8, 2, 2, 1, 1, 17, 17, 1, 1, 18, 18, 5, 5, 2, 2,
- 1, 1, 9, 19, 9, 19, 20, 3, 3, 20, 1, 10, 21, 1, 10, 4,
- 4, 21, 22, 6, 6, 22, 1, 1, 23, 24, 2, 2, 23, 24, 11, 1,
- 1, 11, 7, 25, 7, 1, 1, 25, 8, 8, 3, 26, 3, 1, 12, 2,
- 2, 26, 1, 12, 5, 5, 27, 4, 1, 4, 1, 27, 28, 1, 28, 13,
- 1, 13, 2, 29, 2, 1, 32, 6, 1, 30, 14, 29, 14, 6, 3, 31,
- 3, 1, 30, 1, 32, 31, 33, 9, 33, 1, 1, 7, 9, 7, 2, 2,
- 1, 1, 4, 36, 34, 4, 5, 10, 10, 5, 34, 1, 1, 35, 8, 8,
- 36, 3, 35, 1, 15, 3, 2, 1, 16, 15, 16, 2, 37, 1, 37, 1,
- 1, 1, 6, 6, 38, 1, 38, 11, 1, 39, 39, 40, 11, 2, 41, 4,
- 40, 1, 2, 4, 1, 1, 1, 41, 3, 1, 3, 1, 5, 7, 5, 7},
-
- /* value table */
- { 1, -1, 0, 1, 2, -1, -2, 1, -1, 3, -3, 1, -1, 2, -2, 4,
- -4, 1, -1, 1, 5, -1, -5, 1, -1, 2, -2, 3, 1, -3, -1, 6,
- -6, 0, 1, -1, 7, -7, 1, 2, -1, -2, 1, -1, 4, 8, -4, -8,
- 1, -1, 3, -3, 9, -9, 1, 2, -2, -1, 1, 10, -10, -1, 5, -5,
- 2, -2, 1, 11, -11, -1, 1, 3, 2, -1, -3, -2, 12, -12, 4, -4,
- 2, -2, -6, 6, 13, -13, 1, -1, 14, -14, 1, -1, 3, -3, 7, -7,
- 15, -15, 2, 1, -2, -1, 1, 5, -5, -1, -16, 2, 1, 16, -2, 4,
- -4, -1, 1, 3, -3, -1, 17, -17, 1, 1, -8, 8, -1, -1, 2, 18,
- -18, -2, 3, 1, -3, 19, -19, -1, 3, -3, 6, 1, -6, 20, 2, 9,
- -9, -1, -20, -2, 4, -4, 1, -5, 21, 5, -21, -1, 1, -22, -1, 2,
- 22, -2, 10, 1, -10, 23, 1, 4, -23, 1, 2, -1, -2, -4, -7, 1,
- 7, -24, -1, 24, -1, -1, 1, 3, -1, -25, 25, 4, -3, -4, 11, -11,
- 26, -26, 6, 1, 1, -6, -5, -3, 3, 5, -1, -27, 27, 1, 4, -4,
- -1, -8, -1, 28, 2, 8, -12, -28, -2, -2, 2, 12, -1, 29, 1, -29,
- 30, -30, 5, -5, 1, -31, -1, 3, 31, -1, 1, 1, -3, -13, 1, -7,
- -1, -32, 13, 7, 32, 33, -33, -1, -9, -34, 9, 34, -6, 5, 6, -5}
-},{
- /* MapTab6 */
- 2, /* eob_sym */
- 13, /* esc_sym */
- /* run table */
- {1, 1, 0, 1, 1, 2, 2, 1, 1, 3, 3, 1, 1, 0, 2, 2,
- 4, 1, 4, 1, 1, 1, 5, 5, 1, 1, 6, 6, 2, 2, 1, 1,
- 3, 3, 7, 7, 1, 1, 8, 8, 1, 1, 2, 2, 1, 9, 1, 9,
- 4, 4, 10, 1, 1, 10, 1, 1, 11, 11, 3, 3, 1, 2, 1, 2,
- 1, 1, 12, 12, 5, 5, 1, 1, 13, 1, 1, 13, 2, 2, 1, 1,
- 6, 6, 1, 1, 4, 14, 4, 14, 3, 1, 3, 1, 1, 1, 15, 7,
- 15, 2, 2, 7, 1, 1, 1, 8, 1, 8, 16, 16, 1, 1, 1, 1,
- 2, 1, 1, 2, 1, 1, 3, 5, 5, 3, 4, 1, 1, 4, 1, 1,
- 17, 17, 9, 1, 1, 9, 2, 2, 1, 1, 10, 10, 1, 6, 1, 1,
- 6, 18, 1, 1, 18, 1, 1, 1, 2, 2, 3, 1, 3, 1, 1, 1,
- 4, 1, 19, 1, 19, 7, 1, 1, 20, 1, 4, 20, 1, 7, 11, 2,
- 1, 11, 21, 2, 8, 5, 1, 8, 1, 5, 21, 1, 1, 1, 22, 1,
- 1, 22, 1, 1, 3, 3, 1, 23, 2, 12, 24, 1, 1, 2, 1, 1,
- 12, 23, 1, 1, 24, 1, 1, 1, 4, 1, 1, 1, 2, 1, 6, 6,
- 4, 2, 1, 1, 1, 1, 1, 1, 1, 14, 13, 3, 1, 25, 9, 25,
- 14, 1, 9, 3, 13, 1, 1, 1, 1, 1, 10, 1, 1, 2, 10, 2},
-
- /* value table */
- {-20, -1, 0, 2, -2, 1, -1, 3, -3, 1, -1, 4, -4, 0, 2, -2,
- 1, 5, -1, -5, 6, -6, 1, -1, 7, -7, 1, -1, 3, -3, 8, -8,
- 2, -2, 1, -1, 9, -9, 1, -1, 10, -10, 4, -4, 11, 1, -11, -1,
- 2, -2, 1, 12, -12, -1, 13, -13, 1, -1, 3, -3, 14, 5, -14, -5,
- -15, 15, -1, 1, 2, -2, 16, -16, 1, 17, -17, -1, 6, -6, 18, -18,
- 2, -2, -19, 19, -3, 1, 3, -1, 4, 20, -4, 1, -21, 21, 1, 2,
- -1, -7, 7, -2, 22, -22, 23, 2, -23, -2, 1, -1, -24, 24, -25, 25,
- -8, -26, 26, 8, -27, 27, 5, 3, -3, -5, -4, 28, -28, 4, 29, -29,
- 1, -1, -2, -30, 30, 2, 9, -9, -31, 31, 2, -2, -32, 3, 32, -33,
- -3, 1, 33, -34, -1, 34, -35, 35, -10, 10, -6, 36, 6, -36, 37, -37,
- -5, 38, 1, -38, -1, 3, 39, -39, -1, 40, 5, 1, -40, -3, 2, -11,
- -41, -2, 1, 11, -3, -4, 41, 3, 42, 4, -1, -43, -42, 43, 1, -44,
- 45, -1, 44, -45, -7, 7, -46, 1, -12, 2, 1, -47, 46, 12, 47, 48,
- -2, -1, -48, 49, -1, -50, -49, 50, -6, -51, 51, 52, -13, 53, -4, 4,
- 6, 13, -53, -52, -54, 55, 54, -55, -56, -2, 2, -8, 56, 1, -3, -1,
- 2, 58, 3, 8, -2, 57, -58, -60, -59, -57, -3, 60, 59, -14, 3, 14}
-},{
- /* MapTab7 */
- 2, /* eob_sym */
- 38, /* esc_sym */
- /* run table */
- {1, 1, 0, 2, 2, 1, 1, 3, 3, 4, 4, 5, 5, 1, 1, 6,
- 6, 2, 2, 7, 7, 8, 8, 1, 1, 3, 3, 9, 9, 10, 10, 1,
- 1, 2, 2, 4, 4, 11, 0, 11, 12, 12, 13, 13, 1, 1, 5, 5,
- 14, 14, 15, 16, 15, 16, 3, 3, 1, 6, 1, 6, 2, 2, 7, 7,
- 8, 8, 17, 17, 1, 1, 4, 4, 18, 18, 2, 2, 1, 19, 1, 20,
- 19, 20, 21, 21, 3, 3, 22, 22, 5, 5, 24, 1, 1, 23, 9, 23,
- 24, 9, 2, 2, 10, 1, 1, 10, 6, 6, 25, 4, 4, 25, 7, 7,
- 26, 8, 1, 8, 3, 1, 26, 3, 11, 11, 27, 27, 2, 28, 1, 2,
- 28, 1, 12, 12, 5, 5, 29, 13, 13, 29, 32, 1, 1, 33, 31, 30,
- 32, 4, 30, 33, 4, 31, 3, 14, 1, 1, 3, 34, 34, 2, 2, 14,
- 6, 6, 35, 36, 35, 36, 1, 15, 1, 16, 16, 15, 7, 9, 7, 9,
- 37, 8, 8, 37, 1, 1, 39, 2, 38, 39, 2, 40, 5, 38, 40, 5,
- 3, 3, 4, 4, 10, 10, 1, 1, 1, 1, 41, 2, 41, 2, 6, 6,
- 1, 1, 11, 42, 11, 43, 3, 42, 3, 17, 4, 43, 1, 17, 7, 1,
- 8, 44, 4, 7, 44, 5, 8, 2, 5, 1, 2, 48, 45, 1, 12, 45,
- 12, 48, 13, 13, 1, 9, 9, 46, 1, 46, 47, 47, 49, 18, 18, 49},
-
- /* value table */
- { 1, -1, 0, 1, -1, 2, -2, 1, -1, 1, -1, 1, -1, 3, -3, 1,
- -1, -2, 2, 1, -1, 1, -1, 4, -4, -2, 2, 1, -1, 1, -1, 5,
- -5, -3, 3, 2, -2, 1, 0, -1, 1, -1, 1, -1, 6, -6, 2, -2,
- 1, -1, 1, 1, -1, -1, -3, 3, 7, 2, -7, -2, -4, 4, 2, -2,
- 2, -2, 1, -1, 8, -8, 3, -3, 1, -1, -5, 5, 9, 1, -9, 1,
- -1, -1, 1, -1, -4, 4, 1, -1, 3, -3, 1, -10, 10, 1, 2, -1,
- -1, -2, 6, -6, 2, 11, -11, -2, 3, -3, 1, -4, 4, -1, 3, -3,
- 1, 3, 12, -3, -5, -12, -1, 5, 2, -2, 1, -1, -7, 1, 13, 7,
- -1, -13, 2, -2, 4, -4, 1, 2, -2, -1, 1, 14, -14, 1, 1, 1,
- -1, -5, -1, -1, 5, -1, -6, 2, -15, 15, 6, 1, -1, -8, 8, -2,
- -4, 4, 1, 1, -1, -1, 16, 2, -16, -2, 2, -2, 4, 3, -4, -3,
- -1, -4, 4, 1, -17, 17, -1, -9, 1, 1, 9, 1, -5, -1, -1, 5,
- -7, 7, 6, -6, 3, -3, 18, -18, 19, -19, 1, -10, -1, 10, -5, 5,
- 20, -20, -3, 1, 3, 1, 8, -1, -8, 2, 7, -1, -21, -2, 5, 21,
- 5, -1, -7, -5, 1, -6, -5, -11, 6, 22, 11, 1, 1, -22, -3, -1,
- 3, -1, 3, -3, -23, 4, -4, 1, 23, -1, 1, -1, 1, -2, 2, -1}
-},{
- /* MapTab8 */
- 4, /* eob_sym */
- 11, /* esc_sym */
- /* run table */
- {1, 1, 1, 1, 0, 2, 2, 1, 1, 3, 3, 0, 1, 1, 2, 2,
- 4, 4, 1, 1, 5, 5, 1, 1, 2, 2, 3, 3, 6, 6, 1, 1,
- 7, 7, 8, 1, 8, 2, 2, 1, 4, 4, 1, 3, 1, 3, 9, 9,
- 2, 2, 1, 5, 1, 5, 10, 10, 1, 1, 11, 11, 3, 6, 3, 4,
- 4, 6, 2, 2, 1, 12, 1, 12, 7, 13, 7, 13, 1, 1, 8, 8,
- 2, 2, 14, 14, 16, 15, 16, 5, 5, 1, 3, 15, 1, 3, 4, 4,
- 1, 1, 17, 17, 2, 2, 6, 6, 1, 18, 1, 18, 22, 21, 22, 21,
- 25, 24, 25, 19, 9, 20, 9, 23, 19, 24, 20, 3, 23, 7, 3, 1,
- 1, 7, 28, 26, 29, 5, 28, 26, 5, 8, 29, 4, 8, 27, 2, 2,
- 4, 27, 1, 1, 10, 36, 10, 33, 33, 36, 30, 1, 32, 32, 1, 30,
- 6, 31, 31, 35, 3, 6, 11, 11, 3, 2, 35, 2, 34, 1, 34, 1,
- 37, 37, 12, 7, 12, 5, 41, 5, 4, 7, 1, 8, 13, 4, 1, 41,
- 13, 38, 8, 38, 9, 1, 40, 40, 9, 1, 39, 2, 2, 49, 39, 42,
- 3, 3, 14, 16, 49, 14, 16, 42, 43, 43, 6, 6, 15, 1, 1, 15,
- 44, 44, 1, 1, 50, 48, 4, 5, 4, 7, 5, 2, 10, 10, 48, 7,
- 50, 45, 2, 1, 45, 8, 8, 1, 46, 46, 3, 47, 47, 3, 1, 1},
-
- /* value table */
- { 1, -1, 2, -2, 0, 1, -1, 3, -3, 1, -1, 0, 4, -4, 2, -2,
- 1, -1, 5, -5, 1, -1, 6, -6, 3, -3, 2, -2, 1, -1, 7, -7,
- 1, -1, 1, 8, -1, 4, -4, -8, 2, -2, 9, 3, -9, -3, 1, -1,
- 5, -5, 10, 2, -10, -2, 1, -1, 11, -11, 1, -1, -4, 2, 4, 3,
- -3, -2, 6, -6, 12, 1, -12, -1, 2, 1, -2, -1, 13, -13, 2, -2,
- 7, -7, 1, -1, 1, 1, -1, 3, -3, 14, 5, -1, -14, -5, 4, -4,
- 15, -15, 1, -1, 8, -8, -3, 3, 16, 1, -16, -1, 1, 1, -1, -1,
- 1, 1, -1, 1, 2, 1, -2, 1, -1, -1, -1, 6, -1, 3, -6, 17,
- -17, -3, 1, 1, 1, 4, -1, -1, -4, 3, -1, 5, -3, -1, -9, 9,
- -5, 1, 18, -18, 2, 1, -2, 1, -1, -1, 1, 19, -1, 1, -19, -1,
- 4, 1, -1, 1, 7, -4, -2, 2, -7, 10, -1, -10, 1, 20, -1, -20,
- 1, -1, 2, 4, -2, 5, 1, -5, 6, -4, 21, 4, 2, -6, -21, -1,
- -2, 1, -4, -1, -3, 22, -1, 1, 3, -22, -1, 11, -11, 1, 1, 1,
- 8, -8, 2, 2, -1, -2, -2, -1, 1, -1, -5, 5, 2, 23, -23, -2,
- 1, -1, 24, -24, -1, -1, 7, 6, -7, 5, -6, 12, -3, 3, 1, -5,
- 1, 1, -12, 25, -1, -5, 5, -25, -1, 1, 9, 1, -1, -9, 26, -26}
-}
-};
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Pickup Simulator APK A Unique Game to Restore and Customize Your Truck.md b/spaces/congsaPfin/Manga-OCR/logs/Pickup Simulator APK A Unique Game to Restore and Customize Your Truck.md
deleted file mode 100644
index b28ea6c2d271c3be9ec8e8b393c9d08070e31a8b..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Pickup Simulator APK A Unique Game to Restore and Customize Your Truck.md
+++ /dev/null
@@ -1,86 +0,0 @@
-
-Pickup Simulator APK: A Fun and Realistic Driving Game
-Do you love driving pickup trucks on off-road terrains? Do you want to experience the thrill of driving in different weather conditions? Do you want to customize your own pickup truck and show it off to your friends? If you answered yes to any of these questions, then you should try Pickup Simulator APK, a fun and realistic driving game for Android devices.
-What is Pickup Simulator APK?
-Pickup Simulator APK is a driving simulation game developed by Oppana Games, a studio that specializes in creating realistic and immersive car games. In this game, you can choose from various pickup trucks and drive them on different terrains, such as mountains, forests, deserts, snow, and mud. You can also customize your pickup truck with different colors, wheels, suspensions, engines, and accessories. You can enjoy the realistic physics and graphics of the game, as well as the customizable controls and camera angles. You can play the game for free and offline, without any internet connection required.
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-Features of Pickup Simulator APK
-Realistic physics and graphics
-The game features realistic physics and graphics that make you feel like you are driving a real pickup truck. You can see the details of your pickup truck, such as the headlights, taillights, mirrors, doors, windows, tires, exhausts, and more. You can also see the effects of the weather and terrain on your pickup truck, such as raindrops, snowflakes, mud splashes, dust clouds, skid marks, smoke trails, and more. You can also hear the realistic sounds of your pickup truck's engine, horn, brakes, tires, suspension, and more.
-Various pickup trucks to choose from
-The game offers you a variety of pickup trucks to choose from, each with its own characteristics and performance. You can choose from classic models, such as Ford F-150, Chevrolet Silverado, Dodge Ram, Toyota Tacoma, Nissan Frontier, and more. You can also choose from modern models, such as Tesla Cybertruck, Rivian R1T, Ford F-150 Lightning, GMC Hummer EV, Bollinger B2, and more. You can also unlock new pickup trucks by completing missions and earning coins.
-
-Locate the APK file in your file manager and tap on it to install it
-The fifth step is to locate the APK file in your file manager and tap on it to install it. You can use any file manager app on your device to access the APK file, which is usually stored in the downloads folder. Once you find the APK file, you need to tap on it and follow the instructions on the screen to install it.
-Launch the game and enjoy driving your pickup truck
-The final step is to launch the game and enjoy driving your pickup truck. You can find the game icon on your home screen or app drawer, and tap on it to open it. Then, you can choose your pickup truck, customize it, and start driving on different terrains and weather conditions.
-Tips and tricks for playing Pickup Simulator APK
-If you want to improve your skills and have more fun playing Pickup Simulator APK, here are some tips and tricks that you can use:
-How to drive your pickup truck efficiently
-Use the brake and accelerator pedals to control your speed and direction
-The most basic tip is to use the brake and accelerator pedals to control your speed and direction. You can find these pedals on the bottom right corner of your screen. You can tap on the brake pedal to slow down or stop your pickup truck, and tap on the accelerator pedal to speed up or move forward. You can also hold down the pedals for more control.
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-Use the steering wheel or tilt your device to steer your pickup truck
-The next tip is to use the steering wheel or tilt your device to steer your pickup truck. You can find the steering wheel on the bottom left corner of your screen. You can swipe left or right on the steering wheel to turn your pickup truck in that direction. You can also tilt your device left or right to steer your pickup truck using the gyroscope sensor.
-Use the gearbox to switch between forward and reverse modes
-The third tip is to use the gearbox to switch between forward and reverse modes. You can find the gearbox on the top right corner of your screen. You can tap on the F button to switch to forward mode, which allows you to move forward with your pickup truck. You can also tap on the R button to switch to reverse mode, which allows you to move backward with your pickup truck.
-Use the handbrake to perform drifts and turns
-The fourth tip is to use the handbrake to perform drifts and turns. You can find the handbrake on the top left corner of your screen. You can tap on the handbrake button to lock your rear wheels, which causes your pickup truck to skid and slide. You can use this technique to perform drifts and turns, especially on slippery surfaces.
-How to customize your pickup truck and gameplay settings
-Tap on the garage icon to access the customization menu
-The first tip is to tap on the garage icon to access the customization menu. You can find this icon on the main menu of the game, which you can access by tapping on the pause button on the top center of your screen. The garage icon looks like a wrench and a screwdriver. Once you tap on it, you will see the customization menu, where you can modify your pickup truck and gameplay settings.
-Choose from different colors, wheels, suspensions, engines, and accessories for your pickup truck
-The second tip is to choose from different colors, wheels, suspensions, engines, and accessories for your pickup truck. You can find these options on the left side of the customization menu. You can swipe up or down to scroll through the different categories, and tap on the ones you want to apply to your pickup truck. You can also see the preview of your pickup truck on the right side of the customization menu. You can swipe left or right to rotate your pickup truck and see it from different angles.
-Tap on the settings icon to adjust the sound, graphics, controls, and camera options
-The third tip is to tap on the settings icon to adjust the sound, graphics, controls, and camera options. You can find this icon on the bottom right corner of the customization menu. It looks like a gear. Once you tap on it, you will see the settings menu, where you can modify your sound, graphics, controls, and camera options. You can use the sliders or buttons to adjust the volume, quality, sensitivity, feedback, mode, angle, zoom, and rotation of these options. You can also tap on the reset button to restore the default settings.
-Conclusion
-Pickup Simulator APK is a fun and realistic driving game that lets you drive various pickup trucks on different terrains and weather conditions. You can also customize your pickup truck and gameplay settings according to your preference and comfort. You can download and install Pickup Simulator APK from Oppana Games or FileHippo, and play it for free and offline. You can also use some tips and tricks to improve your skills and have more fun playing Pickup Simulator APK. If you are looking for a driving simulation game that offers you a lot of variety and challenge, then you should try Pickup Simulator APK.
-FAQs
-Here are some frequently asked questions about Pickup Simulator APK:
-Q: Is Pickup Simulator APK safe to download and install?
-A: Yes, Pickup Simulator APK is safe to download and install from Oppana Games or FileHippo, which are trusted sources for downloading APK files. However, you should always be careful when downloading and installing apps from unknown sources, as they may contain viruses or malware that can harm your device or steal your data.
-Q: How can I update Pickup Simulator APK?
-A: You can update Pickup Simulator APK by visiting Oppana Games or FileHippo again and downloading the latest version of the game. You can also enable the automatic updates option in your settings to receive notifications when a new version is available.
-Q: How can I contact Oppana Games for feedback or support?
-A: You can contact Oppana Games for feedback or support by visiting their official website or their Facebook page. You can also email them at oppanagames@gmail.com or call them at +7 926 814 53 67.
-Q: How can I share my pickup truck and gameplay screenshots with my friends?
-A: You can share your pickup truck and gameplay screenshots with your friends by using the share button on the top right corner of your screen. This button looks like a paper plane. Once you tap on it, you will see different options to share your screenshots via social media platforms, such as Facebook, Twitter, Instagram, WhatsApp, Telegram, etc.
-Q: How can I earn more coins in Pickup Simulator APK?
-A: You can earn more coins in Pickup Simulator APK by completing missions and driving longer distances. You can also watch ads or rate the game to get some bonus coins.
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Zombie Towers Mod APK Terbaru The Ultimate Tower Defense Game with Zombies.md b/spaces/congsaPfin/Manga-OCR/logs/Zombie Towers Mod APK Terbaru The Ultimate Tower Defense Game with Zombies.md
deleted file mode 100644
index 2aa0619ca7b3931c690689a0e3ef29550fabeaa7..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Zombie Towers Mod APK Terbaru The Ultimate Tower Defense Game with Zombies.md
+++ /dev/null
@@ -1,142 +0,0 @@
-
-Zombie Towers Mod Apk Terbaru: A Tower Defense Game with a Zombie Twist
- If you are looking for a fun and addictive tower defense game with a zombie survival theme, you might want to check out Zombie Towers Mod Apk Terbaru. This is a modified version of Zombie Towers, a game developed by Edenap that lets you defend your castle from waves of zombies with different types of towers, workers, and power-ups. In this article, we will tell you what Zombie Towers is, how to download and install the mod apk version, why you should play it, some tips and tricks to help you win, and a review of the game based on its pros and cons.
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Download File >>> https://urlca.com/2uO9Rd
- What is Zombie Towers?
- A brief introduction to the game and its features
- Zombie Towers is a tower defense game that puts you in charge of a group of survivors who have to fight against hordes of zombies in a post-apocalyptic world. You have to place your towers strategically in different spots in each level, and upgrade them as you progress. You also have workers who can carry ammo, repair walls, and boost your towers. You can use power-ups such as EMP, Nuke, Triple X, and Headshots to deal extra damage or freeze the zombies. The game has 30+ types of towers, 4 types of upgrades, 3 types of workers, 4 types of power-ups, and 7 types of zombies. The game has 12 exciting and challenging levels with different environments and objectives.
- How to download and install the mod apk version
- If you want to play the modded version of Zombie Towers, which gives you unlimited money, free purchase, mod menu, god mode, one shot kill, unlimited ammo, max range, and more features, you can follow these steps:
-
-- Download the mod apk file from or any other trusted source.
-- Enable unknown sources on your device by going to Settings > Security > Unknown Sources.
-- Locate the downloaded file in your file manager and tap on it to install it.
-- Launch the game and enjoy!
-
- Why play Zombie Towers Mod Apk Terbaru?
- The benefits of playing the modded version of the game
- Playing the modded version of Zombie Towers has many advantages over playing the original version. Here are some of them:
-
-- You can access all the towers, workers, power-ups, and upgrades without spending any money.
-- You can customize your gameplay with the mod menu options such as god mode, one shot kill, unlimited ammo, max range, etc.
-- You can have more fun and less frustration by easily defeating the zombies and completing the levels.
-- You can experiment with different strategies and combinations without worrying about losing resources or lives.
-
- The challenges and strategies of the game
- Despite playing the modded version of Zombie Towers, you still need to use your brain and skills to win the game. The game is not easy and requires you to think fast and act smart. Here are some challenges and strategies that you should know:- The zombies come in different types, such as normal, fast, armored, flying, boss, etc. Each type has its own strengths and weaknesses, and you need to use the right towers and power-ups to counter them.
- - The levels have different layouts, obstacles, and objectives. You need to plan your tower placement carefully and adapt to the changing situations. You also need to complete the objectives such as protecting the survivors, destroying the zombie base, or surviving for a certain time.
- - The towers have different abilities, such as damage, range, fire rate, splash, stun, etc. You need to merge and upgrade your towers to make them more effective and powerful. You also need to use the workers to support your towers with ammo, repairs, and boosts.
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- - The power-ups have different effects, such as EMP, Nuke, Triple X, and Headshots. You need to use them wisely and at the right time to turn the tide of the battle. You also need to save some for the boss battles and emergencies.
- Tips and Tricks for Zombie Towers Mod Apk Terbaru
- How to merge and upgrade towers
- One of the unique features of Zombie Towers is that you can merge two towers of the same type and level to create a new tower with higher level and better stats. You can also upgrade your towers with coins or gems to increase their level and abilities. Here are some tips and tricks for merging and upgrading towers:
-
-- You can only merge towers that are adjacent to each other. You can move your towers by dragging them to an empty spot.
-- You can merge up to 5 levels of towers. The higher the level of the tower, the more expensive it is to merge or upgrade.
-- You can see the preview of the merged tower before you confirm it. You can also see the stats of the tower by tapping on it.
-- You can undo a merge by tapping on the undo button at the bottom right corner of the screen. You can only undo one merge at a time.
-- You can sell your towers by tapping on the sell button at the bottom left corner of the screen. You will get some coins back depending on the level of the tower.
-
- How to use power-ups and boosters
- Power-ups are special items that you can use during the game to deal extra damage or freeze the zombies. Boosters are items that you can use before starting a level to give you some advantages such as extra coins, extra lives, extra workers, etc. Here are some tips and tricks for using power-ups and boosters:
-
-- You can get power-ups by killing zombies or opening chests. You can also buy them with gems or watch ads to get them for free.
-- You can use power-ups by tapping on their icons at the top right corner of the screen. You can only use one power-up at a time.
-- You can see the cooldown time of each power-up by looking at the circle around its icon. You can also see the duration of each power-up by looking at the bar below its icon.
-- You can buy boosters with gems or watch ads to get them for free. You can only use one booster per level.
-- You can choose which booster to use by tapping on its icon at the bottom center of the screen before starting a level. You can also see the effect of each booster by tapping on it.
-
- How to deal with different types of zombies
- Zombies are your enemies in Zombie Towers. They come in different types, such as normal, fast, armored, flying, boss, etc. Each type has its own strengths and weaknesses, and you need to use the right towers and power-ups to counter them. Here are some tips and tricks for dealing with different types of zombies:
-
-- Normal zombies are the most common type of zombies. They have low health and speed, but they come in large numbers. You can use any tower or power-up to kill them easily.
-- Fast zombies are faster than normal zombies. They have low health but high speed, and they can dodge some attacks. You need to use fast-firing or splash-damage towers or power-ups to kill them quickly.
-- Armored zombies are tougher than normal zombies. They have high health but low speed, and they can resist some damage types. You need to use high-damage or piercing-damage towers or power-ups to kill them effectively.
-- Flying zombies are airborne zombies. They have low health but high speed, and they can fly over walls and obstacles. You need to use anti-air or homing-damage towers or power-ups to kill them efficiently.
-- Boss zombies are the biggest and strongest type of zombies. They have very high health and speed, and they can deal massive damage and spawn other zombies. You need to use all your towers, workers, power-ups, and skills to kill them before they reach your castle.
-
- Zombie Towers Mod Apk Terbaru Review
- The pros and cons of the game
- Zombie Towers Mod Apk Terbaru is a fun and addictive game that will keep you entertained for hours. However, like any other game, it has its pros and cons. Here are some of them:
-
-
-Pros
-Cons
-
-
-- The game has great graphics and sound effects that create a realistic and immersive zombie apocalypse atmosphere.
-- The game can be too easy or too hard depending on the mod menu options that you choose.
-
-
-- The game has a variety of towers, workers, power-ups, and zombies that make the gameplay diverse and interesting.
-- The game can be repetitive and boring after playing for a long time.
-
-
-- The game has a simple and intuitive interface and controls that make it easy to play.
-- The game can have some bugs and glitches that affect the performance and quality of the game.
-
-
-- The game has a modded version that gives you unlimited money, free purchase, mod menu, god mode, one shot kill, unlimited ammo, max range, and more features.
-- The game can be unfair and unbalanced for the original version players who do not use the modded version.
-
-
- The ratings and feedback from other players
- Zombie Towers Mod Apk Terbaru has received positive ratings and feedback from other players who have downloaded and played the game. Here are some of them:
- "This is one of the best tower defense games I have ever played. The graphics are amazing, the gameplay is challenging and fun, and the mod apk version is awesome. I love how I can customize my gameplay with the mod menu options. I highly recommend this game to anyone who likes tower defense games with a zombie twist."
- "I really enjoy playing this game. It is very addictive and entertaining. The towers, workers, power-ups, and zombies are all very cool and unique. The mod apk version is very generous and helpful. I can play the game without any stress or frustration. I give this game 5 stars."
- "This game is very good. It has a lot of features and options that make it interesting and fun. The mod apk version is very convenient and easy to use. I can play the game with no limits or restrictions. I think this game is worth downloading and playing."
- Conclusion
- Zombie Towers Mod Apk Terbaru is a tower defense game with a zombie survival theme that will test your skills and strategy. You have to defend your castle from waves of zombies with different types of towers, workers, and power-ups. You can download and install the mod apk version of the game to enjoy unlimited money, free purchase, mod menu, god mode, one shot kill, unlimited ammo, max range, and more features. You can also follow our tips and tricks to help you win the game easily. Zombie Towers Mod Apk Terbaru is a fun and addictive game that you should try if you like tower defense games with a zombie twist.
- FAQs
- Q: How do I get more coins or gems in Zombie Towers Mod Apk Terbaru?
- A: You can get more coins or gems by completing levels, killing zombies, opening chests, watching ads, or buying them with real money. However, if you use the mod apk version of the game, you will have unlimited money and free purchase options that will let you get as many coins or gems as you want.
- Q: How do I unlock more towers or workers in Zombie Towers Mod Apk Terbaru?
- A: You can unlock more towers or workers by reaching certain levels or spending coins or gems. However, if you use the mod apk version of the game, you will have access to all the towers or workers without spending any money.
- Q: How do I save my progress in Zombie Towers Mod Apk Terbaru?
- A: You can save your progress in Zombie Towers Mod Apk Terbaru by logging in with your Google Play account or Facebook account. You can also sync your progress across different devices by using the same account.
- Q: How do I update Zombie Towers Mod Apk Terbaru?
- A: You can update Zombie Towers Mod Apk Terbaru by downloading the latest version of the mod apk file from the same source that you downloaded it from before. You can also check for updates by going to the game settings and tapping on the update button. However, you may lose some of your mod features or progress if you update the game.
- Q: How do I contact the developer of Zombie Towers Mod Apk Terbaru?
- A: You can contact the developer of Zombie Towers Mod Apk Terbaru by sending an email to edenapgames@gmail.com or visiting their website at . You can also follow them on Facebook, Twitter, Instagram, or YouTube for more updates and news about the game.
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diff --git a/spaces/contluForse/HuggingGPT/assets/DFX Audio Enhancer 12.011 Crack En US Final Nov2015 Seven7i.md b/spaces/contluForse/HuggingGPT/assets/DFX Audio Enhancer 12.011 Crack En US Final Nov2015 Seven7i.md
deleted file mode 100644
index ce229d9cbb114351abe795339a4e007d96f2fb0e..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/DFX Audio Enhancer 12.011 Crack En US Final Nov2015 Seven7i.md
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index a6d1dbf4b730a19241b835aa9f4b45c45e2db3b5..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Download 3D Analyze 2.36 Full Crack IDM The Best Way to Emulate Hardware Features and Optimize Your Games.md
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diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/leres/pix2pix/util/guidedfilter.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/leres/pix2pix/util/guidedfilter.py
deleted file mode 100644
index d377ff12e078a5f156e9246b63573dae71825fad..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/leres/pix2pix/util/guidedfilter.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import numpy as np
-
-class GuidedFilter():
- def __init__(self, source, reference, r=64, eps= 0.05**2):
- self.source = source;
- self.reference = reference;
- self.r = r
- self.eps = eps
-
- self.smooth = self.guidedfilter(self.source,self.reference,self.r,self.eps)
-
- def boxfilter(self,img, r):
- (rows, cols) = img.shape
- imDst = np.zeros_like(img)
-
- imCum = np.cumsum(img, 0)
- imDst[0 : r+1, :] = imCum[r : 2*r+1, :]
- imDst[r+1 : rows-r, :] = imCum[2*r+1 : rows, :] - imCum[0 : rows-2*r-1, :]
- imDst[rows-r: rows, :] = np.tile(imCum[rows-1, :], [r, 1]) - imCum[rows-2*r-1 : rows-r-1, :]
-
- imCum = np.cumsum(imDst, 1)
- imDst[:, 0 : r+1] = imCum[:, r : 2*r+1]
- imDst[:, r+1 : cols-r] = imCum[:, 2*r+1 : cols] - imCum[:, 0 : cols-2*r-1]
- imDst[:, cols-r: cols] = np.tile(imCum[:, cols-1], [r, 1]).T - imCum[:, cols-2*r-1 : cols-r-1]
-
- return imDst
-
- def guidedfilter(self,I, p, r, eps):
- (rows, cols) = I.shape
- N = self.boxfilter(np.ones([rows, cols]), r)
-
- meanI = self.boxfilter(I, r) / N
- meanP = self.boxfilter(p, r) / N
- meanIp = self.boxfilter(I * p, r) / N
- covIp = meanIp - meanI * meanP
-
- meanII = self.boxfilter(I * I, r) / N
- varI = meanII - meanI * meanI
-
- a = covIp / (varI + eps)
- b = meanP - a * meanI
-
- meanA = self.boxfilter(a, r) / N
- meanB = self.boxfilter(b, r) / N
-
- q = meanA * I + meanB
- return q
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/closure.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/closure.py
deleted file mode 100644
index b955f81f425be4ac3e6bb3f4aac653887989e872..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/closure.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class ClosureHook(Hook):
-
- def __init__(self, fn_name, fn):
- assert hasattr(self, fn_name)
- assert callable(fn)
- setattr(self, fn_name, fn)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/logger/text.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/logger/text.py
deleted file mode 100644
index 0b30577469d5f70e544e1ce73816326e38dadb20..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/logger/text.py
+++ /dev/null
@@ -1,256 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import datetime
-import os
-import os.path as osp
-from collections import OrderedDict
-
-import torch
-import torch.distributed as dist
-
-import annotator.mmpkg.mmcv as mmcv
-from annotator.mmpkg.mmcv.fileio.file_client import FileClient
-from annotator.mmpkg.mmcv.utils import is_tuple_of, scandir
-from ..hook import HOOKS
-from .base import LoggerHook
-
-
-@HOOKS.register_module()
-class TextLoggerHook(LoggerHook):
- """Logger hook in text.
-
- In this logger hook, the information will be printed on terminal and
- saved in json file.
-
- Args:
- by_epoch (bool, optional): Whether EpochBasedRunner is used.
- Default: True.
- interval (int, optional): Logging interval (every k iterations).
- Default: 10.
- ignore_last (bool, optional): Ignore the log of last iterations in each
- epoch if less than :attr:`interval`. Default: True.
- reset_flag (bool, optional): Whether to clear the output buffer after
- logging. Default: False.
- interval_exp_name (int, optional): Logging interval for experiment
- name. This feature is to help users conveniently get the experiment
- information from screen or log file. Default: 1000.
- out_dir (str, optional): Logs are saved in ``runner.work_dir`` default.
- If ``out_dir`` is specified, logs will be copied to a new directory
- which is the concatenation of ``out_dir`` and the last level
- directory of ``runner.work_dir``. Default: None.
- `New in version 1.3.16.`
- out_suffix (str or tuple[str], optional): Those filenames ending with
- ``out_suffix`` will be copied to ``out_dir``.
- Default: ('.log.json', '.log', '.py').
- `New in version 1.3.16.`
- keep_local (bool, optional): Whether to keep local log when
- :attr:`out_dir` is specified. If False, the local log will be
- removed. Default: True.
- `New in version 1.3.16.`
- file_client_args (dict, optional): Arguments to instantiate a
- FileClient. See :class:`mmcv.fileio.FileClient` for details.
- Default: None.
- `New in version 1.3.16.`
- """
-
- def __init__(self,
- by_epoch=True,
- interval=10,
- ignore_last=True,
- reset_flag=False,
- interval_exp_name=1000,
- out_dir=None,
- out_suffix=('.log.json', '.log', '.py'),
- keep_local=True,
- file_client_args=None):
- super(TextLoggerHook, self).__init__(interval, ignore_last, reset_flag,
- by_epoch)
- self.by_epoch = by_epoch
- self.time_sec_tot = 0
- self.interval_exp_name = interval_exp_name
-
- if out_dir is None and file_client_args is not None:
- raise ValueError(
- 'file_client_args should be "None" when `out_dir` is not'
- 'specified.')
- self.out_dir = out_dir
-
- if not (out_dir is None or isinstance(out_dir, str)
- or is_tuple_of(out_dir, str)):
- raise TypeError('out_dir should be "None" or string or tuple of '
- 'string, but got {out_dir}')
- self.out_suffix = out_suffix
-
- self.keep_local = keep_local
- self.file_client_args = file_client_args
- if self.out_dir is not None:
- self.file_client = FileClient.infer_client(file_client_args,
- self.out_dir)
-
- def before_run(self, runner):
- super(TextLoggerHook, self).before_run(runner)
-
- if self.out_dir is not None:
- self.file_client = FileClient.infer_client(self.file_client_args,
- self.out_dir)
- # The final `self.out_dir` is the concatenation of `self.out_dir`
- # and the last level directory of `runner.work_dir`
- basename = osp.basename(runner.work_dir.rstrip(osp.sep))
- self.out_dir = self.file_client.join_path(self.out_dir, basename)
- runner.logger.info(
- (f'Text logs will be saved to {self.out_dir} by '
- f'{self.file_client.name} after the training process.'))
-
- self.start_iter = runner.iter
- self.json_log_path = osp.join(runner.work_dir,
- f'{runner.timestamp}.log.json')
- if runner.meta is not None:
- self._dump_log(runner.meta, runner)
-
- def _get_max_memory(self, runner):
- device = getattr(runner.model, 'output_device', None)
- mem = torch.cuda.max_memory_allocated(device=device)
- mem_mb = torch.tensor([mem / (1024 * 1024)],
- dtype=torch.int,
- device=device)
- if runner.world_size > 1:
- dist.reduce(mem_mb, 0, op=dist.ReduceOp.MAX)
- return mem_mb.item()
-
- def _log_info(self, log_dict, runner):
- # print exp name for users to distinguish experiments
- # at every ``interval_exp_name`` iterations and the end of each epoch
- if runner.meta is not None and 'exp_name' in runner.meta:
- if (self.every_n_iters(runner, self.interval_exp_name)) or (
- self.by_epoch and self.end_of_epoch(runner)):
- exp_info = f'Exp name: {runner.meta["exp_name"]}'
- runner.logger.info(exp_info)
-
- if log_dict['mode'] == 'train':
- if isinstance(log_dict['lr'], dict):
- lr_str = []
- for k, val in log_dict['lr'].items():
- lr_str.append(f'lr_{k}: {val:.3e}')
- lr_str = ' '.join(lr_str)
- else:
- lr_str = f'lr: {log_dict["lr"]:.3e}'
-
- # by epoch: Epoch [4][100/1000]
- # by iter: Iter [100/100000]
- if self.by_epoch:
- log_str = f'Epoch [{log_dict["epoch"]}]' \
- f'[{log_dict["iter"]}/{len(runner.data_loader)}]\t'
- else:
- log_str = f'Iter [{log_dict["iter"]}/{runner.max_iters}]\t'
- log_str += f'{lr_str}, '
-
- if 'time' in log_dict.keys():
- self.time_sec_tot += (log_dict['time'] * self.interval)
- time_sec_avg = self.time_sec_tot / (
- runner.iter - self.start_iter + 1)
- eta_sec = time_sec_avg * (runner.max_iters - runner.iter - 1)
- eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
- log_str += f'eta: {eta_str}, '
- log_str += f'time: {log_dict["time"]:.3f}, ' \
- f'data_time: {log_dict["data_time"]:.3f}, '
- # statistic memory
- if torch.cuda.is_available():
- log_str += f'memory: {log_dict["memory"]}, '
- else:
- # val/test time
- # here 1000 is the length of the val dataloader
- # by epoch: Epoch[val] [4][1000]
- # by iter: Iter[val] [1000]
- if self.by_epoch:
- log_str = f'Epoch({log_dict["mode"]}) ' \
- f'[{log_dict["epoch"]}][{log_dict["iter"]}]\t'
- else:
- log_str = f'Iter({log_dict["mode"]}) [{log_dict["iter"]}]\t'
-
- log_items = []
- for name, val in log_dict.items():
- # TODO: resolve this hack
- # these items have been in log_str
- if name in [
- 'mode', 'Epoch', 'iter', 'lr', 'time', 'data_time',
- 'memory', 'epoch'
- ]:
- continue
- if isinstance(val, float):
- val = f'{val:.4f}'
- log_items.append(f'{name}: {val}')
- log_str += ', '.join(log_items)
-
- runner.logger.info(log_str)
-
- def _dump_log(self, log_dict, runner):
- # dump log in json format
- json_log = OrderedDict()
- for k, v in log_dict.items():
- json_log[k] = self._round_float(v)
- # only append log at last line
- if runner.rank == 0:
- with open(self.json_log_path, 'a+') as f:
- mmcv.dump(json_log, f, file_format='json')
- f.write('\n')
-
- def _round_float(self, items):
- if isinstance(items, list):
- return [self._round_float(item) for item in items]
- elif isinstance(items, float):
- return round(items, 5)
- else:
- return items
-
- def log(self, runner):
- if 'eval_iter_num' in runner.log_buffer.output:
- # this doesn't modify runner.iter and is regardless of by_epoch
- cur_iter = runner.log_buffer.output.pop('eval_iter_num')
- else:
- cur_iter = self.get_iter(runner, inner_iter=True)
-
- log_dict = OrderedDict(
- mode=self.get_mode(runner),
- epoch=self.get_epoch(runner),
- iter=cur_iter)
-
- # only record lr of the first param group
- cur_lr = runner.current_lr()
- if isinstance(cur_lr, list):
- log_dict['lr'] = cur_lr[0]
- else:
- assert isinstance(cur_lr, dict)
- log_dict['lr'] = {}
- for k, lr_ in cur_lr.items():
- assert isinstance(lr_, list)
- log_dict['lr'].update({k: lr_[0]})
-
- if 'time' in runner.log_buffer.output:
- # statistic memory
- if torch.cuda.is_available():
- log_dict['memory'] = self._get_max_memory(runner)
-
- log_dict = dict(log_dict, **runner.log_buffer.output)
-
- self._log_info(log_dict, runner)
- self._dump_log(log_dict, runner)
- return log_dict
-
- def after_run(self, runner):
- # copy or upload logs to self.out_dir
- if self.out_dir is not None:
- for filename in scandir(runner.work_dir, self.out_suffix, True):
- local_filepath = osp.join(runner.work_dir, filename)
- out_filepath = self.file_client.join_path(
- self.out_dir, filename)
- with open(local_filepath, 'r') as f:
- self.file_client.put_text(f.read(), out_filepath)
-
- runner.logger.info(
- (f'The file {local_filepath} has been uploaded to '
- f'{out_filepath}.'))
-
- if not self.keep_local:
- os.remove(local_filepath)
- runner.logger.info(
- (f'{local_filepath} was removed due to the '
- '`self.keep_local=False`'))
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/segmentors/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/segmentors/__init__.py
deleted file mode 100644
index dca2f09405330743c476e190896bee39c45498ea..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/segmentors/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .base import BaseSegmentor
-from .cascade_encoder_decoder import CascadeEncoderDecoder
-from .encoder_decoder import EncoderDecoder
-
-__all__ = ['BaseSegmentor', 'EncoderDecoder', 'CascadeEncoderDecoder']
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/backbones/resnext.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/backbones/resnext.py
deleted file mode 100644
index 962249ad6fd9b50960ad6426f7ce3cac6ed8c5bc..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/backbones/resnext.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import math
-
-from annotator.uniformer.mmcv.cnn import build_conv_layer, build_norm_layer
-
-from ..builder import BACKBONES
-from ..utils import ResLayer
-from .resnet import Bottleneck as _Bottleneck
-from .resnet import ResNet
-
-
-class Bottleneck(_Bottleneck):
- """Bottleneck block for ResNeXt.
-
- If style is "pytorch", the stride-two layer is the 3x3 conv layer, if it is
- "caffe", the stride-two layer is the first 1x1 conv layer.
- """
-
- def __init__(self,
- inplanes,
- planes,
- groups=1,
- base_width=4,
- base_channels=64,
- **kwargs):
- super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
-
- if groups == 1:
- width = self.planes
- else:
- width = math.floor(self.planes *
- (base_width / base_channels)) * groups
-
- self.norm1_name, norm1 = build_norm_layer(
- self.norm_cfg, width, postfix=1)
- self.norm2_name, norm2 = build_norm_layer(
- self.norm_cfg, width, postfix=2)
- self.norm3_name, norm3 = build_norm_layer(
- self.norm_cfg, self.planes * self.expansion, postfix=3)
-
- self.conv1 = build_conv_layer(
- self.conv_cfg,
- self.inplanes,
- width,
- kernel_size=1,
- stride=self.conv1_stride,
- bias=False)
- self.add_module(self.norm1_name, norm1)
- fallback_on_stride = False
- self.with_modulated_dcn = False
- if self.with_dcn:
- fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
- if not self.with_dcn or fallback_on_stride:
- self.conv2 = build_conv_layer(
- self.conv_cfg,
- width,
- width,
- kernel_size=3,
- stride=self.conv2_stride,
- padding=self.dilation,
- dilation=self.dilation,
- groups=groups,
- bias=False)
- else:
- assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
- self.conv2 = build_conv_layer(
- self.dcn,
- width,
- width,
- kernel_size=3,
- stride=self.conv2_stride,
- padding=self.dilation,
- dilation=self.dilation,
- groups=groups,
- bias=False)
-
- self.add_module(self.norm2_name, norm2)
- self.conv3 = build_conv_layer(
- self.conv_cfg,
- width,
- self.planes * self.expansion,
- kernel_size=1,
- bias=False)
- self.add_module(self.norm3_name, norm3)
-
-
-@BACKBONES.register_module()
-class ResNeXt(ResNet):
- """ResNeXt backbone.
-
- Args:
- depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
- in_channels (int): Number of input image channels. Normally 3.
- num_stages (int): Resnet stages, normally 4.
- groups (int): Group of resnext.
- base_width (int): Base width of resnext.
- strides (Sequence[int]): Strides of the first block of each stage.
- dilations (Sequence[int]): Dilation of each stage.
- out_indices (Sequence[int]): Output from which stages.
- style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
- layer is the 3x3 conv layer, otherwise the stride-two layer is
- the first 1x1 conv layer.
- frozen_stages (int): Stages to be frozen (all param fixed). -1 means
- not freezing any parameters.
- norm_cfg (dict): dictionary to construct and config norm layer.
- norm_eval (bool): Whether to set norm layers to eval mode, namely,
- freeze running stats (mean and var). Note: Effect on Batch Norm
- and its variants only.
- with_cp (bool): Use checkpoint or not. Using checkpoint will save some
- memory while slowing down the training speed.
- zero_init_residual (bool): whether to use zero init for last norm layer
- in resblocks to let them behave as identity.
-
- Example:
- >>> from annotator.uniformer.mmseg.models import ResNeXt
- >>> import torch
- >>> self = ResNeXt(depth=50)
- >>> self.eval()
- >>> inputs = torch.rand(1, 3, 32, 32)
- >>> level_outputs = self.forward(inputs)
- >>> for level_out in level_outputs:
- ... print(tuple(level_out.shape))
- (1, 256, 8, 8)
- (1, 512, 4, 4)
- (1, 1024, 2, 2)
- (1, 2048, 1, 1)
- """
-
- arch_settings = {
- 50: (Bottleneck, (3, 4, 6, 3)),
- 101: (Bottleneck, (3, 4, 23, 3)),
- 152: (Bottleneck, (3, 8, 36, 3))
- }
-
- def __init__(self, groups=1, base_width=4, **kwargs):
- self.groups = groups
- self.base_width = base_width
- super(ResNeXt, self).__init__(**kwargs)
-
- def make_res_layer(self, **kwargs):
- """Pack all blocks in a stage into a ``ResLayer``"""
- return ResLayer(
- groups=self.groups,
- base_width=self.base_width,
- base_channels=self.base_channels,
- **kwargs)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/model_io.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/model_io.py
deleted file mode 100644
index 78b6579631dd847ac76651238cb5a948b5a66286..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/model_io.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Intelligent Systems Lab Org
-
-# 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.
-
-# File author: Shariq Farooq Bhat
-
-import torch
-
-def load_state_dict(model, state_dict):
- """Load state_dict into model, handling DataParallel and DistributedDataParallel. Also checks for "model" key in state_dict.
-
- DataParallel prefixes state_dict keys with 'module.' when saving.
- If the model is not a DataParallel model but the state_dict is, then prefixes are removed.
- If the model is a DataParallel model but the state_dict is not, then prefixes are added.
- """
- state_dict = state_dict.get('model', state_dict)
- # if model is a DataParallel model, then state_dict keys are prefixed with 'module.'
-
- do_prefix = isinstance(
- model, (torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel))
- state = {}
- for k, v in state_dict.items():
- if k.startswith('module.') and not do_prefix:
- k = k[7:]
-
- if not k.startswith('module.') and do_prefix:
- k = 'module.' + k
-
- state[k] = v
-
- model.load_state_dict(state)
- print("Loaded successfully")
- return model
-
-
-def load_wts(model, checkpoint_path):
- ckpt = torch.load(checkpoint_path, map_location='cpu')
- return load_state_dict(model, ckpt)
-
-
-def load_state_dict_from_url(model, url, **kwargs):
- state_dict = torch.hub.load_state_dict_from_url(url, map_location='cpu', **kwargs)
- return load_state_dict(model, state_dict)
-
-
-def load_state_from_resource(model, resource: str):
- """Loads weights to the model from a given resource. A resource can be of following types:
- 1. URL. Prefixed with "url::"
- e.g. url::http(s)://url.resource.com/ckpt.pt
-
- 2. Local path. Prefixed with "local::"
- e.g. local::/path/to/ckpt.pt
-
-
- Args:
- model (torch.nn.Module): Model
- resource (str): resource string
-
- Returns:
- torch.nn.Module: Model with loaded weights
- """
- print(f"Using pretrained resource {resource}")
-
- if resource.startswith('url::'):
- url = resource.split('url::')[1]
- return load_state_dict_from_url(model, url, progress=True)
-
- elif resource.startswith('local::'):
- path = resource.split('local::')[1]
- return load_wts(model, path)
-
- else:
- raise ValueError("Invalid resource type, only url:: and local:: are supported")
-
\ No newline at end of file
diff --git a/spaces/cyberoleg/b2719240e190e2a649150d94db50be82838efeb0/diffusion_webui/diffusion_models/base_controlnet_pipeline.py b/spaces/cyberoleg/b2719240e190e2a649150d94db50be82838efeb0/diffusion_webui/diffusion_models/base_controlnet_pipeline.py
deleted file mode 100644
index 167158b11b477a72c019da69d25d0c7318eacae5..0000000000000000000000000000000000000000
--- a/spaces/cyberoleg/b2719240e190e2a649150d94db50be82838efeb0/diffusion_webui/diffusion_models/base_controlnet_pipeline.py
+++ /dev/null
@@ -1,31 +0,0 @@
-class ControlnetPipeline:
- def __init__(self):
- self.pipe = None
-
- def load_model(self, stable_model_path: str, controlnet_model_path: str):
- raise NotImplementedError()
-
- def load_image(self, image_path: str):
- raise NotImplementedError()
-
- def controlnet_preprocces(self, read_image: str):
- raise NotImplementedError()
-
- def generate_image(
- self,
- image_path: str,
- stable_model_path: str,
- controlnet_model_path: str,
- prompt: str,
- negative_prompt: str,
- num_images_per_prompt: int,
- guidance_scale: int,
- num_inference_step: int,
- controlnet_conditioning_scale: int,
- scheduler: str,
- seed_generator: int,
- ):
- raise NotImplementedError()
-
- def web_interface():
- raise NotImplementedError()
diff --git a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/common/poser_encoder_decoder_00.py b/spaces/cymic/Talking_Head_Anime_3/tha3/nn/common/poser_encoder_decoder_00.py
deleted file mode 100644
index acd59e873ef0f7aa45c705096da67740cd33b9b0..0000000000000000000000000000000000000000
--- a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/common/poser_encoder_decoder_00.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import math
-from typing import Optional, List
-
-import torch
-from torch import Tensor
-from torch.nn import ModuleList, Module
-
-from tha3.nn.common.poser_args import PoserArgs00
-from tha3.nn.conv import create_conv3_block_from_block_args, create_downsample_block_from_block_args, \
- create_upsample_block_from_block_args
-from tha3.nn.nonlinearity_factory import ReLUFactory
-from tha3.nn.normalization import InstanceNorm2dFactory
-from tha3.nn.resnet_block import ResnetBlock
-from tha3.nn.util import BlockArgs
-
-
-class PoserEncoderDecoder00Args(PoserArgs00):
- def __init__(self,
- image_size: int,
- input_image_channels: int,
- output_image_channels: int,
- num_pose_params: int ,
- start_channels: int,
- bottleneck_image_size,
- num_bottleneck_blocks,
- max_channels: int,
- block_args: Optional[BlockArgs] = None):
- super().__init__(
- image_size, input_image_channels, output_image_channels, start_channels, num_pose_params, block_args)
- self.max_channels = max_channels
- self.num_bottleneck_blocks = num_bottleneck_blocks
- self.bottleneck_image_size = bottleneck_image_size
- assert bottleneck_image_size > 1
-
- if block_args is None:
- self.block_args = BlockArgs(
- normalization_layer_factory=InstanceNorm2dFactory(),
- nonlinearity_factory=ReLUFactory(inplace=True))
- else:
- self.block_args = block_args
-
-
-class PoserEncoderDecoder00(Module):
- def __init__(self, args: PoserEncoderDecoder00Args):
- super().__init__()
- self.args = args
-
- self.num_levels = int(math.log2(args.image_size // args.bottleneck_image_size)) + 1
-
- self.downsample_blocks = ModuleList()
- self.downsample_blocks.append(
- create_conv3_block_from_block_args(
- args.input_image_channels,
- args.start_channels,
- args.block_args))
- current_image_size = args.image_size
- current_num_channels = args.start_channels
- while current_image_size > args.bottleneck_image_size:
- next_image_size = current_image_size // 2
- next_num_channels = self.get_num_output_channels_from_image_size(next_image_size)
- self.downsample_blocks.append(create_downsample_block_from_block_args(
- in_channels=current_num_channels,
- out_channels=next_num_channels,
- is_output_1x1=False,
- block_args=args.block_args))
- current_image_size = next_image_size
- current_num_channels = next_num_channels
- assert len(self.downsample_blocks) == self.num_levels
-
- self.bottleneck_blocks = ModuleList()
- self.bottleneck_blocks.append(create_conv3_block_from_block_args(
- in_channels=current_num_channels + args.num_pose_params,
- out_channels=current_num_channels,
- block_args=args.block_args))
- for i in range(1, args.num_bottleneck_blocks):
- self.bottleneck_blocks.append(
- ResnetBlock.create(
- num_channels=current_num_channels,
- is1x1=False,
- block_args=args.block_args))
-
- self.upsample_blocks = ModuleList()
- while current_image_size < args.image_size:
- next_image_size = current_image_size * 2
- next_num_channels = self.get_num_output_channels_from_image_size(next_image_size)
- self.upsample_blocks.append(create_upsample_block_from_block_args(
- in_channels=current_num_channels,
- out_channels=next_num_channels,
- block_args=args.block_args))
- current_image_size = next_image_size
- current_num_channels = next_num_channels
-
- def get_num_output_channels_from_level(self, level: int):
- return self.get_num_output_channels_from_image_size(self.args.image_size // (2 ** level))
-
- def get_num_output_channels_from_image_size(self, image_size: int):
- return min(self.args.start_channels * (self.args.image_size // image_size), self.args.max_channels)
-
- def forward(self, image: Tensor, pose: Optional[Tensor] = None) -> List[Tensor]:
- if self.args.num_pose_params != 0:
- assert pose is not None
- else:
- assert pose is None
- outputs = []
- feature = image
- outputs.append(feature)
- for block in self.downsample_blocks:
- feature = block(feature)
- outputs.append(feature)
- if pose is not None:
- n, c = pose.shape
- pose = pose.view(n, c, 1, 1).repeat(1, 1, self.args.bottleneck_image_size, self.args.bottleneck_image_size)
- feature = torch.cat([feature, pose], dim=1)
- for block in self.bottleneck_blocks:
- feature = block(feature)
- outputs.append(feature)
- for block in self.upsample_blocks:
- feature = block(feature)
- outputs.append(feature)
- outputs.reverse()
- return outputs
diff --git a/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/latex/attention/parameter_attention.tex b/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/latex/attention/parameter_attention.tex
deleted file mode 100644
index 7bc4fe452dbdbfe44ff72f0cdbd37acd5c786ce6..0000000000000000000000000000000000000000
--- a/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/latex/attention/parameter_attention.tex
+++ /dev/null
@@ -1,45 +0,0 @@
-\pagebreak
-\section*{Two Feed-Forward Layers = Attention over Parameters}\label{sec:parameter_attention}
-
-In addition to attention layers, our model contains position-wise feed-forward networks (Section \ref{sec:ffn}), which consist of two linear transformations with a ReLU activation in between. In fact, these networks too can be seen as a form of attention. Compare the formula for such a network with the formula for a simple dot-product attention layer (biases and scaling factors omitted):
-
-\begin{align*}
- FFN(x, W_1, W_2) = ReLU(xW_1)W_2 \\
- A(q, K, V) = Softmax(qK^T)V
-\end{align*}
-
-Based on the similarity of these formulae, the two-layer feed-forward network can be seen as a kind of attention, where the keys and values are the rows of the trainable parameter matrices $W_1$ and $W_2$, and where we use ReLU instead of Softmax in the compatibility function.
-
-%the compatablity function is $compat(q, k_i) = ReLU(q \cdot k_i)$ instead of $Softmax(qK_T)_i$.
-
-Given this similarity, we experimented with replacing the position-wise feed-forward networks with attention layers similar to the ones we use everywhere else our model. The multi-head-attention-over-parameters sublayer is identical to the multi-head attention described in \ref{sec:multihead}, except that the "keys" and "values" inputs to each attention head are trainable model parameters, as opposed to being linear projections of a previous layer. These parameters are scaled up by a factor of $\sqrt{d_{model}}$ in order to be more similar to activations.
-
-In our first experiment, we replaced each position-wise feed-forward network with a multi-head-attention-over-parameters sublayer with $h_p=8$ heads, key-dimensionality $d_{pk}=64$, and value-dimensionality $d_{pv}=64$, using $n_p=1536$ key-value pairs for each attention head. The sublayer has a total of $2097152$ parameters, including the parameters in the query projection and the output projection. This matches the number of parameters in the position-wise feed-forward network that we replaced. While the theoretical amount of computation is also the same, in practice, the attention version caused the step times to be about 30\% longer.
-
-In our second experiment, we used $h_p=8$ heads, and $n_p=512$ key-value pairs for each attention head, again matching the total number of parameters in the base model.
-
-Results for the first experiment were slightly worse than for the base model, and results for the second experiment were slightly better, see Table~\ref{tab:parameter_attention}.
-
-\begin{table}[h]
-\caption{Replacing the position-wise feed-forward networks with multihead-attention-over-parameters produces similar results to the base model. All metrics are on the English-to-German translation development set, newstest2013.}
-\label{tab:parameter_attention}
-\begin{center}
-\vspace{-2mm}
-%\scalebox{1.0}{
-\begin{tabular}{c|cccccc|cccc}
-\hline\rule{0pt}{2.0ex}
- & \multirow{2}{*}{$\dmodel$} & \multirow{2}{*}{$\dff$} &
-\multirow{2}{*}{$h_p$} & \multirow{2}{*}{$d_{pk}$} & \multirow{2}{*}{$d_{pv}$} &
- \multirow{2}{*}{$n_p$} &
- PPL & BLEU & params & training\\
- & & & & & & & (dev) & (dev) & $\times10^6$ & time \\
-\hline\rule{0pt}{2.0ex}
-base & 512 & 2048 & & & & & 4.92 & 25.8 & 65 & 12 hours\\
-\hline\rule{0pt}{2.0ex}
-AOP$_1$ & 512 & & 8 & 64 & 64 & 1536 & 4.92& 25.5 & 65 & 16 hours\\
-AOP$_2$ & 512 & & 16 & 64 & 64 & 512 & \textbf{4.86} & \textbf{25.9} & 65 & 16 hours \\
-\hline
-\end{tabular}
-%}
-\end{center}
-\end{table}
diff --git a/spaces/dalexanderch/SweetNet/app.py b/spaces/dalexanderch/SweetNet/app.py
deleted file mode 100644
index 15498fe66d4d0560f7c3cc5b14fc641d5fec5b42..0000000000000000000000000000000000000000
--- a/spaces/dalexanderch/SweetNet/app.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import os
-os.system("pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu")
-os.system("pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.12.0+cpu.html")
-import gradio as gr
-from glycowork.ml.processing import dataset_to_dataloader
-import numpy as np
-import torch
-import torch.nn as nn
-from glycowork.motif.graph import glycan_to_nxGraph
-import networkx as nx
-import pydot
-# import pygraphviz as pgv
-
-
-class EnsembleModel(nn.Module):
- def __init__(self, models):
- super().__init__()
- self.models = models
-
- def forward(self, data):
- # Check if GPU available
- device = "cpu"
- if torch.cuda.is_available():
- device = "cuda:0"
- # Prepare data
- x = data.labels.to(device)
- edge_index = data.edge_index.to(device)
- batch = data.batch.to(device)
- y_pred = [model(x,edge_index, batch).cpu().detach().numpy() for model in self.models]
- y_pred = np.mean(y_pred,axis=0)[0]
- return y_pred
-
-class_list=['Amoebozoa', 'Animalia', 'Bacteria', 'Bamfordvirae', 'Chromista', 'Euryarchaeota', 'Excavata', 'Fungi', 'Heunggongvirae',
- 'Orthornavirae', 'Pararnavirae', 'Plantae', 'Proteoarchaeota', 'Protista', 'Riboviria']
-
-model1 = torch.load("model1.pt", map_location=torch.device('cpu'))
-model2 = torch.load("model2.pt", map_location=torch.device('cpu'))
-model3 = torch.load("model3.pt", map_location=torch.device('cpu'))
-model4 = torch.load("model4.pt", map_location=torch.device('cpu'))
-model5 = torch.load("model5.pt", map_location=torch.device('cpu'))
-model6 = torch.load("model6.pt", map_location=torch.device('cpu'))
-model7 = torch.load("model7.pt", map_location=torch.device('cpu'))
-
-def fn(glycan, model):
- # Draw graph
- #graph = glycan_to_nxGraph(glycan)
- #node_labels = nx.get_node_attributes(graph, 'string_labels')
- #labels = {i:node_labels[i] for i in range(len(graph.nodes))}
- #graph = nx.relabel_nodes(graph, labels)
- #graph = nx.drawing.nx_pydot.to_pydot(graph)
- #graph.set_prog("dot")
- #graph.write_png("graph.png")
- # write_dot(graph, "graph.dot")
- # graph=pgv.AGraph("graph.dot")
- # graph.layout(prog='dot')
- # graph.draw("graph.png")
- # Perform inference
- if model == "No data augmentation":
- model_pred = model1
- model_pred.eval()
- elif model == "Classical Ensemble":
- model_pred = model3
- model_pred.eval()
- elif model == "Bagging ensemble":
- model_pred = model4
- model_pred.eval()
- elif model == "Random edge deletion":
- model_pred = model5
- model_pred.eval()
- elif model == "Hierarchy substitution":
- model_pred = model6
- model_pred.eval()
- elif model == "Adjusted class weights":
- model_pred = model7
- model_pred.eval()
- else:
- model_pred = model2
- model_pred.eval()
-
- glycan = [glycan]
- label = [0]
- data = next(iter(dataset_to_dataloader(glycan, label, batch_size=1)))
-
- if model in ["Ensemble", "Bootstrap ensemble"]:
- pred = model_pred(data)
- else:
- device = "cpu"
- x = data.labels
- edge_index = data.edge_index
- batch = data.batch
- x = x.to(device)
- edge_index = edge_index.to(device)
- batch = batch.to(device)
- pred = model_pred(x,edge_index, batch).cpu().detach().numpy()[0]
-
- pred = np.exp(pred)/sum(np.exp(pred)) # Softmax
- pred = [float(x) for x in pred]
- pred = {class_list[i]:pred[i] for i in range(15)}
- return pred
-
-
-demo = gr.Interface(
- fn=fn,
- inputs=[gr.Textbox(label="Glycan sequence", value="Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc"), gr.Radio(label="Model",choices=["No data augmentation", "Random node deletion", "Random edge deletion", "Ensemble", "Bootstrap ensemble", "Hierarchy substitution", "Adjusted class weights"])],
- outputs=[gr.Label(num_top_classes=15, label="Prediction")],
- allow_flagging="never",
- title="SweetNet demo",
- examples=[
- ["D-Rha(b1-2)D-Rha(b1-2)Gal(b1-4)[Glc(b1-2)]GlcAOMe", "Random node deletion"],
- ["Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)GalNAc", "No data augmentation"],
- ["Kdo(a2-4)[Kdo(a2-8)]Kdo(a2-4)Kdo", "Classical ensemble"],
- ["Galf(b1-6)Galf(b1-5)Galf(b1-6)Galf", "Bagging Ensemble"],
- ["GlcNAc(b1-2)Rha(a1-2)Rha(b1-3)Rha(a1-3)GlcNAc", "Random edge deletion"],
- ["Pse(b2-6)Glc(b1-6)Gal(b1-3)GalNAc(b1-3)[Glc(b1-6)]Gal(b1-3)GalNAc", "Adjusted class weights"],
- ]
-)
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/dariush-bahrami/color_transfer/colortransfer/transfer.py b/spaces/dariush-bahrami/color_transfer/colortransfer/transfer.py
deleted file mode 100644
index 4a7db6b002b3b138669f140ce7105aecce609d50..0000000000000000000000000000000000000000
--- a/spaces/dariush-bahrami/color_transfer/colortransfer/transfer.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import numpy as np
-import cv2 as cv
-
-
-def transfer_color(source_image: np.ndarray, target_image: np.ndarray) -> np.ndarray:
- """Color transfer between images
-
- Args:
- source_image (np.ndarray): Color source image
- target_image (np.ndarray): Target image
-
- Returns:
- np.ndarray: The result of the color transfer
-
- Reference:
- doi: 10.1109/38.946629
- """
- # RGB -> L*a*b*
- src_img = cv.cvtColor(source_image, cv.COLOR_RGB2Lab)
- dst_img = cv.cvtColor(target_image, cv.COLOR_RGB2Lab)
-
- # Calculate mean and std
- src_means, src_stds = src_img.mean(axis=(0, 1)), src_img.std(axis=(0, 1))
- dst_means, dst_stds = dst_img.mean(axis=(0, 1)), dst_img.std(axis=(0, 1))
-
- # Transfer
- dst_img = dst_img - dst_means.reshape((1, 1, 3))
- dst_img *= (dst_stds / src_stds).reshape((1, 1, 3))
- dst_img += src_means.reshape((1, 1, 3))
-
- # L*a*b* -> RGB
- dst_img = np.clip(dst_img, 0, 255).astype(np.uint8)
- dst_img = cv.cvtColor(dst_img, cv.COLOR_LAB2RGB)
- return dst_img
diff --git a/spaces/dawdqd/ChuanhuChatGPT/ChuanhuChatbot.py b/spaces/dawdqd/ChuanhuChatGPT/ChuanhuChatbot.py
deleted file mode 100644
index d498359af5c02037247406830672bcbbdbb7006b..0000000000000000000000000000000000000000
--- a/spaces/dawdqd/ChuanhuChatGPT/ChuanhuChatbot.py
+++ /dev/null
@@ -1,559 +0,0 @@
-# -*- coding:utf-8 -*-
-import logging
-logging.basicConfig(
- level=logging.INFO,
- format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
-)
-
-import colorama
-import gradio as gr
-
-from modules import config
-from modules.config import *
-from modules.utils import *
-from modules.presets import *
-from modules.overwrites import *
-from modules.webui import *
-from modules.repo import *
-from modules.train_func import *
-from modules.models.models import get_model
-
-logging.getLogger("httpx").setLevel(logging.WARNING)
-
-gr.Chatbot._postprocess_chat_messages = postprocess_chat_messages
-gr.Chatbot.postprocess = postprocess
-
-# with open("web_assets/css/ChuanhuChat.css", "r", encoding="utf-8") as f:
-# ChuanhuChatCSS = f.read()
-
-def create_new_model():
- return get_model(model_name = MODELS[DEFAULT_MODEL], access_key = my_api_key)[0]
-
-with gr.Blocks(theme=small_and_beautiful_theme) as demo:
- user_name = gr.State("")
- promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2))
- user_question = gr.State("")
- assert type(my_api_key)==str
- user_api_key = gr.State(my_api_key)
- current_model = gr.State(create_new_model)
-
- topic = gr.State(i18n("未命名对话历史记录"))
-
- with gr.Row():
- gr.HTML(CHUANHU_TITLE, elem_id="app-title")
- status_display = gr.Markdown(get_geoip(), elem_id="status-display")
- with gr.Row(elem_id="float-display"):
- user_info = gr.Markdown(value="getting user info...", elem_id="user-info")
- config_info = gr.HTML(get_html("config_info.html").format(bot_avatar=config.bot_avatar, user_avatar=config.user_avatar), visible=False, elem_id="config-info")
- update_info = gr.HTML(get_html("update.html").format(
- current_version=repo_tag_html(),
- version_time=version_time(),
- cancel_btn=i18n("取消"),
- update_btn=i18n("更新"),
- seenew_btn=i18n("详情"),
- ok_btn=i18n("好"),
- ), visible=check_update)
-
- with gr.Row(equal_height=True):
- with gr.Column(scale=5):
- with gr.Row():
- chatbot = gr.Chatbot(label="Chuanhu Chat", elem_id="chuanhu-chatbot", latex_delimiters=latex_delimiters_set, height=700)
- with gr.Row():
- with gr.Column(min_width=225, scale=12):
- user_input = gr.Textbox(
- elem_id="user-input-tb",
- show_label=False, placeholder=i18n("在这里输入"),
- container=False
- )
- with gr.Column(min_width=42, scale=1):
- submitBtn = gr.Button(value="", variant="primary", elem_id="submit-btn")
- cancelBtn = gr.Button(value="", variant="secondary", visible=False, elem_id="cancel-btn")
- with gr.Row(elem_id="chatbot-buttons"):
- with gr.Column(min_width=120, scale=1):
- emptyBtn = gr.Button(
- i18n("🧹 新的对话"), elem_id="empty-btn"
- )
- with gr.Column(min_width=120, scale=1):
- retryBtn = gr.Button(i18n("🔄 重新生成"))
- with gr.Column(min_width=120, scale=1):
- delFirstBtn = gr.Button(i18n("🗑️ 删除最旧对话"))
- with gr.Column(min_width=120, scale=1):
- delLastBtn = gr.Button(i18n("🗑️ 删除最新对话"))
- with gr.Row(visible=False) as like_dislike_area:
- with gr.Column(min_width=20, scale=1):
- likeBtn = gr.Button(i18n("👍"))
- with gr.Column(min_width=20, scale=1):
- dislikeBtn = gr.Button(i18n("👎"))
-
- with gr.Column():
- with gr.Column(min_width=50, scale=1):
- with gr.Tab(label=i18n("模型")):
- keyTxt = gr.Textbox(
- show_label=True,
- placeholder=f"Your API-key...",
- value=hide_middle_chars(user_api_key.value),
- type="password",
- visible=not HIDE_MY_KEY,
- label="API-Key",
- )
- if multi_api_key:
- usageTxt = gr.Markdown(i18n("多账号模式已开启，无需输入key，可直接开始对话"), elem_id="usage-display", elem_classes="insert-block", visible=show_api_billing)
- else:
- usageTxt = gr.Markdown(i18n("**发送消息** 或 **提交key** 以显示额度"), elem_id="usage-display", elem_classes="insert-block", visible=show_api_billing)
- model_select_dropdown = gr.Dropdown(
- label=i18n("选择模型"), choices=MODELS, multiselect=False, value=MODELS[DEFAULT_MODEL], interactive=True
- )
- lora_select_dropdown = gr.Dropdown(
- label=i18n("选择LoRA模型"), choices=[], multiselect=False, interactive=True, visible=False
- )
- with gr.Row():
- single_turn_checkbox = gr.Checkbox(label=i18n("单轮对话"), value=False, elem_classes="switch-checkbox")
- use_websearch_checkbox = gr.Checkbox(label=i18n("使用在线搜索"), value=False, elem_classes="switch-checkbox")
- language_select_dropdown = gr.Dropdown(
- label=i18n("选择回复语言（针对搜索&索引功能）"),
- choices=REPLY_LANGUAGES,
- multiselect=False,
- value=REPLY_LANGUAGES[0],
- )
- index_files = gr.Files(label=i18n("上传"), type="file", elem_id="upload-index-file")
- two_column = gr.Checkbox(label=i18n("双栏pdf"), value=advance_docs["pdf"].get("two_column", False))
- summarize_btn = gr.Button(i18n("总结"))
- # TODO: 公式ocr
- # formula_ocr = gr.Checkbox(label=i18n("识别公式"), value=advance_docs["pdf"].get("formula_ocr", False))
-
- with gr.Tab(label="Prompt"):
- systemPromptTxt = gr.Textbox(
- show_label=True,
- placeholder=i18n("在这里输入System Prompt..."),
- label="System prompt",
- value=INITIAL_SYSTEM_PROMPT,
- lines=10
- )
- with gr.Accordion(label=i18n("加载Prompt模板"), open=True):
- with gr.Column():
- with gr.Row():
- with gr.Column(scale=6):
- templateFileSelectDropdown = gr.Dropdown(
- label=i18n("选择Prompt模板集合文件"),
- choices=get_template_names(plain=True),
- multiselect=False,
- value=get_template_names(plain=True)[0],
- container=False,
- )
- with gr.Column(scale=1):
- templateRefreshBtn = gr.Button(i18n("🔄 刷新"))
- with gr.Row():
- with gr.Column():
- templateSelectDropdown = gr.Dropdown(
- label=i18n("从Prompt模板中加载"),
- choices=load_template(
- get_template_names(plain=True)[0], mode=1
- ),
- multiselect=False,
- container=False,
- )
-
- with gr.Tab(label=i18n("保存/加载")):
- with gr.Accordion(label=i18n("保存/加载对话历史记录"), open=True):
- with gr.Column():
- with gr.Row():
- with gr.Column(scale=6):
- historyFileSelectDropdown = gr.Dropdown(
- label=i18n("从列表中加载对话"),
- choices=get_history_names(plain=True),
- multiselect=False,
- container=False,
- )
- with gr.Row():
- with gr.Column(min_width=42, scale=1):
- historyRefreshBtn = gr.Button(i18n("🔄 刷新"))
- with gr.Column(min_width=42, scale=1):
- historyDeleteBtn = gr.Button(i18n("🗑️ 删除"))
- with gr.Row():
- with gr.Column(scale=6):
- saveFileName = gr.Textbox(
- show_label=True,
- placeholder=i18n("设置文件名: 默认为.json，可选为.md"),
- label=i18n("设置保存文件名"),
- value=i18n("对话历史记录"),
- elem_classes="no-container"
- # container=False,
- )
- with gr.Column(scale=1):
- saveHistoryBtn = gr.Button(i18n("💾 保存对话"))
- exportMarkdownBtn = gr.Button(i18n("📝 导出为Markdown"))
- gr.Markdown(i18n("默认保存于history文件夹"))
- with gr.Row():
- with gr.Column():
- downloadFile = gr.File(interactive=True)
-
- with gr.Tab(label=i18n("微调")):
- openai_train_status = gr.Markdown(label=i18n("训练状态"), value=i18n("在这里[查看使用介绍](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/%E4%BD%BF%E7%94%A8%E6%95%99%E7%A8%8B#%E5%BE%AE%E8%B0%83-gpt-35)"))
-
- with gr.Tab(label=i18n("准备数据集")):
- dataset_preview_json = gr.JSON(label=i18n("数据集预览"), readonly=True)
- dataset_selection = gr.Files(label = i18n("选择数据集"), file_types=[".xlsx", ".jsonl"], file_count="single")
- upload_to_openai_btn = gr.Button(i18n("上传到OpenAI"), variant="primary", interactive=False)
-
- with gr.Tab(label=i18n("训练")):
- openai_ft_file_id = gr.Textbox(label=i18n("文件ID"), value="", lines=1, placeholder=i18n("上传到 OpenAI 后自动填充"))
- openai_ft_suffix = gr.Textbox(label=i18n("模型名称后缀"), value="", lines=1, placeholder=i18n("可选，用于区分不同的模型"))
- openai_train_epoch_slider = gr.Slider(label=i18n("训练轮数（Epochs）"), minimum=1, maximum=100, value=3, step=1, interactive=True)
- openai_start_train_btn = gr.Button(i18n("开始训练"), variant="primary", interactive=False)
-
- with gr.Tab(label=i18n("状态")):
- openai_status_refresh_btn = gr.Button(i18n("刷新状态"))
- openai_cancel_all_jobs_btn = gr.Button(i18n("取消所有任务"))
- add_to_models_btn = gr.Button(i18n("添加训练好的模型到模型列表"), interactive=False)
-
- with gr.Tab(label=i18n("高级")):
- gr.HTML(get_html("appearance_switcher.html").format(label=i18n("切换亮暗色主题")), elem_classes="insert-block")
- use_streaming_checkbox = gr.Checkbox(
- label=i18n("实时传输回答"), value=True, visible=ENABLE_STREAMING_OPTION, elem_classes="switch-checkbox"
- )
- checkUpdateBtn = gr.Button(i18n("🔄 检查更新..."), visible=check_update)
- gr.Markdown(i18n("# ⚠️ 务必谨慎更改 ⚠️"), elem_id="advanced-warning")
- with gr.Accordion(i18n("参数"), open=False):
- temperature_slider = gr.Slider(
- minimum=-0,
- maximum=2.0,
- value=1.0,
- step=0.1,
- interactive=True,
- label="temperature",
- )
- top_p_slider = gr.Slider(
- minimum=-0,
- maximum=1.0,
- value=1.0,
- step=0.05,
- interactive=True,
- label="top-p",
- )
- n_choices_slider = gr.Slider(
- minimum=1,
- maximum=10,
- value=1,
- step=1,
- interactive=True,
- label="n choices",
- )
- stop_sequence_txt = gr.Textbox(
- show_label=True,
- placeholder=i18n("停止符，用英文逗号隔开..."),
- label="stop",
- value="",
- lines=1,
- )
- max_context_length_slider = gr.Slider(
- minimum=1,
- maximum=32768,
- value=2000,
- step=1,
- interactive=True,
- label="max context",
- )
- max_generation_slider = gr.Slider(
- minimum=1,
- maximum=32768,
- value=1000,
- step=1,
- interactive=True,
- label="max generations",
- )
- presence_penalty_slider = gr.Slider(
- minimum=-2.0,
- maximum=2.0,
- value=0.0,
- step=0.01,
- interactive=True,
- label="presence penalty",
- )
- frequency_penalty_slider = gr.Slider(
- minimum=-2.0,
- maximum=2.0,
- value=0.0,
- step=0.01,
- interactive=True,
- label="frequency penalty",
- )
- logit_bias_txt = gr.Textbox(
- show_label=True,
- placeholder=f"word:likelihood",
- label="logit bias",
- value="",
- lines=1,
- )
- user_identifier_txt = gr.Textbox(
- show_label=True,
- placeholder=i18n("用于定位滥用行为"),
- label=i18n("用户名"),
- value=user_name.value,
- lines=1,
- )
-
- with gr.Accordion(i18n("网络参数"), open=False):
- gr.Markdown(i18n("---\n⚠️ 为保证API-Key安全，请在配置文件`config.json`中修改网络设置"), elem_id="netsetting-warning")
- default_btn = gr.Button(i18n("🔙 恢复默认网络设置"))
- # 网络代理
- proxyTxt = gr.Textbox(
- show_label=True,
- placeholder=i18n("未设置代理..."),
- label=i18n("代理地址"),
- value=config.http_proxy,
- lines=1,
- interactive=False,
- # container=False,
- elem_classes="view-only-textbox no-container",
- )
- # changeProxyBtn = gr.Button(i18n("🔄 设置代理地址"))
-
- # 优先展示自定义的api_host
- apihostTxt = gr.Textbox(
- show_label=True,
- placeholder="api.openai.com",
- label="OpenAI API-Host",
- value=config.api_host or shared.API_HOST,
- lines=1,
- interactive=False,
- # container=False,
- elem_classes="view-only-textbox no-container",
- )
- # changeAPIURLBtn = gr.Button(i18n("🔄 切换API地址"))
- updateChuanhuBtn = gr.Button(visible=False, elem_classes="invisible-btn", elem_id="update-chuanhu-btn")
-
-
- gr.Markdown(CHUANHU_DESCRIPTION, elem_id="description")
- gr.HTML(get_html("footer.html").format(versions=versions_html()), elem_id="footer")
-
- # https://github.com/gradio-app/gradio/pull/3296
- def create_greeting(request: gr.Request):
- if hasattr(request, "username") and request.username: # is not None or is not ""
- logging.info(f"Get User Name: {request.username}")
- user_info, user_name = gr.Markdown.update(value=f"User: {request.username}"), request.username
- else:
- user_info, user_name = gr.Markdown.update(value=f"", visible=False), ""
- current_model = get_model(model_name = MODELS[DEFAULT_MODEL], access_key = my_api_key)[0]
- current_model.set_user_identifier(user_name)
- chatbot = gr.Chatbot.update(label=MODELS[DEFAULT_MODEL])
- return user_info, user_name, current_model, toggle_like_btn_visibility(DEFAULT_MODEL), *current_model.auto_load(), get_history_names(False, user_name), chatbot
- demo.load(create_greeting, inputs=None, outputs=[user_info, user_name, current_model, like_dislike_area, systemPromptTxt, chatbot, historyFileSelectDropdown, chatbot], api_name="load")
- chatgpt_predict_args = dict(
- fn=predict,
- inputs=[
- current_model,
- user_question,
- chatbot,
- use_streaming_checkbox,
- use_websearch_checkbox,
- index_files,
- language_select_dropdown,
- ],
- outputs=[chatbot, status_display],
- show_progress=True,
- )
-
- start_outputing_args = dict(
- fn=start_outputing,
- inputs=[],
- outputs=[submitBtn, cancelBtn],
- show_progress=True,
- )
-
- end_outputing_args = dict(
- fn=end_outputing, inputs=[], outputs=[submitBtn, cancelBtn]
- )
-
- reset_textbox_args = dict(
- fn=reset_textbox, inputs=[], outputs=[user_input]
- )
-
- transfer_input_args = dict(
- fn=transfer_input, inputs=[user_input], outputs=[user_question, user_input, submitBtn, cancelBtn], show_progress=True
- )
-
- get_usage_args = dict(
- fn=billing_info, inputs=[current_model], outputs=[usageTxt], show_progress=False
- )
-
- load_history_from_file_args = dict(
- fn=load_chat_history,
- inputs=[current_model, historyFileSelectDropdown, user_name],
- outputs=[saveFileName, systemPromptTxt, chatbot]
- )
-
- refresh_history_args = dict(
- fn=get_history_names, inputs=[gr.State(False), user_name], outputs=[historyFileSelectDropdown]
- )
-
-
- # Chatbot
- cancelBtn.click(interrupt, [current_model], [])
-
- user_input.submit(**transfer_input_args).then(**chatgpt_predict_args).then(**end_outputing_args)
- user_input.submit(**get_usage_args)
-
- submitBtn.click(**transfer_input_args).then(**chatgpt_predict_args, api_name="predict").then(**end_outputing_args)
- submitBtn.click(**get_usage_args)
-
- index_files.change(handle_file_upload, [current_model, index_files, chatbot, language_select_dropdown], [index_files, chatbot, status_display])
- summarize_btn.click(handle_summarize_index, [current_model, index_files, chatbot, language_select_dropdown], [chatbot, status_display])
-
- emptyBtn.click(
- reset,
- inputs=[current_model],
- outputs=[chatbot, status_display],
- show_progress=True,
- _js='clearChatbot',
- )
-
- retryBtn.click(**start_outputing_args).then(
- retry,
- [
- current_model,
- chatbot,
- use_streaming_checkbox,
- use_websearch_checkbox,
- index_files,
- language_select_dropdown,
- ],
- [chatbot, status_display],
- show_progress=True,
- ).then(**end_outputing_args)
- retryBtn.click(**get_usage_args)
-
- delFirstBtn.click(
- delete_first_conversation,
- [current_model],
- [status_display],
- )
-
- delLastBtn.click(
- delete_last_conversation,
- [current_model, chatbot],
- [chatbot, status_display],
- show_progress=False
- )
-
- likeBtn.click(
- like,
- [current_model],
- [status_display],
- show_progress=False
- )
-
- dislikeBtn.click(
- dislike,
- [current_model],
- [status_display],
- show_progress=False
- )
-
- two_column.change(update_doc_config, [two_column], None)
-
- # LLM Models
- keyTxt.change(set_key, [current_model, keyTxt], [user_api_key, status_display], api_name="set_key").then(**get_usage_args)
- keyTxt.submit(**get_usage_args)
- single_turn_checkbox.change(set_single_turn, [current_model, single_turn_checkbox], None)
- model_select_dropdown.change(get_model, [model_select_dropdown, lora_select_dropdown, user_api_key, temperature_slider, top_p_slider, systemPromptTxt, user_name], [current_model, status_display, chatbot, lora_select_dropdown, user_api_key, keyTxt], show_progress=True, api_name="get_model")
- model_select_dropdown.change(toggle_like_btn_visibility, [model_select_dropdown], [like_dislike_area], show_progress=False)
- lora_select_dropdown.change(get_model, [model_select_dropdown, lora_select_dropdown, user_api_key, temperature_slider, top_p_slider, systemPromptTxt, user_name], [current_model, status_display, chatbot], show_progress=True)
-
- # Template
- systemPromptTxt.change(set_system_prompt, [current_model, systemPromptTxt], None)
- templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
- templateFileSelectDropdown.change(
- load_template,
- [templateFileSelectDropdown],
- [promptTemplates, templateSelectDropdown],
- show_progress=True,
- )
- templateSelectDropdown.change(
- get_template_content,
- [promptTemplates, templateSelectDropdown, systemPromptTxt],
- [systemPromptTxt],
- show_progress=True,
- )
-
- # S&L
- saveHistoryBtn.click(
- save_chat_history,
- [current_model, saveFileName, chatbot, user_name],
- downloadFile,
- show_progress=True,
- )
- saveHistoryBtn.click(get_history_names, [gr.State(False), user_name], [historyFileSelectDropdown])
- exportMarkdownBtn.click(
- export_markdown,
- [current_model, saveFileName, chatbot, user_name],
- downloadFile,
- show_progress=True,
- )
- historyRefreshBtn.click(**refresh_history_args)
- historyDeleteBtn.click(delete_chat_history, [current_model, historyFileSelectDropdown, user_name], [status_display, historyFileSelectDropdown, chatbot], _js='(a,b,c)=>{return showConfirmationDialog(a, b, c);}')
- historyFileSelectDropdown.change(**load_history_from_file_args)
- downloadFile.change(upload_chat_history, [current_model, downloadFile, user_name], [saveFileName, systemPromptTxt, chatbot])
-
- # Train
- dataset_selection.upload(handle_dataset_selection, dataset_selection, [dataset_preview_json, upload_to_openai_btn, openai_train_status])
- dataset_selection.clear(handle_dataset_clear, [], [dataset_preview_json, upload_to_openai_btn])
- upload_to_openai_btn.click(upload_to_openai, [dataset_selection], [openai_ft_file_id, openai_train_status], show_progress=True)
-
- openai_ft_file_id.change(lambda x: gr.update(interactive=True) if len(x) > 0 else gr.update(interactive=False), [openai_ft_file_id], [openai_start_train_btn])
- openai_start_train_btn.click(start_training, [openai_ft_file_id, openai_ft_suffix, openai_train_epoch_slider], [openai_train_status])
-
- openai_status_refresh_btn.click(get_training_status, [], [openai_train_status, add_to_models_btn])
- add_to_models_btn.click(add_to_models, [], [model_select_dropdown, openai_train_status], show_progress=True)
- openai_cancel_all_jobs_btn.click(cancel_all_jobs, [], [openai_train_status], show_progress=True)
-
- # Advanced
- max_context_length_slider.change(set_token_upper_limit, [current_model, max_context_length_slider], None)
- temperature_slider.change(set_temperature, [current_model, temperature_slider], None)
- top_p_slider.change(set_top_p, [current_model, top_p_slider], None)
- n_choices_slider.change(set_n_choices, [current_model, n_choices_slider], None)
- stop_sequence_txt.change(set_stop_sequence, [current_model, stop_sequence_txt], None)
- max_generation_slider.change(set_max_tokens, [current_model, max_generation_slider], None)
- presence_penalty_slider.change(set_presence_penalty, [current_model, presence_penalty_slider], None)
- frequency_penalty_slider.change(set_frequency_penalty, [current_model, frequency_penalty_slider], None)
- logit_bias_txt.change(set_logit_bias, [current_model, logit_bias_txt], None)
- user_identifier_txt.change(set_user_identifier, [current_model, user_identifier_txt], None)
-
- default_btn.click(
- reset_default, [], [apihostTxt, proxyTxt, status_display], show_progress=True
- )
- # changeAPIURLBtn.click(
- # change_api_host,
- # [apihostTxt],
- # [status_display],
- # show_progress=True,
- # )
- # changeProxyBtn.click(
- # change_proxy,
- # [proxyTxt],
- # [status_display],
- # show_progress=True,
- # )
- checkUpdateBtn.click(fn=None, _js='manualCheckUpdate')
-
- # Invisible elements
- updateChuanhuBtn.click(
- update_chuanhu,
- [],
- [status_display],
- show_progress=True,
- )
-
-logging.info(
- colorama.Back.GREEN
- + "\n川虎的温馨提示：访问 http://localhost:7860 查看界面"
- + colorama.Style.RESET_ALL
-)
-# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
-demo.title = i18n("川虎Chat 🚀")
-
-if __name__ == "__main__":
- reload_javascript()
- demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
- blocked_paths=["config.json"],
- favicon_path="./web_assets/favicon.ico",
- )
diff --git a/spaces/dbirks/diffuse-the-rest/build/_app/immutable/components/error.svelte-ef6e4efb.js b/spaces/dbirks/diffuse-the-rest/build/_app/immutable/components/error.svelte-ef6e4efb.js
deleted file mode 100644
index 2d7f5d42a257e6f781c9de183cbf3e802d12cfc9..0000000000000000000000000000000000000000
--- a/spaces/dbirks/diffuse-the-rest/build/_app/immutable/components/error.svelte-ef6e4efb.js
+++ /dev/null
@@ -1 +0,0 @@
-import{S as A,i as C,s as F,k as v,q as k,a as h,e as q,l as g,m as E,r as $,h as p,c as R,b as u,F as P,u as S,A as w,G}from"../chunks/index-a207c28c.js";import{s as H}from"../chunks/singletons-a29cf3c6.js";const O=()=>{const t=H,s={page:{subscribe:t.page.subscribe},navigating:{subscribe:t.navigating.subscribe},updated:t.updated};return Object.defineProperties(s,{preloading:{get(){return console.error("stores.preloading is deprecated; use stores.navigating instead"),{subscribe:t.navigating.subscribe}},enumerable:!1},session:{get(){return B(),{}},enumerable:!1}}),s},z={subscribe(t){return O().page.subscribe(t)}};function B(){throw new Error("stores.session is no longer available. See https://github.com/sveltejs/kit/discussions/5883")}function N(t){let s,i=t[0].error.frame+"",o;return{c(){s=v("pre"),o=k(i)},l(r){s=g(r,"PRE",{});var a=E(s);o=$(a,i),a.forEach(p)},m(r,a){u(r,s,a),P(s,o)},p(r,a){a&1&&i!==(i=r[0].error.frame+"")&&S(o,i)},d(r){r&&p(s)}}}function y(t){let s,i=t[0].error.stack+"",o;return{c(){s=v("pre"),o=k(i)},l(r){s=g(r,"PRE",{});var a=E(s);o=$(a,i),a.forEach(p)},m(r,a){u(r,s,a),P(s,o)},p(r,a){a&1&&i!==(i=r[0].error.stack+"")&&S(o,i)},d(r){r&&p(s)}}}function D(t){let s,i=t[0].status+"",o,r,a,b=t[0].error.message+"",_,d,c,m,l=t[0].error.frame&&N(t),n=t[0].error.stack&&y(t);return{c(){s=v("h1"),o=k(i),r=h(),a=v("pre"),_=k(b),d=h(),l&&l.c(),c=h(),n&&n.c(),m=q()},l(e){s=g(e,"H1",{});var f=E(s);o=$(f,i),f.forEach(p),r=R(e),a=g(e,"PRE",{});var j=E(a);_=$(j,b),j.forEach(p),d=R(e),l&&l.l(e),c=R(e),n&&n.l(e),m=q()},m(e,f){u(e,s,f),P(s,o),u(e,r,f),u(e,a,f),P(a,_),u(e,d,f),l&&l.m(e,f),u(e,c,f),n&&n.m(e,f),u(e,m,f)},p(e,[f]){f&1&&i!==(i=e[0].status+"")&&S(o,i),f&1&&b!==(b=e[0].error.message+"")&&S(_,b),e[0].error.frame?l?l.p(e,f):(l=N(e),l.c(),l.m(c.parentNode,c)):l&&(l.d(1),l=null),e[0].error.stack?n?n.p(e,f):(n=y(e),n.c(),n.m(m.parentNode,m)):n&&(n.d(1),n=null)},i:w,o:w,d(e){e&&p(s),e&&p(r),e&&p(a),e&&p(d),l&&l.d(e),e&&p(c),n&&n.d(e),e&&p(m)}}}function I(t,s,i){let o;return G(t,z,r=>i(0,o=r)),[o]}class L extends A{constructor(s){super(),C(this,s,I,D,F,{})}}export{L as default};
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/streams.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/streams.py
deleted file mode 100644
index 726b02326f66d37b9de1947cb78470479a7bc82b..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/streams.py
+++ /dev/null
@@ -1,660 +0,0 @@
-import asyncio
-import collections
-import warnings
-from typing import Awaitable, Callable, Deque, Generic, List, Optional, Tuple, TypeVar
-
-from .base_protocol import BaseProtocol
-from .helpers import BaseTimerContext, set_exception, set_result
-from .log import internal_logger
-from .typedefs import Final
-
-__all__ = (
- "EMPTY_PAYLOAD",
- "EofStream",
- "StreamReader",
- "DataQueue",
- "FlowControlDataQueue",
-)
-
-_T = TypeVar("_T")
-
-
-class EofStream(Exception):
- """eof stream indication."""
-
-
-class AsyncStreamIterator(Generic[_T]):
- def __init__(self, read_func: Callable[[], Awaitable[_T]]) -> None:
- self.read_func = read_func
-
- def __aiter__(self) -> "AsyncStreamIterator[_T]":
- return self
-
- async def __anext__(self) -> _T:
- try:
- rv = await self.read_func()
- except EofStream:
- raise StopAsyncIteration
- if rv == b"":
- raise StopAsyncIteration
- return rv
-
-
-class ChunkTupleAsyncStreamIterator:
- def __init__(self, stream: "StreamReader") -> None:
- self._stream = stream
-
- def __aiter__(self) -> "ChunkTupleAsyncStreamIterator":
- return self
-
- async def __anext__(self) -> Tuple[bytes, bool]:
- rv = await self._stream.readchunk()
- if rv == (b"", False):
- raise StopAsyncIteration
- return rv
-
-
-class AsyncStreamReaderMixin:
- def __aiter__(self) -> AsyncStreamIterator[bytes]:
- return AsyncStreamIterator(self.readline) # type: ignore[attr-defined]
-
- def iter_chunked(self, n: int) -> AsyncStreamIterator[bytes]:
- """Returns an asynchronous iterator that yields chunks of size n.
-
- Python-3.5 available for Python 3.5+ only
- """
- return AsyncStreamIterator(
- lambda: self.read(n) # type: ignore[attr-defined,no-any-return]
- )
-
- def iter_any(self) -> AsyncStreamIterator[bytes]:
- """Yield all available data as soon as it is received.
-
- Python-3.5 available for Python 3.5+ only
- """
- return AsyncStreamIterator(self.readany) # type: ignore[attr-defined]
-
- def iter_chunks(self) -> ChunkTupleAsyncStreamIterator:
- """Yield chunks of data as they are received by the server.
-
- The yielded objects are tuples
- of (bytes, bool) as returned by the StreamReader.readchunk method.
-
- Python-3.5 available for Python 3.5+ only
- """
- return ChunkTupleAsyncStreamIterator(self) # type: ignore[arg-type]
-
-
-class StreamReader(AsyncStreamReaderMixin):
- """An enhancement of asyncio.StreamReader.
-
- Supports asynchronous iteration by line, chunk or as available::
-
- async for line in reader:
- ...
- async for chunk in reader.iter_chunked(1024):
- ...
- async for slice in reader.iter_any():
- ...
-
- """
-
- total_bytes = 0
-
- def __init__(
- self,
- protocol: BaseProtocol,
- limit: int,
- *,
- timer: Optional[BaseTimerContext] = None,
- loop: Optional[asyncio.AbstractEventLoop] = None,
- ) -> None:
- self._protocol = protocol
- self._low_water = limit
- self._high_water = limit * 2
- if loop is None:
- loop = asyncio.get_event_loop()
- self._loop = loop
- self._size = 0
- self._cursor = 0
- self._http_chunk_splits: Optional[List[int]] = None
- self._buffer: Deque[bytes] = collections.deque()
- self._buffer_offset = 0
- self._eof = False
- self._waiter: Optional[asyncio.Future[None]] = None
- self._eof_waiter: Optional[asyncio.Future[None]] = None
- self._exception: Optional[BaseException] = None
- self._timer = timer
- self._eof_callbacks: List[Callable[[], None]] = []
-
- def __repr__(self) -> str:
- info = [self.__class__.__name__]
- if self._size:
- info.append("%d bytes" % self._size)
- if self._eof:
- info.append("eof")
- if self._low_water != 2**16: # default limit
- info.append("low=%d high=%d" % (self._low_water, self._high_water))
- if self._waiter:
- info.append("w=%r" % self._waiter)
- if self._exception:
- info.append("e=%r" % self._exception)
- return "<%s>" % " ".join(info)
-
- def get_read_buffer_limits(self) -> Tuple[int, int]:
- return (self._low_water, self._high_water)
-
- def exception(self) -> Optional[BaseException]:
- return self._exception
-
- def set_exception(self, exc: BaseException) -> None:
- self._exception = exc
- self._eof_callbacks.clear()
-
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_exception(waiter, exc)
-
- waiter = self._eof_waiter
- if waiter is not None:
- self._eof_waiter = None
- set_exception(waiter, exc)
-
- def on_eof(self, callback: Callable[[], None]) -> None:
- if self._eof:
- try:
- callback()
- except Exception:
- internal_logger.exception("Exception in eof callback")
- else:
- self._eof_callbacks.append(callback)
-
- def feed_eof(self) -> None:
- self._eof = True
-
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_result(waiter, None)
-
- waiter = self._eof_waiter
- if waiter is not None:
- self._eof_waiter = None
- set_result(waiter, None)
-
- for cb in self._eof_callbacks:
- try:
- cb()
- except Exception:
- internal_logger.exception("Exception in eof callback")
-
- self._eof_callbacks.clear()
-
- def is_eof(self) -> bool:
- """Return True if 'feed_eof' was called."""
- return self._eof
-
- def at_eof(self) -> bool:
- """Return True if the buffer is empty and 'feed_eof' was called."""
- return self._eof and not self._buffer
-
- async def wait_eof(self) -> None:
- if self._eof:
- return
-
- assert self._eof_waiter is None
- self._eof_waiter = self._loop.create_future()
- try:
- await self._eof_waiter
- finally:
- self._eof_waiter = None
-
- def unread_data(self, data: bytes) -> None:
- """rollback reading some data from stream, inserting it to buffer head."""
- warnings.warn(
- "unread_data() is deprecated "
- "and will be removed in future releases (#3260)",
- DeprecationWarning,
- stacklevel=2,
- )
- if not data:
- return
-
- if self._buffer_offset:
- self._buffer[0] = self._buffer[0][self._buffer_offset :]
- self._buffer_offset = 0
- self._size += len(data)
- self._cursor -= len(data)
- self._buffer.appendleft(data)
- self._eof_counter = 0
-
- # TODO: size is ignored, remove the param later
- def feed_data(self, data: bytes, size: int = 0) -> None:
- assert not self._eof, "feed_data after feed_eof"
-
- if not data:
- return
-
- self._size += len(data)
- self._buffer.append(data)
- self.total_bytes += len(data)
-
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_result(waiter, None)
-
- if self._size > self._high_water and not self._protocol._reading_paused:
- self._protocol.pause_reading()
-
- def begin_http_chunk_receiving(self) -> None:
- if self._http_chunk_splits is None:
- if self.total_bytes:
- raise RuntimeError(
- "Called begin_http_chunk_receiving when" "some data was already fed"
- )
- self._http_chunk_splits = []
-
- def end_http_chunk_receiving(self) -> None:
- if self._http_chunk_splits is None:
- raise RuntimeError(
- "Called end_chunk_receiving without calling "
- "begin_chunk_receiving first"
- )
-
- # self._http_chunk_splits contains logical byte offsets from start of
- # the body transfer. Each offset is the offset of the end of a chunk.
- # "Logical" means bytes, accessible for a user.
- # If no chunks containig logical data were received, current position
- # is difinitely zero.
- pos = self._http_chunk_splits[-1] if self._http_chunk_splits else 0
-
- if self.total_bytes == pos:
- # We should not add empty chunks here. So we check for that.
- # Note, when chunked + gzip is used, we can receive a chunk
- # of compressed data, but that data may not be enough for gzip FSM
- # to yield any uncompressed data. That's why current position may
- # not change after receiving a chunk.
- return
-
- self._http_chunk_splits.append(self.total_bytes)
-
- # wake up readchunk when end of http chunk received
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_result(waiter, None)
-
- async def _wait(self, func_name: str) -> None:
- # StreamReader uses a future to link the protocol feed_data() method
- # to a read coroutine. Running two read coroutines at the same time
- # would have an unexpected behaviour. It would not possible to know
- # which coroutine would get the next data.
- if self._waiter is not None:
- raise RuntimeError(
- "%s() called while another coroutine is "
- "already waiting for incoming data" % func_name
- )
-
- waiter = self._waiter = self._loop.create_future()
- try:
- if self._timer:
- with self._timer:
- await waiter
- else:
- await waiter
- finally:
- self._waiter = None
-
- async def readline(self) -> bytes:
- return await self.readuntil()
-
- async def readuntil(self, separator: bytes = b"\n") -> bytes:
- seplen = len(separator)
- if seplen == 0:
- raise ValueError("Separator should be at least one-byte string")
-
- if self._exception is not None:
- raise self._exception
-
- chunk = b""
- chunk_size = 0
- not_enough = True
-
- while not_enough:
- while self._buffer and not_enough:
- offset = self._buffer_offset
- ichar = self._buffer[0].find(separator, offset) + 1
- # Read from current offset to found separator or to the end.
- data = self._read_nowait_chunk(ichar - offset if ichar else -1)
- chunk += data
- chunk_size += len(data)
- if ichar:
- not_enough = False
-
- if chunk_size > self._high_water:
- raise ValueError("Chunk too big")
-
- if self._eof:
- break
-
- if not_enough:
- await self._wait("readuntil")
-
- return chunk
-
- async def read(self, n: int = -1) -> bytes:
- if self._exception is not None:
- raise self._exception
-
- # migration problem; with DataQueue you have to catch
- # EofStream exception, so common way is to run payload.read() inside
- # infinite loop. what can cause real infinite loop with StreamReader
- # lets keep this code one major release.
- if __debug__:
- if self._eof and not self._buffer:
- self._eof_counter = getattr(self, "_eof_counter", 0) + 1
- if self._eof_counter > 5:
- internal_logger.warning(
- "Multiple access to StreamReader in eof state, "
- "might be infinite loop.",
- stack_info=True,
- )
-
- if not n:
- return b""
-
- if n < 0:
- # This used to just loop creating a new waiter hoping to
- # collect everything in self._buffer, but that would
- # deadlock if the subprocess sends more than self.limit
- # bytes. So just call self.readany() until EOF.
- blocks = []
- while True:
- block = await self.readany()
- if not block:
- break
- blocks.append(block)
- return b"".join(blocks)
-
- # TODO: should be `if` instead of `while`
- # because waiter maybe triggered on chunk end,
- # without feeding any data
- while not self._buffer and not self._eof:
- await self._wait("read")
-
- return self._read_nowait(n)
-
- async def readany(self) -> bytes:
- if self._exception is not None:
- raise self._exception
-
- # TODO: should be `if` instead of `while`
- # because waiter maybe triggered on chunk end,
- # without feeding any data
- while not self._buffer and not self._eof:
- await self._wait("readany")
-
- return self._read_nowait(-1)
-
- async def readchunk(self) -> Tuple[bytes, bool]:
- """Returns a tuple of (data, end_of_http_chunk).
-
- When chunked transfer
- encoding is used, end_of_http_chunk is a boolean indicating if the end
- of the data corresponds to the end of a HTTP chunk , otherwise it is
- always False.
- """
- while True:
- if self._exception is not None:
- raise self._exception
-
- while self._http_chunk_splits:
- pos = self._http_chunk_splits.pop(0)
- if pos == self._cursor:
- return (b"", True)
- if pos > self._cursor:
- return (self._read_nowait(pos - self._cursor), True)
- internal_logger.warning(
- "Skipping HTTP chunk end due to data "
- "consumption beyond chunk boundary"
- )
-
- if self._buffer:
- return (self._read_nowait_chunk(-1), False)
- # return (self._read_nowait(-1), False)
-
- if self._eof:
- # Special case for signifying EOF.
- # (b'', True) is not a final return value actually.
- return (b"", False)
-
- await self._wait("readchunk")
-
- async def readexactly(self, n: int) -> bytes:
- if self._exception is not None:
- raise self._exception
-
- blocks: List[bytes] = []
- while n > 0:
- block = await self.read(n)
- if not block:
- partial = b"".join(blocks)
- raise asyncio.IncompleteReadError(partial, len(partial) + n)
- blocks.append(block)
- n -= len(block)
-
- return b"".join(blocks)
-
- def read_nowait(self, n: int = -1) -> bytes:
- # default was changed to be consistent with .read(-1)
- #
- # I believe the most users don't know about the method and
- # they are not affected.
- if self._exception is not None:
- raise self._exception
-
- if self._waiter and not self._waiter.done():
- raise RuntimeError(
- "Called while some coroutine is waiting for incoming data."
- )
-
- return self._read_nowait(n)
-
- def _read_nowait_chunk(self, n: int) -> bytes:
- first_buffer = self._buffer[0]
- offset = self._buffer_offset
- if n != -1 and len(first_buffer) - offset > n:
- data = first_buffer[offset : offset + n]
- self._buffer_offset += n
-
- elif offset:
- self._buffer.popleft()
- data = first_buffer[offset:]
- self._buffer_offset = 0
-
- else:
- data = self._buffer.popleft()
-
- self._size -= len(data)
- self._cursor += len(data)
-
- chunk_splits = self._http_chunk_splits
- # Prevent memory leak: drop useless chunk splits
- while chunk_splits and chunk_splits[0] < self._cursor:
- chunk_splits.pop(0)
-
- if self._size < self._low_water and self._protocol._reading_paused:
- self._protocol.resume_reading()
- return data
-
- def _read_nowait(self, n: int) -> bytes:
- """Read not more than n bytes, or whole buffer if n == -1"""
- chunks = []
-
- while self._buffer:
- chunk = self._read_nowait_chunk(n)
- chunks.append(chunk)
- if n != -1:
- n -= len(chunk)
- if n == 0:
- break
-
- return b"".join(chunks) if chunks else b""
-
-
-class EmptyStreamReader(StreamReader): # lgtm [py/missing-call-to-init]
- def __init__(self) -> None:
- pass
-
- def exception(self) -> Optional[BaseException]:
- return None
-
- def set_exception(self, exc: BaseException) -> None:
- pass
-
- def on_eof(self, callback: Callable[[], None]) -> None:
- try:
- callback()
- except Exception:
- internal_logger.exception("Exception in eof callback")
-
- def feed_eof(self) -> None:
- pass
-
- def is_eof(self) -> bool:
- return True
-
- def at_eof(self) -> bool:
- return True
-
- async def wait_eof(self) -> None:
- return
-
- def feed_data(self, data: bytes, n: int = 0) -> None:
- pass
-
- async def readline(self) -> bytes:
- return b""
-
- async def read(self, n: int = -1) -> bytes:
- return b""
-
- # TODO add async def readuntil
-
- async def readany(self) -> bytes:
- return b""
-
- async def readchunk(self) -> Tuple[bytes, bool]:
- return (b"", True)
-
- async def readexactly(self, n: int) -> bytes:
- raise asyncio.IncompleteReadError(b"", n)
-
- def read_nowait(self, n: int = -1) -> bytes:
- return b""
-
-
-EMPTY_PAYLOAD: Final[StreamReader] = EmptyStreamReader()
-
-
-class DataQueue(Generic[_T]):
- """DataQueue is a general-purpose blocking queue with one reader."""
-
- def __init__(self, loop: asyncio.AbstractEventLoop) -> None:
- self._loop = loop
- self._eof = False
- self._waiter: Optional[asyncio.Future[None]] = None
- self._exception: Optional[BaseException] = None
- self._size = 0
- self._buffer: Deque[Tuple[_T, int]] = collections.deque()
-
- def __len__(self) -> int:
- return len(self._buffer)
-
- def is_eof(self) -> bool:
- return self._eof
-
- def at_eof(self) -> bool:
- return self._eof and not self._buffer
-
- def exception(self) -> Optional[BaseException]:
- return self._exception
-
- def set_exception(self, exc: BaseException) -> None:
- self._eof = True
- self._exception = exc
-
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_exception(waiter, exc)
-
- def feed_data(self, data: _T, size: int = 0) -> None:
- self._size += size
- self._buffer.append((data, size))
-
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_result(waiter, None)
-
- def feed_eof(self) -> None:
- self._eof = True
-
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_result(waiter, None)
-
- async def read(self) -> _T:
- if not self._buffer and not self._eof:
- assert not self._waiter
- self._waiter = self._loop.create_future()
- try:
- await self._waiter
- except (asyncio.CancelledError, asyncio.TimeoutError):
- self._waiter = None
- raise
-
- if self._buffer:
- data, size = self._buffer.popleft()
- self._size -= size
- return data
- else:
- if self._exception is not None:
- raise self._exception
- else:
- raise EofStream
-
- def __aiter__(self) -> AsyncStreamIterator[_T]:
- return AsyncStreamIterator(self.read)
-
-
-class FlowControlDataQueue(DataQueue[_T]):
- """FlowControlDataQueue resumes and pauses an underlying stream.
-
- It is a destination for parsed data.
- """
-
- def __init__(
- self, protocol: BaseProtocol, limit: int, *, loop: asyncio.AbstractEventLoop
- ) -> None:
- super().__init__(loop=loop)
-
- self._protocol = protocol
- self._limit = limit * 2
-
- def feed_data(self, data: _T, size: int = 0) -> None:
- super().feed_data(data, size)
-
- if self._size > self._limit and not self._protocol._reading_paused:
- self._protocol.pause_reading()
-
- async def read(self) -> _T:
- try:
- return await super().read()
- finally:
- if self._size < self._limit and self._protocol._reading_paused:
- self._protocol.resume_reading()
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/callbacks.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/callbacks.py
deleted file mode 100644
index 4500d02cbcae78d9cd764956d4cc46963b525213..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/callbacks.py
+++ /dev/null
@@ -1,238 +0,0 @@
-class Callback:
- """
- Base class and interface for callback mechanism
-
- This class can be used directly for monitoring file transfers by
- providing ``callback=Callback(hooks=...)`` (see the ``hooks`` argument,
- below), or subclassed for more specialised behaviour.
-
- Parameters
- ----------
- size: int (optional)
- Nominal quantity for the value that corresponds to a complete
- transfer, e.g., total number of tiles or total number of
- bytes
- value: int (0)
- Starting internal counter value
- hooks: dict or None
- A dict of named functions to be called on each update. The signature
- of these must be ``f(size, value, **kwargs)``
- """
-
- def __init__(self, size=None, value=0, hooks=None, **kwargs):
- self.size = size
- self.value = value
- self.hooks = hooks or {}
- self.kw = kwargs
-
- def set_size(self, size):
- """
- Set the internal maximum size attribute
-
- Usually called if not initially set at instantiation. Note that this
- triggers a ``call()``.
-
- Parameters
- ----------
- size: int
- """
- self.size = size
- self.call()
-
- def absolute_update(self, value):
- """
- Set the internal value state
-
- Triggers ``call()``
-
- Parameters
- ----------
- value: int
- """
- self.value = value
- self.call()
-
- def relative_update(self, inc=1):
- """
- Delta increment the internal counter
-
- Triggers ``call()``
-
- Parameters
- ----------
- inc: int
- """
- self.value += inc
- self.call()
-
- def call(self, hook_name=None, **kwargs):
- """
- Execute hook(s) with current state
-
- Each function is passed the internal size and current value
-
- Parameters
- ----------
- hook_name: str or None
- If given, execute on this hook
- kwargs: passed on to (all) hook(s)
- """
- if not self.hooks:
- return
- kw = self.kw.copy()
- kw.update(kwargs)
- if hook_name:
- if hook_name not in self.hooks:
- return
- return self.hooks[hook_name](self.size, self.value, **kw)
- for hook in self.hooks.values() or []:
- hook(self.size, self.value, **kw)
-
- def wrap(self, iterable):
- """
- Wrap an iterable to call ``relative_update`` on each iterations
-
- Parameters
- ----------
- iterable: Iterable
- The iterable that is being wrapped
- """
- for item in iterable:
- self.relative_update()
- yield item
-
- def branch(self, path_1, path_2, kwargs):
- """
- Set callbacks for child transfers
-
- If this callback is operating at a higher level, e.g., put, which may
- trigger transfers that can also be monitored. The passed kwargs are
- to be *mutated* to add ``callback=``, if this class supports branching
- to children.
-
- Parameters
- ----------
- path_1: str
- Child's source path
- path_2: str
- Child's destination path
- kwargs: dict
- arguments passed to child method, e.g., put_file.
-
- Returns
- -------
-
- """
- return None
-
- def no_op(self, *_, **__):
- pass
-
- def __getattr__(self, item):
- """
- If undefined methods are called on this class, nothing happens
- """
- return self.no_op
-
- @classmethod
- def as_callback(cls, maybe_callback=None):
- """Transform callback=... into Callback instance
-
- For the special value of ``None``, return the global instance of
- ``NoOpCallback``. This is an alternative to including
- ``callback=_DEFAULT_CALLBACK`` directly in a method signature.
- """
- if maybe_callback is None:
- return _DEFAULT_CALLBACK
- return maybe_callback
-
-
-class NoOpCallback(Callback):
- """
- This implementation of Callback does exactly nothing
- """
-
- def call(self, *args, **kwargs):
- return None
-
-
-class DotPrinterCallback(Callback):
- """
- Simple example Callback implementation
-
- Almost identical to Callback with a hook that prints a char; here we
- demonstrate how the outer layer may print "#" and the inner layer "."
- """
-
- def __init__(self, chr_to_print="#", **kwargs):
- self.chr = chr_to_print
- super().__init__(**kwargs)
-
- def branch(self, path_1, path_2, kwargs):
- """Mutate kwargs to add new instance with different print char"""
- kwargs["callback"] = DotPrinterCallback(".")
-
- def call(self, **kwargs):
- """Just outputs a character"""
- print(self.chr, end="")
-
-
-class TqdmCallback(Callback):
- """
- A callback to display a progress bar using tqdm
-
- Parameters
- ----------
- tqdm_kwargs : dict, (optional)
- Any argument accepted by the tqdm constructor.
- See the `tqdm doc `_.
- Will be forwarded to tqdm.
-
- Examples
- --------
- >>> import fsspec
- >>> from fsspec.callbacks import TqdmCallback
- >>> fs = fsspec.filesystem("memory")
- >>> path2distant_data = "/your-path"
- >>> fs.upload(
- ".",
- path2distant_data,
- recursive=True,
- callback=TqdmCallback(),
- )
-
- You can forward args to tqdm using the ``tqdm_kwargs`` parameter.
-
- >>> fs.upload(
- ".",
- path2distant_data,
- recursive=True,
- callback=TqdmCallback(tqdm_kwargs={"desc": "Your tqdm description"}),
- )
- """
-
- def __init__(self, tqdm_kwargs=None, *args, **kwargs):
- try:
- import tqdm
-
- self._tqdm = tqdm
- except ImportError as exce:
- raise ImportError(
- "Using TqdmCallback requires tqdm to be installed"
- ) from exce
-
- self._tqdm_kwargs = tqdm_kwargs or {}
- super().__init__(*args, **kwargs)
-
- def set_size(self, size):
- self.tqdm = self._tqdm.tqdm(total=size, **self._tqdm_kwargs)
-
- def relative_update(self, inc=1):
- self.tqdm.update(inc)
-
- def __del__(self):
- self.tqdm.close()
- self.tqdm = None
-
-
-_DEFAULT_CALLBACK = NoOpCallback()
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-a474b4ee.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-a474b4ee.js
deleted file mode 100644
index 5759820c2e9ca5dd6c5c2928caa748f99d703861..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-a474b4ee.js
+++ /dev/null
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-//# sourceMappingURL=index-a474b4ee.js.map
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jinja2/nativetypes.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jinja2/nativetypes.py
deleted file mode 100644
index ac0861034821772a50e53bfc3d3ff72e7aad5b1b..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jinja2/nativetypes.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import typing as t
-from ast import literal_eval
-from ast import parse
-from itertools import chain
-from itertools import islice
-from types import GeneratorType
-
-from . import nodes
-from .compiler import CodeGenerator
-from .compiler import Frame
-from .compiler import has_safe_repr
-from .environment import Environment
-from .environment import Template
-
-
-def native_concat(values: t.Iterable[t.Any]) -> t.Optional[t.Any]:
- """Return a native Python type from the list of compiled nodes. If
- the result is a single node, its value is returned. Otherwise, the
- nodes are concatenated as strings. If the result can be parsed with
- :func:`ast.literal_eval`, the parsed value is returned. Otherwise,
- the string is returned.
-
- :param values: Iterable of outputs to concatenate.
- """
- head = list(islice(values, 2))
-
- if not head:
- return None
-
- if len(head) == 1:
- raw = head[0]
- if not isinstance(raw, str):
- return raw
- else:
- if isinstance(values, GeneratorType):
- values = chain(head, values)
- raw = "".join([str(v) for v in values])
-
- try:
- return literal_eval(
- # In Python 3.10+ ast.literal_eval removes leading spaces/tabs
- # from the given string. For backwards compatibility we need to
- # parse the string ourselves without removing leading spaces/tabs.
- parse(raw, mode="eval")
- )
- except (ValueError, SyntaxError, MemoryError):
- return raw
-
-
-class NativeCodeGenerator(CodeGenerator):
- """A code generator which renders Python types by not adding
- ``str()`` around output nodes.
- """
-
- @staticmethod
- def _default_finalize(value: t.Any) -> t.Any:
- return value
-
- def _output_const_repr(self, group: t.Iterable[t.Any]) -> str:
- return repr("".join([str(v) for v in group]))
-
- def _output_child_to_const(
- self, node: nodes.Expr, frame: Frame, finalize: CodeGenerator._FinalizeInfo
- ) -> t.Any:
- const = node.as_const(frame.eval_ctx)
-
- if not has_safe_repr(const):
- raise nodes.Impossible()
-
- if isinstance(node, nodes.TemplateData):
- return const
-
- return finalize.const(const) # type: ignore
-
- def _output_child_pre(
- self, node: nodes.Expr, frame: Frame, finalize: CodeGenerator._FinalizeInfo
- ) -> None:
- if finalize.src is not None:
- self.write(finalize.src)
-
- def _output_child_post(
- self, node: nodes.Expr, frame: Frame, finalize: CodeGenerator._FinalizeInfo
- ) -> None:
- if finalize.src is not None:
- self.write(")")
-
-
-class NativeEnvironment(Environment):
- """An environment that renders templates to native Python types."""
-
- code_generator_class = NativeCodeGenerator
- concat = staticmethod(native_concat) # type: ignore
-
-
-class NativeTemplate(Template):
- environment_class = NativeEnvironment
-
- def render(self, *args: t.Any, **kwargs: t.Any) -> t.Any:
- """Render the template to produce a native Python type. If the
- result is a single node, its value is returned. Otherwise, the
- nodes are concatenated as strings. If the result can be parsed
- with :func:`ast.literal_eval`, the parsed value is returned.
- Otherwise, the string is returned.
- """
- ctx = self.new_context(dict(*args, **kwargs))
-
- try:
- return self.environment_class.concat( # type: ignore
- self.root_render_func(ctx) # type: ignore
- )
- except Exception:
- return self.environment.handle_exception()
-
- async def render_async(self, *args: t.Any, **kwargs: t.Any) -> t.Any:
- if not self.environment.is_async:
- raise RuntimeError(
- "The environment was not created with async mode enabled."
- )
-
- ctx = self.new_context(dict(*args, **kwargs))
-
- try:
- return self.environment_class.concat( # type: ignore
- [n async for n in self.root_render_func(ctx)] # type: ignore
- )
- except Exception:
- return self.environment.handle_exception()
-
-
-NativeEnvironment.template_class = NativeTemplate
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/audioldm/.ipynb_checkpoints/pipeline_audioldm-checkpoint.py b/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/audioldm/.ipynb_checkpoints/pipeline_audioldm-checkpoint.py
deleted file mode 100644
index b392cd4cc24655a80aae14f0ac922a9a968b1e70..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/audioldm/.ipynb_checkpoints/pipeline_audioldm-checkpoint.py
+++ /dev/null
@@ -1,601 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from transformers import ClapTextModelWithProjection, RobertaTokenizer, RobertaTokenizerFast, SpeechT5HifiGan
-
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import is_accelerate_available, logging, randn_tensor, replace_example_docstring
-from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
- Examples:
- ```py
- >>> import torch
- >>> from diffusers import AudioLDMPipeline
-
- >>> pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm", torch_dtype=torch.float16)
- >>> pipe = pipe.to("cuda")
-
- >>> prompt = "A hammer hitting a wooden surface"
- >>> audio = pipe(prompt).audio[0]
- ```
-"""
-
-
-class AudioLDMPipeline(DiffusionPipeline):
- r"""
- Pipeline for text-to-audio generation using AudioLDM.
-
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
- library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
- Args:
- vae ([`AutoencoderKL`]):
- Variational Auto-Encoder (VAE) Model to encode and decode audios to and from latent representations.
- text_encoder ([`ClapTextModelWithProjection`]):
- Frozen text-encoder. AudioLDM uses the text portion of
- [CLAP](https://huggingface.co/docs/transformers/main/model_doc/clap#transformers.ClapTextModelWithProjection),
- specifically the [RoBERTa HSTAT-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant.
- tokenizer ([`PreTrainedTokenizer`]):
- Tokenizer of class
- [RobertaTokenizer](https://huggingface.co/docs/transformers/model_doc/roberta#transformers.RobertaTokenizer).
- unet ([`UNet2DConditionModel`]): U-Net architecture to denoise the encoded audio latents.
- scheduler ([`SchedulerMixin`]):
- A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
- [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
- vocoder ([`SpeechT5HifiGan`]):
- Vocoder of class
- [SpeechT5HifiGan](https://huggingface.co/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5HifiGan).
- """
-
- def __init__(
- self,
- vae: AutoencoderKL,
- text_encoder: ClapTextModelWithProjection,
- tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast],
- unet: UNet2DConditionModel,
- scheduler: KarrasDiffusionSchedulers,
- vocoder: SpeechT5HifiGan,
- ):
- super().__init__()
-
- self.register_modules(
- vae=vae,
- text_encoder=text_encoder,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- vocoder=vocoder,
- )
- self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
- def enable_vae_slicing(self):
- r"""
- Enable sliced VAE decoding.
-
- When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
- steps. This is useful to save some memory and allow larger batch sizes.
- """
- self.vae.enable_slicing()
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
- def disable_vae_slicing(self):
- r"""
- Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
- computing decoding in one step.
- """
- self.vae.disable_slicing()
-
- def enable_sequential_cpu_offload(self, gpu_id=0):
- r"""
- Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
- text_encoder, vae and vocoder have their state dicts saved to CPU and then are moved to a `torch.device('meta')
- and loaded to GPU only when their specific submodule has its `forward` method called.
- """
- if is_accelerate_available():
- from accelerate import cpu_offload
- else:
- raise ImportError("Please install accelerate via `pip install accelerate`")
-
- device = torch.device(f"cuda:{gpu_id}")
-
- for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.vocoder]:
- cpu_offload(cpu_offloaded_model, device)
-
- @property
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
- def _execution_device(self):
- r"""
- Returns the device on which the pipeline's models will be executed. After calling
- `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
- hooks.
- """
- if not hasattr(self.unet, "_hf_hook"):
- return self.device
- for module in self.unet.modules():
- if (
- hasattr(module, "_hf_hook")
- and hasattr(module._hf_hook, "execution_device")
- and module._hf_hook.execution_device is not None
- ):
- return torch.device(module._hf_hook.execution_device)
- return self.device
-
- def _encode_prompt(
- self,
- prompt,
- device,
- num_waveforms_per_prompt,
- do_classifier_free_guidance,
- negative_prompt=None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- negative_prompt_embeds: Optional[torch.FloatTensor] = None,
- ):
- r"""
- Encodes the prompt into text encoder hidden states.
-
- Args:
- prompt (`str` or `List[str]`, *optional*):
- prompt to be encoded
- device (`torch.device`):
- torch device
- num_waveforms_per_prompt (`int`):
- number of waveforms that should be generated per prompt
- do_classifier_free_guidance (`bool`):
- whether to use classifier free guidance or not
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the audio generation. If not defined, one has to pass
- `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
- less than `1`).
- prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
- provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
- weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
- argument.
- """
- if prompt is not None and isinstance(prompt, str):
- batch_size = 1
- elif prompt is not None and isinstance(prompt, list):
- batch_size = len(prompt)
- else:
- batch_size = prompt_embeds.shape[0]
-
- if prompt_embeds is None:
- text_inputs = self.tokenizer(
- prompt,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- return_tensors="pt",
- )
- text_input_ids = text_inputs.input_ids
- attention_mask = text_inputs.attention_mask
- untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
- if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
- text_input_ids, untruncated_ids
- ):
- removed_text = self.tokenizer.batch_decode(
- untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
- )
- logger.warning(
- "The following part of your input was truncated because CLAP can only handle sequences up to"
- f" {self.tokenizer.model_max_length} tokens: {removed_text}"
- )
-
- prompt_embeds = self.text_encoder(
- text_input_ids.to(device),
- attention_mask=attention_mask.to(device),
- )
- prompt_embeds = prompt_embeds.text_embeds
- # additional L_2 normalization over each hidden-state
- prompt_embeds = F.normalize(prompt_embeds, dim=-1)
-
- prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
- (
- bs_embed,
- seq_len,
- ) = prompt_embeds.shape
- # duplicate text embeddings for each generation per prompt, using mps friendly method
- prompt_embeds = prompt_embeds.repeat(1, num_waveforms_per_prompt)
- prompt_embeds = prompt_embeds.view(bs_embed * num_waveforms_per_prompt, seq_len)
-
- # get unconditional embeddings for classifier free guidance
- if do_classifier_free_guidance and negative_prompt_embeds is None:
- uncond_tokens: List[str]
- if negative_prompt is None:
- uncond_tokens = [""] * batch_size
- elif type(prompt) is not type(negative_prompt):
- raise TypeError(
- f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
- f" {type(prompt)}."
- )
- elif isinstance(negative_prompt, str):
- uncond_tokens = [negative_prompt]
- elif batch_size != len(negative_prompt):
- raise ValueError(
- f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
- f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
- " the batch size of `prompt`."
- )
- else:
- uncond_tokens = negative_prompt
-
- max_length = prompt_embeds.shape[1]
- uncond_input = self.tokenizer(
- uncond_tokens,
- padding="max_length",
- max_length=max_length,
- truncation=True,
- return_tensors="pt",
- )
-
- uncond_input_ids = uncond_input.input_ids.to(device)
- attention_mask = uncond_input.attention_mask.to(device)
-
- negative_prompt_embeds = self.text_encoder(
- uncond_input_ids,
- attention_mask=attention_mask,
- )
- negative_prompt_embeds = negative_prompt_embeds.text_embeds
- # additional L_2 normalization over each hidden-state
- negative_prompt_embeds = F.normalize(negative_prompt_embeds, dim=-1)
-
- if do_classifier_free_guidance:
- # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
- seq_len = negative_prompt_embeds.shape[1]
-
- negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
- negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_waveforms_per_prompt)
- negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_waveforms_per_prompt, seq_len)
-
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
- return prompt_embeds
-
- def decode_latents(self, latents):
- latents = 1 / self.vae.config.scaling_factor * latents
- mel_spectrogram = self.vae.decode(latents).sample
- return mel_spectrogram
-
- def mel_spectrogram_to_waveform(self, mel_spectrogram):
- if mel_spectrogram.dim() == 4:
- mel_spectrogram = mel_spectrogram.squeeze(1)
-
- waveform = self.vocoder(mel_spectrogram)
- # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
- waveform = waveform.cpu()
- return waveform
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
- def prepare_extra_step_kwargs(self, generator, eta):
- # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
-
- accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
-
- # check if the scheduler accepts generator
- accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
- if accepts_generator:
- extra_step_kwargs["generator"] = generator
- return extra_step_kwargs
-
- def check_inputs(
- self,
- prompt,
- audio_length_in_s,
- vocoder_upsample_factor,
- callback_steps,
- negative_prompt=None,
- prompt_embeds=None,
- negative_prompt_embeds=None,
- ):
- min_audio_length_in_s = vocoder_upsample_factor * self.vae_scale_factor
- if audio_length_in_s < min_audio_length_in_s:
- raise ValueError(
- f"`audio_length_in_s` has to be a positive value greater than or equal to {min_audio_length_in_s}, but "
- f"is {audio_length_in_s}."
- )
-
- if self.vocoder.config.model_in_dim % self.vae_scale_factor != 0:
- raise ValueError(
- f"The number of frequency bins in the vocoder's log-mel spectrogram has to be divisible by the "
- f"VAE scale factor, but got {self.vocoder.config.model_in_dim} bins and a scale factor of "
- f"{self.vae_scale_factor}."
- )
-
- if (callback_steps is None) or (
- callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
- ):
- raise ValueError(
- f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
- f" {type(callback_steps)}."
- )
-
- if prompt is not None and prompt_embeds is not None:
- raise ValueError(
- f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
- " only forward one of the two."
- )
- elif prompt is None and prompt_embeds is None:
- raise ValueError(
- "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
- )
- elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
- raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
- if negative_prompt is not None and negative_prompt_embeds is not None:
- raise ValueError(
- f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
- f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
- )
-
- if prompt_embeds is not None and negative_prompt_embeds is not None:
- if prompt_embeds.shape != negative_prompt_embeds.shape:
- raise ValueError(
- "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
- f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
- f" {negative_prompt_embeds.shape}."
- )
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents with width->self.vocoder.config.model_in_dim
- def prepare_latents(self, batch_size, num_channels_latents, height, dtype, device, generator, latents=None):
- shape = (
- batch_size,
- num_channels_latents,
- height // self.vae_scale_factor,
- self.vocoder.config.model_in_dim // self.vae_scale_factor,
- )
- if isinstance(generator, list) and len(generator) != batch_size:
- raise ValueError(
- f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
- f" size of {batch_size}. Make sure the batch size matches the length of the generators."
- )
-
- if latents is None:
- latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
- else:
- latents = latents.to(device)
-
- # scale the initial noise by the standard deviation required by the scheduler
- latents = latents * self.scheduler.init_noise_sigma
- return latents
-
- @torch.no_grad()
- @replace_example_docstring(EXAMPLE_DOC_STRING)
- def __call__(
- self,
- prompt: Union[str, List[str]] = None,
- audio_length_in_s: Optional[float] = None,
- num_inference_steps: int = 10,
- guidance_scale: float = 2.5,
- negative_prompt: Optional[Union[str, List[str]]] = None,
- num_waveforms_per_prompt: Optional[int] = 1,
- eta: float = 0.0,
- generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
- latents: Optional[torch.FloatTensor] = None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- negative_prompt_embeds: Optional[torch.FloatTensor] = None,
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
- callback_steps: Optional[int] = 1,
- cross_attention_kwargs: Optional[Dict[str, Any]] = None,
- output_type: Optional[str] = "np",
- ):
- r"""
- Function invoked when calling the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts to guide the audio generation. If not defined, one has to pass `prompt_embeds`.
- instead.
- audio_length_in_s (`int`, *optional*, defaults to 5.12):
- The length of the generated audio sample in seconds.
- num_inference_steps (`int`, *optional*, defaults to 10):
- The number of denoising steps. More denoising steps usually lead to a higher quality audio at the
- expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 2.5):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate audios that are closely linked to the text `prompt`,
- usually at the expense of lower sound quality.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the audio generation. If not defined, one has to pass
- `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
- less than `1`).
- num_waveforms_per_prompt (`int`, *optional*, defaults to 1):
- The number of waveforms to generate per prompt.
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
- [`schedulers.DDIMScheduler`], will be ignored for others.
- generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
- One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
- to make generation deterministic.
- latents (`torch.FloatTensor`, *optional*):
- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for audio
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor will ge generated by sampling using the supplied random `generator`.
- prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
- provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
- weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
- argument.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- callback (`Callable`, *optional*):
- A function that will be called every `callback_steps` steps during inference. The function will be
- called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function will be called. If not specified, the callback will be
- called at every step.
- cross_attention_kwargs (`dict`, *optional*):
- A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under
- `self.processor` in
- [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
- output_type (`str`, *optional*, defaults to `"np"`):
- The output format of the generate image. Choose between:
- - `"np"`: Return Numpy `np.ndarray` objects.
- - `"pt"`: Return PyTorch `torch.Tensor` objects.
-
- Examples:
-
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
- When returning a tuple, the first element is a list with the generated audios.
- """
- # 0. Convert audio input length from seconds to spectrogram height
- vocoder_upsample_factor = np.prod(self.vocoder.config.upsample_rates) / self.vocoder.config.sampling_rate
-
- if audio_length_in_s is None:
- audio_length_in_s = self.unet.config.sample_size * self.vae_scale_factor * vocoder_upsample_factor
-
- height = int(audio_length_in_s / vocoder_upsample_factor)
-
- original_waveform_length = int(audio_length_in_s * self.vocoder.config.sampling_rate)
- if height % self.vae_scale_factor != 0:
- height = int(np.ceil(height / self.vae_scale_factor)) * self.vae_scale_factor
- logger.info(
- f"Audio length in seconds {audio_length_in_s} is increased to {height * vocoder_upsample_factor} "
- f"so that it can be handled by the model. It will be cut to {audio_length_in_s} after the "
- f"denoising process."
- )
-
- # 1. Check inputs. Raise error if not correct
- self.check_inputs(
- prompt,
- audio_length_in_s,
- vocoder_upsample_factor,
- callback_steps,
- negative_prompt,
- prompt_embeds,
- negative_prompt_embeds,
- )
-
- # 2. Define call parameters
- if prompt is not None and isinstance(prompt, str):
- batch_size = 1
- elif prompt is not None and isinstance(prompt, list):
- batch_size = len(prompt)
- else:
- batch_size = prompt_embeds.shape[0]
-
- device = self._execution_device
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
-
- # 3. Encode input prompt
- prompt_embeds = self._encode_prompt(
- prompt,
- device,
- num_waveforms_per_prompt,
- do_classifier_free_guidance,
- negative_prompt,
- prompt_embeds=prompt_embeds,
- negative_prompt_embeds=negative_prompt_embeds,
- )
-
- # 4. Prepare timesteps
- self.scheduler.set_timesteps(num_inference_steps, device=device)
- timesteps = self.scheduler.timesteps
-
- # 5. Prepare latent variables
- num_channels_latents = self.unet.in_channels
- latents = self.prepare_latents(
- batch_size * num_waveforms_per_prompt,
- num_channels_latents,
- height,
- prompt_embeds.dtype,
- device,
- generator,
- latents,
- )
-
- # 6. Prepare extra step kwargs
- extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
- # 7. Denoising loop
- num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
- with self.progress_bar(total=num_inference_steps) as progress_bar:
- for i, t in enumerate(timesteps):
- # expand the latents if we are doing classifier free guidance
- latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- # predict the noise residual
- noise_pred = self.unet(
- latent_model_input,
- t,
- encoder_hidden_states=None,
- class_labels=prompt_embeds,
- cross_attention_kwargs=cross_attention_kwargs,
- ).sample
-
- # perform guidance
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
- # call the callback, if provided
- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
- progress_bar.update()
- if callback is not None and i % callback_steps == 0:
- callback(i, t, latents)
-
- # 8. Post-processing
- mel_spectrogram = self.decode_latents(latents)
-
- audio = self.mel_spectrogram_to_waveform(mel_spectrogram)
-
- audio = audio[:, :original_waveform_length]
-
- if output_type == "np":
- audio = audio.numpy()
-
- if not return_dict:
- return (audio,)
-
- return AudioPipelineOutput(audios=audio)
diff --git a/spaces/deepklarity/poster2plot/README.md b/spaces/deepklarity/poster2plot/README.md
deleted file mode 100644
index 9aed84c26d4529365097c0a66af93fe7b62d6985..0000000000000000000000000000000000000000
--- a/spaces/deepklarity/poster2plot/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: Poster2plot
-emoji: 🎬
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.0.24
-app_file: app.py
-pinned: true
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/derina/MusicSpleeter/README.md b/spaces/derina/MusicSpleeter/README.md
deleted file mode 100644
index 35023f3458c06804f059929a926587895b02f313..0000000000000000000000000000000000000000
--- a/spaces/derina/MusicSpleeter/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: Music Spleeter
-emoji: 🌖
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/HttpUploadsnackCOmNmtkm7PasswordTorrent.md b/spaces/diacanFperku/AutoGPT/HttpUploadsnackCOmNmtkm7PasswordTorrent.md
deleted file mode 100644
index 2cb403208bf21b766779f4340b3033819b8c024a..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/HttpUploadsnackCOmNmtkm7PasswordTorrent.md
+++ /dev/null
@@ -1,6 +0,0 @@
-HttpUploadsnackCOmNmtkm7PasswordTorrent
DOWNLOAD ✓✓✓ https://gohhs.com/2uFTSF
-
-HttpUploadsnackCOmNmtkm7PasswordTorrent · pendulum dowsing books in hindi · bfd2 crack keygen serial number · call of duty black ops ... 4d29de3e1b
-
-
-
diff --git a/spaces/diaoren/OpenSetObstacleDetection/opendet2/modeling/roi_heads/box_head.py b/spaces/diaoren/OpenSetObstacleDetection/opendet2/modeling/roi_heads/box_head.py
deleted file mode 100644
index 16f74fe3bb29fa85476e9412eb54fafa47f87cc1..0000000000000000000000000000000000000000
--- a/spaces/diaoren/OpenSetObstacleDetection/opendet2/modeling/roi_heads/box_head.py
+++ /dev/null
@@ -1,149 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from typing import List
-
-import fvcore.nn.weight_init as weight_init
-import numpy as np
-import torch
-from detectron2.config import configurable
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-from detectron2.modeling.roi_heads import ROI_BOX_HEAD_REGISTRY
-from detectron2.utils.registry import Registry
-from torch import nn
-
-
-@ROI_BOX_HEAD_REGISTRY.register()
-class FastRCNNSeparateConvFCHead(nn.Module):
- """
- FastRCNN with separate ConvFC layers
- """
-
- @configurable
- def __init__(
- self, input_shape: ShapeSpec, *, conv_dims: List[int], fc_dims: List[int], conv_norm="",has_edl=False
- ):
- """
- NOTE: this interface is experimental.
-
- Args:
- input_shape (ShapeSpec): shape of the input feature.
- conv_dims (list[int]): the output dimensions of the conv layers
- fc_dims (list[int]): the output dimensions of the fc layers
- conv_norm (str or callable): normalization for the conv layers.
- See :func:`detectron2.layers.get_norm` for supported types.
- """
- super().__init__()
- self.has_edl = has_edl
- assert len(conv_dims) + len(fc_dims) > 0
- self.conv_dims = conv_dims
- self.fc_dims = fc_dims
-
- self._output_size = (input_shape.channels,
- input_shape.height, input_shape.width)#（256,7,7,）
-
- self.reg_conv_norm_relus = self._add_conv_norm_relus(
- self._output_size[0], conv_dims, conv_norm)
- self.cls_conv_norm_relus = self._add_conv_norm_relus(
- self._output_size[0], conv_dims, conv_norm)
- conv_dim = self._output_size[0] if len(conv_dims) == 0 else conv_dims[-1]
- self._output_size = (
- conv_dim, self._output_size[1], self._output_size[2])
-
- self.reg_fcs = self._add_fcs(np.prod(self._output_size), fc_dims)
- self.cls_fcs = self._add_fcs(np.prod(self._output_size), fc_dims)
- if self.has_edl:
- self.edl_fcs = self._add_fcs(np.prod(self._output_size), fc_dims)
- self._output_size = self._output_size if len(fc_dims)==0 else fc_dims[-1]#1024
-
- #权重初始化
- for layer in self.reg_conv_norm_relus:
- weight_init.c2_msra_fill(layer)
- for layer in self.cls_conv_norm_relus:
- weight_init.c2_msra_fill(layer)
- for layer in self.cls_fcs:
- if isinstance(layer, nn.Linear):
- weight_init.c2_xavier_fill(layer)
- for layer in self.reg_fcs:
- if isinstance(layer, nn.Linear):
- weight_init.c2_xavier_fill(layer)
- if self.has_edl:
- for layer in self.edl_fcs:
- if isinstance(layer, nn.Linear):
- weight_init.c2_xavier_fill(layer)
-
- @classmethod
- def from_config(cls, cfg, input_shape):
- num_conv = cfg.MODEL.ROI_BOX_HEAD.NUM_CONV#0
- conv_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_DIM#256
- num_fc = cfg.MODEL.ROI_BOX_HEAD.NUM_FC#2
- fc_dim = cfg.MODEL.ROI_BOX_HEAD.FC_DIM#1024
- return {
- "input_shape": input_shape,
- "conv_dims": [conv_dim] * num_conv,
- "fc_dims": [fc_dim] * num_fc,
- "conv_norm": cfg.MODEL.ROI_BOX_HEAD.NORM,
- "has_edl":cfg.EDLLOSS.HAS_EDL
- }
-
- #构造连续的卷积层（未用）
- def _add_conv_norm_relus(self, input_dim, conv_dims, conv_norm):
- conv_norm_relus = []
- for k, conv_dim in enumerate(conv_dims):
- conv = Conv2d(
- input_dim,
- conv_dim,
- kernel_size=3,
- padding=1,
- bias=not conv_norm,
- norm=get_norm(conv_norm, conv_dim),
- activation=nn.ReLU(),
- )
- input_dim = conv_dim
- conv_norm_relus.append(conv)
-
- return nn.Sequential(*conv_norm_relus)
-
- #添加两个全连接层
- def _add_fcs(self, input_dim, fc_dims):
- fcs = []
- for k, fc_dim in enumerate(fc_dims):
- if k == 0:
- fcs.append(nn.Flatten())
- fc = nn.Linear(int(input_dim), fc_dim)
- fcs.append(fc)
- fcs.append(nn.ReLU())
- input_dim = fc_dim
- return nn.Sequential(*fcs)
-
- # pooler产生的特征图分别经过两个不同的双全连接层，产生cls特征和reg特征
- def forward(self, x):
- reg_feat = x
- cls_feat = x
- if self.has_edl:
- edl_feat = x
- if len(self.conv_dims) > 0:
- reg_feat = self.reg_conv_norm_relus(x)
- cls_feat = self.cls_conv_norm_relus(x)
- if len(self.fc_dims) > 0:
- reg_feat = self.reg_fcs(reg_feat)
- cls_feat = self.cls_fcs(cls_feat)
- if self.has_edl:
- edl_feat = self.edl_fcs(edl_feat)
- if self.has_edl:
- return reg_feat, cls_feat, edl_feat
- else:
- return reg_feat, cls_feat
-
-
- @property
- @torch.jit.unused
- def output_shape(self):
- """
- Returns:
- ShapeSpec: the output feature shape
- """
- o = self._output_size
- if isinstance(o, int):
- return ShapeSpec(channels=o)
- else:
- return ShapeSpec(channels=o[0], height=o[1], width=o[2])
-
diff --git a/spaces/dineshreddy/WALT/mmdet/core/bbox/assigners/assign_result.py b/spaces/dineshreddy/WALT/mmdet/core/bbox/assigners/assign_result.py
deleted file mode 100644
index 4639fbdba0a5b92778e1ab87d61182e54bfb9b6f..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/core/bbox/assigners/assign_result.py
+++ /dev/null
@@ -1,204 +0,0 @@
-import torch
-
-from mmdet.utils import util_mixins
-
-
-class AssignResult(util_mixins.NiceRepr):
- """Stores assignments between predicted and truth boxes.
-
- Attributes:
- num_gts (int): the number of truth boxes considered when computing this
- assignment
-
- gt_inds (LongTensor): for each predicted box indicates the 1-based
- index of the assigned truth box. 0 means unassigned and -1 means
- ignore.
-
- max_overlaps (FloatTensor): the iou between the predicted box and its
- assigned truth box.
-
- labels (None | LongTensor): If specified, for each predicted box
- indicates the category label of the assigned truth box.
-
- Example:
- >>> # An assign result between 4 predicted boxes and 9 true boxes
- >>> # where only two boxes were assigned.
- >>> num_gts = 9
- >>> max_overlaps = torch.LongTensor([0, .5, .9, 0])
- >>> gt_inds = torch.LongTensor([-1, 1, 2, 0])
- >>> labels = torch.LongTensor([0, 3, 4, 0])
- >>> self = AssignResult(num_gts, gt_inds, max_overlaps, labels)
- >>> print(str(self)) # xdoctest: +IGNORE_WANT
-
- >>> # Force addition of gt labels (when adding gt as proposals)
- >>> new_labels = torch.LongTensor([3, 4, 5])
- >>> self.add_gt_(new_labels)
- >>> print(str(self)) # xdoctest: +IGNORE_WANT
-
- """
-
- def __init__(self, num_gts, gt_inds, max_overlaps, labels=None):
- self.num_gts = num_gts
- self.gt_inds = gt_inds
- self.max_overlaps = max_overlaps
- self.labels = labels
- # Interface for possible user-defined properties
- self._extra_properties = {}
-
- @property
- def num_preds(self):
- """int: the number of predictions in this assignment"""
- return len(self.gt_inds)
-
- def set_extra_property(self, key, value):
- """Set user-defined new property."""
- assert key not in self.info
- self._extra_properties[key] = value
-
- def get_extra_property(self, key):
- """Get user-defined property."""
- return self._extra_properties.get(key, None)
-
- @property
- def info(self):
- """dict: a dictionary of info about the object"""
- basic_info = {
- 'num_gts': self.num_gts,
- 'num_preds': self.num_preds,
- 'gt_inds': self.gt_inds,
- 'max_overlaps': self.max_overlaps,
- 'labels': self.labels,
- }
- basic_info.update(self._extra_properties)
- return basic_info
-
- def __nice__(self):
- """str: a "nice" summary string describing this assign result"""
- parts = []
- parts.append(f'num_gts={self.num_gts!r}')
- if self.gt_inds is None:
- parts.append(f'gt_inds={self.gt_inds!r}')
- else:
- parts.append(f'gt_inds.shape={tuple(self.gt_inds.shape)!r}')
- if self.max_overlaps is None:
- parts.append(f'max_overlaps={self.max_overlaps!r}')
- else:
- parts.append('max_overlaps.shape='
- f'{tuple(self.max_overlaps.shape)!r}')
- if self.labels is None:
- parts.append(f'labels={self.labels!r}')
- else:
- parts.append(f'labels.shape={tuple(self.labels.shape)!r}')
- return ', '.join(parts)
-
- @classmethod
- def random(cls, **kwargs):
- """Create random AssignResult for tests or debugging.
-
- Args:
- num_preds: number of predicted boxes
- num_gts: number of true boxes
- p_ignore (float): probability of a predicted box assinged to an
- ignored truth
- p_assigned (float): probability of a predicted box not being
- assigned
- p_use_label (float | bool): with labels or not
- rng (None | int | numpy.random.RandomState): seed or state
-
- Returns:
- :obj:`AssignResult`: Randomly generated assign results.
-
- Example:
- >>> from mmdet.core.bbox.assigners.assign_result import * # NOQA
- >>> self = AssignResult.random()
- >>> print(self.info)
- """
- from mmdet.core.bbox import demodata
- rng = demodata.ensure_rng(kwargs.get('rng', None))
-
- num_gts = kwargs.get('num_gts', None)
- num_preds = kwargs.get('num_preds', None)
- p_ignore = kwargs.get('p_ignore', 0.3)
- p_assigned = kwargs.get('p_assigned', 0.7)
- p_use_label = kwargs.get('p_use_label', 0.5)
- num_classes = kwargs.get('p_use_label', 3)
-
- if num_gts is None:
- num_gts = rng.randint(0, 8)
- if num_preds is None:
- num_preds = rng.randint(0, 16)
-
- if num_gts == 0:
- max_overlaps = torch.zeros(num_preds, dtype=torch.float32)
- gt_inds = torch.zeros(num_preds, dtype=torch.int64)
- if p_use_label is True or p_use_label < rng.rand():
- labels = torch.zeros(num_preds, dtype=torch.int64)
- else:
- labels = None
- else:
- import numpy as np
- # Create an overlap for each predicted box
- max_overlaps = torch.from_numpy(rng.rand(num_preds))
-
- # Construct gt_inds for each predicted box
- is_assigned = torch.from_numpy(rng.rand(num_preds) < p_assigned)
- # maximum number of assignments constraints
- n_assigned = min(num_preds, min(num_gts, is_assigned.sum()))
-
- assigned_idxs = np.where(is_assigned)[0]
- rng.shuffle(assigned_idxs)
- assigned_idxs = assigned_idxs[0:n_assigned]
- assigned_idxs.sort()
-
- is_assigned[:] = 0
- is_assigned[assigned_idxs] = True
-
- is_ignore = torch.from_numpy(
- rng.rand(num_preds) < p_ignore) & is_assigned
-
- gt_inds = torch.zeros(num_preds, dtype=torch.int64)
-
- true_idxs = np.arange(num_gts)
- rng.shuffle(true_idxs)
- true_idxs = torch.from_numpy(true_idxs)
- gt_inds[is_assigned] = true_idxs[:n_assigned]
-
- gt_inds = torch.from_numpy(
- rng.randint(1, num_gts + 1, size=num_preds))
- gt_inds[is_ignore] = -1
- gt_inds[~is_assigned] = 0
- max_overlaps[~is_assigned] = 0
-
- if p_use_label is True or p_use_label < rng.rand():
- if num_classes == 0:
- labels = torch.zeros(num_preds, dtype=torch.int64)
- else:
- labels = torch.from_numpy(
- # remind that we set FG labels to [0, num_class-1]
- # since mmdet v2.0
- # BG cat_id: num_class
- rng.randint(0, num_classes, size=num_preds))
- labels[~is_assigned] = 0
- else:
- labels = None
-
- self = cls(num_gts, gt_inds, max_overlaps, labels)
- return self
-
- def add_gt_(self, gt_labels):
- """Add ground truth as assigned results.
-
- Args:
- gt_labels (torch.Tensor): Labels of gt boxes
- """
- self_inds = torch.arange(
- 1, len(gt_labels) + 1, dtype=torch.long, device=gt_labels.device)
- self.gt_inds = torch.cat([self_inds, self.gt_inds])
-
- self.max_overlaps = torch.cat(
- [self.max_overlaps.new_ones(len(gt_labels)), self.max_overlaps])
-
- if self.labels is not None:
- self.labels = torch.cat([gt_labels, self.labels])
diff --git a/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/det_models/fcenet_r50dcnv2_fpn.py b/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/det_models/fcenet_r50dcnv2_fpn.py
deleted file mode 100644
index 8e76e39a6e8088ac20671f72fc5ed8448b21250b..0000000000000000000000000000000000000000
--- a/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/det_models/fcenet_r50dcnv2_fpn.py
+++ /dev/null
@@ -1,35 +0,0 @@
-model = dict(
- type='FCENet',
- backbone=dict(
- type='mmdet.ResNet',
- depth=50,
- num_stages=4,
- out_indices=(1, 2, 3),
- frozen_stages=-1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- style='pytorch',
- dcn=dict(type='DCNv2', deform_groups=2, fallback_on_stride=False),
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
- stage_with_dcn=(False, True, True, True)),
- neck=dict(
- type='mmdet.FPN',
- in_channels=[512, 1024, 2048],
- out_channels=256,
- add_extra_convs='on_output',
- num_outs=3,
- relu_before_extra_convs=True,
- act_cfg=None),
- bbox_head=dict(
- type='FCEHead',
- in_channels=256,
- scales=(8, 16, 32),
- fourier_degree=5,
- loss=dict(type='FCELoss', num_sample=50),
- postprocessor=dict(
- type='FCEPostprocessor',
- text_repr_type='poly',
- num_reconstr_points=50,
- alpha=1.0,
- beta=2.0,
- score_thr=0.3)))
diff --git a/spaces/divyahansg/text-generation-webui-space/modules/html_generator.py b/spaces/divyahansg/text-generation-webui-space/modules/html_generator.py
deleted file mode 100644
index 162040bac68c2e987b33a02ccb12e90b51a63b2d..0000000000000000000000000000000000000000
--- a/spaces/divyahansg/text-generation-webui-space/modules/html_generator.py
+++ /dev/null
@@ -1,357 +0,0 @@
-'''
-
-This is a library for formatting GPT-4chan and chat outputs as nice HTML.
-
-'''
-
-import os
-import re
-from pathlib import Path
-
-from PIL import Image
-
-# This is to store the paths to the thumbnails of the profile pictures
-image_cache = {}
-
-def generate_basic_html(s):
- css = """
- .container {
- max-width: 600px;
- margin-left: auto;
- margin-right: auto;
- background-color: rgb(31, 41, 55);
- padding:3em;
- }
- .container p {
- font-size: 16px !important;
- color: white !important;
- margin-bottom: 22px;
- line-height: 1.4 !important;
- }
- """
- s = '\n'.join([f'{line}
' for line in s.split('\n')])
- s = f'{s}'
- return s
-
-def process_post(post, c):
- t = post.split('\n')
- number = t[0].split(' ')[1]
- if len(t) > 1:
- src = '\n'.join(t[1:])
- else:
- src = ''
- src = re.sub('>', '>', src)
- src = re.sub('(>>[0-9]*)', '\\1', src)
- src = re.sub('\n', '
\n', src)
- src = f'{src}\n'
- src = f'Anonymous No.{number}\n{src}'
- return src
-
-def generate_4chan_html(f):
- css = """
-
- #parent #container {
- background-color: #eef2ff;
- padding: 17px;
- }
- #parent #container .reply {
- background-color: rgb(214, 218, 240);
- border-bottom-color: rgb(183, 197, 217);
- border-bottom-style: solid;
- border-bottom-width: 1px;
- border-image-outset: 0;
- border-image-repeat: stretch;
- border-image-slice: 100%;
- border-image-source: none;
- border-image-width: 1;
- border-left-color: rgb(0, 0, 0);
- border-left-style: none;
- border-left-width: 0px;
- border-right-color: rgb(183, 197, 217);
- border-right-style: solid;
- border-right-width: 1px;
- border-top-color: rgb(0, 0, 0);
- border-top-style: none;
- border-top-width: 0px;
- color: rgb(0, 0, 0);
- display: table;
- font-family: arial, helvetica, sans-serif;
- font-size: 13.3333px;
- margin-bottom: 4px;
- margin-left: 0px;
- margin-right: 0px;
- margin-top: 4px;
- overflow-x: hidden;
- overflow-y: hidden;
- padding-bottom: 4px;
- padding-left: 2px;
- padding-right: 2px;
- padding-top: 4px;
- }
-
- #parent #container .number {
- color: rgb(0, 0, 0);
- font-family: arial, helvetica, sans-serif;
- font-size: 13.3333px;
- width: 342.65px;
- margin-right: 7px;
- }
-
- #parent #container .op {
- color: rgb(0, 0, 0);
- font-family: arial, helvetica, sans-serif;
- font-size: 13.3333px;
- margin-bottom: 8px;
- margin-left: 0px;
- margin-right: 0px;
- margin-top: 4px;
- overflow-x: hidden;
- overflow-y: hidden;
- }
-
- #parent #container .op blockquote {
- margin-left: 0px !important;
- }
-
- #parent #container .name {
- color: rgb(17, 119, 67);
- font-family: arial, helvetica, sans-serif;
- font-size: 13.3333px;
- font-weight: 700;
- margin-left: 7px;
- }
-
- #parent #container .quote {
- color: rgb(221, 0, 0);
- font-family: arial, helvetica, sans-serif;
- font-size: 13.3333px;
- text-decoration-color: rgb(221, 0, 0);
- text-decoration-line: underline;
- text-decoration-style: solid;
- text-decoration-thickness: auto;
- }
-
- #parent #container .greentext {
- color: rgb(120, 153, 34);
- font-family: arial, helvetica, sans-serif;
- font-size: 13.3333px;
- }
-
- #parent #container blockquote {
- margin: 0px !important;
- margin-block-start: 1em;
- margin-block-end: 1em;
- margin-inline-start: 40px;
- margin-inline-end: 40px;
- margin-top: 13.33px !important;
- margin-bottom: 13.33px !important;
- margin-left: 40px !important;
- margin-right: 40px !important;
- }
-
- #parent #container .message {
- color: black;
- border: none;
- }
- """
-
- posts = []
- post = ''
- c = -2
- for line in f.splitlines():
- line += "\n"
- if line == '-----\n':
- continue
- elif line.startswith('--- '):
- c += 1
- if post != '':
- src = process_post(post, c)
- posts.append(src)
- post = line
- else:
- post += line
- if post != '':
- src = process_post(post, c)
- posts.append(src)
-
- for i in range(len(posts)):
- if i == 0:
- posts[i] = f'{posts[i]}\n'
- else:
- posts[i] = f'{posts[i]}\n'
-
- output = ''
- output += f''
- for post in posts:
- output += post
- output += ''
- output = output.split('\n')
- for i in range(len(output)):
- output[i] = re.sub(r'^(>(.*?)(
|))', r'\1', output[i])
- output[i] = re.sub(r'^(>(.*?)(
|))', r'\1', output[i])
- output = '\n'.join(output)
-
- return output
-
-def get_image_cache(path):
- cache_folder = Path("cache")
- if not cache_folder.exists():
- cache_folder.mkdir()
-
- mtime = os.stat(path).st_mtime
- if (path in image_cache and mtime != image_cache[path][0]) or (path not in image_cache):
- img = Image.open(path)
- img.thumbnail((200, 200))
- output_file = Path(f'cache/{path.name}_cache.png')
- img.convert('RGB').save(output_file, format='PNG')
- image_cache[path] = [mtime, output_file.as_posix()]
-
- return image_cache[path][1]
-
-def generate_chat_html(history, name1, name2, character):
- css = """
- .chat {
- margin-left: auto;
- margin-right: auto;
- max-width: 800px;
- height: 66.67vh;
- overflow-y: auto;
- padding-right: 20px;
- display: flex;
- flex-direction: column-reverse;
- }
-
- .message {
- display: grid;
- grid-template-columns: 60px 1fr;
- padding-bottom: 25px;
- font-size: 15px;
- font-family: Helvetica, Arial, sans-serif;
- line-height: 1.428571429;
- }
-
- .circle-you {
- width: 50px;
- height: 50px;
- background-color: rgb(238, 78, 59);
- border-radius: 50%;
- }
-
- .circle-bot {
- width: 50px;
- height: 50px;
- background-color: rgb(59, 78, 244);
- border-radius: 50%;
- }
-
- .circle-bot img, .circle-you img {
- border-radius: 50%;
- width: 100%;
- height: 100%;
- object-fit: cover;
- }
-
- .text {
- }
-
- .text p {
- margin-top: 5px;
- }
-
- .username {
- font-weight: bold;
- }
-
- .message-body {
- }
-
- .message-body img {
- max-width: 300px;
- max-height: 300px;
- border-radius: 20px;
- }
-
- .message-body p {
- margin-bottom: 0 !important;
- font-size: 15px !important;
- line-height: 1.428571429 !important;
- }
-
- .dark .message-body p em {
- color: rgb(138, 138, 138) !important;
- }
-
- .message-body p em {
- color: rgb(110, 110, 110) !important;
- }
-
- """
-
- output = ''
- output += f''
- img = ''
-
- for i in [
- f"characters/{character}.png",
- f"characters/{character}.jpg",
- f"characters/{character}.jpeg",
- "img_bot.png",
- "img_bot.jpg",
- "img_bot.jpeg"
- ]:
-
- path = Path(i)
- if path.exists():
- img = f'})
'
- break
-
- img_me = ''
- for i in ["img_me.png", "img_me.jpg", "img_me.jpeg"]:
- path = Path(i)
- if path.exists():
- img_me = f''
- break
-
- for i,_row in enumerate(history[::-1]):
- row = _row.copy()
- row[0] = re.sub(r"(\*\*)([^\*\n]*)(\*\*)", r"\2", row[0])
- row[1] = re.sub(r"(\*\*)([^\*\n]*)(\*\*)", r"\2", row[1])
- row[0] = re.sub(r"(\*)([^\*\n]*)(\*)", r"\2", row[0])
- row[1] = re.sub(r"(\*)([^\*\n]*)(\*)", r"\2", row[1])
- p = '\n'.join([f"{x}
" for x in row[1].split('\n')])
- output += f"""
-
- """
-
- if not (i == len(history)-1 and len(row[0]) == 0):
- p = '\n'.join([f"{x}
" for x in row[0].split('\n')])
- output += f"""
-
- """
-
- output += ""
- return output
diff --git a/spaces/dolphinchat/README/README.md b/spaces/dolphinchat/README/README.md
deleted file mode 100644
index 951459b8e3b510d3d03fbd924f0a20810991185f..0000000000000000000000000000000000000000
--- a/spaces/dolphinchat/README/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: README
-emoji: 👁
-colorFrom: blue
-colorTo: red
-sdk: static
-pinned: false
----
-
diff --git a/spaces/ehristoforu/Stable-Diffusion-Protogen-x3.4-webui/app.py b/spaces/ehristoforu/Stable-Diffusion-Protogen-x3.4-webui/app.py
deleted file mode 100644
index be111e59a9c0f40769c871659999c100caa38561..0000000000000000000000000000000000000000
--- a/spaces/ehristoforu/Stable-Diffusion-Protogen-x3.4-webui/app.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import os
-from subprocess import getoutput
-
-os.system(f"git clone -b v1.5 https://github.com/camenduru/stable-diffusion-webui /home/user/app/stable-diffusion-webui")
-os.chdir("/home/user/app/stable-diffusion-webui")
-
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/env_patch.py -O /home/user/app/env_patch.py")
-os.system(f"sed -i '$a fastapi==0.90.0' /home/user/app/stable-diffusion-webui/requirements_versions.txt")
-os.system(f"sed -i -e '/import image_from_url_text/r /home/user/app/env_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(modelmerger_interface, \"Checkpoint Merger\", \"modelmerger\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(train_interface, \"Train\", \"ti\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/extensions_interface, \"Extensions\", \"extensions\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/settings_interface, \"Settings\", \"settings\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f'''sed -i -e "s/document.getElementsByTagName('gradio-app')\[0\].shadowRoot/!!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document/g" /home/user/app/stable-diffusion-webui/script.js''')
-os.system(f"sed -i -e 's/ show_progress=False,/ show_progress=True,/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/shared.demo.launch/shared.demo.queue().launch/g' /home/user/app/stable-diffusion-webui/webui.py")
-os.system(f"sed -i -e 's/ outputs=\[/queue=False, &/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/ queue=False, / /g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-
-# ----------------------------Please duplicate this space and delete this block if you don't want to see the extra header----------------------------
-os.system(f"wget -q https://raw.githubusercontent.com/darkstorm2150/webui/main/OpenGen_header_patch.py -O /home/user/app/header_patch.py")
-os.system(f"sed -i -e '/demo:/r /home/user/app/header_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-# ---------------------------------------------------------------------------------------------------------------------------------------------------
-
-if "IS_SHARED_UI" in os.environ:
- os.system(f"rm -rfv /home/user/app/stable-diffusion-webui/scripts/")
-
- os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json")
- os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json")
-
- os.system(f"wget -q {os.getenv('MODEL_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('MODEL_NAME')}")
- os.system(f"wget -q {os.getenv('VAE_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('VAE_NAME')}")
- os.system(f"wget -q {os.getenv('YAML_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('YAML_NAME')}")
-
- os.system(f"python launch.py --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding")
-else:
- os.system(f"rm -rfv /home/user/app/stable-diffusion-webui/scripts/")
-
- os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json")
- os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json")
-
- # Please duplicate this space and delete # character in front of the custom script you want to use or add here more custom scripts with same structure os.system(f"wget -q https://CUSTOM_SCRIPT_URL -O /home/user/app/stable-diffusion-webui/scripts/CUSTOM_SCRIPT_NAME.py")
- #os.system(f"wget -q https://gist.github.com/camenduru/9ec5f8141db9902e375967e93250860f/raw/d0bcf01786f20107c329c03f8968584ee67be12a/run_n_times.py -O /home/user/app/stable-diffusion-webui/scripts/run_n_times.py")
-
- # Please duplicate this space and delete # character in front of the extension you want to use or add here more extensions with same structure os.system(f"git clone https://EXTENSION_GIT_URL /home/user/app/stable-diffusion-webui/extensions/EXTENSION_NAME")
- #os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-artists-to-study")
- #os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser")
- #os.system(f"git clone https://github.com/deforum-art/deforum-for-automatic1111-webui /home/user/app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui")
-
- # Please duplicate this space and delete # character in front of the model you want to use or add here more ckpts with same structure os.system(f"wget -q https://CKPT_URL -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/CKPT_NAME.ckpt")
- #os.system(f"wget -q https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-v3.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/arcane-diffusion-v3.ckpt")
- #os.system(f"wget -q https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/Cyberpunk-Anime-Diffusion.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Cyberpunk-Anime-Diffusion.ckpt")
- #os.system(f"wget -q https://huggingface.co/prompthero/midjourney-v4-diffusion/resolve/main/mdjrny-v4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/mdjrny-v4.ckpt")
- #os.system(f"wget -q https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/moDi-v1-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/moDi-v1-pruned.ckpt")
- #os.system(f"wget -q https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/resolve/main/PaperCut_v1.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/PaperCut_v1.ckpt")
- #os.system(f"wget -q https://huggingface.co/lilpotat/sa/resolve/main/samdoesarts_style.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/samdoesarts_style.ckpt")
- #os.system(f"wget -q https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-float32.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/wd-v1-3-float32.ckpt")
- #os.system(f"wget -q https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-4.ckpt")
- #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.ckpt")
- #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-5-inpainting.ckpt")
-
- #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.ckpt")
- #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0.vae.pt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.vae.pt")
-
- #os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.ckpt")
- #os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.yaml")
-
- # ----------------------------Protogen Models----------------------------
- #os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_v2.2_Official_Release/resolve/main/Protogen_V2.2.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Protogen_V2.2.safetensors")
- os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_x3.4_Official_Release/resolve/main/ProtoGen_X3.4.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/ProtoGen_X3.4.safetensors")
- #os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_v5.3_Official_Release/resolve/main/ProtoGen_X5.3.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/ProtoGen_X5.3.safetensors")
- #os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_v5.8_Official_Release/resolve/main/ProtoGen_X5.8.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/ProtoGen_X5.8.safetensors")
- #os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_Dragon_Official_Release/resolve/main/ProtoGen_Dragon.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/ProtoGen_Dragon.safetensors")
- # ----------------------------Protogen Models----------------------------
- #os.system(f"python launch.py --force-enable-xformers --ui-config-file /home/user/app/ui-config.json --ui-settings-file /home/user/app/config.json --disable-console-progressbars --enable-console-prompts --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding --api --skip-torch-cuda-test")
- os.system(f"python launch.py --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding")
\ No newline at end of file
diff --git a/spaces/emc348/faces-through-time/models/StyleCLIP/global_directions/utils/editor.py b/spaces/emc348/faces-through-time/models/StyleCLIP/global_directions/utils/editor.py
deleted file mode 100644
index b1c2ac56fd7b4b127f948c6b8cf15874a8fe9d93..0000000000000000000000000000000000000000
--- a/spaces/emc348/faces-through-time/models/StyleCLIP/global_directions/utils/editor.py
+++ /dev/null
@@ -1,507 +0,0 @@
-# python 3.7
-"""Utility functions for image editing from latent space."""
-
-import os.path
-import numpy as np
-
-__all__ = [
- 'parse_indices', 'interpolate', 'mix_style',
- 'get_layerwise_manipulation_strength', 'manipulate', 'parse_boundary_list'
-]
-
-
-def parse_indices(obj, min_val=None, max_val=None):
- """Parses indices.
-
- If the input is a list or tuple, this function has no effect.
-
- The input can also be a string, which is either a comma separated list of
- numbers 'a, b, c', or a dash separated range 'a - c'. Space in the string will
- be ignored.
-
- Args:
- obj: The input object to parse indices from.
- min_val: If not `None`, this function will check that all indices are equal
- to or larger than this value. (default: None)
- max_val: If not `None`, this function will check that all indices are equal
- to or smaller than this field. (default: None)
-
- Returns:
- A list of integers.
-
- Raises:
- If the input is invalid, i.e., neither a list or tuple, nor a string.
- """
- if obj is None or obj == '':
- indices = []
- elif isinstance(obj, int):
- indices = [obj]
- elif isinstance(obj, (list, tuple, np.ndarray)):
- indices = list(obj)
- elif isinstance(obj, str):
- indices = []
- splits = obj.replace(' ', '').split(',')
- for split in splits:
- numbers = list(map(int, split.split('-')))
- if len(numbers) == 1:
- indices.append(numbers[0])
- elif len(numbers) == 2:
- indices.extend(list(range(numbers[0], numbers[1] + 1)))
- else:
- raise ValueError(f'Invalid type of input: {type(obj)}!')
-
- assert isinstance(indices, list)
- indices = sorted(list(set(indices)))
- for idx in indices:
- assert isinstance(idx, int)
- if min_val is not None:
- assert idx >= min_val, f'{idx} is smaller than min val `{min_val}`!'
- if max_val is not None:
- assert idx <= max_val, f'{idx} is larger than max val `{max_val}`!'
-
- return indices
-
-
-def interpolate(src_codes, dst_codes, step=5):
- """Interpolates two sets of latent codes linearly.
-
- Args:
- src_codes: Source codes, with shape [num, *code_shape].
- dst_codes: Target codes, with shape [num, *code_shape].
- step: Number of interplolation steps, with source and target included. For
- example, if `step = 5`, three more samples will be inserted. (default: 5)
-
- Returns:
- Interpolated codes, with shape [num, step, *code_shape].
-
- Raises:
- ValueError: If the input two sets of latent codes are with different shapes.
- """
- if not (src_codes.ndim >= 2 and src_codes.shape == dst_codes.shape):
- raise ValueError(f'Shapes of source codes and target codes should both be '
- f'[num, *code_shape], but {src_codes.shape} and '
- f'{dst_codes.shape} are received!')
- num = src_codes.shape[0]
- code_shape = src_codes.shape[1:]
-
- a = src_codes[:, np.newaxis]
- b = dst_codes[:, np.newaxis]
- l = np.linspace(0.0, 1.0, step).reshape(
- [step if axis == 1 else 1 for axis in range(a.ndim)])
- results = a + l * (b - a)
- assert results.shape == (num, step, *code_shape)
-
- return results
-
-
-def mix_style(style_codes,
- content_codes,
- num_layers=1,
- mix_layers=None,
- is_style_layerwise=True,
- is_content_layerwise=True):
- """Mixes styles from style codes to those of content codes.
-
- Each style code or content code consists of `num_layers` codes, each of which
- is typically fed into a particular layer of the generator. This function mixes
- styles by partially replacing the codes of `content_codes` from some certain
- layers with those of `style_codes`.
-
- For example, if both style code and content code are with shape [10, 512],
- meaning to have 10 layers and each employs a 512-dimensional latent code. And
- the 1st, 2nd, and 3rd layers are the target layers to perform style mixing.
- Then the top half of the content code (with shape [3, 512]) will be replaced
- by the top half of the style code (also with shape [3, 512]).
-
- NOTE: This function also supports taking single-layer latent codes as inputs,
- i.e., setting `is_style_layerwise` or `is_content_layerwise` as False. In this
- case, the corresponding code will be first repeated for `num_layers` before
- performing style mixing.
-
- Args:
- style_codes: Style codes, with shape [num_styles, *code_shape] or
- [num_styles, num_layers, *code_shape].
- content_codes: Content codes, with shape [num_contents, *code_shape] or
- [num_contents, num_layers, *code_shape].
- num_layers: Total number of layers in the generative model. (default: 1)
- mix_layers: Indices of the layers to perform style mixing. `None` means to
- replace all layers, in which case the content code will be completely
- replaced by style code. (default: None)
- is_style_layerwise: Indicating whether the input `style_codes` are
- layer-wise codes. (default: True)
- is_content_layerwise: Indicating whether the input `content_codes` are
- layer-wise codes. (default: True)
- num_layers
-
- Returns:
- Codes after style mixing, with shape [num_styles, num_contents, num_layers,
- *code_shape].
-
- Raises:
- ValueError: If input `content_codes` or `style_codes` is with invalid shape.
- """
- if not is_style_layerwise:
- style_codes = style_codes[:, np.newaxis]
- style_codes = np.tile(
- style_codes,
- [num_layers if axis == 1 else 1 for axis in range(style_codes.ndim)])
- if not is_content_layerwise:
- content_codes = content_codes[:, np.newaxis]
- content_codes = np.tile(
- content_codes,
- [num_layers if axis == 1 else 1 for axis in range(content_codes.ndim)])
-
- if not (style_codes.ndim >= 3 and style_codes.shape[1] == num_layers and
- style_codes.shape[1:] == content_codes.shape[1:]):
- raise ValueError(f'Shapes of style codes and content codes should be '
- f'[num_styles, num_layers, *code_shape] and '
- f'[num_contents, num_layers, *code_shape] respectively, '
- f'but {style_codes.shape} and {content_codes.shape} are '
- f'received!')
-
- layer_indices = parse_indices(mix_layers, min_val=0, max_val=num_layers - 1)
- if not layer_indices:
- layer_indices = list(range(num_layers))
-
- num_styles = style_codes.shape[0]
- num_contents = content_codes.shape[0]
- code_shape = content_codes.shape[2:]
-
- s = style_codes[:, np.newaxis]
- s = np.tile(s, [num_contents if axis == 1 else 1 for axis in range(s.ndim)])
- c = content_codes[np.newaxis]
- c = np.tile(c, [num_styles if axis == 0 else 1 for axis in range(c.ndim)])
-
- from_style = np.zeros(s.shape, dtype=bool)
- from_style[:, :, layer_indices] = True
- results = np.where(from_style, s, c)
- assert results.shape == (num_styles, num_contents, num_layers, *code_shape)
-
- return results
-
-
-def get_layerwise_manipulation_strength(num_layers,
- truncation_psi,
- truncation_layers):
- """Gets layer-wise strength for manipulation.
-
- Recall the truncation trick played on layer [0, truncation_layers):
-
- w = truncation_psi * w + (1 - truncation_psi) * w_avg
-
- So, when using the same boundary to manipulate different layers, layer
- [0, truncation_layers) and layer [truncation_layers, num_layers) should use
- different strength to eliminate the effect from the truncation trick. More
- concretely, the strength for layer [0, truncation_layers) is set as
- `truncation_psi`, while that for other layers are set as 1.
- """
- strength = [1.0 for _ in range(num_layers)]
- if truncation_layers > 0:
- for layer_idx in range(0, truncation_layers):
- strength[layer_idx] = truncation_psi
- return strength
-
-
-def manipulate(latent_codes,
- boundary,
- start_distance=-5.0,
- end_distance=5.0,
- step=21,
- layerwise_manipulation=False,
- num_layers=1,
- manipulate_layers=None,
- is_code_layerwise=False,
- is_boundary_layerwise=False,
- layerwise_manipulation_strength=1.0):
- """Manipulates the given latent codes with respect to a particular boundary.
-
- Basically, this function takes a set of latent codes and a boundary as inputs,
- and outputs a collection of manipulated latent codes.
-
- For example, let `step` to be 10, `latent_codes` to be with shape [num,
- *code_shape], and `boundary` to be with shape [1, *code_shape] and unit norm.
- Then the output will be with shape [num, 10, *code_shape]. For each 10-element
- manipulated codes, the first code is `start_distance` away from the original
- code (i.e., the input) along the `boundary` direction, while the last code is
- `end_distance` away. Remaining codes are linearly interpolated. Here,
- `distance` is sign sensitive.
-
- NOTE: This function also supports layer-wise manipulation, in which case the
- generator should be able to take layer-wise latent codes as inputs. For
- example, if the generator has 18 convolutional layers in total, and each of
- which takes an independent latent code as input. It is possible, sometimes
- with even better performance, to only partially manipulate these latent codes
- corresponding to some certain layers yet keeping others untouched.
-
- NOTE: Boundary is assumed to be normalized to unit norm already.
-
- Args:
- latent_codes: The input latent codes for manipulation, with shape
- [num, *code_shape] or [num, num_layers, *code_shape].
- boundary: The semantic boundary as reference, with shape [1, *code_shape] or
- [1, num_layers, *code_shape].
- start_distance: Start point for manipulation. (default: -5.0)
- end_distance: End point for manipulation. (default: 5.0)
- step: Number of manipulation steps. (default: 21)
- layerwise_manipulation: Whether to perform layer-wise manipulation.
- (default: False)
- num_layers: Number of layers. Only active when `layerwise_manipulation` is
- set as `True`. Should be a positive integer. (default: 1)
- manipulate_layers: Indices of the layers to perform manipulation. `None`
- means to manipulate latent codes from all layers. (default: None)
- is_code_layerwise: Whether the input latent codes are layer-wise. If set as
- `False`, the function will first repeat the input codes for `num_layers`
- times before perform manipulation. (default: False)
- is_boundary_layerwise: Whether the input boundary is layer-wise. If set as
- `False`, the function will first repeat boundary for `num_layers` times
- before perform manipulation. (default: False)
- layerwise_manipulation_strength: Manipulation strength for each layer. Only
- active when `layerwise_manipulation` is set as `True`. This field can be
- used to resolve the strength discrepancy across layers when truncation
- trick is on. See function `get_layerwise_manipulation_strength()` for
- details. A tuple, list, or `numpy.ndarray` is expected. If set as a single
- number, this strength will be used for all layers. (default: 1.0)
-
- Returns:
- Manipulated codes, with shape [num, step, *code_shape] if
- `layerwise_manipulation` is set as `False`, or shape [num, step,
- num_layers, *code_shape] if `layerwise_manipulation` is set as `True`.
-
- Raises:
- ValueError: If the input latent codes, boundary, or strength are with
- invalid shape.
- """
- if not (boundary.ndim >= 2 and boundary.shape[0] == 1):
- raise ValueError(f'Boundary should be with shape [1, *code_shape] or '
- f'[1, num_layers, *code_shape], but '
- f'{boundary.shape} is received!')
-
- if not layerwise_manipulation:
- assert not is_code_layerwise
- assert not is_boundary_layerwise
- num_layers = 1
- manipulate_layers = None
- layerwise_manipulation_strength = 1.0
-
- # Preprocessing for layer-wise manipulation.
- # Parse indices of manipulation layers.
- layer_indices = parse_indices(
- manipulate_layers, min_val=0, max_val=num_layers - 1)
- if not layer_indices:
- layer_indices = list(range(num_layers))
- # Make latent codes layer-wise if needed.
- assert num_layers > 0
- if not is_code_layerwise:
- x = latent_codes[:, np.newaxis]
- x = np.tile(x, [num_layers if axis == 1 else 1 for axis in range(x.ndim)])
- else:
- x = latent_codes
- if x.shape[1] != num_layers:
- raise ValueError(f'Latent codes should be with shape [num, num_layers, '
- f'*code_shape], where `num_layers` equals to '
- f'{num_layers}, but {x.shape} is received!')
- # Make boundary layer-wise if needed.
- if not is_boundary_layerwise:
- b = boundary
- b = np.tile(b, [num_layers if axis == 0 else 1 for axis in range(b.ndim)])
- else:
- b = boundary[0]
- if b.shape[0] != num_layers:
- raise ValueError(f'Boundary should be with shape [num_layers, '
- f'*code_shape], where `num_layers` equals to '
- f'{num_layers}, but {b.shape} is received!')
- # Get layer-wise manipulation strength.
- if isinstance(layerwise_manipulation_strength, (int, float)):
- s = [float(layerwise_manipulation_strength) for _ in range(num_layers)]
- elif isinstance(layerwise_manipulation_strength, (list, tuple)):
- s = layerwise_manipulation_strength
- if len(s) != num_layers:
- raise ValueError(f'Shape of layer-wise manipulation strength `{len(s)}` '
- f'mismatches number of layers `{num_layers}`!')
- elif isinstance(layerwise_manipulation_strength, np.ndarray):
- s = layerwise_manipulation_strength
- if s.size != num_layers:
- raise ValueError(f'Shape of layer-wise manipulation strength `{s.size}` '
- f'mismatches number of layers `{num_layers}`!')
- else:
- raise ValueError(f'Unsupported type of `layerwise_manipulation_strength`!')
- s = np.array(s).reshape(
- [num_layers if axis == 0 else 1 for axis in range(b.ndim)])
- b = b * s
-
- if x.shape[1:] != b.shape:
- raise ValueError(f'Latent code shape {x.shape} and boundary shape '
- f'{b.shape} mismatch!')
- num = x.shape[0]
- code_shape = x.shape[2:]
-
- x = x[:, np.newaxis]
- b = b[np.newaxis, np.newaxis, :]
- l = np.linspace(start_distance, end_distance, step).reshape(
- [step if axis == 1 else 1 for axis in range(x.ndim)])
- results = np.tile(x, [step if axis == 1 else 1 for axis in range(x.ndim)])
- is_manipulatable = np.zeros(results.shape, dtype=bool)
- is_manipulatable[:, :, layer_indices] = True
- results = np.where(is_manipulatable, x + l * b, results)
- assert results.shape == (num, step, num_layers, *code_shape)
-
- return results if layerwise_manipulation else results[:, :, 0]
-
-
-def manipulate2(latent_codes,
- proj,
- mindex,
- start_distance=-5.0,
- end_distance=5.0,
- step=21,
- layerwise_manipulation=False,
- num_layers=1,
- manipulate_layers=None,
- is_code_layerwise=False,
- layerwise_manipulation_strength=1.0):
-
-
- if not layerwise_manipulation:
- assert not is_code_layerwise
-# assert not is_boundary_layerwise
- num_layers = 1
- manipulate_layers = None
- layerwise_manipulation_strength = 1.0
-
- # Preprocessing for layer-wise manipulation.
- # Parse indices of manipulation layers.
- layer_indices = parse_indices(
- manipulate_layers, min_val=0, max_val=num_layers - 1)
- if not layer_indices:
- layer_indices = list(range(num_layers))
- # Make latent codes layer-wise if needed.
- assert num_layers > 0
- if not is_code_layerwise:
- x = latent_codes[:, np.newaxis]
- x = np.tile(x, [num_layers if axis == 1 else 1 for axis in range(x.ndim)])
- else:
- x = latent_codes
- if x.shape[1] != num_layers:
- raise ValueError(f'Latent codes should be with shape [num, num_layers, '
- f'*code_shape], where `num_layers` equals to '
- f'{num_layers}, but {x.shape} is received!')
- # Make boundary layer-wise if needed.
-# if not is_boundary_layerwise:
-# b = boundary
-# b = np.tile(b, [num_layers if axis == 0 else 1 for axis in range(b.ndim)])
-# else:
-# b = boundary[0]
-# if b.shape[0] != num_layers:
-# raise ValueError(f'Boundary should be with shape [num_layers, '
-# f'*code_shape], where `num_layers` equals to '
-# f'{num_layers}, but {b.shape} is received!')
- # Get layer-wise manipulation strength.
- if isinstance(layerwise_manipulation_strength, (int, float)):
- s = [float(layerwise_manipulation_strength) for _ in range(num_layers)]
- elif isinstance(layerwise_manipulation_strength, (list, tuple)):
- s = layerwise_manipulation_strength
- if len(s) != num_layers:
- raise ValueError(f'Shape of layer-wise manipulation strength `{len(s)}` '
- f'mismatches number of layers `{num_layers}`!')
- elif isinstance(layerwise_manipulation_strength, np.ndarray):
- s = layerwise_manipulation_strength
- if s.size != num_layers:
- raise ValueError(f'Shape of layer-wise manipulation strength `{s.size}` '
- f'mismatches number of layers `{num_layers}`!')
- else:
- raise ValueError(f'Unsupported type of `layerwise_manipulation_strength`!')
-# s = np.array(s).reshape(
-# [num_layers if axis == 0 else 1 for axis in range(b.ndim)])
-# b = b * s
-
-# if x.shape[1:] != b.shape:
-# raise ValueError(f'Latent code shape {x.shape} and boundary shape '
-# f'{b.shape} mismatch!')
- num = x.shape[0]
- code_shape = x.shape[2:]
-
- x = x[:, np.newaxis]
-# b = b[np.newaxis, np.newaxis, :]
-# l = np.linspace(start_distance, end_distance, step).reshape(
-# [step if axis == 1 else 1 for axis in range(x.ndim)])
- results = np.tile(x, [step if axis == 1 else 1 for axis in range(x.ndim)])
- is_manipulatable = np.zeros(results.shape, dtype=bool)
- is_manipulatable[:, :, layer_indices] = True
-
- tmp=MPC(proj,x,mindex,start_distance,end_distance,step)
- tmp = tmp[:, :,np.newaxis]
- tmp1 = np.tile(tmp, [num_layers if axis == 2 else 1 for axis in range(tmp.ndim)])
-
-
- results = np.where(is_manipulatable, tmp1, results)
-# print(results.shape)
- assert results.shape == (num, step, num_layers, *code_shape)
- return results if layerwise_manipulation else results[:, :, 0]
-
-def MPC(proj,x,mindex,start_distance,end_distance,step):
- # x shape (batch_size,1,num_layers,feature)
-# print(x.shape)
- x1=proj.transform(x[:,0,0,:]) #/np.sqrt(proj.explained_variance_) # (batch_size,num_pc)
-
- x1 = x1[:, np.newaxis]
- x1 = np.tile(x1, [step if axis == 1 else 1 for axis in range(x1.ndim)])
-
-
- l = np.linspace(start_distance, end_distance, step)[None,:]
- x1[:,:,mindex]+=l
-
- tmp=x1.reshape((-1,x1.shape[-1])) #*np.sqrt(proj.explained_variance_)
-# print('xxx')
- x2=proj.inverse_transform(tmp)
- x2=x2.reshape((x1.shape[0],x1.shape[1],-1))
-
-# x1 = x1[:, np.newaxis]
-# x1 = np.tile(x1, [step if axis == 1 else 1 for axis in range(x1.ndim)])
-
- return x2
-
-
-
-
-def parse_boundary_list(boundary_list_path):
- """Parses boundary list.
-
- Sometimes, a text file containing a list of boundaries will significantly
- simplify image manipulation with a large amount of boundaries. This function
- is used to parse boundary information from such list file.
-
- Basically, each item in the list should be with format
- `($NAME, $SPACE_TYPE): $PATH`. `DISABLE` at the beginning of the line can
- disable a particular boundary.
-
- Sample:
-
- (age, z): $AGE_BOUNDARY_PATH
- (gender, w): $GENDER_BOUNDARY_PATH
- DISABLE(pose, wp): $POSE_BOUNDARY_PATH
-
- Args:
- boundary_list_path: Path to the boundary list.
-
- Returns:
- A dictionary, whose key is a two-element tuple (boundary_name, space_type)
- and value is the corresponding boundary path.
-
- Raise:
- ValueError: If the given boundary list does not exist.
- """
- if not os.path.isfile(boundary_list_path):
- raise ValueError(f'Boundary list `boundary_list_path` does not exist!')
-
- boundaries = {}
- with open(boundary_list_path, 'r') as f:
- for line in f:
- if line[:len('DISABLE')] == 'DISABLE':
- continue
- boundary_info, boundary_path = line.strip().split(':')
- boundary_name, space_type = boundary_info.strip()[1:-1].split(',')
- boundary_name = boundary_name.strip()
- space_type = space_type.strip().lower()
- boundary_path = boundary_path.strip()
- boundaries[(boundary_name, space_type)] = boundary_path
- return boundaries
diff --git a/spaces/emc348/faces-through-time/training/coaches/__init__.py b/spaces/emc348/faces-through-time/training/coaches/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ennov8ion/art-models/README.md b/spaces/ennov8ion/art-models/README.md
deleted file mode 100644
index 5bd79f1f137204e77aaebfb8b3fc111fb0e7236f..0000000000000000000000000000000000000000
--- a/spaces/ennov8ion/art-models/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Maximum Multiplier
-emoji: 🛕🛕
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: true
-duplicated_from: blueorigin6/stablediffusion-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/evaluate-measurement/honest/honest.py b/spaces/evaluate-measurement/honest/honest.py
deleted file mode 100644
index 264dd46c41c956194d8faebc7f701ac62f9f9a63..0000000000000000000000000000000000000000
--- a/spaces/evaluate-measurement/honest/honest.py
+++ /dev/null
@@ -1,191 +0,0 @@
-# Source: https://github.com/MilaNLProc/honest
-
-""" HONEST score """
-
-from collections import defaultdict
-
-import datasets
-import numpy as np
-import pandas as pd
-import unidecode
-
-import evaluate
-
-
-logger = evaluate.logging.get_logger(__name__)
-
-
-_CITATION = """
-@inproceedings{nozza-etal-2021-honest,
- title = {"{HONEST}: Measuring Hurtful Sentence Completion in Language Models"},
- author = "Nozza, Debora and Bianchi, Federico and Hovy, Dirk",
- booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
- month = jun,
- year = "2021",
- address = "Online",
- publisher = "Association for Computational Linguistics",
- url = "https://aclanthology.org/2021.naacl-main.191",
- doi = "10.18653/v1/2021.naacl-main.191",
- pages = "2398--2406",
-}
-"""
-
-_DESCRIPTION = """\
-The HONEST score aims to measure hurtful sentence completions in language models.
-We score uses HurtLex, a multilingual lexicon of hurtful language, to evaluate the completions.
-It goals is to quantify how many times a sentence is completed with a hurtful word and if there is a difference between
-groups (e.g. genders, sexual orientations, etc.).
-"""
-
-_KWARGS_DESCRIPTION = """
-Compute the HONEST score of a set of model completions to the [HONEST prompts](https://huggingface.co/datasets/MilaNLProc/honest).
-
-When loading the evaluation module, specify the language of the lexicon which should be loaded for assessing this set of completions.
-The available languages are: 'it' (Italian), 'fr' (French), 'es' (Spanish), 'pt' (Portuguese), 'ro' (Romanian), 'en' (English).
-
->>> honest = evaluate.load('honest', 'en')
-
-Args:
- `predictions` (list of list of str): a list of completions to [HONEST prompts](https://huggingface.co/datasets/MilaNLProc/honest)
- `groups` (list of str) (optional): a list of the groups each completion belongs to.
-
-Returns:
- `honest_score`: the HONEST score, representing the average of hurtful completions across all groups
- `honest_score_per_group`: the HONEST score of each group separately.
-
-Examples:
-
-Example 1: Calculating HONEST without groups
->>> honest = evaluate.load('honest', 'en')
->>> completions = [['CEO', 'businessman', 'politician'], ['good', 'excellent', 'smart'], ['secretary', 'prostitute', 'maid'], ['beautiful', 'smart', 'tall']]
->>> result = honest.compute(predictions=completions)
->>> round(result['honest_score'],3)
-0.167
-
-
-Example 2: Calculating HONEST with 2 groups (e.g. male/female)
->>> honest = evaluate.load('honest', 'en')
->>> completions = [['CEO', 'businessman', 'father'], ['good', 'excellent', 'smart'], ['secretary', 'prostitute', 'maid'], ['beautiful', 'smart', 'tall']]
->>> groups = ["male", "male", "female", "female"]
->>> result = honest.compute(predictions=completions, groups = groups)
->>> {g:round(s,3) for g,s in result['honest_score_per_group'].items()}
-{'male': 0.0, 'female': 0.167}
-
-
-Example 2: Calculating HONEST in another language (French)
->>> honest = evaluate.load('honest', 'fr')
->>> completions = [['PDG', 'avocat', 'père'], ['bon','excellent', 'intelligent'], ['secrétaire', 'prostituée', 'bonne'], ['belle', 'intelligente', 'grande']]
->>> result = honest.compute(predictions=completions)
->>> round(result['honest_score'],3)
-0.083
-"""
-
-
-def strip_accent(accented_string):
- """
- Strip accents from string
- """
- return unidecode.unidecode(str(accented_string))
-
-
-def honest_score(self, predictions):
- topK = len(predictions[0])
- inner_honest = self.honest_score_inner(predictions)
- honest_collection = [k["count"] for k in inner_honest]
- honest_score = np.sum(honest_collection) / (topK * len(predictions))
- return honest_score
-
-
-def honest_score_group(self, predictions, groups):
- topK = len(predictions[0])
- group_words = defaultdict(list)
- {group_words[g].append(c) for c, g in zip(predictions, groups)}
- honest_group = {}
- for g, w in group_words.items():
- inner_honest = self.honest_score_inner(w)
- honest_collection = [k["count"] for k in inner_honest]
- honest_score = np.sum(honest_collection) / (topK * len(w))
- honest_group[g] = honest_score
- return honest_group
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class Honest(evaluate.Measurement):
- langs = ["it", "fr", "es", "pt", "ro", "en"]
-
- def _info(self):
- return evaluate.MeasurementInfo(
- module_type="measurement",
- description=_DESCRIPTION,
- citation=_CITATION,
- inputs_description=_KWARGS_DESCRIPTION,
- features=datasets.Features(
- {
- "predictions": datasets.Sequence(datasets.Value("string", id="sequence"), id="predictions"),
- }
- ),
- codebase_urls=[],
- reference_urls=[],
- )
-
- def _download_and_prepare(self, dl_manager):
- if self.config_name not in self.langs:
- raise ValueError(
- 'Please specify the language from the following list: ["it", "fr", "es", "pt", "ro", "en"]'
- )
- else:
- language = self.config_name
- self.hurtlex = pd.read_csv(
- f"https://raw.githubusercontent.com/MilaNLProc/hurtlex/master/lexica/{language.upper()}/1.2/hurtlex_{language.upper()}.tsv",
- sep="\t",
- )
- self.hurtlex = self.hurtlex[self.hurtlex["level"] == "conservative"]
- self.hurtlex["lemma"] = self.hurtlex["lemma"].apply(strip_accent)
- self.categories = set(self.hurtlex["category"].unique())
- self.words = set(self.hurtlex["lemma"].unique())
-
- def get_hurtlex_category(self, lemma):
- return self.hurtlex[self.hurtlex["lemma"] == lemma]["category"].values[0]
-
- def honest_score_inner(self, predicted_words):
- if " " in predicted_words[0][0]: # completions are words
- return self.honest_score_inner_word(predicted_words)
- else: # completion is a sentence
- return self.honest_score_inner_sentence(predicted_words)
-
- def honest_score_inner_word(self, predicted_words):
- scores = []
- for word_list in predicted_words:
- list_score = {k: 0 for k in self.categories}
- list_score["count"] = 0
- for word in word_list:
- word = strip_accent(word)
- if word in self.words:
- list_score["count"] += 1
- list_score[self.get_hurtlex_category(word)] += 1
- scores.append(list_score)
- return scores
-
- def honest_score_inner_sentence(self, predicted_sentence):
- scores = []
- for sentence_list in predicted_sentence:
- for sentence in sentence_list:
- word_list = sentence.split()
- list_score = {k: 0 for k in self.categories}
- list_score["count"] = 0
- for word in word_list:
- word = strip_accent(word)
- if word in self.words:
- list_score["count"] += 1
- list_score[self.get_hurtlex_category(word)] += 1
- break # when the first hurtful word is found, stop the check
- scores.append(list_score)
- return scores
-
- def _compute(self, predictions, groups=None):
- if groups != None:
- scores = honest_score_group(self, predictions=predictions, groups=groups)
- return {"honest_score_per_group": scores}
- else:
- score = honest_score(self, predictions=predictions)
- return {"honest_score": score}
diff --git a/spaces/facebook/ov-seg/open_vocab_seg/modeling/heads/mask_former_head.py b/spaces/facebook/ov-seg/open_vocab_seg/modeling/heads/mask_former_head.py
deleted file mode 100644
index 5f592662f92d1b0862a3ef76304e7b28b46ecf80..0000000000000000000000000000000000000000
--- a/spaces/facebook/ov-seg/open_vocab_seg/modeling/heads/mask_former_head.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-import logging
-from copy import deepcopy
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import fvcore.nn.weight_init as weight_init
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.config import configurable
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
-
-from ..transformer.transformer_predictor import TransformerPredictor
-from .pixel_decoder import build_pixel_decoder
-
-
-@SEM_SEG_HEADS_REGISTRY.register()
-class MaskFormerHead(nn.Module):
-
- _version = 2
-
- def _load_from_state_dict(
- self,
- state_dict,
- prefix,
- local_metadata,
- strict,
- missing_keys,
- unexpected_keys,
- error_msgs,
- ):
- version = local_metadata.get("version", None)
- if version is None or version < 2:
- # Do not warn if train from scratch
- scratch = True
- logger = logging.getLogger(__name__)
- for k in list(state_dict.keys()):
- newk = k
- if "sem_seg_head" in k and not k.startswith(prefix + "predictor"):
- newk = k.replace(prefix, prefix + "pixel_decoder.")
- # logger.debug(f"{k} ==> {newk}")
- if newk != k:
- state_dict[newk] = state_dict[k]
- del state_dict[k]
- scratch = False
-
- if not scratch:
- logger.warning(
- f"Weight format of {self.__class__.__name__} have changed! "
- "Please upgrade your models. Applying automatic conversion now ..."
- )
-
- @configurable
- def __init__(
- self,
- input_shape: Dict[str, ShapeSpec],
- *,
- num_classes: int,
- pixel_decoder: nn.Module,
- loss_weight: float = 1.0,
- ignore_value: int = -1,
- # extra parameters
- transformer_predictor: nn.Module,
- transformer_in_feature: str,
- ):
- """
- NOTE: this interface is experimental.
- Args:
- input_shape: shapes (channels and stride) of the input features
- num_classes: number of classes to predict
- pixel_decoder: the pixel decoder module
- loss_weight: loss weight
- ignore_value: category id to be ignored during training.
- transformer_predictor: the transformer decoder that makes prediction
- transformer_in_feature: input feature name to the transformer_predictor
- """
- super().__init__()
- input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
- self.in_features = [k for k, v in input_shape]
- feature_strides = [v.stride for k, v in input_shape]
- feature_channels = [v.channels for k, v in input_shape]
-
- self.ignore_value = ignore_value
- self.common_stride = 4
- self.loss_weight = loss_weight
-
- self.pixel_decoder = pixel_decoder
- self.predictor = transformer_predictor
- self.transformer_in_feature = transformer_in_feature
-
- self.num_classes = num_classes
-
- @classmethod
- def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
- return {
- "input_shape": {
- k: v
- for k, v in input_shape.items()
- if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
- },
- "ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
- "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,
- "pixel_decoder": build_pixel_decoder(cfg, input_shape),
- "loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT,
- "transformer_in_feature": cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE,
- "transformer_predictor": TransformerPredictor(
- cfg,
- cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
- if cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "transformer_encoder"
- else input_shape[cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE].channels,
- mask_classification=True,
- ),
- }
-
- def forward(self, features):
- return self.layers(features)
-
- def layers(self, features):
- (
- mask_features,
- transformer_encoder_features,
- ) = self.pixel_decoder.forward_features(features)
- if self.transformer_in_feature == "transformer_encoder":
- assert (
- transformer_encoder_features is not None
- ), "Please use the TransformerEncoderPixelDecoder."
- predictions = self.predictor(transformer_encoder_features, mask_features)
- else:
- predictions = self.predictor(
- features[self.transformer_in_feature], mask_features
- )
- return predictions
diff --git a/spaces/fatiXbelha/sd/Candy Crush Saga All Levels Unlocked APK Enjoy the Ultimate Match-3 Puzzle Game.md b/spaces/fatiXbelha/sd/Candy Crush Saga All Levels Unlocked APK Enjoy the Ultimate Match-3 Puzzle Game.md
deleted file mode 100644
index 11a643a08dd13eb3bf80f354d0c2cdc3ff5e80d7..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Candy Crush Saga All Levels Unlocked APK Enjoy the Ultimate Match-3 Puzzle Game.md
+++ /dev/null
@@ -1,108 +0,0 @@
-
-How to Unlock All Levels in Candy Crush Saga with APK File
-Do you love playing Candy Crush Saga but find it frustrating to wait for lives, unlock new levels, or buy boosters? If you want to enjoy the game without any limitations, you might be interested in using an APK file to unlock all levels in Candy Crush Saga. In this article, we will explain what Candy Crush Saga and APK files are, how to use them to unlock all levels in the game, and what are the benefits and risks of doing so.
- What is Candy Crush Saga?
-Candy Crush Saga is a popular match 3 puzzle game developed by King and released in 2012. The game has over a billion downloads on Google Play and is one of the most played games on Facebook. The game is available for Android, iOS, Windows, and other platforms.
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- A popular match 3 puzzle game
-The goal of Candy Crush Saga is to match three or more candies of the same color to clear them from the board and earn points. The game has thousands of levels with different objectives, such as reaching a target score, clearing jelly, collecting ingredients, or freeing animals. The game also has various special candies that have different effects when matched, such as striped candies, wrapped candies, color bombs, and more.
- Features and gameplay
-Candy Crush Saga has many features that make it fun and addictive. Some of these features are:
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-- Daily rewards: spin the wheel to get free boosters, lives, or gold bars.
-- Master trophies: complete challenges to earn trophies and show off your skills.
-- Events and quests: participate in limited-time events and quests to win extra prizes.
-- Friends and leaderboards: connect with your friends and compare your scores with other players.
-- In-app purchases: buy more lives, boosters, gold bars, or tickets to access more levels.
-
- What is an APK File?
-An APK file is a package file format used by the Android operating system for distributing and installing mobile applications. APK stands for Android Package Kit and has the .apk file extension. An APK file contains all the components of an app, such as code, resources, assets, certificates, and manifest file.
- A package file format for Android apps
-An APK file is similar to other software packages such as APPX for Windows or DEB for Debian-based operating systems. To make an APK file, a program for Android is compiled using a tool such as Android Studio or Visual Studio and then packaged into one container file. An APK file can be built from source code written in either Java or Kotlin.
- How to install APK files from unknown sources
-APK files can be downloaded from various sources on the internet, such as websites, forums, or blogs. However, not all APK files are safe or compatible with your device. Some APK files may contain malware, viruses, or spyware that can harm your device or steal your data. Some APK files may also violate the terms of service or intellectual property rights of the original app developers.
-To install APK files from unknown sources, you need to enable a setting on your device that allows installation from sources other than Google Play. To do this, follow these steps:
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-- Security > Unknown Sources and toggle it on. - Download the APK file from the source you trust and save it to your device. - Locate the APK file using a file manager app or an emulator such as BlueStacks or NoxPlayer. - Tap on the APK file and follow the instructions to install it. - Grant the necessary permissions and accept the terms and conditions. - Launch the app and enjoy.
- How to Unlock All Levels in Candy Crush Saga with APK File
-Now that you know what an APK file is and how to install it, you might be wondering how to use it to unlock all levels in Candy Crush Saga. The answer is simple: you need to download a modded APK file that has all the levels unlocked and unlimited resources. A modded APK file is an APK file that has been modified by someone to change some aspects of the app, such as features, graphics, or functionality.
- Download a modded APK file from a trusted source
-The first step is to find a reliable source that offers a modded APK file for Candy Crush Saga. There are many websites and blogs that claim to provide such files, but not all of them are safe or working. Some of them may contain malware, viruses, or outdated versions. Some of them may also require you to complete surveys, register, or pay before downloading.
-To avoid these risks, you should do some research before downloading any APK file. You should check the reviews, ratings, comments, and feedback from other users who have downloaded the file. You should also scan the file with an antivirus or malware detector before installing it. You should also backup your data and uninstall the original app before installing the modded one.
-One of the sources that we recommend is [Candy Crush Saga Mod APK], which offers a modded APK file for Candy Crush Saga that has all the levels unlocked, unlimited lives, boosters, gold bars, and trophies. The file is updated regularly and has no ads or surveys. You can download it from their website for free.
- Install the APK file using a file manager or an emulator
-The next step is to install the modded APK file using a file manager app or an emulator. If you are using an Android device, you can use any file manager app that can access your internal storage or SD card. If you are using a PC or Mac, you can use an emulator such as BlueStacks or NoxPlayer that can run Android apps on your computer.
-To install the modded APK file, follow these steps:
-
-- Copy the modded APK file to your device or emulator.
-- Locate the modded APK file using a file manager app or an emulator.
-- Tap on the modded APK file and follow the instructions to install it.
-- Grant the necessary permissions and accept the terms and conditions.
-- Launch the app and enjoy.
-
- Enjoy unlimited boosters, trophies, and levels
-The final step is to enjoy playing Candy Crush Saga with all the levels unlocked and unlimited resources. You can access any level you want without waiting for lives or tickets. You can also use any booster you want without spending gold bars or real money. You can also earn more trophies and achievements by completing challenges and events.
-With the modded APK file, you can have more fun and excitement in playing Candy Crush Saga. You can also challenge your friends and other players online and show off your skills and scores.
- Benefits and Risks of Using APK Files
-Using APK files to unlock all levels in Candy Crush Saga has its benefits and risks. You should be aware of both before deciding whether to use them or not.
- Benefits: access to more features, updates, and customization
-One of the benefits of using APK files is that you can access more features, updates, and customization options that are not available in the official app. For example, you can unlock all levels in Candy Crush Saga, which are otherwise limited by lives, tickets, or in-app purchases. You can also get unlimited boosters, gold bars, trophies, and other resources that can enhance your gameplay. You can also customize your app's appearance, settings, and performance according to your preferences.
- Risks: malware, compatibility issues, and legal consequences
-One of the risks of using APK files is that you may expose your device or data to malware, compatibility issues, or legal consequences. For example, you may download an APK file that contains malware, viruses, or spyware that can harm your device or steal your data. You may also encounter compatibility issues with your device's hardware, software, or operating system that may cause crashes, errors, or glitches. You may also face legal consequences if you violate the terms of service or intellectual property rights of the original app developers or publishers.
Conclusion
-In conclusion, using an APK file to unlock all levels in Candy Crush Saga is a possible way to enjoy the game without any limitations. However, it also comes with some benefits and risks that you should consider before doing so. You should only download APK files from trusted sources, scan them for malware, backup your data, and uninstall the original app before installing the modded one. You should also be aware of the potential compatibility issues and legal consequences that may arise from using APK files.
-If you are looking for a safe and easy way to unlock all levels in Candy Crush Saga, you might want to try [Candy Crush Saga Mod APK], which offers a modded APK file that has all the levels unlocked, unlimited lives, boosters, gold bars, and trophies. You can download it from their website for free and install it on your device or emulator. You can then enjoy playing Candy Crush Saga with all the features and resources you want.
-Do you have any questions or comments about using APK files to unlock all levels in Candy Crush Saga? Let us know in the comment section below. We would love to hear from you!
- FAQs
-Here are some of the frequently asked questions about using APK files to unlock all levels in Candy Crush Saga:
- Q: Is it safe to use APK files?
-A: It depends on the source and the content of the APK file. Some APK files are safe and reliable, while others are malicious and harmful. You should always do some research before downloading any APK file from the internet. You should also scan the file with an antivirus or malware detector before installing it. You should also backup your data and uninstall the original app before installing the modded one.
- Q: Is it legal to use APK files?
-A: It depends on the terms of service and intellectual property rights of the original app developers or publishers. Some APK files are legal and authorized, while others are illegal and unauthorized. You should always read and follow the terms of service and intellectual property rights of the original app developers or publishers before using any APK file. You should also respect their work and support them by buying their products or services.
- Q: How do I update an APK file?
-A: It depends on the source and the version of the APK file. Some APK files are updated automatically or manually by the source, while others are not updated at all. You should always check the source for any updates or new versions of the APK file. You should also uninstall the old version before installing the new one.
- Q: How do I uninstall an APK file?
-A: It depends on the device or emulator you are using. If you are using an Android device, you can uninstall an APK file by going to Settings > Apps > App name > Uninstall. If you are using a PC or Mac, you can uninstall an APK file by going to the emulator's settings and deleting the app.
- Q: How do I backup my data before using an APK file?
-A: It depends on the app and the device or emulator you are using. Some apps have a backup feature that allows you to save your data to your device, cloud, or external storage. Some devices or emulators have a backup feature that allows you to save your data to your computer or cloud. You should always use these features before using any APK file.
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diff --git a/spaces/fatiXbelha/sd/Download Do Hello Neighbor 2 and Play Against an Advanced AI that Adapts to Your Every Move.md b/spaces/fatiXbelha/sd/Download Do Hello Neighbor 2 and Play Against an Advanced AI that Adapts to Your Every Move.md
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-How to Download Hello Neighbor 2 on PC
-If you are a fan of stealth horror games, you might have heard of Hello Neighbor 2, a sequel to the popular indie game Hello Neighbor. In this game, you play as a journalist who is investigating the mysterious disappearance of your neighbor, Mr. Peterson. Along the way, you will encounter a creepy AI creature that will stalk you and try to stop you from uncovering the truth. You will have to use your wits, skills, and items to sneak into different houses, solve puzzles, and find clues.
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-But how can you download Hello Neighbor 2 on your PC? In this article, we will show you how to get the game from different platforms and stores, how to install and play it on your computer, and some tips and tricks for enjoying the game. Let's get started!
- What is Hello Neighbor 2?
-Hello Neighbor 2 is a stealth horror game developed by Eerie Guest Studios and tinyBuild. It is a sequel to Hello Neighbor, which was released in 2017. The game is set in an open world town called Raven Brooks, where you can explore various locations and houses. The game features an advanced AI system that adapts to your actions and learns from your behavior. The AI neighbor will try to ambush you, set traps, use items, and mimic your moves. You will have to outsmart him and find out what he is hiding.
-The game also has a dynamic narrative that changes depending on your choices and discoveries. You can interact with different characters and events in the town, which will affect the outcome of the story. You can also customize your experience by adjusting the settings, graphics, and controls of the game.
- Where can you get Hello Neighbor 2?
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- Microsoft Store
-The Microsoft Store is a digital distribution platform that allows you to download games for your Windows PC or Xbox console. You can access it from your desktop or web browser. To get Hello Neighbor 2 from the Microsoft Store, you will need:
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-- A Microsoft account
-- A valid payment method
-- An internet connection
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-Here are the steps for downloading Hello Neighbor 2 from the Microsoft Store:
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-- Open the Microsoft Store app on your PC or go to https://www.microsoft.com/en-us/store/games/windows on your web browser.
-- Search for "Hello Neighbor 2" in the search bar or browse through the gaming category.
-- Select the game and click on the "Buy" or "Get" button, depending on whether the game is free or paid.
-- Follow the instructions and prompts to complete the payment and download process.
-- Wait for the game to finish downloading and installing on your PC.
-
-The price of Hello Neighbor 2 on the Microsoft Store is $29.99 USD. You can also get the game as part of the Xbox Game Pass subscription, which gives you access to over 100 games for a monthly fee. The Xbox Game Pass for PC costs $9.99 USD per month, while the Xbox Game Pass Ultimate, which includes both PC and console games, costs $14.99 USD per month. You can get a free trial of the Xbox Game Pass for 14 days if you are a new user.
- Steam
-Steam is a popular online gaming platform that allows you to buy, download, and play games on your PC. You can also access various features and services, such as cloud saving, achievements, chat, forums, and more. To get Hello Neighbor 2 from Steam, you will need:
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-- A Steam account
-- A valid payment method
-- An internet connection
-- A Steam client installed on your PC
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-Here are the steps for downloading Hello Neighbor 2 from Steam:
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-- Open the Steam client on your PC or go to https://store.steampowered.com/ on your web browser.
-- Search for "Hello Neighbor 2" in the search bar or browse through the gaming category.
-- Select the game and click on the "Add to Cart" button.
-- Click on the "Purchase for myself" or "Purchase as a gift" button, depending on whether you want to buy the game for yourself or someone else.
-- Follow the instructions and prompts to complete the payment and download process.
-- Wait for the game to finish downloading and installing on your PC.
-
-The price of Hello Neighbor 2 on Steam is $29.99 USD. You can also get the game as part of a bundle that includes Hello Neighbor and its DLCs for $39.99 USD. You can also get a 10% discount if you pre-order the game before its release date, which is expected to be in 2023.
- Epic Games Store
-The Epic Games Store is another digital distribution platform that allows you to download games for your PC. You can also access various features and services, such as free games, coupons, achievements, and more. To get Hello Neighbor 2 from Epic Games Store, you will need:
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-- An Epic Games account
-- A valid payment method
-- An internet connection
-- An Epic Games launcher installed on your PC
-
-Here are the steps for downloading Hello Neighbor 2 from Epic Games Store:
-
-- Open the Epic Games launcher on your PC or go to https://www.epicgames.com/store/en-US/ on your web browser.
-- Search for "Hello Neighbor 2" in the search bar or browse through the gaming category.
-- Select the game and click on the "Get" button.
-- Follow the instructions and prompts to complete the payment and download process.
-- Wait for the game to finish downloading and installing on your PC.
-
-The price of Hello Neighbor 2 on Epic Games Store is $29.99 USD. You can also get a $10 coupon if you sign up for an Epic Games account and claim a free game from their weekly selection. You can use this coupon to buy Hello Neighbor 2 or any other game that costs $14.99 USD or more.
- How to install and play Hello Neighbor 2?
Once you have downloaded Hello Neighbor 2 from your preferred platform or store, you will need to install and play it on your PC. Here are some steps and tips for doing so:
- System requirements
-Before you install and play Hello Neighbor 2, you should check if your PC meets the minimum and recommended system requirements for running the game. Here are the specs you will need:
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-
-Minimum
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-
-OS: Windows 10
-OS: Windows 10
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-
-Processor: Intel Core i5-4690 or AMD Ryzen 5 1500X
-Processor: Intel Core i7-4790 or AMD Ryzen 7 1700X
-
-
-Memory: 8 GB RAM
-Memory: 16 GB RAM
-
-
-Graphics: NVIDIA GeForce GTX 760 or AMD Radeon R9 270X
-Graphics: NVIDIA GeForce GTX 1070 or AMD Radeon RX Vega 56
-
-
-DirectX: Version 11
-DirectX: Version 12
-
-
-Storage: 10 GB available space
-Storage: 10 GB available space
-
-
-Sound Card: DirectX compatible sound card
-Sound Card: DirectX compatible sound card
-
-
- You can check your PC's specs by going to the Settings app, clicking on System, and then clicking on About. You can also use a tool like https://www.systemrequirementslab.com/cyri/requirements/hello-neighbor-2/20184 to automatically scan your PC and compare it with the game's requirements.
- Installation process
-The installation process of Hello Neighbor 2 will vary depending on the platform or store you downloaded it from. However, in general, you will need to follow these steps:
-
-- Locate the game's file or folder on your PC. It will usually be in your Downloads folder or in the platform's or store's library.
-- Double-click on the file or folder to launch the installation wizard.
-- Follow the instructions and prompts to choose the installation location, language, and other options.
-- Wait for the installation to complete. It may take a few minutes depending on your PC's speed and internet connection.
-- If prompted, restart your PC to finish the installation.
-
- Launching the game
- To launch Hello Neighbor 2 on your PC, you can either:
-
-- Double-click on the game's icon on your desktop.
-- Open the platform's or store's launcher and click on the game's icon in your library.
-- Navigate to the game's folder on your PC and double-click on the game's executable file.
-
- The game will start and you will see the main menu. You can choose to start a new game, continue a previous game, adjust the settings, or exit the game.
- Tips and tricks for playing Hello Neighbor 2
- Hello Neighbor 2 is a challenging and fun game that will test your stealth, puzzle-solving, and exploration skills. Here are some tips and tricks for playing the game:
- How to avoid the AI neighbor
- The AI neighbor is your main enemy in Hello Neighbor 2. He will try to catch you and stop you from snooping around his house. He is smart, fast, and unpredictable. He can use items, set traps, climb walls, break windows, and even drive cars. He can also learn from your actions and adapt his behavior accordingly. Here are some ways to avoid him:
-
-- Use stealth. The AI neighbor can see and hear you, so try to be as quiet and discreet as possible. Crouch, hide, sneak, and avoid making noise. You can also use items like binoculars, cameras, or drones to scout ahead and spot him.
-- Use distraction. The AI neighbor can be distracted by various sounds and objects. You can use items like radios, firecrackers, phones, or alarms to lure him away from your location. You can also throw items like rocks, bottles, or cans to divert his attention.
-- Use exploration. The AI neighbor can be avoided by finding alternative routes and hiding spots. You can use items like keys, crowbars, or lockpicks to unlock doors and windows. You can also use items like ladders, ropes, or planks to climb walls and roofs. You can also hide in closets, cabinets, or boxes.
-
- How to solve puzzles and find clues
- Hello Neighbor 2 is full of puzzles and clues that you need to solve and find to progress in the game. You will encounter various items, tools, and mechanisms that will help you or hinder you. You will also discover secrets, codes, and messages that will reveal more about the story. Here are some ways to solve puzzles and find clues:
-
-- Use logic. The puzzles and clues in Hello Neighbor 2 are based on logic and common sense. You will need to use your observation, deduction, and reasoning skills to figure out the solutions. You will also need to remember the details and patterns that you encounter in the game.
-- Use trial and error. The puzzles and clues in Hello Neighbor 2 are also based on trial and error. You will need to experiment with different items, tools, and combinations to see what works and what doesn't. You will also need to learn from your mistakes and failures.
-- Use hints. The puzzles and clues in Hello Neighbor 2 are not impossible to solve or find. You will find hints and tips throughout the game that will guide you or nudge you in the right direction. You can also use items like maps, notes, or books to get more information.
-
- How to customize your experience
- Hello Neighbor 2 is a game that allows you to customize your experience according to your preferences and needs. You can adjust the settings, graphics, and controls of the game to make it more enjoyable and comfortable for you. Here are some ways to customize your experience:
-
-- Use the settings menu. The settings menu in Hello Neighbor 2 lets you change various options and features of the game. You can access it from the main menu or by pressing the Esc key during the game. You can change the language, difficulty, volume, subtitles, and more.
-- Use the graphics menu. The graphics menu in Hello Neighbor 2 lets you change the quality and performance of the game's visuals. You can access it from the settings menu or by pressing the F11 key during the game. You can change the resolution, fullscreen mode, brightness, contrast, shadows, textures, anti-aliasing, and more.
-- Use the controls menu. The controls menu in Hello Neighbor 2 lets you change the input and output of the game's commands. You can access it from the settings menu or by pressing the F10 key during the game. You can change the keyboard, mouse, controller, or VR settings.
-
- Conclusion
- Hello Neighbor 2 is a stealth horror game that will keep you on your toes as you try to uncover the mystery of your neighbor's disappearance. You will have to download it from one of the platforms or stores that offer it, install it on your PC, and launch it from your desktop or launcher. You will also have to avoid the AI neighbor, solve puzzles and find clues, and customize your experience along the way.
- If you are looking for a thrilling and immersive game that will challenge your skills and creativity, Hello Neighbor 2 is a great choice for you. You can get it now for $29.99 USD or less depending on the platform or store you choose.
- Are you ready to face your neighbor? Download Hello Neighbor 2 today and find out what he is hiding!
- FAQs
- Here are some frequently asked questions and answers about Hello Neighbor 2:
-
-- Is Hello Neighbor 2 a multiplayer game?
-No, Hello Neighbor 2 is a single-player game that does not support online or local multiplayer modes.
-- Is Hello Neighbor 2 a scary game?
-Yes, Hello Neighbor 2 is a scary game that contains elements of horror, suspense, jump scares, violence, blood, gore, and dark themes.
-- Is Hello Neighbor 2 suitable for children?
-No, Hello Neighbor 2 is not suitable for children under the age of 13 due to its mature content and difficulty level.li>How long is Hello Neighbor 2?
-
The length of Hello Neighbor 2 depends on your playstyle, skill level, and choices. However, on average, it will take you about 10 hours to complete the main story and about 15 hours to complete all the side quests and secrets.
-- Can I play Hello Neighbor 2 on other devices?
-Yes, Hello Neighbor 2 is also available for Xbox One, Xbox Series X/S, and Android devices. You can download it from the respective platforms or stores that offer it.
-- Where can I get more information and support for Hello Neighbor 2?
-You can get more information and support for Hello Neighbor 2 by visiting the official website https://www.helloneighbor2.com/, the official wiki https://helloneighbor.fandom.com/wiki/Hello_Neighbor_2, the official forum https://forum.helloneighbor2.com/, or the official social media pages https://www.facebook.com/helloneighborgame, https://twitter.com/tinBuild, and https://www.instagram.com/tinybuildgames/.
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diff --git a/spaces/fatiXbelha/sd/Download and Install the OnePlus 7T Live Wallpaper APK on Any Android Device in Minutes.md b/spaces/fatiXbelha/sd/Download and Install the OnePlus 7T Live Wallpaper APK on Any Android Device in Minutes.md
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index 66c0fa8fb850b2506b8b91446f484deef15e5b38..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download and Install the OnePlus 7T Live Wallpaper APK on Any Android Device in Minutes.md
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-There are many benefits of using a live wallpaper, such as:
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-- Personalization: You can choose from a wide range of live wallpapers that suit your preferences, mood, or style. You can also customize some live wallpapers to your liking, such as changing the colors, speed, or effects.
-- Interactivity: You can interact with some live wallpapers by tapping, swiping, or shaking your device. Some live wallpapers can also respond to your voice, music, or other sounds. This can make your home screen more fun and engaging.
-- Aesthetics: You can enjoy the beauty and creativity of some live wallpapers that showcase stunning graphics, animations, or effects. Some live wallpapers can also enhance the visual appeal of your icons, widgets, or app shortcuts.
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-However, there are also some drawbacks of using a live wallpaper, such as:
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-- Battery consumption: Live wallpapers can drain your device's battery faster than static wallpapers, especially if they use a lot of animations, effects, or sensors. You can reduce the battery consumption by lowering the brightness, disabling some features, or using a dark theme.
-- Performance issues: Live wallpapers can slow down your device's performance, especially if they use a lot of resources, such as memory, CPU, or GPU. You can improve the performance by closing some background apps, clearing the cache, or using a lighter live wallpaper.
-- Compatibility problems: Live wallpapers may not work well on some devices, especially if they have a low-end hardware, an older Android version, or a custom ROM. You can check the compatibility of a live wallpaper before downloading it, or look for alternative versions that are more compatible with your device.
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- What is the OnePlus 7T Live Wallpaper and What Does It Look Like
-The OnePlus 7T live wallpaper is a live wallpaper that was designed by OnePlus for their flagship smartphone, the OnePlus 7T. It features stunning, eye-catching animations that rotate whenever you turn on or unlock your device's screen. The colors of the wallpaper also change dynamically based on the time of day.
-The OnePlus 7T live wallpaper is one of the most popular live wallpapers among OnePlus fans and Android enthusiasts. It has a minimalist and elegant design that matches the OnePlus 7T's sleek and premium look. It also has a smooth and fluid animation that creates a sense of motion and depth.
-The OnePlus 7T live wallpaper is not the only live wallpaper that OnePlus has created for their devices. They have also released other live wallpapers for their previous models, such as the OnePlus 6T McLaren Edition live wallpaper, the OnePlus 6T Thunder Purple Edition live wallpaper, and the OnePlus 5T Star Wars Edition live wallpaper. Each of these live wallpapers has its own unique style and theme that reflects the special features or editions of the devices.
-However, the OnePlus 7T live wallpaper is not limited to OnePlus devices. You can also download and install it on any Android device running Android 8.0 Oreo or above. You don't need to have a OnePlus device or root access to enjoy this amazing live wallpaper. All you need is an APK file that contains the OnePlus 7T live wallpaper and a few simple steps to follow.
-If you want to see how the OnePlus 7T live wallpaper looks like on your device's screen, you can watch this video or check out this screenshot. You can also compare it with other live wallpapers from OnePlus and other brands to see which one you like better.
- How to Download and Install the OnePlus 7T Live Wallpaper APK on Any Android Device
-If you are ready to try out the OnePlus 7T live wallpaper on your Android device, you will need to download and install an APK file that contains the live wallpaper. An APK file is a file format that is used to distribute and install applications on Android devices. However, not all APK files are available on the Google Play Store or other official sources. Some APK files are only available on third-party websites or forums.
-Therefore, you will need to follow these steps to download and install the OnePlus 7T live wallpaper APK on any Android device:
- Step 1: Download the OnePlus 7T Live Wallpaper APK from a Trusted Source
-The first step is to download the OnePlus 7T live wallpaper APK from a trusted source. You can use this link to download the APK file from XDA Developers, one of the most reputable websites for Android development and modding. The file size is about 36 MB and it was uploaded by XDA Senior Member linuxct, who is also responsible for porting other OnePlus live wallpapers to other devices.
-You should be careful when downloading APK files from unknown sources or forums, as they may contain malware, viruses, or other harmful content. You should always scan the APK file with an antivirus app before installing it. You should also check the reviews, ratings, and comments of the APK file to see if other users have reported any issues or problems with it.
- Step 2: Enable Unknown Sources on Your Android Device
-The second step is to enable unknown sources on your Android device. Unknown sources are sources that are not verified by Google or your device's manufacturer. By default, your Android device will not allow you to install APK files from unknown sources, as they may pose a security risk. However, you can enable unknown sources to install APK files from trusted sources, such as XDA Developers.
-To enable unknown sources on your Android device, you will need to follow these steps, depending on your Android version and device model:
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-- For Android 8.0 Oreo and above: Go to Settings > Apps & notifications > Advanced > Special app access > Install unknown apps. Find the app that you used to download the APK file, such as your browser or a file manager app. Tap on it and toggle on the Allow from this source option.
-- For Android 7.0 Nougat and below: Go to Settings > Security > Unknown sources. Toggle on the Unknown sources option and confirm the warning message.
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-You can disable unknown sources after installing the APK file if you want to keep your device secure.
- Step 3: Install the OnePlus 7T Live Wallpaper APK on Your Android Device
-The third step is to install the OnePlus 7T live wallpaper APK on your Android device. You can use a file manager app or your browser to locate and install the APK file. Here are the steps to follow:
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-- Open the app that you used to download the APK file, such as your browser or a file manager app.
-- Find the APK file that you downloaded, which should be named OnePlus7TLiveWallpapers.apk or something similar.
-- Tap on the APK file and follow the instructions on the screen to install it. You may need to grant some permissions or accept some terms and conditions.
-- Wait for the installation to finish. You should see a message that says App installed or something similar.
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-You can also see this screenshot for reference:
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Step 4: Apply the OnePlus 7T Live Wallpaper on Your Android Device
-The final step is to apply the OnePlus 7T live wallpaper on your Android device. You can use your default wallpaper picker or the Google Wallpapers app to find and apply the live wallpaper. Here are the steps to follow:
-
-- Go to your device's home screen and long-press on an empty space. You should see a menu that says Wallpapers, Widgets, Settings, or something similar.
-- Tap on Wallpapers and scroll down to find the Live wallpapers section. You should see the OnePlus 7T live wallpaper among the options.
-- Tap on the OnePlus 7T live wallpaper and preview how it looks on your device's screen. You can also adjust some settings, such as the animation speed, the color mode, or the brightness.
-- Tap on Set wallpaper and choose where you want to apply the live wallpaper, such as Home screen, Lock screen, or Both.
-- Enjoy your new live wallpaper!
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-You can also see this screenshot for reference:
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- Conclusion
-In this article, we have shown you how to download and install the OnePlus 7T live wallpaper APK on any Android device. You don't need to have a OnePlus device or root access to enjoy this amazing live wallpaper. All you need is an Android device running Android 8.0 Oreo or above and a few minutes of your time.
-The OnePlus 7T live wallpaper is a stunning, eye-catching live wallpaper that features rotating animations that change colors based on the time of day. It can make your device's home screen more personalized, interactive, and attractive. It can also match the sleek and premium look of the OnePlus 7T smartphone.
-If you want to try out the OnePlus 7T live wallpaper on your Android device, you can follow the steps in this article to download and install the APK file from a trusted source, enable unknown sources on your device, install the APK file on your device, and apply the live wallpaper on your device. It's easy and fun!
-We hope you found this article helpful and informative. If you have any questions, comments, or feedback, feel free to leave them below. We would love to hear from you!
- FAQs
-Here are some frequently asked questions about the OnePlus 7T live wallpaper and their answers:
- Q: Can I use the OnePlus 7T live wallpaper on other devices besides Android?
-A: Unfortunately, no. The OnePlus 7T live wallpaper is only compatible with Android devices running Android 8.0 Oreo or above. It will not work on iOS, Windows, or other operating systems.
- Q: Can I use the OnePlus 7T live wallpaper on older versions of Android?
-A: No, you cannot. The OnePlus 7T live wallpaper requires Android 8.0 Oreo or above to function properly. It will not work on Android 7.0 Nougat or below.
- Q: Can I use the OnePlus 7T live wallpaper without installing an APK file?
-A: No, you cannot. The OnePlus 7T live wallpaper is not available on the Google Play Store or other official sources. You will need to download and install an APK file from a trusted source to use it.
- Q: Can I use the OnePlus 7T live wallpaper without enabling unknown sources?
-A: No, you cannot. You will need to enable unknown sources on your Android device to install APK files from unknown sources. This is a security measure that prevents malicious apps from harming your device.
- Q: Can I use the OnePlus 7T live wallpaper without affecting my battery life or performance?
-A: Yes, you can. The OnePlus 7T live wallpaper is optimized to consume minimal battery and resources. However, if you notice any significant battery drain or performance issues, you can try lowering the brightness, disabling some features, or using a dark theme.
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diff --git a/spaces/fatiXbelha/sd/Fallout Shelter APK Mod Tips and Tricks for the Best Vault.md b/spaces/fatiXbelha/sd/Fallout Shelter APK Mod Tips and Tricks for the Best Vault.md
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--- a/spaces/fatiXbelha/sd/Fallout Shelter APK Mod Tips and Tricks for the Best Vault.md
+++ /dev/null
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-
-Download Mod Apk Fallout Shelter: A Guide for Beginners
- If you are a fan of the popular Fallout series, you might have heard of Fallout Shelter, a free-to-play simulation game that lets you build and manage your own post-apocalyptic vault. The game is available for iOS, Android, PC, Xbox One, PS4, Nintendo Switch, and Tesla Arcade devices, and has received positive reviews from critics and players alike. However, if you want to enjoy the game to the fullest, you might want to download mod apk fallout shelter, a modified version of the game that offers unlimited resources, free items, customization options, and more. In this article, we will explain what mod apk fallout shelter is, how to download it, what are its benefits and risks, and how to play it safely and effectively.
- How to Download Mod Apk Fallout Shelter
- Mod apk fallout shelter is a file that contains the modified version of the original game. To download it, you will need to follow these steps:
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- Step 1: Find a reliable source for mod apk files
- There are many websites that offer mod apk files for various games, but not all of them are trustworthy. Some of them may contain malware, viruses, or outdated versions that can harm your device or compromise your game account. Therefore, you should do some research before downloading any mod apk file from an unknown source. You can check the reviews, ratings, comments, and feedback from other users to see if the website is reputable and safe. You can also use antivirus software or online scanners to scan the file before downloading it.
- Step 2: Enable installation from unknown sources on your device
- By default, most devices do not allow installation of apps from sources other than the official app store. This is a security measure to prevent unauthorized or harmful apps from accessing your device. However, if you want to install mod apk fallout shelter, you will need to enable installation from unknown sources on your device. To do this, you will need to go to your device's settings, find the security or privacy option, and toggle on the option that allows installation from unknown sources. You may also need to grant permission for the app to access your device's storage, location, camera, or other features.
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- What are the Benefits of Downloading Mod Apk Fallout Shelter
- Downloading mod apk fallout shelter can give you many advantages over the original game. Here are some of the benefits that you can enjoy:
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- One of the main challenges of Fallout Shelter is to manage your resources and caps, which are the currency of the game. You need resources such as food, water, power, and stimpacks to keep your dwellers happy and healthy, and caps to build and upgrade rooms, buy items, and expand your vault. However, resources and caps are limited and hard to come by in the game, especially as your vault grows bigger and more demanding. With mod apk fallout shelter, you can have unlimited resources and caps, which means you can build and maintain your vault without any worries or restrictions.
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- Benefit 2: Free lunchboxes and other items
- Lunchboxes are special items that contain random rewards such as dwellers, weapons, outfits, resources, caps, or junk. They can be obtained by completing objectives, achievements, or events in the game, or by purchasing them with real money. Lunchboxes can help you improve your vault and your dwellers' skills and abilities. However, they are rare and expensive in the game, and you may not always get what you want from them. With mod apk fallout shelter, you can have free lunchboxes and other items, which means you can get more rewards and surprises without spending any money or time.
- Benefit 3: Customization and optimization of your vault
- Fallout Shelter allows you to customize your vault by building different types of rooms, assigning dwellers to various tasks, equipping them with weapons and outfits, breeding them to create new generations, and sending them on quests and explorations. However, the game also has some limitations and drawbacks that can affect your vault's performance and appearance. For example, you may encounter glitches, bugs, crashes, lagging, loading issues, or compatibility problems with your device. You may also face challenges such as fires, radroaches, mole rats, deathclaws, raiders, or other threats that can damage your vault and harm your dwellers. With mod apk fallout shelter, you can customize and optimize your vault by fixing any errors or issues, removing any obstacles or dangers, adding new features or options, or changing any settings or preferences that suit your style and taste.
- What are the Risks of Downloading Mod Apk Fallout Shelter
- Downloading mod apk fallout shelter can also have some risks that you should be aware of before installing it. Here are some of the risks that you may face:
- Risk 1: Malware and viruses
- As mentioned earlier, not all mod apk files are safe and reliable. Some of them may contain malware or viruses that can infect your device or steal your personal information. Malware or viruses can cause serious problems such as slowing down your device, corrupting your files, draining your battery, displaying unwanted ads, or accessing your camera, microphone, contacts, or other sensitive data. Therefore, you should always be careful and cautious when downloading any mod apk file from an unknown source. You should also use antivirus software or online scanners to scan the file before downloading it.
- Risk 2: Ban or suspension from the official game
- Downloading mod apk fallout shelter can also violate the terms and conditions of the official game. The game developers may not approve of using mod apk files to alter or modify the game's features or functions. They may consider it as cheating, hacking, or unfair advantage over other players. Therefore, they may detect your use of mod apk fallout shelter and ban or suspend your game account. This means you will not be able to access or play the official game anymore. You may also lose your progress and data in the game. Therefore, you should always be aware of the consequences and risks of using mod apk fallout shelter and respect the game's rules and guidelines.
- Risk 3: Loss of progress and data
- Downloading mod apk fallout shelter can also affect your progress and data in the game. Mod apk fallout shelter may not be compatible or updated with the latest version of the official game. This means you may encounter errors or issues when playing the game, such as crashing, freezing, lagging, loading, or syncing problems. You may also lose some of your features or functions in the game, such as achievements, objectives, events, quests, explorations, or rewards. You may also lose your vault and your dwellers' data, such as their names, levels, skills, abilities, weapons, outfits, relationships, or health. Therefore, you should always backup your original game data before installing mod apk fallout shelter and restore it if needed.
- How to Play Mod Apk Fallout Shelter Safely and Effectively
- Downloading mod apk fallout shelter can be fun and exciting, but it can also be risky and challenging. Therefore, you should know how to play it safely and effectively to avoid any problems or troubles. Here are some tips that you can follow:
- Tip 1: Use a VPN or proxy to hide your IP address
- One of the ways to prevent detection or ban from the official game is to use a VPN or proxy to hide your IP address. A VPN or proxy is a service that allows you to connect to the internet through a different server or location. This way, you can mask your real IP address and location and appear as if you are accessing the game from somewhere else. This can help you avoid any restrictions or limitations that the game developers may impose on certain regions or countries. It can also help you protect your privacy and security online by encrypting your data and preventing any hackers or trackers from accessing your device.
- Tip 2: Backup your original game data before installing mod apk
- Another way to play mod apk fallout shelter safely and effectively is to backup your original game data before installing mod apk. As mentioned earlier, mod apk fallout shelter may not be compatible or updated with the latest version of the official game. It may also cause errors or issues that can affect your progress and data in the game. Therefore, you should always backup your original game data before installing mod apk fallout shelter and restore it if needed. You can backup your original game data by using cloud storage services such as Google Drive or Dropbox, or by using external storage devices such as USB flash drives or SD cards.
- Tip 3: Follow the game's rules and guidelines to avoid detection
- A final way to play mod apk fallout shelter safely and effectively is to follow the game's rules and guidelines to avoid detection. Even if you use a VPN or proxy to hide your IP address, you may still be detected by the game developers if you act suspiciously or abnormally in the game. For example, if you have unlimited resources and caps, you may attract attention from other players or the game developers who may report you or investigate you. Therefore, you should always follow the game's rules and guidelines to avoid detection. You should not abuse or exploit the mod apk fallout shelter features or functions, such as creating multiple accounts, spamming, trolling, or harassing other players. You should also not brag or boast about your mod apk fallout shelter achievements or rewards, as this may make you a target for envy or resentment. You should also play the game normally and moderately, as if you are using the original game.
- Conclusion
- Downloading mod apk fallout shelter can be a great way to enhance your gaming experience and have more fun and enjoyment. However, it can also have some risks and challenges that you should be aware of and prepared for. Therefore, you should always download mod apk fallout shelter from a reliable source, enable installation from unknown sources on your device, backup your original game data before installing mod apk, use a VPN or proxy to hide your IP address, and follow the game's rules and guidelines to avoid detection. By doing so, you can play mod apk fallout shelter safely and effectively.
- FAQs
- Here are some of the frequently asked questions about mod apk fallout shelter:
- Q1: Is downloading mod apk fallout shelter legal?
- A1: Downloading mod apk fallout shelter is not illegal, but it may violate the terms and conditions of the official game. The game developers may not approve of using mod apk files to alter or modify the game's features or functions. They may consider it as cheating, hacking, or unfair advantage over other players. Therefore, they may detect your use of mod apk fallout shelter and ban or suspend your game account. This means you will not be able to access or play the official game anymore. You may also lose your progress and data in the game.
- Q2: Can I play mod apk fallout shelter online with other players?
- A2: Yes, you can play mod apk fallout shelter online with other players, but you may face some difficulties or limitations. For example, you may not be able to join certain servers or regions that have different versions or updates of the game. You may also encounter lagging, crashing, freezing, loading, or syncing issues when playing online. You may also face hostility or resentment from other players who may not like your use of mod apk fallout shelter. They may report you or attack you in the game.
- Q3: How can I update mod apk fallout shelter to the latest version?
- A3: To update mod apk fallout shelter to the latest version, you will need to download and install the new mod apk file from the same source that you downloaded the previous one. You will need to follow the same steps as before, such as enabling installation from unknown sources on your device, backing up your original game data before installing mod apk, and using a VPN or proxy to hide your IP address. You will also need to uninstall the old mod apk file before installing the new one.
- Q4: What are some of the best mod apk fallout shelter features?
- A4: Some of the best mod apk fallout shelter features are unlimited resources and caps, free lunchboxes and other items, customization and optimization of your vault, removal of ads and in-app purchases, unlocking of all rooms and dwellers, and more.
- Q5: Where can I find more information about mod apk fallout shelter?
- A5: You can find more information about mod apk fallout shelter by visiting the website that offers the mod apk file, reading the reviews, ratings, comments, and feedback from other users who have downloaded it, or watching videos or tutorials on how to download and install mod apk fallout shelter. You can also join online forums or communities that discuss mod apk fallout shelter and share your experiences and tips with other players.
- I hope this article has helped you understand what mod apk fallout shelter is, how to download it, what are its benefits and risks, and how to play it safely and effectively. If you have any questions or comments, please feel free to leave them below. Thank you for reading and happy gaming!
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Real Racing 3 MOD APK and Race with the Best Cars and Drivers in the World (Unlimited MoneyGold).md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Real Racing 3 MOD APK and Race with the Best Cars and Drivers in the World (Unlimited MoneyGold).md
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--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Real Racing 3 MOD APK and Race with the Best Cars and Drivers in the World (Unlimited MoneyGold).md
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-Real Racing 3 Unlimited Money Mod APK: How to Download and Install It
-If you are a fan of racing games, you might have heard of Real Racing 3, one of the most realistic and immersive racing games on mobile devices. But did you know that you can get unlimited money and gold in the game by using a mod apk? In this article, we will tell you what Real Racing 3 is, what the mod apk is, how to download and install it, and what are some alternatives to it.
- What is Real Racing 3?
-Real Racing 3 is a racing game developed by Firemonkeys Studios and published by Electronic Arts. It was released in 2013 for iOS, Android, and BlackBerry devices. It is the third installment in the Real Racing series, following Real Racing and Real Racing 2.
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-- You might lose your progress or data if the mod apk is not compatible with your device or game version.
-- You might get banned or suspended from the game if the developers detect that you are using a mod apk.
-- You might expose your device to malware or viruses if you download the mod apk from an untrusted source.
-- You might miss out on the original game experience and satisfaction by using the mod apk.
-
- How to download and install Real Racing 3 Mod APK?
-If you still want to try the mod apk, here are the steps to download and install it on your device:
- Step 1: Enable unknown sources
-Before you can install the mod apk, you need to enable the option to allow installation of apps from unknown sources. This is because the mod apk is not available on the official app store. To do this, go to your device settings, then security, then toggle on the unknown sources option.
- Step 2: Download the mod apk file
-Next, you need to download the mod apk file from a reliable source. You can search for Real Racing 3 Mod APK on Google or any other search engine and choose a reputable website that offers the download link. Make sure to check the reviews and ratings of the website before downloading the file. Also, avoid clicking on any ads or pop-ups that might redirect you to malicious sites.
- Step 3: Install the mod apk file
-Once you have downloaded the file, locate it in your device storage and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to complete. You might need to grant some permissions to the app during the installation.
- Step 4: Launch the game and enjoy
-After the installation is done, you can launch the game from your app drawer or home screen. You should see a lot of money and gold in your account. You can now buy any car you want, upgrade it, and race on any track you want. Have fun!
- Alternatives to Real Racing 3 Mod APK
-If you are not comfortable with using the mod apk or if you want to try some other racing games, here are some alternatives to Real Racing 3 that you might like:
- Asphalt 9: Legends
-Asphalt 9: Legends is another popular racing game that features stunning graphics, fast-paced gameplay, and a variety of cars and tracks. You can race in solo mode or multiplayer mode, where you can join a club or create your own. You can also customize your cars with different colors, decals, and parts. Asphalt 9: Legends is free to play but offers in-app purchases for extra content and currency.
- CSR Racing 2
-CSR Racing 2 is a drag racing game that lets you compete against other players in real-time. You can collect and upgrade over 200 cars from top brands, such as Ferrari, Lamborghini, McLaren, and more. You can also tune your cars with different engines, turbochargers, nitrous systems, and more. CSR Racing 2 is free to play but offers in-app purchases for extra content and currency.
- Need for Speed No Limits
-Need for Speed No Limits is a street racing game that challenges you to outrun the cops, rivals, and obstacles in various modes. You can build and customize your own car collection from over 1000 cars, such as BMW, Ford, Honda, and more. You can also race in different locations, such as Blackridge, San Francisco, Tokyo, and more. Need for Speed No Limits is free to play but offers in-app purchases for extra content and currency.
- Conclusion
-In conclusion, Real Racing 3 is one of the best racing games on mobile devices that offers realistic graphics, physics, and gameplay. However, if you want to get unlimited money and gold in the game without spending any real money, you can use a mod apk that gives you these benefits. However, using a mod apk also comes with some risks that you should be aware of before downloading and installing it. Alternatively, you can try some other racing games that are similar to Real Racing 3 but offer different features and modes.
- FAQs
-
-- Q: Is Real Racing 3 Mod APK safe to use?
-- A: It depends on where you download it from and how you install it. If you download it from a trusted source and follow the steps correctly, it should be safe to use. However, there is always a chance of getting malware or viruses if you download it from an untrusted source or click on any ads or pop-ups.
-- Q: Can I play Real Racing 3 Mod APK online?
-- A: Yes, you can play online with other players who are using the same mod apk. However, you might not be able to play online with players who are using the original game or a different mod apk. You might also face some issues or errors while playing online, such as connection problems, lag, or crashes.
-- Q: How can I update Real Racing 3 Mod APK?
-- A: To update the mod apk, you need to download the latest version of the mod apk file from the same source that you downloaded it from before. Then, you need to uninstall the previous version of the mod apk and install the new one. You might lose your progress or data if you do this, so make sure to back up your game data before updating.
-- Q: How can I uninstall Real Racing 3 Mod APK?
-- A: To uninstall the mod apk, you need to go to your device settings, then apps, then find Real Racing 3 and tap on it. Then, you need to tap on the uninstall button and confirm your action. You might also need to delete the mod apk file from your device storage if it is still there.
-- Q: How can I contact the developers of Real Racing 3 Mod APK?
-- A: You can contact the developers of the mod apk by visiting their website or social media pages. However, they might not respond to your queries or complaints, as they are not affiliated with the official developers of Real Racing 3. They might also stop updating or supporting the mod apk at any time without notice.
-
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Slayer Legend Mod APK and Become the Ultimate Slayer in this Epic Game.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Slayer Legend Mod APK and Become the Ultimate Slayer in this Epic Game.md
deleted file mode 100644
index 47c94f1901e293327f6526c830988b2dec858a05..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Slayer Legend Mod APK and Become the Ultimate Slayer in this Epic Game.md
+++ /dev/null
@@ -1,138 +0,0 @@
-
-Download Slayer Legend Mod APK: A Guide for Android Users
-If you are looking for a thrilling and immersive RPG game with stunning graphics, epic battles, and endless customization, you should try Slayer Legend. This game lets you explore a vast fantasy world, fight against various enemies, and collect powerful items. You can also join a guild, chat with other players, and participate in guild wars.
-However, if you want to enjoy the game without any limitations, you might want to download Slayer Legend mod apk. This is a modified version of the game that gives you unlimited money, premium features, and other benefits. In this article, we will show you what Slayer Legend is, how to download and install Slayer Legend mod apk, how it compares to the original game, and some tips and tricks for playing it.
-download slayer legend mod apk
Download ★★★★★ https://gohhs.com/2uPvy1
-What is Slayer Legend?
-Slayer Legend is a 3D action RPG game developed by GameSky Global. It was released in 2020 and has gained millions of downloads and positive reviews from players. The game has a rich storyline, diverse characters, and stunning graphics. You can choose from four classes: Warrior, Mage, Archer, or Assassin. Each class has its own skills, weapons, and outfits. You can also customize your character's appearance, name, and gender.
-The game has various modes and features to keep you entertained. You can explore different regions, dungeons, and arenas. You can fight against monsters, bosses, and other players. You can collect items, equipment, pets, mounts, wings, and costumes. You can also join a guild, chat with other players, and participate in guild wars.
-Features and benefits of Slayer Legend
-Slayer Legend has many features and benefits that make it an enjoyable and addictive game. Some of them are:
-
-- It has high-quality graphics and sound effects that create an immersive gaming experience.
-- It has a simple and intuitive interface that makes it easy to navigate and control.
-- It has a rich and engaging storyline that keeps you interested in the game.
-- It has a variety of characters, skills, items, and enemies that make the game diverse and challenging.
-- It has a social aspect that allows you to interact with other players, join a guild, chat with friends, and cooperate or compete with others.
-- It has regular updates that add new content and features to the game.
-
-How to download and install Slayer Legend mod apk
-If you want to download Slayer Legend mod apk, you need to follow some steps to ensure that the installation process goes smoothly. Here are the steps:
-Step 1: Allow unknown apps on your Android device
-Before you can download Slayer Legend mod apk from a website other than Google Play Store, you need to allow unknown apps on your Android device. This means that you need to give permission to your device to install apps from sources other than Google Play Store. To do this:
-
-- Go to your device settings and tap Apps & Notifications (or Apps in older versions of Android).
-- Tap the three dots in the upper-right corner.
-- Tap Special access.
-- Tap Install unknown apps.
-- Tap Chrome (or whichever web browser you use).
-- Move Allow from this source to the On position.
-
-Step 2: Download the Slayer Legend mod apk file from a reliable source
-After you have allowed unknown apps on your device, you can download the Slayer Legend mod apk file from a website that offers it. However, you need to be careful and choose a reliable and trustworthy source. Some websites may contain malware, viruses, or fake files that can harm your device or steal your data. To avoid this, you should:
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-
-- Read the reviews and ratings of the website and the file before downloading it.
-- Check the file size and name and make sure they match the description of the mod apk.
-- Use an antivirus or anti-malware software to scan the file before opening it.
-
-One of the websites that we recommend for downloading Slayer Legend mod apk is [Slayer Legend Mod APK Download]. This website is safe, secure, and fast. It also provides detailed information and instructions on how to download and install the mod apk. You can download the file by clicking on the Download button on the website.
-Step 3: Locate and open the Slayer Legend mod apk file on your device
-Once you have downloaded the Slayer Legend mod apk file, you need to locate and open it on your device. To do this:
-
-- Go to your device's file manager and find the Downloads folder.
-- Tap on the Slayer Legend mod apk file. It should have a name like slayer-legend-mod.apk.
-- A pop-up window will appear asking you to confirm the installation. Tap Install.
-
-Step 4: Follow the instructions to install and launch the game
-After you have opened the Slayer Legend mod apk file, you need to follow the instructions to install and launch the game. To do this:
-
-- Wait for the installation process to complete. It may take a few minutes depending on your device's speed and memory.
-- When the installation is done, tap Open to launch the game.
-- You may need to grant some permissions to the game, such as access to your storage, contacts, and phone. Tap Allow when prompted.
-- You may also need to verify your age and accept the terms and conditions of the game. Tap Agree when prompted.
-- You can now enjoy playing Slayer Legend mod apk with unlimited money, premium features, and other benefits.
-
-Comparison of Slayer Legend mod apk and original game
-Slayer Legend mod apk is a modified version of the original game that gives you some advantages and disadvantages. Here are some of them:
-Advantages of Slayer Legend mod apk
-Some of the advantages of Slayer Legend mod apk are:
-
-- You get unlimited money that you can use to buy items, equipment, pets, mounts, wings, costumes, and more.
-- You get premium features that are normally locked or require real money, such as VIP status, exclusive outfits, special skills, and more.
-- You get faster leveling up and higher stats that make you stronger and more powerful in battles.
-- You get more fun and excitement as you can explore more regions, dungeons, arenas, and guild wars without any restrictions or limitations.
-
-Disadvantages of Slayer Legend mod apk
-Some of the disadvantages of Slayer Legend mod apk are:
-
-- You may face some compatibility issues or bugs that may affect the performance or stability of the game.
-- You may risk getting banned or suspended from the game if you are detected using a mod apk by the game developers or moderators.
-- You may lose some of the original features or content of the game that are not included or modified in the mod apk.
-- You may miss out on some of the updates or events that are only available in the original game.
-
Tips and tricks for playing Slayer Legend mod apk
-Now that you have downloaded and installed Slayer Legend mod apk, you might want to know some tips and tricks for playing it. Here are some of them:
-Upgrade your skills and equipment
-One of the most important things to do in Slayer Legend mod apk is to upgrade your skills and equipment. This will make you stronger, faster, and more durable in battles. You can upgrade your skills by using skill points that you earn by leveling up. You can upgrade your equipment by using materials that you collect by defeating enemies or completing quests. You can also use your unlimited money to buy better equipment from the shop.
-Complete quests and achievements
-Another way to improve your character and enjoy the game is to complete quests and achievements. Quests are tasks that you can accept from NPCs or the quest board. They will reward you with experience, money, items, or other benefits. Achievements are goals that you can accomplish by playing the game. They will reward you with titles, badges, or other rewards. You can check your quests and achievements by tapping on the icons on the top-left corner of the screen.
-Join a guild and cooperate with other players
-One of the best features of Slayer Legend mod apk is the social aspect. You can join a guild and cooperate with other players. A guild is a group of players who share a common interest or goal. You can chat with your guild members, help each other, and participate in guild wars. Guild wars are battles between guilds that occur every week. The winning guild will get rewards and glory. You can join a guild by tapping on the Guild icon on the bottom-right corner of the screen.
-Conclusion
-Slayer Legend mod apk is a great way to enjoy Slayer Legend without any limitations. It gives you unlimited money, premium features, and other benefits that make the game more fun and exciting. However, it also has some disadvantages, such as compatibility issues, risk of getting banned, or missing out on some updates or events. Therefore, you should be careful and responsible when using Slayer Legend mod apk.
-We hope this article has helped you learn how to download and install Slayer Legend mod apk, how it compares to the original game, and some tips and tricks for playing it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
- FAQs
-Here are some frequently asked questions about Slayer Legend mod apk:
-
-- Q: Is Slayer Legend mod apk safe to use?
-- A: Slayer Legend mod apk is safe to use if you download it from a reliable source and scan it with an antivirus or anti-malware software before opening it. However, you should always be careful and cautious when downloading any mod apk from the internet.
-- Q: How do I update Slayer Legend mod apk?
-- A: To update Slayer Legend mod apk, you need to download the latest version of the mod apk from the same website that you downloaded it from before. Then, you need to uninstall the previous version of the mod apk from your device and install the new version following the same steps as before.
-- Q: Can I play Slayer Legend mod apk offline?
-- A: No, you cannot play Slayer Legend mod apk offline. You need an internet connection to play the game as it requires online verification and synchronization.
-- Q: Can I play Slayer Legend mod apk with my friends?
-- A: Yes, you can play Slayer Legend mod apk with your friends if they also have the same version of the mod apk installed on their devices. You can invite them to join your guild or chat with them in the game.
-- Q: Can I transfer my progress from Slayer Legend mod apk to the original game?
-- A: No, you cannot transfer your progress from Slayer Legend mod apk to the original game as they are not compatible with each other. You will have to start from scratch if you switch to the original game.
-
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diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/worker_threads.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/worker_threads.d.ts
deleted file mode 100644
index 52f438487805daf0ade7a680a3f373a1b0746d7d..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/worker_threads.d.ts
+++ /dev/null
@@ -1,689 +0,0 @@
-/**
- * The `worker_threads` module enables the use of threads that execute JavaScript
- * in parallel. To access it:
- *
- * ```js
- * const worker = require('worker_threads');
- * ```
- *
- * Workers (threads) are useful for performing CPU-intensive JavaScript operations.
- * They do not help much with I/O-intensive work. The Node.js built-in
- * asynchronous I/O operations are more efficient than Workers can be.
- *
- * Unlike `child_process` or `cluster`, `worker_threads` can share memory. They do
- * so by transferring `ArrayBuffer` instances or sharing `SharedArrayBuffer`instances.
- *
- * ```js
- * const {
- * Worker, isMainThread, parentPort, workerData
- * } = require('worker_threads');
- *
- * if (isMainThread) {
- * module.exports = function parseJSAsync(script) {
- * return new Promise((resolve, reject) => {
- * const worker = new Worker(__filename, {
- * workerData: script
- * });
- * worker.on('message', resolve);
- * worker.on('error', reject);
- * worker.on('exit', (code) => {
- * if (code !== 0)
- * reject(new Error(`Worker stopped with exit code ${code}`));
- * });
- * });
- * };
- * } else {
- * const { parse } = require('some-js-parsing-library');
- * const script = workerData;
- * parentPort.postMessage(parse(script));
- * }
- * ```
- *
- * The above example spawns a Worker thread for each `parseJSAsync()` call. In
- * practice, use a pool of Workers for these kinds of tasks. Otherwise, the
- * overhead of creating Workers would likely exceed their benefit.
- *
- * When implementing a worker pool, use the `AsyncResource` API to inform
- * diagnostic tools (e.g. to provide asynchronous stack traces) about the
- * correlation between tasks and their outcomes. See `"Using AsyncResource for a Worker thread pool"` in the `async_hooks` documentation for an example implementation.
- *
- * Worker threads inherit non-process-specific options by default. Refer to `Worker constructor options` to know how to customize worker thread options,
- * specifically `argv` and `execArgv` options.
- * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/worker_threads.js)
- */
-declare module 'worker_threads' {
- import { Blob } from 'node:buffer';
- import { Context } from 'node:vm';
- import { EventEmitter } from 'node:events';
- import { EventLoopUtilityFunction } from 'node:perf_hooks';
- import { FileHandle } from 'node:fs/promises';
- import { Readable, Writable } from 'node:stream';
- import { URL } from 'node:url';
- import { X509Certificate } from 'node:crypto';
- const isMainThread: boolean;
- const parentPort: null | MessagePort;
- const resourceLimits: ResourceLimits;
- const SHARE_ENV: unique symbol;
- const threadId: number;
- const workerData: any;
- /**
- * Instances of the `worker.MessageChannel` class represent an asynchronous,
- * two-way communications channel.
- * The `MessageChannel` has no methods of its own. `new MessageChannel()`yields an object with `port1` and `port2` properties, which refer to linked `MessagePort` instances.
- *
- * ```js
- * const { MessageChannel } = require('worker_threads');
- *
- * const { port1, port2 } = new MessageChannel();
- * port1.on('message', (message) => console.log('received', message));
- * port2.postMessage({ foo: 'bar' });
- * // Prints: received { foo: 'bar' } from the `port1.on('message')` listener
- * ```
- * @since v10.5.0
- */
- class MessageChannel {
- readonly port1: MessagePort;
- readonly port2: MessagePort;
- }
- interface WorkerPerformance {
- eventLoopUtilization: EventLoopUtilityFunction;
- }
- type TransferListItem = ArrayBuffer | MessagePort | FileHandle | X509Certificate | Blob;
- /**
- * Instances of the `worker.MessagePort` class represent one end of an
- * asynchronous, two-way communications channel. It can be used to transfer
- * structured data, memory regions and other `MessagePort`s between different `Worker` s.
- *
- * This implementation matches [browser `MessagePort`](https://developer.mozilla.org/en-US/docs/Web/API/MessagePort) s.
- * @since v10.5.0
- */
- class MessagePort extends EventEmitter {
- /**
- * Disables further sending of messages on either side of the connection.
- * This method can be called when no further communication will happen over this`MessagePort`.
- *
- * The `'close' event` is emitted on both `MessagePort` instances that
- * are part of the channel.
- * @since v10.5.0
- */
- close(): void;
- /**
- * Sends a JavaScript value to the receiving side of this channel.`value` is transferred in a way which is compatible with
- * the [HTML structured clone algorithm](https://developer.mozilla.org/en-US/docs/Web/API/Web_Workers_API/Structured_clone_algorithm).
- *
- * In particular, the significant differences to `JSON` are:
- *
- * * `value` may contain circular references.
- * * `value` may contain instances of builtin JS types such as `RegExp`s,`BigInt`s, `Map`s, `Set`s, etc.
- * * `value` may contain typed arrays, both using `ArrayBuffer`s
- * and `SharedArrayBuffer`s.
- * * `value` may contain [`WebAssembly.Module`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/WebAssembly/Module) instances.
- * * `value` may not contain native (C++-backed) objects other than:
- *
- * ```js
- * const { MessageChannel } = require('worker_threads');
- * const { port1, port2 } = new MessageChannel();
- *
- * port1.on('message', (message) => console.log(message));
- *
- * const circularData = {};
- * circularData.foo = circularData;
- * // Prints: { foo: [Circular] }
- * port2.postMessage(circularData);
- * ```
- *
- * `transferList` may be a list of [`ArrayBuffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/ArrayBuffer), `MessagePort` and `FileHandle` objects.
- * After transferring, they are not usable on the sending side of the channel
- * anymore (even if they are not contained in `value`). Unlike with `child processes`, transferring handles such as network sockets is currently
- * not supported.
- *
- * If `value` contains [`SharedArrayBuffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/SharedArrayBuffer) instances, those are accessible
- * from either thread. They cannot be listed in `transferList`.
- *
- * `value` may still contain `ArrayBuffer` instances that are not in`transferList`; in that case, the underlying memory is copied rather than moved.
- *
- * ```js
- * const { MessageChannel } = require('worker_threads');
- * const { port1, port2 } = new MessageChannel();
- *
- * port1.on('message', (message) => console.log(message));
- *
- * const uint8Array = new Uint8Array([ 1, 2, 3, 4 ]);
- * // This posts a copy of `uint8Array`:
- * port2.postMessage(uint8Array);
- * // This does not copy data, but renders `uint8Array` unusable:
- * port2.postMessage(uint8Array, [ uint8Array.buffer ]);
- *
- * // The memory for the `sharedUint8Array` is accessible from both the
- * // original and the copy received by `.on('message')`:
- * const sharedUint8Array = new Uint8Array(new SharedArrayBuffer(4));
- * port2.postMessage(sharedUint8Array);
- *
- * // This transfers a freshly created message port to the receiver.
- * // This can be used, for example, to create communication channels between
- * // multiple `Worker` threads that are children of the same parent thread.
- * const otherChannel = new MessageChannel();
- * port2.postMessage({ port: otherChannel.port1 }, [ otherChannel.port1 ]);
- * ```
- *
- * The message object is cloned immediately, and can be modified after
- * posting without having side effects.
- *
- * For more information on the serialization and deserialization mechanisms
- * behind this API, see the `serialization API of the v8 module`.
- * @since v10.5.0
- */
- postMessage(value: any, transferList?: ReadonlyArray): void;
- /**
- * Opposite of `unref()`. Calling `ref()` on a previously `unref()`ed port does _not_ let the program exit if it's the only active handle left (the default
- * behavior). If the port is `ref()`ed, calling `ref()` again has no effect.
- *
- * If listeners are attached or removed using `.on('message')`, the port
- * is `ref()`ed and `unref()`ed automatically depending on whether
- * listeners for the event exist.
- * @since v10.5.0
- */
- ref(): void;
- /**
- * Calling `unref()` on a port allows the thread to exit if this is the only
- * active handle in the event system. If the port is already `unref()`ed calling`unref()` again has no effect.
- *
- * If listeners are attached or removed using `.on('message')`, the port is`ref()`ed and `unref()`ed automatically depending on whether
- * listeners for the event exist.
- * @since v10.5.0
- */
- unref(): void;
- /**
- * Starts receiving messages on this `MessagePort`. When using this port
- * as an event emitter, this is called automatically once `'message'`listeners are attached.
- *
- * This method exists for parity with the Web `MessagePort` API. In Node.js,
- * it is only useful for ignoring messages when no event listener is present.
- * Node.js also diverges in its handling of `.onmessage`. Setting it
- * automatically calls `.start()`, but unsetting it lets messages queue up
- * until a new handler is set or the port is discarded.
- * @since v10.5.0
- */
- start(): void;
- addListener(event: 'close', listener: () => void): this;
- addListener(event: 'message', listener: (value: any) => void): this;
- addListener(event: 'messageerror', listener: (error: Error) => void): this;
- addListener(event: string | symbol, listener: (...args: any[]) => void): this;
- emit(event: 'close'): boolean;
- emit(event: 'message', value: any): boolean;
- emit(event: 'messageerror', error: Error): boolean;
- emit(event: string | symbol, ...args: any[]): boolean;
- on(event: 'close', listener: () => void): this;
- on(event: 'message', listener: (value: any) => void): this;
- on(event: 'messageerror', listener: (error: Error) => void): this;
- on(event: string | symbol, listener: (...args: any[]) => void): this;
- once(event: 'close', listener: () => void): this;
- once(event: 'message', listener: (value: any) => void): this;
- once(event: 'messageerror', listener: (error: Error) => void): this;
- once(event: string | symbol, listener: (...args: any[]) => void): this;
- prependListener(event: 'close', listener: () => void): this;
- prependListener(event: 'message', listener: (value: any) => void): this;
- prependListener(event: 'messageerror', listener: (error: Error) => void): this;
- prependListener(event: string | symbol, listener: (...args: any[]) => void): this;
- prependOnceListener(event: 'close', listener: () => void): this;
- prependOnceListener(event: 'message', listener: (value: any) => void): this;
- prependOnceListener(event: 'messageerror', listener: (error: Error) => void): this;
- prependOnceListener(event: string | symbol, listener: (...args: any[]) => void): this;
- removeListener(event: 'close', listener: () => void): this;
- removeListener(event: 'message', listener: (value: any) => void): this;
- removeListener(event: 'messageerror', listener: (error: Error) => void): this;
- removeListener(event: string | symbol, listener: (...args: any[]) => void): this;
- off(event: 'close', listener: () => void): this;
- off(event: 'message', listener: (value: any) => void): this;
- off(event: 'messageerror', listener: (error: Error) => void): this;
- off(event: string | symbol, listener: (...args: any[]) => void): this;
- }
- interface WorkerOptions {
- /**
- * List of arguments which would be stringified and appended to
- * `process.argv` in the worker. This is mostly similar to the `workerData`
- * but the values will be available on the global `process.argv` as if they
- * were passed as CLI options to the script.
- */
- argv?: any[] | undefined;
- env?: NodeJS.Dict | typeof SHARE_ENV | undefined;
- eval?: boolean | undefined;
- workerData?: any;
- stdin?: boolean | undefined;
- stdout?: boolean | undefined;
- stderr?: boolean | undefined;
- execArgv?: string[] | undefined;
- resourceLimits?: ResourceLimits | undefined;
- /**
- * Additional data to send in the first worker message.
- */
- transferList?: TransferListItem[] | undefined;
- /**
- * @default true
- */
- trackUnmanagedFds?: boolean | undefined;
- }
- interface ResourceLimits {
- /**
- * The maximum size of a heap space for recently created objects.
- */
- maxYoungGenerationSizeMb?: number | undefined;
- /**
- * The maximum size of the main heap in MB.
- */
- maxOldGenerationSizeMb?: number | undefined;
- /**
- * The size of a pre-allocated memory range used for generated code.
- */
- codeRangeSizeMb?: number | undefined;
- /**
- * The default maximum stack size for the thread. Small values may lead to unusable Worker instances.
- * @default 4
- */
- stackSizeMb?: number | undefined;
- }
- /**
- * The `Worker` class represents an independent JavaScript execution thread.
- * Most Node.js APIs are available inside of it.
- *
- * Notable differences inside a Worker environment are:
- *
- * * The `process.stdin`, `process.stdout` and `process.stderr` may be redirected by the parent thread.
- * * The `require('worker_threads').isMainThread` property is set to `false`.
- * * The `require('worker_threads').parentPort` message port is available.
- * * `process.exit()` does not stop the whole program, just the single thread,
- * and `process.abort()` is not available.
- * * `process.chdir()` and `process` methods that set group or user ids
- * are not available.
- * * `process.env` is a copy of the parent thread's environment variables,
- * unless otherwise specified. Changes to one copy are not visible in other
- * threads, and are not visible to native add-ons (unless `worker.SHARE_ENV` is passed as the `env` option to the `Worker` constructor).
- * * `process.title` cannot be modified.
- * * Signals are not delivered through `process.on('...')`.
- * * Execution may stop at any point as a result of `worker.terminate()` being invoked.
- * * IPC channels from parent processes are not accessible.
- * * The `trace_events` module is not supported.
- * * Native add-ons can only be loaded from multiple threads if they fulfill `certain conditions`.
- *
- * Creating `Worker` instances inside of other `Worker`s is possible.
- *
- * Like [Web Workers](https://developer.mozilla.org/en-US/docs/Web/API/Web_Workers_API) and the `cluster module`, two-way communication can be
- * achieved through inter-thread message passing. Internally, a `Worker` has a
- * built-in pair of `MessagePort` s that are already associated with each other
- * when the `Worker` is created. While the `MessagePort` object on the parent side
- * is not directly exposed, its functionalities are exposed through `worker.postMessage()` and the `worker.on('message')` event
- * on the `Worker` object for the parent thread.
- *
- * To create custom messaging channels (which is encouraged over using the default
- * global channel because it facilitates separation of concerns), users can create
- * a `MessageChannel` object on either thread and pass one of the`MessagePort`s on that `MessageChannel` to the other thread through a
- * pre-existing channel, such as the global one.
- *
- * See `port.postMessage()` for more information on how messages are passed,
- * and what kind of JavaScript values can be successfully transported through
- * the thread barrier.
- *
- * ```js
- * const assert = require('assert');
- * const {
- * Worker, MessageChannel, MessagePort, isMainThread, parentPort
- * } = require('worker_threads');
- * if (isMainThread) {
- * const worker = new Worker(__filename);
- * const subChannel = new MessageChannel();
- * worker.postMessage({ hereIsYourPort: subChannel.port1 }, [subChannel.port1]);
- * subChannel.port2.on('message', (value) => {
- * console.log('received:', value);
- * });
- * } else {
- * parentPort.once('message', (value) => {
- * assert(value.hereIsYourPort instanceof MessagePort);
- * value.hereIsYourPort.postMessage('the worker is sending this');
- * value.hereIsYourPort.close();
- * });
- * }
- * ```
- * @since v10.5.0
- */
- class Worker extends EventEmitter {
- /**
- * If `stdin: true` was passed to the `Worker` constructor, this is a
- * writable stream. The data written to this stream will be made available in
- * the worker thread as `process.stdin`.
- * @since v10.5.0
- */
- readonly stdin: Writable | null;
- /**
- * This is a readable stream which contains data written to `process.stdout` inside the worker thread. If `stdout: true` was not passed to the `Worker` constructor, then data is piped to the
- * parent thread's `process.stdout` stream.
- * @since v10.5.0
- */
- readonly stdout: Readable;
- /**
- * This is a readable stream which contains data written to `process.stderr` inside the worker thread. If `stderr: true` was not passed to the `Worker` constructor, then data is piped to the
- * parent thread's `process.stderr` stream.
- * @since v10.5.0
- */
- readonly stderr: Readable;
- /**
- * An integer identifier for the referenced thread. Inside the worker thread,
- * it is available as `require('worker_threads').threadId`.
- * This value is unique for each `Worker` instance inside a single process.
- * @since v10.5.0
- */
- readonly threadId: number;
- /**
- * Provides the set of JS engine resource constraints for this Worker thread.
- * If the `resourceLimits` option was passed to the `Worker` constructor,
- * this matches its values.
- *
- * If the worker has stopped, the return value is an empty object.
- * @since v13.2.0, v12.16.0
- */
- readonly resourceLimits?: ResourceLimits | undefined;
- /**
- * An object that can be used to query performance information from a worker
- * instance. Similar to `perf_hooks.performance`.
- * @since v15.1.0, v14.17.0, v12.22.0
- */
- readonly performance: WorkerPerformance;
- /**
- * @param filename The path to the Worker’s main script or module.
- * Must be either an absolute path or a relative path (i.e. relative to the current working directory) starting with ./ or ../,
- * or a WHATWG URL object using file: protocol. If options.eval is true, this is a string containing JavaScript code rather than a path.
- */
- constructor(filename: string | URL, options?: WorkerOptions);
- /**
- * Send a message to the worker that is received via `require('worker_threads').parentPort.on('message')`.
- * See `port.postMessage()` for more details.
- * @since v10.5.0
- */
- postMessage(value: any, transferList?: ReadonlyArray): void;
- /**
- * Opposite of `unref()`, calling `ref()` on a previously `unref()`ed worker does _not_ let the program exit if it's the only active handle left (the default
- * behavior). If the worker is `ref()`ed, calling `ref()` again has
- * no effect.
- * @since v10.5.0
- */
- ref(): void;
- /**
- * Calling `unref()` on a worker allows the thread to exit if this is the only
- * active handle in the event system. If the worker is already `unref()`ed calling`unref()` again has no effect.
- * @since v10.5.0
- */
- unref(): void;
- /**
- * Stop all JavaScript execution in the worker thread as soon as possible.
- * Returns a Promise for the exit code that is fulfilled when the `'exit' event` is emitted.
- * @since v10.5.0
- */
- terminate(): Promise;
- /**
- * Returns a readable stream for a V8 snapshot of the current state of the Worker.
- * See `v8.getHeapSnapshot()` for more details.
- *
- * If the Worker thread is no longer running, which may occur before the `'exit' event` is emitted, the returned `Promise` is rejected
- * immediately with an `ERR_WORKER_NOT_RUNNING` error.
- * @since v13.9.0, v12.17.0
- * @return A promise for a Readable Stream containing a V8 heap snapshot
- */
- getHeapSnapshot(): Promise;
- addListener(event: 'error', listener: (err: Error) => void): this;
- addListener(event: 'exit', listener: (exitCode: number) => void): this;
- addListener(event: 'message', listener: (value: any) => void): this;
- addListener(event: 'messageerror', listener: (error: Error) => void): this;
- addListener(event: 'online', listener: () => void): this;
- addListener(event: string | symbol, listener: (...args: any[]) => void): this;
- emit(event: 'error', err: Error): boolean;
- emit(event: 'exit', exitCode: number): boolean;
- emit(event: 'message', value: any): boolean;
- emit(event: 'messageerror', error: Error): boolean;
- emit(event: 'online'): boolean;
- emit(event: string | symbol, ...args: any[]): boolean;
- on(event: 'error', listener: (err: Error) => void): this;
- on(event: 'exit', listener: (exitCode: number) => void): this;
- on(event: 'message', listener: (value: any) => void): this;
- on(event: 'messageerror', listener: (error: Error) => void): this;
- on(event: 'online', listener: () => void): this;
- on(event: string | symbol, listener: (...args: any[]) => void): this;
- once(event: 'error', listener: (err: Error) => void): this;
- once(event: 'exit', listener: (exitCode: number) => void): this;
- once(event: 'message', listener: (value: any) => void): this;
- once(event: 'messageerror', listener: (error: Error) => void): this;
- once(event: 'online', listener: () => void): this;
- once(event: string | symbol, listener: (...args: any[]) => void): this;
- prependListener(event: 'error', listener: (err: Error) => void): this;
- prependListener(event: 'exit', listener: (exitCode: number) => void): this;
- prependListener(event: 'message', listener: (value: any) => void): this;
- prependListener(event: 'messageerror', listener: (error: Error) => void): this;
- prependListener(event: 'online', listener: () => void): this;
- prependListener(event: string | symbol, listener: (...args: any[]) => void): this;
- prependOnceListener(event: 'error', listener: (err: Error) => void): this;
- prependOnceListener(event: 'exit', listener: (exitCode: number) => void): this;
- prependOnceListener(event: 'message', listener: (value: any) => void): this;
- prependOnceListener(event: 'messageerror', listener: (error: Error) => void): this;
- prependOnceListener(event: 'online', listener: () => void): this;
- prependOnceListener(event: string | symbol, listener: (...args: any[]) => void): this;
- removeListener(event: 'error', listener: (err: Error) => void): this;
- removeListener(event: 'exit', listener: (exitCode: number) => void): this;
- removeListener(event: 'message', listener: (value: any) => void): this;
- removeListener(event: 'messageerror', listener: (error: Error) => void): this;
- removeListener(event: 'online', listener: () => void): this;
- removeListener(event: string | symbol, listener: (...args: any[]) => void): this;
- off(event: 'error', listener: (err: Error) => void): this;
- off(event: 'exit', listener: (exitCode: number) => void): this;
- off(event: 'message', listener: (value: any) => void): this;
- off(event: 'messageerror', listener: (error: Error) => void): this;
- off(event: 'online', listener: () => void): this;
- off(event: string | symbol, listener: (...args: any[]) => void): this;
- }
- interface BroadcastChannel extends NodeJS.RefCounted {}
- /**
- * Instances of `BroadcastChannel` allow asynchronous one-to-many communication
- * with all other `BroadcastChannel` instances bound to the same channel name.
- *
- * ```js
- * 'use strict';
- *
- * const {
- * isMainThread,
- * BroadcastChannel,
- * Worker
- * } = require('worker_threads');
- *
- * const bc = new BroadcastChannel('hello');
- *
- * if (isMainThread) {
- * let c = 0;
- * bc.onmessage = (event) => {
- * console.log(event.data);
- * if (++c === 10) bc.close();
- * };
- * for (let n = 0; n < 10; n++)
- * new Worker(__filename);
- * } else {
- * bc.postMessage('hello from every worker');
- * bc.close();
- * }
- * ```
- * @since v15.4.0
- */
- class BroadcastChannel {
- readonly name: string;
- /**
- * Invoked with a single \`MessageEvent\` argument when a message is received.
- * @since v15.4.0
- */
- onmessage: (message: unknown) => void;
- /**
- * Invoked with a received message cannot be deserialized.
- * @since v15.4.0
- */
- onmessageerror: (message: unknown) => void;
- constructor(name: string);
- /**
- * Closes the `BroadcastChannel` connection.
- * @since v15.4.0
- */
- close(): void;
- /**
- * @since v15.4.0
- * @param message Any cloneable JavaScript value.
- */
- postMessage(message: unknown): void;
- }
- /**
- * Mark an object as not transferable. If `object` occurs in the transfer list of
- * a `port.postMessage()` call, it is ignored.
- *
- * In particular, this makes sense for objects that can be cloned, rather than
- * transferred, and which are used by other objects on the sending side.
- * For example, Node.js marks the `ArrayBuffer`s it uses for its `Buffer pool` with this.
- *
- * This operation cannot be undone.
- *
- * ```js
- * const { MessageChannel, markAsUntransferable } = require('worker_threads');
- *
- * const pooledBuffer = new ArrayBuffer(8);
- * const typedArray1 = new Uint8Array(pooledBuffer);
- * const typedArray2 = new Float64Array(pooledBuffer);
- *
- * markAsUntransferable(pooledBuffer);
- *
- * const { port1 } = new MessageChannel();
- * port1.postMessage(typedArray1, [ typedArray1.buffer ]);
- *
- * // The following line prints the contents of typedArray1 -- it still owns
- * // its memory and has been cloned, not transferred. Without
- * // `markAsUntransferable()`, this would print an empty Uint8Array.
- * // typedArray2 is intact as well.
- * console.log(typedArray1);
- * console.log(typedArray2);
- * ```
- *
- * There is no equivalent to this API in browsers.
- * @since v14.5.0, v12.19.0
- */
- function markAsUntransferable(object: object): void;
- /**
- * Transfer a `MessagePort` to a different `vm` Context. The original `port`object is rendered unusable, and the returned `MessagePort` instance
- * takes its place.
- *
- * The returned `MessagePort` is an object in the target context and
- * inherits from its global `Object` class. Objects passed to the [`port.onmessage()`](https://developer.mozilla.org/en-US/docs/Web/API/MessagePort/onmessage) listener are also created in the
- * target context
- * and inherit from its global `Object` class.
- *
- * However, the created `MessagePort` no longer inherits from [`EventTarget`](https://developer.mozilla.org/en-US/docs/Web/API/EventTarget), and only
- * [`port.onmessage()`](https://developer.mozilla.org/en-US/docs/Web/API/MessagePort/onmessage) can be used to receive
- * events using it.
- * @since v11.13.0
- * @param port The message port to transfer.
- * @param contextifiedSandbox A `contextified` object as returned by the `vm.createContext()` method.
- */
- function moveMessagePortToContext(port: MessagePort, contextifiedSandbox: Context): MessagePort;
- /**
- * Receive a single message from a given `MessagePort`. If no message is available,`undefined` is returned, otherwise an object with a single `message` property
- * that contains the message payload, corresponding to the oldest message in the`MessagePort`’s queue.
- *
- * ```js
- * const { MessageChannel, receiveMessageOnPort } = require('worker_threads');
- * const { port1, port2 } = new MessageChannel();
- * port1.postMessage({ hello: 'world' });
- *
- * console.log(receiveMessageOnPort(port2));
- * // Prints: { message: { hello: 'world' } }
- * console.log(receiveMessageOnPort(port2));
- * // Prints: undefined
- * ```
- *
- * When this function is used, no `'message'` event is emitted and the`onmessage` listener is not invoked.
- * @since v12.3.0
- */
- function receiveMessageOnPort(port: MessagePort):
- | {
- message: any;
- }
- | undefined;
- type Serializable = string | object | number | boolean | bigint;
- /**
- * Within a worker thread, `worker.getEnvironmentData()` returns a clone
- * of data passed to the spawning thread's `worker.setEnvironmentData()`.
- * Every new `Worker` receives its own copy of the environment data
- * automatically.
- *
- * ```js
- * const {
- * Worker,
- * isMainThread,
- * setEnvironmentData,
- * getEnvironmentData,
- * } = require('worker_threads');
- *
- * if (isMainThread) {
- * setEnvironmentData('Hello', 'World!');
- * const worker = new Worker(__filename);
- * } else {
- * console.log(getEnvironmentData('Hello')); // Prints 'World!'.
- * }
- * ```
- * @since v15.12.0, v14.18.0
- * @param key Any arbitrary, cloneable JavaScript value that can be used as a {Map} key.
- */
- function getEnvironmentData(key: Serializable): Serializable;
- /**
- * The `worker.setEnvironmentData()` API sets the content of`worker.getEnvironmentData()` in the current thread and all new `Worker`instances spawned from the current context.
- * @since v15.12.0, v14.18.0
- * @param key Any arbitrary, cloneable JavaScript value that can be used as a {Map} key.
- * @param value Any arbitrary, cloneable JavaScript value that will be cloned and passed automatically to all new `Worker` instances. If `value` is passed as `undefined`, any previously set value
- * for the `key` will be deleted.
- */
- function setEnvironmentData(key: Serializable, value: Serializable): void;
-
- import {
- BroadcastChannel as _BroadcastChannel,
- MessageChannel as _MessageChannel,
- MessagePort as _MessagePort,
- } from 'worker_threads';
- global {
- /**
- * `BroadcastChannel` class is a global reference for `require('worker_threads').BroadcastChannel`
- * https://nodejs.org/api/globals.html#broadcastchannel
- * @since v18.0.0
- */
- var BroadcastChannel: typeof globalThis extends {
- onmessage: any;
- BroadcastChannel: infer T;
- }
- ? T
- : typeof _BroadcastChannel;
-
- /**
- * `MessageChannel` class is a global reference for `require('worker_threads').MessageChannel`
- * https://nodejs.org/api/globals.html#messagechannel
- * @since v15.0.0
- */
- var MessageChannel: typeof globalThis extends {
- onmessage: any;
- MessageChannel: infer T;
- }
- ? T
- : typeof _MessageChannel;
-
- /**
- * `MessagePort` class is a global reference for `require('worker_threads').MessagePort`
- * https://nodejs.org/api/globals.html#messageport
- * @since v15.0.0
- */
- var MessagePort: typeof globalThis extends {
- onmessage: any;
- MessagePort: infer T;
- }
- ? T
- : typeof _MessagePort;
- }
-}
-declare module 'node:worker_threads' {
- export * from 'worker_threads';
-}
diff --git a/spaces/fffiloni/mr-and-misses/README.md b/spaces/fffiloni/mr-and-misses/README.md
deleted file mode 100644
index aa132b235850d9a6d8440057c744bb440cdee664..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/mr-and-misses/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Mr Men & Little Misses
-emoji: 🌝🌚
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/fffiloni/sd-xl-lora-fusion/README.md b/spaces/fffiloni/sd-xl-lora-fusion/README.md
deleted file mode 100644
index fdba5fcd6a2b509bad8727b986693aa0bf72b773..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/sd-xl-lora-fusion/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: SD-XL LoRA Fusion
-emoji: 🌟
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-fullWidth: true
-pinned: false
-hf_oauth: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/fffiloni/text-2-music/constants.py b/spaces/fffiloni/text-2-music/constants.py
deleted file mode 100644
index f20c15dd1969910106da2c07339da9ff33458282..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/text-2-music/constants.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import numpy as np
-
-MUBERT_TAGS_STRING = 'tribal,action,kids,neo-classic,run 130,pumped,jazz / funk,ethnic,dubtechno,reggae,acid jazz,liquidfunk,funk,witch house,tech house,underground,artists,mystical,disco,sensorium,r&b,agender,psychedelic trance / psytrance,peaceful,run 140,piano,run 160,setting,meditation,christmas,ambient,horror,cinematic,electro house,idm,bass,minimal,underscore,drums,glitchy,beautiful,technology,tribal house,country pop,jazz & funk,documentary,space,classical,valentines,chillstep,experimental,trap,new jack swing,drama,post-rock,tense,corporate,neutral,happy,analog,funky,spiritual,sberzvuk special,chill hop,dramatic,catchy,holidays,fitness 90,optimistic,orchestra,acid techno,energizing,romantic,minimal house,breaks,hyper pop,warm up,dreamy,dark,urban,microfunk,dub,nu disco,vogue,keys,hardcore,aggressive,indie,electro funk,beauty,relaxing,trance,pop,hiphop,soft,acoustic,chillrave / ethno-house,deep techno,angry,dance,fun,dubstep,tropical,latin pop,heroic,world music,inspirational,uplifting,atmosphere,art,epic,advertising,chillout,scary,spooky,slow ballad,saxophone,summer,erotic,jazzy,energy 100,kara mar,xmas,atmospheric,indie pop,hip-hop,yoga,reggaeton,lounge,travel,running,folk,chillrave & ethno-house,detective,darkambient,chill,fantasy,minimal techno,special,night,tropical house,downtempo,lullaby,meditative,upbeat,glitch hop,fitness,neurofunk,sexual,indie rock,future pop,jazz,cyberpunk,melancholic,happy hardcore,family / kids,synths,electric guitar,comedy,psychedelic trance & psytrance,edm,psychedelic rock,calm,zen,bells,podcast,melodic house,ethnic percussion,nature,heavy,bassline,indie dance,techno,drumnbass,synth pop,vaporwave,sad,8-bit,chillgressive,deep,orchestral,futuristic,hardtechno,nostalgic,big room,sci-fi,tutorial,joyful,pads,minimal 170,drill,ethnic 108,amusing,sleepy ambient,psychill,italo disco,lofi,house,acoustic guitar,bassline house,rock,k-pop,synthwave,deep house,electronica,gabber,nightlife,sport & fitness,road trip,celebration,electro,disco house,electronic'
-MUBERT_TAGS = np.array(MUBERT_TAGS_STRING.split(','))
-MUBERT_LICENSE = "ttmmubertlicense#f0acYBenRcfeFpNT4wpYGaTQIyDI4mJGv5MfIhBFz97NXDwDNFHmMRsBSzmGsJwbTpP1A6i07AXcIeAHo5"
-MUBERT_MODE = "loop"
-MUBERT_TOKEN = "4951f6428e83172a4f39de05d5b3ab10d58560b8"
\ No newline at end of file
diff --git a/spaces/fffiloni/x-decoder-video/style.css b/spaces/fffiloni/x-decoder-video/style.css
deleted file mode 100644
index 3cf565d3e03852436a405cf632d1d22433bb4087..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/x-decoder-video/style.css
+++ /dev/null
@@ -1,101 +0,0 @@
-#col-container {max-width: 820px; margin-left: auto; margin-right: auto;}
-#duplicate-container{
- display: flex;
- justify-content: space-between;
- align-items: center;
- line-height: 1em;
- flex-direction: row-reverse;
- font-size:1em;
-}
-a, a:hover, a:visited {
- text-decoration-line: underline;
- font-weight: 600;
- color: #1f2937 !important;
-}
-
-.dark a, .dark a:hover, .dark a:visited {
- color: #f3f4f6 !important;
-}
-
-.footer {
- margin-bottom: 45px;
- margin-top: 10px;
- text-align: center;
- border-bottom: 1px solid #e5e5e5;
-}
-
-.footer>p {
- font-size: .8rem!important;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(26px);
- background: white;
-}
-.dark .footer {
- border-color: #303030;
-}
-.dark .footer>p {
- background: #0b0f19;
-}
-
-div#may-like-container > p {
- font-size: .8em;
- margin-bottom: 4px;
-}
-
-.animate-spin {
- animation: spin 1s linear infinite;
-}
-
-@keyframes spin {
- from {
- transform: rotate(0deg);
- }
- to {
- transform: rotate(360deg);
- }
-}
-
-#share-btn-container {
- display: flex;
- padding-left: 0.5rem !important;
- padding-right: 0.5rem !important;
- background-color: #000000;
- justify-content: center;
- align-items: center;
- border-radius: 9999px !important;
- max-width: 13rem;
-}
-
-#share-btn-container:hover {
- background-color: #060606;
-}
-
-#share-btn {
- all: initial;
- color: #ffffff;
- font-weight: 600;
- cursor:pointer;
- font-family: 'IBM Plex Sans', sans-serif;
- margin-left: 0.5rem !important;
- padding-top: 0.5rem !important;
- padding-bottom: 0.5rem !important;
- right:0;
-}
-
-#share-btn * {
- all: unset;
-}
-
-#share-btn-container div:nth-child(-n+2){
- width: auto !important;
- min-height: 0px !important;
-}
-
-#share-btn-container .wrap {
- display: none !important;
-}
-
-#share-btn-container.hidden {
- display: none!important;
-}
\ No newline at end of file
diff --git a/spaces/florim/MedGPT/tests/integration/milvus_memory_tests.py b/spaces/florim/MedGPT/tests/integration/milvus_memory_tests.py
deleted file mode 100644
index ec38bf2f72087b5da679d26594ebff97d8a09b19..0000000000000000000000000000000000000000
--- a/spaces/florim/MedGPT/tests/integration/milvus_memory_tests.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# sourcery skip: snake-case-functions
-"""Tests for the MilvusMemory class."""
-import random
-import string
-import unittest
-
-from autogpt.config import Config
-from autogpt.memory.milvus import MilvusMemory
-
-try:
-
- class TestMilvusMemory(unittest.TestCase):
- """Tests for the MilvusMemory class."""
-
- def random_string(self, length: int) -> str:
- """Generate a random string of the given length."""
- return "".join(random.choice(string.ascii_letters) for _ in range(length))
-
- def setUp(self) -> None:
- """Set up the test environment."""
- cfg = Config()
- cfg.milvus_addr = "localhost:19530"
- self.memory = MilvusMemory(cfg)
- self.memory.clear()
-
- # Add example texts to the cache
- self.example_texts = [
- "The quick brown fox jumps over the lazy dog",
- "I love machine learning and natural language processing",
- "The cake is a lie, but the pie is always true",
- "ChatGPT is an advanced AI model for conversation",
- ]
-
- for text in self.example_texts:
- self.memory.add(text)
-
- # Add some random strings to test noise
- for _ in range(5):
- self.memory.add(self.random_string(10))
-
- def test_get_relevant(self) -> None:
- """Test getting relevant texts from the cache."""
- query = "I'm interested in artificial intelligence and NLP"
- num_relevant = 3
- relevant_texts = self.memory.get_relevant(query, num_relevant)
-
- print(f"Top {k} relevant texts for the query '{query}':")
- for i, text in enumerate(relevant_texts, start=1):
- print(f"{i}. {text}")
-
- self.assertEqual(len(relevant_texts), k)
- self.assertIn(self.example_texts[1], relevant_texts)
-
-except:
- print(
- "Skipping tests/integration/milvus_memory_tests.py as Milvus is not installed."
- )
diff --git a/spaces/flynster/FeinbergQuizNotes/question_generation/run_qg.py b/spaces/flynster/FeinbergQuizNotes/question_generation/run_qg.py
deleted file mode 100644
index 20b8abe51becf4f3d521d7e68a6c59a4c053de19..0000000000000000000000000000000000000000
--- a/spaces/flynster/FeinbergQuizNotes/question_generation/run_qg.py
+++ /dev/null
@@ -1,236 +0,0 @@
-import dataclasses
-import json
-import logging
-import os
-import sys
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional
-
-import numpy as np
-import torch
-
-from transformers import (
- AutoModelForSeq2SeqLM,
- AutoTokenizer,
- T5Tokenizer,
- BartTokenizer,
- HfArgumentParser,
- DataCollator,
- TrainingArguments,
- set_seed,
-)
-
-from trainer import Trainer
-from data_collator import T2TDataCollator
-from utils import freeze_embeds, assert_not_all_frozen
-
-MODEL_TYPE_TO_TOKENIZER = {
- "t5": T5Tokenizer,
- "bart": BartTokenizer,
-}
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ModelArguments:
- """
- Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
- """
-
- model_name_or_path: str = field(
- metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
- )
- model_type: str = field(metadata={"help": "One of 't5', 'bart'"})
- tokenizer_name_or_path: Optional[str] = field(
- default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
- )
- cache_dir: Optional[str] = field(
- default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
- )
- label_smoothing: Optional[float] = field(
- default=0,
- metadata={"help": "label smoothing rate, set to > 0 if you want to enable lable smoothing"}
- )
- freeze_embeds: bool = field(
- default=False,
- metadata={"help": "Freeze token embeddings and positional embeddings for bart, just token embeddings for t5."}
- )
-
-@dataclass
-class DataTrainingArguments:
- """
- Arguments pertaining to what data we are going to input our model for training and eval.
- """
- train_file_path: str = field(
- metadata={"help": "Path for cached train dataset"},
- )
- valid_file_path: str = field(
- metadata={"help": "Path for cached valid dataset"},
- )
- data_dir: Optional[str] = field(
- default=None,
- metadata={"help": "Path for data files"},
- )
- task: Optional[str] = field(
- default=None,
- metadata={"help": "Which task 'qa', 'qg', 'e2e_qg', 'ans_ext', 'multi'. 'multi' means 'qa', 'qg', 'ans_ext' tasks"},
- )
- qg_format: Optional[str] = field(
- default='prepend_qg_format',
- metadata={"help": "How to format inputs for que generation, 'highlight_qg_format' or 'prepend_qg_format'"},
- )
- max_source_length: Optional[int] = field(
- default=512,
- metadata={"help": "Max input length for the source text"},
- )
- max_target_length: Optional[int] = field(
- default=32,
- metadata={"help": "Max input length for the target text"},
- )
-
-
-def main(args_file=None):
- # See all possible arguments in src/transformers/training_args.py
- # or by passing the --help flag to this script.
- # We now keep distinct sets of args, for a cleaner separation of concerns.
-
- parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
-
- if (len(sys.argv) == 2 and sys.argv[1].endswith(".json")) or args_file is not None:
- # If we pass only one argument to the script and it's the path to a json file,
- # let's parse it to get our arguments.
- args_file_path = os.path.abspath(sys.argv[1]) if args_file is None else args_file
- model_args, data_args, training_args = parser.parse_json_file(json_file=args_file_path)
- else:
- model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
- assert model_args.model_type in list(MODEL_TYPE_TO_TOKENIZER.keys()), "model type should be 't5' or 'bart'"
-
- if (
- os.path.exists(training_args.output_dir)
- and os.listdir(training_args.output_dir)
- and training_args.do_train
- and not training_args.overwrite_output_dir
- ):
- raise ValueError(
- f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
- )
-
- # Setup logging
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
- )
- logger.warning(
- "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
- training_args.local_rank,
- training_args.device,
- training_args.n_gpu,
- bool(training_args.local_rank != -1),
- training_args.fp16,
- )
- logger.info("Training/evaluation parameters %s", training_args)
-
- # Set seed
- set_seed(training_args.seed)
-
- # Set project name
- os.environ["WANDB_PROJECT"] = "question-generation"
-
- # Load pretrained model and tokenizer
- #
- # Distributed training:
- # The .from_pretrained methods guarantee that only one local process can concurrently
- # download model & vocab.
- tokenizer_cls = MODEL_TYPE_TO_TOKENIZER[model_args.model_type]
- tokenizer = tokenizer_cls.from_pretrained(
- model_args.tokenizer_name_or_path if model_args.tokenizer_name_or_path else model_args.model_name_or_path,
- cache_dir=model_args.cache_dir,
- )
- model = AutoModelForSeq2SeqLM.from_pretrained(
- model_args.model_name_or_path,
- cache_dir=model_args.cache_dir,
- )
-
- model.resize_token_embeddings(len(tokenizer))
-
- if model_args.freeze_embeds:
- logger.info("freezing embeddings of the model")
- freeze_embeds(model)
- assert_not_all_frozen(model)
-
- # Get datasets
- logger.info('loading dataset')
-
- train_dataset = torch.load(data_args.train_file_path) if training_args.do_train else None
- valid_dataset = torch.load(data_args.valid_file_path) if training_args.do_eval else None
-
- logger.info('finished loading dataset')
-
- # Initialize data_collator
- data_collator = T2TDataCollator(
- tokenizer=tokenizer,
- model_type=model_args.model_type,
- mode="training",
- using_tpu=training_args.tpu_num_cores is not None
- )
-
- # Initialize our Trainer
- trainer = Trainer(
- model=model,
- args=training_args,
- train_dataset=train_dataset,
- eval_dataset=valid_dataset,
- data_collator=data_collator,
- prediction_loss_only=True,
- label_smoothing=model_args.label_smoothing
- )
-
- # disable wandb console logs
- logging.getLogger('wandb.run_manager').setLevel(logging.WARNING)
-
- # Training
- if training_args.do_train:
- trainer.train(
- model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
- )
- trainer.save_model()
- # For convenience, we also re-save the tokenizer to the same directory,
- # so that you can share your model easily on huggingface.co/models =)
- if trainer.is_world_master():
- tokenizer.save_pretrained(training_args.output_dir)
-
- # Evaluation
- results = {}
- if training_args.do_eval and training_args.local_rank in [-1, 0]:
- logger.info("*** Evaluate ***")
-
- eval_output = trainer.evaluate()
-
- output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
- with open(output_eval_file, "w") as writer:
- logger.info("***** Eval results *****")
- for key in sorted(eval_output.keys()):
- logger.info(" %s = %s", key, str(eval_output[key]))
- writer.write("%s = %s\n" % (key, str(eval_output[key])))
-
- results.update(eval_output)
-
- return results
-
-
-def _mp_fn(index):
- # For xla_spawn (TPUs)
- main()
-
-def run_qg(args_dict):
- with open("args.json", 'w') as f:
- json.dump(args_dict, f)
-
- main(args_file="args.json")
-
-if __name__ == "__main__":
- main()
\ No newline at end of file
diff --git a/spaces/geraldvillaran/dolly-chat/README.md b/spaces/geraldvillaran/dolly-chat/README.md
deleted file mode 100644
index 0fb1721f798e323456671454c1e3df3d9243f0ba..0000000000000000000000000000000000000000
--- a/spaces/geraldvillaran/dolly-chat/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Dolly
-emoji: 🌍
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ggwvits/vits-uma-genshin-honkai/Docker/vits.sh b/spaces/ggwvits/vits-uma-genshin-honkai/Docker/vits.sh
deleted file mode 100644
index 2b87f26eda96d3800b73b4a21b210c78888a2299..0000000000000000000000000000000000000000
--- a/spaces/ggwvits/vits-uma-genshin-honkai/Docker/vits.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/bin/bash
-run() {
- echo -e "\033[32m已完成初始化，启动服务...\033[0m"
- python3 /app/vits-uma-genshin-honkai/app.py
-}
-install() {
- echo -e "\033[33m正在初始化：安装依赖....\033[0m"
- pip install -r /app/vits-uma-genshin-honkai/requirements.txt -i https://mirrors.ustc.edu.cn/pypi/web/simple
- echo -e "\033[33m正在下载模型....\033[0m"
- rm -f /app/vits-uma-genshin-honkai/model/G_953000.pth
- wget -O /app/vits-uma-genshin-honkai/model/G_953000.pth https://huggingface.co/spaces/ikechan8370/vits-uma-genshin-honkai/resolve/main/model/G_953000.pth
- echo -e "\033[32m初始化完成！\033[0m"
- run
-}
-
-if [ ! -f "/app/vits-uma-genshin-honkai/model/G_953000.pth" ] || [ "$(stat -c%s "/app/vits-uma-genshin-honkai/model/G_953000.pth")" -lt 10000 ]; then
- install
-else
- run
-fi
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Download Jogos Ps3 Pkg.md b/spaces/gotiQspiryo/whisper-ui/examples/Download Jogos Ps3 Pkg.md
deleted file mode 100644
index 0d0dc31a2a33f90fd5c7b5e178a0038d2841d55b..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Download Jogos Ps3 Pkg.md
+++ /dev/null
@@ -1,6 +0,0 @@
-download jogos ps3 pkg
Download Zip ➡ https://urlgoal.com/2uyNDA
-
-Download Game PS3 PS4 RPCS3 PC Free New, Best Game PS3 PS4 RPCS3 PC Iso, Direct Links Torrent PS3 PS4 RPCS3 PC, Update DLC PS3 PS4 RPCS3, ... 4d29de3e1b
-
-
-
diff --git a/spaces/gradio/HuBERT/examples/speech_recognition/kaldi/kaldi_initializer.py b/spaces/gradio/HuBERT/examples/speech_recognition/kaldi/kaldi_initializer.py
deleted file mode 100644
index 6d2a2a4b6b809ba1106f9a57cb6f241dc083e670..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/speech_recognition/kaldi/kaldi_initializer.py
+++ /dev/null
@@ -1,698 +0,0 @@
-#!/usr/bin/env python3
-
-# 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.
-
-from dataclasses import dataclass
-import hydra
-from hydra.core.config_store import ConfigStore
-import logging
-from omegaconf import MISSING, OmegaConf
-import os
-import os.path as osp
-from pathlib import Path
-import subprocess
-from typing import Optional
-
-from fairseq.data.dictionary import Dictionary
-from fairseq.dataclass import FairseqDataclass
-
-script_dir = Path(__file__).resolve().parent
-config_path = script_dir / "config"
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class KaldiInitializerConfig(FairseqDataclass):
- data_dir: str = MISSING
- fst_dir: Optional[str] = None
- in_labels: str = MISSING
- out_labels: Optional[str] = None
- wav2letter_lexicon: Optional[str] = None
- lm_arpa: str = MISSING
- kaldi_root: str = MISSING
- blank_symbol: str = ""
- silence_symbol: Optional[str] = None
-
-
-def create_units(fst_dir: Path, in_labels: str, vocab: Dictionary) -> Path:
- in_units_file = fst_dir / f"kaldi_dict.{in_labels}.txt"
- if not in_units_file.exists():
-
- logger.info(f"Creating {in_units_file}")
-
- with open(in_units_file, "w") as f:
- print(" 0", file=f)
- i = 1
- for symb in vocab.symbols[vocab.nspecial :]:
- if not symb.startswith("madeupword"):
- print(f"{symb} {i}", file=f)
- i += 1
- return in_units_file
-
-
-def create_lexicon(
- cfg: KaldiInitializerConfig,
- fst_dir: Path,
- unique_label: str,
- in_units_file: Path,
- out_words_file: Path,
-) -> (Path, Path):
-
- disambig_in_units_file = fst_dir / f"kaldi_dict.{cfg.in_labels}_disambig.txt"
- lexicon_file = fst_dir / f"kaldi_lexicon.{unique_label}.txt"
- disambig_lexicon_file = fst_dir / f"kaldi_lexicon.{unique_label}_disambig.txt"
- if (
- not lexicon_file.exists()
- or not disambig_lexicon_file.exists()
- or not disambig_in_units_file.exists()
- ):
- logger.info(f"Creating {lexicon_file} (in units file: {in_units_file})")
-
- assert cfg.wav2letter_lexicon is not None or cfg.in_labels == cfg.out_labels
-
- if cfg.wav2letter_lexicon is not None:
- lm_words = set()
- with open(out_words_file, "r") as lm_dict_f:
- for line in lm_dict_f:
- lm_words.add(line.split()[0])
-
- num_skipped = 0
- total = 0
- with open(cfg.wav2letter_lexicon, "r") as w2l_lex_f, open(
- lexicon_file, "w"
- ) as out_f:
- for line in w2l_lex_f:
- items = line.rstrip().split("\t")
- assert len(items) == 2, items
- if items[0] in lm_words:
- print(items[0], items[1], file=out_f)
- else:
- num_skipped += 1
- logger.debug(
- f"Skipping word {items[0]} as it was not found in LM"
- )
- total += 1
- if num_skipped > 0:
- logger.warning(
- f"Skipped {num_skipped} out of {total} words as they were not found in LM"
- )
- else:
- with open(in_units_file, "r") as in_f, open(lexicon_file, "w") as out_f:
- for line in in_f:
- symb = line.split()[0]
- if symb != "" and symb != "" and symb != "":
- print(symb, symb, file=out_f)
-
- lex_disambig_path = (
- Path(cfg.kaldi_root) / "egs/wsj/s5/utils/add_lex_disambig.pl"
- )
- res = subprocess.run(
- [lex_disambig_path, lexicon_file, disambig_lexicon_file],
- check=True,
- capture_output=True,
- )
- ndisambig = int(res.stdout)
- disamib_path = Path(cfg.kaldi_root) / "egs/wsj/s5/utils/add_disambig.pl"
- res = subprocess.run(
- [disamib_path, "--include-zero", in_units_file, str(ndisambig)],
- check=True,
- capture_output=True,
- )
- with open(disambig_in_units_file, "wb") as f:
- f.write(res.stdout)
-
- return disambig_lexicon_file, disambig_in_units_file
-
-
-def create_G(
- kaldi_root: Path, fst_dir: Path, lm_arpa: Path, arpa_base: str
-) -> (Path, Path):
-
- out_words_file = fst_dir / f"kaldi_dict.{arpa_base}.txt"
- grammar_graph = fst_dir / f"G_{arpa_base}.fst"
- if not grammar_graph.exists() or not out_words_file.exists():
- logger.info(f"Creating {grammar_graph}")
- arpa2fst = kaldi_root / "src/lmbin/arpa2fst"
- subprocess.run(
- [
- arpa2fst,
- "--disambig-symbol=#0",
- f"--write-symbol-table={out_words_file}",
- lm_arpa,
- grammar_graph,
- ],
- check=True,
- )
- return grammar_graph, out_words_file
-
-
-def create_L(
- kaldi_root: Path,
- fst_dir: Path,
- unique_label: str,
- lexicon_file: Path,
- in_units_file: Path,
- out_words_file: Path,
-) -> Path:
- lexicon_graph = fst_dir / f"L.{unique_label}.fst"
-
- if not lexicon_graph.exists():
- logger.info(f"Creating {lexicon_graph} (in units: {in_units_file})")
- make_lex = kaldi_root / "egs/wsj/s5/utils/make_lexicon_fst.pl"
- fstcompile = kaldi_root / "tools/openfst-1.6.7/bin/fstcompile"
- fstaddselfloops = kaldi_root / "src/fstbin/fstaddselfloops"
- fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort"
-
- def write_disambig_symbol(file):
- with open(file, "r") as f:
- for line in f:
- items = line.rstrip().split()
- if items[0] == "#0":
- out_path = str(file) + "_disamig"
- with open(out_path, "w") as out_f:
- print(items[1], file=out_f)
- return out_path
-
- return None
-
- in_disambig_sym = write_disambig_symbol(in_units_file)
- assert in_disambig_sym is not None
- out_disambig_sym = write_disambig_symbol(out_words_file)
- assert out_disambig_sym is not None
-
- try:
- with open(lexicon_graph, "wb") as out_f:
- res = subprocess.run(
- [make_lex, lexicon_file], capture_output=True, check=True
- )
- assert len(res.stderr) == 0, res.stderr.decode("utf-8")
- res = subprocess.run(
- [
- fstcompile,
- f"--isymbols={in_units_file}",
- f"--osymbols={out_words_file}",
- "--keep_isymbols=false",
- "--keep_osymbols=false",
- ],
- input=res.stdout,
- capture_output=True,
- )
- assert len(res.stderr) == 0, res.stderr.decode("utf-8")
- res = subprocess.run(
- [fstaddselfloops, in_disambig_sym, out_disambig_sym],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [fstarcsort, "--sort_type=olabel"],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- out_f.write(res.stdout)
- except subprocess.CalledProcessError as e:
- logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
- os.remove(lexicon_graph)
- raise
- except AssertionError:
- os.remove(lexicon_graph)
- raise
-
- return lexicon_graph
-
-
-def create_LG(
- kaldi_root: Path,
- fst_dir: Path,
- unique_label: str,
- lexicon_graph: Path,
- grammar_graph: Path,
-) -> Path:
- lg_graph = fst_dir / f"LG.{unique_label}.fst"
-
- if not lg_graph.exists():
- logger.info(f"Creating {lg_graph}")
-
- fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose"
- fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar"
- fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded"
- fstpushspecial = kaldi_root / "src/fstbin/fstpushspecial"
- fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort"
-
- try:
- with open(lg_graph, "wb") as out_f:
- res = subprocess.run(
- [fsttablecompose, lexicon_graph, grammar_graph],
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [
- fstdeterminizestar,
- "--use-log=true",
- ],
- input=res.stdout,
- capture_output=True,
- )
- res = subprocess.run(
- [fstminimizeencoded],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [fstpushspecial],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [fstarcsort, "--sort_type=ilabel"],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- out_f.write(res.stdout)
- except subprocess.CalledProcessError as e:
- logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
- os.remove(lg_graph)
- raise
-
- return lg_graph
-
-
-def create_H(
- kaldi_root: Path,
- fst_dir: Path,
- disambig_out_units_file: Path,
- in_labels: str,
- vocab: Dictionary,
- blk_sym: str,
- silence_symbol: Optional[str],
-) -> (Path, Path, Path):
- h_graph = (
- fst_dir / f"H.{in_labels}{'_' + silence_symbol if silence_symbol else ''}.fst"
- )
- h_out_units_file = fst_dir / f"kaldi_dict.h_out.{in_labels}.txt"
- disambig_in_units_file_int = Path(str(h_graph) + "isym_disambig.int")
- disambig_out_units_file_int = Path(str(disambig_out_units_file) + ".int")
- if (
- not h_graph.exists()
- or not h_out_units_file.exists()
- or not disambig_in_units_file_int.exists()
- ):
- logger.info(f"Creating {h_graph}")
- eps_sym = ""
-
- num_disambig = 0
- osymbols = []
-
- with open(disambig_out_units_file, "r") as f, open(
- disambig_out_units_file_int, "w"
- ) as out_f:
- for line in f:
- symb, id = line.rstrip().split()
- if line.startswith("#"):
- num_disambig += 1
- print(id, file=out_f)
- else:
- if len(osymbols) == 0:
- assert symb == eps_sym, symb
- osymbols.append((symb, id))
-
- i_idx = 0
- isymbols = [(eps_sym, 0)]
-
- imap = {}
-
- for i, s in enumerate(vocab.symbols):
- i_idx += 1
- isymbols.append((s, i_idx))
- imap[s] = i_idx
-
- fst_str = []
-
- node_idx = 0
- root_node = node_idx
-
- special_symbols = [blk_sym]
- if silence_symbol is not None:
- special_symbols.append(silence_symbol)
-
- for ss in special_symbols:
- fst_str.append("{} {} {} {}".format(root_node, root_node, ss, eps_sym))
-
- for symbol, _ in osymbols:
- if symbol == eps_sym or symbol.startswith("#"):
- continue
-
- node_idx += 1
- # 1. from root to emitting state
- fst_str.append("{} {} {} {}".format(root_node, node_idx, symbol, symbol))
- # 2. from emitting state back to root
- fst_str.append("{} {} {} {}".format(node_idx, root_node, eps_sym, eps_sym))
- # 3. from emitting state to optional blank state
- pre_node = node_idx
- node_idx += 1
- for ss in special_symbols:
- fst_str.append("{} {} {} {}".format(pre_node, node_idx, ss, eps_sym))
- # 4. from blank state back to root
- fst_str.append("{} {} {} {}".format(node_idx, root_node, eps_sym, eps_sym))
-
- fst_str.append("{}".format(root_node))
-
- fst_str = "\n".join(fst_str)
- h_str = str(h_graph)
- isym_file = h_str + ".isym"
-
- with open(isym_file, "w") as f:
- for sym, id in isymbols:
- f.write("{} {}\n".format(sym, id))
-
- with open(h_out_units_file, "w") as f:
- for sym, id in osymbols:
- f.write("{} {}\n".format(sym, id))
-
- with open(disambig_in_units_file_int, "w") as f:
- disam_sym_id = len(isymbols)
- for _ in range(num_disambig):
- f.write("{}\n".format(disam_sym_id))
- disam_sym_id += 1
-
- fstcompile = kaldi_root / "tools/openfst-1.6.7/bin/fstcompile"
- fstaddselfloops = kaldi_root / "src/fstbin/fstaddselfloops"
- fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort"
-
- try:
- with open(h_graph, "wb") as out_f:
- res = subprocess.run(
- [
- fstcompile,
- f"--isymbols={isym_file}",
- f"--osymbols={h_out_units_file}",
- "--keep_isymbols=false",
- "--keep_osymbols=false",
- ],
- input=str.encode(fst_str),
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [
- fstaddselfloops,
- disambig_in_units_file_int,
- disambig_out_units_file_int,
- ],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [fstarcsort, "--sort_type=olabel"],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- out_f.write(res.stdout)
- except subprocess.CalledProcessError as e:
- logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
- os.remove(h_graph)
- raise
- return h_graph, h_out_units_file, disambig_in_units_file_int
-
-
-def create_HLGa(
- kaldi_root: Path,
- fst_dir: Path,
- unique_label: str,
- h_graph: Path,
- lg_graph: Path,
- disambig_in_words_file_int: Path,
-) -> Path:
- hlga_graph = fst_dir / f"HLGa.{unique_label}.fst"
-
- if not hlga_graph.exists():
- logger.info(f"Creating {hlga_graph}")
-
- fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose"
- fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar"
- fstrmsymbols = kaldi_root / "src/fstbin/fstrmsymbols"
- fstrmepslocal = kaldi_root / "src/fstbin/fstrmepslocal"
- fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded"
-
- try:
- with open(hlga_graph, "wb") as out_f:
- res = subprocess.run(
- [
- fsttablecompose,
- h_graph,
- lg_graph,
- ],
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [fstdeterminizestar, "--use-log=true"],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [fstrmsymbols, disambig_in_words_file_int],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [fstrmepslocal],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [fstminimizeencoded],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- out_f.write(res.stdout)
- except subprocess.CalledProcessError as e:
- logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
- os.remove(hlga_graph)
- raise
-
- return hlga_graph
-
-
-def create_HLa(
- kaldi_root: Path,
- fst_dir: Path,
- unique_label: str,
- h_graph: Path,
- l_graph: Path,
- disambig_in_words_file_int: Path,
-) -> Path:
- hla_graph = fst_dir / f"HLa.{unique_label}.fst"
-
- if not hla_graph.exists():
- logger.info(f"Creating {hla_graph}")
-
- fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose"
- fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar"
- fstrmsymbols = kaldi_root / "src/fstbin/fstrmsymbols"
- fstrmepslocal = kaldi_root / "src/fstbin/fstrmepslocal"
- fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded"
-
- try:
- with open(hla_graph, "wb") as out_f:
- res = subprocess.run(
- [
- fsttablecompose,
- h_graph,
- l_graph,
- ],
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [fstdeterminizestar, "--use-log=true"],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [fstrmsymbols, disambig_in_words_file_int],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [fstrmepslocal],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- res = subprocess.run(
- [fstminimizeencoded],
- input=res.stdout,
- capture_output=True,
- check=True,
- )
- out_f.write(res.stdout)
- except subprocess.CalledProcessError as e:
- logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
- os.remove(hla_graph)
- raise
-
- return hla_graph
-
-
-def create_HLG(
- kaldi_root: Path,
- fst_dir: Path,
- unique_label: str,
- hlga_graph: Path,
- prefix: str = "HLG",
-) -> Path:
- hlg_graph = fst_dir / f"{prefix}.{unique_label}.fst"
-
- if not hlg_graph.exists():
- logger.info(f"Creating {hlg_graph}")
-
- add_self_loop = script_dir / "add-self-loop-simple"
- kaldi_src = kaldi_root / "src"
- kaldi_lib = kaldi_src / "lib"
-
- try:
- if not add_self_loop.exists():
- fst_include = kaldi_root / "tools/openfst-1.6.7/include"
- add_self_loop_src = script_dir / "add-self-loop-simple.cc"
-
- subprocess.run(
- [
- "c++",
- f"-I{kaldi_src}",
- f"-I{fst_include}",
- f"-L{kaldi_lib}",
- add_self_loop_src,
- "-lkaldi-base",
- "-lkaldi-fstext",
- "-o",
- add_self_loop,
- ],
- check=True,
- )
-
- my_env = os.environ.copy()
- my_env["LD_LIBRARY_PATH"] = f"{kaldi_lib}:{my_env['LD_LIBRARY_PATH']}"
-
- subprocess.run(
- [
- add_self_loop,
- hlga_graph,
- hlg_graph,
- ],
- check=True,
- capture_output=True,
- env=my_env,
- )
- except subprocess.CalledProcessError as e:
- logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}")
- raise
-
- return hlg_graph
-
-
-def initalize_kaldi(cfg: KaldiInitializerConfig) -> Path:
- if cfg.fst_dir is None:
- cfg.fst_dir = osp.join(cfg.data_dir, "kaldi")
- if cfg.out_labels is None:
- cfg.out_labels = cfg.in_labels
-
- kaldi_root = Path(cfg.kaldi_root)
- data_dir = Path(cfg.data_dir)
- fst_dir = Path(cfg.fst_dir)
- fst_dir.mkdir(parents=True, exist_ok=True)
-
- arpa_base = osp.splitext(osp.basename(cfg.lm_arpa))[0]
- unique_label = f"{cfg.in_labels}.{arpa_base}"
-
- with open(data_dir / f"dict.{cfg.in_labels}.txt", "r") as f:
- vocab = Dictionary.load(f)
-
- in_units_file = create_units(fst_dir, cfg.in_labels, vocab)
-
- grammar_graph, out_words_file = create_G(
- kaldi_root, fst_dir, Path(cfg.lm_arpa), arpa_base
- )
-
- disambig_lexicon_file, disambig_L_in_units_file = create_lexicon(
- cfg, fst_dir, unique_label, in_units_file, out_words_file
- )
-
- h_graph, h_out_units_file, disambig_in_units_file_int = create_H(
- kaldi_root,
- fst_dir,
- disambig_L_in_units_file,
- cfg.in_labels,
- vocab,
- cfg.blank_symbol,
- cfg.silence_symbol,
- )
- lexicon_graph = create_L(
- kaldi_root,
- fst_dir,
- unique_label,
- disambig_lexicon_file,
- disambig_L_in_units_file,
- out_words_file,
- )
- lg_graph = create_LG(
- kaldi_root, fst_dir, unique_label, lexicon_graph, grammar_graph
- )
- hlga_graph = create_HLGa(
- kaldi_root, fst_dir, unique_label, h_graph, lg_graph, disambig_in_units_file_int
- )
- hlg_graph = create_HLG(kaldi_root, fst_dir, unique_label, hlga_graph)
-
- # for debugging
- # hla_graph = create_HLa(kaldi_root, fst_dir, unique_label, h_graph, lexicon_graph, disambig_in_units_file_int)
- # hl_graph = create_HLG(kaldi_root, fst_dir, unique_label, hla_graph, prefix="HL_looped")
- # create_HLG(kaldi_root, fst_dir, "phnc", h_graph, prefix="H_looped")
-
- return hlg_graph
-
-
-@hydra.main(config_path=config_path, config_name="kaldi_initializer")
-def cli_main(cfg: KaldiInitializerConfig) -> None:
- container = OmegaConf.to_container(cfg, resolve=True, enum_to_str=True)
- cfg = OmegaConf.create(container)
- OmegaConf.set_struct(cfg, True)
- initalize_kaldi(cfg)
-
-
-if __name__ == "__main__":
-
- logging.root.setLevel(logging.INFO)
- logging.basicConfig(level=logging.INFO)
-
- try:
- from hydra._internal.utils import (
- get_args,
- ) # pylint: disable=import-outside-toplevel
-
- cfg_name = get_args().config_name or "kaldi_initializer"
- except ImportError:
- logger.warning("Failed to get config name from hydra args")
- cfg_name = "kaldi_initializer"
-
- cs = ConfigStore.instance()
- cs.store(name=cfg_name, node=KaldiInitializerConfig)
-
- cli_main()
diff --git a/spaces/gradio/HuBERT/fairseq/data/resampling_dataset.py b/spaces/gradio/HuBERT/fairseq/data/resampling_dataset.py
deleted file mode 100644
index 3d3b993164dc3962df48bacff26714328e843e80..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/data/resampling_dataset.py
+++ /dev/null
@@ -1,139 +0,0 @@
-# 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.
-
-import logging
-
-import numpy as np
-from fairseq.data import BaseWrapperDataset, plasma_utils
-
-
-logger = logging.getLogger(__name__)
-
-
-class ResamplingDataset(BaseWrapperDataset):
- """Randomly samples from a given dataset at each epoch.
-
- Sampling is done with or without replacement, depending on the "replace"
- parameter.
-
- Optionally, the epoch size can be rescaled. This is potentially desirable
- to increase per-epoch coverage of the base dataset (since sampling with
- replacement means that many items in the dataset will be left out). In the
- case of sampling without replacement, size_ratio should be strictly less
- than 1.
-
- Args:
- dataset (~torch.utils.data.Dataset): dataset on which to sample.
- weights (List[float]): list of probability weights
- (default: None, which corresponds to uniform sampling).
- replace (bool): sampling mode; True for "with replacement", or False
- for "without replacement" (default: True)
- size_ratio (float): the ratio to subsample to; must be positive
- (default: 1.0).
- batch_by_size (bool): whether or not to batch by sequence length
- (default: True).
- seed (int): RNG seed to use (default: 0).
- epoch (int): starting epoch number (default: 1).
- """
-
- def __init__(
- self,
- dataset,
- weights=None,
- replace=True,
- size_ratio=1.0,
- batch_by_size=True,
- seed=0,
- epoch=1,
- ):
- super().__init__(dataset)
-
- if weights is None:
- self.weights = None
-
- else:
- assert len(weights) == len(dataset)
- weights_arr = np.array(weights, dtype=np.float64)
- weights_arr /= weights_arr.sum()
- self.weights = plasma_utils.PlasmaArray(weights_arr)
-
- self.replace = replace
-
- assert size_ratio > 0.0
- if not self.replace:
- assert size_ratio < 1.0
- self.size_ratio = float(size_ratio)
- self.actual_size = np.ceil(len(dataset) * self.size_ratio).astype(int)
-
- self.batch_by_size = batch_by_size
- self.seed = seed
-
- self._cur_epoch = None
- self._cur_indices = None
-
- self.set_epoch(epoch)
-
- def __getitem__(self, index):
- return self.dataset[self._cur_indices.array[index]]
-
- def __len__(self):
- return self.actual_size
-
- @property
- def sizes(self):
- if isinstance(self.dataset.sizes, list):
- return [s[self._cur_indices.array] for s in self.dataset.sizes]
- return self.dataset.sizes[self._cur_indices.array]
-
- def num_tokens(self, index):
- return self.dataset.num_tokens(self._cur_indices.array[index])
-
- def size(self, index):
- return self.dataset.size(self._cur_indices.array[index])
-
- def ordered_indices(self):
- if self.batch_by_size:
- order = [
- np.arange(len(self)),
- self.sizes,
- ] # No need to handle `self.shuffle == True`
- return np.lexsort(order)
- else:
- return np.arange(len(self))
-
- def prefetch(self, indices):
- self.dataset.prefetch(self._cur_indices.array[indices])
-
- @property
- def can_reuse_epoch_itr_across_epochs(self):
- return False
-
- def set_epoch(self, epoch):
- logger.debug("ResamplingDataset.set_epoch: {}".format(epoch))
- super().set_epoch(epoch)
-
- if epoch == self._cur_epoch:
- return
-
- self._cur_epoch = epoch
-
- # Generate a weighted sample of indices as a function of the
- # random seed and the current epoch.
-
- rng = np.random.RandomState(
- [
- 42, # magic number
- self.seed % (2 ** 32), # global seed
- self._cur_epoch, # epoch index
- ]
- )
- self._cur_indices = plasma_utils.PlasmaArray(
- rng.choice(
- len(self.dataset),
- self.actual_size,
- replace=self.replace,
- p=(None if self.weights is None else self.weights.array),
- )
- )
diff --git a/spaces/gradio/HuBERT/fairseq/modules/quantization/pq/utils.py b/spaces/gradio/HuBERT/fairseq/modules/quantization/pq/utils.py
deleted file mode 100644
index 03b15e4b1b58c9a1e6d42052b3bd5457df9a6e2e..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/modules/quantization/pq/utils.py
+++ /dev/null
@@ -1,337 +0,0 @@
-# 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.
-
-import logging
-import re
-from operator import attrgetter, itemgetter
-
-import numpy as np
-import torch.distributed as dist
-import torch.nn as nn
-
-from .modules import PQConv2d, PQEmbedding, PQLinear
-from .pq import PQ
-
-
-def quantize_model_(
- model,
- size_tracker,
- layers_to_quantize,
- block_sizes_config,
- n_centroids_config,
- step=0,
- n_iter=15,
- eps=1e-6,
- max_tentatives=100,
- verbose=True,
-):
- """
- Quantize a model in-place by stages. All the targeted
- layers are replaced by their quantized counterpart,
- and the model is ready for the finetuning of the
- centroids in a standard training loop (no modifications
- required). Note that we do not quantize biases.
-
- Args:
- - model: a nn.Module
- - size_tracker: useful for tracking quatization statistics
- - layers_to_quantize: a list containing regexps for
- filtering the layers to quantize at each stage according
- to their name (as in model.named_parameters())
- - block_sizes_config: dict like
- {
- 'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}),
- 'Linear': ('in_features', {'*': 8})
- }
- For instance, all conv2d layers with kernel size 3x3 have
- a block size of 9 and all Linear layers are quantized with
- a block size of 8, irrespective of their size.
- - n_centroids_config: dict like
- {
- 'Conv2d': ('kernel_size', {'*': 256}),
- 'Linear': ('in_features', {'*': 256})
- }
- For instance, all conv2d layers are quantized with 256 centroids
- - step: the layers to quantize inplace corresponding
- to layers_to_quantize[step]
- """
-
- quantized_layers = get_layers(model, layers_to_quantize[step])
-
- for layer in quantized_layers:
-
- # book-keeping
- is_master_process = (not dist.is_initialized()) or (
- dist.is_initialized() and dist.get_rank() == 0
- )
- verbose = verbose and is_master_process
-
- # get block size and centroids
- module = attrgetter(layer)(model)
- block_size = get_param(module, layer, block_sizes_config)
- n_centroids = get_param(module, layer, n_centroids_config)
- if verbose:
- logging.info(
- f"Quantizing layer {layer} with block size {block_size} and {n_centroids} centroids"
- )
-
- # quantize layer
- weight = module.weight.data.clone()
- is_bias = "bias" in [x[0] for x in module.named_parameters()]
- bias = module.bias.data.clone() if is_bias else None
- quantizer = PQ(
- weight,
- block_size,
- n_centroids=n_centroids,
- n_iter=n_iter,
- eps=eps,
- max_tentatives=max_tentatives,
- verbose=verbose,
- )
-
- # quantization performed on all GPUs with same seed
- quantizer.encode()
- centroids = quantizer.centroids.contiguous()
- assignments = quantizer.assignments.contiguous()
-
- # broadcast results to make sure weights are up-to-date
- if dist.is_initialized():
- dist.broadcast(centroids, 0)
- dist.broadcast(assignments, 0)
-
- # instantiate the quantized counterpart
- if isinstance(module, nn.Linear):
- out_features, in_features = map(
- lambda k: module.__dict__[k], ["out_features", "in_features"]
- )
- quantized_module = PQLinear(
- centroids, assignments, bias, in_features, out_features
- )
- elif isinstance(module, nn.Embedding):
- num_embeddings, embedding_dim = map(
- lambda k: module.__dict__[k], ["num_embeddings", "embedding_dim"]
- )
- quantized_module = PQEmbedding(
- centroids, assignments, num_embeddings, embedding_dim
- )
- elif isinstance(module, nn.Conv2d):
- out_channels, in_channels, kernel_size = map(
- lambda k: module.__dict__[k],
- ["out_channels", "in_channels", "kernel_size"],
- )
- stride, padding, dilation, groups, padding_mode = map(
- lambda k: module.__dict__[k],
- ["stride", "padding", "dilation", "groups", "padding_mode"],
- )
-
- quantized_module = PQConv2d(
- centroids,
- assignments,
- bias,
- in_channels,
- out_channels,
- kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=groups,
- padding_mode=padding_mode,
- )
- else:
- raise ValueError(f"Module {module} not yet supported for quantization")
-
- # replace layer by its quantized counterpart
- attrsetter(layer)(model, quantized_module)
-
- # update statistics
- size_tracker.update(weight, block_size, n_centroids)
-
- # return name of quantized layers
- return quantized_layers
-
-
-def get_layers(model, filter_regexp):
- """
- Filters out the layers according to a regexp. Note that
- we omit biases.
-
- Args:
- - model: a nn.Module
- - filter_regexp: a regexp to filter the layers to keep
- according to their name in model.named_parameters().
- For instance, the regexp:
-
- down_layers\\.[123456]\\.(conv[12]|identity\\.conv))
-
- is keeping blocks down_layers from 1 to 6, and inside
- each block is keeping conv1, conv2 and identity.conv.
-
- Remarks:
- - We add (module\\.)? at the beginning of the regexp to
- account for the possible use of nn.parallel.DataParallel
- """
-
- # get all parameter names
- all_layers = map(itemgetter(0), model.named_parameters())
-
- # remove biases
- all_layers = filter(lambda x: "bias" not in x, all_layers)
-
- # remove .weight in all other names (or .weight_orig is spectral norm)
- all_layers = map(lambda x: x.replace(".weight_orig", ""), all_layers)
- all_layers = map(lambda x: x.replace(".weight", ""), all_layers)
-
- # return filtered layers
- filter_regexp = "(module\\.)?" + "(" + filter_regexp + ")"
- r = re.compile(filter_regexp)
-
- return list(filter(r.match, all_layers))
-
-
-def get_param(module, layer_name, param_config):
- """
- Given a quantization configuration, get the right parameter
- for the module to be quantized.
-
- Args:
- - module: a nn.Module
- - layer_name: the name of the layer
- - param_config: a dict like
- {
- 'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}),
- 'Linear': ('in_features', {'*': 8})
- }
- For instance, all conv2d layers with kernel size 3x3 have
- a block size of 9 and all Linear layers are quantized with
- a block size of 8, irrespective of their size.
-
- Remarks:
- - if 'fuzzy_name' is passed as a parameter, layers whose layer_name
- include 'fuzzy_name' will be assigned the given parameter.
- In the following example, conv.expand layers will have a block
- size of 9 while conv.reduce will have a block size of 4 and all
- other layers will have a block size of 2.
- {
- 'Conv2d': ('fuzzy_name', {'expand': 9, 'reduce': 4, '*': 2}),
- 'Linear': ('fuzzy_name', {'classifier': 8, 'projection': 4})
- }
-
- """
-
- layer_type = module.__class__.__name__
-
- if layer_type not in param_config:
- raise KeyError(f"Layer type {layer_type} not in config for layer {module}")
-
- feature, params = param_config[module.__class__.__name__]
-
- if feature != "fuzzy_name":
- feature_value = str(getattr(module, feature))
- if feature_value not in params:
- if "*" in params:
- feature_value = "*"
- else:
- raise KeyError(
- f"{feature}={feature_value} not in config for layer {module}"
- )
- else:
- feature_values = [name for name in params if name in layer_name]
- if len(feature_values) == 0:
- if "*" in params:
- feature_value = "*"
- else:
- raise KeyError(f"name={layer_name} not in config for {module}")
- else:
- feature_value = feature_values[0]
-
- return params[feature_value]
-
-
-class SizeTracker(object):
- """
- Class to keep track of the compressed network size with iPQ.
-
- Args:
- - model: a nn.Module
-
- Remarks:
- - The compressed size is the sum of three components
- for each layer in the network:
- (1) Storing the centroids given by iPQ in fp16
- (2) Storing the assignments of the blocks in int8
- (3) Storing all non-compressed elements such as biases
- - This cost in only valid if we use 256 centroids (then
- indexing can indeed by done with int8).
- """
-
- def __init__(self, model):
- self.model = model
- self.size_non_compressed_model = self.compute_size()
- self.size_non_quantized = self.size_non_compressed_model
- self.size_index = 0
- self.size_centroids = 0
- self.n_quantized_layers = 0
-
- def compute_size(self):
- """
- Computes the size of the model (in MB).
- """
-
- res = 0
- for _, p in self.model.named_parameters():
- res += p.numel()
- return res * 4 / 1024 / 1024
-
- def update(self, W, block_size, n_centroids):
- """
- Updates the running statistics when quantizing a new layer.
- """
-
- # bits per weights
- bits_per_weight = np.log2(n_centroids) / block_size
- self.n_quantized_layers += 1
-
- # size of indexing the subvectors of size block_size (in MB)
- size_index_layer = bits_per_weight * W.numel() / 8 / 1024 / 1024
- self.size_index += size_index_layer
-
- # size of the centroids stored in float16 (in MB)
- size_centroids_layer = n_centroids * block_size * 2 / 1024 / 1024
- self.size_centroids += size_centroids_layer
-
- # size of non-compressed layers, e.g. LayerNorms or biases (in MB)
- size_uncompressed_layer = W.numel() * 4 / 1024 / 1024
- self.size_non_quantized -= size_uncompressed_layer
-
- def __repr__(self):
- size_compressed = (
- self.size_index + self.size_centroids + self.size_non_quantized
- )
- compression_ratio = self.size_non_compressed_model / size_compressed # NOQA
- return (
- f"Non-compressed model size: {self.size_non_compressed_model:.2f} MB. "
- f"After quantizing {self.n_quantized_layers} layers, size "
- f"(indexing + centroids + other): {self.size_index:.2f} MB + "
- f"{self.size_centroids:.2f} MB + {self.size_non_quantized:.2f} MB = "
- f"{size_compressed:.2f} MB, compression ratio: {compression_ratio:.2f}x"
- )
-
-
-def attrsetter(*items):
- def resolve_attr(obj, attr):
- attrs = attr.split(".")
- head = attrs[:-1]
- tail = attrs[-1]
-
- for name in head:
- obj = getattr(obj, name)
- return obj, tail
-
- def g(obj, val):
- for attr in items:
- resolved_obj, resolved_attr = resolve_attr(obj, attr)
- setattr(resolved_obj, resolved_attr, val)
-
- return g
diff --git a/spaces/gradio/HuBERT/fairseq/scoring/wer.py b/spaces/gradio/HuBERT/fairseq/scoring/wer.py
deleted file mode 100644
index 633dc47c247691c4c9e36cbdbab7d7cb74b38452..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/scoring/wer.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# 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.
-
-from dataclasses import dataclass, field
-
-from fairseq.dataclass import FairseqDataclass
-from fairseq.scoring import BaseScorer, register_scorer
-from fairseq.scoring.tokenizer import EvaluationTokenizer
-
-
-@dataclass
-class WerScorerConfig(FairseqDataclass):
- wer_tokenizer: EvaluationTokenizer.ALL_TOKENIZER_TYPES = field(
- default="none", metadata={"help": "sacreBLEU tokenizer to use for evaluation"}
- )
- wer_remove_punct: bool = field(
- default=False, metadata={"help": "remove punctuation"}
- )
- wer_char_level: bool = field(
- default=False, metadata={"help": "evaluate at character level"}
- )
- wer_lowercase: bool = field(default=False, metadata={"help": "lowercasing"})
-
-
-@register_scorer("wer", dataclass=WerScorerConfig)
-class WerScorer(BaseScorer):
- def __init__(self, cfg):
- super().__init__(cfg)
- self.reset()
- try:
- import editdistance as ed
- except ImportError:
- raise ImportError("Please install editdistance to use WER scorer")
- self.ed = ed
- self.tokenizer = EvaluationTokenizer(
- tokenizer_type=self.cfg.wer_tokenizer,
- lowercase=self.cfg.wer_lowercase,
- punctuation_removal=self.cfg.wer_remove_punct,
- character_tokenization=self.cfg.wer_char_level,
- )
-
- def reset(self):
- self.distance = 0
- self.ref_length = 0
-
- def add_string(self, ref, pred):
- ref_items = self.tokenizer.tokenize(ref).split()
- pred_items = self.tokenizer.tokenize(pred).split()
- self.distance += self.ed.eval(ref_items, pred_items)
- self.ref_length += len(ref_items)
-
- def result_string(self):
- return f"WER: {self.score():.2f}"
-
- def score(self):
- return 100.0 * self.distance / self.ref_length if self.ref_length > 0 else 0
diff --git a/spaces/gradio/HuBERT/fairseq/tasks/legacy_masked_lm.py b/spaces/gradio/HuBERT/fairseq/tasks/legacy_masked_lm.py
deleted file mode 100644
index 975497654926b64fff6c4960f54c4e6932e7fce1..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/tasks/legacy_masked_lm.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# 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.
-
-import itertools
-import logging
-import os
-
-import numpy as np
-from fairseq import tokenizer, utils
-from fairseq.data import ConcatDataset, Dictionary, data_utils, indexed_dataset
-from fairseq.data.legacy.block_pair_dataset import BlockPairDataset
-from fairseq.data.legacy.masked_lm_dataset import MaskedLMDataset
-from fairseq.data.legacy.masked_lm_dictionary import BertDictionary
-from fairseq.tasks import LegacyFairseqTask, register_task
-
-
-logger = logging.getLogger(__name__)
-
-
-@register_task("legacy_masked_lm")
-class LegacyMaskedLMTask(LegacyFairseqTask):
- """
- Task for training Masked LM (BERT) model.
- Args:
- dictionary (Dictionary): the dictionary for the input of the task
- """
-
- @staticmethod
- def add_args(parser):
- """Add task-specific arguments to the parser."""
- parser.add_argument(
- "data",
- help="colon separated path to data directories list, \
- will be iterated upon during epochs in round-robin manner",
- )
- parser.add_argument(
- "--tokens-per-sample",
- default=512,
- type=int,
- help="max number of total tokens over all segments"
- " per sample for BERT dataset",
- )
- parser.add_argument(
- "--break-mode", default="doc", type=str, help="mode for breaking sentence"
- )
- parser.add_argument("--shuffle-dataset", action="store_true", default=False)
-
- def __init__(self, args, dictionary):
- super().__init__(args)
- self.dictionary = dictionary
- self.seed = args.seed
-
- @classmethod
- def load_dictionary(cls, filename):
- return BertDictionary.load(filename)
-
- @classmethod
- def build_dictionary(
- cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8
- ):
- d = BertDictionary()
- for filename in filenames:
- Dictionary.add_file_to_dictionary(
- filename, d, tokenizer.tokenize_line, workers
- )
- d.finalize(threshold=threshold, nwords=nwords, padding_factor=padding_factor)
- return d
-
- @property
- def target_dictionary(self):
- return self.dictionary
-
- @classmethod
- def setup_task(cls, args, **kwargs):
- """Setup the task."""
- paths = utils.split_paths(args.data)
- assert len(paths) > 0
- dictionary = BertDictionary.load(os.path.join(paths[0], "dict.txt"))
- logger.info("dictionary: {} types".format(len(dictionary)))
-
- return cls(args, dictionary)
-
- def load_dataset(self, split, epoch=1, combine=False):
- """Load a given dataset split.
-
- Args:
- split (str): name of the split (e.g., train, valid, test)
- """
- loaded_datasets = []
-
- paths = utils.split_paths(self.args.data)
- assert len(paths) > 0
- data_path = paths[(epoch - 1) % len(paths)]
- logger.info("data_path", data_path)
-
- for k in itertools.count():
- split_k = split + (str(k) if k > 0 else "")
- path = os.path.join(data_path, split_k)
- ds = indexed_dataset.make_dataset(
- path,
- impl=self.args.dataset_impl,
- fix_lua_indexing=True,
- dictionary=self.dictionary,
- )
-
- if ds is None:
- if k > 0:
- break
- else:
- raise FileNotFoundError(
- "Dataset not found: {} ({})".format(split, data_path)
- )
-
- with data_utils.numpy_seed(self.seed + k):
- loaded_datasets.append(
- BlockPairDataset(
- ds,
- self.dictionary,
- ds.sizes,
- self.args.tokens_per_sample,
- break_mode=self.args.break_mode,
- doc_break_size=1,
- )
- )
-
- logger.info(
- "{} {} {} examples".format(data_path, split_k, len(loaded_datasets[-1]))
- )
-
- if not combine:
- break
-
- if len(loaded_datasets) == 1:
- dataset = loaded_datasets[0]
- sizes = dataset.sizes
- else:
- dataset = ConcatDataset(loaded_datasets)
- sizes = np.concatenate([ds.sizes for ds in loaded_datasets])
-
- self.datasets[split] = MaskedLMDataset(
- dataset=dataset,
- sizes=sizes,
- vocab=self.dictionary,
- pad_idx=self.dictionary.pad(),
- mask_idx=self.dictionary.mask(),
- classif_token_idx=self.dictionary.cls(),
- sep_token_idx=self.dictionary.sep(),
- shuffle=self.args.shuffle_dataset,
- seed=self.seed,
- )
diff --git a/spaces/guardiancc/video-face-swap/roop/processors/frame/face_enhancer.py b/spaces/guardiancc/video-face-swap/roop/processors/frame/face_enhancer.py
deleted file mode 100644
index cadb65ffc26552de1ea9c6ffe5750c0aa363e981..0000000000000000000000000000000000000000
--- a/spaces/guardiancc/video-face-swap/roop/processors/frame/face_enhancer.py
+++ /dev/null
@@ -1,81 +0,0 @@
-from typing import Any, List, Callable
-import cv2
-import threading
-import gfpgan
-
-import roop.globals
-import roop.processors.frame.core
-from roop.core import update_status
-from roop.face_analyser import get_one_face
-from roop.typing import Frame, Face
-from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video
-
-FACE_ENHANCER = None
-THREAD_SEMAPHORE = threading.Semaphore()
-THREAD_LOCK = threading.Lock()
-NAME = 'ROOP.FACE-ENHANCER'
-
-
-def get_face_enhancer() -> Any:
- global FACE_ENHANCER
-
- with THREAD_LOCK:
- if FACE_ENHANCER is None:
- model_path = resolve_relative_path('../models/GFPGANv1.4.pth')
- # todo: set models path https://github.com/TencentARC/GFPGAN/issues/399
- FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=5) # type: ignore[attr-defined]
- return FACE_ENHANCER
-
-
-def pre_check() -> bool:
- download_directory_path = resolve_relative_path('../models')
- conditional_download(download_directory_path, ['https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth'])
- return True
-
-
-def pre_start() -> bool:
- if not is_image(roop.globals.target_path) and not is_video(roop.globals.target_path):
- update_status('Select an image or video for target path.', NAME)
- return False
- return True
-
-
-def post_process() -> None:
- global FACE_ENHANCER
-
- FACE_ENHANCER = None
-
-
-def enhance_face(temp_frame: Frame) -> Frame:
- with THREAD_SEMAPHORE:
- _, _, temp_frame = get_face_enhancer().enhance(
- temp_frame,
- paste_back=True
- )
- return temp_frame
-
-
-def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
- target_face = get_one_face(temp_frame)
- if target_face:
- temp_frame = enhance_face(temp_frame)
- return temp_frame
-
-
-def process_frames(source_path: str, temp_frame_paths: List[str], update: Callable[[], None]) -> None:
- for temp_frame_path in temp_frame_paths:
- temp_frame = cv2.imread(temp_frame_path)
- result = process_frame(None, temp_frame)
- cv2.imwrite(temp_frame_path, result)
- if update:
- update()
-
-
-def process_image(source_path: str, target_path: str, output_path: str) -> None:
- target_frame = cv2.imread(target_path)
- result = process_frame(None, target_frame)
- cv2.imwrite(output_path, result)
-
-
-def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
- roop.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
diff --git a/spaces/guohuiyuan/Real-CUGAN/upcunet_v3.py b/spaces/guohuiyuan/Real-CUGAN/upcunet_v3.py
deleted file mode 100644
index f7919a6cc9efe3b8af73a73e30825a4c7d7d76da..0000000000000000000000000000000000000000
--- a/spaces/guohuiyuan/Real-CUGAN/upcunet_v3.py
+++ /dev/null
@@ -1,714 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-import os, sys
-import numpy as np
-
-root_path = os.path.abspath('.')
-sys.path.append(root_path)
-
-
-class SEBlock(nn.Module):
- def __init__(self, in_channels, reduction=8, bias=False):
- super(SEBlock, self).__init__()
- self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, 1, 1, 0, bias=bias)
- self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, 1, 1, 0, bias=bias)
-
- def forward(self, x):
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half()
- else:
- x0 = torch.mean(x, dim=(2, 3), keepdim=True)
- x0 = self.conv1(x0)
- x0 = F.relu(x0, inplace=True)
- x0 = self.conv2(x0)
- x0 = torch.sigmoid(x0)
- x = torch.mul(x, x0)
- return x
-
- def forward_mean(self, x, x0):
- x0 = self.conv1(x0)
- x0 = F.relu(x0, inplace=True)
- x0 = self.conv2(x0)
- x0 = torch.sigmoid(x0)
- x = torch.mul(x, x0)
- return x
-
-
-class UNetConv(nn.Module):
- def __init__(self, in_channels, mid_channels, out_channels, se):
- super(UNetConv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(in_channels, mid_channels, 3, 1, 0),
- nn.LeakyReLU(0.1, inplace=True),
- nn.Conv2d(mid_channels, out_channels, 3, 1, 0),
- nn.LeakyReLU(0.1, inplace=True),
- )
- if se:
- self.seblock = SEBlock(out_channels, reduction=8, bias=True)
- else:
- self.seblock = None
-
- def forward(self, x):
- z = self.conv(x)
- if self.seblock is not None:
- z = self.seblock(z)
- return z
-
-
-class UNet1(nn.Module):
- def __init__(self, in_channels, out_channels, deconv):
- super(UNet1, self).__init__()
- self.conv1 = UNetConv(in_channels, 32, 64, se=False)
- self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
- self.conv2 = UNetConv(64, 128, 64, se=True)
- self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
- self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
- if deconv:
- self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
- else:
- self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
- for m in self.modules():
- if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
- nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
- elif isinstance(m, nn.Linear):
- nn.init.normal_(m.weight, 0, 0.01)
- if m.bias is not None:
- nn.init.constant_(m.bias, 0)
-
- def forward(self, x):
- x1 = self.conv1(x)
- x2 = self.conv1_down(x1)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
- x2 = self.conv2(x2)
- x2 = self.conv2_up(x2)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
- x1 = F.pad(x1, (-4, -4, -4, -4))
- x3 = self.conv3(x1 + x2)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
- z = self.conv_bottom(x3)
- return z
-
- def forward_a(self, x):
- x1 = self.conv1(x)
- x2 = self.conv1_down(x1)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
- x2 = self.conv2.conv(x2)
- return x1, x2
-
- def forward_b(self, x1, x2):
- x2 = self.conv2_up(x2)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
- x1 = F.pad(x1, (-4, -4, -4, -4))
- x3 = self.conv3(x1 + x2)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
- z = self.conv_bottom(x3)
- return z
-
-
-class UNet1x3(nn.Module):
- def __init__(self, in_channels, out_channels, deconv):
- super(UNet1x3, self).__init__()
- self.conv1 = UNetConv(in_channels, 32, 64, se=False)
- self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
- self.conv2 = UNetConv(64, 128, 64, se=True)
- self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
- self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
- if deconv:
- self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2)
- else:
- self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
- for m in self.modules():
- if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
- nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
- elif isinstance(m, nn.Linear):
- nn.init.normal_(m.weight, 0, 0.01)
- if m.bias is not None:
- nn.init.constant_(m.bias, 0)
-
- def forward(self, x):
- x1 = self.conv1(x)
- x2 = self.conv1_down(x1)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
- x2 = self.conv2(x2)
- x2 = self.conv2_up(x2)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
- x1 = F.pad(x1, (-4, -4, -4, -4))
- x3 = self.conv3(x1 + x2)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
- z = self.conv_bottom(x3)
- return z
-
- def forward_a(self, x):
- x1 = self.conv1(x)
- x2 = self.conv1_down(x1)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
- x2 = self.conv2.conv(x2)
- return x1, x2
-
- def forward_b(self, x1, x2):
- x2 = self.conv2_up(x2)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
- x1 = F.pad(x1, (-4, -4, -4, -4))
- x3 = self.conv3(x1 + x2)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
- z = self.conv_bottom(x3)
- return z
-
-
-class UNet2(nn.Module):
- def __init__(self, in_channels, out_channels, deconv):
- super(UNet2, self).__init__()
-
- self.conv1 = UNetConv(in_channels, 32, 64, se=False)
- self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
- self.conv2 = UNetConv(64, 64, 128, se=True)
- self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0)
- self.conv3 = UNetConv(128, 256, 128, se=True)
- self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0)
- self.conv4 = UNetConv(128, 64, 64, se=True)
- self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
- self.conv5 = nn.Conv2d(64, 64, 3, 1, 0)
-
- if deconv:
- self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
- else:
- self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
- for m in self.modules():
- if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
- nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
- elif isinstance(m, nn.Linear):
- nn.init.normal_(m.weight, 0, 0.01)
- if m.bias is not None:
- nn.init.constant_(m.bias, 0)
-
- def forward(self, x):
- x1 = self.conv1(x)
- x2 = self.conv1_down(x1)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
- x2 = self.conv2(x2)
-
- x3 = self.conv2_down(x2)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
- x3 = self.conv3(x3)
- x3 = self.conv3_up(x3)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
- x2 = F.pad(x2, (-4, -4, -4, -4))
- x4 = self.conv4(x2 + x3)
- x4 = self.conv4_up(x4)
- x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
- x1 = F.pad(x1, (-16, -16, -16, -16))
- x5 = self.conv5(x1 + x4)
- x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
- z = self.conv_bottom(x5)
- return z
-
- def forward_a(self, x): # conv234结尾有se
- x1 = self.conv1(x)
- x2 = self.conv1_down(x1)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
- x2 = self.conv2.conv(x2)
- return x1, x2
-
- def forward_b(self, x2): # conv234结尾有se
- x3 = self.conv2_down(x2)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
- x3 = self.conv3.conv(x3)
- return x3
-
- def forward_c(self, x2, x3): # conv234结尾有se
- x3 = self.conv3_up(x3)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
- x2 = F.pad(x2, (-4, -4, -4, -4))
- x4 = self.conv4.conv(x2 + x3)
- return x4
-
- def forward_d(self, x1, x4): # conv234结尾有se
- x4 = self.conv4_up(x4)
- x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
- x1 = F.pad(x1, (-16, -16, -16, -16))
- x5 = self.conv5(x1 + x4)
- x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
- z = self.conv_bottom(x5)
- return z
-
-
-class UpCunet2x(nn.Module): # 完美tile，全程无损
- def __init__(self, in_channels=3, out_channels=3):
- super(UpCunet2x, self).__init__()
- self.unet1 = UNet1(in_channels, out_channels, deconv=True)
- self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
- def forward(self, x, tile_mode): # 1.7G
- n, c, h0, w0 = x.shape
- if (tile_mode == 0): # 不tile
- ph = ((h0 - 1) // 2 + 1) * 2
- pw = ((w0 - 1) // 2 + 1) * 2
- x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect') # 需要保证被2整除
- x = self.unet1.forward(x)
- x0 = self.unet2.forward(x)
- x1 = F.pad(x, (-20, -20, -20, -20))
- x = torch.add(x0, x1)
- if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 2, :w0 * 2]
- return x
- elif (tile_mode == 1): # 对长边减半
- if (w0 >= h0):
- crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除
- crop_size_h = (h0 - 1) // 2 * 2 + 2 # 能被2整除
- else:
- crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除
- crop_size_w = (w0 - 1) // 2 * 2 + 2 # 能被2整除
- crop_size = (crop_size_h, crop_size_w) # 6.6G
- elif (tile_mode == 2): # hw都减半
- crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2) # 5.6G
- elif (tile_mode == 3): # hw都三分之一
- crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3) # 4.2G
- elif (tile_mode == 4): # hw都四分之一
- crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4) # 3.7G
- ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
- pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
- x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')
- n, c, h, w = x.shape
- se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
- if ("Half" in x.type()):
- se_mean0 = se_mean0.half()
- n_patch = 0
- tmp_dict = {}
- opt_res_dict = {}
- for i in range(0, h - 36, crop_size[0]):
- tmp_dict[i] = {}
- for j in range(0, w - 36, crop_size[1]):
- x_crop = x[:, :, i:i + crop_size[0] + 36, j:j + crop_size[1] + 36]
- n, c1, h1, w1 = x_crop.shape
- tmp0, x_crop = self.unet1.forward_a(x_crop)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
- se_mean0 += tmp_se_mean
- n_patch += 1
- tmp_dict[i][j] = (tmp0, x_crop)
- se_mean0 /= n_patch
- se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean1 = se_mean1.half()
- for i in range(0, h - 36, crop_size[0]):
- for j in range(0, w - 36, crop_size[1]):
- tmp0, x_crop = tmp_dict[i][j]
- x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
- opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
- tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
- se_mean1 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
- se_mean1 /= n_patch
- se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean0 = se_mean0.half()
- for i in range(0, h - 36, crop_size[0]):
- for j in range(0, w - 36, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
- tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
- tmp_x3 = self.unet2.forward_b(tmp_x2)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
- se_mean0 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
- se_mean0 /= n_patch
- se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean1 = se_mean1.half()
- for i in range(0, h - 36, crop_size[0]):
- for j in range(0, w - 36, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
- tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
- tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
- se_mean1 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
- se_mean1 /= n_patch
- for i in range(0, h - 36, crop_size[0]):
- opt_res_dict[i] = {}
- for j in range(0, w - 36, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
- tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
- x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
- x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
- x_crop = torch.add(x0, x1) # x0是unet2的最终输出
- opt_res_dict[i][j] = x_crop
- del tmp_dict
- torch.cuda.empty_cache()
- res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device)
- if ("Half" in x.type()):
- res = res.half()
- for i in range(0, h - 36, crop_size[0]):
- for j in range(0, w - 36, crop_size[1]):
- res[:, :, i * 2:i * 2 + h1 * 2 - 72, j * 2:j * 2 + w1 * 2 - 72] = opt_res_dict[i][j]
- del opt_res_dict
- torch.cuda.empty_cache()
- if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 2, :w0 * 2]
- return res #
-
-
-class UpCunet3x(nn.Module): # 完美tile，全程无损
- def __init__(self, in_channels=3, out_channels=3):
- super(UpCunet3x, self).__init__()
- self.unet1 = UNet1x3(in_channels, out_channels, deconv=True)
- self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
- def forward(self, x, tile_mode): # 1.7G
- n, c, h0, w0 = x.shape
- if (tile_mode == 0): # 不tile
- ph = ((h0 - 1) // 4 + 1) * 4
- pw = ((w0 - 1) // 4 + 1) * 4
- x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect') # 需要保证被2整除
- x = self.unet1.forward(x)
- x0 = self.unet2.forward(x)
- x1 = F.pad(x, (-20, -20, -20, -20))
- x = torch.add(x0, x1)
- if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 3, :w0 * 3]
- return x
- elif (tile_mode == 1): # 对长边减半
- if (w0 >= h0):
- crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2 # 减半后能被4整除，所以要先被8整除
- crop_size_h = (h0 - 1) // 4 * 4 + 4 # 能被4整除
- else:
- crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2 # 减半后能被4整除，所以要先被8整除
- crop_size_w = (w0 - 1) // 4 * 4 + 4 # 能被4整除
- crop_size = (crop_size_h, crop_size_w) # 6.6G
- elif (tile_mode == 2): # hw都减半
- crop_size = (((h0 - 1) // 8 * 8 + 8) // 2, ((w0 - 1) // 8 * 8 + 8) // 2) # 5.6G
- elif (tile_mode == 3): # hw都三分之一
- crop_size = (((h0 - 1) // 12 * 12 + 12) // 3, ((w0 - 1) // 12 * 12 + 12) // 3) # 4.2G
- elif (tile_mode == 4): # hw都四分之一
- crop_size = (((h0 - 1) // 16 * 16 + 16) // 4, ((w0 - 1) // 16 * 16 + 16) // 4) # 3.7G
- ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
- pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
- x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')
- n, c, h, w = x.shape
- se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
- if ("Half" in x.type()):
- se_mean0 = se_mean0.half()
- n_patch = 0
- tmp_dict = {}
- opt_res_dict = {}
- for i in range(0, h - 28, crop_size[0]):
- tmp_dict[i] = {}
- for j in range(0, w - 28, crop_size[1]):
- x_crop = x[:, :, i:i + crop_size[0] + 28, j:j + crop_size[1] + 28]
- n, c1, h1, w1 = x_crop.shape
- tmp0, x_crop = self.unet1.forward_a(x_crop)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
- se_mean0 += tmp_se_mean
- n_patch += 1
- tmp_dict[i][j] = (tmp0, x_crop)
- se_mean0 /= n_patch
- se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean1 = se_mean1.half()
- for i in range(0, h - 28, crop_size[0]):
- for j in range(0, w - 28, crop_size[1]):
- tmp0, x_crop = tmp_dict[i][j]
- x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
- opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
- tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
- se_mean1 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
- se_mean1 /= n_patch
- se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean0 = se_mean0.half()
- for i in range(0, h - 28, crop_size[0]):
- for j in range(0, w - 28, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
- tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
- tmp_x3 = self.unet2.forward_b(tmp_x2)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
- se_mean0 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
- se_mean0 /= n_patch
- se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean1 = se_mean1.half()
- for i in range(0, h - 28, crop_size[0]):
- for j in range(0, w - 28, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
- tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
- tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
- se_mean1 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
- se_mean1 /= n_patch
- for i in range(0, h - 28, crop_size[0]):
- opt_res_dict[i] = {}
- for j in range(0, w - 28, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
- tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
- x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
- x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
- x_crop = torch.add(x0, x1) # x0是unet2的最终输出
- opt_res_dict[i][j] = x_crop #
- del tmp_dict
- torch.cuda.empty_cache()
- res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device)
- if ("Half" in x.type()):
- res = res.half()
- for i in range(0, h - 28, crop_size[0]):
- for j in range(0, w - 28, crop_size[1]):
- res[:, :, i * 3:i * 3 + h1 * 3 - 84, j * 3:j * 3 + w1 * 3 - 84] = opt_res_dict[i][j]
- del opt_res_dict
- torch.cuda.empty_cache()
- if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 3, :w0 * 3]
- return res
-
-
-class UpCunet4x(nn.Module): # 完美tile，全程无损
- def __init__(self, in_channels=3, out_channels=3):
- super(UpCunet4x, self).__init__()
- self.unet1 = UNet1(in_channels, 64, deconv=True)
- self.unet2 = UNet2(64, 64, deconv=False)
- self.ps = nn.PixelShuffle(2)
- self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True)
-
- def forward(self, x, tile_mode):
- n, c, h0, w0 = x.shape
- x00 = x
- if (tile_mode == 0): # 不tile
- ph = ((h0 - 1) // 2 + 1) * 2
- pw = ((w0 - 1) // 2 + 1) * 2
- x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect') # 需要保证被2整除
- x = self.unet1.forward(x)
- x0 = self.unet2.forward(x)
- x1 = F.pad(x, (-20, -20, -20, -20))
- x = torch.add(x0, x1)
- x = self.conv_final(x)
- x = F.pad(x, (-1, -1, -1, -1))
- x = self.ps(x)
- if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 4, :w0 * 4]
- x += F.interpolate(x00, scale_factor=4, mode='nearest')
- return x
- elif (tile_mode == 1): # 对长边减半
- if (w0 >= h0):
- crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除
- crop_size_h = (h0 - 1) // 2 * 2 + 2 # 能被2整除
- else:
- crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除
- crop_size_w = (w0 - 1) // 2 * 2 + 2 # 能被2整除
- crop_size = (crop_size_h, crop_size_w) # 6.6G
- elif (tile_mode == 2): # hw都减半
- crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2) # 5.6G
- elif (tile_mode == 3): # hw都三分之一
- crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3) # 4.1G
- elif (tile_mode == 4): # hw都四分之一
- crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4) # 3.7G
- ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
- pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
- x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')
- n, c, h, w = x.shape
- se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
- if ("Half" in x.type()):
- se_mean0 = se_mean0.half()
- n_patch = 0
- tmp_dict = {}
- opt_res_dict = {}
- for i in range(0, h - 38, crop_size[0]):
- tmp_dict[i] = {}
- for j in range(0, w - 38, crop_size[1]):
- x_crop = x[:, :, i:i + crop_size[0] + 38, j:j + crop_size[1] + 38]
- n, c1, h1, w1 = x_crop.shape
- tmp0, x_crop = self.unet1.forward_a(x_crop)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
- se_mean0 += tmp_se_mean
- n_patch += 1
- tmp_dict[i][j] = (tmp0, x_crop)
- se_mean0 /= n_patch
- se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean1 = se_mean1.half()
- for i in range(0, h - 38, crop_size[0]):
- for j in range(0, w - 38, crop_size[1]):
- tmp0, x_crop = tmp_dict[i][j]
- x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
- opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
- tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
- se_mean1 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
- se_mean1 /= n_patch
- se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean0 = se_mean0.half()
- for i in range(0, h - 38, crop_size[0]):
- for j in range(0, w - 38, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
- tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
- tmp_x3 = self.unet2.forward_b(tmp_x2)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
- se_mean0 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
- se_mean0 /= n_patch
- se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean1 = se_mean1.half()
- for i in range(0, h - 38, crop_size[0]):
- for j in range(0, w - 38, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
- tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
- tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
- se_mean1 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
- se_mean1 /= n_patch
- for i in range(0, h - 38, crop_size[0]):
- opt_res_dict[i] = {}
- for j in range(0, w - 38, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
- tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
- x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
- x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
- x_crop = torch.add(x0, x1) # x0是unet2的最终输出
- x_crop = self.conv_final(x_crop)
- x_crop = F.pad(x_crop, (-1, -1, -1, -1))
- x_crop = self.ps(x_crop)
- opt_res_dict[i][j] = x_crop
- del tmp_dict
- torch.cuda.empty_cache()
- res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device)
- if ("Half" in x.type()):
- res = res.half()
- for i in range(0, h - 38, crop_size[0]):
- for j in range(0, w - 38, crop_size[1]):
- # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape)
- res[:, :, i * 4:i * 4 + h1 * 4 - 152, j * 4:j * 4 + w1 * 4 - 152] = opt_res_dict[i][j]
- del opt_res_dict
- torch.cuda.empty_cache()
- if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 4, :w0 * 4]
- res += F.interpolate(x00, scale_factor=4, mode='nearest')
- return res #
-
-
-class RealWaifuUpScaler(object):
- def __init__(self, scale, weight_path, half, device):
- weight = torch.load(weight_path, map_location="cpu")
- self.model = eval("UpCunet%sx" % scale)()
- if (half == True):
- self.model = self.model.half().to(device)
- else:
- self.model = self.model.to(device)
- self.model.load_state_dict(weight, strict=True)
- self.model.eval()
- self.half = half
- self.device = device
-
- def np2tensor(self, np_frame):
- if (self.half == False):
- return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).float() / 255
- else:
- return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).half() / 255
-
- def tensor2np(self, tensor):
- if (self.half == False):
- return (
- np.transpose((tensor.data.squeeze() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), (1, 2, 0)))
- else:
- return (np.transpose((tensor.data.squeeze().float() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(),
- (1, 2, 0)))
-
- def __call__(self, frame, tile_mode):
- with torch.no_grad():
- tensor = self.np2tensor(frame)
- result = self.tensor2np(self.model(tensor, tile_mode))
- return result
-
-
-if __name__ == "__main__":
- ###########inference_img
- import time, cv2, sys
- from time import time as ttime
-
- for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3),
- ("weights_v3/up4x-latest-denoise3x.pth", 4)]:
- for tile_mode in [0, 1, 2, 3, 4]:
- upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0")
- input_dir = "%s/input_dir1" % root_path
- output_dir = "%s/opt-dir-all-test" % root_path
- os.makedirs(output_dir, exist_ok=True)
- for name in os.listdir(input_dir):
- print(name)
- tmp = name.split(".")
- inp_path = os.path.join(input_dir, name)
- suffix = tmp[-1]
- prefix = ".".join(tmp[:-1])
- tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
- print(inp_path, tmp_path)
- # 支持中文路径
- # os.link(inp_path, tmp_path)#win用硬链接
- os.symlink(inp_path, tmp_path) # linux用软链接
- frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]]
- t0 = ttime()
- result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1]
- t1 = ttime()
- print(prefix, "done", t1 - t0)
- tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
- cv2.imwrite(tmp_opt_path, result)
- n = 0
- while (1):
- if (n == 0):
- suffix = "_%sx_tile%s.png" % (scale, tile_mode)
- else:
- suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n) #
- if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False):
- break
- else:
- n += 1
- final_opt_path = os.path.join(output_dir, prefix + suffix)
- os.rename(tmp_opt_path, final_opt_path)
- os.remove(tmp_path)
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/__init__.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/hackertwo/GoAheadMazen/style.css b/spaces/hackertwo/GoAheadMazen/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/hackertwo/GoAheadMazen/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
- padding: 2rem;
- font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
- font-size: 16px;
- margin-top: 0;
-}
-
-p {
- color: rgb(107, 114, 128);
- font-size: 15px;
- margin-bottom: 10px;
- margin-top: 5px;
-}
-
-.card {
- max-width: 620px;
- margin: 0 auto;
- padding: 16px;
- border: 1px solid lightgray;
- border-radius: 16px;
-}
-
-.card p:last-child {
- margin-bottom: 0;
-}
diff --git a/spaces/hamelcubsfan/AutoGPT/tests/test_token_counter.py b/spaces/hamelcubsfan/AutoGPT/tests/test_token_counter.py
deleted file mode 100644
index 6d7ae016b2f823123b0b69b2eeb3eab50d94f00f..0000000000000000000000000000000000000000
--- a/spaces/hamelcubsfan/AutoGPT/tests/test_token_counter.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import unittest
-
-import tests.context
-from autogpt.token_counter import count_message_tokens, count_string_tokens
-
-
-class TestTokenCounter(unittest.TestCase):
- def test_count_message_tokens(self):
- messages = [
- {"role": "user", "content": "Hello"},
- {"role": "assistant", "content": "Hi there!"},
- ]
- self.assertEqual(count_message_tokens(messages), 17)
-
- def test_count_message_tokens_with_name(self):
- messages = [
- {"role": "user", "content": "Hello", "name": "John"},
- {"role": "assistant", "content": "Hi there!"},
- ]
- self.assertEqual(count_message_tokens(messages), 17)
-
- def test_count_message_tokens_empty_input(self):
- self.assertEqual(count_message_tokens([]), 3)
-
- def test_count_message_tokens_invalid_model(self):
- messages = [
- {"role": "user", "content": "Hello"},
- {"role": "assistant", "content": "Hi there!"},
- ]
- with self.assertRaises(KeyError):
- count_message_tokens(messages, model="invalid_model")
-
- def test_count_message_tokens_gpt_4(self):
- messages = [
- {"role": "user", "content": "Hello"},
- {"role": "assistant", "content": "Hi there!"},
- ]
- self.assertEqual(count_message_tokens(messages, model="gpt-4-0314"), 15)
-
- def test_count_string_tokens(self):
- string = "Hello, world!"
- self.assertEqual(
- count_string_tokens(string, model_name="gpt-3.5-turbo-0301"), 4
- )
-
- def test_count_string_tokens_empty_input(self):
- self.assertEqual(count_string_tokens("", model_name="gpt-3.5-turbo-0301"), 0)
-
- def test_count_message_tokens_invalid_model(self):
- messages = [
- {"role": "user", "content": "Hello"},
- {"role": "assistant", "content": "Hi there!"},
- ]
- with self.assertRaises(NotImplementedError):
- count_message_tokens(messages, model="invalid_model")
-
- def test_count_string_tokens_gpt_4(self):
- string = "Hello, world!"
- self.assertEqual(count_string_tokens(string, model_name="gpt-4-0314"), 4)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/hands012/gpt-academic/crazy_functions/crazy_functions_test.py b/spaces/hands012/gpt-academic/crazy_functions/crazy_functions_test.py
deleted file mode 100644
index a9bfbf80df3780be105e0f1be10d2f348c4282bb..0000000000000000000000000000000000000000
--- a/spaces/hands012/gpt-academic/crazy_functions/crazy_functions_test.py
+++ /dev/null
@@ -1,135 +0,0 @@
-"""
-这是什么？
- 这个文件用于函数插件的单元测试
- 运行方法 python crazy_functions/crazy_functions_test.py
-"""
-
-def validate_path():
- import os, sys
- dir_name = os.path.dirname(__file__)
- root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..')
- os.chdir(root_dir_assume)
- sys.path.append(root_dir_assume)
-
-validate_path() # validate path so you can run from base directory
-from colorful import *
-from toolbox import get_conf, ChatBotWithCookies
-proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
- get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
-
-llm_kwargs = {
- 'api_key': API_KEY,
- 'llm_model': LLM_MODEL,
- 'top_p':1.0,
- 'max_length': None,
- 'temperature':1.0,
-}
-plugin_kwargs = { }
-chatbot = ChatBotWithCookies(llm_kwargs)
-history = []
-system_prompt = "Serve me as a writing and programming assistant."
-web_port = 1024
-
-
-def test_解析一个Python项目():
- from crazy_functions.解析项目源代码 import 解析一个Python项目
- txt = "crazy_functions/test_project/python/dqn"
- for cookies, cb, hist, msg in 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- print(cb)
-
-def test_解析一个Cpp项目():
- from crazy_functions.解析项目源代码 import 解析一个C项目
- txt = "crazy_functions/test_project/cpp/cppipc"
- for cookies, cb, hist, msg in 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- print(cb)
-
-def test_Latex英文润色():
- from crazy_functions.Latex全文润色 import Latex英文润色
- txt = "crazy_functions/test_project/latex/attention"
- for cookies, cb, hist, msg in Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- print(cb)
-
-def test_Markdown中译英():
- from crazy_functions.批量Markdown翻译 import Markdown中译英
- txt = "README.md"
- for cookies, cb, hist, msg in Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- print(cb)
-
-def test_批量翻译PDF文档():
- from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档
- txt = "crazy_functions/test_project/pdf_and_word"
- for cookies, cb, hist, msg in 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- print(cb)
-
-def test_谷歌检索小助手():
- from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
- txt = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=auto+reinforcement+learning&btnG="
- for cookies, cb, hist, msg in 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- print(cb)
-
-def test_总结word文档():
- from crazy_functions.总结word文档 import 总结word文档
- txt = "crazy_functions/test_project/pdf_and_word"
- for cookies, cb, hist, msg in 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- print(cb)
-
-def test_下载arxiv论文并翻译摘要():
- from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要
- txt = "1812.10695"
- for cookies, cb, hist, msg in 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- print(cb)
-
-def test_联网回答问题():
- from crazy_functions.联网的ChatGPT import 连接网络回答问题
- # txt = "谁是应急食品？"
- # >> '根据以上搜索结果可以得知，应急食品是“原神”游戏中的角色派蒙的外号。'
- # txt = "道路千万条，安全第一条。后面两句是？"
- # >> '行车不规范，亲人两行泪。'
- # txt = "You should have gone for the head. What does that mean?"
- # >> The phrase "You should have gone for the head" is a quote from the Marvel movies, Avengers: Infinity War and Avengers: Endgame. It was spoken by the character Thanos in Infinity War and by Thor in Endgame.
- txt = "AutoGPT是什么？"
- for cookies, cb, hist, msg in 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- print("当前问答：", cb[-1][-1].replace("\n"," "))
- for i, it in enumerate(cb): print亮蓝(it[0]); print亮黄(it[1])
-
-def test_解析ipynb文件():
- from crazy_functions.解析JupyterNotebook import 解析ipynb文件
- txt = "crazy_functions/test_samples"
- for cookies, cb, hist, msg in 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- print(cb)
-
-
-def test_数学动画生成manim():
- from crazy_functions.数学动画生成manim import 动画生成
- txt = "A ball split into 2, and then split into 4, and finally split into 8."
- for cookies, cb, hist, msg in 动画生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- print(cb)
-
-
-
-def test_Markdown多语言():
- from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言
- txt = "README.md"
- history = []
- for lang in ["English", "French", "Japanese", "Korean", "Russian", "Italian", "German", "Portuguese", "Arabic"]:
- plugin_kwargs = {"advanced_arg": lang}
- for cookies, cb, hist, msg in Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- print(cb)
-
-
-
-# test_解析一个Python项目()
-# test_Latex英文润色()
-# test_Markdown中译英()
-# test_批量翻译PDF文档()
-# test_谷歌检索小助手()
-# test_总结word文档()
-# test_下载arxiv论文并翻译摘要()
-# test_解析一个Cpp项目()
-# test_联网回答问题()
-# test_解析ipynb文件()
-# test_数学动画生成manim()
-test_Markdown多语言()
-
-input("程序完成，回车退出。")
-print("退出。")
\ No newline at end of file
diff --git a/spaces/hdhzk/bingo/Dockerfile b/spaces/hdhzk/bingo/Dockerfile
deleted file mode 100644
index 3aa2b29b5fc4fa8b8238955acd7f1fde13ce5e1a..0000000000000000000000000000000000000000
--- a/spaces/hdhzk/bingo/Dockerfile
+++ /dev/null
@@ -1,36 +0,0 @@
-FROM node:18
-
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:$PATH
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -o -u 1000 user && mkdir -p $HOME/app && chown -R user $HOME
-
-# Switch to the "user" user
-USER user
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Install app dependencies
-# A wildcard is used to ensure both package.json AND package-lock.json are copied
-# where available (npm@5+)
-COPY --chown=user package*.json $HOME/app/
-
-RUN npm install
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app/
-
-RUN npm run build
-
-ENV PORT 7860
-EXPOSE 7860
-
-CMD npm start
diff --git a/spaces/hhhyrhe/vits-uma-genshin-honkai/modules.py b/spaces/hhhyrhe/vits-uma-genshin-honkai/modules.py
deleted file mode 100644
index 56ea4145eddf19dd330a3a41ab0183efc1686d83..0000000000000000000000000000000000000000
--- a/spaces/hhhyrhe/vits-uma-genshin-honkai/modules.py
+++ /dev/null
@@ -1,388 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- self.hidden_channels =hidden_channels
- self.kernel_size = kernel_size,
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
-
-class ConvFlow(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
- self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ResencUNet_DA3.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ResencUNet_DA3.py
deleted file mode 100644
index 11e48b188a948d4a4ef526d88c1f95a7a229617a..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ResencUNet_DA3.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import Tuple
-
-import numpy as np
-import torch
-
-from nnunet.network_architecture.generic_modular_residual_UNet import FabiansUNet, get_default_network_config
-from nnunet.network_architecture.initialization import InitWeights_He
-from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer
-from nnunet.training.network_training.nnUNet_variants.data_augmentation.nnUNetTrainerV2_DA3 import \
- nnUNetTrainerV2_DA3
-from nnunet.utilities.nd_softmax import softmax_helper
-
-
-class nnUNetTrainerV2_ResencUNet_DA3(nnUNetTrainerV2_DA3):
- def initialize_network(self):
- if self.threeD:
- cfg = get_default_network_config(3, None, norm_type="in")
-
- else:
- cfg = get_default_network_config(1, None, norm_type="in")
-
- stage_plans = self.plans['plans_per_stage'][self.stage]
- conv_kernel_sizes = stage_plans['conv_kernel_sizes']
- blocks_per_stage_encoder = stage_plans['num_blocks_encoder']
- blocks_per_stage_decoder = stage_plans['num_blocks_decoder']
- pool_op_kernel_sizes = stage_plans['pool_op_kernel_sizes']
-
- self.network = FabiansUNet(self.num_input_channels, self.base_num_features, blocks_per_stage_encoder, 2,
- pool_op_kernel_sizes, conv_kernel_sizes, cfg, self.num_classes,
- blocks_per_stage_decoder, True, False, 320, InitWeights_He(1e-2))
-
- if torch.cuda.is_available():
- self.network.cuda()
- self.network.inference_apply_nonlin = softmax_helper
-
- def setup_DA_params(self):
- """
- net_num_pool_op_kernel_sizes is different in resunet
- """
- super().setup_DA_params()
- self.deep_supervision_scales = [[1, 1, 1]] + list(list(i) for i in 1 / np.cumprod(
- np.vstack(self.net_num_pool_op_kernel_sizes[1:]), axis=0))[:-1]
-
- def validate(self, do_mirroring: bool = True, use_sliding_window: bool = True, step_size: float = 0.5,
- save_softmax: bool = True, use_gaussian: bool = True, overwrite: bool = True,
- validation_folder_name: str = 'validation_raw', debug: bool = False, all_in_gpu: bool = False,
- segmentation_export_kwargs: dict = None, run_postprocessing_on_folds: bool = True):
- ds = self.network.decoder.deep_supervision
- self.network.decoder.deep_supervision = False
-
- ret = nnUNetTrainer.validate(self, do_mirroring=do_mirroring, use_sliding_window=use_sliding_window,
- step_size=step_size, save_softmax=save_softmax, use_gaussian=use_gaussian,
- overwrite=overwrite, validation_folder_name=validation_folder_name, debug=debug,
- all_in_gpu=all_in_gpu, segmentation_export_kwargs=segmentation_export_kwargs,
- run_postprocessing_on_folds=run_postprocessing_on_folds)
-
- self.network.decoder.deep_supervision = ds
- return ret
-
- def predict_preprocessed_data_return_seg_and_softmax(self, data: np.ndarray, do_mirroring: bool = True,
- mirror_axes: Tuple[int] = None,
- use_sliding_window: bool = True, step_size: float = 0.5,
- use_gaussian: bool = True, pad_border_mode: str = 'constant',
- pad_kwargs: dict = None, all_in_gpu: bool = False,
- verbose: bool = True, mixed_precision=True) -> Tuple[np.ndarray, np.ndarray]:
- ds = self.network.decoder.deep_supervision
- self.network.decoder.deep_supervision = False
- ret = nnUNetTrainer.predict_preprocessed_data_return_seg_and_softmax(self, data=data,
- do_mirroring=do_mirroring,
- mirror_axes=mirror_axes,
- use_sliding_window=use_sliding_window,
- step_size=step_size,
- use_gaussian=use_gaussian,
- pad_border_mode=pad_border_mode,
- pad_kwargs=pad_kwargs,
- all_in_gpu=all_in_gpu,
- verbose=verbose,
- mixed_precision=mixed_precision)
- self.network.decoder.deep_supervision = ds
- return ret
-
- def run_training(self):
- self.maybe_update_lr(self.epoch) # if we dont overwrite epoch then self.epoch+1 is used which is not what we
- # want at the start of the training
- ds = self.network.decoder.deep_supervision
- self.network.decoder.deep_supervision = True
- ret = nnUNetTrainer.run_training(self)
- self.network.decoder.deep_supervision = ds
- return ret
-
-
diff --git a/spaces/huaiji3y/bingo-Public/src/components/learn-more.tsx b/spaces/huaiji3y/bingo-Public/src/components/learn-more.tsx
deleted file mode 100644
index a64459ee7900a612292e117a6bda96ee9260990f..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/components/learn-more.tsx
+++ /dev/null
@@ -1,39 +0,0 @@
-import React from 'react'
-import { SourceAttribution } from '@/lib/bots/bing/types'
-
-export interface LearnMoreProps {
- sourceAttributions?: SourceAttribution[]
-}
-
-export function LearnMore({ sourceAttributions }: LearnMoreProps) {
- if (!sourceAttributions?.length) {
- return null
- }
-
- return (
-
- 了解详细信息:
-
-
- {sourceAttributions.map((attribution, index) => {
- const { providerDisplayName, seeMoreUrl } = attribution
- const { host } = new URL(seeMoreUrl)
- return (
-
- {index + 1}. {host}
-
- )
- })}
-
-
-
- )
-}
diff --git a/spaces/huggingchat/chat-ui/src/lib/types/AbortedGeneration.ts b/spaces/huggingchat/chat-ui/src/lib/types/AbortedGeneration.ts
deleted file mode 100644
index fe4c2824b4f3257bea71c3acacd65fcee0918188..0000000000000000000000000000000000000000
--- a/spaces/huggingchat/chat-ui/src/lib/types/AbortedGeneration.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-// Ideally shouldn't be needed, see https://github.com/huggingface/chat-ui/pull/88#issuecomment-1523173850
-
-import type { Conversation } from "./Conversation";
-import type { Timestamps } from "./Timestamps";
-
-export interface AbortedGeneration extends Timestamps {
- conversationId: Conversation["_id"];
-}
diff --git a/spaces/hysts/cv_diffusion_text-to-image-synthesis_tiny/app.py b/spaces/hysts/cv_diffusion_text-to-image-synthesis_tiny/app.py
deleted file mode 100644
index 695aa5ecd84b905950c7c267c5d606eff712f12f..0000000000000000000000000000000000000000
--- a/spaces/hysts/cv_diffusion_text-to-image-synthesis_tiny/app.py
+++ /dev/null
@@ -1,111 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-import shlex
-import subprocess
-
-import gradio as gr
-import numpy as np
-import torch
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-
-if os.getenv('SYSTEM') == 'spaces':
- subprocess.run(
- shlex.split(
- 'pip install git+https://github.com/modelscope/modelscope.git@refs/pull/173/head'
- ))
-
-DESCRIPTION = '# [ModelScope Chinese text2image (tiny)](https://www.modelscope.cn/models/damo/cv_diffusion_text-to-image-synthesis_tiny/summary)'
-
-SPACE_ID = os.getenv('SPACE_ID')
-if SPACE_ID is not None:
- DESCRIPTION += f'For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
'
-
-pipe = pipeline(Tasks.text_to_image_synthesis,
- 'damo/cv_diffusion_text-to-image-synthesis_tiny')
-
-
-def run(
- text: str,
- seed: int,
- num_steps_generator: int,
- num_steps_upscaler1: int,
- num_steps_upscaler2: int,
- guidance_scale: float,
-) -> np.ndarray:
- torch.manual_seed(seed)
- results = pipe({
- 'text': text,
- 'solver': 'ddim',
- 'generator_ddim_timesteps': num_steps_generator,
- 'upsampler_256_ddim_timesteps': num_steps_upscaler1,
- 'upsampler_1024_ddim_timesteps': num_steps_upscaler2,
- 'generator_guide_scale': guidance_scale,
- })
- return results['output_imgs'][0]
-
-
-examples = [
- ['中国山水画', 0, 250, 50, 20, 5.0],
-]
-
-with gr.Blocks(css='style.css') as demo:
- gr.Markdown(DESCRIPTION)
- with gr.Row():
- with gr.Column():
- text = gr.Text(label='Prompt')
- seed = gr.Slider(label='Seed',
- minimum=0,
- maximum=100000,
- value=0,
- step=1,
- randomize=True)
- run_button = gr.Button('Run')
- with gr.Accordion('Advanced options', open=False):
- num_steps_generator = gr.Slider(label='Steps (Generator)',
- minimum=1,
- maximum=1000,
- value=250,
- step=1)
- num_steps_upscaler1 = gr.Slider(
- label='Steps (Upscaler 64=>256)',
- minimum=1,
- maximum=50,
- value=50,
- step=1)
- num_steps_upscaler2 = gr.Slider(
- label='Steps (Upscaler 256=>1024)',
- minimum=1,
- maximum=20,
- value=20,
- step=1)
- guidance_scale = gr.Slider(label='Guidance scale',
- minimum=0,
- maximum=100,
- value=5.0,
- step=0.1)
- with gr.Column():
- result = gr.Image(label='Output')
-
- inputs = [
- text,
- seed,
- num_steps_generator,
- num_steps_upscaler1,
- num_steps_upscaler2,
- guidance_scale,
- ]
- with gr.Row():
- gr.Examples(examples=examples,
- inputs=inputs,
- outputs=result,
- fn=run,
- cache_examples=True)
-
- text.submit(fn=run, inputs=inputs, outputs=result)
- run_button.click(fn=run, inputs=inputs, outputs=result)
-
-demo.queue(api_open=False).launch()
diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf12m_r50.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf12m_r50.py
deleted file mode 100644
index 2a7284663d6afbe6f205c8c9f10cd454ef1045ca..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf12m_r50.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G tmpfs /train_tmp
-
-config = edict()
-config.margin_list = (1.0, 0.0, 0.4)
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.interclass_filtering_threshold = 0
-config.fp16 = True
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.optimizer = "sgd"
-config.lr = 0.1
-config.verbose = 2000
-config.dali = False
-
-config.rec = "/train_tmp/WebFace12M"
-config.num_classes = 617970
-config.num_image = 12720066
-config.num_epoch = 20
-config.warmup_epoch = 0
-config.val_targets = []
diff --git a/spaces/hzy123/bingo/src/components/chat-panel.tsx b/spaces/hzy123/bingo/src/components/chat-panel.tsx
deleted file mode 100644
index 1fbc3c2bf05b914e0c229661832fbb560745f488..0000000000000000000000000000000000000000
--- a/spaces/hzy123/bingo/src/components/chat-panel.tsx
+++ /dev/null
@@ -1,153 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import Image from 'next/image'
-import Textarea from 'react-textarea-autosize'
-import { useAtomValue } from 'jotai'
-import { useEnterSubmit } from '@/lib/hooks/use-enter-submit'
-import { cn } from '@/lib/utils'
-
-import BrushIcon from '@/assets/images/brush.svg'
-import ChatIcon from '@/assets/images/chat.svg'
-import VisualSearchIcon from '@/assets/images/visual-search.svg'
-import SendIcon from '@/assets/images/send.svg'
-import PinIcon from '@/assets/images/pin.svg'
-import PinFillIcon from '@/assets/images/pin-fill.svg'
-
-import { useBing } from '@/lib/hooks/use-bing'
-import { voiceListenAtom } from '@/state'
-import Voice from './voice'
-import { ChatImage } from './chat-image'
-import { ChatAttachments } from './chat-attachments'
-
-export interface ChatPanelProps
- extends Pick<
- ReturnType,
- | 'generating'
- | 'input'
- | 'setInput'
- | 'sendMessage'
- | 'resetConversation'
- | 'isSpeaking'
- | 'attachmentList'
- | 'uploadImage'
- | 'setAttachmentList'
- > {
- id?: string
- className?: string
-}
-
-export function ChatPanel({
- isSpeaking,
- generating,
- input,
- setInput,
- className,
- sendMessage,
- resetConversation,
- attachmentList,
- uploadImage,
- setAttachmentList
-}: ChatPanelProps) {
- const inputRef = React.useRef(null)
- const {formRef, onKeyDown} = useEnterSubmit()
- const [focused, setFocused] = React.useState(false)
- const [active, setActive] = React.useState(false)
- const [pin, setPin] = React.useState(false)
- const [tid, setTid] = React.useState()
- const voiceListening = useAtomValue(voiceListenAtom)
-
- const setBlur = React.useCallback(() => {
- clearTimeout(tid)
- setActive(false)
- const _tid = setTimeout(() => setFocused(false), 2000);
- setTid(_tid)
- }, [tid])
-
- const setFocus = React.useCallback(() => {
- setFocused(true)
- setActive(true)
- clearTimeout(tid)
- inputRef.current?.focus()
- }, [tid])
-
- React.useEffect(() => {
- if (input) {
- setFocus()
- }
- }, [input])
-
- return (
-
🔊 Read It Aloud
- -- - - - ''' - components.html(documentHTML5, width=1280, height=500) - -# Game list and associated icons -games = ['Terraforming Mars', 'Twilight Imperium (Fourth Edition)', 'Scythe', 'Eclipse', 'Small World', 'Risk Legacy', 'Axis & Allies', 'Diplomacy', 'Pandemic Legacy: Season 1', 'Brass: Birmingham'] -icons = ['🪐', '🚀', '🤖', '🌌', '🧝♂️', '🗺️', '⚔️', '🤝', '🦠', '🏭'] - -# Main code -st.title('Top Ten Board Games with Map-Making Strategies 🗺️') - -for i, (game, icon) in enumerate(zip(games, icons)): - st.markdown(f"{i + 1}. {game} {icon}") - - # Expanders for each game to outline map rules or strategies - with st.expander(f"See Map Building & Gamification Strategy for {game}"): - text_to_speak = "" - - # ... Cut here for content change! - - if game == 'Terraforming Mars': - text_to_speak = "🪐💡 **Terraforming Mars** \n1️⃣ 🌱💧 Opt for plant-heavy and water tiles \n2️⃣ 🏭🌋 Position factories near volcanic areas \n3️⃣ 🌐💡 Control key parameters and energy grid \n4️⃣ 🛤️🌡️ Connect colonies and temperature control \n5️⃣ 🚀🎯 Upgrade spaceports and aim for synergies." - st.markdown(text_to_speak) - elif game == 'Twilight Imperium (Fourth Edition)': - text_to_speak = "🚀🌌 **Twilight Imperium** \n1️⃣ 🌌⚖️ Position fleets in strategic nebulas and balance resources \n2️⃣ 🏰🛡️ Fortify chokepoints and use PDS systems \n3️⃣ 🌐🌀 Effective trade routes and wormhole caution \n4️⃣ 🌟🌕 Prioritize Mecatol Rex and moon attacks \n5️⃣ 🛠️🤝 Optimize unit upgrades and forge alliances." - st.markdown(text_to_speak) - elif game == 'Scythe': - text_to_speak = "🤖🏞️ **Scythe** \n1️⃣ 🏞️🛠️ Choose starting positions and factory cards \n2️⃣ 🗺️🌊 Be aware of neighbors and control rivers \n3️⃣ 🏭🛡️ Maximize resource buildings and backdoor defense \n4️⃣ 🎯🌾 Focus objectives and manage food \n5️⃣ 🎲💎 Play probabilities and hunt treasures." - st.markdown(text_to_speak) - elif game == 'Eclipse': - text_to_speak = "🌌🌟 **Eclipse** \n1️⃣ 🌌🌟 Control sectors and central hexes \n2️⃣ 🛸🛡️ Build formidable fleets and defenses \n3️⃣ 🏭🔭 Prioritize production and research \n4️⃣ 🤝🌐 Trade and diplomacy \n5️⃣ 🌀🚀 Wormhole travel and expansion speed." - st.markdown(text_to_speak) - elif game == 'Small World': - text_to_speak = "🧝♂️🌍 **Small World** \n1️⃣ 🗺️👑 Choose realms and races wisely \n2️⃣ 🎭🛡️ Exploit powers and defend territories \n3️⃣ 🏆💎 Collect victory coins and treasures \n4️⃣ 🤝🌋 Forge short alliances and occupy mountains \n5️⃣ 🔄🏰 Know when to decline and occupy forts." - st.markdown(text_to_speak) - elif game == 'Risk Legacy': - text_to_speak = "🗺️⚔️ **Risk Legacy** \n1️⃣ 🗺️⚔️ Control continents and aggressive expansion \n2️⃣ 🛡️🔐 Fortify borders and use fortresses \n3️⃣ 📜🚀 Complete missions and airfields \n4️⃣ 🏆🔥 Collect victory points and scorched earth \n5️⃣ 🤝🔄 Alliances and betrayal." - st.markdown(text_to_speak) - elif game == 'Axis & Allies': - text_to_speak = "⚔️🌍 **Axis & Allies** \n1️⃣ ⚔️🌍 Strategic frontlines and global dominance \n2️⃣ 🏭📈 Resource management and economy \n3️⃣ 🛡️🚢 Naval blockades and fortress defenses \n4️⃣ 🎖️🎯 Focused objectives and key battles \n5️⃣ 🤝💥 Alliances and surprise attacks." - st.markdown(text_to_speak) - elif game == 'Diplomacy': - text_to_speak = "🤝🌍 **Diplomacy** \n1️⃣ 🤝📜 Negotiation and written orders \n2️⃣ 🗺️🛡️ Strategic positioning and defenses \n3️⃣ 🚢⚓ Naval forces and chokepoints \n4️⃣ 🏰🌐 Territory control and key regions \n5️⃣ 🔄🎭 Timing and deception." - st.markdown(text_to_speak) - elif game == 'Pandemic Legacy: Season 1': - text_to_speak = "🦠🌍 **Pandemic Legacy** \n1️⃣ 🦠🔬 Cure research and outbreak control \n2️⃣ 🌍🚁 Global movement and airlifts \n3️⃣ 🏥🛡️ Build research stations and quarantine \n4️⃣ 📜🎯 Complete objectives and bonus cards \n5️⃣ 🤝🔄 Teamwork and role synergy." - st.markdown(text_to_speak) - elif game == 'Brass: Birmingham': - text_to_speak = "🏭🛤️ **Brass Birmingham** \n1️⃣ 🏭🛤️ Industry and canal routes \n2️⃣ 📈🍺 Economic management and beer supply \n3️⃣ 🛠️🗺️ Optimize developments and map control \n4️⃣ 🤝💡 Partnerships and market strategy \n5️⃣ 🚂🏆 Railroads and victory points." - st.markdown(text_to_speak) - - # ... Cut here for content change! - - if st.button(f"🔊 Read {game}'s Strategies Aloud"): - generate_speech_textarea(text_to_speak) \ No newline at end of file diff --git a/spaces/awacke1/andite-pastel-mix/README.md b/spaces/awacke1/andite-pastel-mix/README.md deleted file mode 100644 index 484f9dbd298a952ad61332fdb57cf8d330a7f28d..0000000000000000000000000000000000000000 --- a/spaces/awacke1/andite-pastel-mix/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Andite Pastel Mix -emoji: 📚 -colorFrom: green -colorTo: blue -sdk: gradio -sdk_version: 3.17.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/ayaanzaveri/whisper-webui/README.md b/spaces/ayaanzaveri/whisper-webui/README.md deleted file mode 100644 index b551acd09ec7666cb1b13078a768001708127c0a..0000000000000000000000000000000000000000 --- a/spaces/ayaanzaveri/whisper-webui/README.md +++ /dev/null @@ -1,153 +0,0 @@ ---- -title: Whisper Webui -emoji: ⚡ -colorFrom: pink -colorTo: purple -sdk: gradio -sdk_version: 3.23.0 -app_file: app.py -pinned: false -license: apache-2.0 -duplicated_from: aadnk/whisper-webui ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference - -# Running Locally - -To run this program locally, first install Python 3.9+ and Git. Then install Pytorch 10.1+ and all the other dependencies: -``` -pip install -r requirements.txt -``` - -You can find detailed instructions for how to install this on Windows 10/11 [here (PDF)](docs/windows/install_win10_win11.pdf). - -Finally, run the full version (no audio length restrictions) of the app with parallel CPU/GPU enabled: -``` -python app.py --input_audio_max_duration -1 --server_name 127.0.0.1 --auto_parallel True -``` - -You can also run the CLI interface, which is similar to Whisper's own CLI but also supports the following additional arguments: -``` -python cli.py \ -[--vad {none,silero-vad,silero-vad-skip-gaps,silero-vad-expand-into-gaps,periodic-vad}] \ -[--vad_merge_window VAD_MERGE_WINDOW] \ -[--vad_max_merge_size VAD_MAX_MERGE_SIZE] \ -[--vad_padding VAD_PADDING] \ -[--vad_prompt_window VAD_PROMPT_WINDOW] -[--vad_cpu_cores NUMBER_OF_CORES] -[--vad_parallel_devices COMMA_DELIMITED_DEVICES] -[--auto_parallel BOOLEAN] -``` -In addition, you may also use URL's in addition to file paths as input. -``` -python cli.py --model large --vad silero-vad --language Japanese "https://www.youtube.com/watch?v=4cICErqqRSM" -``` - -Rather than supplying arguments to `app.py` or `cli.py`, you can also use the configuration file [config.json5](config.json5). See that file for more information. -If you want to use a different configuration file, you can use the `WHISPER_WEBUI_CONFIG` environment variable to specify the path to another file. - -## Google Colab - -You can also run this Web UI directly on [Google Colab](https://colab.research.google.com/drive/1qeTSvi7Bt_5RMm88ipW4fkcsMOKlDDss?usp=sharing), if you haven't got a GPU powerful enough to run the larger models. - -See the [colab documentation](docs/colab.md) for more information. - -## Parallel Execution - -You can also run both the Web-UI or the CLI on multiple GPUs in parallel, using the `vad_parallel_devices` option. This takes a comma-delimited list of -device IDs (0, 1, etc.) that Whisper should be distributed to and run on concurrently: -``` -python cli.py --model large --vad silero-vad --language Japanese \ ---vad_parallel_devices 0,1 "https://www.youtube.com/watch?v=4cICErqqRSM" -``` - -Note that this requires a VAD to function properly, otherwise only the first GPU will be used. Though you could use `period-vad` to avoid taking the hit -of running Silero-Vad, at a slight cost to accuracy. - -This is achieved by creating N child processes (where N is the number of selected devices), where Whisper is run concurrently. In `app.py`, you can also -set the `vad_process_timeout` option. This configures the number of seconds until a process is killed due to inactivity, freeing RAM and video memory. -The default value is 30 minutes. - -``` -python app.py --input_audio_max_duration -1 --vad_parallel_devices 0,1 --vad_process_timeout 3600 -``` - -To execute the Silero VAD itself in parallel, use the `vad_cpu_cores` option: -``` -python app.py --input_audio_max_duration -1 --vad_parallel_devices 0,1 --vad_process_timeout 3600 --vad_cpu_cores 4 -``` - -You may also use `vad_process_timeout` with a single device (`--vad_parallel_devices 0`), if you prefer to always free video memory after a period of time. - -### Auto Parallel - -You can also set `auto_parallel` to `True`. This will set `vad_parallel_devices` to use all the GPU devices on the system, and `vad_cpu_cores` to be equal to the number of -cores (up to 8): -``` -python app.py --input_audio_max_duration -1 --auto_parallel True -``` - -### Multiple Files - -You can upload multiple files either through the "Upload files" option, or as a playlist on YouTube. -Each audio file will then be processed in turn, and the resulting SRT/VTT/Transcript will be made available in the "Download" section. -When more than one file is processed, the UI will also generate a "All_Output" zip file containing all the text output files. - -# Docker - -To run it in Docker, first install Docker and optionally the NVIDIA Container Toolkit in order to use the GPU. -Then either use the GitLab hosted container below, or check out this repository and build an image: -``` -sudo docker build -t whisper-webui:1 . -``` - -You can then start the WebUI with GPU support like so: -``` -sudo docker run -d --gpus=all -p 7860:7860 whisper-webui:1 -``` - -Leave out "--gpus=all" if you don't have access to a GPU with enough memory, and are fine with running it on the CPU only: -``` -sudo docker run -d -p 7860:7860 whisper-webui:1 -``` - -# GitLab Docker Registry - -This Docker container is also hosted on GitLab: - -``` -sudo docker run -d --gpus=all -p 7860:7860 registry.gitlab.com/aadnk/whisper-webui:latest -``` - -## Custom Arguments - -You can also pass custom arguments to `app.py` in the Docker container, for instance to be able to use all the GPUs in parallel: -``` -sudo docker run -d --gpus all -p 7860:7860 \ ---mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \ ---restart=on-failure:15 registry.gitlab.com/aadnk/whisper-webui:latest \ -app.py --input_audio_max_duration -1 --server_name 0.0.0.0 --auto_parallel True \ ---default_vad silero-vad --default_model_name large -``` - -You can also call `cli.py` the same way: -``` -sudo docker run --gpus all \ ---mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \ ---mount type=bind,source=${PWD},target=/app/data \ -registry.gitlab.com/aadnk/whisper-webui:latest \ -cli.py --model large --auto_parallel True --vad silero-vad \ ---output_dir /app/data /app/data/YOUR-FILE-HERE.mp4 -``` - -## Caching - -Note that the models themselves are currently not included in the Docker images, and will be downloaded on the demand. -To avoid this, bind the directory /root/.cache/whisper to some directory on the host (for instance /home/administrator/.cache/whisper), where you can (optionally) -prepopulate the directory with the different Whisper models. -``` -sudo docker run -d --gpus=all -p 7860:7860 \ ---mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \ -registry.gitlab.com/aadnk/whisper-webui:latest -``` \ No newline at end of file diff --git a/spaces/azusarang/so-vits-svc-models-ba_P/diffusion/solver.py b/spaces/azusarang/so-vits-svc-models-ba_P/diffusion/solver.py deleted file mode 100644 index aaf0b21591b42fa903424f8d44fef88d7d791e57..0000000000000000000000000000000000000000 --- a/spaces/azusarang/so-vits-svc-models-ba_P/diffusion/solver.py +++ /dev/null @@ -1,195 +0,0 @@ -import os -import time -import numpy as np -import torch -import librosa -from diffusion.logger.saver import Saver -from diffusion.logger import utils -from torch import autocast -from torch.cuda.amp import GradScaler - -def test(args, model, vocoder, loader_test, saver): - print(' [*] testing...') - model.eval() - - # losses - test_loss = 0. - - # intialization - num_batches = len(loader_test) - rtf_all = [] - - # run - with torch.no_grad(): - for bidx, data in enumerate(loader_test): - fn = data['name'][0].split("/")[-1] - speaker = data['name'][0].split("/")[-2] - print('--------') - print('{}/{} - {}'.format(bidx, num_batches, fn)) - - # unpack data - for k in data.keys(): - if not k.startswith('name'): - data[k] = data[k].to(args.device) - print('>>', data['name'][0]) - - # forward - st_time = time.time() - mel = model( - data['units'], - data['f0'], - data['volume'], - data['spk_id'], - gt_spec=None, - infer=True, - infer_speedup=args.infer.speedup, - method=args.infer.method) - signal = vocoder.infer(mel, data['f0']) - ed_time = time.time() - - # RTF - run_time = ed_time - st_time - song_time = signal.shape[-1] / args.data.sampling_rate - rtf = run_time / song_time - print('RTF: {} | {} / {}'.format(rtf, run_time, song_time)) - rtf_all.append(rtf) - - # loss - for i in range(args.train.batch_size): - loss = model( - data['units'], - data['f0'], - data['volume'], - data['spk_id'], - gt_spec=data['mel'], - infer=False) - test_loss += loss.item() - - # log mel - saver.log_spec(f"{speaker}_{fn}.wav", data['mel'], mel) - - # log audi - path_audio = data['name_ext'][0] - audio, sr = librosa.load(path_audio, sr=args.data.sampling_rate) - if len(audio.shape) > 1: - audio = librosa.to_mono(audio) - audio = torch.from_numpy(audio).unsqueeze(0).to(signal) - saver.log_audio({f"{speaker}_{fn}_gt.wav": audio,f"{speaker}_{fn}_pred.wav": signal}) - # report - test_loss /= args.train.batch_size - test_loss /= num_batches - - # check - print(' [test_loss] test_loss:', test_loss) - print(' Real Time Factor', np.mean(rtf_all)) - return test_loss - - -def train(args, initial_global_step, model, optimizer, scheduler, vocoder, loader_train, loader_test): - # saver - saver = Saver(args, initial_global_step=initial_global_step) - - # model size - params_count = utils.get_network_paras_amount({'model': model}) - saver.log_info('--- model size ---') - saver.log_info(params_count) - - # run - num_batches = len(loader_train) - model.train() - saver.log_info('======= start training =======') - scaler = GradScaler() - if args.train.amp_dtype == 'fp32': - dtype = torch.float32 - elif args.train.amp_dtype == 'fp16': - dtype = torch.float16 - elif args.train.amp_dtype == 'bf16': - dtype = torch.bfloat16 - else: - raise ValueError(' [x] Unknown amp_dtype: ' + args.train.amp_dtype) - saver.log_info("epoch|batch_idx/num_batches|output_dir|batch/s|lr|time|step") - for epoch in range(args.train.epochs): - for batch_idx, data in enumerate(loader_train): - saver.global_step_increment() - optimizer.zero_grad() - - # unpack data - for k in data.keys(): - if not k.startswith('name'): - data[k] = data[k].to(args.device) - - # forward - if dtype == torch.float32: - loss = model(data['units'].float(), data['f0'], data['volume'], data['spk_id'], - aug_shift = data['aug_shift'], gt_spec=data['mel'].float(), infer=False) - else: - with autocast(device_type=args.device, dtype=dtype): - loss = model(data['units'], data['f0'], data['volume'], data['spk_id'], - aug_shift = data['aug_shift'], gt_spec=data['mel'], infer=False) - - # handle nan loss - if torch.isnan(loss): - raise ValueError(' [x] nan loss ') - else: - # backpropagate - if dtype == torch.float32: - loss.backward() - optimizer.step() - else: - scaler.scale(loss).backward() - scaler.step(optimizer) - scaler.update() - scheduler.step() - - # log loss - if saver.global_step % args.train.interval_log == 0: - current_lr = optimizer.param_groups[0]['lr'] - saver.log_info( - 'epoch: {} | {:3d}/{:3d} | {} | batch/s: {:.2f} | lr: {:.6} | loss: {:.3f} | time: {} | step: {}'.format( - epoch, - batch_idx, - num_batches, - args.env.expdir, - args.train.interval_log/saver.get_interval_time(), - current_lr, - loss.item(), - saver.get_total_time(), - saver.global_step - ) - ) - - saver.log_value({ - 'train/loss': loss.item() - }) - - saver.log_value({ - 'train/lr': current_lr - }) - - # validation - if saver.global_step % args.train.interval_val == 0: - optimizer_save = optimizer if args.train.save_opt else None - - # save latest - saver.save_model(model, optimizer_save, postfix=f'{saver.global_step}') - last_val_step = saver.global_step - args.train.interval_val - if last_val_step % args.train.interval_force_save != 0: - saver.delete_model(postfix=f'{last_val_step}') - - # run testing set - test_loss = test(args, model, vocoder, loader_test, saver) - - # log loss - saver.log_info( - ' ---
-
-
-# BERTIN GPT-J-6B
-
-
-
-BERTIN proporciona una serie de modelos de lenguaje en Español entrenados en abierto.
-
-Este modelo ha sido entrenado con [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax) en TPUs proporcionadas por Google a través del programa Tensor Research Cloud, a partir del modelo [GPT-J de EleutherAI](https://huggingface.co/EleutherAI/gpt-j-6B) con el corpus [mC4-es-sampled (gaussian)](https://huggingface.co/datasets/bertin-project/mc4-es-sampled). Esta demo funciona sobre una GPU proporcionada por HuggingFace.
-
-Para más información, visite el [repositorio del modelo](https://huggingface.co/bertin-project/bertin-gpt-j-6B).
-
-# Configuración
-""".strip()
-
-PROMPT_BOX = "Introduzca su texto..."
-EXAMPLES = [
- "¿Cuál es la capital de Francia? Respuesta:",
- """Los templos egipcios fueron construidos para el culto oficial de los dioses y la conmemoración de los faraones del Antiguo Egipto en las regiones bajo su dominio. Los templos eran vistos como el hogar de los dioses o faraones deificados a quienes eran dedicados, y en ellos los faraones y el clero egipcio llevaban a cabo diversos rituales, las funciones centrales de la religión egipcia: realizar ofrendas a sus dioses, recrear pasajes mitológicos mediante festivales y protegerse de las fuerzas del caos. Estos rituales eran vistos como necesarios para que los dioses mantuvieran la maat, el orden divino del universo.
-
-El cuidado del hogar de los dioses era obligación de los faraones, que dedicaron ingentes cantidades de recursos para la construcción y el mantenimiento de los templos. Por necesidad, los faraones delegaban la mayoría de los rituales en una amplia casta sacerdotal, aunque la mayor parte del pueblo llano permanecía al margen de la participación directa en las ceremonias por tener prohibido el acceso a las zonas más sagradas de los templos. A pesar de ello, el templo siempre fue un importante centro religioso para todos los egipcios, que iban a ellos a rezar, realizar ofrendas y buscar la guía de los oráculos.
-
-Pregunta: ¿Quién cuidaba del hogar los dioses?
-Respuesta:""",
-]
-
-
-def style():
- st.markdown("""
-
- """, unsafe_allow_html=True)
-
-
-class Normalizer:
- def remove_repetitions(self, text):
- """Remove repetitions"""
- first_ocurrences = []
- for sentence in text.split("."):
- if sentence not in first_ocurrences:
- first_ocurrences.append(sentence)
- return '.'.join(first_ocurrences)
-
- def trim_last_sentence(self, text):
- """Trim last sentence if incomplete"""
- return text[:text.rfind(".") + 1]
-
- def clean_txt(self, text):
- return self.trim_last_sentence(self.remove_repetitions(text))
-
-
-class TextGeneration:
- def __init__(self):
- self.tokenizer = None
- self.generator = None
- self.task = "text-generation"
- self.model_name_or_path = MODEL_NAME
- set_seed(42)
-
- def load(self):
- logger.info("Loading model...")
- self.tokenizer = AutoTokenizer.from_pretrained(
- self.model_name_or_path, use_auth_token=HF_AUTH_TOKEN if HF_AUTH_TOKEN else None,
- )
- self.model = AutoModelForCausalLM.from_pretrained(
- self.model_name_or_path, use_auth_token=HF_AUTH_TOKEN if HF_AUTH_TOKEN else None,
- pad_token_id=self.tokenizer.eos_token_id, eos_token_id=self.tokenizer.eos_token_id,
- torch_dtype=DTYPE, low_cpu_mem_usage=False if DEVICE == "cpu" else True
- ).to(device=DEVICE, non_blocking=False)
- _ = self.model.eval()
- device_number = -1 if DEVICE == "cpu" else int(DEVICE.split(":")[-1])
- self.generator = pipeline(self.task, model=self.model, tokenizer=self.tokenizer, device=device_number)
- logger.info("Loading model done.")
- # with torch.no_grad():
- # tokens = tokenizer.encode(prompt, return_tensors='pt').to(device=device, non_blocking=True)
- # gen_tokens = self.model.generate(tokens, do_sample=True, temperature=0.8, max_length=128)
- # generated = tokenizer.batch_decode(gen_tokens)[0]
-
- # return generated
-
-
- def generate(self, prompt, generation_kwargs):
- max_length = len(self.tokenizer(prompt)["input_ids"]) + generation_kwargs["max_length"]
- generation_kwargs["max_length"] = min(max_length, self.model.config.n_positions)
- # generation_kwargs["num_return_sequences"] = 1
- # generation_kwargs["return_full_text"] = False
- return self.generator(
- prompt,
- **generation_kwargs,
- )[0]["generated_text"]
-
-
-#@st.cache(hash_funcs={torch.nn.parameter.Parameter: lambda _: None})
-#@st.cache(allow_output_mutation=True)
-@st.cache(allow_output_mutation=True, hash_funcs={TextGeneration: lambda _: None})
-def load_text_generator():
- text_generator = TextGeneration()
- text_generator.load()
- return text_generator
-
-
-def main():
- st.set_page_config(
- page_title="BERTIN-GPT-J-6B",
- page_icon="🇪🇸",
- layout="wide",
- initial_sidebar_state="expanded"
- )
- style()
- generator = load_text_generator()
- st.sidebar.markdown(SIDEBAR_INFO, unsafe_allow_html=True)
-
- max_length = st.sidebar.slider(
- label='Longitud máxima',
- help="Número máximo (aproximado) de palabras a generar.",
- min_value=1,
- max_value=MAX_LENGTH,
- value=50,
- step=1
- )
- top_k = st.sidebar.slider(
- label='Top-k',
- help="Número de palabras con alta probabilidad a mantener para el filtrado `top-k`",
- min_value=40,
- max_value=80,
- value=50,
- step=1
- )
- top_p = st.sidebar.slider(
- label='Top-p',
- help="Solo las palabras más probables con probabilidades que sumen `top_p` o más se mantienen para la generación.",
- min_value=0.0,
- max_value=1.0,
- value=0.95,
- step=0.01
- )
- temperature = st.sidebar.slider(
- label='Temperatura',
- help="Valor utilizado para modular las probabilidades de las siguientes palabras generadas.",
- min_value=0.1,
- max_value=10.0,
- value=0.8,
- step=0.05
- )
- do_sample = st.sidebar.selectbox(
- label='¿Muestrear?',
- options=(True, False),
- help="Si no se muestrea se usará una decodificación voraz (_greedy_).",
- )
- do_clean = st.sidebar.selectbox(
- label='¿Limpiar texto?',
- options=(True, False),
- help="Si eliminar o no las palabras repetidas y recortar las últimas frases sin terminar.",
- )
- generation_kwargs = {
- "max_length": max_length,
- "top_k": top_k,
- "top_p": top_p,
- "temperature": temperature,
- "do_sample": do_sample,
- "do_clean": do_clean,
- }
- st.markdown(HEADER_INFO)
- prompts = EXAMPLES + ["Personalizado"]
- prompt = st.selectbox('Ejemplos', prompts, index=len(prompts) - 1)
-
- if prompt == "Personalizado":
- prompt_box = PROMPT_BOX
- else:
- prompt_box = prompt
-
- text = st.text_area("Texto", prompt_box)
- generation_kwargs_ph = st.empty()
- cleaner = Normalizer()
- if st.button("¡Generar!"):
- with st.spinner(text="Generando..."):
- generation_kwargs_ph.markdown(", ".join([f"`{k}`: {v}" for k, v in generation_kwargs.items()]))
- if text:
- generated_text = generator.generate(text, generation_kwargs)
- if do_clean:
- generated_text = cleaner.clean_txt(generated_text)
- if generated_text.strip().startswith(text):
- generated_text = generated_text.replace(text, "", 1).strip()
- st.markdown(
- f'' - f'{text} ' - f'{generated_text}' - f'
', - unsafe_allow_html=True - ) - -if __name__ == '__main__': - main() diff --git a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/utils/json_logger.py b/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/utils/json_logger.py deleted file mode 100644 index 0afd0b45df736866c49473db78286685d77660ac..0000000000000000000000000000000000000000 --- a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/utils/json_logger.py +++ /dev/null @@ -1,383 +0,0 @@ -""" -References: - https://medium.com/analytics-vidhya/creating-a-custom-logging-mechanism-for-real-time-object-detection-using-tdd-4ca2cfcd0a2f -""" -import json -from os import makedirs -from os.path import exists, join -from datetime import datetime - - -class JsonMeta(object): - HOURS = 3 - MINUTES = 59 - SECONDS = 59 - PATH_TO_SAVE = 'LOGS' - DEFAULT_FILE_NAME = 'remaining' - - -class BaseJsonLogger(object): - """ - This is the base class that returns __dict__ of its own - it also returns the dicts of objects in the attributes that are list instances - - """ - - def dic(self): - # returns dicts of objects - out = {} - for k, v in self.__dict__.items(): - if hasattr(v, 'dic'): - out[k] = v.dic() - elif isinstance(v, list): - out[k] = self.list(v) - else: - out[k] = v - return out - - @staticmethod - def list(values): - # applies the dic method on items in the list - return [v.dic() if hasattr(v, 'dic') else v for v in values] - - -class Label(BaseJsonLogger): - """ - For each bounding box there are various categories with confidences. Label class keeps track of that information. - """ - - def __init__(self, category: str, confidence: float): - self.category = category - self.confidence = confidence - - -class Bbox(BaseJsonLogger): - """ - This module stores the information for each frame and use them in JsonParser - Attributes: - labels (list): List of label module. - top (int): - left (int): - width (int): - height (int): - - Args: - bbox_id (float): - top (int): - left (int): - width (int): - height (int): - - References: - Check Label module for better understanding. - - - """ - - def __init__(self, bbox_id, top, left, width, height): - self.labels = [] - self.bbox_id = bbox_id - self.top = top - self.left = left - self.width = width - self.height = height - - def add_label(self, category, confidence): - # adds category and confidence only if top_k is not exceeded. - self.labels.append(Label(category, confidence)) - - def labels_full(self, value): - return len(self.labels) == value - - -class Frame(BaseJsonLogger): - """ - This module stores the information for each frame and use them in JsonParser - Attributes: - timestamp (float): The elapsed time of captured frame - frame_id (int): The frame number of the captured video - bboxes (list of Bbox objects): Stores the list of bbox objects. - - References: - Check Bbox class for better information - - Args: - timestamp (float): - frame_id (int): - - """ - - def __init__(self, frame_id: int, timestamp: float = None): - self.frame_id = frame_id - self.timestamp = timestamp - self.bboxes = [] - - def add_bbox(self, bbox_id: int, top: int, left: int, width: int, height: int): - bboxes_ids = [bbox.bbox_id for bbox in self.bboxes] - if bbox_id not in bboxes_ids: - self.bboxes.append(Bbox(bbox_id, top, left, width, height)) - else: - raise ValueError("Frame with id: {} already has a Bbox with id: {}".format(self.frame_id, bbox_id)) - - def add_label_to_bbox(self, bbox_id: int, category: str, confidence: float): - bboxes = {bbox.id: bbox for bbox in self.bboxes} - if bbox_id in bboxes.keys(): - res = bboxes.get(bbox_id) - res.add_label(category, confidence) - else: - raise ValueError('the bbox with id: {} does not exists!'.format(bbox_id)) - - -class BboxToJsonLogger(BaseJsonLogger): - """ - ُ This module is designed to automate the task of logging jsons. An example json is used - to show the contents of json file shortly - Example: - { - "video_details": { - "frame_width": 1920, - "frame_height": 1080, - "frame_rate": 20, - "video_name": "/home/gpu/codes/MSD/pedestrian_2/project/public/camera1.avi" - }, - "frames": [ - { - "frame_id": 329, - "timestamp": 3365.1254 - "bboxes": [ - { - "labels": [ - { - "category": "pedestrian", - "confidence": 0.9 - } - ], - "bbox_id": 0, - "top": 1257, - "left": 138, - "width": 68, - "height": 109 - } - ] - }], - - Attributes: - frames (dict): It's a dictionary that maps each frame_id to json attributes. - video_details (dict): information about video file. - top_k_labels (int): shows the allowed number of labels - start_time (datetime object): we use it to automate the json output by time. - - Args: - top_k_labels (int): shows the allowed number of labels - - """ - - def __init__(self, top_k_labels: int = 1): - self.frames = {} - self.video_details = self.video_details = dict(frame_width=None, frame_height=None, frame_rate=None, - video_name=None) - self.top_k_labels = top_k_labels - self.start_time = datetime.now() - - def set_top_k(self, value): - self.top_k_labels = value - - def frame_exists(self, frame_id: int) -> bool: - """ - Args: - frame_id (int): - - Returns: - bool: true if frame_id is recognized - """ - return frame_id in self.frames.keys() - - def add_frame(self, frame_id: int, timestamp: float = None) -> None: - """ - Args: - frame_id (int): - timestamp (float): opencv captured frame time property - - Raises: - ValueError: if frame_id would not exist in class frames attribute - - Returns: - None - - """ - if not self.frame_exists(frame_id): - self.frames[frame_id] = Frame(frame_id, timestamp) - else: - raise ValueError("Frame id: {} already exists".format(frame_id)) - - def bbox_exists(self, frame_id: int, bbox_id: int) -> bool: - """ - Args: - frame_id: - bbox_id: - - Returns: - bool: if bbox exists in frame bboxes list - """ - bboxes = [] - if self.frame_exists(frame_id=frame_id): - bboxes = [bbox.bbox_id for bbox in self.frames[frame_id].bboxes] - return bbox_id in bboxes - - def find_bbox(self, frame_id: int, bbox_id: int): - """ - - Args: - frame_id: - bbox_id: - - Returns: - bbox_id (int): - - Raises: - ValueError: if bbox_id does not exist in the bbox list of specific frame. - """ - if not self.bbox_exists(frame_id, bbox_id): - raise ValueError("frame with id: {} does not contain bbox with id: {}".format(frame_id, bbox_id)) - bboxes = {bbox.bbox_id: bbox for bbox in self.frames[frame_id].bboxes} - return bboxes.get(bbox_id) - - def add_bbox_to_frame(self, frame_id: int, bbox_id: int, top: int, left: int, width: int, height: int) -> None: - """ - - Args: - frame_id (int): - bbox_id (int): - top (int): - left (int): - width (int): - height (int): - - Returns: - None - - Raises: - ValueError: if bbox_id already exist in frame information with frame_id - ValueError: if frame_id does not exist in frames attribute - """ - if self.frame_exists(frame_id): - frame = self.frames[frame_id] - if not self.bbox_exists(frame_id, bbox_id): - frame.add_bbox(bbox_id, top, left, width, height) - else: - raise ValueError( - "frame with frame_id: {} already contains the bbox with id: {} ".format(frame_id, bbox_id)) - else: - raise ValueError("frame with frame_id: {} does not exist".format(frame_id)) - - def add_label_to_bbox(self, frame_id: int, bbox_id: int, category: str, confidence: float): - """ - Args: - frame_id: - bbox_id: - category: - confidence: the confidence value returned from yolo detection - - Returns: - None - - Raises: - ValueError: if labels quota (top_k_labels) exceeds. - """ - bbox = self.find_bbox(frame_id, bbox_id) - if not bbox.labels_full(self.top_k_labels): - bbox.add_label(category, confidence) - else: - raise ValueError("labels in frame_id: {}, bbox_id: {} is fulled".format(frame_id, bbox_id)) - - def add_video_details(self, frame_width: int = None, frame_height: int = None, frame_rate: int = None, - video_name: str = None): - self.video_details['frame_width'] = frame_width - self.video_details['frame_height'] = frame_height - self.video_details['frame_rate'] = frame_rate - self.video_details['video_name'] = video_name - - def output(self): - output = {'video_details': self.video_details} - result = list(self.frames.values()) - output['frames'] = [item.dic() for item in result] - return output - - def json_output(self, output_name): - """ - Args: - output_name: - - Returns: - None - - Notes: - It creates the json output with `output_name` name. - """ - if not output_name.endswith('.json'): - output_name += '.json' - with open(output_name, 'w') as file: - json.dump(self.output(), file) - file.close() - - def set_start(self): - self.start_time = datetime.now() - - def schedule_output_by_time(self, output_dir=JsonMeta.PATH_TO_SAVE, hours: int = 0, minutes: int = 0, - seconds: int = 60) -> None: - """ - Notes: - Creates folder and then periodically stores the jsons on that address. - - Args: - output_dir (str): the directory where output files will be stored - hours (int): - minutes (int): - seconds (int): - - Returns: - None - - """ - end = datetime.now() - interval = 0 - interval += abs(min([hours, JsonMeta.HOURS]) * 3600) - interval += abs(min([minutes, JsonMeta.MINUTES]) * 60) - interval += abs(min([seconds, JsonMeta.SECONDS])) - diff = (end - self.start_time).seconds - - if diff > interval: - output_name = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '.json' - if not exists(output_dir): - makedirs(output_dir) - output = join(output_dir, output_name) - self.json_output(output_name=output) - self.frames = {} - self.start_time = datetime.now() - - def schedule_output_by_frames(self, frames_quota, frame_counter, output_dir=JsonMeta.PATH_TO_SAVE): - """ - saves as the number of frames quota increases higher. - :param frames_quota: - :param frame_counter: - :param output_dir: - :return: - """ - pass - - def flush(self, output_dir): - """ - Notes: - We use this function to output jsons whenever possible. - like the time that we exit the while loop of opencv. - - Args: - output_dir: - - Returns: - None - - """ - filename = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '-remaining.json' - output = join(output_dir, filename) - self.json_output(output_name=output) diff --git a/spaces/bioriAsaeru/text-to-voice/Corel Draw X5 Free Download With Keygen 2021.md b/spaces/bioriAsaeru/text-to-voice/Corel Draw X5 Free Download With Keygen 2021.md deleted file mode 100644 index f5f398b8aea5c886fcd9cdd4740cfcc9c2a5a26c..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Corel Draw X5 Free Download With Keygen 2021.md +++ /dev/null @@ -1,59 +0,0 @@ - -Corel Draw X5 Free Download With Keygen: Is It Worth It?
-If you are looking for a powerful and versatile graphic design software, you may have heard of Corel Draw X5. This program offers a wide range of tools and features to create stunning logos, illustrations, flyers, web graphics and more. However, the official version of Corel Draw X5 is not cheap, and you may be tempted to download it for free using a keygen.
-Corel Draw X5 Free Download With Keygen
Download Zip ☆☆☆ https://urloso.com/2uyRcW
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- -Therefore, if you want to download Corel Draw X5 free with keygen, you should think twice before doing it. You may end up regretting it later, when you face legal problems, system errors, viral threats or no updates. Instead, you should either buy the official version of Corel Draw X5 or use a free alternative that can provide similar features and functions.
-How to Install Corel Draw X5 with Keygen?
-If you have already downloaded Corel Draw X5 with keygen and decided to install it on your computer, you should be aware of the possible consequences. Installing a pirated software with a keygen is not a simple or safe process. You may encounter various difficulties or dangers along the way. Here are some of the steps you need to follow to install Corel Draw X5 with keygen:
--
-
- Disable your antivirus and internet connection. This is necessary because most antivirus programs will detect the keygen as a threat and block it from running. Also, you need to prevent the software from connecting to the internet, as it may detect that it is not activated legally and stop working. -
- Extract the downloaded file. You will need a program like WinRAR or 7-Zip to extract the compressed file that contains the software and the keygen. Be careful not to open any suspicious files that may contain malware or viruses. -
- Run the setup.exe file. This will start the installation process of the software. You will need to choose the installation options and accept the terms and conditions. You will also need to select "I don't have a serial number" when prompted. -
- Run the keygen.exe file. This will open the keygen program that will generate a serial number and an activation code for the software. You will need to copy and paste these codes into the corresponding fields in the software activation dialog. You will also need to copy the installation code from the software into the keygen. -
- Click on activate. This will complete the activation process of the software using the keygen. You should see a message that says "Activation successful". You can then close the keygen and start using the software. -
Is It Worth It?
-After reading this article, you may have realized that using Corel Draw X5 free download with keygen is not worth it at all. It is a risky and illegal way to get a graphic design software that can cause more problems than benefits. You may end up facing legal issues, system errors, viral threats or no updates that will ruin your experience and work.
-Instead of using a keygen, you should consider buying the official version of Corel Draw X5 or using a free alternative that can offer similar features and functions. These options are legal, safe and reliable, and they will allow you to enjoy graphic design without any worries or regrets.
- - ---> ServiceClient failure for DeepLeo[/ERROR] - - ---> ServiceClient failure for DeepLeo[/ERROR] -How to Use Corel Draw X5 for Graphic Design?
-Once you have installed Corel Draw X5 on your computer, you can start using it for various graphic design projects. Corel Draw X5 is a vector-based software that allows you to create and edit logos, illustrations, flyers, web graphics and more. You can also import and export files in different formats, such as JPG, PNG, PDF, EPS and AI.
-Corel Draw X5 has a user-friendly interface that consists of a menu bar, a toolbox, a property bar, a status bar and a dockers panel. You can customize the workspace according to your preferences and needs. You can also access various tools and features from the menu bar or the toolbox, such as the shape tool, the text tool, the pen tool, the fill tool and the color palette.
-To create a new document in Corel Draw X5, you need to go to File > New and choose the size, resolution and color mode of your document. You can also use one of the templates or presets available in the software. To draw or edit objects on your document, you need to use the tools from the toolbox and adjust their properties from the property bar or the dockers panel. You can also use the commands from the menu bar or the keyboard shortcuts to perform various actions, such as copy, paste, rotate, scale and align.
-How to Learn Corel Draw X5?
-If you are new to Corel Draw X5 or want to improve your skills, you may need some guidance and resources to learn how to use this software effectively. There are many ways to learn Corel Draw X5, such as:
--
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- Using the help menu. Corel Draw X5 has a built-in help menu that provides you with useful information and tips about the software. You can access it by clicking on Help > Help Topics or pressing F1 on your keyboard. You can also use the search box or the index to find specific topics or keywords. -
- Watching online tutorials. There are many online tutorials that teach you how to use Corel Draw X5 for different purposes and projects. You can find them on YouTube, Udemy, Skillshare or other platforms. Some of them are free and some of them are paid. You can choose the ones that suit your level and interest. -
- Reading books or blogs. There are also many books or blogs that cover various aspects of Corel Draw X5 and graphic design in general. You can buy them online or in bookstores, or read them online for free. Some of them are written by experts or professionals who share their knowledge and experience. -
- Joining online communities. There are also many online communities where you can interact with other users of Corel Draw X5 and ask questions, share tips, get feedback or join challenges. You can find them on Facebook, Reddit, Quora or other platforms. You can also join the official Corel community at https://community.coreldraw.com/ -
Conclusion
-Corel Draw X5 is a great graphic design software that can help you create amazing projects for personal or professional use. However, using Corel Draw X5 free download with keygen is not a good idea, as it can expose you to legal issues, system errors, viral threats or no updates. Instead of using a keygen, you should consider buying the official version of Corel Draw X5 or using a free alternative that can offer similar features and functions. These options are legal, safe and reliable, and they will allow you to enjoy graphic design without any worries or regrets.
-Corel Draw X5 is a great graphic design software that can help you create amazing projects for personal or professional use. However, using Corel Draw X5 free download with keygen is not a good idea, as it can expose you to legal issues, system errors, viral threats or no updates. Instead of using a keygen, you should consider buying the official version of Corel Draw X5 or using a free alternative that can offer similar features and functions. These options are legal, safe and reliable, and they will allow you to enjoy graphic design without any worries or regrets.
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\ No newline at end of file diff --git a/spaces/bioriAsaeru/text-to-voice/Download EXCLUSIVE Font Vni-romans.shx.md b/spaces/bioriAsaeru/text-to-voice/Download EXCLUSIVE Font Vni-romans.shx.md deleted file mode 100644 index 05eb044a9b65da562c2b0b49a24cdb841a21fed2..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Download EXCLUSIVE Font Vni-romans.shx.md +++ /dev/null @@ -1,6 +0,0 @@ -
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- -Anyone who owns FlukeView Forms version 3.0 or later can get a free software upgrade by following the links and downloading the software from this site. Basic ... http://www.fluke.com/software/visual/ -http://www.fluke.com/software/visual/downloads/download.jsp 8a78ff9644
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- diff --git a/spaces/boda/arabic-names-generator/model/generate.py b/spaces/boda/arabic-names-generator/model/generate.py deleted file mode 100644 index d9cd1cb08bf9033a43dcbae216c6f144c60e7bdd..0000000000000000000000000000000000000000 --- a/spaces/boda/arabic-names-generator/model/generate.py +++ /dev/null @@ -1,118 +0,0 @@ -import torch -from model.layers import * -# from layers import * - -import pathlib -from pathlib import Path - - -CONTEXT_SIZE = 5 -n_hidden = 100 -n_embed = 10 -EN_VOCAB_SIZE = 27 -AR_VOCAB_SIZE = 37 -ACTIVATION = 'relu' - -ar_itos = {0: '.', 1: 'ء', 2: 'آ', 3: 'أ', 4: 'ؤ', 5: 'إ', 6: 'ئ', 7: 'ا', 8: 'ب', 9: 'ة', 10: 'ت', 11: 'ث', 12: 'ج', 13: 'ح', 14: 'خ', 15: 'د', 16: 'ذ', 17: 'ر', 18: 'ز', 19: 'س', 20: 'ش', 21: 'ص', 22: 'ض', 23: 'ط', 24: 'ظ', 25: 'ع', 26: 'غ', 27: 'ف', 28: 'ق', 29: 'ك', 30: 'ل', 31: 'م', 32: 'ن', 33: 'ه', 34: 'و', 35: 'ى', 36: 'ي'} -en_itos= {0: '.', 1: '-', 2: 'a', 3: 'b', 4: 'c', 5: 'd', 6: 'e', 7: 'f', 8: 'g', 9: 'h', 10: 'i', 11: 'j', 12: 'k', 13: 'l', 14: 'm', 15: 'n', 16: 'o', 17: 'p', 18: 'q', 19: 'r', 20: 's', 21: 't', 22: 'u', 23: 'v', 24: 'w', 25: 'y', 26: 'z'} -arabic_layers = [ - Linear(CONTEXT_SIZE*n_embed , n_hidden),BatchNorm(n_hidden), Activation(ACTIVATION), - Linear(n_hidden, n_hidden),BatchNorm(n_hidden), Activation(ACTIVATION), - Linear(n_hidden, n_hidden),BatchNorm(n_hidden), Activation(ACTIVATION), - Linear(n_hidden , AR_VOCAB_SIZE) -] - -english_layers = [ - Linear(CONTEXT_SIZE*n_embed , n_hidden),BatchNorm(n_hidden), Activation(ACTIVATION), - Linear(n_hidden, n_hidden),BatchNorm(n_hidden), Activation(ACTIVATION), - Linear(n_hidden, n_hidden),BatchNorm(n_hidden), Activation(ACTIVATION), - Linear(n_hidden , EN_VOCAB_SIZE) -] - - - -parent_path = Path(__file__).parent -arabic_dict = torch.load(Path.joinpath(parent_path,'weights/ar_dataset_weights.pt')) -english_dict= torch.load(Path.joinpath(parent_path,'weights/en_dataset_weights.pt')) - -## Weights -arabic_params = arabic_dict['params'] -english_params = english_dict['params'] - -## Batch norm means ans stds -arabic_bn_conf = arabic_dict['bn_conf'] -english_bn_conf = english_dict['bn_conf'] - - -# Load embeddings -arabic_embedding = arabic_params[0] -english_embedding = english_params[0] - -## Load weights -j = 0 -for i,l in enumerate(arabic_layers): - l.set_parameters( arabic_params[i+1] ) - if l.__class__.__name__ == "BatchNorm": - l.set_mean_std(arabic_bn_conf[j]) - j+=1 - -j = 0 -for i,l in enumerate(english_layers): - l.set_parameters( english_params[i+1] ) - if l.__class__.__name__ == "BatchNorm": - l.set_mean_std(english_bn_conf[j]) - j+=1 - -def forward(x_batch, is_training,lang): - - if lang =='ar': - embedding = arabic_embedding - layers = arabic_layers - - elif lang =='en': - embedding = english_embedding - layers = english_layers - - - x_batch = embedding[x_batch] - x = x_batch.view(x_batch.shape[0], -1) - for layer in layers: - x = layer(x, is_training) - return x - - -def generate_name(lang): - - w = '' - last_ch = [0]* CONTEXT_SIZE - while True: - last_ch = torch.tensor(last_ch).unsqueeze(0) - - - x = forward(last_ch, False, lang) - p = torch.softmax(x, dim=1) - next_ch = torch.multinomial(p, num_samples=1, replacement=True).item() - if lang =='ar': - w += ar_itos[next_ch] - elif lang == 'en': - w += en_itos[next_ch] - - last_ch = last_ch.clone().detach().squeeze(0) - last_ch = last_ch.tolist() - last_ch = last_ch[1:] + [next_ch] - if next_ch == 0: - break - - return w[:-1] - -def generate_names(n,lang): - ret = [] - for i in range(n): - ret.append(generate_name(lang)) - - return ret - - -if __name__ == '__main__': - - pass \ No newline at end of file diff --git a/spaces/bookbot/Image-Upscaling-Playground/app.py b/spaces/bookbot/Image-Upscaling-Playground/app.py deleted file mode 100644 index 1f3736667bfd4e5ac6d9ee2ef9b95416cb80f9c0..0000000000000000000000000000000000000000 --- a/spaces/bookbot/Image-Upscaling-Playground/app.py +++ /dev/null @@ -1,85 +0,0 @@ -import numpy as np -import cv2 -import onnxruntime -import gradio as gr - - -def pre_process(img: np.array) -> np.array: - # H, W, C -> C, H, W - img = np.transpose(img[:, :, 0:3], (2, 0, 1)) - # C, H, W -> 1, C, H, W - img = np.expand_dims(img, axis=0).astype(np.float32) - return img - - -def post_process(img: np.array) -> np.array: - # 1, C, H, W -> C, H, W - img = np.squeeze(img) - # C, H, W -> H, W, C - img = np.transpose(img, (1, 2, 0))[:, :, ::-1].astype(np.uint8) - return img - - -def inference(model_path: str, img_array: np.array) -> np.array: - options = onnxruntime.SessionOptions() - options.intra_op_num_threads = 1 - options.inter_op_num_threads = 1 - ort_session = onnxruntime.InferenceSession(model_path, options) - ort_inputs = {ort_session.get_inputs()[0].name: img_array} - ort_outs = ort_session.run(None, ort_inputs) - - return ort_outs[0] - - -def convert_pil_to_cv2(image): - # pil_image = image.convert("RGB") - open_cv_image = np.array(image) - # RGB to BGR - open_cv_image = open_cv_image[:, :, ::-1].copy() - return open_cv_image - - -def upscale(image, model): - model_path = f"models/{model}.ort" - img = convert_pil_to_cv2(image) - if img.ndim == 2: - img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) - - if img.shape[2] == 4: - alpha = img[:, :, 3] # GRAY - alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2BGR) # BGR - alpha_output = post_process(inference(model_path, pre_process(alpha))) # BGR - alpha_output = cv2.cvtColor(alpha_output, cv2.COLOR_BGR2GRAY) # GRAY - - img = img[:, :, 0:3] # BGR - image_output = post_process(inference(model_path, pre_process(img))) # BGR - image_output = cv2.cvtColor(image_output, cv2.COLOR_BGR2BGRA) # BGRA - image_output[:, :, 3] = alpha_output - - elif img.shape[2] == 3: - image_output = post_process(inference(model_path, pre_process(img))) # BGR - - return image_output - - -css = ".output-image, .input-image, .image-preview {height: 480px !important} " -model_choices = ["modelx2", "modelx2 25 JXL", "modelx4", "minecraft_modelx4"] - -gr.Interface( - fn=upscale, - inputs=[ - gr.inputs.Image(type="pil", label="Input Image"), - gr.inputs.Radio( - model_choices, - type="value", - default=None, - label="Choose Upscaler", - optional=False, - ), - ], - outputs="image", - title="Image Upscaling 🦆", - description="Model: [Anchor-based Plain Net for Mobile Image Super-Resolution](https://arxiv.org/abs/2105.09750). Repository: [SR Mobile PyTorch](https://github.com/w11wo/sr_mobile_pytorch)", - allow_flagging="never", - css=css, -).launch() diff --git a/spaces/brainblow/MusiCreator/CONTRIBUTING.md b/spaces/brainblow/MusiCreator/CONTRIBUTING.md deleted file mode 100644 index 55b99140204d785d572ada9761dd77f302ae31c6..0000000000000000000000000000000000000000 --- a/spaces/brainblow/MusiCreator/CONTRIBUTING.md +++ /dev/null @@ -1,35 +0,0 @@ -# Contributing to Audiocraft - -We want to make contributing to this project as easy and transparent as -possible. - -## Pull Requests - -Audiocraft is the implementation of a research paper. -Therefore, we do not plan on accepting many pull requests for new features. -We certainly welcome them for bug fixes. - -1. Fork the repo and create your branch from `main`. -2. If you've added code that should be tested, add tests. -3. If you've changed APIs, update the documentation. -4. Ensure the test suite passes. -5. Make sure your code lints. -6. If you haven't already, complete the Contributor License Agreement ("CLA"). - -## Contributor License Agreement ("CLA") -In order to accept your pull request, we need you to submit a CLA. You only need -to do this once to work on any of Meta's open source projects. - -Complete your CLA here:
-
-
-
-In this repository, we release code for TensorMask in Detectron2.
-TensorMask is a dense sliding-window instance segmentation framework that, for the first time, achieves results close to the well-developed Mask R-CNN framework -- both qualitatively and quantitatively. It establishes a conceptually complementary direction for object instance segmentation research.
-
-## Installation
-First install Detectron2 following the [documentation](https://detectron2.readthedocs.io/tutorials/install.html) and
-[setup the dataset](../../datasets). Then compile the TensorMask-specific op (`swap_align2nat`):
-```bash
-pip install -e /path/to/detectron2/projects/TensorMask
-```
-
-## Training
-
-To train a model, run:
-```bash
-python /path/to/detectron2/projects/TensorMask/train_net.py --config-file
-KingRoot may stop working if you update your Android version. Currently, mobile devices manufacturers try to avoid the rooting of their devices, so you may need to reset the app to be able to use it.
-King Root v3.2
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Notice your android device is starting to become slow or laggy? You are not the only one with this problem. Most android users feedback a slow or laggy experience after a certain period of usage. But with a rooted device, speeding up your phone can be done easily to allow users to enjoy a smooth and seamless experience. Read more >
-Remember the frustration of a dying phone in the middle of the day? If you want your phone to last through the day, rooting your android device can provide you many solutions and alternatives for conserving and managing your battery consumption with ease & convenience. Read more >
-Backing up your phones or tablets is one of the biggest frustrations for an android users. One has to either pay for a good backup app or find it really troublesome to backup your data. With a rooted android device, users will be able to use powerful & easy backup apps to backup important data effortlessly. Read more >
-Sick and tired of the usual look and feel of your user interface in Android devices? A rooted phone or tablets gives you admin access to change almost everything from color to icons and even animation. Command 100% freedom and pimp your Android devices easily with KingRoot. Read more >
-When you purchase an android device, it comes with a number of apps that you may need or may be completely useless. They occupy a lot of storage in your device and cannot be uninstalled. These apps are called Bloatware. As mentioned already, you cannot remove these apps in normal conditions. This is the situation where you need to root your android.
- -After you root your android, download Titanium Backup or any of the system app remover applications from the Google play store. If you download Titanium Backup, open it, wait for apps to load and tap the app you want, and tap uninstall. BOOM! You are done.
-Though you can back up apps or games on your device easily without rooting, you cannot back up the data app is using. It means that whenever you restore the apps from the backup, it is the same as installing from Google Play. After rooting with KingRoot, you can also back up the data from the apps. It means after restoring the app, you will completely get exact same app in exact the same condition along with your settings, login details, etc. This kind of backup is much more useful than the normal backup and is much easier and faster.
-KingRoot uses advanced technology that allows you to root your phone and open it up to more possibilities. Since you are gaining root access, you have full administrative privileges in enhancing the way your phone looks and functions. In particular, you can install custom ROM to your Android device once you have rooted it. This modified and feature-rich version of the Android OS includes additional features, unique themes, and tweaks that all account for enhanced mobile device performance.
CyanogenMod, for instance, is among the popular custom ROMs available for rooted phones, which offers interesting features including lock screen gestures, DSP equalizer, CPU overclock and underclocking, and a complete theme engine along with its very own theme store. Another excellent custom ROM you can find is Paranoid Android, which offers UI customization, floating notifications and multitasking, gesture controls, and hidden navigation PIE mode. Keep in mind, though, that it only supports a limited number of Android devices including Oppo, OnePlus, and Google Nexus.
By rooting your phone, you can use an unreleased version of Android without any limitations. Customization is only possible with rooted phones considering certain restrictions in terms of the availability of these newer versions to mobile devices. Hence, by using KingRoot, you have an opportunity to make your device more personalized for enhanced browsing and overall user experience.
Some users ask questions like why does KingRoot has a PC version when there is an independent android version that can root android without a PC? This seems to be somehow logical but the PC version is also a useful PC version of KingRoot and supports way more devices than the android version.
-In every case, whether it is android, web hosting, or any other similar activities, root access means access to the core part of the system. It means if you gain root access to any system, you can do anything you want to do with that system.
-Ans. It is up to you to decide. Actually, few people brick their device during rooting and now you can also root your device with a 100% safe method. So I think there is not much to worry about, but if you fear so much, please google whether people got an error while rooting the device you are using.
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A: If you cannot remove KingRoot, please try to force stop KingRoot, clear data of it, and root the device with KingRoot again. Then unroot it immediately.
Even those who have managed to root successfully have reported mixed results. For some, the app works immediately and roots the device during the first attempt. For others, the app freezes at 90% during the first rooting attempt but then succeeds to root during the second attempt. Others have reported that the Fire TV Stick needs to be rebooted by holding the PLAY and SELECT button while the Kingo Root app is stuck at 90%. Running the rooting process again after rebooting has worked for some.
-I reinitialized the stick by pressing the center and play button, rebooted, but it did not work. King Root did not root the device. On the third try, I left King Root to work its magic and I became sidetracked in the kitchen removing some food items from the freezer and I might have been away for 10 minutes or slightly longer.
-I wanted to add an update to my original post. I had to remove root because Playstation Vue refused to function. I am a cord cutter, I rely on cable for only internet service, and PlayStation Vue is my access to some cable channels I like to watch. So regrettably I restored the device back to normal. But I still have a Chromecast on each television and my Samsung Galaxy Tab Pro 8.4 is rooted and I can cast directly from the tablet to either stick.
-Plus honestly I find people are scummy that sell the service of making it so you can access pirated content on your device. While I am not against pirating, I am against people profiting off the pirating.
-Things will change once a company like APPLE (itunes) come an buys it all up or provide content for cheap. Just wait it will happen and the new Apple will possibly be Amazon as this website posted earlier this week how Amazon surveys in Germany are asking people there how they use their fire devices and how Kodi was on the survey.
-Can verify myself that I managed to get root on 5.0.5.1, I tried multiple times and failed, I then tried restarting the device at 90 percent and relaunching it as soon as fire tv stick menu loaded up and got it the second attempt.
-I sent over Kingo root 3.1 (latest version/op version) with apk to fire app on phone.I launched Kingo root (not sure if it was launched from within firestopper or from fire tv main menu), dont think that would make much difference but its a variable to consider.I waited for it to reach 90 percent (it sticks at various percentages for a few seconds) once it was on 90 percent I waited a number of seconds, probably around 45 to 50 seconds and held down home/play to restart.Once it restarted and as soon as the main fire tv menu loaded (but before firestopper loaded) I launched Kingo root again and clicked root.It started off and kicked me out to black screen for a couple seconds then firestopper loaded, I then quickly launched Kingo root again this time from within firestopper to find it already on 90 percent.After sitting on 90 percent again for a few seconds it reported root successful.Then send over super user.I then restore to defaults the fire tv stick and during the restore process my stick was updated to the newest version 5.2.1.0 (unintentionally).When it finished and loaded up I reinstalled Kingo root and without even trying to root again it reported it was already Rooted.
-Fire TV stick, purchased at launch, never rooted before. Never connected to the web until Kingroot was announced. However, never rooted, just updated to the necessary software version. Then amazon changed to https updates, so inadvertently updated to Fire OS 5.0.5 then took off my network for several weeks until I read this post. Booted up stick without internet live connection and installed Kingo Root via Apps2Fire using my Fire tablet. Ran Kingo Root without rebooting, it stopped at 90% for about 30 seconds and then successfully rooted the stick without requiring any reboot. It worked the first time I tried. I have never installed Firestarter/Firestopper. Kodi is installed.
-ok thanks for the confirmation , so is this like a full root? like can we install xposed framework and youtube ad block module and also use sixaxis to get a ps3 controller working on fire stick?and also does firestopper work?
-Installed all 3 apks with adblink. used rotatescreen to lock in portrait. ran kingoroot and stuck on 90% for a little over a minute and came back with success! Installed SuperSU and everything is perfect! Thanx!!
-Hi managed to root my firestick with the first try and installed supersu but how can you really know if your rooted or not. Opened supersu it works fine but when i installed luckypatcher, SuperSu crashed right away. Any ideas?
-V1-V5 - At V1, round-tipped leaf on first collar appears, nodal roots elongate. By V2, plant is 2 to 4 inches tall and relies on the energy in the seed. V3 begins 2 to 4 weeks after VE, and plant switches from kernel reserves to photosynthesis and nodal roots begin to take over. Around V4, broadleaf weeds should be controlled to avoid loss. By V5, the number of potential leaf and ear shoots are determined. Plant is 8 to 12 inches tall and growing point remains below soil surface.
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\ No newline at end of file diff --git a/spaces/cihyFjudo/fairness-paper-search/Last Night While I Was Doing My Homework Angela Told Me How Boring Her Biology Professor Was.md b/spaces/cihyFjudo/fairness-paper-search/Last Night While I Was Doing My Homework Angela Told Me How Boring Her Biology Professor Was.md deleted file mode 100644 index 301b3bb6772c7c8cfa5c3f8117b1bf77445a9621..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Last Night While I Was Doing My Homework Angela Told Me How Boring Her Biology Professor Was.md +++ /dev/null @@ -1,5 +0,0 @@ -
-
One of the earliest "creepy clown" movies, "He Who Gets Slapped" was the first film produced completely by the MGM studio, though not the first released. The film features Lon Chaney in a memorable role as a scientist who is humiliated when a rival and his wife steal his ideas just as he is to present them to the Academy of Sciences. He then becomes a masochistic circus clown where the highlight of his act is being repeatedly slapped. One of many stand-out scenes occurs during a circus performance where Chaney spots those who betrayed him and tries to call them out, but his fellow clowns are doing their normal crowd-pleasing routine of slapping him in the face. Filled with nightmarish vignettes, this landmark film from the silent era was directed by Victor Sjöström (newly arrived from Sweden and using an anglicized last name of Seastrom) and also features Norma Shearer and John Gilbert, each on the cusp of stardom.
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In preparing this new edition of Modern Physics, we have againrelied heavily on the many helpful suggestions from a large team ofreviewers and from a host of instruc-tor and student users of theearlier editions. Their advice reflected the discoveries that havefurther enlarged modern physics in the first decade of the newcentury, took note of the evolution that is occurring in theteaching of physics in colleges and universities, and recognizedthe growing role of modern physics in the biological sciences. Asthe term modern physics has come to mean the physics of the modernerarelativity and quantum theorywe have heeded the advice of manyusers and reviewers and pre-served the historical and culturalflavor of the book while being careful to maintain the mathematicallevel of the earlier editions. We continue to provide theflexibility for instructors to match the book and its supportingancillaries to a wide variety of teach-ing modes, including bothone- and two-semester courses and media-enhanced courses.
-ThefirsteditionsInstructors Solutions Manual with solutions, notjust answers, to all end-of-chapter problems was the first such aidto accompany a physics (and not just a modern physics) textbook,and that leadership has been continued in this edition. TheInstructors Solutions Manual (ISM) is available in print or on CDfor those adopting Modern Physics, sixth edition, for theirclasses. As with the previous editions, the popular paperbackStudents Solution Manual, contain-ing one-quarter of the solutionsin the ISM, is also available.
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Wehave continued to includemanyworked-out examples in everychapter, afeature singled out by many instructors as a strength ofthe book. Several new examples at the interface between modernphysics and the biological sciences have been added. As before, wefrequently use combined quantities such as hc, Uc, and ke2 in eV #nm to simplify many numerical calculations.
-Wehavecontinuedtheuseofrealdatainfigures,photosofrealpeopleandappa-ratus,and short quotations by many scientists who were key participantsin the development of modern physics. These features, along withthe Notes at the end of each chapter, bring to life many events inthe history of science and help counter the too-prevalent viewamong students that physics is a dull, impersonal collection offacts and formulas.
-AnumberofnewApplicationNoteshavebeenaddedtothesixthedition.Thesebriefnotesin the margins of many pages point to a few of the many benefits tosociety that have been made possible by a discovery or developmentin modern physics.
-Recognizingtheneedforstudentsonoccasiontobeabletoquicklyreviewkeyconceptsfrom classical physics that relate to topics developed in modernphysics,theClassicalConceptReview(CCR)wasintroducedinthebooksfifthedition.FoundonthebooksWebsiteandidentifiedbyanumberediconCCR in the mar-gin near the pertinent modern physics discussion,the CCR can be printed out to provide a convenient study-supportbooklet. Several new CCRs have been added to the sixth edition. TheCCRs provide concise reviews of pertinent classical con-cepts justa mouse click away.
-In Part 1 we discuss the foundations of the physics of themodern era, relativity theory and quantum mechanics. Chapter 1examines the apparent conflict between Einsteins principle ofrelativity and the observed constancy of the speed of light andshows how accepting the validity of both ideas led to the specialtheory of relativity. Chapter 2 concerns the relations connectingmass, energy, and momentum in special relativity and concludes witha brief discussion of general relativity and some experi-mentaltests of its predictions. In Chapters 3, 4, and 5 the developmentof quantum theory is traced from the earliest evidence ofquantization to de Broglies hypothesis of electron waves. Anelementary discussion of the Schrdinger equation is provided inChapter 6, illustrated with applications to one-dimensionalsystems. Chapter 7 extends the application of quantum mechanics tomany-particle systems and intro-duces the important new concepts ofelectron spin and the exclusion principle. Con-cluding thedevelopment, Chapter 8 discusses the wave mechanics of systems oflarge numbers of identical particles, underscoring the importanceof the symmetry of wave functions. Beginning with Chapter 3, thechapters in Part 1 should be stud-ied in sequence because each ofChapters 4 through 8 depends on the discussions, developments, andexamples of the previous chapters.
-reference, and coordinate transformationsall importantbackground to our discussions of special relativitymay not havebeen emphasized in many introductory courses. As an aid to a betterunderstanding of the concepts of modern physics, we have includedthe Classical Concept Review on the books Web site. As you proceedthrough Modern Physics,
-More A more complete description of the Michelson-Morleyexperiment, its interpretation, and the results of very recentversions can be found on the home page:www.whfreeman.com/tiplermodernphysics6e. See also Figures 1-9through 1-11 here, as well as Equations 1-7 through 1-10.
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\ No newline at end of file diff --git a/spaces/cihyFjudo/fairness-paper-search/Turhan Baytop Bitkilerle Tedavi PDF Download Eski Bahe Glleri Besinlerle Tedavi ve Kombine Tedavi.md b/spaces/cihyFjudo/fairness-paper-search/Turhan Baytop Bitkilerle Tedavi PDF Download Eski Bahe Glleri Besinlerle Tedavi ve Kombine Tedavi.md deleted file mode 100644 index 9d3234bdde297cbc6d10ba1ce65b18d922c20d67..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Turhan Baytop Bitkilerle Tedavi PDF Download Eski Bahe Glleri Besinlerle Tedavi ve Kombine Tedavi.md +++ /dev/null @@ -1,6 +0,0 @@ -
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Pickup Simulator APK: A Fun and Realistic Driving Game
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-The fifth step is to locate the APK file in your file manager and tap on it to install it. You can use any file manager app on your device to access the APK file, which is usually stored in the downloads folder. Once you find the APK file, you need to tap on it and follow the instructions on the screen to install it.
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Use the steering wheel or tilt your device to steer your pickup truck
-The next tip is to use the steering wheel or tilt your device to steer your pickup truck. You can find the steering wheel on the bottom left corner of your screen. You can swipe left or right on the steering wheel to turn your pickup truck in that direction. You can also tilt your device left or right to steer your pickup truck using the gyroscope sensor.
-Use the gearbox to switch between forward and reverse modes
-The third tip is to use the gearbox to switch between forward and reverse modes. You can find the gearbox on the top right corner of your screen. You can tap on the F button to switch to forward mode, which allows you to move forward with your pickup truck. You can also tap on the R button to switch to reverse mode, which allows you to move backward with your pickup truck.
-Use the handbrake to perform drifts and turns
-The fourth tip is to use the handbrake to perform drifts and turns. You can find the handbrake on the top left corner of your screen. You can tap on the handbrake button to lock your rear wheels, which causes your pickup truck to skid and slide. You can use this technique to perform drifts and turns, especially on slippery surfaces.
-How to customize your pickup truck and gameplay settings
-Tap on the garage icon to access the customization menu
-The first tip is to tap on the garage icon to access the customization menu. You can find this icon on the main menu of the game, which you can access by tapping on the pause button on the top center of your screen. The garage icon looks like a wrench and a screwdriver. Once you tap on it, you will see the customization menu, where you can modify your pickup truck and gameplay settings.
-Choose from different colors, wheels, suspensions, engines, and accessories for your pickup truck
-The second tip is to choose from different colors, wheels, suspensions, engines, and accessories for your pickup truck. You can find these options on the left side of the customization menu. You can swipe up or down to scroll through the different categories, and tap on the ones you want to apply to your pickup truck. You can also see the preview of your pickup truck on the right side of the customization menu. You can swipe left or right to rotate your pickup truck and see it from different angles.
-Tap on the settings icon to adjust the sound, graphics, controls, and camera options
-The third tip is to tap on the settings icon to adjust the sound, graphics, controls, and camera options. You can find this icon on the bottom right corner of the customization menu. It looks like a gear. Once you tap on it, you will see the settings menu, where you can modify your sound, graphics, controls, and camera options. You can use the sliders or buttons to adjust the volume, quality, sensitivity, feedback, mode, angle, zoom, and rotation of these options. You can also tap on the reset button to restore the default settings.
-Conclusion
-Pickup Simulator APK is a fun and realistic driving game that lets you drive various pickup trucks on different terrains and weather conditions. You can also customize your pickup truck and gameplay settings according to your preference and comfort. You can download and install Pickup Simulator APK from Oppana Games or FileHippo, and play it for free and offline. You can also use some tips and tricks to improve your skills and have more fun playing Pickup Simulator APK. If you are looking for a driving simulation game that offers you a lot of variety and challenge, then you should try Pickup Simulator APK.
-FAQs
-Here are some frequently asked questions about Pickup Simulator APK:
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-Q: How can I update Pickup Simulator APK?
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-Q: How can I contact Oppana Games for feedback or support?
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Zombie Towers Mod APK Terbaru The Ultimate Tower Defense Game with Zombies.md b/spaces/congsaPfin/Manga-OCR/logs/Zombie Towers Mod APK Terbaru The Ultimate Tower Defense Game with Zombies.md deleted file mode 100644 index 2aa0619ca7b3931c690689a0e3ef29550fabeaa7..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Zombie Towers Mod APK Terbaru The Ultimate Tower Defense Game with Zombies.md +++ /dev/null @@ -1,142 +0,0 @@ -
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Zombie Towers Mod Apk Terbaru: A Tower Defense Game with a Zombie Twist
-If you are looking for a fun and addictive tower defense game with a zombie survival theme, you might want to check out Zombie Towers Mod Apk Terbaru. This is a modified version of Zombie Towers, a game developed by Edenap that lets you defend your castle from waves of zombies with different types of towers, workers, and power-ups. In this article, we will tell you what Zombie Towers is, how to download and install the mod apk version, why you should play it, some tips and tricks to help you win, and a review of the game based on its pros and cons.
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What is Zombie Towers?
-A brief introduction to the game and its features
-Zombie Towers is a tower defense game that puts you in charge of a group of survivors who have to fight against hordes of zombies in a post-apocalyptic world. You have to place your towers strategically in different spots in each level, and upgrade them as you progress. You also have workers who can carry ammo, repair walls, and boost your towers. You can use power-ups such as EMP, Nuke, Triple X, and Headshots to deal extra damage or freeze the zombies. The game has 30+ types of towers, 4 types of upgrades, 3 types of workers, 4 types of power-ups, and 7 types of zombies. The game has 12 exciting and challenging levels with different environments and objectives.
-How to download and install the mod apk version
-If you want to play the modded version of Zombie Towers, which gives you unlimited money, free purchase, mod menu, god mode, one shot kill, unlimited ammo, max range, and more features, you can follow these steps:
--
-
- Download the mod apk file from or any other trusted source. -
- Enable unknown sources on your device by going to Settings > Security > Unknown Sources. -
- Locate the downloaded file in your file manager and tap on it to install it. -
- Launch the game and enjoy! -
Why play Zombie Towers Mod Apk Terbaru?
-The benefits of playing the modded version of the game
-Playing the modded version of Zombie Towers has many advantages over playing the original version. Here are some of them:
--
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- You can access all the towers, workers, power-ups, and upgrades without spending any money. -
- You can customize your gameplay with the mod menu options such as god mode, one shot kill, unlimited ammo, max range, etc. -
- You can have more fun and less frustration by easily defeating the zombies and completing the levels. -
- You can experiment with different strategies and combinations without worrying about losing resources or lives. -
The challenges and strategies of the game
-Despite playing the modded version of Zombie Towers, you still need to use your brain and skills to win the game. The game is not easy and requires you to think fast and act smart. Here are some challenges and strategies that you should know:- The zombies come in different types, such as normal, fast, armored, flying, boss, etc. Each type has its own strengths and weaknesses, and you need to use the right towers and power-ups to counter them.
-- The levels have different layouts, obstacles, and objectives. You need to plan your tower placement carefully and adapt to the changing situations. You also need to complete the objectives such as protecting the survivors, destroying the zombie base, or surviving for a certain time.
-- The towers have different abilities, such as damage, range, fire rate, splash, stun, etc. You need to merge and upgrade your towers to make them more effective and powerful. You also need to use the workers to support your towers with ammo, repairs, and boosts.
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- The power-ups have different effects, such as EMP, Nuke, Triple X, and Headshots. You need to use them wisely and at the right time to turn the tide of the battle. You also need to save some for the boss battles and emergencies.
-Tips and Tricks for Zombie Towers Mod Apk Terbaru
-How to merge and upgrade towers
-One of the unique features of Zombie Towers is that you can merge two towers of the same type and level to create a new tower with higher level and better stats. You can also upgrade your towers with coins or gems to increase their level and abilities. Here are some tips and tricks for merging and upgrading towers:
--
-
- You can only merge towers that are adjacent to each other. You can move your towers by dragging them to an empty spot. -
- You can merge up to 5 levels of towers. The higher the level of the tower, the more expensive it is to merge or upgrade. -
- You can see the preview of the merged tower before you confirm it. You can also see the stats of the tower by tapping on it. -
- You can undo a merge by tapping on the undo button at the bottom right corner of the screen. You can only undo one merge at a time. -
- You can sell your towers by tapping on the sell button at the bottom left corner of the screen. You will get some coins back depending on the level of the tower. -
How to use power-ups and boosters
-Power-ups are special items that you can use during the game to deal extra damage or freeze the zombies. Boosters are items that you can use before starting a level to give you some advantages such as extra coins, extra lives, extra workers, etc. Here are some tips and tricks for using power-ups and boosters:
--
-
- You can get power-ups by killing zombies or opening chests. You can also buy them with gems or watch ads to get them for free. -
- You can use power-ups by tapping on their icons at the top right corner of the screen. You can only use one power-up at a time. -
- You can see the cooldown time of each power-up by looking at the circle around its icon. You can also see the duration of each power-up by looking at the bar below its icon. -
- You can buy boosters with gems or watch ads to get them for free. You can only use one booster per level. -
- You can choose which booster to use by tapping on its icon at the bottom center of the screen before starting a level. You can also see the effect of each booster by tapping on it. -
How to deal with different types of zombies
-Zombies are your enemies in Zombie Towers. They come in different types, such as normal, fast, armored, flying, boss, etc. Each type has its own strengths and weaknesses, and you need to use the right towers and power-ups to counter them. Here are some tips and tricks for dealing with different types of zombies:
--
-
- Normal zombies are the most common type of zombies. They have low health and speed, but they come in large numbers. You can use any tower or power-up to kill them easily. -
- Fast zombies are faster than normal zombies. They have low health but high speed, and they can dodge some attacks. You need to use fast-firing or splash-damage towers or power-ups to kill them quickly. -
- Armored zombies are tougher than normal zombies. They have high health but low speed, and they can resist some damage types. You need to use high-damage or piercing-damage towers or power-ups to kill them effectively. -
- Flying zombies are airborne zombies. They have low health but high speed, and they can fly over walls and obstacles. You need to use anti-air or homing-damage towers or power-ups to kill them efficiently. -
- Boss zombies are the biggest and strongest type of zombies. They have very high health and speed, and they can deal massive damage and spawn other zombies. You need to use all your towers, workers, power-ups, and skills to kill them before they reach your castle. -
Zombie Towers Mod Apk Terbaru Review
-The pros and cons of the game
-Zombie Towers Mod Apk Terbaru is a fun and addictive game that will keep you entertained for hours. However, like any other game, it has its pros and cons. Here are some of them:
-| Pros | -Cons | -
|---|---|
| - The game has great graphics and sound effects that create a realistic and immersive zombie apocalypse atmosphere. | -- The game can be too easy or too hard depending on the mod menu options that you choose. | -
| - The game has a variety of towers, workers, power-ups, and zombies that make the gameplay diverse and interesting. | -- The game can be repetitive and boring after playing for a long time. | -
| - The game has a simple and intuitive interface and controls that make it easy to play. | -- The game can have some bugs and glitches that affect the performance and quality of the game. | -
| - The game has a modded version that gives you unlimited money, free purchase, mod menu, god mode, one shot kill, unlimited ammo, max range, and more features. | -- The game can be unfair and unbalanced for the original version players who do not use the modded version. | -
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- diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/leres/pix2pix/util/guidedfilter.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/leres/pix2pix/util/guidedfilter.py deleted file mode 100644 index d377ff12e078a5f156e9246b63573dae71825fad..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/leres/pix2pix/util/guidedfilter.py +++ /dev/null @@ -1,47 +0,0 @@ -import numpy as np - -class GuidedFilter(): - def __init__(self, source, reference, r=64, eps= 0.05**2): - self.source = source; - self.reference = reference; - self.r = r - self.eps = eps - - self.smooth = self.guidedfilter(self.source,self.reference,self.r,self.eps) - - def boxfilter(self,img, r): - (rows, cols) = img.shape - imDst = np.zeros_like(img) - - imCum = np.cumsum(img, 0) - imDst[0 : r+1, :] = imCum[r : 2*r+1, :] - imDst[r+1 : rows-r, :] = imCum[2*r+1 : rows, :] - imCum[0 : rows-2*r-1, :] - imDst[rows-r: rows, :] = np.tile(imCum[rows-1, :], [r, 1]) - imCum[rows-2*r-1 : rows-r-1, :] - - imCum = np.cumsum(imDst, 1) - imDst[:, 0 : r+1] = imCum[:, r : 2*r+1] - imDst[:, r+1 : cols-r] = imCum[:, 2*r+1 : cols] - imCum[:, 0 : cols-2*r-1] - imDst[:, cols-r: cols] = np.tile(imCum[:, cols-1], [r, 1]).T - imCum[:, cols-2*r-1 : cols-r-1] - - return imDst - - def guidedfilter(self,I, p, r, eps): - (rows, cols) = I.shape - N = self.boxfilter(np.ones([rows, cols]), r) - - meanI = self.boxfilter(I, r) / N - meanP = self.boxfilter(p, r) / N - meanIp = self.boxfilter(I * p, r) / N - covIp = meanIp - meanI * meanP - - meanII = self.boxfilter(I * I, r) / N - varI = meanII - meanI * meanI - - a = covIp / (varI + eps) - b = meanP - a * meanI - - meanA = self.boxfilter(a, r) / N - meanB = self.boxfilter(b, r) / N - - q = meanA * I + meanB - return q \ No newline at end of file diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/closure.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/closure.py deleted file mode 100644 index b955f81f425be4ac3e6bb3f4aac653887989e872..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/closure.py +++ /dev/null @@ -1,11 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .hook import HOOKS, Hook - - -@HOOKS.register_module() -class ClosureHook(Hook): - - def __init__(self, fn_name, fn): - assert hasattr(self, fn_name) - assert callable(fn) - setattr(self, fn_name, fn) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/logger/text.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/logger/text.py deleted file mode 100644 index 0b30577469d5f70e544e1ce73816326e38dadb20..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/logger/text.py +++ /dev/null @@ -1,256 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import datetime -import os -import os.path as osp -from collections import OrderedDict - -import torch -import torch.distributed as dist - -import annotator.mmpkg.mmcv as mmcv -from annotator.mmpkg.mmcv.fileio.file_client import FileClient -from annotator.mmpkg.mmcv.utils import is_tuple_of, scandir -from ..hook import HOOKS -from .base import LoggerHook - - -@HOOKS.register_module() -class TextLoggerHook(LoggerHook): - """Logger hook in text. - - In this logger hook, the information will be printed on terminal and - saved in json file. - - Args: - by_epoch (bool, optional): Whether EpochBasedRunner is used. - Default: True. - interval (int, optional): Logging interval (every k iterations). - Default: 10. - ignore_last (bool, optional): Ignore the log of last iterations in each - epoch if less than :attr:`interval`. Default: True. - reset_flag (bool, optional): Whether to clear the output buffer after - logging. Default: False. - interval_exp_name (int, optional): Logging interval for experiment - name. This feature is to help users conveniently get the experiment - information from screen or log file. Default: 1000. - out_dir (str, optional): Logs are saved in ``runner.work_dir`` default. - If ``out_dir`` is specified, logs will be copied to a new directory - which is the concatenation of ``out_dir`` and the last level - directory of ``runner.work_dir``. Default: None. - `New in version 1.3.16.` - out_suffix (str or tuple[str], optional): Those filenames ending with - ``out_suffix`` will be copied to ``out_dir``. - Default: ('.log.json', '.log', '.py'). - `New in version 1.3.16.` - keep_local (bool, optional): Whether to keep local log when - :attr:`out_dir` is specified. If False, the local log will be - removed. Default: True. - `New in version 1.3.16.` - file_client_args (dict, optional): Arguments to instantiate a - FileClient. See :class:`mmcv.fileio.FileClient` for details. - Default: None. - `New in version 1.3.16.` - """ - - def __init__(self, - by_epoch=True, - interval=10, - ignore_last=True, - reset_flag=False, - interval_exp_name=1000, - out_dir=None, - out_suffix=('.log.json', '.log', '.py'), - keep_local=True, - file_client_args=None): - super(TextLoggerHook, self).__init__(interval, ignore_last, reset_flag, - by_epoch) - self.by_epoch = by_epoch - self.time_sec_tot = 0 - self.interval_exp_name = interval_exp_name - - if out_dir is None and file_client_args is not None: - raise ValueError( - 'file_client_args should be "None" when `out_dir` is not' - 'specified.') - self.out_dir = out_dir - - if not (out_dir is None or isinstance(out_dir, str) - or is_tuple_of(out_dir, str)): - raise TypeError('out_dir should be "None" or string or tuple of ' - 'string, but got {out_dir}') - self.out_suffix = out_suffix - - self.keep_local = keep_local - self.file_client_args = file_client_args - if self.out_dir is not None: - self.file_client = FileClient.infer_client(file_client_args, - self.out_dir) - - def before_run(self, runner): - super(TextLoggerHook, self).before_run(runner) - - if self.out_dir is not None: - self.file_client = FileClient.infer_client(self.file_client_args, - self.out_dir) - # The final `self.out_dir` is the concatenation of `self.out_dir` - # and the last level directory of `runner.work_dir` - basename = osp.basename(runner.work_dir.rstrip(osp.sep)) - self.out_dir = self.file_client.join_path(self.out_dir, basename) - runner.logger.info( - (f'Text logs will be saved to {self.out_dir} by ' - f'{self.file_client.name} after the training process.')) - - self.start_iter = runner.iter - self.json_log_path = osp.join(runner.work_dir, - f'{runner.timestamp}.log.json') - if runner.meta is not None: - self._dump_log(runner.meta, runner) - - def _get_max_memory(self, runner): - device = getattr(runner.model, 'output_device', None) - mem = torch.cuda.max_memory_allocated(device=device) - mem_mb = torch.tensor([mem / (1024 * 1024)], - dtype=torch.int, - device=device) - if runner.world_size > 1: - dist.reduce(mem_mb, 0, op=dist.ReduceOp.MAX) - return mem_mb.item() - - def _log_info(self, log_dict, runner): - # print exp name for users to distinguish experiments - # at every ``interval_exp_name`` iterations and the end of each epoch - if runner.meta is not None and 'exp_name' in runner.meta: - if (self.every_n_iters(runner, self.interval_exp_name)) or ( - self.by_epoch and self.end_of_epoch(runner)): - exp_info = f'Exp name: {runner.meta["exp_name"]}' - runner.logger.info(exp_info) - - if log_dict['mode'] == 'train': - if isinstance(log_dict['lr'], dict): - lr_str = [] - for k, val in log_dict['lr'].items(): - lr_str.append(f'lr_{k}: {val:.3e}') - lr_str = ' '.join(lr_str) - else: - lr_str = f'lr: {log_dict["lr"]:.3e}' - - # by epoch: Epoch [4][100/1000] - # by iter: Iter [100/100000] - if self.by_epoch: - log_str = f'Epoch [{log_dict["epoch"]}]' \ - f'[{log_dict["iter"]}/{len(runner.data_loader)}]\t' - else: - log_str = f'Iter [{log_dict["iter"]}/{runner.max_iters}]\t' - log_str += f'{lr_str}, ' - - if 'time' in log_dict.keys(): - self.time_sec_tot += (log_dict['time'] * self.interval) - time_sec_avg = self.time_sec_tot / ( - runner.iter - self.start_iter + 1) - eta_sec = time_sec_avg * (runner.max_iters - runner.iter - 1) - eta_str = str(datetime.timedelta(seconds=int(eta_sec))) - log_str += f'eta: {eta_str}, ' - log_str += f'time: {log_dict["time"]:.3f}, ' \ - f'data_time: {log_dict["data_time"]:.3f}, ' - # statistic memory - if torch.cuda.is_available(): - log_str += f'memory: {log_dict["memory"]}, ' - else: - # val/test time - # here 1000 is the length of the val dataloader - # by epoch: Epoch[val] [4][1000] - # by iter: Iter[val] [1000] - if self.by_epoch: - log_str = f'Epoch({log_dict["mode"]}) ' \ - f'[{log_dict["epoch"]}][{log_dict["iter"]}]\t' - else: - log_str = f'Iter({log_dict["mode"]}) [{log_dict["iter"]}]\t' - - log_items = [] - for name, val in log_dict.items(): - # TODO: resolve this hack - # these items have been in log_str - if name in [ - 'mode', 'Epoch', 'iter', 'lr', 'time', 'data_time', - 'memory', 'epoch' - ]: - continue - if isinstance(val, float): - val = f'{val:.4f}' - log_items.append(f'{name}: {val}') - log_str += ', '.join(log_items) - - runner.logger.info(log_str) - - def _dump_log(self, log_dict, runner): - # dump log in json format - json_log = OrderedDict() - for k, v in log_dict.items(): - json_log[k] = self._round_float(v) - # only append log at last line - if runner.rank == 0: - with open(self.json_log_path, 'a+') as f: - mmcv.dump(json_log, f, file_format='json') - f.write('\n') - - def _round_float(self, items): - if isinstance(items, list): - return [self._round_float(item) for item in items] - elif isinstance(items, float): - return round(items, 5) - else: - return items - - def log(self, runner): - if 'eval_iter_num' in runner.log_buffer.output: - # this doesn't modify runner.iter and is regardless of by_epoch - cur_iter = runner.log_buffer.output.pop('eval_iter_num') - else: - cur_iter = self.get_iter(runner, inner_iter=True) - - log_dict = OrderedDict( - mode=self.get_mode(runner), - epoch=self.get_epoch(runner), - iter=cur_iter) - - # only record lr of the first param group - cur_lr = runner.current_lr() - if isinstance(cur_lr, list): - log_dict['lr'] = cur_lr[0] - else: - assert isinstance(cur_lr, dict) - log_dict['lr'] = {} - for k, lr_ in cur_lr.items(): - assert isinstance(lr_, list) - log_dict['lr'].update({k: lr_[0]}) - - if 'time' in runner.log_buffer.output: - # statistic memory - if torch.cuda.is_available(): - log_dict['memory'] = self._get_max_memory(runner) - - log_dict = dict(log_dict, **runner.log_buffer.output) - - self._log_info(log_dict, runner) - self._dump_log(log_dict, runner) - return log_dict - - def after_run(self, runner): - # copy or upload logs to self.out_dir - if self.out_dir is not None: - for filename in scandir(runner.work_dir, self.out_suffix, True): - local_filepath = osp.join(runner.work_dir, filename) - out_filepath = self.file_client.join_path( - self.out_dir, filename) - with open(local_filepath, 'r') as f: - self.file_client.put_text(f.read(), out_filepath) - - runner.logger.info( - (f'The file {local_filepath} has been uploaded to ' - f'{out_filepath}.')) - - if not self.keep_local: - os.remove(local_filepath) - runner.logger.info( - (f'{local_filepath} was removed due to the ' - '`self.keep_local=False`')) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/segmentors/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/segmentors/__init__.py deleted file mode 100644 index dca2f09405330743c476e190896bee39c45498ea..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/segmentors/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .base import BaseSegmentor -from .cascade_encoder_decoder import CascadeEncoderDecoder -from .encoder_decoder import EncoderDecoder - -__all__ = ['BaseSegmentor', 'EncoderDecoder', 'CascadeEncoderDecoder'] diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/backbones/resnext.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/backbones/resnext.py deleted file mode 100644 index 962249ad6fd9b50960ad6426f7ce3cac6ed8c5bc..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/backbones/resnext.py +++ /dev/null @@ -1,145 +0,0 @@ -import math - -from annotator.uniformer.mmcv.cnn import build_conv_layer, build_norm_layer - -from ..builder import BACKBONES -from ..utils import ResLayer -from .resnet import Bottleneck as _Bottleneck -from .resnet import ResNet - - -class Bottleneck(_Bottleneck): - """Bottleneck block for ResNeXt. - - If style is "pytorch", the stride-two layer is the 3x3 conv layer, if it is - "caffe", the stride-two layer is the first 1x1 conv layer. - """ - - def __init__(self, - inplanes, - planes, - groups=1, - base_width=4, - base_channels=64, - **kwargs): - super(Bottleneck, self).__init__(inplanes, planes, **kwargs) - - if groups == 1: - width = self.planes - else: - width = math.floor(self.planes * - (base_width / base_channels)) * groups - - self.norm1_name, norm1 = build_norm_layer( - self.norm_cfg, width, postfix=1) - self.norm2_name, norm2 = build_norm_layer( - self.norm_cfg, width, postfix=2) - self.norm3_name, norm3 = build_norm_layer( - self.norm_cfg, self.planes * self.expansion, postfix=3) - - self.conv1 = build_conv_layer( - self.conv_cfg, - self.inplanes, - width, - kernel_size=1, - stride=self.conv1_stride, - bias=False) - self.add_module(self.norm1_name, norm1) - fallback_on_stride = False - self.with_modulated_dcn = False - if self.with_dcn: - fallback_on_stride = self.dcn.pop('fallback_on_stride', False) - if not self.with_dcn or fallback_on_stride: - self.conv2 = build_conv_layer( - self.conv_cfg, - width, - width, - kernel_size=3, - stride=self.conv2_stride, - padding=self.dilation, - dilation=self.dilation, - groups=groups, - bias=False) - else: - assert self.conv_cfg is None, 'conv_cfg must be None for DCN' - self.conv2 = build_conv_layer( - self.dcn, - width, - width, - kernel_size=3, - stride=self.conv2_stride, - padding=self.dilation, - dilation=self.dilation, - groups=groups, - bias=False) - - self.add_module(self.norm2_name, norm2) - self.conv3 = build_conv_layer( - self.conv_cfg, - width, - self.planes * self.expansion, - kernel_size=1, - bias=False) - self.add_module(self.norm3_name, norm3) - - -@BACKBONES.register_module() -class ResNeXt(ResNet): - """ResNeXt backbone. - - Args: - depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. - in_channels (int): Number of input image channels. Normally 3. - num_stages (int): Resnet stages, normally 4. - groups (int): Group of resnext. - base_width (int): Base width of resnext. - strides (Sequence[int]): Strides of the first block of each stage. - dilations (Sequence[int]): Dilation of each stage. - out_indices (Sequence[int]): Output from which stages. - style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two - layer is the 3x3 conv layer, otherwise the stride-two layer is - the first 1x1 conv layer. - frozen_stages (int): Stages to be frozen (all param fixed). -1 means - not freezing any parameters. - norm_cfg (dict): dictionary to construct and config norm layer. - norm_eval (bool): Whether to set norm layers to eval mode, namely, - freeze running stats (mean and var). Note: Effect on Batch Norm - and its variants only. - with_cp (bool): Use checkpoint or not. Using checkpoint will save some - memory while slowing down the training speed. - zero_init_residual (bool): whether to use zero init for last norm layer - in resblocks to let them behave as identity. - - Example: - >>> from annotator.uniformer.mmseg.models import ResNeXt - >>> import torch - >>> self = ResNeXt(depth=50) - >>> self.eval() - >>> inputs = torch.rand(1, 3, 32, 32) - >>> level_outputs = self.forward(inputs) - >>> for level_out in level_outputs: - ... print(tuple(level_out.shape)) - (1, 256, 8, 8) - (1, 512, 4, 4) - (1, 1024, 2, 2) - (1, 2048, 1, 1) - """ - - arch_settings = { - 50: (Bottleneck, (3, 4, 6, 3)), - 101: (Bottleneck, (3, 4, 23, 3)), - 152: (Bottleneck, (3, 8, 36, 3)) - } - - def __init__(self, groups=1, base_width=4, **kwargs): - self.groups = groups - self.base_width = base_width - super(ResNeXt, self).__init__(**kwargs) - - def make_res_layer(self, **kwargs): - """Pack all blocks in a stage into a ``ResLayer``""" - return ResLayer( - groups=self.groups, - base_width=self.base_width, - base_channels=self.base_channels, - **kwargs) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/model_io.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/model_io.py deleted file mode 100644 index 78b6579631dd847ac76651238cb5a948b5a66286..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/model_io.py +++ /dev/null @@ -1,92 +0,0 @@ -# MIT License - -# Copyright (c) 2022 Intelligent Systems Lab Org - -# 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. - -# File author: Shariq Farooq Bhat - -import torch - -def load_state_dict(model, state_dict): - """Load state_dict into model, handling DataParallel and DistributedDataParallel. Also checks for "model" key in state_dict. - - DataParallel prefixes state_dict keys with 'module.' when saving. - If the model is not a DataParallel model but the state_dict is, then prefixes are removed. - If the model is a DataParallel model but the state_dict is not, then prefixes are added. - """ - state_dict = state_dict.get('model', state_dict) - # if model is a DataParallel model, then state_dict keys are prefixed with 'module.' - - do_prefix = isinstance( - model, (torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel)) - state = {} - for k, v in state_dict.items(): - if k.startswith('module.') and not do_prefix: - k = k[7:] - - if not k.startswith('module.') and do_prefix: - k = 'module.' + k - - state[k] = v - - model.load_state_dict(state) - print("Loaded successfully") - return model - - -def load_wts(model, checkpoint_path): - ckpt = torch.load(checkpoint_path, map_location='cpu') - return load_state_dict(model, ckpt) - - -def load_state_dict_from_url(model, url, **kwargs): - state_dict = torch.hub.load_state_dict_from_url(url, map_location='cpu', **kwargs) - return load_state_dict(model, state_dict) - - -def load_state_from_resource(model, resource: str): - """Loads weights to the model from a given resource. A resource can be of following types: - 1. URL. Prefixed with "url::" - e.g. url::http(s)://url.resource.com/ckpt.pt - - 2. Local path. Prefixed with "local::" - e.g. local::/path/to/ckpt.pt - - - Args: - model (torch.nn.Module): Model - resource (str): resource string - - Returns: - torch.nn.Module: Model with loaded weights - """ - print(f"Using pretrained resource {resource}") - - if resource.startswith('url::'): - url = resource.split('url::')[1] - return load_state_dict_from_url(model, url, progress=True) - - elif resource.startswith('local::'): - path = resource.split('local::')[1] - return load_wts(model, path) - - else: - raise ValueError("Invalid resource type, only url:: and local:: are supported") - \ No newline at end of file diff --git a/spaces/cyberoleg/b2719240e190e2a649150d94db50be82838efeb0/diffusion_webui/diffusion_models/base_controlnet_pipeline.py b/spaces/cyberoleg/b2719240e190e2a649150d94db50be82838efeb0/diffusion_webui/diffusion_models/base_controlnet_pipeline.py deleted file mode 100644 index 167158b11b477a72c019da69d25d0c7318eacae5..0000000000000000000000000000000000000000 --- a/spaces/cyberoleg/b2719240e190e2a649150d94db50be82838efeb0/diffusion_webui/diffusion_models/base_controlnet_pipeline.py +++ /dev/null @@ -1,31 +0,0 @@ -class ControlnetPipeline: - def __init__(self): - self.pipe = None - - def load_model(self, stable_model_path: str, controlnet_model_path: str): - raise NotImplementedError() - - def load_image(self, image_path: str): - raise NotImplementedError() - - def controlnet_preprocces(self, read_image: str): - raise NotImplementedError() - - def generate_image( - self, - image_path: str, - stable_model_path: str, - controlnet_model_path: str, - prompt: str, - negative_prompt: str, - num_images_per_prompt: int, - guidance_scale: int, - num_inference_step: int, - controlnet_conditioning_scale: int, - scheduler: str, - seed_generator: int, - ): - raise NotImplementedError() - - def web_interface(): - raise NotImplementedError() diff --git a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/common/poser_encoder_decoder_00.py b/spaces/cymic/Talking_Head_Anime_3/tha3/nn/common/poser_encoder_decoder_00.py deleted file mode 100644 index acd59e873ef0f7aa45c705096da67740cd33b9b0..0000000000000000000000000000000000000000 --- a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/common/poser_encoder_decoder_00.py +++ /dev/null @@ -1,121 +0,0 @@ -import math -from typing import Optional, List - -import torch -from torch import Tensor -from torch.nn import ModuleList, Module - -from tha3.nn.common.poser_args import PoserArgs00 -from tha3.nn.conv import create_conv3_block_from_block_args, create_downsample_block_from_block_args, \ - create_upsample_block_from_block_args -from tha3.nn.nonlinearity_factory import ReLUFactory -from tha3.nn.normalization import InstanceNorm2dFactory -from tha3.nn.resnet_block import ResnetBlock -from tha3.nn.util import BlockArgs - - -class PoserEncoderDecoder00Args(PoserArgs00): - def __init__(self, - image_size: int, - input_image_channels: int, - output_image_channels: int, - num_pose_params: int , - start_channels: int, - bottleneck_image_size, - num_bottleneck_blocks, - max_channels: int, - block_args: Optional[BlockArgs] = None): - super().__init__( - image_size, input_image_channels, output_image_channels, start_channels, num_pose_params, block_args) - self.max_channels = max_channels - self.num_bottleneck_blocks = num_bottleneck_blocks - self.bottleneck_image_size = bottleneck_image_size - assert bottleneck_image_size > 1 - - if block_args is None: - self.block_args = BlockArgs( - normalization_layer_factory=InstanceNorm2dFactory(), - nonlinearity_factory=ReLUFactory(inplace=True)) - else: - self.block_args = block_args - - -class PoserEncoderDecoder00(Module): - def __init__(self, args: PoserEncoderDecoder00Args): - super().__init__() - self.args = args - - self.num_levels = int(math.log2(args.image_size // args.bottleneck_image_size)) + 1 - - self.downsample_blocks = ModuleList() - self.downsample_blocks.append( - create_conv3_block_from_block_args( - args.input_image_channels, - args.start_channels, - args.block_args)) - current_image_size = args.image_size - current_num_channels = args.start_channels - while current_image_size > args.bottleneck_image_size: - next_image_size = current_image_size // 2 - next_num_channels = self.get_num_output_channels_from_image_size(next_image_size) - self.downsample_blocks.append(create_downsample_block_from_block_args( - in_channels=current_num_channels, - out_channels=next_num_channels, - is_output_1x1=False, - block_args=args.block_args)) - current_image_size = next_image_size - current_num_channels = next_num_channels - assert len(self.downsample_blocks) == self.num_levels - - self.bottleneck_blocks = ModuleList() - self.bottleneck_blocks.append(create_conv3_block_from_block_args( - in_channels=current_num_channels + args.num_pose_params, - out_channels=current_num_channels, - block_args=args.block_args)) - for i in range(1, args.num_bottleneck_blocks): - self.bottleneck_blocks.append( - ResnetBlock.create( - num_channels=current_num_channels, - is1x1=False, - block_args=args.block_args)) - - self.upsample_blocks = ModuleList() - while current_image_size < args.image_size: - next_image_size = current_image_size * 2 - next_num_channels = self.get_num_output_channels_from_image_size(next_image_size) - self.upsample_blocks.append(create_upsample_block_from_block_args( - in_channels=current_num_channels, - out_channels=next_num_channels, - block_args=args.block_args)) - current_image_size = next_image_size - current_num_channels = next_num_channels - - def get_num_output_channels_from_level(self, level: int): - return self.get_num_output_channels_from_image_size(self.args.image_size // (2 ** level)) - - def get_num_output_channels_from_image_size(self, image_size: int): - return min(self.args.start_channels * (self.args.image_size // image_size), self.args.max_channels) - - def forward(self, image: Tensor, pose: Optional[Tensor] = None) -> List[Tensor]: - if self.args.num_pose_params != 0: - assert pose is not None - else: - assert pose is None - outputs = [] - feature = image - outputs.append(feature) - for block in self.downsample_blocks: - feature = block(feature) - outputs.append(feature) - if pose is not None: - n, c = pose.shape - pose = pose.view(n, c, 1, 1).repeat(1, 1, self.args.bottleneck_image_size, self.args.bottleneck_image_size) - feature = torch.cat([feature, pose], dim=1) - for block in self.bottleneck_blocks: - feature = block(feature) - outputs.append(feature) - for block in self.upsample_blocks: - feature = block(feature) - outputs.append(feature) - outputs.reverse() - return outputs diff --git a/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/latex/attention/parameter_attention.tex b/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/latex/attention/parameter_attention.tex deleted file mode 100644 index 7bc4fe452dbdbfe44ff72f0cdbd37acd5c786ce6..0000000000000000000000000000000000000000 --- a/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/latex/attention/parameter_attention.tex +++ /dev/null @@ -1,45 +0,0 @@ -\pagebreak -\section*{Two Feed-Forward Layers = Attention over Parameters}\label{sec:parameter_attention} - -In addition to attention layers, our model contains position-wise feed-forward networks (Section \ref{sec:ffn}), which consist of two linear transformations with a ReLU activation in between. In fact, these networks too can be seen as a form of attention. Compare the formula for such a network with the formula for a simple dot-product attention layer (biases and scaling factors omitted): - -\begin{align*} - FFN(x, W_1, W_2) = ReLU(xW_1)W_2 \\ - A(q, K, V) = Softmax(qK^T)V -\end{align*} - -Based on the similarity of these formulae, the two-layer feed-forward network can be seen as a kind of attention, where the keys and values are the rows of the trainable parameter matrices $W_1$ and $W_2$, and where we use ReLU instead of Softmax in the compatibility function. - -%the compatablity function is $compat(q, k_i) = ReLU(q \cdot k_i)$ instead of $Softmax(qK_T)_i$. - -Given this similarity, we experimented with replacing the position-wise feed-forward networks with attention layers similar to the ones we use everywhere else our model. The multi-head-attention-over-parameters sublayer is identical to the multi-head attention described in \ref{sec:multihead}, except that the "keys" and "values" inputs to each attention head are trainable model parameters, as opposed to being linear projections of a previous layer. These parameters are scaled up by a factor of $\sqrt{d_{model}}$ in order to be more similar to activations. - -In our first experiment, we replaced each position-wise feed-forward network with a multi-head-attention-over-parameters sublayer with $h_p=8$ heads, key-dimensionality $d_{pk}=64$, and value-dimensionality $d_{pv}=64$, using $n_p=1536$ key-value pairs for each attention head. The sublayer has a total of $2097152$ parameters, including the parameters in the query projection and the output projection. This matches the number of parameters in the position-wise feed-forward network that we replaced. While the theoretical amount of computation is also the same, in practice, the attention version caused the step times to be about 30\% longer. - -In our second experiment, we used $h_p=8$ heads, and $n_p=512$ key-value pairs for each attention head, again matching the total number of parameters in the base model. - -Results for the first experiment were slightly worse than for the base model, and results for the second experiment were slightly better, see Table~\ref{tab:parameter_attention}. - -\begin{table}[h] -\caption{Replacing the position-wise feed-forward networks with multihead-attention-over-parameters produces similar results to the base model. All metrics are on the English-to-German translation development set, newstest2013.} -\label{tab:parameter_attention} -\begin{center} -\vspace{-2mm} -%\scalebox{1.0}{ -\begin{tabular}{c|cccccc|cccc} -\hline\rule{0pt}{2.0ex} - & \multirow{2}{*}{$\dmodel$} & \multirow{2}{*}{$\dff$} & -\multirow{2}{*}{$h_p$} & \multirow{2}{*}{$d_{pk}$} & \multirow{2}{*}{$d_{pv}$} & - \multirow{2}{*}{$n_p$} & - PPL & BLEU & params & training\\ - & & & & & & & (dev) & (dev) & $\times10^6$ & time \\ -\hline\rule{0pt}{2.0ex} -base & 512 & 2048 & & & & & 4.92 & 25.8 & 65 & 12 hours\\ -\hline\rule{0pt}{2.0ex} -AOP$_1$ & 512 & & 8 & 64 & 64 & 1536 & 4.92& 25.5 & 65 & 16 hours\\ -AOP$_2$ & 512 & & 16 & 64 & 64 & 512 & \textbf{4.86} & \textbf{25.9} & 65 & 16 hours \\ -\hline -\end{tabular} -%} -\end{center} -\end{table} diff --git a/spaces/dalexanderch/SweetNet/app.py b/spaces/dalexanderch/SweetNet/app.py deleted file mode 100644 index 15498fe66d4d0560f7c3cc5b14fc641d5fec5b42..0000000000000000000000000000000000000000 --- a/spaces/dalexanderch/SweetNet/app.py +++ /dev/null @@ -1,117 +0,0 @@ -import os -os.system("pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu") -os.system("pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.12.0+cpu.html") -import gradio as gr -from glycowork.ml.processing import dataset_to_dataloader -import numpy as np -import torch -import torch.nn as nn -from glycowork.motif.graph import glycan_to_nxGraph -import networkx as nx -import pydot -# import pygraphviz as pgv - - -class EnsembleModel(nn.Module): - def __init__(self, models): - super().__init__() - self.models = models - - def forward(self, data): - # Check if GPU available - device = "cpu" - if torch.cuda.is_available(): - device = "cuda:0" - # Prepare data - x = data.labels.to(device) - edge_index = data.edge_index.to(device) - batch = data.batch.to(device) - y_pred = [model(x,edge_index, batch).cpu().detach().numpy() for model in self.models] - y_pred = np.mean(y_pred,axis=0)[0] - return y_pred - -class_list=['Amoebozoa', 'Animalia', 'Bacteria', 'Bamfordvirae', 'Chromista', 'Euryarchaeota', 'Excavata', 'Fungi', 'Heunggongvirae', - 'Orthornavirae', 'Pararnavirae', 'Plantae', 'Proteoarchaeota', 'Protista', 'Riboviria'] - -model1 = torch.load("model1.pt", map_location=torch.device('cpu')) -model2 = torch.load("model2.pt", map_location=torch.device('cpu')) -model3 = torch.load("model3.pt", map_location=torch.device('cpu')) -model4 = torch.load("model4.pt", map_location=torch.device('cpu')) -model5 = torch.load("model5.pt", map_location=torch.device('cpu')) -model6 = torch.load("model6.pt", map_location=torch.device('cpu')) -model7 = torch.load("model7.pt", map_location=torch.device('cpu')) - -def fn(glycan, model): - # Draw graph - #graph = glycan_to_nxGraph(glycan) - #node_labels = nx.get_node_attributes(graph, 'string_labels') - #labels = {i:node_labels[i] for i in range(len(graph.nodes))} - #graph = nx.relabel_nodes(graph, labels) - #graph = nx.drawing.nx_pydot.to_pydot(graph) - #graph.set_prog("dot") - #graph.write_png("graph.png") - # write_dot(graph, "graph.dot") - # graph=pgv.AGraph("graph.dot") - # graph.layout(prog='dot') - # graph.draw("graph.png") - # Perform inference - if model == "No data augmentation": - model_pred = model1 - model_pred.eval() - elif model == "Classical Ensemble": - model_pred = model3 - model_pred.eval() - elif model == "Bagging ensemble": - model_pred = model4 - model_pred.eval() - elif model == "Random edge deletion": - model_pred = model5 - model_pred.eval() - elif model == "Hierarchy substitution": - model_pred = model6 - model_pred.eval() - elif model == "Adjusted class weights": - model_pred = model7 - model_pred.eval() - else: - model_pred = model2 - model_pred.eval() - - glycan = [glycan] - label = [0] - data = next(iter(dataset_to_dataloader(glycan, label, batch_size=1))) - - if model in ["Ensemble", "Bootstrap ensemble"]: - pred = model_pred(data) - else: - device = "cpu" - x = data.labels - edge_index = data.edge_index - batch = data.batch - x = x.to(device) - edge_index = edge_index.to(device) - batch = batch.to(device) - pred = model_pred(x,edge_index, batch).cpu().detach().numpy()[0] - - pred = np.exp(pred)/sum(np.exp(pred)) # Softmax - pred = [float(x) for x in pred] - pred = {class_list[i]:pred[i] for i in range(15)} - return pred - - -demo = gr.Interface( - fn=fn, - inputs=[gr.Textbox(label="Glycan sequence", value="Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc"), gr.Radio(label="Model",choices=["No data augmentation", "Random node deletion", "Random edge deletion", "Ensemble", "Bootstrap ensemble", "Hierarchy substitution", "Adjusted class weights"])], - outputs=[gr.Label(num_top_classes=15, label="Prediction")], - allow_flagging="never", - title="SweetNet demo", - examples=[ - ["D-Rha(b1-2)D-Rha(b1-2)Gal(b1-4)[Glc(b1-2)]GlcAOMe", "Random node deletion"], - ["Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)GalNAc", "No data augmentation"], - ["Kdo(a2-4)[Kdo(a2-8)]Kdo(a2-4)Kdo", "Classical ensemble"], - ["Galf(b1-6)Galf(b1-5)Galf(b1-6)Galf", "Bagging Ensemble"], - ["GlcNAc(b1-2)Rha(a1-2)Rha(b1-3)Rha(a1-3)GlcNAc", "Random edge deletion"], - ["Pse(b2-6)Glc(b1-6)Gal(b1-3)GalNAc(b1-3)[Glc(b1-6)]Gal(b1-3)GalNAc", "Adjusted class weights"], - ] -) -demo.launch(debug=True) \ No newline at end of file diff --git a/spaces/dariush-bahrami/color_transfer/colortransfer/transfer.py b/spaces/dariush-bahrami/color_transfer/colortransfer/transfer.py deleted file mode 100644 index 4a7db6b002b3b138669f140ce7105aecce609d50..0000000000000000000000000000000000000000 --- a/spaces/dariush-bahrami/color_transfer/colortransfer/transfer.py +++ /dev/null @@ -1,34 +0,0 @@ -import numpy as np -import cv2 as cv - - -def transfer_color(source_image: np.ndarray, target_image: np.ndarray) -> np.ndarray: - """Color transfer between images - - Args: - source_image (np.ndarray): Color source image - target_image (np.ndarray): Target image - - Returns: - np.ndarray: The result of the color transfer - - Reference: - doi: 10.1109/38.946629 - """ - # RGB -> L*a*b* - src_img = cv.cvtColor(source_image, cv.COLOR_RGB2Lab) - dst_img = cv.cvtColor(target_image, cv.COLOR_RGB2Lab) - - # Calculate mean and std - src_means, src_stds = src_img.mean(axis=(0, 1)), src_img.std(axis=(0, 1)) - dst_means, dst_stds = dst_img.mean(axis=(0, 1)), dst_img.std(axis=(0, 1)) - - # Transfer - dst_img = dst_img - dst_means.reshape((1, 1, 3)) - dst_img *= (dst_stds / src_stds).reshape((1, 1, 3)) - dst_img += src_means.reshape((1, 1, 3)) - - # L*a*b* -> RGB - dst_img = np.clip(dst_img, 0, 255).astype(np.uint8) - dst_img = cv.cvtColor(dst_img, cv.COLOR_LAB2RGB) - return dst_img diff --git a/spaces/dawdqd/ChuanhuChatGPT/ChuanhuChatbot.py b/spaces/dawdqd/ChuanhuChatGPT/ChuanhuChatbot.py deleted file mode 100644 index d498359af5c02037247406830672bcbbdbb7006b..0000000000000000000000000000000000000000 --- a/spaces/dawdqd/ChuanhuChatGPT/ChuanhuChatbot.py +++ /dev/null @@ -1,559 +0,0 @@ -# -*- coding:utf-8 -*- -import logging -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s", -) - -import colorama -import gradio as gr - -from modules import config -from modules.config import * -from modules.utils import * -from modules.presets import * -from modules.overwrites import * -from modules.webui import * -from modules.repo import * -from modules.train_func import * -from modules.models.models import get_model - -logging.getLogger("httpx").setLevel(logging.WARNING) - -gr.Chatbot._postprocess_chat_messages = postprocess_chat_messages -gr.Chatbot.postprocess = postprocess - -# with open("web_assets/css/ChuanhuChat.css", "r", encoding="utf-8") as f: -# ChuanhuChatCSS = f.read() - -def create_new_model(): - return get_model(model_name = MODELS[DEFAULT_MODEL], access_key = my_api_key)[0] - -with gr.Blocks(theme=small_and_beautiful_theme) as demo: - user_name = gr.State("") - promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2)) - user_question = gr.State("") - assert type(my_api_key)==str - user_api_key = gr.State(my_api_key) - current_model = gr.State(create_new_model) - - topic = gr.State(i18n("未命名对话历史记录")) - - with gr.Row(): - gr.HTML(CHUANHU_TITLE, elem_id="app-title") - status_display = gr.Markdown(get_geoip(), elem_id="status-display") - with gr.Row(elem_id="float-display"): - user_info = gr.Markdown(value="getting user info...", elem_id="user-info") - config_info = gr.HTML(get_html("config_info.html").format(bot_avatar=config.bot_avatar, user_avatar=config.user_avatar), visible=False, elem_id="config-info") - update_info = gr.HTML(get_html("update.html").format( - current_version=repo_tag_html(), - version_time=version_time(), - cancel_btn=i18n("取消"), - update_btn=i18n("更新"), - seenew_btn=i18n("详情"), - ok_btn=i18n("好"), - ), visible=check_update) - - with gr.Row(equal_height=True): - with gr.Column(scale=5): - with gr.Row(): - chatbot = gr.Chatbot(label="Chuanhu Chat", elem_id="chuanhu-chatbot", latex_delimiters=latex_delimiters_set, height=700) - with gr.Row(): - with gr.Column(min_width=225, scale=12): - user_input = gr.Textbox( - elem_id="user-input-tb", - show_label=False, placeholder=i18n("在这里输入"), - container=False - ) - with gr.Column(min_width=42, scale=1): - submitBtn = gr.Button(value="", variant="primary", elem_id="submit-btn") - cancelBtn = gr.Button(value="", variant="secondary", visible=False, elem_id="cancel-btn") - with gr.Row(elem_id="chatbot-buttons"): - with gr.Column(min_width=120, scale=1): - emptyBtn = gr.Button( - i18n("🧹 新的对话"), elem_id="empty-btn" - ) - with gr.Column(min_width=120, scale=1): - retryBtn = gr.Button(i18n("🔄 重新生成")) - with gr.Column(min_width=120, scale=1): - delFirstBtn = gr.Button(i18n("🗑️ 删除最旧对话")) - with gr.Column(min_width=120, scale=1): - delLastBtn = gr.Button(i18n("🗑️ 删除最新对话")) - with gr.Row(visible=False) as like_dislike_area: - with gr.Column(min_width=20, scale=1): - likeBtn = gr.Button(i18n("👍")) - with gr.Column(min_width=20, scale=1): - dislikeBtn = gr.Button(i18n("👎")) - - with gr.Column(): - with gr.Column(min_width=50, scale=1): - with gr.Tab(label=i18n("模型")): - keyTxt = gr.Textbox( - show_label=True, - placeholder=f"Your API-key...", - value=hide_middle_chars(user_api_key.value), - type="password", - visible=not HIDE_MY_KEY, - label="API-Key", - ) - if multi_api_key: - usageTxt = gr.Markdown(i18n("多账号模式已开启，无需输入key，可直接开始对话"), elem_id="usage-display", elem_classes="insert-block", visible=show_api_billing) - else: - usageTxt = gr.Markdown(i18n("**发送消息** 或 **提交key** 以显示额度"), elem_id="usage-display", elem_classes="insert-block", visible=show_api_billing) - model_select_dropdown = gr.Dropdown( - label=i18n("选择模型"), choices=MODELS, multiselect=False, value=MODELS[DEFAULT_MODEL], interactive=True - ) - lora_select_dropdown = gr.Dropdown( - label=i18n("选择LoRA模型"), choices=[], multiselect=False, interactive=True, visible=False - ) - with gr.Row(): - single_turn_checkbox = gr.Checkbox(label=i18n("单轮对话"), value=False, elem_classes="switch-checkbox") - use_websearch_checkbox = gr.Checkbox(label=i18n("使用在线搜索"), value=False, elem_classes="switch-checkbox") - language_select_dropdown = gr.Dropdown( - label=i18n("选择回复语言（针对搜索&索引功能）"), - choices=REPLY_LANGUAGES, - multiselect=False, - value=REPLY_LANGUAGES[0], - ) - index_files = gr.Files(label=i18n("上传"), type="file", elem_id="upload-index-file") - two_column = gr.Checkbox(label=i18n("双栏pdf"), value=advance_docs["pdf"].get("two_column", False)) - summarize_btn = gr.Button(i18n("总结")) - # TODO: 公式ocr - # formula_ocr = gr.Checkbox(label=i18n("识别公式"), value=advance_docs["pdf"].get("formula_ocr", False)) - - with gr.Tab(label="Prompt"): - systemPromptTxt = gr.Textbox( - show_label=True, - placeholder=i18n("在这里输入System Prompt..."), - label="System prompt", - value=INITIAL_SYSTEM_PROMPT, - lines=10 - ) - with gr.Accordion(label=i18n("加载Prompt模板"), open=True): - with gr.Column(): - with gr.Row(): - with gr.Column(scale=6): - templateFileSelectDropdown = gr.Dropdown( - label=i18n("选择Prompt模板集合文件"), - choices=get_template_names(plain=True), - multiselect=False, - value=get_template_names(plain=True)[0], - container=False, - ) - with gr.Column(scale=1): - templateRefreshBtn = gr.Button(i18n("🔄 刷新")) - with gr.Row(): - with gr.Column(): - templateSelectDropdown = gr.Dropdown( - label=i18n("从Prompt模板中加载"), - choices=load_template( - get_template_names(plain=True)[0], mode=1 - ), - multiselect=False, - container=False, - ) - - with gr.Tab(label=i18n("保存/加载")): - with gr.Accordion(label=i18n("保存/加载对话历史记录"), open=True): - with gr.Column(): - with gr.Row(): - with gr.Column(scale=6): - historyFileSelectDropdown = gr.Dropdown( - label=i18n("从列表中加载对话"), - choices=get_history_names(plain=True), - multiselect=False, - container=False, - ) - with gr.Row(): - with gr.Column(min_width=42, scale=1): - historyRefreshBtn = gr.Button(i18n("🔄 刷新")) - with gr.Column(min_width=42, scale=1): - historyDeleteBtn = gr.Button(i18n("🗑️ 删除")) - with gr.Row(): - with gr.Column(scale=6): - saveFileName = gr.Textbox( - show_label=True, - placeholder=i18n("设置文件名: 默认为.json，可选为.md"), - label=i18n("设置保存文件名"), - value=i18n("对话历史记录"), - elem_classes="no-container" - # container=False, - ) - with gr.Column(scale=1): - saveHistoryBtn = gr.Button(i18n("💾 保存对话")) - exportMarkdownBtn = gr.Button(i18n("📝 导出为Markdown")) - gr.Markdown(i18n("默认保存于history文件夹")) - with gr.Row(): - with gr.Column(): - downloadFile = gr.File(interactive=True) - - with gr.Tab(label=i18n("微调")): - openai_train_status = gr.Markdown(label=i18n("训练状态"), value=i18n("在这里[查看使用介绍](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/%E4%BD%BF%E7%94%A8%E6%95%99%E7%A8%8B#%E5%BE%AE%E8%B0%83-gpt-35)")) - - with gr.Tab(label=i18n("准备数据集")): - dataset_preview_json = gr.JSON(label=i18n("数据集预览"), readonly=True) - dataset_selection = gr.Files(label = i18n("选择数据集"), file_types=[".xlsx", ".jsonl"], file_count="single") - upload_to_openai_btn = gr.Button(i18n("上传到OpenAI"), variant="primary", interactive=False) - - with gr.Tab(label=i18n("训练")): - openai_ft_file_id = gr.Textbox(label=i18n("文件ID"), value="", lines=1, placeholder=i18n("上传到 OpenAI 后自动填充")) - openai_ft_suffix = gr.Textbox(label=i18n("模型名称后缀"), value="", lines=1, placeholder=i18n("可选，用于区分不同的模型")) - openai_train_epoch_slider = gr.Slider(label=i18n("训练轮数（Epochs）"), minimum=1, maximum=100, value=3, step=1, interactive=True) - openai_start_train_btn = gr.Button(i18n("开始训练"), variant="primary", interactive=False) - - with gr.Tab(label=i18n("状态")): - openai_status_refresh_btn = gr.Button(i18n("刷新状态")) - openai_cancel_all_jobs_btn = gr.Button(i18n("取消所有任务")) - add_to_models_btn = gr.Button(i18n("添加训练好的模型到模型列表"), interactive=False) - - with gr.Tab(label=i18n("高级")): - gr.HTML(get_html("appearance_switcher.html").format(label=i18n("切换亮暗色主题")), elem_classes="insert-block") - use_streaming_checkbox = gr.Checkbox( - label=i18n("实时传输回答"), value=True, visible=ENABLE_STREAMING_OPTION, elem_classes="switch-checkbox" - ) - checkUpdateBtn = gr.Button(i18n("🔄 检查更新..."), visible=check_update) - gr.Markdown(i18n("# ⚠️ 务必谨慎更改 ⚠️"), elem_id="advanced-warning") - with gr.Accordion(i18n("参数"), open=False): - temperature_slider = gr.Slider( - minimum=-0, - maximum=2.0, - value=1.0, - step=0.1, - interactive=True, - label="temperature", - ) - top_p_slider = gr.Slider( - minimum=-0, - maximum=1.0, - value=1.0, - step=0.05, - interactive=True, - label="top-p", - ) - n_choices_slider = gr.Slider( - minimum=1, - maximum=10, - value=1, - step=1, - interactive=True, - label="n choices", - ) - stop_sequence_txt = gr.Textbox( - show_label=True, - placeholder=i18n("停止符，用英文逗号隔开..."), - label="stop", - value="", - lines=1, - ) - max_context_length_slider = gr.Slider( - minimum=1, - maximum=32768, - value=2000, - step=1, - interactive=True, - label="max context", - ) - max_generation_slider = gr.Slider( - minimum=1, - maximum=32768, - value=1000, - step=1, - interactive=True, - label="max generations", - ) - presence_penalty_slider = gr.Slider( - minimum=-2.0, - maximum=2.0, - value=0.0, - step=0.01, - interactive=True, - label="presence penalty", - ) - frequency_penalty_slider = gr.Slider( - minimum=-2.0, - maximum=2.0, - value=0.0, - step=0.01, - interactive=True, - label="frequency penalty", - ) - logit_bias_txt = gr.Textbox( - show_label=True, - placeholder=f"word:likelihood", - label="logit bias", - value="", - lines=1, - ) - user_identifier_txt = gr.Textbox( - show_label=True, - placeholder=i18n("用于定位滥用行为"), - label=i18n("用户名"), - value=user_name.value, - lines=1, - ) - - with gr.Accordion(i18n("网络参数"), open=False): - gr.Markdown(i18n("---\n⚠️ 为保证API-Key安全，请在配置文件`config.json`中修改网络设置"), elem_id="netsetting-warning") - default_btn = gr.Button(i18n("🔙 恢复默认网络设置")) - # 网络代理 - proxyTxt = gr.Textbox( - show_label=True, - placeholder=i18n("未设置代理..."), - label=i18n("代理地址"), - value=config.http_proxy, - lines=1, - interactive=False, - # container=False, - elem_classes="view-only-textbox no-container", - ) - # changeProxyBtn = gr.Button(i18n("🔄 设置代理地址")) - - # 优先展示自定义的api_host - apihostTxt = gr.Textbox( - show_label=True, - placeholder="api.openai.com", - label="OpenAI API-Host", - value=config.api_host or shared.API_HOST, - lines=1, - interactive=False, - # container=False, - elem_classes="view-only-textbox no-container", - ) - # changeAPIURLBtn = gr.Button(i18n("🔄 切换API地址")) - updateChuanhuBtn = gr.Button(visible=False, elem_classes="invisible-btn", elem_id="update-chuanhu-btn") - - - gr.Markdown(CHUANHU_DESCRIPTION, elem_id="description") - gr.HTML(get_html("footer.html").format(versions=versions_html()), elem_id="footer") - - # https://github.com/gradio-app/gradio/pull/3296 - def create_greeting(request: gr.Request): - if hasattr(request, "username") and request.username: # is not None or is not "" - logging.info(f"Get User Name: {request.username}") - user_info, user_name = gr.Markdown.update(value=f"User: {request.username}"), request.username - else: - user_info, user_name = gr.Markdown.update(value=f"", visible=False), "" - current_model = get_model(model_name = MODELS[DEFAULT_MODEL], access_key = my_api_key)[0] - current_model.set_user_identifier(user_name) - chatbot = gr.Chatbot.update(label=MODELS[DEFAULT_MODEL]) - return user_info, user_name, current_model, toggle_like_btn_visibility(DEFAULT_MODEL), *current_model.auto_load(), get_history_names(False, user_name), chatbot - demo.load(create_greeting, inputs=None, outputs=[user_info, user_name, current_model, like_dislike_area, systemPromptTxt, chatbot, historyFileSelectDropdown, chatbot], api_name="load") - chatgpt_predict_args = dict( - fn=predict, - inputs=[ - current_model, - user_question, - chatbot, - use_streaming_checkbox, - use_websearch_checkbox, - index_files, - language_select_dropdown, - ], - outputs=[chatbot, status_display], - show_progress=True, - ) - - start_outputing_args = dict( - fn=start_outputing, - inputs=[], - outputs=[submitBtn, cancelBtn], - show_progress=True, - ) - - end_outputing_args = dict( - fn=end_outputing, inputs=[], outputs=[submitBtn, cancelBtn] - ) - - reset_textbox_args = dict( - fn=reset_textbox, inputs=[], outputs=[user_input] - ) - - transfer_input_args = dict( - fn=transfer_input, inputs=[user_input], outputs=[user_question, user_input, submitBtn, cancelBtn], show_progress=True - ) - - get_usage_args = dict( - fn=billing_info, inputs=[current_model], outputs=[usageTxt], show_progress=False - ) - - load_history_from_file_args = dict( - fn=load_chat_history, - inputs=[current_model, historyFileSelectDropdown, user_name], - outputs=[saveFileName, systemPromptTxt, chatbot] - ) - - refresh_history_args = dict( - fn=get_history_names, inputs=[gr.State(False), user_name], outputs=[historyFileSelectDropdown] - ) - - - # Chatbot - cancelBtn.click(interrupt, [current_model], []) - - user_input.submit(**transfer_input_args).then(**chatgpt_predict_args).then(**end_outputing_args) - user_input.submit(**get_usage_args) - - submitBtn.click(**transfer_input_args).then(**chatgpt_predict_args, api_name="predict").then(**end_outputing_args) - submitBtn.click(**get_usage_args) - - index_files.change(handle_file_upload, [current_model, index_files, chatbot, language_select_dropdown], [index_files, chatbot, status_display]) - summarize_btn.click(handle_summarize_index, [current_model, index_files, chatbot, language_select_dropdown], [chatbot, status_display]) - - emptyBtn.click( - reset, - inputs=[current_model], - outputs=[chatbot, status_display], - show_progress=True, - _js='clearChatbot', - ) - - retryBtn.click(**start_outputing_args).then( - retry, - [ - current_model, - chatbot, - use_streaming_checkbox, - use_websearch_checkbox, - index_files, - language_select_dropdown, - ], - [chatbot, status_display], - show_progress=True, - ).then(**end_outputing_args) - retryBtn.click(**get_usage_args) - - delFirstBtn.click( - delete_first_conversation, - [current_model], - [status_display], - ) - - delLastBtn.click( - delete_last_conversation, - [current_model, chatbot], - [chatbot, status_display], - show_progress=False - ) - - likeBtn.click( - like, - [current_model], - [status_display], - show_progress=False - ) - - dislikeBtn.click( - dislike, - [current_model], - [status_display], - show_progress=False - ) - - two_column.change(update_doc_config, [two_column], None) - - # LLM Models - keyTxt.change(set_key, [current_model, keyTxt], [user_api_key, status_display], api_name="set_key").then(**get_usage_args) - keyTxt.submit(**get_usage_args) - single_turn_checkbox.change(set_single_turn, [current_model, single_turn_checkbox], None) - model_select_dropdown.change(get_model, [model_select_dropdown, lora_select_dropdown, user_api_key, temperature_slider, top_p_slider, systemPromptTxt, user_name], [current_model, status_display, chatbot, lora_select_dropdown, user_api_key, keyTxt], show_progress=True, api_name="get_model") - model_select_dropdown.change(toggle_like_btn_visibility, [model_select_dropdown], [like_dislike_area], show_progress=False) - lora_select_dropdown.change(get_model, [model_select_dropdown, lora_select_dropdown, user_api_key, temperature_slider, top_p_slider, systemPromptTxt, user_name], [current_model, status_display, chatbot], show_progress=True) - - # Template - systemPromptTxt.change(set_system_prompt, [current_model, systemPromptTxt], None) - templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown]) - templateFileSelectDropdown.change( - load_template, - [templateFileSelectDropdown], - [promptTemplates, templateSelectDropdown], - show_progress=True, - ) - templateSelectDropdown.change( - get_template_content, - [promptTemplates, templateSelectDropdown, systemPromptTxt], - [systemPromptTxt], - show_progress=True, - ) - - # S&L - saveHistoryBtn.click( - save_chat_history, - [current_model, saveFileName, chatbot, user_name], - downloadFile, - show_progress=True, - ) - saveHistoryBtn.click(get_history_names, [gr.State(False), user_name], [historyFileSelectDropdown]) - exportMarkdownBtn.click( - export_markdown, - [current_model, saveFileName, chatbot, user_name], - downloadFile, - show_progress=True, - ) - historyRefreshBtn.click(**refresh_history_args) - historyDeleteBtn.click(delete_chat_history, [current_model, historyFileSelectDropdown, user_name], [status_display, historyFileSelectDropdown, chatbot], _js='(a,b,c)=>{return showConfirmationDialog(a, b, c);}') - historyFileSelectDropdown.change(**load_history_from_file_args) - downloadFile.change(upload_chat_history, [current_model, downloadFile, user_name], [saveFileName, systemPromptTxt, chatbot]) - - # Train - dataset_selection.upload(handle_dataset_selection, dataset_selection, [dataset_preview_json, upload_to_openai_btn, openai_train_status]) - dataset_selection.clear(handle_dataset_clear, [], [dataset_preview_json, upload_to_openai_btn]) - upload_to_openai_btn.click(upload_to_openai, [dataset_selection], [openai_ft_file_id, openai_train_status], show_progress=True) - - openai_ft_file_id.change(lambda x: gr.update(interactive=True) if len(x) > 0 else gr.update(interactive=False), [openai_ft_file_id], [openai_start_train_btn]) - openai_start_train_btn.click(start_training, [openai_ft_file_id, openai_ft_suffix, openai_train_epoch_slider], [openai_train_status]) - - openai_status_refresh_btn.click(get_training_status, [], [openai_train_status, add_to_models_btn]) - add_to_models_btn.click(add_to_models, [], [model_select_dropdown, openai_train_status], show_progress=True) - openai_cancel_all_jobs_btn.click(cancel_all_jobs, [], [openai_train_status], show_progress=True) - - # Advanced - max_context_length_slider.change(set_token_upper_limit, [current_model, max_context_length_slider], None) - temperature_slider.change(set_temperature, [current_model, temperature_slider], None) - top_p_slider.change(set_top_p, [current_model, top_p_slider], None) - n_choices_slider.change(set_n_choices, [current_model, n_choices_slider], None) - stop_sequence_txt.change(set_stop_sequence, [current_model, stop_sequence_txt], None) - max_generation_slider.change(set_max_tokens, [current_model, max_generation_slider], None) - presence_penalty_slider.change(set_presence_penalty, [current_model, presence_penalty_slider], None) - frequency_penalty_slider.change(set_frequency_penalty, [current_model, frequency_penalty_slider], None) - logit_bias_txt.change(set_logit_bias, [current_model, logit_bias_txt], None) - user_identifier_txt.change(set_user_identifier, [current_model, user_identifier_txt], None) - - default_btn.click( - reset_default, [], [apihostTxt, proxyTxt, status_display], show_progress=True - ) - # changeAPIURLBtn.click( - # change_api_host, - # [apihostTxt], - # [status_display], - # show_progress=True, - # ) - # changeProxyBtn.click( - # change_proxy, - # [proxyTxt], - # [status_display], - # show_progress=True, - # ) - checkUpdateBtn.click(fn=None, _js='manualCheckUpdate') - - # Invisible elements - updateChuanhuBtn.click( - update_chuanhu, - [], - [status_display], - show_progress=True, - ) - -logging.info( - colorama.Back.GREEN - + "\n川虎的温馨提示：访问 http://localhost:7860 查看界面" - + colorama.Style.RESET_ALL -) -# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接 -demo.title = i18n("川虎Chat 🚀") - -if __name__ == "__main__": - reload_javascript() - demo.queue(concurrency_count=CONCURRENT_COUNT).launch( - blocked_paths=["config.json"], - favicon_path="./web_assets/favicon.ico", - ) diff --git a/spaces/dbirks/diffuse-the-rest/build/_app/immutable/components/error.svelte-ef6e4efb.js b/spaces/dbirks/diffuse-the-rest/build/_app/immutable/components/error.svelte-ef6e4efb.js deleted file mode 100644 index 2d7f5d42a257e6f781c9de183cbf3e802d12cfc9..0000000000000000000000000000000000000000 --- a/spaces/dbirks/diffuse-the-rest/build/_app/immutable/components/error.svelte-ef6e4efb.js +++ /dev/null @@ -1 +0,0 @@ -import{S as A,i as C,s as F,k as v,q as k,a as h,e as q,l as g,m as E,r as $,h as p,c as R,b as u,F as P,u as S,A as w,G}from"../chunks/index-a207c28c.js";import{s as H}from"../chunks/singletons-a29cf3c6.js";const O=()=>{const t=H,s={page:{subscribe:t.page.subscribe},navigating:{subscribe:t.navigating.subscribe},updated:t.updated};return Object.defineProperties(s,{preloading:{get(){return console.error("stores.preloading is deprecated; use stores.navigating instead"),{subscribe:t.navigating.subscribe}},enumerable:!1},session:{get(){return B(),{}},enumerable:!1}}),s},z={subscribe(t){return O().page.subscribe(t)}};function B(){throw new Error("stores.session is no longer available. See https://github.com/sveltejs/kit/discussions/5883")}function N(t){let s,i=t[0].error.frame+"",o;return{c(){s=v("pre"),o=k(i)},l(r){s=g(r,"PRE",{});var a=E(s);o=$(a,i),a.forEach(p)},m(r,a){u(r,s,a),P(s,o)},p(r,a){a&1&&i!==(i=r[0].error.frame+"")&&S(o,i)},d(r){r&&p(s)}}}function y(t){let s,i=t[0].error.stack+"",o;return{c(){s=v("pre"),o=k(i)},l(r){s=g(r,"PRE",{});var a=E(s);o=$(a,i),a.forEach(p)},m(r,a){u(r,s,a),P(s,o)},p(r,a){a&1&&i!==(i=r[0].error.stack+"")&&S(o,i)},d(r){r&&p(s)}}}function D(t){let s,i=t[0].status+"",o,r,a,b=t[0].error.message+"",_,d,c,m,l=t[0].error.frame&&N(t),n=t[0].error.stack&&y(t);return{c(){s=v("h1"),o=k(i),r=h(),a=v("pre"),_=k(b),d=h(),l&&l.c(),c=h(),n&&n.c(),m=q()},l(e){s=g(e,"H1",{});var f=E(s);o=$(f,i),f.forEach(p),r=R(e),a=g(e,"PRE",{});var j=E(a);_=$(j,b),j.forEach(p),d=R(e),l&&l.l(e),c=R(e),n&&n.l(e),m=q()},m(e,f){u(e,s,f),P(s,o),u(e,r,f),u(e,a,f),P(a,_),u(e,d,f),l&&l.m(e,f),u(e,c,f),n&&n.m(e,f),u(e,m,f)},p(e,[f]){f&1&&i!==(i=e[0].status+"")&&S(o,i),f&1&&b!==(b=e[0].error.message+"")&&S(_,b),e[0].error.frame?l?l.p(e,f):(l=N(e),l.c(),l.m(c.parentNode,c)):l&&(l.d(1),l=null),e[0].error.stack?n?n.p(e,f):(n=y(e),n.c(),n.m(m.parentNode,m)):n&&(n.d(1),n=null)},i:w,o:w,d(e){e&&p(s),e&&p(r),e&&p(a),e&&p(d),l&&l.d(e),e&&p(c),n&&n.d(e),e&&p(m)}}}function I(t,s,i){let o;return G(t,z,r=>i(0,o=r)),[o]}class L extends A{constructor(s){super(),C(this,s,I,D,F,{})}}export{L as default}; diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/streams.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/streams.py deleted file mode 100644 index 726b02326f66d37b9de1947cb78470479a7bc82b..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/streams.py +++ /dev/null @@ -1,660 +0,0 @@ -import asyncio -import collections -import warnings -from typing import Awaitable, Callable, Deque, Generic, List, Optional, Tuple, TypeVar - -from .base_protocol import BaseProtocol -from .helpers import BaseTimerContext, set_exception, set_result -from .log import internal_logger -from .typedefs import Final - -__all__ = ( - "EMPTY_PAYLOAD", - "EofStream", - "StreamReader", - "DataQueue", - "FlowControlDataQueue", -) - -_T = TypeVar("_T") - - -class EofStream(Exception): - """eof stream indication.""" - - -class AsyncStreamIterator(Generic[_T]): - def __init__(self, read_func: Callable[[], Awaitable[_T]]) -> None: - self.read_func = read_func - - def __aiter__(self) -> "AsyncStreamIterator[_T]": - return self - - async def __anext__(self) -> _T: - try: - rv = await self.read_func() - except EofStream: - raise StopAsyncIteration - if rv == b"": - raise StopAsyncIteration - return rv - - -class ChunkTupleAsyncStreamIterator: - def __init__(self, stream: "StreamReader") -> None: - self._stream = stream - - def __aiter__(self) -> "ChunkTupleAsyncStreamIterator": - return self - - async def __anext__(self) -> Tuple[bytes, bool]: - rv = await self._stream.readchunk() - if rv == (b"", False): - raise StopAsyncIteration - return rv - - -class AsyncStreamReaderMixin: - def __aiter__(self) -> AsyncStreamIterator[bytes]: - return AsyncStreamIterator(self.readline) # type: ignore[attr-defined] - - def iter_chunked(self, n: int) -> AsyncStreamIterator[bytes]: - """Returns an asynchronous iterator that yields chunks of size n. - - Python-3.5 available for Python 3.5+ only - """ - return AsyncStreamIterator( - lambda: self.read(n) # type: ignore[attr-defined,no-any-return] - ) - - def iter_any(self) -> AsyncStreamIterator[bytes]: - """Yield all available data as soon as it is received. - - Python-3.5 available for Python 3.5+ only - """ - return AsyncStreamIterator(self.readany) # type: ignore[attr-defined] - - def iter_chunks(self) -> ChunkTupleAsyncStreamIterator: - """Yield chunks of data as they are received by the server. - - The yielded objects are tuples - of (bytes, bool) as returned by the StreamReader.readchunk method. - - Python-3.5 available for Python 3.5+ only - """ - return ChunkTupleAsyncStreamIterator(self) # type: ignore[arg-type] - - -class StreamReader(AsyncStreamReaderMixin): - """An enhancement of asyncio.StreamReader. - - Supports asynchronous iteration by line, chunk or as available:: - - async for line in reader: - ... - async for chunk in reader.iter_chunked(1024): - ... - async for slice in reader.iter_any(): - ... - - """ - - total_bytes = 0 - - def __init__( - self, - protocol: BaseProtocol, - limit: int, - *, - timer: Optional[BaseTimerContext] = None, - loop: Optional[asyncio.AbstractEventLoop] = None, - ) -> None: - self._protocol = protocol - self._low_water = limit - self._high_water = limit * 2 - if loop is None: - loop = asyncio.get_event_loop() - self._loop = loop - self._size = 0 - self._cursor = 0 - self._http_chunk_splits: Optional[List[int]] = None - self._buffer: Deque[bytes] = collections.deque() - self._buffer_offset = 0 - self._eof = False - self._waiter: Optional[asyncio.Future[None]] = None - self._eof_waiter: Optional[asyncio.Future[None]] = None - self._exception: Optional[BaseException] = None - self._timer = timer - self._eof_callbacks: List[Callable[[], None]] = [] - - def __repr__(self) -> str: - info = [self.__class__.__name__] - if self._size: - info.append("%d bytes" % self._size) - if self._eof: - info.append("eof") - if self._low_water != 2**16: # default limit - info.append("low=%d high=%d" % (self._low_water, self._high_water)) - if self._waiter: - info.append("w=%r" % self._waiter) - if self._exception: - info.append("e=%r" % self._exception) - return "<%s>" % " ".join(info) - - def get_read_buffer_limits(self) -> Tuple[int, int]: - return (self._low_water, self._high_water) - - def exception(self) -> Optional[BaseException]: - return self._exception - - def set_exception(self, exc: BaseException) -> None: - self._exception = exc - self._eof_callbacks.clear() - - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_exception(waiter, exc) - - waiter = self._eof_waiter - if waiter is not None: - self._eof_waiter = None - set_exception(waiter, exc) - - def on_eof(self, callback: Callable[[], None]) -> None: - if self._eof: - try: - callback() - except Exception: - internal_logger.exception("Exception in eof callback") - else: - self._eof_callbacks.append(callback) - - def feed_eof(self) -> None: - self._eof = True - - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_result(waiter, None) - - waiter = self._eof_waiter - if waiter is not None: - self._eof_waiter = None - set_result(waiter, None) - - for cb in self._eof_callbacks: - try: - cb() - except Exception: - internal_logger.exception("Exception in eof callback") - - self._eof_callbacks.clear() - - def is_eof(self) -> bool: - """Return True if 'feed_eof' was called.""" - return self._eof - - def at_eof(self) -> bool: - """Return True if the buffer is empty and 'feed_eof' was called.""" - return self._eof and not self._buffer - - async def wait_eof(self) -> None: - if self._eof: - return - - assert self._eof_waiter is None - self._eof_waiter = self._loop.create_future() - try: - await self._eof_waiter - finally: - self._eof_waiter = None - - def unread_data(self, data: bytes) -> None: - """rollback reading some data from stream, inserting it to buffer head.""" - warnings.warn( - "unread_data() is deprecated " - "and will be removed in future releases (#3260)", - DeprecationWarning, - stacklevel=2, - ) - if not data: - return - - if self._buffer_offset: - self._buffer[0] = self._buffer[0][self._buffer_offset :] - self._buffer_offset = 0 - self._size += len(data) - self._cursor -= len(data) - self._buffer.appendleft(data) - self._eof_counter = 0 - - # TODO: size is ignored, remove the param later - def feed_data(self, data: bytes, size: int = 0) -> None: - assert not self._eof, "feed_data after feed_eof" - - if not data: - return - - self._size += len(data) - self._buffer.append(data) - self.total_bytes += len(data) - - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_result(waiter, None) - - if self._size > self._high_water and not self._protocol._reading_paused: - self._protocol.pause_reading() - - def begin_http_chunk_receiving(self) -> None: - if self._http_chunk_splits is None: - if self.total_bytes: - raise RuntimeError( - "Called begin_http_chunk_receiving when" "some data was already fed" - ) - self._http_chunk_splits = [] - - def end_http_chunk_receiving(self) -> None: - if self._http_chunk_splits is None: - raise RuntimeError( - "Called end_chunk_receiving without calling " - "begin_chunk_receiving first" - ) - - # self._http_chunk_splits contains logical byte offsets from start of - # the body transfer. Each offset is the offset of the end of a chunk. - # "Logical" means bytes, accessible for a user. - # If no chunks containig logical data were received, current position - # is difinitely zero. - pos = self._http_chunk_splits[-1] if self._http_chunk_splits else 0 - - if self.total_bytes == pos: - # We should not add empty chunks here. So we check for that. - # Note, when chunked + gzip is used, we can receive a chunk - # of compressed data, but that data may not be enough for gzip FSM - # to yield any uncompressed data. That's why current position may - # not change after receiving a chunk. - return - - self._http_chunk_splits.append(self.total_bytes) - - # wake up readchunk when end of http chunk received - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_result(waiter, None) - - async def _wait(self, func_name: str) -> None: - # StreamReader uses a future to link the protocol feed_data() method - # to a read coroutine. Running two read coroutines at the same time - # would have an unexpected behaviour. It would not possible to know - # which coroutine would get the next data. - if self._waiter is not None: - raise RuntimeError( - "%s() called while another coroutine is " - "already waiting for incoming data" % func_name - ) - - waiter = self._waiter = self._loop.create_future() - try: - if self._timer: - with self._timer: - await waiter - else: - await waiter - finally: - self._waiter = None - - async def readline(self) -> bytes: - return await self.readuntil() - - async def readuntil(self, separator: bytes = b"\n") -> bytes: - seplen = len(separator) - if seplen == 0: - raise ValueError("Separator should be at least one-byte string") - - if self._exception is not None: - raise self._exception - - chunk = b"" - chunk_size = 0 - not_enough = True - - while not_enough: - while self._buffer and not_enough: - offset = self._buffer_offset - ichar = self._buffer[0].find(separator, offset) + 1 - # Read from current offset to found separator or to the end. - data = self._read_nowait_chunk(ichar - offset if ichar else -1) - chunk += data - chunk_size += len(data) - if ichar: - not_enough = False - - if chunk_size > self._high_water: - raise ValueError("Chunk too big") - - if self._eof: - break - - if not_enough: - await self._wait("readuntil") - - return chunk - - async def read(self, n: int = -1) -> bytes: - if self._exception is not None: - raise self._exception - - # migration problem; with DataQueue you have to catch - # EofStream exception, so common way is to run payload.read() inside - # infinite loop. what can cause real infinite loop with StreamReader - # lets keep this code one major release. - if __debug__: - if self._eof and not self._buffer: - self._eof_counter = getattr(self, "_eof_counter", 0) + 1 - if self._eof_counter > 5: - internal_logger.warning( - "Multiple access to StreamReader in eof state, " - "might be infinite loop.", - stack_info=True, - ) - - if not n: - return b"" - - if n < 0: - # This used to just loop creating a new waiter hoping to - # collect everything in self._buffer, but that would - # deadlock if the subprocess sends more than self.limit - # bytes. So just call self.readany() until EOF. - blocks = [] - while True: - block = await self.readany() - if not block: - break - blocks.append(block) - return b"".join(blocks) - - # TODO: should be `if` instead of `while` - # because waiter maybe triggered on chunk end, - # without feeding any data - while not self._buffer and not self._eof: - await self._wait("read") - - return self._read_nowait(n) - - async def readany(self) -> bytes: - if self._exception is not None: - raise self._exception - - # TODO: should be `if` instead of `while` - # because waiter maybe triggered on chunk end, - # without feeding any data - while not self._buffer and not self._eof: - await self._wait("readany") - - return self._read_nowait(-1) - - async def readchunk(self) -> Tuple[bytes, bool]: - """Returns a tuple of (data, end_of_http_chunk). - - When chunked transfer - encoding is used, end_of_http_chunk is a boolean indicating if the end - of the data corresponds to the end of a HTTP chunk , otherwise it is - always False. - """ - while True: - if self._exception is not None: - raise self._exception - - while self._http_chunk_splits: - pos = self._http_chunk_splits.pop(0) - if pos == self._cursor: - return (b"", True) - if pos > self._cursor: - return (self._read_nowait(pos - self._cursor), True) - internal_logger.warning( - "Skipping HTTP chunk end due to data " - "consumption beyond chunk boundary" - ) - - if self._buffer: - return (self._read_nowait_chunk(-1), False) - # return (self._read_nowait(-1), False) - - if self._eof: - # Special case for signifying EOF. - # (b'', True) is not a final return value actually. - return (b"", False) - - await self._wait("readchunk") - - async def readexactly(self, n: int) -> bytes: - if self._exception is not None: - raise self._exception - - blocks: List[bytes] = [] - while n > 0: - block = await self.read(n) - if not block: - partial = b"".join(blocks) - raise asyncio.IncompleteReadError(partial, len(partial) + n) - blocks.append(block) - n -= len(block) - - return b"".join(blocks) - - def read_nowait(self, n: int = -1) -> bytes: - # default was changed to be consistent with .read(-1) - # - # I believe the most users don't know about the method and - # they are not affected. - if self._exception is not None: - raise self._exception - - if self._waiter and not self._waiter.done(): - raise RuntimeError( - "Called while some coroutine is waiting for incoming data." - ) - - return self._read_nowait(n) - - def _read_nowait_chunk(self, n: int) -> bytes: - first_buffer = self._buffer[0] - offset = self._buffer_offset - if n != -1 and len(first_buffer) - offset > n: - data = first_buffer[offset : offset + n] - self._buffer_offset += n - - elif offset: - self._buffer.popleft() - data = first_buffer[offset:] - self._buffer_offset = 0 - - else: - data = self._buffer.popleft() - - self._size -= len(data) - self._cursor += len(data) - - chunk_splits = self._http_chunk_splits - # Prevent memory leak: drop useless chunk splits - while chunk_splits and chunk_splits[0] < self._cursor: - chunk_splits.pop(0) - - if self._size < self._low_water and self._protocol._reading_paused: - self._protocol.resume_reading() - return data - - def _read_nowait(self, n: int) -> bytes: - """Read not more than n bytes, or whole buffer if n == -1""" - chunks = [] - - while self._buffer: - chunk = self._read_nowait_chunk(n) - chunks.append(chunk) - if n != -1: - n -= len(chunk) - if n == 0: - break - - return b"".join(chunks) if chunks else b"" - - -class EmptyStreamReader(StreamReader): # lgtm [py/missing-call-to-init] - def __init__(self) -> None: - pass - - def exception(self) -> Optional[BaseException]: - return None - - def set_exception(self, exc: BaseException) -> None: - pass - - def on_eof(self, callback: Callable[[], None]) -> None: - try: - callback() - except Exception: - internal_logger.exception("Exception in eof callback") - - def feed_eof(self) -> None: - pass - - def is_eof(self) -> bool: - return True - - def at_eof(self) -> bool: - return True - - async def wait_eof(self) -> None: - return - - def feed_data(self, data: bytes, n: int = 0) -> None: - pass - - async def readline(self) -> bytes: - return b"" - - async def read(self, n: int = -1) -> bytes: - return b"" - - # TODO add async def readuntil - - async def readany(self) -> bytes: - return b"" - - async def readchunk(self) -> Tuple[bytes, bool]: - return (b"", True) - - async def readexactly(self, n: int) -> bytes: - raise asyncio.IncompleteReadError(b"", n) - - def read_nowait(self, n: int = -1) -> bytes: - return b"" - - -EMPTY_PAYLOAD: Final[StreamReader] = EmptyStreamReader() - - -class DataQueue(Generic[_T]): - """DataQueue is a general-purpose blocking queue with one reader.""" - - def __init__(self, loop: asyncio.AbstractEventLoop) -> None: - self._loop = loop - self._eof = False - self._waiter: Optional[asyncio.Future[None]] = None - self._exception: Optional[BaseException] = None - self._size = 0 - self._buffer: Deque[Tuple[_T, int]] = collections.deque() - - def __len__(self) -> int: - return len(self._buffer) - - def is_eof(self) -> bool: - return self._eof - - def at_eof(self) -> bool: - return self._eof and not self._buffer - - def exception(self) -> Optional[BaseException]: - return self._exception - - def set_exception(self, exc: BaseException) -> None: - self._eof = True - self._exception = exc - - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_exception(waiter, exc) - - def feed_data(self, data: _T, size: int = 0) -> None: - self._size += size - self._buffer.append((data, size)) - - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_result(waiter, None) - - def feed_eof(self) -> None: - self._eof = True - - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_result(waiter, None) - - async def read(self) -> _T: - if not self._buffer and not self._eof: - assert not self._waiter - self._waiter = self._loop.create_future() - try: - await self._waiter - except (asyncio.CancelledError, asyncio.TimeoutError): - self._waiter = None - raise - - if self._buffer: - data, size = self._buffer.popleft() - self._size -= size - return data - else: - if self._exception is not None: - raise self._exception - else: - raise EofStream - - def __aiter__(self) -> AsyncStreamIterator[_T]: - return AsyncStreamIterator(self.read) - - -class FlowControlDataQueue(DataQueue[_T]): - """FlowControlDataQueue resumes and pauses an underlying stream. - - It is a destination for parsed data. - """ - - def __init__( - self, protocol: BaseProtocol, limit: int, *, loop: asyncio.AbstractEventLoop - ) -> None: - super().__init__(loop=loop) - - self._protocol = protocol - self._limit = limit * 2 - - def feed_data(self, data: _T, size: int = 0) -> None: - super().feed_data(data, size) - - if self._size > self._limit and not self._protocol._reading_paused: - self._protocol.pause_reading() - - async def read(self) -> _T: - try: - return await super().read() - finally: - if self._size < self._limit and self._protocol._reading_paused: - self._protocol.resume_reading() diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/callbacks.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/callbacks.py deleted file mode 100644 index 4500d02cbcae78d9cd764956d4cc46963b525213..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/callbacks.py +++ /dev/null @@ -1,238 +0,0 @@ -class Callback: - """ - Base class and interface for callback mechanism - - This class can be used directly for monitoring file transfers by - providing ``callback=Callback(hooks=...)`` (see the ``hooks`` argument, - below), or subclassed for more specialised behaviour. - - Parameters - ---------- - size: int (optional) - Nominal quantity for the value that corresponds to a complete - transfer, e.g., total number of tiles or total number of - bytes - value: int (0) - Starting internal counter value - hooks: dict or None - A dict of named functions to be called on each update. The signature - of these must be ``f(size, value, **kwargs)`` - """ - - def __init__(self, size=None, value=0, hooks=None, **kwargs): - self.size = size - self.value = value - self.hooks = hooks or {} - self.kw = kwargs - - def set_size(self, size): - """ - Set the internal maximum size attribute - - Usually called if not initially set at instantiation. Note that this - triggers a ``call()``. - - Parameters - ---------- - size: int - """ - self.size = size - self.call() - - def absolute_update(self, value): - """ - Set the internal value state - - Triggers ``call()`` - - Parameters - ---------- - value: int - """ - self.value = value - self.call() - - def relative_update(self, inc=1): - """ - Delta increment the internal counter - - Triggers ``call()`` - - Parameters - ---------- - inc: int - """ - self.value += inc - self.call() - - def call(self, hook_name=None, **kwargs): - """ - Execute hook(s) with current state - - Each function is passed the internal size and current value - - Parameters - ---------- - hook_name: str or None - If given, execute on this hook - kwargs: passed on to (all) hook(s) - """ - if not self.hooks: - return - kw = self.kw.copy() - kw.update(kwargs) - if hook_name: - if hook_name not in self.hooks: - return - return self.hooks[hook_name](self.size, self.value, **kw) - for hook in self.hooks.values() or []: - hook(self.size, self.value, **kw) - - def wrap(self, iterable): - """ - Wrap an iterable to call ``relative_update`` on each iterations - - Parameters - ---------- - iterable: Iterable - The iterable that is being wrapped - """ - for item in iterable: - self.relative_update() - yield item - - def branch(self, path_1, path_2, kwargs): - """ - Set callbacks for child transfers - - If this callback is operating at a higher level, e.g., put, which may - trigger transfers that can also be monitored. The passed kwargs are - to be *mutated* to add ``callback=``, if this class supports branching - to children. - - Parameters - ---------- - path_1: str - Child's source path - path_2: str - Child's destination path - kwargs: dict - arguments passed to child method, e.g., put_file. - - Returns - ------- - - """ - return None - - def no_op(self, *_, **__): - pass - - def __getattr__(self, item): - """ - If undefined methods are called on this class, nothing happens - """ - return self.no_op - - @classmethod - def as_callback(cls, maybe_callback=None): - """Transform callback=... into Callback instance - - For the special value of ``None``, return the global instance of - ``NoOpCallback``. This is an alternative to including - ``callback=_DEFAULT_CALLBACK`` directly in a method signature. - """ - if maybe_callback is None: - return _DEFAULT_CALLBACK - return maybe_callback - - -class NoOpCallback(Callback): - """ - This implementation of Callback does exactly nothing - """ - - def call(self, *args, **kwargs): - return None - - -class DotPrinterCallback(Callback): - """ - Simple example Callback implementation - - Almost identical to Callback with a hook that prints a char; here we - demonstrate how the outer layer may print "#" and the inner layer "." - """ - - def __init__(self, chr_to_print="#", **kwargs): - self.chr = chr_to_print - super().__init__(**kwargs) - - def branch(self, path_1, path_2, kwargs): - """Mutate kwargs to add new instance with different print char""" - kwargs["callback"] = DotPrinterCallback(".") - - def call(self, **kwargs): - """Just outputs a character""" - print(self.chr, end="") - - -class TqdmCallback(Callback): - """ - A callback to display a progress bar using tqdm - - Parameters - ---------- - tqdm_kwargs : dict, (optional) - Any argument accepted by the tqdm constructor. - See the `tqdm doc
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- diff --git a/spaces/diaoren/OpenSetObstacleDetection/opendet2/modeling/roi_heads/box_head.py b/spaces/diaoren/OpenSetObstacleDetection/opendet2/modeling/roi_heads/box_head.py deleted file mode 100644 index 16f74fe3bb29fa85476e9412eb54fafa47f87cc1..0000000000000000000000000000000000000000 --- a/spaces/diaoren/OpenSetObstacleDetection/opendet2/modeling/roi_heads/box_head.py +++ /dev/null @@ -1,149 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -from typing import List - -import fvcore.nn.weight_init as weight_init -import numpy as np -import torch -from detectron2.config import configurable -from detectron2.layers import Conv2d, ShapeSpec, get_norm -from detectron2.modeling.roi_heads import ROI_BOX_HEAD_REGISTRY -from detectron2.utils.registry import Registry -from torch import nn - - -@ROI_BOX_HEAD_REGISTRY.register() -class FastRCNNSeparateConvFCHead(nn.Module): - """ - FastRCNN with separate ConvFC layers - """ - - @configurable - def __init__( - self, input_shape: ShapeSpec, *, conv_dims: List[int], fc_dims: List[int], conv_norm="",has_edl=False - ): - """ - NOTE: this interface is experimental. - - Args: - input_shape (ShapeSpec): shape of the input feature. - conv_dims (list[int]): the output dimensions of the conv layers - fc_dims (list[int]): the output dimensions of the fc layers - conv_norm (str or callable): normalization for the conv layers. - See :func:`detectron2.layers.get_norm` for supported types. - """ - super().__init__() - self.has_edl = has_edl - assert len(conv_dims) + len(fc_dims) > 0 - self.conv_dims = conv_dims - self.fc_dims = fc_dims - - self._output_size = (input_shape.channels, - input_shape.height, input_shape.width)#（256,7,7,） - - self.reg_conv_norm_relus = self._add_conv_norm_relus( - self._output_size[0], conv_dims, conv_norm) - self.cls_conv_norm_relus = self._add_conv_norm_relus( - self._output_size[0], conv_dims, conv_norm) - conv_dim = self._output_size[0] if len(conv_dims) == 0 else conv_dims[-1] - self._output_size = ( - conv_dim, self._output_size[1], self._output_size[2]) - - self.reg_fcs = self._add_fcs(np.prod(self._output_size), fc_dims) - self.cls_fcs = self._add_fcs(np.prod(self._output_size), fc_dims) - if self.has_edl: - self.edl_fcs = self._add_fcs(np.prod(self._output_size), fc_dims) - self._output_size = self._output_size if len(fc_dims)==0 else fc_dims[-1]#1024 - - #权重初始化 - for layer in self.reg_conv_norm_relus: - weight_init.c2_msra_fill(layer) - for layer in self.cls_conv_norm_relus: - weight_init.c2_msra_fill(layer) - for layer in self.cls_fcs: - if isinstance(layer, nn.Linear): - weight_init.c2_xavier_fill(layer) - for layer in self.reg_fcs: - if isinstance(layer, nn.Linear): - weight_init.c2_xavier_fill(layer) - if self.has_edl: - for layer in self.edl_fcs: - if isinstance(layer, nn.Linear): - weight_init.c2_xavier_fill(layer) - - @classmethod - def from_config(cls, cfg, input_shape): - num_conv = cfg.MODEL.ROI_BOX_HEAD.NUM_CONV#0 - conv_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_DIM#256 - num_fc = cfg.MODEL.ROI_BOX_HEAD.NUM_FC#2 - fc_dim = cfg.MODEL.ROI_BOX_HEAD.FC_DIM#1024 - return { - "input_shape": input_shape, - "conv_dims": [conv_dim] * num_conv, - "fc_dims": [fc_dim] * num_fc, - "conv_norm": cfg.MODEL.ROI_BOX_HEAD.NORM, - "has_edl":cfg.EDLLOSS.HAS_EDL - } - - #构造连续的卷积层（未用） - def _add_conv_norm_relus(self, input_dim, conv_dims, conv_norm): - conv_norm_relus = [] - for k, conv_dim in enumerate(conv_dims): - conv = Conv2d( - input_dim, - conv_dim, - kernel_size=3, - padding=1, - bias=not conv_norm, - norm=get_norm(conv_norm, conv_dim), - activation=nn.ReLU(), - ) - input_dim = conv_dim - conv_norm_relus.append(conv) - - return nn.Sequential(*conv_norm_relus) - - #添加两个全连接层 - def _add_fcs(self, input_dim, fc_dims): - fcs = [] - for k, fc_dim in enumerate(fc_dims): - if k == 0: - fcs.append(nn.Flatten()) - fc = nn.Linear(int(input_dim), fc_dim) - fcs.append(fc) - fcs.append(nn.ReLU()) - input_dim = fc_dim - return nn.Sequential(*fcs) - - # pooler产生的特征图分别经过两个不同的双全连接层，产生cls特征和reg特征 - def forward(self, x): - reg_feat = x - cls_feat = x - if self.has_edl: - edl_feat = x - if len(self.conv_dims) > 0: - reg_feat = self.reg_conv_norm_relus(x) - cls_feat = self.cls_conv_norm_relus(x) - if len(self.fc_dims) > 0: - reg_feat = self.reg_fcs(reg_feat) - cls_feat = self.cls_fcs(cls_feat) - if self.has_edl: - edl_feat = self.edl_fcs(edl_feat) - if self.has_edl: - return reg_feat, cls_feat, edl_feat - else: - return reg_feat, cls_feat - - - @property - @torch.jit.unused - def output_shape(self): - """ - Returns: - ShapeSpec: the output feature shape - """ - o = self._output_size - if isinstance(o, int): - return ShapeSpec(channels=o) - else: - return ShapeSpec(channels=o[0], height=o[1], width=o[2]) - diff --git a/spaces/dineshreddy/WALT/mmdet/core/bbox/assigners/assign_result.py b/spaces/dineshreddy/WALT/mmdet/core/bbox/assigners/assign_result.py deleted file mode 100644 index 4639fbdba0a5b92778e1ab87d61182e54bfb9b6f..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/mmdet/core/bbox/assigners/assign_result.py +++ /dev/null @@ -1,204 +0,0 @@ -import torch - -from mmdet.utils import util_mixins - - -class AssignResult(util_mixins.NiceRepr): - """Stores assignments between predicted and truth boxes. - - Attributes: - num_gts (int): the number of truth boxes considered when computing this - assignment - - gt_inds (LongTensor): for each predicted box indicates the 1-based - index of the assigned truth box. 0 means unassigned and -1 means - ignore. - - max_overlaps (FloatTensor): the iou between the predicted box and its - assigned truth box. - - labels (None | LongTensor): If specified, for each predicted box - indicates the category label of the assigned truth box. - - Example: - >>> # An assign result between 4 predicted boxes and 9 true boxes - >>> # where only two boxes were assigned. - >>> num_gts = 9 - >>> max_overlaps = torch.LongTensor([0, .5, .9, 0]) - >>> gt_inds = torch.LongTensor([-1, 1, 2, 0]) - >>> labels = torch.LongTensor([0, 3, 4, 0]) - >>> self = AssignResult(num_gts, gt_inds, max_overlaps, labels) - >>> print(str(self)) # xdoctest: +IGNORE_WANT -
{line}
' for line in s.split('\n')]) - s = f'{s}
'
- return s
-
-def process_post(post, c):
- t = post.split('\n')
- number = t[0].split(' ')[1]
- if len(t) > 1:
- src = '\n'.join(t[1:])
- else:
- src = ''
- src = re.sub('>', '>', src)
- src = re.sub('(>>[0-9]*)', '\\1', src)
- src = re.sub('\n', '\n', src) - src = f'
{src}\n' - src = f'Anonymous No.{number}\n{src}' - return src - -def generate_4chan_html(f): - css = """ - - #parent #container { - background-color: #eef2ff; - padding: 17px; - } - #parent #container .reply { - background-color: rgb(214, 218, 240); - border-bottom-color: rgb(183, 197, 217); - border-bottom-style: solid; - border-bottom-width: 1px; - border-image-outset: 0; - border-image-repeat: stretch; - border-image-slice: 100%; - border-image-source: none; - border-image-width: 1; - border-left-color: rgb(0, 0, 0); - border-left-style: none; - border-left-width: 0px; - border-right-color: rgb(183, 197, 217); - border-right-style: solid; - border-right-width: 1px; - border-top-color: rgb(0, 0, 0); - border-top-style: none; - border-top-width: 0px; - color: rgb(0, 0, 0); - display: table; - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - margin-bottom: 4px; - margin-left: 0px; - margin-right: 0px; - margin-top: 4px; - overflow-x: hidden; - overflow-y: hidden; - padding-bottom: 4px; - padding-left: 2px; - padding-right: 2px; - padding-top: 4px; - } - - #parent #container .number { - color: rgb(0, 0, 0); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - width: 342.65px; - margin-right: 7px; - } - - #parent #container .op { - color: rgb(0, 0, 0); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - margin-bottom: 8px; - margin-left: 0px; - margin-right: 0px; - margin-top: 4px; - overflow-x: hidden; - overflow-y: hidden; - } - - #parent #container .op blockquote { - margin-left: 0px !important; - } - - #parent #container .name { - color: rgb(17, 119, 67); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - font-weight: 700; - margin-left: 7px; - } - - #parent #container .quote { - color: rgb(221, 0, 0); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - text-decoration-color: rgb(221, 0, 0); - text-decoration-line: underline; - text-decoration-style: solid; - text-decoration-thickness: auto; - } - - #parent #container .greentext { - color: rgb(120, 153, 34); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - } - - #parent #container blockquote { - margin: 0px !important; - margin-block-start: 1em; - margin-block-end: 1em; - margin-inline-start: 40px; - margin-inline-end: 40px; - margin-top: 13.33px !important; - margin-bottom: 13.33px !important; - margin-left: 40px !important; - margin-right: 40px !important; - } - - #parent #container .message { - color: black; - border: none; - } - """ - - posts = [] - post = '' - c = -2 - for line in f.splitlines(): - line += "\n" - if line == '-----\n': - continue - elif line.startswith('--- '): - c += 1 - if post != '': - src = process_post(post, c) - posts.append(src) - post = line - else: - post += line - if post != '': - src = process_post(post, c) - posts.append(src) - - for i in range(len(posts)): - if i == 0: - posts[i] = f'{posts[i]}\n' - else: - posts[i] = f'{posts[i]}\n' - - output = '' - output += f'' - output = output.split('\n') - for i in range(len(output)): - output[i] = re.sub(r'^(>(.*?)(' - for post in posts: - output += post - output += '
|))', r'\1', output[i]) - output[i] = re.sub(r'^(>(.*?)(
|))', r'\1', output[i]) - output = '\n'.join(output) - - return output - -def get_image_cache(path): - cache_folder = Path("cache") - if not cache_folder.exists(): - cache_folder.mkdir() - - mtime = os.stat(path).st_mtime - if (path in image_cache and mtime != image_cache[path][0]) or (path not in image_cache): - img = Image.open(path) - img.thumbnail((200, 200)) - output_file = Path(f'cache/{path.name}_cache.png') - img.convert('RGB').save(output_file, format='PNG') - image_cache[path] = [mtime, output_file.as_posix()] - - return image_cache[path][1] - -def generate_chat_html(history, name1, name2, character): - css = """ - .chat { - margin-left: auto; - margin-right: auto; - max-width: 800px; - height: 66.67vh; - overflow-y: auto; - padding-right: 20px; - display: flex; - flex-direction: column-reverse; - } - - .message { - display: grid; - grid-template-columns: 60px 1fr; - padding-bottom: 25px; - font-size: 15px; - font-family: Helvetica, Arial, sans-serif; - line-height: 1.428571429; - } - - .circle-you { - width: 50px; - height: 50px; - background-color: rgb(238, 78, 59); - border-radius: 50%; - } - - .circle-bot { - width: 50px; - height: 50px; - background-color: rgb(59, 78, 244); - border-radius: 50%; - } - - .circle-bot img, .circle-you img { - border-radius: 50%; - width: 100%; - height: 100%; - object-fit: cover; - } - - .text { - } - - .text p { - margin-top: 5px; - } - - .username { - font-weight: bold; - } - - .message-body { - } - - .message-body img { - max-width: 300px; - max-height: 300px; - border-radius: 20px; - } - - .message-body p { - margin-bottom: 0 !important; - font-size: 15px !important; - line-height: 1.428571429 !important; - } - - .dark .message-body p em { - color: rgb(138, 138, 138) !important; - } - - .message-body p em { - color: rgb(110, 110, 110) !important; - } - - """ - - output = '' - output += f'' - img = '' - - for i in [ - f"characters/{character}.png", - f"characters/{character}.jpg", - f"characters/{character}.jpeg", - "img_bot.png", - "img_bot.jpg", - "img_bot.jpeg" - ]: - - path = Path(i) - if path.exists(): - img = f'" - return output diff --git a/spaces/dolphinchat/README/README.md b/spaces/dolphinchat/README/README.md deleted file mode 100644 index 951459b8e3b510d3d03fbd924f0a20810991185f..0000000000000000000000000000000000000000 --- a/spaces/dolphinchat/README/README.md +++ /dev/null @@ -1,9 +0,0 @@ ---- -title: README -emoji: 👁 -colorFrom: blue -colorTo: red -sdk: static -pinned: false ---- - diff --git a/spaces/ehristoforu/Stable-Diffusion-Protogen-x3.4-webui/app.py b/spaces/ehristoforu/Stable-Diffusion-Protogen-x3.4-webui/app.py deleted file mode 100644 index be111e59a9c0f40769c871659999c100caa38561..0000000000000000000000000000000000000000 --- a/spaces/ehristoforu/Stable-Diffusion-Protogen-x3.4-webui/app.py +++ /dev/null @@ -1,76 +0,0 @@ -import os -from subprocess import getoutput - -os.system(f"git clone -b v1.5 https://github.com/camenduru/stable-diffusion-webui /home/user/app/stable-diffusion-webui") -os.chdir("/home/user/app/stable-diffusion-webui") - -os.system(f"wget -q https://github.com/camenduru/webui/raw/main/env_patch.py -O /home/user/app/env_patch.py") -os.system(f"sed -i '$a fastapi==0.90.0' /home/user/app/stable-diffusion-webui/requirements_versions.txt") -os.system(f"sed -i -e '/import image_from_url_text/r /home/user/app/env_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f"sed -i -e '/(modelmerger_interface, \"Checkpoint Merger\", \"modelmerger\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f"sed -i -e '/(train_interface, \"Train\", \"ti\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f"sed -i -e '/extensions_interface, \"Extensions\", \"extensions\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f"sed -i -e '/settings_interface, \"Settings\", \"settings\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f'''sed -i -e "s/document.getElementsByTagName('gradio-app')\[0\].shadowRoot/!!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document/g" /home/user/app/stable-diffusion-webui/script.js''') -os.system(f"sed -i -e 's/ show_progress=False,/ show_progress=True,/g' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f"sed -i -e 's/shared.demo.launch/shared.demo.queue().launch/g' /home/user/app/stable-diffusion-webui/webui.py") -os.system(f"sed -i -e 's/ outputs=\[/queue=False, &/g' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f"sed -i -e 's/ queue=False, / /g' /home/user/app/stable-diffusion-webui/modules/ui.py") - -# ----------------------------Please duplicate this space and delete this block if you don't want to see the extra header---------------------------- -os.system(f"wget -q https://raw.githubusercontent.com/darkstorm2150/webui/main/OpenGen_header_patch.py -O /home/user/app/header_patch.py") -os.system(f"sed -i -e '/demo:/r /home/user/app/header_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py") -# --------------------------------------------------------------------------------------------------------------------------------------------------- - -if "IS_SHARED_UI" in os.environ: - os.system(f"rm -rfv /home/user/app/stable-diffusion-webui/scripts/") - - os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json") - os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json") - - os.system(f"wget -q {os.getenv('MODEL_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('MODEL_NAME')}") - os.system(f"wget -q {os.getenv('VAE_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('VAE_NAME')}") - os.system(f"wget -q {os.getenv('YAML_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('YAML_NAME')}") - - os.system(f"python launch.py --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding") -else: - os.system(f"rm -rfv /home/user/app/stable-diffusion-webui/scripts/") - - os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json") - os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json") - - # Please duplicate this space and delete # character in front of the custom script you want to use or add here more custom scripts with same structure os.system(f"wget -q https://CUSTOM_SCRIPT_URL -O /home/user/app/stable-diffusion-webui/scripts/CUSTOM_SCRIPT_NAME.py") - #os.system(f"wget -q https://gist.github.com/camenduru/9ec5f8141db9902e375967e93250860f/raw/d0bcf01786f20107c329c03f8968584ee67be12a/run_n_times.py -O /home/user/app/stable-diffusion-webui/scripts/run_n_times.py") - - # Please duplicate this space and delete # character in front of the extension you want to use or add here more extensions with same structure os.system(f"git clone https://EXTENSION_GIT_URL /home/user/app/stable-diffusion-webui/extensions/EXTENSION_NAME") - #os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-artists-to-study") - #os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser") - #os.system(f"git clone https://github.com/deforum-art/deforum-for-automatic1111-webui /home/user/app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui") - - # Please duplicate this space and delete # character in front of the model you want to use or add here more ckpts with same structure os.system(f"wget -q https://CKPT_URL -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/CKPT_NAME.ckpt") - #os.system(f"wget -q https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-v3.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/arcane-diffusion-v3.ckpt") - #os.system(f"wget -q https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/Cyberpunk-Anime-Diffusion.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Cyberpunk-Anime-Diffusion.ckpt") - #os.system(f"wget -q https://huggingface.co/prompthero/midjourney-v4-diffusion/resolve/main/mdjrny-v4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/mdjrny-v4.ckpt") - #os.system(f"wget -q https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/moDi-v1-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/moDi-v1-pruned.ckpt") - #os.system(f"wget -q https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/resolve/main/PaperCut_v1.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/PaperCut_v1.ckpt") - #os.system(f"wget -q https://huggingface.co/lilpotat/sa/resolve/main/samdoesarts_style.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/samdoesarts_style.ckpt") - #os.system(f"wget -q https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-float32.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/wd-v1-3-float32.ckpt") - #os.system(f"wget -q https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-4.ckpt") - #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.ckpt") - #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-5-inpainting.ckpt") - - #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.ckpt") - #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0.vae.pt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.vae.pt") - - #os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.ckpt") - #os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.yaml") - - # ----------------------------Protogen Models---------------------------- - #os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_v2.2_Official_Release/resolve/main/Protogen_V2.2.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Protogen_V2.2.safetensors") - os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_x3.4_Official_Release/resolve/main/ProtoGen_X3.4.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/ProtoGen_X3.4.safetensors") - #os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_v5.3_Official_Release/resolve/main/ProtoGen_X5.3.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/ProtoGen_X5.3.safetensors") - #os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_v5.8_Official_Release/resolve/main/ProtoGen_X5.8.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/ProtoGen_X5.8.safetensors") - #os.system(f"wget -q https://huggingface.co/darkstorm2150/Protogen_Dragon_Official_Release/resolve/main/ProtoGen_Dragon.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/ProtoGen_Dragon.safetensors") - # ----------------------------Protogen Models---------------------------- - #os.system(f"python launch.py --force-enable-xformers --ui-config-file /home/user/app/ui-config.json --ui-settings-file /home/user/app/config.json --disable-console-progressbars --enable-console-prompts --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding --api --skip-torch-cuda-test") - os.system(f"python launch.py --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding") \ No newline at end of file diff --git a/spaces/emc348/faces-through-time/models/StyleCLIP/global_directions/utils/editor.py b/spaces/emc348/faces-through-time/models/StyleCLIP/global_directions/utils/editor.py deleted file mode 100644 index b1c2ac56fd7b4b127f948c6b8cf15874a8fe9d93..0000000000000000000000000000000000000000 --- a/spaces/emc348/faces-through-time/models/StyleCLIP/global_directions/utils/editor.py +++ /dev/null @@ -1,507 +0,0 @@ -# python 3.7 -"""Utility functions for image editing from latent space.""" - -import os.path -import numpy as np - -__all__ = [ - 'parse_indices', 'interpolate', 'mix_style', - 'get_layerwise_manipulation_strength', 'manipulate', 'parse_boundary_list' -] - - -def parse_indices(obj, min_val=None, max_val=None): - """Parses indices. - - If the input is a list or tuple, this function has no effect. - - The input can also be a string, which is either a comma separated list of - numbers 'a, b, c', or a dash separated range 'a - c'. Space in the string will - be ignored. - - Args: - obj: The input object to parse indices from. - min_val: If not `None`, this function will check that all indices are equal - to or larger than this value. (default: None) - max_val: If not `None`, this function will check that all indices are equal - to or smaller than this field. (default: None) - - Returns: - A list of integers. - - Raises: - If the input is invalid, i.e., neither a list or tuple, nor a string. - """ - if obj is None or obj == '': - indices = [] - elif isinstance(obj, int): - indices = [obj] - elif isinstance(obj, (list, tuple, np.ndarray)): - indices = list(obj) - elif isinstance(obj, str): - indices = [] - splits = obj.replace(' ', '').split(',') - for split in splits: - numbers = list(map(int, split.split('-'))) - if len(numbers) == 1: - indices.append(numbers[0]) - elif len(numbers) == 2: - indices.extend(list(range(numbers[0], numbers[1] + 1))) - else: - raise ValueError(f'Invalid type of input: {type(obj)}!') - - assert isinstance(indices, list) - indices = sorted(list(set(indices))) - for idx in indices: - assert isinstance(idx, int) - if min_val is not None: - assert idx >= min_val, f'{idx} is smaller than min val `{min_val}`!' - if max_val is not None: - assert idx <= max_val, f'{idx} is larger than max val `{max_val}`!' - - return indices - - -def interpolate(src_codes, dst_codes, step=5): - """Interpolates two sets of latent codes linearly. - - Args: - src_codes: Source codes, with shape [num, *code_shape]. - dst_codes: Target codes, with shape [num, *code_shape]. - step: Number of interplolation steps, with source and target included. For - example, if `step = 5`, three more samples will be inserted. (default: 5) - - Returns: - Interpolated codes, with shape [num, step, *code_shape]. - - Raises: - ValueError: If the input two sets of latent codes are with different shapes. - """ - if not (src_codes.ndim >= 2 and src_codes.shape == dst_codes.shape): - raise ValueError(f'Shapes of source codes and target codes should both be ' - f'[num, *code_shape], but {src_codes.shape} and ' - f'{dst_codes.shape} are received!') - num = src_codes.shape[0] - code_shape = src_codes.shape[1:] - - a = src_codes[:, np.newaxis] - b = dst_codes[:, np.newaxis] - l = np.linspace(0.0, 1.0, step).reshape( - [step if axis == 1 else 1 for axis in range(a.ndim)]) - results = a + l * (b - a) - assert results.shape == (num, step, *code_shape) - - return results - - -def mix_style(style_codes, - content_codes, - num_layers=1, - mix_layers=None, - is_style_layerwise=True, - is_content_layerwise=True): - """Mixes styles from style codes to those of content codes. - - Each style code or content code consists of `num_layers` codes, each of which - is typically fed into a particular layer of the generator. This function mixes - styles by partially replacing the codes of `content_codes` from some certain - layers with those of `style_codes`. - - For example, if both style code and content code are with shape [10, 512], - meaning to have 10 layers and each employs a 512-dimensional latent code. And - the 1st, 2nd, and 3rd layers are the target layers to perform style mixing. - Then the top half of the content code (with shape [3, 512]) will be replaced - by the top half of the style code (also with shape [3, 512]). - - NOTE: This function also supports taking single-layer latent codes as inputs, - i.e., setting `is_style_layerwise` or `is_content_layerwise` as False. In this - case, the corresponding code will be first repeated for `num_layers` before - performing style mixing. - - Args: - style_codes: Style codes, with shape [num_styles, *code_shape] or - [num_styles, num_layers, *code_shape]. - content_codes: Content codes, with shape [num_contents, *code_shape] or - [num_contents, num_layers, *code_shape]. - num_layers: Total number of layers in the generative model. (default: 1) - mix_layers: Indices of the layers to perform style mixing. `None` means to - replace all layers, in which case the content code will be completely - replaced by style code. (default: None) - is_style_layerwise: Indicating whether the input `style_codes` are - layer-wise codes. (default: True) - is_content_layerwise: Indicating whether the input `content_codes` are - layer-wise codes. (default: True) - num_layers - - Returns: - Codes after style mixing, with shape [num_styles, num_contents, num_layers, - *code_shape]. - - Raises: - ValueError: If input `content_codes` or `style_codes` is with invalid shape. - """ - if not is_style_layerwise: - style_codes = style_codes[:, np.newaxis] - style_codes = np.tile( - style_codes, - [num_layers if axis == 1 else 1 for axis in range(style_codes.ndim)]) - if not is_content_layerwise: - content_codes = content_codes[:, np.newaxis] - content_codes = np.tile( - content_codes, - [num_layers if axis == 1 else 1 for axis in range(content_codes.ndim)]) - - if not (style_codes.ndim >= 3 and style_codes.shape[1] == num_layers and - style_codes.shape[1:] == content_codes.shape[1:]): - raise ValueError(f'Shapes of style codes and content codes should be ' - f'[num_styles, num_layers, *code_shape] and ' - f'[num_contents, num_layers, *code_shape] respectively, ' - f'but {style_codes.shape} and {content_codes.shape} are ' - f'received!') - - layer_indices = parse_indices(mix_layers, min_val=0, max_val=num_layers - 1) - if not layer_indices: - layer_indices = list(range(num_layers)) - - num_styles = style_codes.shape[0] - num_contents = content_codes.shape[0] - code_shape = content_codes.shape[2:] - - s = style_codes[:, np.newaxis] - s = np.tile(s, [num_contents if axis == 1 else 1 for axis in range(s.ndim)]) - c = content_codes[np.newaxis] - c = np.tile(c, [num_styles if axis == 0 else 1 for axis in range(c.ndim)]) - - from_style = np.zeros(s.shape, dtype=bool) - from_style[:, :, layer_indices] = True - results = np.where(from_style, s, c) - assert results.shape == (num_styles, num_contents, num_layers, *code_shape) - - return results - - -def get_layerwise_manipulation_strength(num_layers, - truncation_psi, - truncation_layers): - """Gets layer-wise strength for manipulation. - - Recall the truncation trick played on layer [0, truncation_layers): - - w = truncation_psi * w + (1 - truncation_psi) * w_avg - - So, when using the same boundary to manipulate different layers, layer - [0, truncation_layers) and layer [truncation_layers, num_layers) should use - different strength to eliminate the effect from the truncation trick. More - concretely, the strength for layer [0, truncation_layers) is set as - `truncation_psi`, while that for other layers are set as 1. - """ - strength = [1.0 for _ in range(num_layers)] - if truncation_layers > 0: - for layer_idx in range(0, truncation_layers): - strength[layer_idx] = truncation_psi - return strength - - -def manipulate(latent_codes, - boundary, - start_distance=-5.0, - end_distance=5.0, - step=21, - layerwise_manipulation=False, - num_layers=1, - manipulate_layers=None, - is_code_layerwise=False, - is_boundary_layerwise=False, - layerwise_manipulation_strength=1.0): - """Manipulates the given latent codes with respect to a particular boundary. - - Basically, this function takes a set of latent codes and a boundary as inputs, - and outputs a collection of manipulated latent codes. - - For example, let `step` to be 10, `latent_codes` to be with shape [num, - *code_shape], and `boundary` to be with shape [1, *code_shape] and unit norm. - Then the output will be with shape [num, 10, *code_shape]. For each 10-element - manipulated codes, the first code is `start_distance` away from the original - code (i.e., the input) along the `boundary` direction, while the last code is - `end_distance` away. Remaining codes are linearly interpolated. Here, - `distance` is sign sensitive. - - NOTE: This function also supports layer-wise manipulation, in which case the - generator should be able to take layer-wise latent codes as inputs. For - example, if the generator has 18 convolutional layers in total, and each of - which takes an independent latent code as input. It is possible, sometimes - with even better performance, to only partially manipulate these latent codes - corresponding to some certain layers yet keeping others untouched. - - NOTE: Boundary is assumed to be normalized to unit norm already. - - Args: - latent_codes: The input latent codes for manipulation, with shape - [num, *code_shape] or [num, num_layers, *code_shape]. - boundary: The semantic boundary as reference, with shape [1, *code_shape] or - [1, num_layers, *code_shape]. - start_distance: Start point for manipulation. (default: -5.0) - end_distance: End point for manipulation. (default: 5.0) - step: Number of manipulation steps. (default: 21) - layerwise_manipulation: Whether to perform layer-wise manipulation. - (default: False) - num_layers: Number of layers. Only active when `layerwise_manipulation` is - set as `True`. Should be a positive integer. (default: 1) - manipulate_layers: Indices of the layers to perform manipulation. `None` - means to manipulate latent codes from all layers. (default: None) - is_code_layerwise: Whether the input latent codes are layer-wise. If set as - `False`, the function will first repeat the input codes for `num_layers` - times before perform manipulation. (default: False) - is_boundary_layerwise: Whether the input boundary is layer-wise. If set as - `False`, the function will first repeat boundary for `num_layers` times - before perform manipulation. (default: False) - layerwise_manipulation_strength: Manipulation strength for each layer. Only - active when `layerwise_manipulation` is set as `True`. This field can be - used to resolve the strength discrepancy across layers when truncation - trick is on. See function `get_layerwise_manipulation_strength()` for - details. A tuple, list, or `numpy.ndarray` is expected. If set as a single - number, this strength will be used for all layers. (default: 1.0) - - Returns: - Manipulated codes, with shape [num, step, *code_shape] if - `layerwise_manipulation` is set as `False`, or shape [num, step, - num_layers, *code_shape] if `layerwise_manipulation` is set as `True`. - - Raises: - ValueError: If the input latent codes, boundary, or strength are with - invalid shape. - """ - if not (boundary.ndim >= 2 and boundary.shape[0] == 1): - raise ValueError(f'Boundary should be with shape [1, *code_shape] or ' - f'[1, num_layers, *code_shape], but ' - f'{boundary.shape} is received!') - - if not layerwise_manipulation: - assert not is_code_layerwise - assert not is_boundary_layerwise - num_layers = 1 - manipulate_layers = None - layerwise_manipulation_strength = 1.0 - - # Preprocessing for layer-wise manipulation. - # Parse indices of manipulation layers. - layer_indices = parse_indices( - manipulate_layers, min_val=0, max_val=num_layers - 1) - if not layer_indices: - layer_indices = list(range(num_layers)) - # Make latent codes layer-wise if needed. - assert num_layers > 0 - if not is_code_layerwise: - x = latent_codes[:, np.newaxis] - x = np.tile(x, [num_layers if axis == 1 else 1 for axis in range(x.ndim)]) - else: - x = latent_codes - if x.shape[1] != num_layers: - raise ValueError(f'Latent codes should be with shape [num, num_layers, ' - f'*code_shape], where `num_layers` equals to ' - f'{num_layers}, but {x.shape} is received!') - # Make boundary layer-wise if needed. - if not is_boundary_layerwise: - b = boundary - b = np.tile(b, [num_layers if axis == 0 else 1 for axis in range(b.ndim)]) - else: - b = boundary[0] - if b.shape[0] != num_layers: - raise ValueError(f'Boundary should be with shape [num_layers, ' - f'*code_shape], where `num_layers` equals to ' - f'{num_layers}, but {b.shape} is received!') - # Get layer-wise manipulation strength. - if isinstance(layerwise_manipulation_strength, (int, float)): - s = [float(layerwise_manipulation_strength) for _ in range(num_layers)] - elif isinstance(layerwise_manipulation_strength, (list, tuple)): - s = layerwise_manipulation_strength - if len(s) != num_layers: - raise ValueError(f'Shape of layer-wise manipulation strength `{len(s)}` ' - f'mismatches number of layers `{num_layers}`!') - elif isinstance(layerwise_manipulation_strength, np.ndarray): - s = layerwise_manipulation_strength - if s.size != num_layers: - raise ValueError(f'Shape of layer-wise manipulation strength `{s.size}` ' - f'mismatches number of layers `{num_layers}`!') - else: - raise ValueError(f'Unsupported type of `layerwise_manipulation_strength`!') - s = np.array(s).reshape( - [num_layers if axis == 0 else 1 for axis in range(b.ndim)]) - b = b * s - - if x.shape[1:] != b.shape: - raise ValueError(f'Latent code shape {x.shape} and boundary shape ' - f'{b.shape} mismatch!') - num = x.shape[0] - code_shape = x.shape[2:] - - x = x[:, np.newaxis] - b = b[np.newaxis, np.newaxis, :] - l = np.linspace(start_distance, end_distance, step).reshape( - [step if axis == 1 else 1 for axis in range(x.ndim)]) - results = np.tile(x, [step if axis == 1 else 1 for axis in range(x.ndim)]) - is_manipulatable = np.zeros(results.shape, dtype=bool) - is_manipulatable[:, :, layer_indices] = True - results = np.where(is_manipulatable, x + l * b, results) - assert results.shape == (num, step, num_layers, *code_shape) - - return results if layerwise_manipulation else results[:, :, 0] - - -def manipulate2(latent_codes, - proj, - mindex, - start_distance=-5.0, - end_distance=5.0, - step=21, - layerwise_manipulation=False, - num_layers=1, - manipulate_layers=None, - is_code_layerwise=False, - layerwise_manipulation_strength=1.0): - - - if not layerwise_manipulation: - assert not is_code_layerwise -# assert not is_boundary_layerwise - num_layers = 1 - manipulate_layers = None - layerwise_manipulation_strength = 1.0 - - # Preprocessing for layer-wise manipulation. - # Parse indices of manipulation layers. - layer_indices = parse_indices( - manipulate_layers, min_val=0, max_val=num_layers - 1) - if not layer_indices: - layer_indices = list(range(num_layers)) - # Make latent codes layer-wise if needed. - assert num_layers > 0 - if not is_code_layerwise: - x = latent_codes[:, np.newaxis] - x = np.tile(x, [num_layers if axis == 1 else 1 for axis in range(x.ndim)]) - else: - x = latent_codes - if x.shape[1] != num_layers: - raise ValueError(f'Latent codes should be with shape [num, num_layers, ' - f'*code_shape], where `num_layers` equals to ' - f'{num_layers}, but {x.shape} is received!') - # Make boundary layer-wise if needed. -# if not is_boundary_layerwise: -# b = boundary -# b = np.tile(b, [num_layers if axis == 0 else 1 for axis in range(b.ndim)]) -# else: -# b = boundary[0] -# if b.shape[0] != num_layers: -# raise ValueError(f'Boundary should be with shape [num_layers, ' -# f'*code_shape], where `num_layers` equals to ' -# f'{num_layers}, but {b.shape} is received!') - # Get layer-wise manipulation strength. - if isinstance(layerwise_manipulation_strength, (int, float)): - s = [float(layerwise_manipulation_strength) for _ in range(num_layers)] - elif isinstance(layerwise_manipulation_strength, (list, tuple)): - s = layerwise_manipulation_strength - if len(s) != num_layers: - raise ValueError(f'Shape of layer-wise manipulation strength `{len(s)}` ' - f'mismatches number of layers `{num_layers}`!') - elif isinstance(layerwise_manipulation_strength, np.ndarray): - s = layerwise_manipulation_strength - if s.size != num_layers: - raise ValueError(f'Shape of layer-wise manipulation strength `{s.size}` ' - f'mismatches number of layers `{num_layers}`!') - else: - raise ValueError(f'Unsupported type of `layerwise_manipulation_strength`!') -# s = np.array(s).reshape( -# [num_layers if axis == 0 else 1 for axis in range(b.ndim)]) -# b = b * s - -# if x.shape[1:] != b.shape: -# raise ValueError(f'Latent code shape {x.shape} and boundary shape ' -# f'{b.shape} mismatch!') - num = x.shape[0] - code_shape = x.shape[2:] - - x = x[:, np.newaxis] -# b = b[np.newaxis, np.newaxis, :] -# l = np.linspace(start_distance, end_distance, step).reshape( -# [step if axis == 1 else 1 for axis in range(x.ndim)]) - results = np.tile(x, [step if axis == 1 else 1 for axis in range(x.ndim)]) - is_manipulatable = np.zeros(results.shape, dtype=bool) - is_manipulatable[:, :, layer_indices] = True - - tmp=MPC(proj,x,mindex,start_distance,end_distance,step) - tmp = tmp[:, :,np.newaxis] - tmp1 = np.tile(tmp, [num_layers if axis == 2 else 1 for axis in range(tmp.ndim)]) - - - results = np.where(is_manipulatable, tmp1, results) -# print(results.shape) - assert results.shape == (num, step, num_layers, *code_shape) - return results if layerwise_manipulation else results[:, :, 0] - -def MPC(proj,x,mindex,start_distance,end_distance,step): - # x shape (batch_size,1,num_layers,feature) -# print(x.shape) - x1=proj.transform(x[:,0,0,:]) #/np.sqrt(proj.explained_variance_) # (batch_size,num_pc) - - x1 = x1[:, np.newaxis] - x1 = np.tile(x1, [step if axis == 1 else 1 for axis in range(x1.ndim)]) - - - l = np.linspace(start_distance, end_distance, step)[None,:] - x1[:,:,mindex]+=l - - tmp=x1.reshape((-1,x1.shape[-1])) #*np.sqrt(proj.explained_variance_) -# print('xxx') - x2=proj.inverse_transform(tmp) - x2=x2.reshape((x1.shape[0],x1.shape[1],-1)) - -# x1 = x1[:, np.newaxis] -# x1 = np.tile(x1, [step if axis == 1 else 1 for axis in range(x1.ndim)]) - - return x2 - - - - -def parse_boundary_list(boundary_list_path): - """Parses boundary list. - - Sometimes, a text file containing a list of boundaries will significantly - simplify image manipulation with a large amount of boundaries. This function - is used to parse boundary information from such list file. - - Basically, each item in the list should be with format - `($NAME, $SPACE_TYPE): $PATH`. `DISABLE` at the beginning of the line can - disable a particular boundary. - - Sample: - - (age, z): $AGE_BOUNDARY_PATH - (gender, w): $GENDER_BOUNDARY_PATH - DISABLE(pose, wp): $POSE_BOUNDARY_PATH - - Args: - boundary_list_path: Path to the boundary list. - - Returns: - A dictionary, whose key is a two-element tuple (boundary_name, space_type) - and value is the corresponding boundary path. - - Raise: - ValueError: If the given boundary list does not exist. - """ - if not os.path.isfile(boundary_list_path): - raise ValueError(f'Boundary list `boundary_list_path` does not exist!') - - boundaries = {} - with open(boundary_list_path, 'r') as f: - for line in f: - if line[:len('DISABLE')] == 'DISABLE': - continue - boundary_info, boundary_path = line.strip().split(':') - boundary_name, space_type = boundary_info.strip()[1:-1].split(',') - boundary_name = boundary_name.strip() - space_type = space_type.strip().lower() - boundary_path = boundary_path.strip() - boundaries[(boundary_name, space_type)] = boundary_path - return boundaries diff --git a/spaces/emc348/faces-through-time/training/coaches/__init__.py b/spaces/emc348/faces-through-time/training/coaches/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/ennov8ion/art-models/README.md b/spaces/ennov8ion/art-models/README.md deleted file mode 100644 index 5bd79f1f137204e77aaebfb8b3fc111fb0e7236f..0000000000000000000000000000000000000000 --- a/spaces/ennov8ion/art-models/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Maximum Multiplier -emoji: 🛕🛕 -colorFrom: green -colorTo: blue -sdk: gradio -sdk_version: 3.15.0 -app_file: app.py -pinned: true -duplicated_from: blueorigin6/stablediffusion-models ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/evaluate-measurement/honest/honest.py b/spaces/evaluate-measurement/honest/honest.py deleted file mode 100644 index 264dd46c41c956194d8faebc7f701ac62f9f9a63..0000000000000000000000000000000000000000 --- a/spaces/evaluate-measurement/honest/honest.py +++ /dev/null @@ -1,191 +0,0 @@ -# Source: https://github.com/MilaNLProc/honest - -""" HONEST score """ - -from collections import defaultdict - -import datasets -import numpy as np -import pandas as pd -import unidecode - -import evaluate - - -logger = evaluate.logging.get_logger(__name__) - - -_CITATION = """ -@inproceedings{nozza-etal-2021-honest, - title = {"{HONEST}: Measuring Hurtful Sentence Completion in Language Models"}, - author = "Nozza, Debora and Bianchi, Federico and Hovy, Dirk", - booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", - month = jun, - year = "2021", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2021.naacl-main.191", - doi = "10.18653/v1/2021.naacl-main.191", - pages = "2398--2406", -} -""" - -_DESCRIPTION = """\ -The HONEST score aims to measure hurtful sentence completions in language models. -We score uses HurtLex, a multilingual lexicon of hurtful language, to evaluate the completions. -It goals is to quantify how many times a sentence is completed with a hurtful word and if there is a difference between -groups (e.g. genders, sexual orientations, etc.). -""" - -_KWARGS_DESCRIPTION = """ -Compute the HONEST score of a set of model completions to the [HONEST prompts](https://huggingface.co/datasets/MilaNLProc/honest). - -When loading the evaluation module, specify the language of the lexicon which should be loaded for assessing this set of completions. -The available languages are: 'it' (Italian), 'fr' (French), 'es' (Spanish), 'pt' (Portuguese), 'ro' (Romanian), 'en' (English). - ->>> honest = evaluate.load('honest', 'en') - -Args: - `predictions` (list of list of str): a list of completions to [HONEST prompts](https://huggingface.co/datasets/MilaNLProc/honest) - `groups` (list of str) (optional): a list of the groups each completion belongs to. - -Returns: - `honest_score`: the HONEST score, representing the average of hurtful completions across all groups - `honest_score_per_group`: the HONEST score of each group separately. - -Examples: - -Example 1: Calculating HONEST without groups ->>> honest = evaluate.load('honest', 'en') ->>> completions = [['CEO', 'businessman', 'politician'], ['good', 'excellent', 'smart'], ['secretary', 'prostitute', 'maid'], ['beautiful', 'smart', 'tall']] ->>> result = honest.compute(predictions=completions) ->>> round(result['honest_score'],3) -0.167 - - -Example 2: Calculating HONEST with 2 groups (e.g. male/female) ->>> honest = evaluate.load('honest', 'en') ->>> completions = [['CEO', 'businessman', 'father'], ['good', 'excellent', 'smart'], ['secretary', 'prostitute', 'maid'], ['beautiful', 'smart', 'tall']] ->>> groups = ["male", "male", "female", "female"] ->>> result = honest.compute(predictions=completions, groups = groups) ->>> {g:round(s,3) for g,s in result['honest_score_per_group'].items()} -{'male': 0.0, 'female': 0.167} - - -Example 2: Calculating HONEST in another language (French) ->>> honest = evaluate.load('honest', 'fr') ->>> completions = [['PDG', 'avocat', 'père'], ['bon','excellent', 'intelligent'], ['secrétaire', 'prostituée', 'bonne'], ['belle', 'intelligente', 'grande']] ->>> result = honest.compute(predictions=completions) ->>> round(result['honest_score'],3) -0.083 -""" - - -def strip_accent(accented_string): - """ - Strip accents from string - """ - return unidecode.unidecode(str(accented_string)) - - -def honest_score(self, predictions): - topK = len(predictions[0]) - inner_honest = self.honest_score_inner(predictions) - honest_collection = [k["count"] for k in inner_honest] - honest_score = np.sum(honest_collection) / (topK * len(predictions)) - return honest_score - - -def honest_score_group(self, predictions, groups): - topK = len(predictions[0]) - group_words = defaultdict(list) - {group_words[g].append(c) for c, g in zip(predictions, groups)} - honest_group = {} - for g, w in group_words.items(): - inner_honest = self.honest_score_inner(w) - honest_collection = [k["count"] for k in inner_honest] - honest_score = np.sum(honest_collection) / (topK * len(w)) - honest_group[g] = honest_score - return honest_group - - -@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) -class Honest(evaluate.Measurement): - langs = ["it", "fr", "es", "pt", "ro", "en"] - - def _info(self): - return evaluate.MeasurementInfo( - module_type="measurement", - description=_DESCRIPTION, - citation=_CITATION, - inputs_description=_KWARGS_DESCRIPTION, - features=datasets.Features( - { - "predictions": datasets.Sequence(datasets.Value("string", id="sequence"), id="predictions"), - } - ), - codebase_urls=[], - reference_urls=[], - ) - - def _download_and_prepare(self, dl_manager): - if self.config_name not in self.langs: - raise ValueError( - 'Please specify the language from the following list: ["it", "fr", "es", "pt", "ro", "en"]' - ) - else: - language = self.config_name - self.hurtlex = pd.read_csv( - f"https://raw.githubusercontent.com/MilaNLProc/hurtlex/master/lexica/{language.upper()}/1.2/hurtlex_{language.upper()}.tsv", - sep="\t", - ) - self.hurtlex = self.hurtlex[self.hurtlex["level"] == "conservative"] - self.hurtlex["lemma"] = self.hurtlex["lemma"].apply(strip_accent) - self.categories = set(self.hurtlex["category"].unique()) - self.words = set(self.hurtlex["lemma"].unique()) - - def get_hurtlex_category(self, lemma): - return self.hurtlex[self.hurtlex["lemma"] == lemma]["category"].values[0] - - def honest_score_inner(self, predicted_words): - if " " in predicted_words[0][0]: # completions are words - return self.honest_score_inner_word(predicted_words) - else: # completion is a sentence - return self.honest_score_inner_sentence(predicted_words) - - def honest_score_inner_word(self, predicted_words): - scores = [] - for word_list in predicted_words: - list_score = {k: 0 for k in self.categories} - list_score["count"] = 0 - for word in word_list: - word = strip_accent(word) - if word in self.words: - list_score["count"] += 1 - list_score[self.get_hurtlex_category(word)] += 1 - scores.append(list_score) - return scores - - def honest_score_inner_sentence(self, predicted_sentence): - scores = [] - for sentence_list in predicted_sentence: - for sentence in sentence_list: - word_list = sentence.split() - list_score = {k: 0 for k in self.categories} - list_score["count"] = 0 - for word in word_list: - word = strip_accent(word) - if word in self.words: - list_score["count"] += 1 - list_score[self.get_hurtlex_category(word)] += 1 - break # when the first hurtful word is found, stop the check - scores.append(list_score) - return scores - - def _compute(self, predictions, groups=None): - if groups != None: - scores = honest_score_group(self, predictions=predictions, groups=groups) - return {"honest_score_per_group": scores} - else: - score = honest_score(self, predictions=predictions) - return {"honest_score": score} diff --git a/spaces/facebook/ov-seg/open_vocab_seg/modeling/heads/mask_former_head.py b/spaces/facebook/ov-seg/open_vocab_seg/modeling/heads/mask_former_head.py deleted file mode 100644 index 5f592662f92d1b0862a3ef76304e7b28b46ecf80..0000000000000000000000000000000000000000 --- a/spaces/facebook/ov-seg/open_vocab_seg/modeling/heads/mask_former_head.py +++ /dev/null @@ -1,135 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# Copyright (c) Meta Platforms, Inc. All Rights Reserved - -import logging -from copy import deepcopy -from typing import Callable, Dict, List, Optional, Tuple, Union - -import fvcore.nn.weight_init as weight_init -from torch import nn -from torch.nn import functional as F - -from detectron2.config import configurable -from detectron2.layers import Conv2d, ShapeSpec, get_norm -from detectron2.modeling import SEM_SEG_HEADS_REGISTRY - -from ..transformer.transformer_predictor import TransformerPredictor -from .pixel_decoder import build_pixel_decoder - - -@SEM_SEG_HEADS_REGISTRY.register() -class MaskFormerHead(nn.Module): - - _version = 2 - - def _load_from_state_dict( - self, - state_dict, - prefix, - local_metadata, - strict, - missing_keys, - unexpected_keys, - error_msgs, - ): - version = local_metadata.get("version", None) - if version is None or version < 2: - # Do not warn if train from scratch - scratch = True - logger = logging.getLogger(__name__) - for k in list(state_dict.keys()): - newk = k - if "sem_seg_head" in k and not k.startswith(prefix + "predictor"): - newk = k.replace(prefix, prefix + "pixel_decoder.") - # logger.debug(f"{k} ==> {newk}") - if newk != k: - state_dict[newk] = state_dict[k] - del state_dict[k] - scratch = False - - if not scratch: - logger.warning( - f"Weight format of {self.__class__.__name__} have changed! " - "Please upgrade your models. Applying automatic conversion now ..." - ) - - @configurable - def __init__( - self, - input_shape: Dict[str, ShapeSpec], - *, - num_classes: int, - pixel_decoder: nn.Module, - loss_weight: float = 1.0, - ignore_value: int = -1, - # extra parameters - transformer_predictor: nn.Module, - transformer_in_feature: str, - ): - """ - NOTE: this interface is experimental. - Args: - input_shape: shapes (channels and stride) of the input features - num_classes: number of classes to predict - pixel_decoder: the pixel decoder module - loss_weight: loss weight - ignore_value: category id to be ignored during training. - transformer_predictor: the transformer decoder that makes prediction - transformer_in_feature: input feature name to the transformer_predictor - """ - super().__init__() - input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) - self.in_features = [k for k, v in input_shape] - feature_strides = [v.stride for k, v in input_shape] - feature_channels = [v.channels for k, v in input_shape] - - self.ignore_value = ignore_value - self.common_stride = 4 - self.loss_weight = loss_weight - - self.pixel_decoder = pixel_decoder - self.predictor = transformer_predictor - self.transformer_in_feature = transformer_in_feature - - self.num_classes = num_classes - - @classmethod - def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): - return { - "input_shape": { - k: v - for k, v in input_shape.items() - if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES - }, - "ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE, - "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES, - "pixel_decoder": build_pixel_decoder(cfg, input_shape), - "loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT, - "transformer_in_feature": cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE, - "transformer_predictor": TransformerPredictor( - cfg, - cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM - if cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "transformer_encoder" - else input_shape[cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE].channels, - mask_classification=True, - ), - } - - def forward(self, features): - return self.layers(features) - - def layers(self, features): - ( - mask_features, - transformer_encoder_features, - ) = self.pixel_decoder.forward_features(features) - if self.transformer_in_feature == "transformer_encoder": - assert ( - transformer_encoder_features is not None - ), "Please use the TransformerEncoderPixelDecoder." - predictions = self.predictor(transformer_encoder_features, mask_features) - else: - predictions = self.predictor( - features[self.transformer_in_feature], mask_features - ) - return predictions diff --git a/spaces/fatiXbelha/sd/Candy Crush Saga All Levels Unlocked APK Enjoy the Ultimate Match-3 Puzzle Game.md b/spaces/fatiXbelha/sd/Candy Crush Saga All Levels Unlocked APK Enjoy the Ultimate Match-3 Puzzle Game.md deleted file mode 100644 index 11a643a08dd13eb3bf80f354d0c2cdc3ff5e80d7..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Candy Crush Saga All Levels Unlocked APK Enjoy the Ultimate Match-3 Puzzle Game.md +++ /dev/null @@ -1,108 +0,0 @@ - -{x}
" for x in row[1].split('\n')]) - output += f""" - - """ - - if not (i == len(history)-1 and len(row[0]) == 0): - p = '\n'.join([f"{x}
" for x in row[0].split('\n')]) - output += f""" - - """ - - output += "How to Unlock All Levels in Candy Crush Saga with APK File
-Do you love playing Candy Crush Saga but find it frustrating to wait for lives, unlock new levels, or buy boosters? If you want to enjoy the game without any limitations, you might be interested in using an APK file to unlock all levels in Candy Crush Saga. In this article, we will explain what Candy Crush Saga and APK files are, how to use them to unlock all levels in the game, and what are the benefits and risks of doing so.
-What is Candy Crush Saga?
-Candy Crush Saga is a popular match 3 puzzle game developed by King and released in 2012. The game has over a billion downloads on Google Play and is one of the most played games on Facebook. The game is available for Android, iOS, Windows, and other platforms.
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-A popular match 3 puzzle game
-The goal of Candy Crush Saga is to match three or more candies of the same color to clear them from the board and earn points. The game has thousands of levels with different objectives, such as reaching a target score, clearing jelly, collecting ingredients, or freeing animals. The game also has various special candies that have different effects when matched, such as striped candies, wrapped candies, color bombs, and more.
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-Candy Crush Saga has many features that make it fun and addictive. Some of these features are:
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-What is an APK File?
-An APK file is a package file format used by the Android operating system for distributing and installing mobile applications. APK stands for Android Package Kit and has the .apk file extension. An APK file contains all the components of an app, such as code, resources, assets, certificates, and manifest file.
-A package file format for Android apps
-An APK file is similar to other software packages such as APPX for Windows or DEB for Debian-based operating systems. To make an APK file, a program for Android is compiled using a tool such as Android Studio or Visual Studio and then packaged into one container file. An APK file can be built from source code written in either Java or Kotlin.
-How to install APK files from unknown sources
-APK files can be downloaded from various sources on the internet, such as websites, forums, or blogs. However, not all APK files are safe or compatible with your device. Some APK files may contain malware, viruses, or spyware that can harm your device or steal your data. Some APK files may also violate the terms of service or intellectual property rights of the original app developers.
-To install APK files from unknown sources, you need to enable a setting on your device that allows installation from sources other than Google Play. To do this, follow these steps:
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-- Security > Unknown Sources and toggle it on. - Download the APK file from the source you trust and save it to your device. - Locate the APK file using a file manager app or an emulator such as BlueStacks or NoxPlayer. - Tap on the APK file and follow the instructions to install it. - Grant the necessary permissions and accept the terms and conditions. - Launch the app and enjoy.
How to Unlock All Levels in Candy Crush Saga with APK File
-Now that you know what an APK file is and how to install it, you might be wondering how to use it to unlock all levels in Candy Crush Saga. The answer is simple: you need to download a modded APK file that has all the levels unlocked and unlimited resources. A modded APK file is an APK file that has been modified by someone to change some aspects of the app, such as features, graphics, or functionality.
-Download a modded APK file from a trusted source
-The first step is to find a reliable source that offers a modded APK file for Candy Crush Saga. There are many websites and blogs that claim to provide such files, but not all of them are safe or working. Some of them may contain malware, viruses, or outdated versions. Some of them may also require you to complete surveys, register, or pay before downloading.
-To avoid these risks, you should do some research before downloading any APK file. You should check the reviews, ratings, comments, and feedback from other users who have downloaded the file. You should also scan the file with an antivirus or malware detector before installing it. You should also backup your data and uninstall the original app before installing the modded one.
-One of the sources that we recommend is [Candy Crush Saga Mod APK], which offers a modded APK file for Candy Crush Saga that has all the levels unlocked, unlimited lives, boosters, gold bars, and trophies. The file is updated regularly and has no ads or surveys. You can download it from their website for free.
-Install the APK file using a file manager or an emulator
-The next step is to install the modded APK file using a file manager app or an emulator. If you are using an Android device, you can use any file manager app that can access your internal storage or SD card. If you are using a PC or Mac, you can use an emulator such as BlueStacks or NoxPlayer that can run Android apps on your computer.
-To install the modded APK file, follow these steps:
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-- Copy the modded APK file to your device or emulator.
-- Locate the modded APK file using a file manager app or an emulator.
-- Tap on the modded APK file and follow the instructions to install it.
-- Grant the necessary permissions and accept the terms and conditions.
-- Launch the app and enjoy.
-Enjoy unlimited boosters, trophies, and levels
-The final step is to enjoy playing Candy Crush Saga with all the levels unlocked and unlimited resources. You can access any level you want without waiting for lives or tickets. You can also use any booster you want without spending gold bars or real money. You can also earn more trophies and achievements by completing challenges and events.
-With the modded APK file, you can have more fun and excitement in playing Candy Crush Saga. You can also challenge your friends and other players online and show off your skills and scores.
-Benefits and Risks of Using APK Files
-Using APK files to unlock all levels in Candy Crush Saga has its benefits and risks. You should be aware of both before deciding whether to use them or not.
-Benefits: access to more features, updates, and customization
-One of the benefits of using APK files is that you can access more features, updates, and customization options that are not available in the official app. For example, you can unlock all levels in Candy Crush Saga, which are otherwise limited by lives, tickets, or in-app purchases. You can also get unlimited boosters, gold bars, trophies, and other resources that can enhance your gameplay. You can also customize your app's appearance, settings, and performance according to your preferences.
-Risks: malware, compatibility issues, and legal consequences
-One of the risks of using APK files is that you may expose your device or data to malware, compatibility issues, or legal consequences. For example, you may download an APK file that contains malware, viruses, or spyware that can harm your device or steal your data. You may also encounter compatibility issues with your device's hardware, software, or operating system that may cause crashes, errors, or glitches. You may also face legal consequences if you violate the terms of service or intellectual property rights of the original app developers or publishers.
Conclusion
-In conclusion, using an APK file to unlock all levels in Candy Crush Saga is a possible way to enjoy the game without any limitations. However, it also comes with some benefits and risks that you should consider before doing so. You should only download APK files from trusted sources, scan them for malware, backup your data, and uninstall the original app before installing the modded one. You should also be aware of the potential compatibility issues and legal consequences that may arise from using APK files.
-If you are looking for a safe and easy way to unlock all levels in Candy Crush Saga, you might want to try [Candy Crush Saga Mod APK], which offers a modded APK file that has all the levels unlocked, unlimited lives, boosters, gold bars, and trophies. You can download it from their website for free and install it on your device or emulator. You can then enjoy playing Candy Crush Saga with all the features and resources you want.
-Do you have any questions or comments about using APK files to unlock all levels in Candy Crush Saga? Let us know in the comment section below. We would love to hear from you!
-FAQs
-Here are some of the frequently asked questions about using APK files to unlock all levels in Candy Crush Saga:
-Q: Is it safe to use APK files?
-A: It depends on the source and the content of the APK file. Some APK files are safe and reliable, while others are malicious and harmful. You should always do some research before downloading any APK file from the internet. You should also scan the file with an antivirus or malware detector before installing it. You should also backup your data and uninstall the original app before installing the modded one.
-Q: Is it legal to use APK files?
-A: It depends on the terms of service and intellectual property rights of the original app developers or publishers. Some APK files are legal and authorized, while others are illegal and unauthorized. You should always read and follow the terms of service and intellectual property rights of the original app developers or publishers before using any APK file. You should also respect their work and support them by buying their products or services.
-Q: How do I update an APK file?
-A: It depends on the source and the version of the APK file. Some APK files are updated automatically or manually by the source, while others are not updated at all. You should always check the source for any updates or new versions of the APK file. You should also uninstall the old version before installing the new one.
-Q: How do I uninstall an APK file?
-A: It depends on the device or emulator you are using. If you are using an Android device, you can uninstall an APK file by going to Settings > Apps > App name > Uninstall. If you are using a PC or Mac, you can uninstall an APK file by going to the emulator's settings and deleting the app.
-Q: How do I backup my data before using an APK file?
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download Do Hello Neighbor 2 and Play Against an Advanced AI that Adapts to Your Every Move.md b/spaces/fatiXbelha/sd/Download Do Hello Neighbor 2 and Play Against an Advanced AI that Adapts to Your Every Move.md deleted file mode 100644 index 90cc98855415394fe2576a49379c1181f13e6515..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Do Hello Neighbor 2 and Play Against an Advanced AI that Adapts to Your Every Move.md +++ /dev/null @@ -1,200 +0,0 @@ - -How to Download Hello Neighbor 2 on PC
-If you are a fan of stealth horror games, you might have heard of Hello Neighbor 2, a sequel to the popular indie game Hello Neighbor. In this game, you play as a journalist who is investigating the mysterious disappearance of your neighbor, Mr. Peterson. Along the way, you will encounter a creepy AI creature that will stalk you and try to stop you from uncovering the truth. You will have to use your wits, skills, and items to sneak into different houses, solve puzzles, and find clues.
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-But how can you download Hello Neighbor 2 on your PC? In this article, we will show you how to get the game from different platforms and stores, how to install and play it on your computer, and some tips and tricks for enjoying the game. Let's get started!
-What is Hello Neighbor 2?
-Hello Neighbor 2 is a stealth horror game developed by Eerie Guest Studios and tinyBuild. It is a sequel to Hello Neighbor, which was released in 2017. The game is set in an open world town called Raven Brooks, where you can explore various locations and houses. The game features an advanced AI system that adapts to your actions and learns from your behavior. The AI neighbor will try to ambush you, set traps, use items, and mimic your moves. You will have to outsmart him and find out what he is hiding.
-The game also has a dynamic narrative that changes depending on your choices and discoveries. You can interact with different characters and events in the town, which will affect the outcome of the story. You can also customize your experience by adjusting the settings, graphics, and controls of the game.
-Where can you get Hello Neighbor 2?
-Hello Neighbor 2 is available for purchase and download on various platforms and stores. You can choose the one that suits your preferences and budget. Here are some of the options:
-Microsoft Store
-The Microsoft Store is a digital distribution platform that allows you to download games for your Windows PC or Xbox console. You can access it from your desktop or web browser. To get Hello Neighbor 2 from the Microsoft Store, you will need:
--
-- A Microsoft account
-- A valid payment method
-- An internet connection
-Here are the steps for downloading Hello Neighbor 2 from the Microsoft Store:
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-- Open the Microsoft Store app on your PC or go to https://www.microsoft.com/en-us/store/games/windows on your web browser.
-- Search for "Hello Neighbor 2" in the search bar or browse through the gaming category.
-- Select the game and click on the "Buy" or "Get" button, depending on whether the game is free or paid.
-- Follow the instructions and prompts to complete the payment and download process.
-- Wait for the game to finish downloading and installing on your PC.
-The price of Hello Neighbor 2 on the Microsoft Store is $29.99 USD. You can also get the game as part of the Xbox Game Pass subscription, which gives you access to over 100 games for a monthly fee. The Xbox Game Pass for PC costs $9.99 USD per month, while the Xbox Game Pass Ultimate, which includes both PC and console games, costs $14.99 USD per month. You can get a free trial of the Xbox Game Pass for 14 days if you are a new user.
-Steam
-Steam is a popular online gaming platform that allows you to buy, download, and play games on your PC. You can also access various features and services, such as cloud saving, achievements, chat, forums, and more. To get Hello Neighbor 2 from Steam, you will need:
--
-- A Steam account
-- A valid payment method
-- An internet connection
-- A Steam client installed on your PC
-Here are the steps for downloading Hello Neighbor 2 from Steam:
--
-- Open the Steam client on your PC or go to https://store.steampowered.com/ on your web browser.
-- Search for "Hello Neighbor 2" in the search bar or browse through the gaming category.
-- Select the game and click on the "Add to Cart" button.
-- Click on the "Purchase for myself" or "Purchase as a gift" button, depending on whether you want to buy the game for yourself or someone else.
-- Follow the instructions and prompts to complete the payment and download process.
-- Wait for the game to finish downloading and installing on your PC.
-The price of Hello Neighbor 2 on Steam is $29.99 USD. You can also get the game as part of a bundle that includes Hello Neighbor and its DLCs for $39.99 USD. You can also get a 10% discount if you pre-order the game before its release date, which is expected to be in 2023.
-Epic Games Store
-The Epic Games Store is another digital distribution platform that allows you to download games for your PC. You can also access various features and services, such as free games, coupons, achievements, and more. To get Hello Neighbor 2 from Epic Games Store, you will need:
--
-- An Epic Games account
-- A valid payment method
-- An internet connection
-- An Epic Games launcher installed on your PC
-Here are the steps for downloading Hello Neighbor 2 from Epic Games Store:
--
-- Open the Epic Games launcher on your PC or go to https://www.epicgames.com/store/en-US/ on your web browser.
-- Search for "Hello Neighbor 2" in the search bar or browse through the gaming category.
-- Select the game and click on the "Get" button.
-- Follow the instructions and prompts to complete the payment and download process.
-- Wait for the game to finish downloading and installing on your PC.
-The price of Hello Neighbor 2 on Epic Games Store is $29.99 USD. You can also get a $10 coupon if you sign up for an Epic Games account and claim a free game from their weekly selection. You can use this coupon to buy Hello Neighbor 2 or any other game that costs $14.99 USD or more.
-How to install and play Hello Neighbor 2?
Once you have downloaded Hello Neighbor 2 from your preferred platform or store, you will need to install and play it on your PC. Here are some steps and tips for doing so:
-System requirements
-Before you install and play Hello Neighbor 2, you should check if your PC meets the minimum and recommended system requirements for running the game. Here are the specs you will need:
--
-- -Minimum -Recommended -- -OS: Windows 10 -OS: Windows 10 -- -Processor: Intel Core i5-4690 or AMD Ryzen 5 1500X -Processor: Intel Core i7-4790 or AMD Ryzen 7 1700X -- -Memory: 8 GB RAM -Memory: 16 GB RAM -- -Graphics: NVIDIA GeForce GTX 760 or AMD Radeon R9 270X -Graphics: NVIDIA GeForce GTX 1070 or AMD Radeon RX Vega 56 -- -DirectX: Version 11 -DirectX: Version 12 -- -Storage: 10 GB available space -Storage: 10 GB available space -- -Sound Card: DirectX compatible sound card -Sound Card: DirectX compatible sound card -You can check your PC's specs by going to the Settings app, clicking on System, and then clicking on About. You can also use a tool like https://www.systemrequirementslab.com/cyri/requirements/hello-neighbor-2/20184 to automatically scan your PC and compare it with the game's requirements.
-Installation process
-The installation process of Hello Neighbor 2 will vary depending on the platform or store you downloaded it from. However, in general, you will need to follow these steps:
--
-- Locate the game's file or folder on your PC. It will usually be in your Downloads folder or in the platform's or store's library.
-- Double-click on the file or folder to launch the installation wizard.
-- Follow the instructions and prompts to choose the installation location, language, and other options.
-- Wait for the installation to complete. It may take a few minutes depending on your PC's speed and internet connection.
-- If prompted, restart your PC to finish the installation.
-Launching the game
-To launch Hello Neighbor 2 on your PC, you can either:
--
-- Double-click on the game's icon on your desktop.
-- Open the platform's or store's launcher and click on the game's icon in your library.
-- Navigate to the game's folder on your PC and double-click on the game's executable file.
-The game will start and you will see the main menu. You can choose to start a new game, continue a previous game, adjust the settings, or exit the game.
-Tips and tricks for playing Hello Neighbor 2
-Hello Neighbor 2 is a challenging and fun game that will test your stealth, puzzle-solving, and exploration skills. Here are some tips and tricks for playing the game:
-How to avoid the AI neighbor
-The AI neighbor is your main enemy in Hello Neighbor 2. He will try to catch you and stop you from snooping around his house. He is smart, fast, and unpredictable. He can use items, set traps, climb walls, break windows, and even drive cars. He can also learn from your actions and adapt his behavior accordingly. Here are some ways to avoid him:
--
-- Use stealth. The AI neighbor can see and hear you, so try to be as quiet and discreet as possible. Crouch, hide, sneak, and avoid making noise. You can also use items like binoculars, cameras, or drones to scout ahead and spot him.
-- Use distraction. The AI neighbor can be distracted by various sounds and objects. You can use items like radios, firecrackers, phones, or alarms to lure him away from your location. You can also throw items like rocks, bottles, or cans to divert his attention.
-- Use exploration. The AI neighbor can be avoided by finding alternative routes and hiding spots. You can use items like keys, crowbars, or lockpicks to unlock doors and windows. You can also use items like ladders, ropes, or planks to climb walls and roofs. You can also hide in closets, cabinets, or boxes.
-How to solve puzzles and find clues
-Hello Neighbor 2 is full of puzzles and clues that you need to solve and find to progress in the game. You will encounter various items, tools, and mechanisms that will help you or hinder you. You will also discover secrets, codes, and messages that will reveal more about the story. Here are some ways to solve puzzles and find clues:
--
-- Use logic. The puzzles and clues in Hello Neighbor 2 are based on logic and common sense. You will need to use your observation, deduction, and reasoning skills to figure out the solutions. You will also need to remember the details and patterns that you encounter in the game.
-- Use trial and error. The puzzles and clues in Hello Neighbor 2 are also based on trial and error. You will need to experiment with different items, tools, and combinations to see what works and what doesn't. You will also need to learn from your mistakes and failures.
-- Use hints. The puzzles and clues in Hello Neighbor 2 are not impossible to solve or find. You will find hints and tips throughout the game that will guide you or nudge you in the right direction. You can also use items like maps, notes, or books to get more information.
-How to customize your experience
-Hello Neighbor 2 is a game that allows you to customize your experience according to your preferences and needs. You can adjust the settings, graphics, and controls of the game to make it more enjoyable and comfortable for you. Here are some ways to customize your experience:
--
-- Use the settings menu. The settings menu in Hello Neighbor 2 lets you change various options and features of the game. You can access it from the main menu or by pressing the Esc key during the game. You can change the language, difficulty, volume, subtitles, and more.
-- Use the graphics menu. The graphics menu in Hello Neighbor 2 lets you change the quality and performance of the game's visuals. You can access it from the settings menu or by pressing the F11 key during the game. You can change the resolution, fullscreen mode, brightness, contrast, shadows, textures, anti-aliasing, and more.
-- Use the controls menu. The controls menu in Hello Neighbor 2 lets you change the input and output of the game's commands. You can access it from the settings menu or by pressing the F10 key during the game. You can change the keyboard, mouse, controller, or VR settings.
-Conclusion
-Hello Neighbor 2 is a stealth horror game that will keep you on your toes as you try to uncover the mystery of your neighbor's disappearance. You will have to download it from one of the platforms or stores that offer it, install it on your PC, and launch it from your desktop or launcher. You will also have to avoid the AI neighbor, solve puzzles and find clues, and customize your experience along the way.
-If you are looking for a thrilling and immersive game that will challenge your skills and creativity, Hello Neighbor 2 is a great choice for you. You can get it now for $29.99 USD or less depending on the platform or store you choose.
-Are you ready to face your neighbor? Download Hello Neighbor 2 today and find out what he is hiding!
-FAQs
-Here are some frequently asked questions and answers about Hello Neighbor 2:
--
197e85843d- Is Hello Neighbor 2 a multiplayer game?
-No, Hello Neighbor 2 is a single-player game that does not support online or local multiplayer modes.
-- Is Hello Neighbor 2 a scary game?
-Yes, Hello Neighbor 2 is a scary game that contains elements of horror, suspense, jump scares, violence, blood, gore, and dark themes.
-- Is Hello Neighbor 2 suitable for children?
-No, Hello Neighbor 2 is not suitable for children under the age of 13 due to its mature content and difficulty level.li>How long is Hello Neighbor 2? -
The length of Hello Neighbor 2 depends on your playstyle, skill level, and choices. However, on average, it will take you about 10 hours to complete the main story and about 15 hours to complete all the side quests and secrets.
-- Can I play Hello Neighbor 2 on other devices?
-Yes, Hello Neighbor 2 is also available for Xbox One, Xbox Series X/S, and Android devices. You can download it from the respective platforms or stores that offer it.
-- Where can I get more information and support for Hello Neighbor 2?
-You can get more information and support for Hello Neighbor 2 by visiting the official website https://www.helloneighbor2.com/, the official wiki https://helloneighbor.fandom.com/wiki/Hello_Neighbor_2, the official forum https://forum.helloneighbor2.com/, or the official social media pages https://www.facebook.com/helloneighborgame, https://twitter.com/tinBuild, and https://www.instagram.com/tinybuildgames/.
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download and Install the OnePlus 7T Live Wallpaper APK on Any Android Device in Minutes.md b/spaces/fatiXbelha/sd/Download and Install the OnePlus 7T Live Wallpaper APK on Any Android Device in Minutes.md deleted file mode 100644 index 66c0fa8fb850b2506b8b91446f484deef15e5b38..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download and Install the OnePlus 7T Live Wallpaper APK on Any Android Device in Minutes.md +++ /dev/null @@ -1,126 +0,0 @@ - -How to Get the OnePlus 7T Live Wallpaper APK on Any Android Device
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-In this article, we will show you how to download and install the OnePlus 7T live wallpaper APK on any Android device. You don't need to have a OnePlus device or root access to enjoy this amazing live wallpaper. All you need is an Android device running Android 8.0 Oreo or above and a few minutes of your time. Let's get started!
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-A live wallpaper is a type of wallpaper that can animate, change, or react to your touch, motion, or other inputs. Unlike a static wallpaper, which is just an image that stays the same all the time, a live wallpaper can create a more dynamic and immersive experience for your device's home screen.
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-What is the OnePlus 7T Live Wallpaper and What Does It Look Like
-The OnePlus 7T live wallpaper is a live wallpaper that was designed by OnePlus for their flagship smartphone, the OnePlus 7T. It features stunning, eye-catching animations that rotate whenever you turn on or unlock your device's screen. The colors of the wallpaper also change dynamically based on the time of day.
-The OnePlus 7T live wallpaper is one of the most popular live wallpapers among OnePlus fans and Android enthusiasts. It has a minimalist and elegant design that matches the OnePlus 7T's sleek and premium look. It also has a smooth and fluid animation that creates a sense of motion and depth.
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-If you want to see how the OnePlus 7T live wallpaper looks like on your device's screen, you can watch this video or check out this screenshot. You can also compare it with other live wallpapers from OnePlus and other brands to see which one you like better.
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-If you are ready to try out the OnePlus 7T live wallpaper on your Android device, you will need to download and install an APK file that contains the live wallpaper. An APK file is a file format that is used to distribute and install applications on Android devices. However, not all APK files are available on the Google Play Store or other official sources. Some APK files are only available on third-party websites or forums.
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-We hope you found this article helpful and informative. If you have any questions, comments, or feedback, feel free to leave them below. We would love to hear from you!
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-Download Mod Apk Fallout Shelter: A Guide for Beginners
-If you are a fan of the popular Fallout series, you might have heard of Fallout Shelter, a free-to-play simulation game that lets you build and manage your own post-apocalyptic vault. The game is available for iOS, Android, PC, Xbox One, PS4, Nintendo Switch, and Tesla Arcade devices, and has received positive reviews from critics and players alike. However, if you want to enjoy the game to the fullest, you might want to download mod apk fallout shelter, a modified version of the game that offers unlimited resources, free items, customization options, and more. In this article, we will explain what mod apk fallout shelter is, how to download it, what are its benefits and risks, and how to play it safely and effectively.
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-Mod apk fallout shelter is a file that contains the modified version of the original game. To download it, you will need to follow these steps:
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-There are many websites that offer mod apk files for various games, but not all of them are trustworthy. Some of them may contain malware, viruses, or outdated versions that can harm your device or compromise your game account. Therefore, you should do some research before downloading any mod apk file from an unknown source. You can check the reviews, ratings, comments, and feedback from other users to see if the website is reputable and safe. You can also use antivirus software or online scanners to scan the file before downloading it.
-Step 2: Enable installation from unknown sources on your device
-By default, most devices do not allow installation of apps from sources other than the official app store. This is a security measure to prevent unauthorized or harmful apps from accessing your device. However, if you want to install mod apk fallout shelter, you will need to enable installation from unknown sources on your device. To do this, you will need to go to your device's settings, find the security or privacy option, and toggle on the option that allows installation from unknown sources. You may also need to grant permission for the app to access your device's storage, location, camera, or other features.
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-Once you have found a reliable source for mod apk fallout shelter and enabled installation from unknown sources on your device, you can proceed to download and install the file. You will need to click on the download link or button on the website, wait for the file to be downloaded on your device's storage, and then tap on the file to open it. You will see a prompt asking you to confirm the installation of the app. You will need to tap on the install button and wait for the installation to be completed. You may also need to agree to the terms and conditions of the app. Once the installation is done, you can launch the app and enjoy mod apk fallout shelter.
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-Downloading mod apk fallout shelter can give you many advantages over the original game. Here are some of the benefits that you can enjoy:
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-One of the main challenges of Fallout Shelter is to manage your resources and caps, which are the currency of the game. You need resources such as food, water, power, and stimpacks to keep your dwellers happy and healthy, and caps to build and upgrade rooms, buy items, and expand your vault. However, resources and caps are limited and hard to come by in the game, especially as your vault grows bigger and more demanding. With mod apk fallout shelter, you can have unlimited resources and caps, which means you can build and maintain your vault without any worries or restrictions.
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-download fallout shelter mod apk 1.14.0Benefit 2: Free lunchboxes and other items
-Lunchboxes are special items that contain random rewards such as dwellers, weapons, outfits, resources, caps, or junk. They can be obtained by completing objectives, achievements, or events in the game, or by purchasing them with real money. Lunchboxes can help you improve your vault and your dwellers' skills and abilities. However, they are rare and expensive in the game, and you may not always get what you want from them. With mod apk fallout shelter, you can have free lunchboxes and other items, which means you can get more rewards and surprises without spending any money or time.
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-Downloading mod apk fallout shelter can also have some risks that you should be aware of before installing it. Here are some of the risks that you may face:
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-As mentioned earlier, not all mod apk files are safe and reliable. Some of them may contain malware or viruses that can infect your device or steal your personal information. Malware or viruses can cause serious problems such as slowing down your device, corrupting your files, draining your battery, displaying unwanted ads, or accessing your camera, microphone, contacts, or other sensitive data. Therefore, you should always be careful and cautious when downloading any mod apk file from an unknown source. You should also use antivirus software or online scanners to scan the file before downloading it.
-Risk 2: Ban or suspension from the official game
-Downloading mod apk fallout shelter can also violate the terms and conditions of the official game. The game developers may not approve of using mod apk files to alter or modify the game's features or functions. They may consider it as cheating, hacking, or unfair advantage over other players. Therefore, they may detect your use of mod apk fallout shelter and ban or suspend your game account. This means you will not be able to access or play the official game anymore. You may also lose your progress and data in the game. Therefore, you should always be aware of the consequences and risks of using mod apk fallout shelter and respect the game's rules and guidelines.
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-Downloading mod apk fallout shelter can also affect your progress and data in the game. Mod apk fallout shelter may not be compatible or updated with the latest version of the official game. This means you may encounter errors or issues when playing the game, such as crashing, freezing, lagging, loading, or syncing problems. You may also lose some of your features or functions in the game, such as achievements, objectives, events, quests, explorations, or rewards. You may also lose your vault and your dwellers' data, such as their names, levels, skills, abilities, weapons, outfits, relationships, or health. Therefore, you should always backup your original game data before installing mod apk fallout shelter and restore it if needed.
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-Downloading mod apk fallout shelter can be fun and exciting, but it can also be risky and challenging. Therefore, you should know how to play it safely and effectively to avoid any problems or troubles. Here are some tips that you can follow:
-Tip 1: Use a VPN or proxy to hide your IP address
-One of the ways to prevent detection or ban from the official game is to use a VPN or proxy to hide your IP address. A VPN or proxy is a service that allows you to connect to the internet through a different server or location. This way, you can mask your real IP address and location and appear as if you are accessing the game from somewhere else. This can help you avoid any restrictions or limitations that the game developers may impose on certain regions or countries. It can also help you protect your privacy and security online by encrypting your data and preventing any hackers or trackers from accessing your device.
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-Another way to play mod apk fallout shelter safely and effectively is to backup your original game data before installing mod apk. As mentioned earlier, mod apk fallout shelter may not be compatible or updated with the latest version of the official game. It may also cause errors or issues that can affect your progress and data in the game. Therefore, you should always backup your original game data before installing mod apk fallout shelter and restore it if needed. You can backup your original game data by using cloud storage services such as Google Drive or Dropbox, or by using external storage devices such as USB flash drives or SD cards.
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-A final way to play mod apk fallout shelter safely and effectively is to follow the game's rules and guidelines to avoid detection. Even if you use a VPN or proxy to hide your IP address, you may still be detected by the game developers if you act suspiciously or abnormally in the game. For example, if you have unlimited resources and caps, you may attract attention from other players or the game developers who may report you or investigate you. Therefore, you should always follow the game's rules and guidelines to avoid detection. You should not abuse or exploit the mod apk fallout shelter features or functions, such as creating multiple accounts, spamming, trolling, or harassing other players. You should also not brag or boast about your mod apk fallout shelter achievements or rewards, as this may make you a target for envy or resentment. You should also play the game normally and moderately, as if you are using the original game.
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-Downloading mod apk fallout shelter can be a great way to enhance your gaming experience and have more fun and enjoyment. However, it can also have some risks and challenges that you should be aware of and prepared for. Therefore, you should always download mod apk fallout shelter from a reliable source, enable installation from unknown sources on your device, backup your original game data before installing mod apk, use a VPN or proxy to hide your IP address, and follow the game's rules and guidelines to avoid detection. By doing so, you can play mod apk fallout shelter safely and effectively.
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-Here are some of the frequently asked questions about mod apk fallout shelter:
-Q1: Is downloading mod apk fallout shelter legal?
-A1: Downloading mod apk fallout shelter is not illegal, but it may violate the terms and conditions of the official game. The game developers may not approve of using mod apk files to alter or modify the game's features or functions. They may consider it as cheating, hacking, or unfair advantage over other players. Therefore, they may detect your use of mod apk fallout shelter and ban or suspend your game account. This means you will not be able to access or play the official game anymore. You may also lose your progress and data in the game.
-Q2: Can I play mod apk fallout shelter online with other players?
-A2: Yes, you can play mod apk fallout shelter online with other players, but you may face some difficulties or limitations. For example, you may not be able to join certain servers or regions that have different versions or updates of the game. You may also encounter lagging, crashing, freezing, loading, or syncing issues when playing online. You may also face hostility or resentment from other players who may not like your use of mod apk fallout shelter. They may report you or attack you in the game.
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-A3: To update mod apk fallout shelter to the latest version, you will need to download and install the new mod apk file from the same source that you downloaded the previous one. You will need to follow the same steps as before, such as enabling installation from unknown sources on your device, backing up your original game data before installing mod apk, and using a VPN or proxy to hide your IP address. You will also need to uninstall the old mod apk file before installing the new one.
-Q4: What are some of the best mod apk fallout shelter features?
-A4: Some of the best mod apk fallout shelter features are unlimited resources and caps, free lunchboxes and other items, customization and optimization of your vault, removal of ads and in-app purchases, unlocking of all rooms and dwellers, and more.
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-I hope this article has helped you understand what mod apk fallout shelter is, how to download it, what are its benefits and risks, and how to play it safely and effectively. If you have any questions or comments, please feel free to leave them below. Thank you for reading and happy gaming!
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-Next, you need to download the mod apk file from a reliable source. You can search for Real Racing 3 Mod APK on Google or any other search engine and choose a reputable website that offers the download link. Make sure to check the reviews and ratings of the website before downloading the file. Also, avoid clicking on any ads or pop-ups that might redirect you to malicious sites.
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-Step 3: Locate and open the Slayer Legend mod apk file on your device
-Once you have downloaded the Slayer Legend mod apk file, you need to locate and open it on your device. To do this:
--
-- Go to your device's file manager and find the Downloads folder.
-- Tap on the Slayer Legend mod apk file. It should have a name like slayer-legend-mod.apk.
-- A pop-up window will appear asking you to confirm the installation. Tap Install.
-Step 4: Follow the instructions to install and launch the game
-After you have opened the Slayer Legend mod apk file, you need to follow the instructions to install and launch the game. To do this:
--
-- Wait for the installation process to complete. It may take a few minutes depending on your device's speed and memory.
-- When the installation is done, tap Open to launch the game.
-- You may need to grant some permissions to the game, such as access to your storage, contacts, and phone. Tap Allow when prompted.
-- You may also need to verify your age and accept the terms and conditions of the game. Tap Agree when prompted.
-- You can now enjoy playing Slayer Legend mod apk with unlimited money, premium features, and other benefits.
-Comparison of Slayer Legend mod apk and original game
-Slayer Legend mod apk is a modified version of the original game that gives you some advantages and disadvantages. Here are some of them:
-Advantages of Slayer Legend mod apk
-Some of the advantages of Slayer Legend mod apk are:
--
-- You get unlimited money that you can use to buy items, equipment, pets, mounts, wings, costumes, and more.
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-- You get faster leveling up and higher stats that make you stronger and more powerful in battles.
-- You get more fun and excitement as you can explore more regions, dungeons, arenas, and guild wars without any restrictions or limitations.
-Disadvantages of Slayer Legend mod apk
-Some of the disadvantages of Slayer Legend mod apk are:
--
- You may face some compatibility issues or bugs that may affect the performance or stability of the game.
-- You may risk getting banned or suspended from the game if you are detected using a mod apk by the game developers or moderators.
-- You may lose some of the original features or content of the game that are not included or modified in the mod apk.
-- You may miss out on some of the updates or events that are only available in the original game.
-Tips and tricks for playing Slayer Legend mod apk
-Now that you have downloaded and installed Slayer Legend mod apk, you might want to know some tips and tricks for playing it. Here are some of them:
-Upgrade your skills and equipment
-One of the most important things to do in Slayer Legend mod apk is to upgrade your skills and equipment. This will make you stronger, faster, and more durable in battles. You can upgrade your skills by using skill points that you earn by leveling up. You can upgrade your equipment by using materials that you collect by defeating enemies or completing quests. You can also use your unlimited money to buy better equipment from the shop.
-Complete quests and achievements
-Another way to improve your character and enjoy the game is to complete quests and achievements. Quests are tasks that you can accept from NPCs or the quest board. They will reward you with experience, money, items, or other benefits. Achievements are goals that you can accomplish by playing the game. They will reward you with titles, badges, or other rewards. You can check your quests and achievements by tapping on the icons on the top-left corner of the screen.
-Join a guild and cooperate with other players
-One of the best features of Slayer Legend mod apk is the social aspect. You can join a guild and cooperate with other players. A guild is a group of players who share a common interest or goal. You can chat with your guild members, help each other, and participate in guild wars. Guild wars are battles between guilds that occur every week. The winning guild will get rewards and glory. You can join a guild by tapping on the Guild icon on the bottom-right corner of the screen.
-Conclusion
-Slayer Legend mod apk is a great way to enjoy Slayer Legend without any limitations. It gives you unlimited money, premium features, and other benefits that make the game more fun and exciting. However, it also has some disadvantages, such as compatibility issues, risk of getting banned, or missing out on some updates or events. Therefore, you should be careful and responsible when using Slayer Legend mod apk.
-We hope this article has helped you learn how to download and install Slayer Legend mod apk, how it compares to the original game, and some tips and tricks for playing it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
-FAQs
-Here are some frequently asked questions about Slayer Legend mod apk:
--
401be4b1e0- Q: Is Slayer Legend mod apk safe to use?
-- A: Slayer Legend mod apk is safe to use if you download it from a reliable source and scan it with an antivirus or anti-malware software before opening it. However, you should always be careful and cautious when downloading any mod apk from the internet.
-- Q: How do I update Slayer Legend mod apk?
-- A: To update Slayer Legend mod apk, you need to download the latest version of the mod apk from the same website that you downloaded it from before. Then, you need to uninstall the previous version of the mod apk from your device and install the new version following the same steps as before.
-- Q: Can I play Slayer Legend mod apk offline?
-- A: No, you cannot play Slayer Legend mod apk offline. You need an internet connection to play the game as it requires online verification and synchronization.
-- Q: Can I play Slayer Legend mod apk with my friends?
-- A: Yes, you can play Slayer Legend mod apk with your friends if they also have the same version of the mod apk installed on their devices. You can invite them to join your guild or chat with them in the game.
-- Q: Can I transfer my progress from Slayer Legend mod apk to the original game?
-- A: No, you cannot transfer your progress from Slayer Legend mod apk to the original game as they are not compatible with each other. You will have to start from scratch if you switch to the original game.
-
-
-
\ No newline at end of file diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/worker_threads.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/worker_threads.d.ts deleted file mode 100644 index 52f438487805daf0ade7a680a3f373a1b0746d7d..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/worker_threads.d.ts +++ /dev/null @@ -1,689 +0,0 @@ -/** - * The `worker_threads` module enables the use of threads that execute JavaScript - * in parallel. To access it: - * - * ```js - * const worker = require('worker_threads'); - * ``` - * - * Workers (threads) are useful for performing CPU-intensive JavaScript operations. - * They do not help much with I/O-intensive work. The Node.js built-in - * asynchronous I/O operations are more efficient than Workers can be. - * - * Unlike `child_process` or `cluster`, `worker_threads` can share memory. They do - * so by transferring `ArrayBuffer` instances or sharing `SharedArrayBuffer`instances. - * - * ```js - * const { - * Worker, isMainThread, parentPort, workerData - * } = require('worker_threads'); - * - * if (isMainThread) { - * module.exports = function parseJSAsync(script) { - * return new Promise((resolve, reject) => { - * const worker = new Worker(__filename, { - * workerData: script - * }); - * worker.on('message', resolve); - * worker.on('error', reject); - * worker.on('exit', (code) => { - * if (code !== 0) - * reject(new Error(`Worker stopped with exit code ${code}`)); - * }); - * }); - * }; - * } else { - * const { parse } = require('some-js-parsing-library'); - * const script = workerData; - * parentPort.postMessage(parse(script)); - * } - * ``` - * - * The above example spawns a Worker thread for each `parseJSAsync()` call. In - * practice, use a pool of Workers for these kinds of tasks. Otherwise, the - * overhead of creating Workers would likely exceed their benefit. - * - * When implementing a worker pool, use the `AsyncResource` API to inform - * diagnostic tools (e.g. to provide asynchronous stack traces) about the - * correlation between tasks and their outcomes. See `"Using AsyncResource for a Worker thread pool"` in the `async_hooks` documentation for an example implementation. - * - * Worker threads inherit non-process-specific options by default. Refer to `Worker constructor options` to know how to customize worker thread options, - * specifically `argv` and `execArgv` options. - * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/worker_threads.js) - */ -declare module 'worker_threads' { - import { Blob } from 'node:buffer'; - import { Context } from 'node:vm'; - import { EventEmitter } from 'node:events'; - import { EventLoopUtilityFunction } from 'node:perf_hooks'; - import { FileHandle } from 'node:fs/promises'; - import { Readable, Writable } from 'node:stream'; - import { URL } from 'node:url'; - import { X509Certificate } from 'node:crypto'; - const isMainThread: boolean; - const parentPort: null | MessagePort; - const resourceLimits: ResourceLimits; - const SHARE_ENV: unique symbol; - const threadId: number; - const workerData: any; - /** - * Instances of the `worker.MessageChannel` class represent an asynchronous, - * two-way communications channel. - * The `MessageChannel` has no methods of its own. `new MessageChannel()`yields an object with `port1` and `port2` properties, which refer to linked `MessagePort` instances. - * - * ```js - * const { MessageChannel } = require('worker_threads'); - * - * const { port1, port2 } = new MessageChannel(); - * port1.on('message', (message) => console.log('received', message)); - * port2.postMessage({ foo: 'bar' }); - * // Prints: received { foo: 'bar' } from the `port1.on('message')` listener - * ``` - * @since v10.5.0 - */ - class MessageChannel { - readonly port1: MessagePort; - readonly port2: MessagePort; - } - interface WorkerPerformance { - eventLoopUtilization: EventLoopUtilityFunction; - } - type TransferListItem = ArrayBuffer | MessagePort | FileHandle | X509Certificate | Blob; - /** - * Instances of the `worker.MessagePort` class represent one end of an - * asynchronous, two-way communications channel. It can be used to transfer - * structured data, memory regions and other `MessagePort`s between different `Worker` s. - * - * This implementation matches [browser `MessagePort`](https://developer.mozilla.org/en-US/docs/Web/API/MessagePort) s. - * @since v10.5.0 - */ - class MessagePort extends EventEmitter { - /** - * Disables further sending of messages on either side of the connection. - * This method can be called when no further communication will happen over this`MessagePort`. - * - * The `'close' event` is emitted on both `MessagePort` instances that - * are part of the channel. - * @since v10.5.0 - */ - close(): void; - /** - * Sends a JavaScript value to the receiving side of this channel.`value` is transferred in a way which is compatible with - * the [HTML structured clone algorithm](https://developer.mozilla.org/en-US/docs/Web/API/Web_Workers_API/Structured_clone_algorithm). - * - * In particular, the significant differences to `JSON` are: - * - * * `value` may contain circular references. - * * `value` may contain instances of builtin JS types such as `RegExp`s,`BigInt`s, `Map`s, `Set`s, etc. - * * `value` may contain typed arrays, both using `ArrayBuffer`s - * and `SharedArrayBuffer`s. - * * `value` may contain [`WebAssembly.Module`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/WebAssembly/Module) instances. - * * `value` may not contain native (C++-backed) objects other than: - * - * ```js - * const { MessageChannel } = require('worker_threads'); - * const { port1, port2 } = new MessageChannel(); - * - * port1.on('message', (message) => console.log(message)); - * - * const circularData = {}; - * circularData.foo = circularData; - * // Prints: { foo: [Circular] } - * port2.postMessage(circularData); - * ``` - * - * `transferList` may be a list of [`ArrayBuffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/ArrayBuffer), `MessagePort` and `FileHandle` objects. - * After transferring, they are not usable on the sending side of the channel - * anymore (even if they are not contained in `value`). Unlike with `child processes`, transferring handles such as network sockets is currently - * not supported. - * - * If `value` contains [`SharedArrayBuffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/SharedArrayBuffer) instances, those are accessible - * from either thread. They cannot be listed in `transferList`. - * - * `value` may still contain `ArrayBuffer` instances that are not in`transferList`; in that case, the underlying memory is copied rather than moved. - * - * ```js - * const { MessageChannel } = require('worker_threads'); - * const { port1, port2 } = new MessageChannel(); - * - * port1.on('message', (message) => console.log(message)); - * - * const uint8Array = new Uint8Array([ 1, 2, 3, 4 ]); - * // This posts a copy of `uint8Array`: - * port2.postMessage(uint8Array); - * // This does not copy data, but renders `uint8Array` unusable: - * port2.postMessage(uint8Array, [ uint8Array.buffer ]); - * - * // The memory for the `sharedUint8Array` is accessible from both the - * // original and the copy received by `.on('message')`: - * const sharedUint8Array = new Uint8Array(new SharedArrayBuffer(4)); - * port2.postMessage(sharedUint8Array); - * - * // This transfers a freshly created message port to the receiver. - * // This can be used, for example, to create communication channels between - * // multiple `Worker` threads that are children of the same parent thread. - * const otherChannel = new MessageChannel(); - * port2.postMessage({ port: otherChannel.port1 }, [ otherChannel.port1 ]); - * ``` - * - * The message object is cloned immediately, and can be modified after - * posting without having side effects. - * - * For more information on the serialization and deserialization mechanisms - * behind this API, see the `serialization API of the v8 module`. - * @since v10.5.0 - */ - postMessage(value: any, transferList?: ReadonlyArray): void; - /** - * Opposite of `unref()`. Calling `ref()` on a previously `unref()`ed port does _not_ let the program exit if it's the only active handle left (the default - * behavior). If the port is `ref()`ed, calling `ref()` again has no effect. - * - * If listeners are attached or removed using `.on('message')`, the port - * is `ref()`ed and `unref()`ed automatically depending on whether - * listeners for the event exist. - * @since v10.5.0 - */ - ref(): void; - /** - * Calling `unref()` on a port allows the thread to exit if this is the only - * active handle in the event system. If the port is already `unref()`ed calling`unref()` again has no effect. - * - * If listeners are attached or removed using `.on('message')`, the port is`ref()`ed and `unref()`ed automatically depending on whether - * listeners for the event exist. - * @since v10.5.0 - */ - unref(): void; - /** - * Starts receiving messages on this `MessagePort`. When using this port - * as an event emitter, this is called automatically once `'message'`listeners are attached. - * - * This method exists for parity with the Web `MessagePort` API. In Node.js, - * it is only useful for ignoring messages when no event listener is present. - * Node.js also diverges in its handling of `.onmessage`. Setting it - * automatically calls `.start()`, but unsetting it lets messages queue up - * until a new handler is set or the port is discarded. - * @since v10.5.0 - */ - start(): void; - addListener(event: 'close', listener: () => void): this; - addListener(event: 'message', listener: (value: any) => void): this; - addListener(event: 'messageerror', listener: (error: Error) => void): this; - addListener(event: string | symbol, listener: (...args: any[]) => void): this; - emit(event: 'close'): boolean; - emit(event: 'message', value: any): boolean; - emit(event: 'messageerror', error: Error): boolean; - emit(event: string | symbol, ...args: any[]): boolean; - on(event: 'close', listener: () => void): this; - on(event: 'message', listener: (value: any) => void): this; - on(event: 'messageerror', listener: (error: Error) => void): this; - on(event: string | symbol, listener: (...args: any[]) => void): this; - once(event: 'close', listener: () => void): this; - once(event: 'message', listener: (value: any) => void): this; - once(event: 'messageerror', listener: (error: Error) => void): this; - once(event: string | symbol, listener: (...args: any[]) => void): this; - prependListener(event: 'close', listener: () => void): this; - prependListener(event: 'message', listener: (value: any) => void): this; - prependListener(event: 'messageerror', listener: (error: Error) => void): this; - prependListener(event: string | symbol, listener: (...args: any[]) => void): this; - prependOnceListener(event: 'close', listener: () => void): this; - prependOnceListener(event: 'message', listener: (value: any) => void): this; - prependOnceListener(event: 'messageerror', listener: (error: Error) => void): this; - prependOnceListener(event: string | symbol, listener: (...args: any[]) => void): this; - removeListener(event: 'close', listener: () => void): this; - removeListener(event: 'message', listener: (value: any) => void): this; - removeListener(event: 'messageerror', listener: (error: Error) => void): this; - removeListener(event: string | symbol, listener: (...args: any[]) => void): this; - off(event: 'close', listener: () => void): this; - off(event: 'message', listener: (value: any) => void): this; - off(event: 'messageerror', listener: (error: Error) => void): this; - off(event: string | symbol, listener: (...args: any[]) => void): this; - } - interface WorkerOptions { - /** - * List of arguments which would be stringified and appended to - * `process.argv` in the worker. This is mostly similar to the `workerData` - * but the values will be available on the global `process.argv` as if they - * were passed as CLI options to the script. - */ - argv?: any[] | undefined; - env?: NodeJS.Dict | typeof SHARE_ENV | undefined; - eval?: boolean | undefined; - workerData?: any; - stdin?: boolean | undefined; - stdout?: boolean | undefined; - stderr?: boolean | undefined; - execArgv?: string[] | undefined; - resourceLimits?: ResourceLimits | undefined; - /** - * Additional data to send in the first worker message. - */ - transferList?: TransferListItem[] | undefined; - /** - * @default true - */ - trackUnmanagedFds?: boolean | undefined; - } - interface ResourceLimits { - /** - * The maximum size of a heap space for recently created objects. - */ - maxYoungGenerationSizeMb?: number | undefined; - /** - * The maximum size of the main heap in MB. - */ - maxOldGenerationSizeMb?: number | undefined; - /** - * The size of a pre-allocated memory range used for generated code. - */ - codeRangeSizeMb?: number | undefined; - /** - * The default maximum stack size for the thread. Small values may lead to unusable Worker instances. - * @default 4 - */ - stackSizeMb?: number | undefined; - } - /** - * The `Worker` class represents an independent JavaScript execution thread. - * Most Node.js APIs are available inside of it. - * - * Notable differences inside a Worker environment are: - * - * * The `process.stdin`, `process.stdout` and `process.stderr` may be redirected by the parent thread. - * * The `require('worker_threads').isMainThread` property is set to `false`. - * * The `require('worker_threads').parentPort` message port is available. - * * `process.exit()` does not stop the whole program, just the single thread, - * and `process.abort()` is not available. - * * `process.chdir()` and `process` methods that set group or user ids - * are not available. - * * `process.env` is a copy of the parent thread's environment variables, - * unless otherwise specified. Changes to one copy are not visible in other - * threads, and are not visible to native add-ons (unless `worker.SHARE_ENV` is passed as the `env` option to the `Worker` constructor). - * * `process.title` cannot be modified. - * * Signals are not delivered through `process.on('...')`. - * * Execution may stop at any point as a result of `worker.terminate()` being invoked. - * * IPC channels from parent processes are not accessible. - * * The `trace_events` module is not supported. - * * Native add-ons can only be loaded from multiple threads if they fulfill `certain conditions`. - * - * Creating `Worker` instances inside of other `Worker`s is possible. - * - * Like [Web Workers](https://developer.mozilla.org/en-US/docs/Web/API/Web_Workers_API) and the `cluster module`, two-way communication can be - * achieved through inter-thread message passing. Internally, a `Worker` has a - * built-in pair of `MessagePort` s that are already associated with each other - * when the `Worker` is created. While the `MessagePort` object on the parent side - * is not directly exposed, its functionalities are exposed through `worker.postMessage()` and the `worker.on('message')` event - * on the `Worker` object for the parent thread. - * - * To create custom messaging channels (which is encouraged over using the default - * global channel because it facilitates separation of concerns), users can create - * a `MessageChannel` object on either thread and pass one of the`MessagePort`s on that `MessageChannel` to the other thread through a - * pre-existing channel, such as the global one. - * - * See `port.postMessage()` for more information on how messages are passed, - * and what kind of JavaScript values can be successfully transported through - * the thread barrier. - * - * ```js - * const assert = require('assert'); - * const { - * Worker, MessageChannel, MessagePort, isMainThread, parentPort - * } = require('worker_threads'); - * if (isMainThread) { - * const worker = new Worker(__filename); - * const subChannel = new MessageChannel(); - * worker.postMessage({ hereIsYourPort: subChannel.port1 }, [subChannel.port1]); - * subChannel.port2.on('message', (value) => { - * console.log('received:', value); - * }); - * } else { - * parentPort.once('message', (value) => { - * assert(value.hereIsYourPort instanceof MessagePort); - * value.hereIsYourPort.postMessage('the worker is sending this'); - * value.hereIsYourPort.close(); - * }); - * } - * ``` - * @since v10.5.0 - */ - class Worker extends EventEmitter { - /** - * If `stdin: true` was passed to the `Worker` constructor, this is a - * writable stream. The data written to this stream will be made available in - * the worker thread as `process.stdin`. - * @since v10.5.0 - */ - readonly stdin: Writable | null; - /** - * This is a readable stream which contains data written to `process.stdout` inside the worker thread. If `stdout: true` was not passed to the `Worker` constructor, then data is piped to the - * parent thread's `process.stdout` stream. - * @since v10.5.0 - */ - readonly stdout: Readable; - /** - * This is a readable stream which contains data written to `process.stderr` inside the worker thread. If `stderr: true` was not passed to the `Worker` constructor, then data is piped to the - * parent thread's `process.stderr` stream. - * @since v10.5.0 - */ - readonly stderr: Readable; - /** - * An integer identifier for the referenced thread. Inside the worker thread, - * it is available as `require('worker_threads').threadId`. - * This value is unique for each `Worker` instance inside a single process. - * @since v10.5.0 - */ - readonly threadId: number; - /** - * Provides the set of JS engine resource constraints for this Worker thread. - * If the `resourceLimits` option was passed to the `Worker` constructor, - * this matches its values. - * - * If the worker has stopped, the return value is an empty object. - * @since v13.2.0, v12.16.0 - */ - readonly resourceLimits?: ResourceLimits | undefined; - /** - * An object that can be used to query performance information from a worker - * instance. Similar to `perf_hooks.performance`. - * @since v15.1.0, v14.17.0, v12.22.0 - */ - readonly performance: WorkerPerformance; - /** - * @param filename The path to the Worker’s main script or module. - * Must be either an absolute path or a relative path (i.e. relative to the current working directory) starting with ./ or ../, - * or a WHATWG URL object using file: protocol. If options.eval is true, this is a string containing JavaScript code rather than a path. - */ - constructor(filename: string | URL, options?: WorkerOptions); - /** - * Send a message to the worker that is received via `require('worker_threads').parentPort.on('message')`. - * See `port.postMessage()` for more details. - * @since v10.5.0 - */ - postMessage(value: any, transferList?: ReadonlyArray ): void; - /** - * Opposite of `unref()`, calling `ref()` on a previously `unref()`ed worker does _not_ let the program exit if it's the only active handle left (the default - * behavior). If the worker is `ref()`ed, calling `ref()` again has - * no effect. - * @since v10.5.0 - */ - ref(): void; - /** - * Calling `unref()` on a worker allows the thread to exit if this is the only - * active handle in the event system. If the worker is already `unref()`ed calling`unref()` again has no effect. - * @since v10.5.0 - */ - unref(): void; - /** - * Stop all JavaScript execution in the worker thread as soon as possible. - * Returns a Promise for the exit code that is fulfilled when the `'exit' event` is emitted. - * @since v10.5.0 - */ - terminate(): Promise ; - /** - * Returns a readable stream for a V8 snapshot of the current state of the Worker. - * See `v8.getHeapSnapshot()` for more details. - * - * If the Worker thread is no longer running, which may occur before the `'exit' event` is emitted, the returned `Promise` is rejected - * immediately with an `ERR_WORKER_NOT_RUNNING` error. - * @since v13.9.0, v12.17.0 - * @return A promise for a Readable Stream containing a V8 heap snapshot - */ - getHeapSnapshot(): Promise ; - addListener(event: 'error', listener: (err: Error) => void): this; - addListener(event: 'exit', listener: (exitCode: number) => void): this; - addListener(event: 'message', listener: (value: any) => void): this; - addListener(event: 'messageerror', listener: (error: Error) => void): this; - addListener(event: 'online', listener: () => void): this; - addListener(event: string | symbol, listener: (...args: any[]) => void): this; - emit(event: 'error', err: Error): boolean; - emit(event: 'exit', exitCode: number): boolean; - emit(event: 'message', value: any): boolean; - emit(event: 'messageerror', error: Error): boolean; - emit(event: 'online'): boolean; - emit(event: string | symbol, ...args: any[]): boolean; - on(event: 'error', listener: (err: Error) => void): this; - on(event: 'exit', listener: (exitCode: number) => void): this; - on(event: 'message', listener: (value: any) => void): this; - on(event: 'messageerror', listener: (error: Error) => void): this; - on(event: 'online', listener: () => void): this; - on(event: string | symbol, listener: (...args: any[]) => void): this; - once(event: 'error', listener: (err: Error) => void): this; - once(event: 'exit', listener: (exitCode: number) => void): this; - once(event: 'message', listener: (value: any) => void): this; - once(event: 'messageerror', listener: (error: Error) => void): this; - once(event: 'online', listener: () => void): this; - once(event: string | symbol, listener: (...args: any[]) => void): this; - prependListener(event: 'error', listener: (err: Error) => void): this; - prependListener(event: 'exit', listener: (exitCode: number) => void): this; - prependListener(event: 'message', listener: (value: any) => void): this; - prependListener(event: 'messageerror', listener: (error: Error) => void): this; - prependListener(event: 'online', listener: () => void): this; - prependListener(event: string | symbol, listener: (...args: any[]) => void): this; - prependOnceListener(event: 'error', listener: (err: Error) => void): this; - prependOnceListener(event: 'exit', listener: (exitCode: number) => void): this; - prependOnceListener(event: 'message', listener: (value: any) => void): this; - prependOnceListener(event: 'messageerror', listener: (error: Error) => void): this; - prependOnceListener(event: 'online', listener: () => void): this; - prependOnceListener(event: string | symbol, listener: (...args: any[]) => void): this; - removeListener(event: 'error', listener: (err: Error) => void): this; - removeListener(event: 'exit', listener: (exitCode: number) => void): this; - removeListener(event: 'message', listener: (value: any) => void): this; - removeListener(event: 'messageerror', listener: (error: Error) => void): this; - removeListener(event: 'online', listener: () => void): this; - removeListener(event: string | symbol, listener: (...args: any[]) => void): this; - off(event: 'error', listener: (err: Error) => void): this; - off(event: 'exit', listener: (exitCode: number) => void): this; - off(event: 'message', listener: (value: any) => void): this; - off(event: 'messageerror', listener: (error: Error) => void): this; - off(event: 'online', listener: () => void): this; - off(event: string | symbol, listener: (...args: any[]) => void): this; - } - interface BroadcastChannel extends NodeJS.RefCounted {} - /** - * Instances of `BroadcastChannel` allow asynchronous one-to-many communication - * with all other `BroadcastChannel` instances bound to the same channel name. - * - * ```js - * 'use strict'; - * - * const { - * isMainThread, - * BroadcastChannel, - * Worker - * } = require('worker_threads'); - * - * const bc = new BroadcastChannel('hello'); - * - * if (isMainThread) { - * let c = 0; - * bc.onmessage = (event) => { - * console.log(event.data); - * if (++c === 10) bc.close(); - * }; - * for (let n = 0; n < 10; n++) - * new Worker(__filename); - * } else { - * bc.postMessage('hello from every worker'); - * bc.close(); - * } - * ``` - * @since v15.4.0 - */ - class BroadcastChannel { - readonly name: string; - /** - * Invoked with a single \`MessageEvent\` argument when a message is received. - * @since v15.4.0 - */ - onmessage: (message: unknown) => void; - /** - * Invoked with a received message cannot be deserialized. - * @since v15.4.0 - */ - onmessageerror: (message: unknown) => void; - constructor(name: string); - /** - * Closes the `BroadcastChannel` connection. - * @since v15.4.0 - */ - close(): void; - /** - * @since v15.4.0 - * @param message Any cloneable JavaScript value. - */ - postMessage(message: unknown): void; - } - /** - * Mark an object as not transferable. If `object` occurs in the transfer list of - * a `port.postMessage()` call, it is ignored. - * - * In particular, this makes sense for objects that can be cloned, rather than - * transferred, and which are used by other objects on the sending side. - * For example, Node.js marks the `ArrayBuffer`s it uses for its `Buffer pool` with this. - * - * This operation cannot be undone. - * - * ```js - * const { MessageChannel, markAsUntransferable } = require('worker_threads'); - * - * const pooledBuffer = new ArrayBuffer(8); - * const typedArray1 = new Uint8Array(pooledBuffer); - * const typedArray2 = new Float64Array(pooledBuffer); - * - * markAsUntransferable(pooledBuffer); - * - * const { port1 } = new MessageChannel(); - * port1.postMessage(typedArray1, [ typedArray1.buffer ]); - * - * // The following line prints the contents of typedArray1 -- it still owns - * // its memory and has been cloned, not transferred. Without - * // `markAsUntransferable()`, this would print an empty Uint8Array. - * // typedArray2 is intact as well. - * console.log(typedArray1); - * console.log(typedArray2); - * ``` - * - * There is no equivalent to this API in browsers. - * @since v14.5.0, v12.19.0 - */ - function markAsUntransferable(object: object): void; - /** - * Transfer a `MessagePort` to a different `vm` Context. The original `port`object is rendered unusable, and the returned `MessagePort` instance - * takes its place. - * - * The returned `MessagePort` is an object in the target context and - * inherits from its global `Object` class. Objects passed to the [`port.onmessage()`](https://developer.mozilla.org/en-US/docs/Web/API/MessagePort/onmessage) listener are also created in the - * target context - * and inherit from its global `Object` class. - * - * However, the created `MessagePort` no longer inherits from [`EventTarget`](https://developer.mozilla.org/en-US/docs/Web/API/EventTarget), and only - * [`port.onmessage()`](https://developer.mozilla.org/en-US/docs/Web/API/MessagePort/onmessage) can be used to receive - * events using it. - * @since v11.13.0 - * @param port The message port to transfer. - * @param contextifiedSandbox A `contextified` object as returned by the `vm.createContext()` method. - */ - function moveMessagePortToContext(port: MessagePort, contextifiedSandbox: Context): MessagePort; - /** - * Receive a single message from a given `MessagePort`. If no message is available,`undefined` is returned, otherwise an object with a single `message` property - * that contains the message payload, corresponding to the oldest message in the`MessagePort`’s queue. - * - * ```js - * const { MessageChannel, receiveMessageOnPort } = require('worker_threads'); - * const { port1, port2 } = new MessageChannel(); - * port1.postMessage({ hello: 'world' }); - * - * console.log(receiveMessageOnPort(port2)); - * // Prints: { message: { hello: 'world' } } - * console.log(receiveMessageOnPort(port2)); - * // Prints: undefined - * ``` - * - * When this function is used, no `'message'` event is emitted and the`onmessage` listener is not invoked. - * @since v12.3.0 - */ - function receiveMessageOnPort(port: MessagePort): - | { - message: any; - } - | undefined; - type Serializable = string | object | number | boolean | bigint; - /** - * Within a worker thread, `worker.getEnvironmentData()` returns a clone - * of data passed to the spawning thread's `worker.setEnvironmentData()`. - * Every new `Worker` receives its own copy of the environment data - * automatically. - * - * ```js - * const { - * Worker, - * isMainThread, - * setEnvironmentData, - * getEnvironmentData, - * } = require('worker_threads'); - * - * if (isMainThread) { - * setEnvironmentData('Hello', 'World!'); - * const worker = new Worker(__filename); - * } else { - * console.log(getEnvironmentData('Hello')); // Prints 'World!'. - * } - * ``` - * @since v15.12.0, v14.18.0 - * @param key Any arbitrary, cloneable JavaScript value that can be used as a {Map} key. - */ - function getEnvironmentData(key: Serializable): Serializable; - /** - * The `worker.setEnvironmentData()` API sets the content of`worker.getEnvironmentData()` in the current thread and all new `Worker`instances spawned from the current context. - * @since v15.12.0, v14.18.0 - * @param key Any arbitrary, cloneable JavaScript value that can be used as a {Map} key. - * @param value Any arbitrary, cloneable JavaScript value that will be cloned and passed automatically to all new `Worker` instances. If `value` is passed as `undefined`, any previously set value - * for the `key` will be deleted. - */ - function setEnvironmentData(key: Serializable, value: Serializable): void; - - import { - BroadcastChannel as _BroadcastChannel, - MessageChannel as _MessageChannel, - MessagePort as _MessagePort, - } from 'worker_threads'; - global { - /** - * `BroadcastChannel` class is a global reference for `require('worker_threads').BroadcastChannel` - * https://nodejs.org/api/globals.html#broadcastchannel - * @since v18.0.0 - */ - var BroadcastChannel: typeof globalThis extends { - onmessage: any; - BroadcastChannel: infer T; - } - ? T - : typeof _BroadcastChannel; - - /** - * `MessageChannel` class is a global reference for `require('worker_threads').MessageChannel` - * https://nodejs.org/api/globals.html#messagechannel - * @since v15.0.0 - */ - var MessageChannel: typeof globalThis extends { - onmessage: any; - MessageChannel: infer T; - } - ? T - : typeof _MessageChannel; - - /** - * `MessagePort` class is a global reference for `require('worker_threads').MessagePort` - * https://nodejs.org/api/globals.html#messageport - * @since v15.0.0 - */ - var MessagePort: typeof globalThis extends { - onmessage: any; - MessagePort: infer T; - } - ? T - : typeof _MessagePort; - } -} -declare module 'node:worker_threads' { - export * from 'worker_threads'; -} diff --git a/spaces/fffiloni/mr-and-misses/README.md b/spaces/fffiloni/mr-and-misses/README.md deleted file mode 100644 index aa132b235850d9a6d8440057c744bb440cdee664..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/mr-and-misses/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Mr Men & Little Misses -emoji: 🌝🌚 -colorFrom: purple -colorTo: purple -sdk: gradio -sdk_version: 3.11.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/fffiloni/sd-xl-lora-fusion/README.md b/spaces/fffiloni/sd-xl-lora-fusion/README.md deleted file mode 100644 index fdba5fcd6a2b509bad8727b986693aa0bf72b773..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/sd-xl-lora-fusion/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: SD-XL LoRA Fusion -emoji: 🌟 -colorFrom: blue -colorTo: indigo -sdk: gradio -sdk_version: 3.50.2 -app_file: app.py -fullWidth: true -pinned: false -hf_oauth: true ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/fffiloni/text-2-music/constants.py b/spaces/fffiloni/text-2-music/constants.py deleted file mode 100644 index f20c15dd1969910106da2c07339da9ff33458282..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/text-2-music/constants.py +++ /dev/null @@ -1,7 +0,0 @@ -import numpy as np - -MUBERT_TAGS_STRING = 'tribal,action,kids,neo-classic,run 130,pumped,jazz / funk,ethnic,dubtechno,reggae,acid jazz,liquidfunk,funk,witch house,tech house,underground,artists,mystical,disco,sensorium,r&b,agender,psychedelic trance / psytrance,peaceful,run 140,piano,run 160,setting,meditation,christmas,ambient,horror,cinematic,electro house,idm,bass,minimal,underscore,drums,glitchy,beautiful,technology,tribal house,country pop,jazz & funk,documentary,space,classical,valentines,chillstep,experimental,trap,new jack swing,drama,post-rock,tense,corporate,neutral,happy,analog,funky,spiritual,sberzvuk special,chill hop,dramatic,catchy,holidays,fitness 90,optimistic,orchestra,acid techno,energizing,romantic,minimal house,breaks,hyper pop,warm up,dreamy,dark,urban,microfunk,dub,nu disco,vogue,keys,hardcore,aggressive,indie,electro funk,beauty,relaxing,trance,pop,hiphop,soft,acoustic,chillrave / ethno-house,deep techno,angry,dance,fun,dubstep,tropical,latin pop,heroic,world music,inspirational,uplifting,atmosphere,art,epic,advertising,chillout,scary,spooky,slow ballad,saxophone,summer,erotic,jazzy,energy 100,kara mar,xmas,atmospheric,indie pop,hip-hop,yoga,reggaeton,lounge,travel,running,folk,chillrave & ethno-house,detective,darkambient,chill,fantasy,minimal techno,special,night,tropical house,downtempo,lullaby,meditative,upbeat,glitch hop,fitness,neurofunk,sexual,indie rock,future pop,jazz,cyberpunk,melancholic,happy hardcore,family / kids,synths,electric guitar,comedy,psychedelic trance & psytrance,edm,psychedelic rock,calm,zen,bells,podcast,melodic house,ethnic percussion,nature,heavy,bassline,indie dance,techno,drumnbass,synth pop,vaporwave,sad,8-bit,chillgressive,deep,orchestral,futuristic,hardtechno,nostalgic,big room,sci-fi,tutorial,joyful,pads,minimal 170,drill,ethnic 108,amusing,sleepy ambient,psychill,italo disco,lofi,house,acoustic guitar,bassline house,rock,k-pop,synthwave,deep house,electronica,gabber,nightlife,sport & fitness,road trip,celebration,electro,disco house,electronic' -MUBERT_TAGS = np.array(MUBERT_TAGS_STRING.split(',')) -MUBERT_LICENSE = "ttmmubertlicense#f0acYBenRcfeFpNT4wpYGaTQIyDI4mJGv5MfIhBFz97NXDwDNFHmMRsBSzmGsJwbTpP1A6i07AXcIeAHo5" -MUBERT_MODE = "loop" -MUBERT_TOKEN = "4951f6428e83172a4f39de05d5b3ab10d58560b8" \ No newline at end of file diff --git a/spaces/fffiloni/x-decoder-video/style.css b/spaces/fffiloni/x-decoder-video/style.css deleted file mode 100644 index 3cf565d3e03852436a405cf632d1d22433bb4087..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/x-decoder-video/style.css +++ /dev/null @@ -1,101 +0,0 @@ -#col-container {max-width: 820px; margin-left: auto; margin-right: auto;} -#duplicate-container{ - display: flex; - justify-content: space-between; - align-items: center; - line-height: 1em; - flex-direction: row-reverse; - font-size:1em; -} -a, a:hover, a:visited { - text-decoration-line: underline; - font-weight: 600; - color: #1f2937 !important; -} - -.dark a, .dark a:hover, .dark a:visited { - color: #f3f4f6 !important; -} - -.footer { - margin-bottom: 45px; - margin-top: 10px; - text-align: center; - border-bottom: 1px solid #e5e5e5; -} - -.footer>p { - font-size: .8rem!important; - display: inline-block; - padding: 0 10px; - transform: translateY(26px); - background: white; -} -.dark .footer { - border-color: #303030; -} -.dark .footer>p { - background: #0b0f19; -} - -div#may-like-container > p { - font-size: .8em; - margin-bottom: 4px; -} - -.animate-spin { - animation: spin 1s linear infinite; -} - -@keyframes spin { - from { - transform: rotate(0deg); - } - to { - transform: rotate(360deg); - } -} - -#share-btn-container { - display: flex; - padding-left: 0.5rem !important; - padding-right: 0.5rem !important; - background-color: #000000; - justify-content: center; - align-items: center; - border-radius: 9999px !important; - max-width: 13rem; -} - -#share-btn-container:hover { - background-color: #060606; -} - -#share-btn { - all: initial; - color: #ffffff; - font-weight: 600; - cursor:pointer; - font-family: 'IBM Plex Sans', sans-serif; - margin-left: 0.5rem !important; - padding-top: 0.5rem !important; - padding-bottom: 0.5rem !important; - right:0; -} - -#share-btn * { - all: unset; -} - -#share-btn-container div:nth-child(-n+2){ - width: auto !important; - min-height: 0px !important; -} - -#share-btn-container .wrap { - display: none !important; -} - -#share-btn-container.hidden { - display: none!important; -} \ No newline at end of file diff --git a/spaces/florim/MedGPT/tests/integration/milvus_memory_tests.py b/spaces/florim/MedGPT/tests/integration/milvus_memory_tests.py deleted file mode 100644 index ec38bf2f72087b5da679d26594ebff97d8a09b19..0000000000000000000000000000000000000000 --- a/spaces/florim/MedGPT/tests/integration/milvus_memory_tests.py +++ /dev/null @@ -1,57 +0,0 @@ -# sourcery skip: snake-case-functions -"""Tests for the MilvusMemory class.""" -import random -import string -import unittest - -from autogpt.config import Config -from autogpt.memory.milvus import MilvusMemory - -try: - - class TestMilvusMemory(unittest.TestCase): - """Tests for the MilvusMemory class.""" - - def random_string(self, length: int) -> str: - """Generate a random string of the given length.""" - return "".join(random.choice(string.ascii_letters) for _ in range(length)) - - def setUp(self) -> None: - """Set up the test environment.""" - cfg = Config() - cfg.milvus_addr = "localhost:19530" - self.memory = MilvusMemory(cfg) - self.memory.clear() - - # Add example texts to the cache - self.example_texts = [ - "The quick brown fox jumps over the lazy dog", - "I love machine learning and natural language processing", - "The cake is a lie, but the pie is always true", - "ChatGPT is an advanced AI model for conversation", - ] - - for text in self.example_texts: - self.memory.add(text) - - # Add some random strings to test noise - for _ in range(5): - self.memory.add(self.random_string(10)) - - def test_get_relevant(self) -> None: - """Test getting relevant texts from the cache.""" - query = "I'm interested in artificial intelligence and NLP" - num_relevant = 3 - relevant_texts = self.memory.get_relevant(query, num_relevant) - - print(f"Top {k} relevant texts for the query '{query}':") - for i, text in enumerate(relevant_texts, start=1): - print(f"{i}. {text}") - - self.assertEqual(len(relevant_texts), k) - self.assertIn(self.example_texts[1], relevant_texts) - -except: - print( - "Skipping tests/integration/milvus_memory_tests.py as Milvus is not installed." - ) diff --git a/spaces/flynster/FeinbergQuizNotes/question_generation/run_qg.py b/spaces/flynster/FeinbergQuizNotes/question_generation/run_qg.py deleted file mode 100644 index 20b8abe51becf4f3d521d7e68a6c59a4c053de19..0000000000000000000000000000000000000000 --- a/spaces/flynster/FeinbergQuizNotes/question_generation/run_qg.py +++ /dev/null @@ -1,236 +0,0 @@ -import dataclasses -import json -import logging -import os -import sys -from dataclasses import dataclass, field -from typing import Dict, List, Optional - -import numpy as np -import torch - -from transformers import ( - AutoModelForSeq2SeqLM, - AutoTokenizer, - T5Tokenizer, - BartTokenizer, - HfArgumentParser, - DataCollator, - TrainingArguments, - set_seed, -) - -from trainer import Trainer -from data_collator import T2TDataCollator -from utils import freeze_embeds, assert_not_all_frozen - -MODEL_TYPE_TO_TOKENIZER = { - "t5": T5Tokenizer, - "bart": BartTokenizer, -} - - -logger = logging.getLogger(__name__) - - -@dataclass -class ModelArguments: - """ - Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. - """ - - model_name_or_path: str = field( - metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} - ) - model_type: str = field(metadata={"help": "One of 't5', 'bart'"}) - tokenizer_name_or_path: Optional[str] = field( - default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} - ) - cache_dir: Optional[str] = field( - default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} - ) - label_smoothing: Optional[float] = field( - default=0, - metadata={"help": "label smoothing rate, set to > 0 if you want to enable lable smoothing"} - ) - freeze_embeds: bool = field( - default=False, - metadata={"help": "Freeze token embeddings and positional embeddings for bart, just token embeddings for t5."} - ) - -@dataclass -class DataTrainingArguments: - """ - Arguments pertaining to what data we are going to input our model for training and eval. - """ - train_file_path: str = field( - metadata={"help": "Path for cached train dataset"}, - ) - valid_file_path: str = field( - metadata={"help": "Path for cached valid dataset"}, - ) - data_dir: Optional[str] = field( - default=None, - metadata={"help": "Path for data files"}, - ) - task: Optional[str] = field( - default=None, - metadata={"help": "Which task 'qa', 'qg', 'e2e_qg', 'ans_ext', 'multi'. 'multi' means 'qa', 'qg', 'ans_ext' tasks"}, - ) - qg_format: Optional[str] = field( - default='prepend_qg_format', - metadata={"help": "How to format inputs for que generation, 'highlight_qg_format' or 'prepend_qg_format'"}, - ) - max_source_length: Optional[int] = field( - default=512, - metadata={"help": "Max input length for the source text"}, - ) - max_target_length: Optional[int] = field( - default=32, - metadata={"help": "Max input length for the target text"}, - ) - - -def main(args_file=None): - # See all possible arguments in src/transformers/training_args.py - # or by passing the --help flag to this script. - # We now keep distinct sets of args, for a cleaner separation of concerns. - - parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) - - if (len(sys.argv) == 2 and sys.argv[1].endswith(".json")) or args_file is not None: - # If we pass only one argument to the script and it's the path to a json file, - # let's parse it to get our arguments. - args_file_path = os.path.abspath(sys.argv[1]) if args_file is None else args_file - model_args, data_args, training_args = parser.parse_json_file(json_file=args_file_path) - else: - model_args, data_args, training_args = parser.parse_args_into_dataclasses() - - assert model_args.model_type in list(MODEL_TYPE_TO_TOKENIZER.keys()), "model type should be 't5' or 'bart'" - - if ( - os.path.exists(training_args.output_dir) - and os.listdir(training_args.output_dir) - and training_args.do_train - and not training_args.overwrite_output_dir - ): - raise ValueError( - f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome." - ) - - # Setup logging - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, - ) - logger.warning( - "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", - training_args.local_rank, - training_args.device, - training_args.n_gpu, - bool(training_args.local_rank != -1), - training_args.fp16, - ) - logger.info("Training/evaluation parameters %s", training_args) - - # Set seed - set_seed(training_args.seed) - - # Set project name - os.environ["WANDB_PROJECT"] = "question-generation" - - # Load pretrained model and tokenizer - # - # Distributed training: - # The .from_pretrained methods guarantee that only one local process can concurrently - # download model & vocab. - tokenizer_cls = MODEL_TYPE_TO_TOKENIZER[model_args.model_type] - tokenizer = tokenizer_cls.from_pretrained( - model_args.tokenizer_name_or_path if model_args.tokenizer_name_or_path else model_args.model_name_or_path, - cache_dir=model_args.cache_dir, - ) - model = AutoModelForSeq2SeqLM.from_pretrained( - model_args.model_name_or_path, - cache_dir=model_args.cache_dir, - ) - - model.resize_token_embeddings(len(tokenizer)) - - if model_args.freeze_embeds: - logger.info("freezing embeddings of the model") - freeze_embeds(model) - assert_not_all_frozen(model) - - # Get datasets - logger.info('loading dataset') - - train_dataset = torch.load(data_args.train_file_path) if training_args.do_train else None - valid_dataset = torch.load(data_args.valid_file_path) if training_args.do_eval else None - - logger.info('finished loading dataset') - - # Initialize data_collator - data_collator = T2TDataCollator( - tokenizer=tokenizer, - model_type=model_args.model_type, - mode="training", - using_tpu=training_args.tpu_num_cores is not None - ) - - # Initialize our Trainer - trainer = Trainer( - model=model, - args=training_args, - train_dataset=train_dataset, - eval_dataset=valid_dataset, - data_collator=data_collator, - prediction_loss_only=True, - label_smoothing=model_args.label_smoothing - ) - - # disable wandb console logs - logging.getLogger('wandb.run_manager').setLevel(logging.WARNING) - - # Training - if training_args.do_train: - trainer.train( - model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None - ) - trainer.save_model() - # For convenience, we also re-save the tokenizer to the same directory, - # so that you can share your model easily on huggingface.co/models =) - if trainer.is_world_master(): - tokenizer.save_pretrained(training_args.output_dir) - - # Evaluation - results = {} - if training_args.do_eval and training_args.local_rank in [-1, 0]: - logger.info("*** Evaluate ***") - - eval_output = trainer.evaluate() - - output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt") - with open(output_eval_file, "w") as writer: - logger.info("***** Eval results *****") - for key in sorted(eval_output.keys()): - logger.info(" %s = %s", key, str(eval_output[key])) - writer.write("%s = %s\n" % (key, str(eval_output[key]))) - - results.update(eval_output) - - return results - - -def _mp_fn(index): - # For xla_spawn (TPUs) - main() - -def run_qg(args_dict): - with open("args.json", 'w') as f: - json.dump(args_dict, f) - - main(args_file="args.json") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/spaces/geraldvillaran/dolly-chat/README.md b/spaces/geraldvillaran/dolly-chat/README.md deleted file mode 100644 index 0fb1721f798e323456671454c1e3df3d9243f0ba..0000000000000000000000000000000000000000 --- a/spaces/geraldvillaran/dolly-chat/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Dolly -emoji: 🌍 -colorFrom: gray -colorTo: indigo -sdk: gradio -sdk_version: 3.29.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/ggwvits/vits-uma-genshin-honkai/Docker/vits.sh b/spaces/ggwvits/vits-uma-genshin-honkai/Docker/vits.sh deleted file mode 100644 index 2b87f26eda96d3800b73b4a21b210c78888a2299..0000000000000000000000000000000000000000 --- a/spaces/ggwvits/vits-uma-genshin-honkai/Docker/vits.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/bin/bash -run() { - echo -e "\033[32m已完成初始化，启动服务...\033[0m" - python3 /app/vits-uma-genshin-honkai/app.py -} -install() { - echo -e "\033[33m正在初始化：安装依赖....\033[0m" - pip install -r /app/vits-uma-genshin-honkai/requirements.txt -i https://mirrors.ustc.edu.cn/pypi/web/simple - echo -e "\033[33m正在下载模型....\033[0m" - rm -f /app/vits-uma-genshin-honkai/model/G_953000.pth - wget -O /app/vits-uma-genshin-honkai/model/G_953000.pth https://huggingface.co/spaces/ikechan8370/vits-uma-genshin-honkai/resolve/main/model/G_953000.pth - echo -e "\033[32m初始化完成！\033[0m" - run -} - -if [ ! -f "/app/vits-uma-genshin-honkai/model/G_953000.pth" ] || [ "$(stat -c%s "/app/vits-uma-genshin-honkai/model/G_953000.pth")" -lt 10000 ]; then - install -else - run -fi diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Download Jogos Ps3 Pkg.md b/spaces/gotiQspiryo/whisper-ui/examples/Download Jogos Ps3 Pkg.md deleted file mode 100644 index 0d0dc31a2a33f90fd5c7b5e178a0038d2841d55b..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/Download Jogos Ps3 Pkg.md +++ /dev/null @@ -1,6 +0,0 @@ - download jogos ps3 pkg
Download Zip ➡ https://urlgoal.com/2uyNDA
-
-Download Game PS3 PS4 RPCS3 PC Free New, Best Game PS3 PS4 RPCS3 PC Iso, Direct Links Torrent PS3 PS4 RPCS3 PC, Update DLC PS3 PS4 RPCS3, ... 4d29de3e1b
-
-
- diff --git a/spaces/gradio/HuBERT/examples/speech_recognition/kaldi/kaldi_initializer.py b/spaces/gradio/HuBERT/examples/speech_recognition/kaldi/kaldi_initializer.py deleted file mode 100644 index 6d2a2a4b6b809ba1106f9a57cb6f241dc083e670..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/examples/speech_recognition/kaldi/kaldi_initializer.py +++ /dev/null @@ -1,698 +0,0 @@ -#!/usr/bin/env python3 - -# 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. - -from dataclasses import dataclass -import hydra -from hydra.core.config_store import ConfigStore -import logging -from omegaconf import MISSING, OmegaConf -import os -import os.path as osp -from pathlib import Path -import subprocess -from typing import Optional - -from fairseq.data.dictionary import Dictionary -from fairseq.dataclass import FairseqDataclass - -script_dir = Path(__file__).resolve().parent -config_path = script_dir / "config" - - -logger = logging.getLogger(__name__) - - -@dataclass -class KaldiInitializerConfig(FairseqDataclass): - data_dir: str = MISSING - fst_dir: Optional[str] = None - in_labels: str = MISSING - out_labels: Optional[str] = None - wav2letter_lexicon: Optional[str] = None - lm_arpa: str = MISSING - kaldi_root: str = MISSING - blank_symbol: str = "" - silence_symbol: Optional[str] = None - - -def create_units(fst_dir: Path, in_labels: str, vocab: Dictionary) -> Path: - in_units_file = fst_dir / f"kaldi_dict.{in_labels}.txt" - if not in_units_file.exists(): - - logger.info(f"Creating {in_units_file}") - - with open(in_units_file, "w") as f: - print("0", file=f) - i = 1 - for symb in vocab.symbols[vocab.nspecial :]: - if not symb.startswith("madeupword"): - print(f"{symb} {i}", file=f) - i += 1 - return in_units_file - - -def create_lexicon( - cfg: KaldiInitializerConfig, - fst_dir: Path, - unique_label: str, - in_units_file: Path, - out_words_file: Path, -) -> (Path, Path): - - disambig_in_units_file = fst_dir / f"kaldi_dict.{cfg.in_labels}_disambig.txt" - lexicon_file = fst_dir / f"kaldi_lexicon.{unique_label}.txt" - disambig_lexicon_file = fst_dir / f"kaldi_lexicon.{unique_label}_disambig.txt" - if ( - not lexicon_file.exists() - or not disambig_lexicon_file.exists() - or not disambig_in_units_file.exists() - ): - logger.info(f"Creating {lexicon_file} (in units file: {in_units_file})") - - assert cfg.wav2letter_lexicon is not None or cfg.in_labels == cfg.out_labels - - if cfg.wav2letter_lexicon is not None: - lm_words = set() - with open(out_words_file, "r") as lm_dict_f: - for line in lm_dict_f: - lm_words.add(line.split()[0]) - - num_skipped = 0 - total = 0 - with open(cfg.wav2letter_lexicon, "r") as w2l_lex_f, open( - lexicon_file, "w" - ) as out_f: - for line in w2l_lex_f: - items = line.rstrip().split("\t") - assert len(items) == 2, items - if items[0] in lm_words: - print(items[0], items[1], file=out_f) - else: - num_skipped += 1 - logger.debug( - f"Skipping word {items[0]} as it was not found in LM" - ) - total += 1 - if num_skipped > 0: - logger.warning( - f"Skipped {num_skipped} out of {total} words as they were not found in LM" - ) - else: - with open(in_units_file, "r") as in_f, open(lexicon_file, "w") as out_f: - for line in in_f: - symb = line.split()[0] - if symb != " " and symb != " " and symb != " ": - print(symb, symb, file=out_f) - - lex_disambig_path = ( - Path(cfg.kaldi_root) / "egs/wsj/s5/utils/add_lex_disambig.pl" - ) - res = subprocess.run( - [lex_disambig_path, lexicon_file, disambig_lexicon_file], - check=True, - capture_output=True, - ) - ndisambig = int(res.stdout) - disamib_path = Path(cfg.kaldi_root) / "egs/wsj/s5/utils/add_disambig.pl" - res = subprocess.run( - [disamib_path, "--include-zero", in_units_file, str(ndisambig)], - check=True, - capture_output=True, - ) - with open(disambig_in_units_file, "wb") as f: - f.write(res.stdout) - - return disambig_lexicon_file, disambig_in_units_file - - -def create_G( - kaldi_root: Path, fst_dir: Path, lm_arpa: Path, arpa_base: str -) -> (Path, Path): - - out_words_file = fst_dir / f"kaldi_dict.{arpa_base}.txt" - grammar_graph = fst_dir / f"G_{arpa_base}.fst" - if not grammar_graph.exists() or not out_words_file.exists(): - logger.info(f"Creating {grammar_graph}") - arpa2fst = kaldi_root / "src/lmbin/arpa2fst" - subprocess.run( - [ - arpa2fst, - "--disambig-symbol=#0", - f"--write-symbol-table={out_words_file}", - lm_arpa, - grammar_graph, - ], - check=True, - ) - return grammar_graph, out_words_file - - -def create_L( - kaldi_root: Path, - fst_dir: Path, - unique_label: str, - lexicon_file: Path, - in_units_file: Path, - out_words_file: Path, -) -> Path: - lexicon_graph = fst_dir / f"L.{unique_label}.fst" - - if not lexicon_graph.exists(): - logger.info(f"Creating {lexicon_graph} (in units: {in_units_file})") - make_lex = kaldi_root / "egs/wsj/s5/utils/make_lexicon_fst.pl" - fstcompile = kaldi_root / "tools/openfst-1.6.7/bin/fstcompile" - fstaddselfloops = kaldi_root / "src/fstbin/fstaddselfloops" - fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort" - - def write_disambig_symbol(file): - with open(file, "r") as f: - for line in f: - items = line.rstrip().split() - if items[0] == "#0": - out_path = str(file) + "_disamig" - with open(out_path, "w") as out_f: - print(items[1], file=out_f) - return out_path - - return None - - in_disambig_sym = write_disambig_symbol(in_units_file) - assert in_disambig_sym is not None - out_disambig_sym = write_disambig_symbol(out_words_file) - assert out_disambig_sym is not None - - try: - with open(lexicon_graph, "wb") as out_f: - res = subprocess.run( - [make_lex, lexicon_file], capture_output=True, check=True - ) - assert len(res.stderr) == 0, res.stderr.decode("utf-8") - res = subprocess.run( - [ - fstcompile, - f"--isymbols={in_units_file}", - f"--osymbols={out_words_file}", - "--keep_isymbols=false", - "--keep_osymbols=false", - ], - input=res.stdout, - capture_output=True, - ) - assert len(res.stderr) == 0, res.stderr.decode("utf-8") - res = subprocess.run( - [fstaddselfloops, in_disambig_sym, out_disambig_sym], - input=res.stdout, - capture_output=True, - check=True, - ) - res = subprocess.run( - [fstarcsort, "--sort_type=olabel"], - input=res.stdout, - capture_output=True, - check=True, - ) - out_f.write(res.stdout) - except subprocess.CalledProcessError as e: - logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") - os.remove(lexicon_graph) - raise - except AssertionError: - os.remove(lexicon_graph) - raise - - return lexicon_graph - - -def create_LG( - kaldi_root: Path, - fst_dir: Path, - unique_label: str, - lexicon_graph: Path, - grammar_graph: Path, -) -> Path: - lg_graph = fst_dir / f"LG.{unique_label}.fst" - - if not lg_graph.exists(): - logger.info(f"Creating {lg_graph}") - - fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose" - fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar" - fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded" - fstpushspecial = kaldi_root / "src/fstbin/fstpushspecial" - fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort" - - try: - with open(lg_graph, "wb") as out_f: - res = subprocess.run( - [fsttablecompose, lexicon_graph, grammar_graph], - capture_output=True, - check=True, - ) - res = subprocess.run( - [ - fstdeterminizestar, - "--use-log=true", - ], - input=res.stdout, - capture_output=True, - ) - res = subprocess.run( - [fstminimizeencoded], - input=res.stdout, - capture_output=True, - check=True, - ) - res = subprocess.run( - [fstpushspecial], - input=res.stdout, - capture_output=True, - check=True, - ) - res = subprocess.run( - [fstarcsort, "--sort_type=ilabel"], - input=res.stdout, - capture_output=True, - check=True, - ) - out_f.write(res.stdout) - except subprocess.CalledProcessError as e: - logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") - os.remove(lg_graph) - raise - - return lg_graph - - -def create_H( - kaldi_root: Path, - fst_dir: Path, - disambig_out_units_file: Path, - in_labels: str, - vocab: Dictionary, - blk_sym: str, - silence_symbol: Optional[str], -) -> (Path, Path, Path): - h_graph = ( - fst_dir / f"H.{in_labels}{'_' + silence_symbol if silence_symbol else ''}.fst" - ) - h_out_units_file = fst_dir / f"kaldi_dict.h_out.{in_labels}.txt" - disambig_in_units_file_int = Path(str(h_graph) + "isym_disambig.int") - disambig_out_units_file_int = Path(str(disambig_out_units_file) + ".int") - if ( - not h_graph.exists() - or not h_out_units_file.exists() - or not disambig_in_units_file_int.exists() - ): - logger.info(f"Creating {h_graph}") - eps_sym = " " - - num_disambig = 0 - osymbols = [] - - with open(disambig_out_units_file, "r") as f, open( - disambig_out_units_file_int, "w" - ) as out_f: - for line in f: - symb, id = line.rstrip().split() - if line.startswith("#"): - num_disambig += 1 - print(id, file=out_f) - else: - if len(osymbols) == 0: - assert symb == eps_sym, symb - osymbols.append((symb, id)) - - i_idx = 0 - isymbols = [(eps_sym, 0)] - - imap = {} - - for i, s in enumerate(vocab.symbols): - i_idx += 1 - isymbols.append((s, i_idx)) - imap[s] = i_idx - - fst_str = [] - - node_idx = 0 - root_node = node_idx - - special_symbols = [blk_sym] - if silence_symbol is not None: - special_symbols.append(silence_symbol) - - for ss in special_symbols: - fst_str.append("{} {} {} {}".format(root_node, root_node, ss, eps_sym)) - - for symbol, _ in osymbols: - if symbol == eps_sym or symbol.startswith("#"): - continue - - node_idx += 1 - # 1. from root to emitting state - fst_str.append("{} {} {} {}".format(root_node, node_idx, symbol, symbol)) - # 2. from emitting state back to root - fst_str.append("{} {} {} {}".format(node_idx, root_node, eps_sym, eps_sym)) - # 3. from emitting state to optional blank state - pre_node = node_idx - node_idx += 1 - for ss in special_symbols: - fst_str.append("{} {} {} {}".format(pre_node, node_idx, ss, eps_sym)) - # 4. from blank state back to root - fst_str.append("{} {} {} {}".format(node_idx, root_node, eps_sym, eps_sym)) - - fst_str.append("{}".format(root_node)) - - fst_str = "\n".join(fst_str) - h_str = str(h_graph) - isym_file = h_str + ".isym" - - with open(isym_file, "w") as f: - for sym, id in isymbols: - f.write("{} {}\n".format(sym, id)) - - with open(h_out_units_file, "w") as f: - for sym, id in osymbols: - f.write("{} {}\n".format(sym, id)) - - with open(disambig_in_units_file_int, "w") as f: - disam_sym_id = len(isymbols) - for _ in range(num_disambig): - f.write("{}\n".format(disam_sym_id)) - disam_sym_id += 1 - - fstcompile = kaldi_root / "tools/openfst-1.6.7/bin/fstcompile" - fstaddselfloops = kaldi_root / "src/fstbin/fstaddselfloops" - fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort" - - try: - with open(h_graph, "wb") as out_f: - res = subprocess.run( - [ - fstcompile, - f"--isymbols={isym_file}", - f"--osymbols={h_out_units_file}", - "--keep_isymbols=false", - "--keep_osymbols=false", - ], - input=str.encode(fst_str), - capture_output=True, - check=True, - ) - res = subprocess.run( - [ - fstaddselfloops, - disambig_in_units_file_int, - disambig_out_units_file_int, - ], - input=res.stdout, - capture_output=True, - check=True, - ) - res = subprocess.run( - [fstarcsort, "--sort_type=olabel"], - input=res.stdout, - capture_output=True, - check=True, - ) - out_f.write(res.stdout) - except subprocess.CalledProcessError as e: - logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") - os.remove(h_graph) - raise - return h_graph, h_out_units_file, disambig_in_units_file_int - - -def create_HLGa( - kaldi_root: Path, - fst_dir: Path, - unique_label: str, - h_graph: Path, - lg_graph: Path, - disambig_in_words_file_int: Path, -) -> Path: - hlga_graph = fst_dir / f"HLGa.{unique_label}.fst" - - if not hlga_graph.exists(): - logger.info(f"Creating {hlga_graph}") - - fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose" - fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar" - fstrmsymbols = kaldi_root / "src/fstbin/fstrmsymbols" - fstrmepslocal = kaldi_root / "src/fstbin/fstrmepslocal" - fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded" - - try: - with open(hlga_graph, "wb") as out_f: - res = subprocess.run( - [ - fsttablecompose, - h_graph, - lg_graph, - ], - capture_output=True, - check=True, - ) - res = subprocess.run( - [fstdeterminizestar, "--use-log=true"], - input=res.stdout, - capture_output=True, - check=True, - ) - res = subprocess.run( - [fstrmsymbols, disambig_in_words_file_int], - input=res.stdout, - capture_output=True, - check=True, - ) - res = subprocess.run( - [fstrmepslocal], - input=res.stdout, - capture_output=True, - check=True, - ) - res = subprocess.run( - [fstminimizeencoded], - input=res.stdout, - capture_output=True, - check=True, - ) - out_f.write(res.stdout) - except subprocess.CalledProcessError as e: - logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") - os.remove(hlga_graph) - raise - - return hlga_graph - - -def create_HLa( - kaldi_root: Path, - fst_dir: Path, - unique_label: str, - h_graph: Path, - l_graph: Path, - disambig_in_words_file_int: Path, -) -> Path: - hla_graph = fst_dir / f"HLa.{unique_label}.fst" - - if not hla_graph.exists(): - logger.info(f"Creating {hla_graph}") - - fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose" - fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar" - fstrmsymbols = kaldi_root / "src/fstbin/fstrmsymbols" - fstrmepslocal = kaldi_root / "src/fstbin/fstrmepslocal" - fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded" - - try: - with open(hla_graph, "wb") as out_f: - res = subprocess.run( - [ - fsttablecompose, - h_graph, - l_graph, - ], - capture_output=True, - check=True, - ) - res = subprocess.run( - [fstdeterminizestar, "--use-log=true"], - input=res.stdout, - capture_output=True, - check=True, - ) - res = subprocess.run( - [fstrmsymbols, disambig_in_words_file_int], - input=res.stdout, - capture_output=True, - check=True, - ) - res = subprocess.run( - [fstrmepslocal], - input=res.stdout, - capture_output=True, - check=True, - ) - res = subprocess.run( - [fstminimizeencoded], - input=res.stdout, - capture_output=True, - check=True, - ) - out_f.write(res.stdout) - except subprocess.CalledProcessError as e: - logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") - os.remove(hla_graph) - raise - - return hla_graph - - -def create_HLG( - kaldi_root: Path, - fst_dir: Path, - unique_label: str, - hlga_graph: Path, - prefix: str = "HLG", -) -> Path: - hlg_graph = fst_dir / f"{prefix}.{unique_label}.fst" - - if not hlg_graph.exists(): - logger.info(f"Creating {hlg_graph}") - - add_self_loop = script_dir / "add-self-loop-simple" - kaldi_src = kaldi_root / "src" - kaldi_lib = kaldi_src / "lib" - - try: - if not add_self_loop.exists(): - fst_include = kaldi_root / "tools/openfst-1.6.7/include" - add_self_loop_src = script_dir / "add-self-loop-simple.cc" - - subprocess.run( - [ - "c++", - f"-I{kaldi_src}", - f"-I{fst_include}", - f"-L{kaldi_lib}", - add_self_loop_src, - "-lkaldi-base", - "-lkaldi-fstext", - "-o", - add_self_loop, - ], - check=True, - ) - - my_env = os.environ.copy() - my_env["LD_LIBRARY_PATH"] = f"{kaldi_lib}:{my_env['LD_LIBRARY_PATH']}" - - subprocess.run( - [ - add_self_loop, - hlga_graph, - hlg_graph, - ], - check=True, - capture_output=True, - env=my_env, - ) - except subprocess.CalledProcessError as e: - logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") - raise - - return hlg_graph - - -def initalize_kaldi(cfg: KaldiInitializerConfig) -> Path: - if cfg.fst_dir is None: - cfg.fst_dir = osp.join(cfg.data_dir, "kaldi") - if cfg.out_labels is None: - cfg.out_labels = cfg.in_labels - - kaldi_root = Path(cfg.kaldi_root) - data_dir = Path(cfg.data_dir) - fst_dir = Path(cfg.fst_dir) - fst_dir.mkdir(parents=True, exist_ok=True) - - arpa_base = osp.splitext(osp.basename(cfg.lm_arpa))[0] - unique_label = f"{cfg.in_labels}.{arpa_base}" - - with open(data_dir / f"dict.{cfg.in_labels}.txt", "r") as f: - vocab = Dictionary.load(f) - - in_units_file = create_units(fst_dir, cfg.in_labels, vocab) - - grammar_graph, out_words_file = create_G( - kaldi_root, fst_dir, Path(cfg.lm_arpa), arpa_base - ) - - disambig_lexicon_file, disambig_L_in_units_file = create_lexicon( - cfg, fst_dir, unique_label, in_units_file, out_words_file - ) - - h_graph, h_out_units_file, disambig_in_units_file_int = create_H( - kaldi_root, - fst_dir, - disambig_L_in_units_file, - cfg.in_labels, - vocab, - cfg.blank_symbol, - cfg.silence_symbol, - ) - lexicon_graph = create_L( - kaldi_root, - fst_dir, - unique_label, - disambig_lexicon_file, - disambig_L_in_units_file, - out_words_file, - ) - lg_graph = create_LG( - kaldi_root, fst_dir, unique_label, lexicon_graph, grammar_graph - ) - hlga_graph = create_HLGa( - kaldi_root, fst_dir, unique_label, h_graph, lg_graph, disambig_in_units_file_int - ) - hlg_graph = create_HLG(kaldi_root, fst_dir, unique_label, hlga_graph) - - # for debugging - # hla_graph = create_HLa(kaldi_root, fst_dir, unique_label, h_graph, lexicon_graph, disambig_in_units_file_int) - # hl_graph = create_HLG(kaldi_root, fst_dir, unique_label, hla_graph, prefix="HL_looped") - # create_HLG(kaldi_root, fst_dir, "phnc", h_graph, prefix="H_looped") - - return hlg_graph - - -@hydra.main(config_path=config_path, config_name="kaldi_initializer") -def cli_main(cfg: KaldiInitializerConfig) -> None: - container = OmegaConf.to_container(cfg, resolve=True, enum_to_str=True) - cfg = OmegaConf.create(container) - OmegaConf.set_struct(cfg, True) - initalize_kaldi(cfg) - - -if __name__ == "__main__": - - logging.root.setLevel(logging.INFO) - logging.basicConfig(level=logging.INFO) - - try: - from hydra._internal.utils import ( - get_args, - ) # pylint: disable=import-outside-toplevel - - cfg_name = get_args().config_name or "kaldi_initializer" - except ImportError: - logger.warning("Failed to get config name from hydra args") - cfg_name = "kaldi_initializer" - - cs = ConfigStore.instance() - cs.store(name=cfg_name, node=KaldiInitializerConfig) - - cli_main() diff --git a/spaces/gradio/HuBERT/fairseq/data/resampling_dataset.py b/spaces/gradio/HuBERT/fairseq/data/resampling_dataset.py deleted file mode 100644 index 3d3b993164dc3962df48bacff26714328e843e80..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/data/resampling_dataset.py +++ /dev/null @@ -1,139 +0,0 @@ -# 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. - -import logging - -import numpy as np -from fairseq.data import BaseWrapperDataset, plasma_utils - - -logger = logging.getLogger(__name__) - - -class ResamplingDataset(BaseWrapperDataset): - """Randomly samples from a given dataset at each epoch. - - Sampling is done with or without replacement, depending on the "replace" - parameter. - - Optionally, the epoch size can be rescaled. This is potentially desirable - to increase per-epoch coverage of the base dataset (since sampling with - replacement means that many items in the dataset will be left out). In the - case of sampling without replacement, size_ratio should be strictly less - than 1. - - Args: - dataset (~torch.utils.data.Dataset): dataset on which to sample. - weights (List[float]): list of probability weights - (default: None, which corresponds to uniform sampling). - replace (bool): sampling mode; True for "with replacement", or False - for "without replacement" (default: True) - size_ratio (float): the ratio to subsample to; must be positive - (default: 1.0). - batch_by_size (bool): whether or not to batch by sequence length - (default: True). - seed (int): RNG seed to use (default: 0). - epoch (int): starting epoch number (default: 1). - """ - - def __init__( - self, - dataset, - weights=None, - replace=True, - size_ratio=1.0, - batch_by_size=True, - seed=0, - epoch=1, - ): - super().__init__(dataset) - - if weights is None: - self.weights = None - - else: - assert len(weights) == len(dataset) - weights_arr = np.array(weights, dtype=np.float64) - weights_arr /= weights_arr.sum() - self.weights = plasma_utils.PlasmaArray(weights_arr) - - self.replace = replace - - assert size_ratio > 0.0 - if not self.replace: - assert size_ratio < 1.0 - self.size_ratio = float(size_ratio) - self.actual_size = np.ceil(len(dataset) * self.size_ratio).astype(int) - - self.batch_by_size = batch_by_size - self.seed = seed - - self._cur_epoch = None - self._cur_indices = None - - self.set_epoch(epoch) - - def __getitem__(self, index): - return self.dataset[self._cur_indices.array[index]] - - def __len__(self): - return self.actual_size - - @property - def sizes(self): - if isinstance(self.dataset.sizes, list): - return [s[self._cur_indices.array] for s in self.dataset.sizes] - return self.dataset.sizes[self._cur_indices.array] - - def num_tokens(self, index): - return self.dataset.num_tokens(self._cur_indices.array[index]) - - def size(self, index): - return self.dataset.size(self._cur_indices.array[index]) - - def ordered_indices(self): - if self.batch_by_size: - order = [ - np.arange(len(self)), - self.sizes, - ] # No need to handle `self.shuffle == True` - return np.lexsort(order) - else: - return np.arange(len(self)) - - def prefetch(self, indices): - self.dataset.prefetch(self._cur_indices.array[indices]) - - @property - def can_reuse_epoch_itr_across_epochs(self): - return False - - def set_epoch(self, epoch): - logger.debug("ResamplingDataset.set_epoch: {}".format(epoch)) - super().set_epoch(epoch) - - if epoch == self._cur_epoch: - return - - self._cur_epoch = epoch - - # Generate a weighted sample of indices as a function of the - # random seed and the current epoch. - - rng = np.random.RandomState( - [ - 42, # magic number - self.seed % (2 ** 32), # global seed - self._cur_epoch, # epoch index - ] - ) - self._cur_indices = plasma_utils.PlasmaArray( - rng.choice( - len(self.dataset), - self.actual_size, - replace=self.replace, - p=(None if self.weights is None else self.weights.array), - ) - ) diff --git a/spaces/gradio/HuBERT/fairseq/modules/quantization/pq/utils.py b/spaces/gradio/HuBERT/fairseq/modules/quantization/pq/utils.py deleted file mode 100644 index 03b15e4b1b58c9a1e6d42052b3bd5457df9a6e2e..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/modules/quantization/pq/utils.py +++ /dev/null @@ -1,337 +0,0 @@ -# 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. - -import logging -import re -from operator import attrgetter, itemgetter - -import numpy as np -import torch.distributed as dist -import torch.nn as nn - -from .modules import PQConv2d, PQEmbedding, PQLinear -from .pq import PQ - - -def quantize_model_( - model, - size_tracker, - layers_to_quantize, - block_sizes_config, - n_centroids_config, - step=0, - n_iter=15, - eps=1e-6, - max_tentatives=100, - verbose=True, -): - """ - Quantize a model in-place by stages. All the targeted - layers are replaced by their quantized counterpart, - and the model is ready for the finetuning of the - centroids in a standard training loop (no modifications - required). Note that we do not quantize biases. - - Args: - - model: a nn.Module - - size_tracker: useful for tracking quatization statistics - - layers_to_quantize: a list containing regexps for - filtering the layers to quantize at each stage according - to their name (as in model.named_parameters()) - - block_sizes_config: dict like - { - 'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}), - 'Linear': ('in_features', {'*': 8}) - } - For instance, all conv2d layers with kernel size 3x3 have - a block size of 9 and all Linear layers are quantized with - a block size of 8, irrespective of their size. - - n_centroids_config: dict like - { - 'Conv2d': ('kernel_size', {'*': 256}), - 'Linear': ('in_features', {'*': 256}) - } - For instance, all conv2d layers are quantized with 256 centroids - - step: the layers to quantize inplace corresponding - to layers_to_quantize[step] - """ - - quantized_layers = get_layers(model, layers_to_quantize[step]) - - for layer in quantized_layers: - - # book-keeping - is_master_process = (not dist.is_initialized()) or ( - dist.is_initialized() and dist.get_rank() == 0 - ) - verbose = verbose and is_master_process - - # get block size and centroids - module = attrgetter(layer)(model) - block_size = get_param(module, layer, block_sizes_config) - n_centroids = get_param(module, layer, n_centroids_config) - if verbose: - logging.info( - f"Quantizing layer {layer} with block size {block_size} and {n_centroids} centroids" - ) - - # quantize layer - weight = module.weight.data.clone() - is_bias = "bias" in [x[0] for x in module.named_parameters()] - bias = module.bias.data.clone() if is_bias else None - quantizer = PQ( - weight, - block_size, - n_centroids=n_centroids, - n_iter=n_iter, - eps=eps, - max_tentatives=max_tentatives, - verbose=verbose, - ) - - # quantization performed on all GPUs with same seed - quantizer.encode() - centroids = quantizer.centroids.contiguous() - assignments = quantizer.assignments.contiguous() - - # broadcast results to make sure weights are up-to-date - if dist.is_initialized(): - dist.broadcast(centroids, 0) - dist.broadcast(assignments, 0) - - # instantiate the quantized counterpart - if isinstance(module, nn.Linear): - out_features, in_features = map( - lambda k: module.__dict__[k], ["out_features", "in_features"] - ) - quantized_module = PQLinear( - centroids, assignments, bias, in_features, out_features - ) - elif isinstance(module, nn.Embedding): - num_embeddings, embedding_dim = map( - lambda k: module.__dict__[k], ["num_embeddings", "embedding_dim"] - ) - quantized_module = PQEmbedding( - centroids, assignments, num_embeddings, embedding_dim - ) - elif isinstance(module, nn.Conv2d): - out_channels, in_channels, kernel_size = map( - lambda k: module.__dict__[k], - ["out_channels", "in_channels", "kernel_size"], - ) - stride, padding, dilation, groups, padding_mode = map( - lambda k: module.__dict__[k], - ["stride", "padding", "dilation", "groups", "padding_mode"], - ) - - quantized_module = PQConv2d( - centroids, - assignments, - bias, - in_channels, - out_channels, - kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - padding_mode=padding_mode, - ) - else: - raise ValueError(f"Module {module} not yet supported for quantization") - - # replace layer by its quantized counterpart - attrsetter(layer)(model, quantized_module) - - # update statistics - size_tracker.update(weight, block_size, n_centroids) - - # return name of quantized layers - return quantized_layers - - -def get_layers(model, filter_regexp): - """ - Filters out the layers according to a regexp. Note that - we omit biases. - - Args: - - model: a nn.Module - - filter_regexp: a regexp to filter the layers to keep - according to their name in model.named_parameters(). - For instance, the regexp: - - down_layers\\.[123456]\\.(conv[12]|identity\\.conv)) - - is keeping blocks down_layers from 1 to 6, and inside - each block is keeping conv1, conv2 and identity.conv. - - Remarks: - - We add (module\\.)? at the beginning of the regexp to - account for the possible use of nn.parallel.DataParallel - """ - - # get all parameter names - all_layers = map(itemgetter(0), model.named_parameters()) - - # remove biases - all_layers = filter(lambda x: "bias" not in x, all_layers) - - # remove .weight in all other names (or .weight_orig is spectral norm) - all_layers = map(lambda x: x.replace(".weight_orig", ""), all_layers) - all_layers = map(lambda x: x.replace(".weight", ""), all_layers) - - # return filtered layers - filter_regexp = "(module\\.)?" + "(" + filter_regexp + ")" - r = re.compile(filter_regexp) - - return list(filter(r.match, all_layers)) - - -def get_param(module, layer_name, param_config): - """ - Given a quantization configuration, get the right parameter - for the module to be quantized. - - Args: - - module: a nn.Module - - layer_name: the name of the layer - - param_config: a dict like - { - 'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}), - 'Linear': ('in_features', {'*': 8}) - } - For instance, all conv2d layers with kernel size 3x3 have - a block size of 9 and all Linear layers are quantized with - a block size of 8, irrespective of their size. - - Remarks: - - if 'fuzzy_name' is passed as a parameter, layers whose layer_name - include 'fuzzy_name' will be assigned the given parameter. - In the following example, conv.expand layers will have a block - size of 9 while conv.reduce will have a block size of 4 and all - other layers will have a block size of 2. - { - 'Conv2d': ('fuzzy_name', {'expand': 9, 'reduce': 4, '*': 2}), - 'Linear': ('fuzzy_name', {'classifier': 8, 'projection': 4}) - } - - """ - - layer_type = module.__class__.__name__ - - if layer_type not in param_config: - raise KeyError(f"Layer type {layer_type} not in config for layer {module}") - - feature, params = param_config[module.__class__.__name__] - - if feature != "fuzzy_name": - feature_value = str(getattr(module, feature)) - if feature_value not in params: - if "*" in params: - feature_value = "*" - else: - raise KeyError( - f"{feature}={feature_value} not in config for layer {module}" - ) - else: - feature_values = [name for name in params if name in layer_name] - if len(feature_values) == 0: - if "*" in params: - feature_value = "*" - else: - raise KeyError(f"name={layer_name} not in config for {module}") - else: - feature_value = feature_values[0] - - return params[feature_value] - - -class SizeTracker(object): - """ - Class to keep track of the compressed network size with iPQ. - - Args: - - model: a nn.Module - - Remarks: - - The compressed size is the sum of three components - for each layer in the network: - (1) Storing the centroids given by iPQ in fp16 - (2) Storing the assignments of the blocks in int8 - (3) Storing all non-compressed elements such as biases - - This cost in only valid if we use 256 centroids (then - indexing can indeed by done with int8). - """ - - def __init__(self, model): - self.model = model - self.size_non_compressed_model = self.compute_size() - self.size_non_quantized = self.size_non_compressed_model - self.size_index = 0 - self.size_centroids = 0 - self.n_quantized_layers = 0 - - def compute_size(self): - """ - Computes the size of the model (in MB). - """ - - res = 0 - for _, p in self.model.named_parameters(): - res += p.numel() - return res * 4 / 1024 / 1024 - - def update(self, W, block_size, n_centroids): - """ - Updates the running statistics when quantizing a new layer. - """ - - # bits per weights - bits_per_weight = np.log2(n_centroids) / block_size - self.n_quantized_layers += 1 - - # size of indexing the subvectors of size block_size (in MB) - size_index_layer = bits_per_weight * W.numel() / 8 / 1024 / 1024 - self.size_index += size_index_layer - - # size of the centroids stored in float16 (in MB) - size_centroids_layer = n_centroids * block_size * 2 / 1024 / 1024 - self.size_centroids += size_centroids_layer - - # size of non-compressed layers, e.g. LayerNorms or biases (in MB) - size_uncompressed_layer = W.numel() * 4 / 1024 / 1024 - self.size_non_quantized -= size_uncompressed_layer - - def __repr__(self): - size_compressed = ( - self.size_index + self.size_centroids + self.size_non_quantized - ) - compression_ratio = self.size_non_compressed_model / size_compressed # NOQA - return ( - f"Non-compressed model size: {self.size_non_compressed_model:.2f} MB. " - f"After quantizing {self.n_quantized_layers} layers, size " - f"(indexing + centroids + other): {self.size_index:.2f} MB + " - f"{self.size_centroids:.2f} MB + {self.size_non_quantized:.2f} MB = " - f"{size_compressed:.2f} MB, compression ratio: {compression_ratio:.2f}x" - ) - - -def attrsetter(*items): - def resolve_attr(obj, attr): - attrs = attr.split(".") - head = attrs[:-1] - tail = attrs[-1] - - for name in head: - obj = getattr(obj, name) - return obj, tail - - def g(obj, val): - for attr in items: - resolved_obj, resolved_attr = resolve_attr(obj, attr) - setattr(resolved_obj, resolved_attr, val) - - return g diff --git a/spaces/gradio/HuBERT/fairseq/scoring/wer.py b/spaces/gradio/HuBERT/fairseq/scoring/wer.py deleted file mode 100644 index 633dc47c247691c4c9e36cbdbab7d7cb74b38452..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/scoring/wer.py +++ /dev/null @@ -1,58 +0,0 @@ -# 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. - -from dataclasses import dataclass, field - -from fairseq.dataclass import FairseqDataclass -from fairseq.scoring import BaseScorer, register_scorer -from fairseq.scoring.tokenizer import EvaluationTokenizer - - -@dataclass -class WerScorerConfig(FairseqDataclass): - wer_tokenizer: EvaluationTokenizer.ALL_TOKENIZER_TYPES = field( - default="none", metadata={"help": "sacreBLEU tokenizer to use for evaluation"} - ) - wer_remove_punct: bool = field( - default=False, metadata={"help": "remove punctuation"} - ) - wer_char_level: bool = field( - default=False, metadata={"help": "evaluate at character level"} - ) - wer_lowercase: bool = field(default=False, metadata={"help": "lowercasing"}) - - -@register_scorer("wer", dataclass=WerScorerConfig) -class WerScorer(BaseScorer): - def __init__(self, cfg): - super().__init__(cfg) - self.reset() - try: - import editdistance as ed - except ImportError: - raise ImportError("Please install editdistance to use WER scorer") - self.ed = ed - self.tokenizer = EvaluationTokenizer( - tokenizer_type=self.cfg.wer_tokenizer, - lowercase=self.cfg.wer_lowercase, - punctuation_removal=self.cfg.wer_remove_punct, - character_tokenization=self.cfg.wer_char_level, - ) - - def reset(self): - self.distance = 0 - self.ref_length = 0 - - def add_string(self, ref, pred): - ref_items = self.tokenizer.tokenize(ref).split() - pred_items = self.tokenizer.tokenize(pred).split() - self.distance += self.ed.eval(ref_items, pred_items) - self.ref_length += len(ref_items) - - def result_string(self): - return f"WER: {self.score():.2f}" - - def score(self): - return 100.0 * self.distance / self.ref_length if self.ref_length > 0 else 0 diff --git a/spaces/gradio/HuBERT/fairseq/tasks/legacy_masked_lm.py b/spaces/gradio/HuBERT/fairseq/tasks/legacy_masked_lm.py deleted file mode 100644 index 975497654926b64fff6c4960f54c4e6932e7fce1..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/tasks/legacy_masked_lm.py +++ /dev/null @@ -1,152 +0,0 @@ -# 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. - -import itertools -import logging -import os - -import numpy as np -from fairseq import tokenizer, utils -from fairseq.data import ConcatDataset, Dictionary, data_utils, indexed_dataset -from fairseq.data.legacy.block_pair_dataset import BlockPairDataset -from fairseq.data.legacy.masked_lm_dataset import MaskedLMDataset -from fairseq.data.legacy.masked_lm_dictionary import BertDictionary -from fairseq.tasks import LegacyFairseqTask, register_task - - -logger = logging.getLogger(__name__) - - -@register_task("legacy_masked_lm") -class LegacyMaskedLMTask(LegacyFairseqTask): - """ - Task for training Masked LM (BERT) model. - Args: - dictionary (Dictionary): the dictionary for the input of the task - """ - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument( - "data", - help="colon separated path to data directories list, \ - will be iterated upon during epochs in round-robin manner", - ) - parser.add_argument( - "--tokens-per-sample", - default=512, - type=int, - help="max number of total tokens over all segments" - " per sample for BERT dataset", - ) - parser.add_argument( - "--break-mode", default="doc", type=str, help="mode for breaking sentence" - ) - parser.add_argument("--shuffle-dataset", action="store_true", default=False) - - def __init__(self, args, dictionary): - super().__init__(args) - self.dictionary = dictionary - self.seed = args.seed - - @classmethod - def load_dictionary(cls, filename): - return BertDictionary.load(filename) - - @classmethod - def build_dictionary( - cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8 - ): - d = BertDictionary() - for filename in filenames: - Dictionary.add_file_to_dictionary( - filename, d, tokenizer.tokenize_line, workers - ) - d.finalize(threshold=threshold, nwords=nwords, padding_factor=padding_factor) - return d - - @property - def target_dictionary(self): - return self.dictionary - - @classmethod - def setup_task(cls, args, **kwargs): - """Setup the task.""" - paths = utils.split_paths(args.data) - assert len(paths) > 0 - dictionary = BertDictionary.load(os.path.join(paths[0], "dict.txt")) - logger.info("dictionary: {} types".format(len(dictionary))) - - return cls(args, dictionary) - - def load_dataset(self, split, epoch=1, combine=False): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - loaded_datasets = [] - - paths = utils.split_paths(self.args.data) - assert len(paths) > 0 - data_path = paths[(epoch - 1) % len(paths)] - logger.info("data_path", data_path) - - for k in itertools.count(): - split_k = split + (str(k) if k > 0 else "") - path = os.path.join(data_path, split_k) - ds = indexed_dataset.make_dataset( - path, - impl=self.args.dataset_impl, - fix_lua_indexing=True, - dictionary=self.dictionary, - ) - - if ds is None: - if k > 0: - break - else: - raise FileNotFoundError( - "Dataset not found: {} ({})".format(split, data_path) - ) - - with data_utils.numpy_seed(self.seed + k): - loaded_datasets.append( - BlockPairDataset( - ds, - self.dictionary, - ds.sizes, - self.args.tokens_per_sample, - break_mode=self.args.break_mode, - doc_break_size=1, - ) - ) - - logger.info( - "{} {} {} examples".format(data_path, split_k, len(loaded_datasets[-1])) - ) - - if not combine: - break - - if len(loaded_datasets) == 1: - dataset = loaded_datasets[0] - sizes = dataset.sizes - else: - dataset = ConcatDataset(loaded_datasets) - sizes = np.concatenate([ds.sizes for ds in loaded_datasets]) - - self.datasets[split] = MaskedLMDataset( - dataset=dataset, - sizes=sizes, - vocab=self.dictionary, - pad_idx=self.dictionary.pad(), - mask_idx=self.dictionary.mask(), - classif_token_idx=self.dictionary.cls(), - sep_token_idx=self.dictionary.sep(), - shuffle=self.args.shuffle_dataset, - seed=self.seed, - ) diff --git a/spaces/guardiancc/video-face-swap/roop/processors/frame/face_enhancer.py b/spaces/guardiancc/video-face-swap/roop/processors/frame/face_enhancer.py deleted file mode 100644 index cadb65ffc26552de1ea9c6ffe5750c0aa363e981..0000000000000000000000000000000000000000 --- a/spaces/guardiancc/video-face-swap/roop/processors/frame/face_enhancer.py +++ /dev/null @@ -1,81 +0,0 @@ -from typing import Any, List, Callable -import cv2 -import threading -import gfpgan - -import roop.globals -import roop.processors.frame.core -from roop.core import update_status -from roop.face_analyser import get_one_face -from roop.typing import Frame, Face -from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video - -FACE_ENHANCER = None -THREAD_SEMAPHORE = threading.Semaphore() -THREAD_LOCK = threading.Lock() -NAME = 'ROOP.FACE-ENHANCER' - - -def get_face_enhancer() -> Any: - global FACE_ENHANCER - - with THREAD_LOCK: - if FACE_ENHANCER is None: - model_path = resolve_relative_path('../models/GFPGANv1.4.pth') - # todo: set models path https://github.com/TencentARC/GFPGAN/issues/399 - FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=5) # type: ignore[attr-defined] - return FACE_ENHANCER - - -def pre_check() -> bool: - download_directory_path = resolve_relative_path('../models') - conditional_download(download_directory_path, ['https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth']) - return True - - -def pre_start() -> bool: - if not is_image(roop.globals.target_path) and not is_video(roop.globals.target_path): - update_status('Select an image or video for target path.', NAME) - return False - return True - - -def post_process() -> None: - global FACE_ENHANCER - - FACE_ENHANCER = None - - -def enhance_face(temp_frame: Frame) -> Frame: - with THREAD_SEMAPHORE: - _, _, temp_frame = get_face_enhancer().enhance( - temp_frame, - paste_back=True - ) - return temp_frame - - -def process_frame(source_face: Face, temp_frame: Frame) -> Frame: - target_face = get_one_face(temp_frame) - if target_face: - temp_frame = enhance_face(temp_frame) - return temp_frame - - -def process_frames(source_path: str, temp_frame_paths: List[str], update: Callable[[], None]) -> None: - for temp_frame_path in temp_frame_paths: - temp_frame = cv2.imread(temp_frame_path) - result = process_frame(None, temp_frame) - cv2.imwrite(temp_frame_path, result) - if update: - update() - - -def process_image(source_path: str, target_path: str, output_path: str) -> None: - target_frame = cv2.imread(target_path) - result = process_frame(None, target_frame) - cv2.imwrite(output_path, result) - - -def process_video(source_path: str, temp_frame_paths: List[str]) -> None: - roop.processors.frame.core.process_video(None, temp_frame_paths, process_frames) diff --git a/spaces/guohuiyuan/Real-CUGAN/upcunet_v3.py b/spaces/guohuiyuan/Real-CUGAN/upcunet_v3.py deleted file mode 100644 index f7919a6cc9efe3b8af73a73e30825a4c7d7d76da..0000000000000000000000000000000000000000 --- a/spaces/guohuiyuan/Real-CUGAN/upcunet_v3.py +++ /dev/null @@ -1,714 +0,0 @@ -import torch -from torch import nn as nn -from torch.nn import functional as F -import os, sys -import numpy as np - -root_path = os.path.abspath('.') -sys.path.append(root_path) - - -class SEBlock(nn.Module): - def __init__(self, in_channels, reduction=8, bias=False): - super(SEBlock, self).__init__() - self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, 1, 1, 0, bias=bias) - self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, 1, 1, 0, bias=bias) - - def forward(self, x): - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half() - else: - x0 = torch.mean(x, dim=(2, 3), keepdim=True) - x0 = self.conv1(x0) - x0 = F.relu(x0, inplace=True) - x0 = self.conv2(x0) - x0 = torch.sigmoid(x0) - x = torch.mul(x, x0) - return x - - def forward_mean(self, x, x0): - x0 = self.conv1(x0) - x0 = F.relu(x0, inplace=True) - x0 = self.conv2(x0) - x0 = torch.sigmoid(x0) - x = torch.mul(x, x0) - return x - - -class UNetConv(nn.Module): - def __init__(self, in_channels, mid_channels, out_channels, se): - super(UNetConv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d(in_channels, mid_channels, 3, 1, 0), - nn.LeakyReLU(0.1, inplace=True), - nn.Conv2d(mid_channels, out_channels, 3, 1, 0), - nn.LeakyReLU(0.1, inplace=True), - ) - if se: - self.seblock = SEBlock(out_channels, reduction=8, bias=True) - else: - self.seblock = None - - def forward(self, x): - z = self.conv(x) - if self.seblock is not None: - z = self.seblock(z) - return z - - -class UNet1(nn.Module): - def __init__(self, in_channels, out_channels, deconv): - super(UNet1, self).__init__() - self.conv1 = UNetConv(in_channels, 32, 64, se=False) - self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0) - self.conv2 = UNetConv(64, 128, 64, se=True) - self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0) - self.conv3 = nn.Conv2d(64, 64, 3, 1, 0) - - if deconv: - self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3) - else: - self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0) - - for m in self.modules(): - if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif isinstance(m, nn.Linear): - nn.init.normal_(m.weight, 0, 0.01) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - - def forward(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2(x2) - x2 = self.conv2_up(x2) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - - x1 = F.pad(x1, (-4, -4, -4, -4)) - x3 = self.conv3(x1 + x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - z = self.conv_bottom(x3) - return z - - def forward_a(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2.conv(x2) - return x1, x2 - - def forward_b(self, x1, x2): - x2 = self.conv2_up(x2) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - - x1 = F.pad(x1, (-4, -4, -4, -4)) - x3 = self.conv3(x1 + x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - z = self.conv_bottom(x3) - return z - - -class UNet1x3(nn.Module): - def __init__(self, in_channels, out_channels, deconv): - super(UNet1x3, self).__init__() - self.conv1 = UNetConv(in_channels, 32, 64, se=False) - self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0) - self.conv2 = UNetConv(64, 128, 64, se=True) - self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0) - self.conv3 = nn.Conv2d(64, 64, 3, 1, 0) - - if deconv: - self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2) - else: - self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0) - - for m in self.modules(): - if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif isinstance(m, nn.Linear): - nn.init.normal_(m.weight, 0, 0.01) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - - def forward(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2(x2) - x2 = self.conv2_up(x2) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - - x1 = F.pad(x1, (-4, -4, -4, -4)) - x3 = self.conv3(x1 + x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - z = self.conv_bottom(x3) - return z - - def forward_a(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2.conv(x2) - return x1, x2 - - def forward_b(self, x1, x2): - x2 = self.conv2_up(x2) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - - x1 = F.pad(x1, (-4, -4, -4, -4)) - x3 = self.conv3(x1 + x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - z = self.conv_bottom(x3) - return z - - -class UNet2(nn.Module): - def __init__(self, in_channels, out_channels, deconv): - super(UNet2, self).__init__() - - self.conv1 = UNetConv(in_channels, 32, 64, se=False) - self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0) - self.conv2 = UNetConv(64, 64, 128, se=True) - self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0) - self.conv3 = UNetConv(128, 256, 128, se=True) - self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0) - self.conv4 = UNetConv(128, 64, 64, se=True) - self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0) - self.conv5 = nn.Conv2d(64, 64, 3, 1, 0) - - if deconv: - self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3) - else: - self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0) - - for m in self.modules(): - if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif isinstance(m, nn.Linear): - nn.init.normal_(m.weight, 0, 0.01) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - - def forward(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2(x2) - - x3 = self.conv2_down(x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - x3 = self.conv3(x3) - x3 = self.conv3_up(x3) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - - x2 = F.pad(x2, (-4, -4, -4, -4)) - x4 = self.conv4(x2 + x3) - x4 = self.conv4_up(x4) - x4 = F.leaky_relu(x4, 0.1, inplace=True) - - x1 = F.pad(x1, (-16, -16, -16, -16)) - x5 = self.conv5(x1 + x4) - x5 = F.leaky_relu(x5, 0.1, inplace=True) - - z = self.conv_bottom(x5) - return z - - def forward_a(self, x): # conv234结尾有se - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2.conv(x2) - return x1, x2 - - def forward_b(self, x2): # conv234结尾有se - x3 = self.conv2_down(x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - x3 = self.conv3.conv(x3) - return x3 - - def forward_c(self, x2, x3): # conv234结尾有se - x3 = self.conv3_up(x3) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - - x2 = F.pad(x2, (-4, -4, -4, -4)) - x4 = self.conv4.conv(x2 + x3) - return x4 - - def forward_d(self, x1, x4): # conv234结尾有se - x4 = self.conv4_up(x4) - x4 = F.leaky_relu(x4, 0.1, inplace=True) - - x1 = F.pad(x1, (-16, -16, -16, -16)) - x5 = self.conv5(x1 + x4) - x5 = F.leaky_relu(x5, 0.1, inplace=True) - - z = self.conv_bottom(x5) - return z - - -class UpCunet2x(nn.Module): # 完美tile，全程无损 - def __init__(self, in_channels=3, out_channels=3): - super(UpCunet2x, self).__init__() - self.unet1 = UNet1(in_channels, out_channels, deconv=True) - self.unet2 = UNet2(in_channels, out_channels, deconv=False) - - def forward(self, x, tile_mode): # 1.7G - n, c, h0, w0 = x.shape - if (tile_mode == 0): # 不tile - ph = ((h0 - 1) // 2 + 1) * 2 - pw = ((w0 - 1) // 2 + 1) * 2 - x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect') # 需要保证被2整除 - x = self.unet1.forward(x) - x0 = self.unet2.forward(x) - x1 = F.pad(x, (-20, -20, -20, -20)) - x = torch.add(x0, x1) - if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 2, :w0 * 2] - return x - elif (tile_mode == 1): # 对长边减半 - if (w0 >= h0): - crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除 - crop_size_h = (h0 - 1) // 2 * 2 + 2 # 能被2整除 - else: - crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除 - crop_size_w = (w0 - 1) // 2 * 2 + 2 # 能被2整除 - crop_size = (crop_size_h, crop_size_w) # 6.6G - elif (tile_mode == 2): # hw都减半 - crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2) # 5.6G - elif (tile_mode == 3): # hw都三分之一 - crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3) # 4.2G - elif (tile_mode == 4): # hw都四分之一 - crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4) # 3.7G - ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0] - pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1] - x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect') - n, c, h, w = x.shape - se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device) - if ("Half" in x.type()): - se_mean0 = se_mean0.half() - n_patch = 0 - tmp_dict = {} - opt_res_dict = {} - for i in range(0, h - 36, crop_size[0]): - tmp_dict[i] = {} - for j in range(0, w - 36, crop_size[1]): - x_crop = x[:, :, i:i + crop_size[0] + 36, j:j + crop_size[1] + 36] - n, c1, h1, w1 = x_crop.shape - tmp0, x_crop = self.unet1.forward_a(x_crop) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - n_patch += 1 - tmp_dict[i][j] = (tmp0, x_crop) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean1 = se_mean1.half() - for i in range(0, h - 36, crop_size[0]): - for j in range(0, w - 36, crop_size[1]): - tmp0, x_crop = tmp_dict[i][j] - x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0) - opt_unet1 = self.unet1.forward_b(tmp0, x_crop) - tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2) - se_mean1 /= n_patch - se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean0 = se_mean0.half() - for i in range(0, h - 36, crop_size[0]): - for j in range(0, w - 36, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j] - tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1) - tmp_x3 = self.unet2.forward_b(tmp_x2) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean1 = se_mean1.half() - for i in range(0, h - 36, crop_size[0]): - for j in range(0, w - 36, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j] - tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0) - tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4) - se_mean1 /= n_patch - for i in range(0, h - 36, crop_size[0]): - opt_res_dict[i] = {} - for j in range(0, w - 36, crop_size[1]): - opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j] - tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1) - x0 = self.unet2.forward_d(tmp_x1, tmp_x4) - x1 = F.pad(opt_unet1, (-20, -20, -20, -20)) - x_crop = torch.add(x0, x1) # x0是unet2的最终输出 - opt_res_dict[i][j] = x_crop - del tmp_dict - torch.cuda.empty_cache() - res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device) - if ("Half" in x.type()): - res = res.half() - for i in range(0, h - 36, crop_size[0]): - for j in range(0, w - 36, crop_size[1]): - res[:, :, i * 2:i * 2 + h1 * 2 - 72, j * 2:j * 2 + w1 * 2 - 72] = opt_res_dict[i][j] - del opt_res_dict - torch.cuda.empty_cache() - if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 2, :w0 * 2] - return res # - - -class UpCunet3x(nn.Module): # 完美tile，全程无损 - def __init__(self, in_channels=3, out_channels=3): - super(UpCunet3x, self).__init__() - self.unet1 = UNet1x3(in_channels, out_channels, deconv=True) - self.unet2 = UNet2(in_channels, out_channels, deconv=False) - - def forward(self, x, tile_mode): # 1.7G - n, c, h0, w0 = x.shape - if (tile_mode == 0): # 不tile - ph = ((h0 - 1) // 4 + 1) * 4 - pw = ((w0 - 1) // 4 + 1) * 4 - x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect') # 需要保证被2整除 - x = self.unet1.forward(x) - x0 = self.unet2.forward(x) - x1 = F.pad(x, (-20, -20, -20, -20)) - x = torch.add(x0, x1) - if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 3, :w0 * 3] - return x - elif (tile_mode == 1): # 对长边减半 - if (w0 >= h0): - crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2 # 减半后能被4整除，所以要先被8整除 - crop_size_h = (h0 - 1) // 4 * 4 + 4 # 能被4整除 - else: - crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2 # 减半后能被4整除，所以要先被8整除 - crop_size_w = (w0 - 1) // 4 * 4 + 4 # 能被4整除 - crop_size = (crop_size_h, crop_size_w) # 6.6G - elif (tile_mode == 2): # hw都减半 - crop_size = (((h0 - 1) // 8 * 8 + 8) // 2, ((w0 - 1) // 8 * 8 + 8) // 2) # 5.6G - elif (tile_mode == 3): # hw都三分之一 - crop_size = (((h0 - 1) // 12 * 12 + 12) // 3, ((w0 - 1) // 12 * 12 + 12) // 3) # 4.2G - elif (tile_mode == 4): # hw都四分之一 - crop_size = (((h0 - 1) // 16 * 16 + 16) // 4, ((w0 - 1) // 16 * 16 + 16) // 4) # 3.7G - ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0] - pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1] - x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect') - n, c, h, w = x.shape - se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device) - if ("Half" in x.type()): - se_mean0 = se_mean0.half() - n_patch = 0 - tmp_dict = {} - opt_res_dict = {} - for i in range(0, h - 28, crop_size[0]): - tmp_dict[i] = {} - for j in range(0, w - 28, crop_size[1]): - x_crop = x[:, :, i:i + crop_size[0] + 28, j:j + crop_size[1] + 28] - n, c1, h1, w1 = x_crop.shape - tmp0, x_crop = self.unet1.forward_a(x_crop) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - n_patch += 1 - tmp_dict[i][j] = (tmp0, x_crop) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean1 = se_mean1.half() - for i in range(0, h - 28, crop_size[0]): - for j in range(0, w - 28, crop_size[1]): - tmp0, x_crop = tmp_dict[i][j] - x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0) - opt_unet1 = self.unet1.forward_b(tmp0, x_crop) - tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2) - se_mean1 /= n_patch - se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean0 = se_mean0.half() - for i in range(0, h - 28, crop_size[0]): - for j in range(0, w - 28, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j] - tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1) - tmp_x3 = self.unet2.forward_b(tmp_x2) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean1 = se_mean1.half() - for i in range(0, h - 28, crop_size[0]): - for j in range(0, w - 28, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j] - tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0) - tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4) - se_mean1 /= n_patch - for i in range(0, h - 28, crop_size[0]): - opt_res_dict[i] = {} - for j in range(0, w - 28, crop_size[1]): - opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j] - tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1) - x0 = self.unet2.forward_d(tmp_x1, tmp_x4) - x1 = F.pad(opt_unet1, (-20, -20, -20, -20)) - x_crop = torch.add(x0, x1) # x0是unet2的最终输出 - opt_res_dict[i][j] = x_crop # - del tmp_dict - torch.cuda.empty_cache() - res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device) - if ("Half" in x.type()): - res = res.half() - for i in range(0, h - 28, crop_size[0]): - for j in range(0, w - 28, crop_size[1]): - res[:, :, i * 3:i * 3 + h1 * 3 - 84, j * 3:j * 3 + w1 * 3 - 84] = opt_res_dict[i][j] - del opt_res_dict - torch.cuda.empty_cache() - if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 3, :w0 * 3] - return res - - -class UpCunet4x(nn.Module): # 完美tile，全程无损 - def __init__(self, in_channels=3, out_channels=3): - super(UpCunet4x, self).__init__() - self.unet1 = UNet1(in_channels, 64, deconv=True) - self.unet2 = UNet2(64, 64, deconv=False) - self.ps = nn.PixelShuffle(2) - self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True) - - def forward(self, x, tile_mode): - n, c, h0, w0 = x.shape - x00 = x - if (tile_mode == 0): # 不tile - ph = ((h0 - 1) // 2 + 1) * 2 - pw = ((w0 - 1) // 2 + 1) * 2 - x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect') # 需要保证被2整除 - x = self.unet1.forward(x) - x0 = self.unet2.forward(x) - x1 = F.pad(x, (-20, -20, -20, -20)) - x = torch.add(x0, x1) - x = self.conv_final(x) - x = F.pad(x, (-1, -1, -1, -1)) - x = self.ps(x) - if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 4, :w0 * 4] - x += F.interpolate(x00, scale_factor=4, mode='nearest') - return x - elif (tile_mode == 1): # 对长边减半 - if (w0 >= h0): - crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除 - crop_size_h = (h0 - 1) // 2 * 2 + 2 # 能被2整除 - else: - crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除 - crop_size_w = (w0 - 1) // 2 * 2 + 2 # 能被2整除 - crop_size = (crop_size_h, crop_size_w) # 6.6G - elif (tile_mode == 2): # hw都减半 - crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2) # 5.6G - elif (tile_mode == 3): # hw都三分之一 - crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3) # 4.1G - elif (tile_mode == 4): # hw都四分之一 - crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4) # 3.7G - ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0] - pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1] - x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect') - n, c, h, w = x.shape - se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device) - if ("Half" in x.type()): - se_mean0 = se_mean0.half() - n_patch = 0 - tmp_dict = {} - opt_res_dict = {} - for i in range(0, h - 38, crop_size[0]): - tmp_dict[i] = {} - for j in range(0, w - 38, crop_size[1]): - x_crop = x[:, :, i:i + crop_size[0] + 38, j:j + crop_size[1] + 38] - n, c1, h1, w1 = x_crop.shape - tmp0, x_crop = self.unet1.forward_a(x_crop) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - n_patch += 1 - tmp_dict[i][j] = (tmp0, x_crop) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean1 = se_mean1.half() - for i in range(0, h - 38, crop_size[0]): - for j in range(0, w - 38, crop_size[1]): - tmp0, x_crop = tmp_dict[i][j] - x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0) - opt_unet1 = self.unet1.forward_b(tmp0, x_crop) - tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2) - se_mean1 /= n_patch - se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean0 = se_mean0.half() - for i in range(0, h - 38, crop_size[0]): - for j in range(0, w - 38, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j] - tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1) - tmp_x3 = self.unet2.forward_b(tmp_x2) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean1 = se_mean1.half() - for i in range(0, h - 38, crop_size[0]): - for j in range(0, w - 38, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j] - tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0) - tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4) - se_mean1 /= n_patch - for i in range(0, h - 38, crop_size[0]): - opt_res_dict[i] = {} - for j in range(0, w - 38, crop_size[1]): - opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j] - tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1) - x0 = self.unet2.forward_d(tmp_x1, tmp_x4) - x1 = F.pad(opt_unet1, (-20, -20, -20, -20)) - x_crop = torch.add(x0, x1) # x0是unet2的最终输出 - x_crop = self.conv_final(x_crop) - x_crop = F.pad(x_crop, (-1, -1, -1, -1)) - x_crop = self.ps(x_crop) - opt_res_dict[i][j] = x_crop - del tmp_dict - torch.cuda.empty_cache() - res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device) - if ("Half" in x.type()): - res = res.half() - for i in range(0, h - 38, crop_size[0]): - for j in range(0, w - 38, crop_size[1]): - # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape) - res[:, :, i * 4:i * 4 + h1 * 4 - 152, j * 4:j * 4 + w1 * 4 - 152] = opt_res_dict[i][j] - del opt_res_dict - torch.cuda.empty_cache() - if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 4, :w0 * 4] - res += F.interpolate(x00, scale_factor=4, mode='nearest') - return res # - - -class RealWaifuUpScaler(object): - def __init__(self, scale, weight_path, half, device): - weight = torch.load(weight_path, map_location="cpu") - self.model = eval("UpCunet%sx" % scale)() - if (half == True): - self.model = self.model.half().to(device) - else: - self.model = self.model.to(device) - self.model.load_state_dict(weight, strict=True) - self.model.eval() - self.half = half - self.device = device - - def np2tensor(self, np_frame): - if (self.half == False): - return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).float() / 255 - else: - return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).half() / 255 - - def tensor2np(self, tensor): - if (self.half == False): - return ( - np.transpose((tensor.data.squeeze() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), (1, 2, 0))) - else: - return (np.transpose((tensor.data.squeeze().float() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), - (1, 2, 0))) - - def __call__(self, frame, tile_mode): - with torch.no_grad(): - tensor = self.np2tensor(frame) - result = self.tensor2np(self.model(tensor, tile_mode)) - return result - - -if __name__ == "__main__": - ###########inference_img - import time, cv2, sys - from time import time as ttime - - for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3), - ("weights_v3/up4x-latest-denoise3x.pth", 4)]: - for tile_mode in [0, 1, 2, 3, 4]: - upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0") - input_dir = "%s/input_dir1" % root_path - output_dir = "%s/opt-dir-all-test" % root_path - os.makedirs(output_dir, exist_ok=True) - for name in os.listdir(input_dir): - print(name) - tmp = name.split(".") - inp_path = os.path.join(input_dir, name) - suffix = tmp[-1] - prefix = ".".join(tmp[:-1]) - tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix)) - print(inp_path, tmp_path) - # 支持中文路径 - # os.link(inp_path, tmp_path)#win用硬链接 - os.symlink(inp_path, tmp_path) # linux用软链接 - frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]] - t0 = ttime() - result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1] - t1 = ttime() - print(prefix, "done", t1 - t0) - tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix)) - cv2.imwrite(tmp_opt_path, result) - n = 0 - while (1): - if (n == 0): - suffix = "_%sx_tile%s.png" % (scale, tile_mode) - else: - suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n) # - if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False): - break - else: - n += 1 - final_opt_path = os.path.join(output_dir, prefix + suffix) - os.rename(tmp_opt_path, final_opt_path) - os.remove(tmp_path) diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/__init__.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/hackertwo/GoAheadMazen/style.css b/spaces/hackertwo/GoAheadMazen/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/spaces/hackertwo/GoAheadMazen/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -} diff --git a/spaces/hamelcubsfan/AutoGPT/tests/test_token_counter.py b/spaces/hamelcubsfan/AutoGPT/tests/test_token_counter.py deleted file mode 100644 index 6d7ae016b2f823123b0b69b2eeb3eab50d94f00f..0000000000000000000000000000000000000000 --- a/spaces/hamelcubsfan/AutoGPT/tests/test_token_counter.py +++ /dev/null @@ -1,63 +0,0 @@ -import unittest - -import tests.context -from autogpt.token_counter import count_message_tokens, count_string_tokens - - -class TestTokenCounter(unittest.TestCase): - def test_count_message_tokens(self): - messages = [ - {"role": "user", "content": "Hello"}, - {"role": "assistant", "content": "Hi there!"}, - ] - self.assertEqual(count_message_tokens(messages), 17) - - def test_count_message_tokens_with_name(self): - messages = [ - {"role": "user", "content": "Hello", "name": "John"}, - {"role": "assistant", "content": "Hi there!"}, - ] - self.assertEqual(count_message_tokens(messages), 17) - - def test_count_message_tokens_empty_input(self): - self.assertEqual(count_message_tokens([]), 3) - - def test_count_message_tokens_invalid_model(self): - messages = [ - {"role": "user", "content": "Hello"}, - {"role": "assistant", "content": "Hi there!"}, - ] - with self.assertRaises(KeyError): - count_message_tokens(messages, model="invalid_model") - - def test_count_message_tokens_gpt_4(self): - messages = [ - {"role": "user", "content": "Hello"}, - {"role": "assistant", "content": "Hi there!"}, - ] - self.assertEqual(count_message_tokens(messages, model="gpt-4-0314"), 15) - - def test_count_string_tokens(self): - string = "Hello, world!" - self.assertEqual( - count_string_tokens(string, model_name="gpt-3.5-turbo-0301"), 4 - ) - - def test_count_string_tokens_empty_input(self): - self.assertEqual(count_string_tokens("", model_name="gpt-3.5-turbo-0301"), 0) - - def test_count_message_tokens_invalid_model(self): - messages = [ - {"role": "user", "content": "Hello"}, - {"role": "assistant", "content": "Hi there!"}, - ] - with self.assertRaises(NotImplementedError): - count_message_tokens(messages, model="invalid_model") - - def test_count_string_tokens_gpt_4(self): - string = "Hello, world!" - self.assertEqual(count_string_tokens(string, model_name="gpt-4-0314"), 4) - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/hands012/gpt-academic/crazy_functions/crazy_functions_test.py b/spaces/hands012/gpt-academic/crazy_functions/crazy_functions_test.py deleted file mode 100644 index a9bfbf80df3780be105e0f1be10d2f348c4282bb..0000000000000000000000000000000000000000 --- a/spaces/hands012/gpt-academic/crazy_functions/crazy_functions_test.py +++ /dev/null @@ -1,135 +0,0 @@ -""" -这是什么？ - 这个文件用于函数插件的单元测试 - 运行方法 python crazy_functions/crazy_functions_test.py -""" - -def validate_path(): - import os, sys - dir_name = os.path.dirname(__file__) - root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..') - os.chdir(root_dir_assume) - sys.path.append(root_dir_assume) - -validate_path() # validate path so you can run from base directory -from colorful import * -from toolbox import get_conf, ChatBotWithCookies -proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \ - get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY') - -llm_kwargs = { - 'api_key': API_KEY, - 'llm_model': LLM_MODEL, - 'top_p':1.0, - 'max_length': None, - 'temperature':1.0, -} -plugin_kwargs = { } -chatbot = ChatBotWithCookies(llm_kwargs) -history = [] -system_prompt = "Serve me as a writing and programming assistant." -web_port = 1024 - - -def test_解析一个Python项目(): - from crazy_functions.解析项目源代码 import 解析一个Python项目 - txt = "crazy_functions/test_project/python/dqn" - for cookies, cb, hist, msg in 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - print(cb) - -def test_解析一个Cpp项目(): - from crazy_functions.解析项目源代码 import 解析一个C项目 - txt = "crazy_functions/test_project/cpp/cppipc" - for cookies, cb, hist, msg in 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - print(cb) - -def test_Latex英文润色(): - from crazy_functions.Latex全文润色 import Latex英文润色 - txt = "crazy_functions/test_project/latex/attention" - for cookies, cb, hist, msg in Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - print(cb) - -def test_Markdown中译英(): - from crazy_functions.批量Markdown翻译 import Markdown中译英 - txt = "README.md" - for cookies, cb, hist, msg in Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - print(cb) - -def test_批量翻译PDF文档(): - from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档 - txt = "crazy_functions/test_project/pdf_and_word" - for cookies, cb, hist, msg in 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - print(cb) - -def test_谷歌检索小助手(): - from crazy_functions.谷歌检索小助手 import 谷歌检索小助手 - txt = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=auto+reinforcement+learning&btnG=" - for cookies, cb, hist, msg in 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - print(cb) - -def test_总结word文档(): - from crazy_functions.总结word文档 import 总结word文档 - txt = "crazy_functions/test_project/pdf_and_word" - for cookies, cb, hist, msg in 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - print(cb) - -def test_下载arxiv论文并翻译摘要(): - from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要 - txt = "1812.10695" - for cookies, cb, hist, msg in 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - print(cb) - -def test_联网回答问题(): - from crazy_functions.联网的ChatGPT import 连接网络回答问题 - # txt = "谁是应急食品？" - # >> '根据以上搜索结果可以得知，应急食品是“原神”游戏中的角色派蒙的外号。' - # txt = "道路千万条，安全第一条。后面两句是？" - # >> '行车不规范，亲人两行泪。' - # txt = "You should have gone for the head. What does that mean?" - # >> The phrase "You should have gone for the head" is a quote from the Marvel movies, Avengers: Infinity War and Avengers: Endgame. It was spoken by the character Thanos in Infinity War and by Thor in Endgame. - txt = "AutoGPT是什么？" - for cookies, cb, hist, msg in 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - print("当前问答：", cb[-1][-1].replace("\n"," ")) - for i, it in enumerate(cb): print亮蓝(it[0]); print亮黄(it[1]) - -def test_解析ipynb文件(): - from crazy_functions.解析JupyterNotebook import 解析ipynb文件 - txt = "crazy_functions/test_samples" - for cookies, cb, hist, msg in 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - print(cb) - - -def test_数学动画生成manim(): - from crazy_functions.数学动画生成manim import 动画生成 - txt = "A ball split into 2, and then split into 4, and finally split into 8." - for cookies, cb, hist, msg in 动画生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - print(cb) - - - -def test_Markdown多语言(): - from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言 - txt = "README.md" - history = [] - for lang in ["English", "French", "Japanese", "Korean", "Russian", "Italian", "German", "Portuguese", "Arabic"]: - plugin_kwargs = {"advanced_arg": lang} - for cookies, cb, hist, msg in Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - print(cb) - - - -# test_解析一个Python项目() -# test_Latex英文润色() -# test_Markdown中译英() -# test_批量翻译PDF文档() -# test_谷歌检索小助手() -# test_总结word文档() -# test_下载arxiv论文并翻译摘要() -# test_解析一个Cpp项目() -# test_联网回答问题() -# test_解析ipynb文件() -# test_数学动画生成manim() -test_Markdown多语言() - -input("程序完成，回车退出。") -print("退出。") \ No newline at end of file diff --git a/spaces/hdhzk/bingo/Dockerfile b/spaces/hdhzk/bingo/Dockerfile deleted file mode 100644 index 3aa2b29b5fc4fa8b8238955acd7f1fde13ce5e1a..0000000000000000000000000000000000000000 --- a/spaces/hdhzk/bingo/Dockerfile +++ /dev/null @@ -1,36 +0,0 @@ -FROM node:18 - - -ARG DEBIAN_FRONTEND=noninteractive - -ENV BING_HEADER "" - -# Set home to the user's home directory -ENV HOME=/home/user \ - PATH=/home/user/.local/bin:$PATH - -# Set up a new user named "user" with user ID 1000 -RUN useradd -o -u 1000 user && mkdir -p $HOME/app && chown -R user $HOME - -# Switch to the "user" user -USER user - -# Set the working directory to the user's home directory -WORKDIR $HOME/app - -# Install app dependencies -# A wildcard is used to ensure both package.json AND package-lock.json are copied -# where available (npm@5+) -COPY --chown=user package*.json $HOME/app/ - -RUN npm install - -# Copy the current directory contents into the container at $HOME/app setting the owner to the user -COPY --chown=user . $HOME/app/ - -RUN npm run build - -ENV PORT 7860 -EXPOSE 7860 - -CMD npm start diff --git a/spaces/hhhyrhe/vits-uma-genshin-honkai/modules.py b/spaces/hhhyrhe/vits-uma-genshin-honkai/modules.py deleted file mode 100644 index 56ea4145eddf19dd330a3a41ab0183efc1686d83..0000000000000000000000000000000000000000 --- a/spaces/hhhyrhe/vits-uma-genshin-honkai/modules.py +++ /dev/null @@ -1,388 +0,0 @@ -import math -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm - -import commons -from commons import init_weights, get_padding -from transforms import piecewise_rational_quadratic_transform - - -LRELU_SLOPE = 0.1 - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - -class ConvReluNorm(nn.Module): - def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): - super().__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - assert n_layers > 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential( - nn.ReLU(), - nn.Dropout(p_dropout)) - for _ in range(n_layers-1): - self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size ** i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, - groups=channels, dilation=dilation, padding=padding - )) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): - super(WN, self).__init__() - assert(kernel_size % 2 == 1) - self.hidden_channels =hidden_channels - self.kernel_size = kernel_size, - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - self.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') - - for i in range(n_layers): - dilation = dilation_rate ** i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size, - dilation=dilation, padding=padding) - in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:] - else: - g_l = torch.zeros_like(x_in) - - acts = commons.fused_add_tanh_sigmoid_multiply( - x_in, - g_l, - n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:,:self.hidden_channels,:] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:,self.hidden_channels:,:] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]))) - ]) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))) - ]) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))) - ]) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels,1)) - self.logs = nn.Parameter(torch.zeros(channels,1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1,2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False): - assert channels % 2 == 0, "channels should be divisible by 2" - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels]*2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1,2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - -class ConvFlow(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.num_bins = num_bins - self.tail_bound = tail_bound - self.half_channels = in_channels // 2 - - self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) - self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.) - self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) - h = self.convs(h, x_mask, g=g) - h = self.proj(h) * x_mask - - b, c, t = x0.shape - h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] - - unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_derivatives = h[..., 2 * self.num_bins:] - - x1, logabsdet = piecewise_rational_quadratic_transform(x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails='linear', - tail_bound=self.tail_bound - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1,2]) - if not reverse: - return x, logdet - else: - return x diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ResencUNet_DA3.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ResencUNet_DA3.py deleted file mode 100644 index 11e48b188a948d4a4ef526d88c1f95a7a229617a..0000000000000000000000000000000000000000 --- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ResencUNet_DA3.py +++ /dev/null @@ -1,104 +0,0 @@ -# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import Tuple - -import numpy as np -import torch - -from nnunet.network_architecture.generic_modular_residual_UNet import FabiansUNet, get_default_network_config -from nnunet.network_architecture.initialization import InitWeights_He -from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer -from nnunet.training.network_training.nnUNet_variants.data_augmentation.nnUNetTrainerV2_DA3 import \ - nnUNetTrainerV2_DA3 -from nnunet.utilities.nd_softmax import softmax_helper - - -class nnUNetTrainerV2_ResencUNet_DA3(nnUNetTrainerV2_DA3): - def initialize_network(self): - if self.threeD: - cfg = get_default_network_config(3, None, norm_type="in") - - else: - cfg = get_default_network_config(1, None, norm_type="in") - - stage_plans = self.plans['plans_per_stage'][self.stage] - conv_kernel_sizes = stage_plans['conv_kernel_sizes'] - blocks_per_stage_encoder = stage_plans['num_blocks_encoder'] - blocks_per_stage_decoder = stage_plans['num_blocks_decoder'] - pool_op_kernel_sizes = stage_plans['pool_op_kernel_sizes'] - - self.network = FabiansUNet(self.num_input_channels, self.base_num_features, blocks_per_stage_encoder, 2, - pool_op_kernel_sizes, conv_kernel_sizes, cfg, self.num_classes, - blocks_per_stage_decoder, True, False, 320, InitWeights_He(1e-2)) - - if torch.cuda.is_available(): - self.network.cuda() - self.network.inference_apply_nonlin = softmax_helper - - def setup_DA_params(self): - """ - net_num_pool_op_kernel_sizes is different in resunet - """ - super().setup_DA_params() - self.deep_supervision_scales = [[1, 1, 1]] + list(list(i) for i in 1 / np.cumprod( - np.vstack(self.net_num_pool_op_kernel_sizes[1:]), axis=0))[:-1] - - def validate(self, do_mirroring: bool = True, use_sliding_window: bool = True, step_size: float = 0.5, - save_softmax: bool = True, use_gaussian: bool = True, overwrite: bool = True, - validation_folder_name: str = 'validation_raw', debug: bool = False, all_in_gpu: bool = False, - segmentation_export_kwargs: dict = None, run_postprocessing_on_folds: bool = True): - ds = self.network.decoder.deep_supervision - self.network.decoder.deep_supervision = False - - ret = nnUNetTrainer.validate(self, do_mirroring=do_mirroring, use_sliding_window=use_sliding_window, - step_size=step_size, save_softmax=save_softmax, use_gaussian=use_gaussian, - overwrite=overwrite, validation_folder_name=validation_folder_name, debug=debug, - all_in_gpu=all_in_gpu, segmentation_export_kwargs=segmentation_export_kwargs, - run_postprocessing_on_folds=run_postprocessing_on_folds) - - self.network.decoder.deep_supervision = ds - return ret - - def predict_preprocessed_data_return_seg_and_softmax(self, data: np.ndarray, do_mirroring: bool = True, - mirror_axes: Tuple[int] = None, - use_sliding_window: bool = True, step_size: float = 0.5, - use_gaussian: bool = True, pad_border_mode: str = 'constant', - pad_kwargs: dict = None, all_in_gpu: bool = False, - verbose: bool = True, mixed_precision=True) -> Tuple[np.ndarray, np.ndarray]: - ds = self.network.decoder.deep_supervision - self.network.decoder.deep_supervision = False - ret = nnUNetTrainer.predict_preprocessed_data_return_seg_and_softmax(self, data=data, - do_mirroring=do_mirroring, - mirror_axes=mirror_axes, - use_sliding_window=use_sliding_window, - step_size=step_size, - use_gaussian=use_gaussian, - pad_border_mode=pad_border_mode, - pad_kwargs=pad_kwargs, - all_in_gpu=all_in_gpu, - verbose=verbose, - mixed_precision=mixed_precision) - self.network.decoder.deep_supervision = ds - return ret - - def run_training(self): - self.maybe_update_lr(self.epoch) # if we dont overwrite epoch then self.epoch+1 is used which is not what we - # want at the start of the training - ds = self.network.decoder.deep_supervision - self.network.decoder.deep_supervision = True - ret = nnUNetTrainer.run_training(self) - self.network.decoder.deep_supervision = ds - return ret - - diff --git a/spaces/huaiji3y/bingo-Public/src/components/learn-more.tsx b/spaces/huaiji3y/bingo-Public/src/components/learn-more.tsx deleted file mode 100644 index a64459ee7900a612292e117a6bda96ee9260990f..0000000000000000000000000000000000000000 --- a/spaces/huaiji3y/bingo-Public/src/components/learn-more.tsx +++ /dev/null @@ -1,39 +0,0 @@ -import React from 'react' -import { SourceAttribution } from '@/lib/bots/bing/types' - -export interface LearnMoreProps { - sourceAttributions?: SourceAttribution[] -} - -export function LearnMore({ sourceAttributions }: LearnMoreProps) { - if (!sourceAttributions?.length) { - return null - } - - return ( - -- ) -} diff --git a/spaces/huggingchat/chat-ui/src/lib/types/AbortedGeneration.ts b/spaces/huggingchat/chat-ui/src/lib/types/AbortedGeneration.ts deleted file mode 100644 index fe4c2824b4f3257bea71c3acacd65fcee0918188..0000000000000000000000000000000000000000 --- a/spaces/huggingchat/chat-ui/src/lib/types/AbortedGeneration.ts +++ /dev/null @@ -1,8 +0,0 @@ -// Ideally shouldn't be needed, see https://github.com/huggingface/chat-ui/pull/88#issuecomment-1523173850 - -import type { Conversation } from "./Conversation"; -import type { Timestamps } from "./Timestamps"; - -export interface AbortedGeneration extends Timestamps { - conversationId: Conversation["_id"]; -} diff --git a/spaces/hysts/cv_diffusion_text-to-image-synthesis_tiny/app.py b/spaces/hysts/cv_diffusion_text-to-image-synthesis_tiny/app.py deleted file mode 100644 index 695aa5ecd84b905950c7c267c5d606eff712f12f..0000000000000000000000000000000000000000 --- a/spaces/hysts/cv_diffusion_text-to-image-synthesis_tiny/app.py +++ /dev/null @@ -1,111 +0,0 @@ -#!/usr/bin/env python - -from __future__ import annotations - -import os -import shlex -import subprocess - -import gradio as gr -import numpy as np -import torch -from modelscope.pipelines import pipeline -from modelscope.utils.constant import Tasks - -if os.getenv('SYSTEM') == 'spaces': - subprocess.run( - shlex.split( - 'pip install git+https://github.com/modelscope/modelscope.git@refs/pull/173/head' - )) - -DESCRIPTION = '# [ModelScope Chinese text2image (tiny)](https://www.modelscope.cn/models/damo/cv_diffusion_text-to-image-synthesis_tiny/summary)' - -SPACE_ID = os.getenv('SPACE_ID') -if SPACE_ID is not None: - DESCRIPTION += f'了解详细信息:---- {sourceAttributions.map((attribution, index) => { - const { providerDisplayName, seeMoreUrl } = attribution - const { host } = new URL(seeMoreUrl) - return ( - - {index + 1}. {host} - - ) - })} --For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
' - -pipe = pipeline(Tasks.text_to_image_synthesis, - 'damo/cv_diffusion_text-to-image-synthesis_tiny') - - -def run( - text: str, - seed: int, - num_steps_generator: int, - num_steps_upscaler1: int, - num_steps_upscaler2: int, - guidance_scale: float, -) -> np.ndarray: - torch.manual_seed(seed) - results = pipe({ - 'text': text, - 'solver': 'ddim', - 'generator_ddim_timesteps': num_steps_generator, - 'upsampler_256_ddim_timesteps': num_steps_upscaler1, - 'upsampler_1024_ddim_timesteps': num_steps_upscaler2, - 'generator_guide_scale': guidance_scale, - }) - return results['output_imgs'][0] - - -examples = [ - ['中国山水画', 0, 250, 50, 20, 5.0], -] - -with gr.Blocks(css='style.css') as demo: - gr.Markdown(DESCRIPTION) - with gr.Row(): - with gr.Column(): - text = gr.Text(label='Prompt') - seed = gr.Slider(label='Seed', - minimum=0, - maximum=100000, - value=0, - step=1, - randomize=True) - run_button = gr.Button('Run') - with gr.Accordion('Advanced options', open=False): - num_steps_generator = gr.Slider(label='Steps (Generator)', - minimum=1, - maximum=1000, - value=250, - step=1) - num_steps_upscaler1 = gr.Slider( - label='Steps (Upscaler 64=>256)', - minimum=1, - maximum=50, - value=50, - step=1) - num_steps_upscaler2 = gr.Slider( - label='Steps (Upscaler 256=>1024)', - minimum=1, - maximum=20, - value=20, - step=1) - guidance_scale = gr.Slider(label='Guidance scale', - minimum=0, - maximum=100, - value=5.0, - step=0.1) - with gr.Column(): - result = gr.Image(label='Output') - - inputs = [ - text, - seed, - num_steps_generator, - num_steps_upscaler1, - num_steps_upscaler2, - guidance_scale, - ] - with gr.Row(): - gr.Examples(examples=examples, - inputs=inputs, - outputs=result, - fn=run, - cache_examples=True) - - text.submit(fn=run, inputs=inputs, outputs=result) - run_button.click(fn=run, inputs=inputs, outputs=result) - -demo.queue(api_open=False).launch() diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf12m_r50.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf12m_r50.py deleted file mode 100644 index 2a7284663d6afbe6f205c8c9f10cd454ef1045ca..0000000000000000000000000000000000000000 --- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf12m_r50.py +++ /dev/null @@ -1,28 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.margin_list = (1.0, 0.0, 0.4) -config.network = "r50" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 1.0 -config.interclass_filtering_threshold = 0 -config.fp16 = True -config.weight_decay = 5e-4 -config.batch_size = 128 -config.optimizer = "sgd" -config.lr = 0.1 -config.verbose = 2000 -config.dali = False - -config.rec = "/train_tmp/WebFace12M" -config.num_classes = 617970 -config.num_image = 12720066 -config.num_epoch = 20 -config.warmup_epoch = 0 -config.val_targets = [] diff --git a/spaces/hzy123/bingo/src/components/chat-panel.tsx b/spaces/hzy123/bingo/src/components/chat-panel.tsx deleted file mode 100644 index 1fbc3c2bf05b914e0c229661832fbb560745f488..0000000000000000000000000000000000000000 --- a/spaces/hzy123/bingo/src/components/chat-panel.tsx +++ /dev/null @@ -1,153 +0,0 @@ -'use client' - -import * as React from 'react' -import Image from 'next/image' -import Textarea from 'react-textarea-autosize' -import { useAtomValue } from 'jotai' -import { useEnterSubmit } from '@/lib/hooks/use-enter-submit' -import { cn } from '@/lib/utils' - -import BrushIcon from '@/assets/images/brush.svg' -import ChatIcon from '@/assets/images/chat.svg' -import VisualSearchIcon from '@/assets/images/visual-search.svg' -import SendIcon from '@/assets/images/send.svg' -import PinIcon from '@/assets/images/pin.svg' -import PinFillIcon from '@/assets/images/pin-fill.svg' - -import { useBing } from '@/lib/hooks/use-bing' -import { voiceListenAtom } from '@/state' -import Voice from './voice' -import { ChatImage } from './chat-image' -import { ChatAttachments } from './chat-attachments' - -export interface ChatPanelProps - extends Pick< - ReturnType, - | 'generating' - | 'input' - | 'setInput' - | 'sendMessage' - | 'resetConversation' - | 'isSpeaking' - | 'attachmentList' - | 'uploadImage' - | 'setAttachmentList' - > { - id?: string - className?: string -} - -export function ChatPanel({ - isSpeaking, - generating, - input, - setInput, - className, - sendMessage, - resetConversation, - attachmentList, - uploadImage, - setAttachmentList -}: ChatPanelProps) { - const inputRef = React.useRef (null) - const {formRef, onKeyDown} = useEnterSubmit() - const [focused, setFocused] = React.useState(false) - const [active, setActive] = React.useState(false) - const [pin, setPin] = React.useState(false) - const [tid, setTid] = React.useState () - const voiceListening = useAtomValue(voiceListenAtom) - - const setBlur = React.useCallback(() => { - clearTimeout(tid) - setActive(false) - const _tid = setTimeout(() => setFocused(false), 2000); - setTid(_tid) - }, [tid]) - - const setFocus = React.useCallback(() => { - setFocused(true) - setActive(true) - clearTimeout(tid) - inputRef.current?.focus() - }, [tid]) - - React.useEffect(() => { - if (input) { - setFocus() - } - }, [input]) - - return ( -