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-This page is about Aurora 3D Templates,contains Aurora Printable Disney ... 3D Presentation 16.01.07 Multilingual Full Keygen,Aurora 3D Text & Logo Maker and more... ... Aurora 3D Text & Logo Maker 20.01.30 + Crack. Aurora 3D Templates Aurora 3D Text & Logo Maker - Free download and software reviews - CNETÂ ... 1fdad05405
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Elysium 720p In Dual Audio Hindi [REPACK].md b/spaces/1gistliPinn/ChatGPT4/Examples/Elysium 720p In Dual Audio Hindi [REPACK].md
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Elysium: A Dystopian Sci-Fi Thriller in Hindi
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Elysium is a 2013 American dystopian science fiction action film written, produced, and directed by Neill Blomkamp. It was Blomkamp's second directorial effort. The film stars Matt Damon and Jodie Foster alongside Sharlto Copley, Alice Braga, Diego Luna, Wagner Moura, and William Fichtner. [^2^]
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The film takes place on both a ravaged Earth and a luxurious artificial world called Elysium. The planetâs citizens live in poverty while the rich and powerful live on Elysium, an orbiting space station just outside of Earthâs atmosphere. Spider, a hacker living on Earth in Los Angeles, runs three space shuttle flights to Elysium to smuggle people in to use their Med-Bays, devices that can heal any disease or condition. Elysium Defense Secretary Delacourt shoots down two of the spacecraft in space, killing everyone on board, and has everyone on the shuttle that does reach Elysium arrested and deported. Elysium President Patel reprimands Delacourt for her actions, threatening discharge for any more actions of a similar manner. In retaliation, she offers Armadyne Corp CEO John Carlyle defense contracts for life in exchange for a program that will allow Delacourt to conduct a coup and install herself as president. Carlyle writes the program and stores it inside his brain. [^2^]
On Earth, parolee Max Da Costa is working for Armadyne Corp as a laborer when he is accidentally exposed to a lethal dose of radiation. He is given medication and told he has five days to live after being dismissed by Carlyle from Armadyne. Max and his friend Julio approach Spider and make a deal: If Max can successfully steal information from a powerful Elysium citizen, in exchange Spider will give Max a shuttle ride to Elysium to use a Med-Bay to cure his condition. Max agrees to steal the information from Carlyle's brain, unaware that it contains the coup program. [^2^]
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When Delacourt learns that the information she needs to become president was stolen from Carlyle's brain, she sends the notorious agent Kruger to hunt down Max and recover the software at any cost. Max must fight for his life and his chance to reach Elysium before it's too late. [^2^]
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Elysium is a film that offers deliberate social commentary that explores political and sociological themes such as immigration, overpopulation, transhumanism, health care, worker exploitation, the justice system, technology, and social class issues. [^2^] It received positive reviews from critics but was considered disappointing after Blomkamp's first film District 9. [^2^] It grossed $286 million worldwide and was released on DVD and Blu-ray on December 17, 2013. [^2^]
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If you are looking for a thrilling and thought-provoking sci-fi film with amazing visuals and action scenes, you should watch Elysium in Hindi dubbed dual audio 720p quality. You can download it from this link [^1^] or watch it online on various streaming platforms.
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-August 14, 2020 - IBM Lotus Domino Server 8.5.3 64-bit. Note that Lotus Domino 8.5 was a 64-bit version of the application, and the internal data structures are . 1 byte.
-There are currently 64-bit versions available in the environment, so we cannot use 64-bit types and data structures, but we can use 32-bit types and data structures such as int32 which are .4 bytes in size.
-How can I tell which version is using 64-bit or 32-bit frameworks and use 64-bit versions to work with 64-bit applications, and also use 32-bit versions to work with 32-bit applications?
-Gratitude 8a78ff9644
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Android Gameplay How to Sync Your Progress and Earn Rewards Across Devices.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Android Gameplay How to Sync Your Progress and Earn Rewards Across Devices.md
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Android Gameplay: A Guide for Gamers and Developers
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Android gameplay is also popular because it offers a wide range of games in different genres, catering to different tastes and preferences. Whether you are looking for adventure, arcade, puzzle, racing, strategy, or any other type of game, you can find it on Android. Here are some of the best Android games in different genres that you can try out:
Adventure games are games that involve exploration, story, puzzles, and interaction with characters and environments. Adventure games often have immersive graphics, sound effects, and voice acting that create a rich and engaging experience. Some of the best adventure games on Android are:
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The Room series: The Room is a series of puzzle games that challenge you to solve mysterious and intricate contraptions in a dark and atmospheric setting. The games have stunning graphics, realistic physics, and captivating stories that will keep you hooked. The latest game in the series is The Room: Old Sins, which takes you to a haunted dollhouse where you must uncover the secrets of a missing engineer and his wife.
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Lara Croft GO: Lara Croft GO is a turn-based puzzle game that follows the iconic heroine as she explores ancient ruins and faces deadly enemies. The game has beautiful visuals, clever puzzles, and a relaxing soundtrack that make it a perfect game for casual gamers. You can also unlock new outfits and collectibles as you progress through the game.
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Life is Strange: Life is Strange is a choice-based adventure game that tells the story of Max Caulfield, a teenage girl who discovers that she can rewind time and change the course of events. The game has a compelling plot, realistic characters, and multiple endings that depend on your choices. You can also use your camera to take photos and collect memories along the way.
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Arcade Games
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Arcade games are games that are fast-paced, simple, and addictive. Arcade games usually have simple controls, high scores, and endless levels that test your reflexes and skills. Some of the best arcade games on Android are:
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Subway Surfers: Subway Surfers is an endless runner game that has you running away from the police on a subway track. You can swipe left or right to dodge obstacles, jump or roll to avoid trains, and collect coins and power-ups to boost your score. You can also customize your character and unlock new hoverboards and jetpacks.
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Fruit Ninja: Fruit Ninja is a classic arcade game that has you slicing fruits with your finger as they fly across the screen. You can play in different modes, such as Classic, Arcade, Zen, or Online Multiplayer, and use different blades and dojos to enhance your gameplay. You can also unlock achievements and compete with your friends on the leaderboards.
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Angry Birds 2: Angry Birds 2 is the sequel to the popular arcade game that has you launching birds at pigs using a slingshot. The game has improved graphics, new levels, new birds, new powers, and new challenges that make it more fun and exciting than ever. You can also join clans and play with other players online.
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Puzzle Games
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Puzzle games are games that require logic, strategy, and problem-solving skills. Puzzle games often have relaxing graphics, sound effects, and music that create a soothing and satisfying experience. Some of the best puzzle games on Android are:
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Monument Valley 2: Monument Valley 2 is a beautiful puzzle game that has you guiding a mother and her child through a world of impossible architecture and optical illusions. The game has stunning visuals, enchanting music, and clever puzzles that will challenge your perception and imagination.
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Candy Crush Saga: Candy Crush Saga is a popular puzzle game that has you matching candies of the same color to clear them from the board. The game has hundreds of levels, each with different goals and obstacles. You can also use boosters and power-ups to help you along the way. You can also play with your friends and compete for the highest score.
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2048: 2048 is a simple but addictive puzzle game that has you sliding tiles with numbers on them to combine them and create larger numbers. The game ends when you reach the 2048 tile or when there are no more moves left. You can also try different modes and variations of the game, such as 4x4, 5x5, 6x6, or Fibonacci.
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Racing Games
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Racing games are games that involve driving or riding vehicles at high speeds and competing with other racers or against the clock. Racing games often have realistic graphics, sound effects, and physics that create a thrilling and immersive experience. Some of the best racing games on Android are:
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Asphalt 9: Legends: Asphalt 9: Legends is a stunning racing game that has you driving some of the most prestigious cars in the world on exotic locations. The game has amazing graphics, smooth controls, and a variety of modes and events. You can also customize your cars, join clubs, and play with other players online.
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Mario Kart Tour: Mario Kart Tour is a fun racing game that features characters and tracks from the Mario franchise. The game has colorful graphics, catchy music, and easy controls. You can also use items and power-ups to boost your speed or hinder your opponents. You can also play with your friends and compete in rankings and tournaments.
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Real Racing 3: Real Racing 3 is a realistic racing game that has you driving some of the most authentic cars on real tracks around the world. The game has impressive graphics, realistic physics, and a variety of modes and challenges. You can also upgrade your cars, join teams, and play with other players online.
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Strategy Games
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Strategy games are games that involve planning, decision-making, and resource management. Strategy games often have complex and challenging gameplay that require tactical thinking and long-term vision. Some of the best strategy games on Android are:
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Clash of Clans: Clash of Clans is a popular strategy game that has you building and defending your own village from other players. You can also join clans, train troops, and attack other villages to loot resources and trophies. You can also participate in clan wars, events, and seasons to earn rewards and bonuses.
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Plants vs. Zombies 2: Plants vs. Zombies 2 is a fun strategy game that has you planting and growing plants to fend off waves of zombies. The game has colorful graphics, humorous characters, and a variety of modes and levels. You can also collect and upgrade your plants, travel through time and space, and face boss battles.
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Civilization VI: Civilization VI is a classic strategy game that has you leading a civilization from the ancient to the modern era. You can choose from different leaders, cultures, and policies to shape your civilization's history and destiny. You can also explore, expand, exploit, and exterminate other civilizations on a randomly generated map.
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Other Genres
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Of course, there are many other genres of games that you can enjoy on Android, such as role-playing, simulation, sports, trivia, word, and more. You can browse the Google Play Store to discover new and trending games in different categories. You can also check out some of the best Android games of 2023 according to TechRadar and PCMag.
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The Best Android Gaming Tips and Tricks
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Now that you have some ideas of what games to play on Android, you might want to know some tips and tricks to enhance your gaming experience. Here are some of the best Android gaming tips and tricks that you can use:
Android has some digital wellbeing features that can help you manage your screen time and avoid distractions while gaming. For example, you can use Focus mode to pause notifications from certain apps while you play. You can also use Bedtime mode to dim your screen and mute sounds at night. You can access these features from the Settings app or the Quick Settings panel.
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How to use voice search and commands
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Android has a built-in voice assistant called Google Assistant that can help you search for games, launch apps, control settings, and more with your voice. You can activate Google Assistant by saying "Hey Google" or by tapping the microphone icon on the search bar or the home screen. You can then ask Google Assistant questions or give commands related to gaming, such as "What are some good racing games?" or "Turn on Do Not Disturb mode".
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How to uninstall unwanted apps
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How to sync your progress across devices
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If you have more than one Android device, you might want to sync your game progress across them so that you can continue playing where you left off. You can do this by using Google Play Games, a service that lets you save your game data, achievements, and leaderboards online. You can sign in to Google Play Games with your Google account and enable cloud save for the games that support it. You can also use Google Play Games to play with other players online and discover new games.
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How to earn rewards with Google Play Points
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Google Play Points is a program that rewards you for using the Google Play Store. You can earn points by downloading and playing games, making in-app purchases, subscribing to services, and more. You can then redeem your points for rewards, such as discounts, coupons, free apps, and more. You can also use your points to support causes that you care about. You can join Google Play Points for free and start earning points today.
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The Mobile Gaming Market Statistics and Trends
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Android gameplay is not only fun and exciting, but also lucrative and influential. The mobile gaming market is one of the fastest-growing and most profitable segments of the gaming industry. Here are some of the mobile gaming market statistics and trends that you should know:
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The global and U.S. revenue and user numbers
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According to Statista, the global mobile gaming market generated $86.3 billion in revenue in 2020, accounting for 49% of the total gaming market. The number of mobile gamers worldwide reached 2.7 billion in 2020, representing 34% of the global population. The U.S. mobile gaming market generated $13.9 billion in revenue in 2020, ranking second after China. The number of mobile gamers in the U.S. reached 203 million in 2020, representing 61% of the U.S. population.
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The most downloaded and highest-grossing apps
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According to Statista, the most-downloaded Android gaming app worldwide in September 2022 was Garena Free Fire, with 63 million downloads. The second-most downloaded app was Subway Surfers, with 40 million downloads, followed by Among Us, with 38 million downloads. The highest-grossing Android gaming app worldwide in September 2022 was Honor of Kings, with $240 million in revenue. The second-highest grossing app was PUBG Mobile, with $198 million in revenue, followed by Genshin Impact, with $156 million in revenue.
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The most popular genres and platforms
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The future projections and opportunities
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According to Statista, the global mobile gaming market is expected to grow to $116.4 billion in revenue by 2024, with a compound annual growth rate of 7.7%. The number of mobile gamers worldwide is expected to reach 3.1 billion by 2024, with a compound annual growth rate of 3.6%. The U.S. mobile gaming market is expected to grow to $18.8 billion in revenue by 2024, with a compound annual growth rate of 7.8%. The number of mobile gamers in the U.S. is expected to reach 222 million by 2024, with a compound annual growth rate of 2.3%.
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The mobile gaming market offers many opportunities for both gamers and developers, as technology, innovation, and creativity continue to evolve and improve. Some of the trends and opportunities that are shaping the future of mobile gaming are:
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Cloud gaming: Cloud gaming is a service that allows gamers to stream games from remote servers without downloading or installing them on their devices. Cloud gaming enables gamers to access high-quality games on any device, regardless of their hardware specifications or storage capacity. Cloud gaming also reduces the cost and complexity of game development and distribution for developers. Some of the cloud gaming services that are available or in development are Google Stadia, Microsoft xCloud, Amazon Luna, and Nvidia GeForce Now.
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5G technology: 5G technology is the next generation of wireless communication that offers faster speed, lower latency, higher bandwidth, and more reliability than 4G technology. 5G technology enables gamers to enjoy smoother and more immersive gameplay, especially for online multiplayer and cloud gaming. 5G technology also allows developers to create more complex and realistic games that can leverage the full potential of mobile devices.
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Augmented reality and virtual reality: Augmented reality (AR) and virtual reality (VR) are technologies that create interactive and immersive experiences by overlaying digital elements on the real world or creating a simulated environment. AR and VR enable gamers to experience games in a new and exciting way, as they can interact with their surroundings and feel more immersed in the game world. AR and VR also offer new possibilities for game design and storytelling for developers. Some of the AR and VR games that are available or in development are Pokemon Go, Harry Potter: Wizards Unite, Minecraft Earth, Beat Saber, Half-Life: Alyx, and The Walking Dead: Saints & Sinners.
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Conclusion
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Android gameplay is a fascinating and diverse topic that covers many aspects of gaming on Android devices. Android gameplay offers many benefits for both gamers and developers, such as entertainment, challenge, learning, creativity, market potential, platform flexibility, community support, and more. Android gameplay also offers a wide range of games in different genres, such as adventure, arcade, puzzle, racing, strategy, and more. Android gameplay also has some tips and tricks that can enhance your gaming experience, such as using digital wellbeing features, voice search and commands, uninstalling unwanted apps, syncing your progress across devices, and earning rewards with Google Play Points. Android gameplay also has some statistics and trends that show its growth and popularity in the global and U.S. markets, as well as its future projections and opportunities in terms of technology, innovation, and creativity.
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If you are interested in android gameplay, you can try out some of the games that we have recommended in this article or explore other games on the Google Play Store. You can also use Google Play Games to save your game data online, play with other players online, and discover new games. You can also use Google Assistant to search for games, launch apps, control settings, and more with your voice.
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If you are a developer or aspiring to be one, you can use Android Studio to create your own games for Android devices. You can also use Firebase to add features such as authentication, database, storage, analytics, and more to your games. You can also use Google Play Console to publish your games on the Google Play Store and reach millions of users worldwide.
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We hope that this article has given you some useful information and insights about android gameplay. We also hope that you have enjoyed reading it as much as we have enjoyed writing it. Thank you for your time and attention.
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Frequently Asked Questions
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Here are some frequently asked questions about android gameplay that you might find helpful:
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What are some of the advantages of android gameplay over other platforms?
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Some of the advantages of android gameplay over other platforms are:
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Android devices have a larger and more diverse user base than other devices
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Android devices have a more open and flexible platform than other devices
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Android devices have a more diverse and dynamic range of games than other devices
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Android devices have more features and functions that can enhance gaming than other devices
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What are some of the challenges or drawbacks of android gameplay?
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Some of the challenges or drawbacks of android gameplay are:
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Android devices have a lower performance and battery life than other devices
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Android devices have a higher risk of malware and security issues than other devices
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Android devices have a more fragmented and inconsistent platform than other devices
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Android devices have a more competitive and saturated market than other devices
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How can I improve my android gameplay experience?
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Some of the ways that you can improve your android gameplay experience are:
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Choose games that are compatible and optimized for your device model and specifications
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Update your device software and apps regularly to ensure smooth and secure performance
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Clear your device storage and cache frequently to free up space and speed up your device
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Adjust your device settings and preferences to suit your gaming needs and preferences
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Use accessories such as headphones, controllers, stands, chargers, and more to enhance your gaming comfort and convenience
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How can I learn more about android gameplay?
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Some of the ways that you can learn more about android gameplay are:
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Read blogs, magazines, reviews, guides, and news about android gameplay online or offline
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Watch videos, podcasts, streams, tutorials, and demos about android gameplay online or offline
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Join forums, communities, groups, and events about android gameplay online or offline
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Ask questions, share opinions, give feedback, and exchange tips about android gameplay online or offline
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Play games, experiment with features, explore genres, and discover new games on Android
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What are some of the best resources for android gameplay?
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Some of the best resources for android gameplay are:
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The Google Play Store: The Google Play Store is the official app store for Android devices that offers millions of games in different categories. You can browse, download, update, rate, review, and share games on the Google Play Store. You can also access Google Play Games, Google Play Points, Google Play Pass, and Google Play Protect on the Google Play Store.
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The Android Developers website: The Android Developers website is the official website for Android developers that offers tools, documentation, guides, tutorials, courses, and more for creating games for Android devices. You can access Android Studio, Firebase, Google Play Console, Google Play Services, and more on the Android Developers website.
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The Android Authority website: The Android Authority website is one of the leading websites for Android news, reviews, tips, tricks, and more. You can find articles, videos, podcasts, newsletters, deals, and more about android gameplay on the Android Authority website.
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Bloons TD 6 APK: A Fun and Challenging Tower Defense Game
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If you are a fan of tower defense games, you might have heard of Bloons TD 6, the latest installment in the popular Bloons TD series. But did you know that you can download and play Bloons TD 6 APK on your device for free? In this article, we will tell you everything you need to know about Bloons TD 6 APK, including what it is, how to download and install it, how to play and enjoy it, and more. Read on to find out more!
Bloons TD 6 is a 3D tower defense game developed and published by Ninja Kiwi, a New Zealand-based game studio. It was released in June 2018 for iOS, Android, Windows, and Mac platforms. It is the sixth main game in the Bloons TD series, which has been around since 2007.
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The history and features of the Bloons TD series
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The Bloons TD series is one of the most popular and successful tower defense franchises in the world. It has over a billion downloads across all platforms, and has received positive reviews from critics and players alike. The series is known for its colorful graphics, humorous animations, addictive gameplay, and diverse content.
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The premise of the series is simple: you have to stop the invading balloons (called bloons) from reaching the end of the path by placing various monkey towers along the way. Each monkey tower has its own unique abilities, upgrades, and activated powers that can help you pop the bloons. The bloons come in different types, colors, and sizes, each with their own characteristics and resistances. Some bloons can even contain other bloons inside them, making them harder to pop.
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The series has evolved over time, adding new features and improvements with each game. Some of the notable features include:
Co-op mode: You can team up with up to three other players online or locally to play any map or mode together.
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Heroes: You can choose from a roster of 14 heroes, each with their own personality, voiceover, signature upgrades, and special abilities. Heroes level up automatically during the game, making them more powerful as you progress.
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Monkey Knowledge: You can unlock over 100 meta-upgrades that add passive bonuses to your monkey towers or gameplay mechanics.
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Trophy Store: You can earn trophies by completing various achievements or events, and use them to buy cosmetic items that customize your monkeys, bloons, animations, music, and more.
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Content Browser: You can create your own challenges and odysseys using various settings and modifiers, and share them with other players online. You can also browse and play the most liked and played community content.
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The gameplay and content of Bloons TD 6
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Bloons TD 6 offers a huge amount of gameplay and content for players of all skill levels and preferences. The game has over 60 handcrafted maps, each with their own theme, layout, difficulty, and special rules. The maps range from easy to expert, and some of them have alternate versions that change the bloon spawns or tower placements.
Continuing the article:
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The advantages and disadvantages of downloading the APK version
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Bloons TD 6 is a premium game that costs $6.99 on the official app stores. However, you can also download and play Bloons TD 6 APK for free from various third-party sources. APK stands for Android Package Kit, and it is the file format used by Android devices to install and distribute apps. By downloading Bloons TD 6 APK, you can enjoy the game without paying anything.
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However, there are also some drawbacks and risks of using Bloons TD 6 APK. Here are some of them:
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You may not get the latest updates and features of the game, as the APK version may not be updated as frequently as the official version.
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You may encounter compatibility issues or bugs that affect the performance or stability of the game.
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You may not be able to access some online features or modes of the game, such as co-op, boss events, odysseys, or content browser.
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You may violate the terms of service or privacy policy of Ninja Kiwi, and risk getting banned or suspended from the game.
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You may expose your device to malware or viruses that can harm your data or security.
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Therefore, you should weigh the pros and cons of downloading Bloons TD 6 APK before deciding to do so. You should also make sure that you download Bloons TD 6 APK from a reputable and trustworthy source, and scan it with an antivirus software before installing it.
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How to download and install Bloons TD 6 APK on your device?
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If you have decided to download and install Bloons TD 6 APK on your device, you will need to follow some steps to do so. The steps may vary depending on whether you are using an Android device or a PC. Here are the steps for both platforms:
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The steps to download and install Bloons TD 6 APK on Android
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Go to a reliable website that offers Bloons TD 6 APK, such as . Make sure that the website is safe and secure, and that the APK file is compatible with your device.
-
Tap on the download button to start downloading Bloons TD 6 APK. You may need to allow downloads from unknown sources in your device settings.
-
Once the download is complete, locate the APK file in your device storage and tap on it to start installing it. You may need to grant some permissions for the app to run properly.
-
Wait for the installation to finish, and then launch Bloons TD 6 from your app drawer or home screen. Enjoy!
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The steps to download and install Bloons TD 6 APK on PC
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Go to a reliable website that offers Bloons TD 6 APK, such as . Make sure that the website is safe and secure, and that the APK file is compatible with your PC.
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Click on the download button to start downloading Bloons TD 6 APK. You may need to save it in a folder where you can easily find it later.
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Download and install an Android emulator on your PC, such as BlueStacks, NoxPlayer, or LDPlayer. An Android emulator is a software that allows you to run Android apps on your PC.
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Launch the Android emulator and sign in with your Google account. You may need to create one if you don't have one already.
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Drag and drop the Bloons TD 6 APK file into the emulator window, or use the built-in browser to locate and install it. You may need to allow installations from unknown sources in the emulator settings.
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Wait for the installation to finish, and then launch Bloons TD 6 from the emulator app drawer or home screen. Enjoy!
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The precautions and risks of using Bloons TD 6 APK
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As mentioned earlier, using Bloons TD 6 APK comes with some potential dangers and disadvantages. Therefore, you should take some precautions and be aware of the risks before playing Bloons TD 6 APK. Here are some tips to help you:
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Always backup your data before installing or updating Bloons TD 6 APK. You can use a cloud service or an external storage device to do so.
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Always scan Bloons TD 6 APK with an antivirus software before installing it. You can use a reputable antivirus app on your device or PC, such as Avast, McAfee, or Kaspersky.
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Always check the reviews and ratings of Bloons TD 6 APK on the website where you download
Continuing the article:
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Always check the reviews and ratings of Bloons TD 6 APK on the website where you download it. You can also read the comments and feedback from other users who have tried it. This can help you avoid fake or malicious APK files.
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Always update Bloons TD 6 APK whenever a new version is available. This can help you fix any bugs or glitches, and enjoy the latest features and content of the game.
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Always respect the rights and policies of Ninja Kiwi, the developer and publisher of Bloons TD 6. Do not use Bloons TD 6 APK to cheat, hack, or exploit the game. Do not distribute or share Bloons TD 6 APK without permission. Do not claim ownership or credit for Bloons TD 6 APK.
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By following these tips, you can reduce the risks and enhance the experience of playing Bloons TD 6 APK. However, you should still be careful and responsible when using Bloons TD 6 APK, as there is no guarantee that it will work perfectly or safely on your device or PC.
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How to play and enjoy Bloons TD 6?
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Bloons TD 6 is a fun and challenging tower defense game that can keep you entertained for hours. Whether you are a beginner or an expert, you can find something to suit your taste and skill level in Bloons TD 6. Here are some tips on how to play and enjoy Bloons TD 6:
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The basic tips and tricks for beginners
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If you are new to Bloons TD 6, you may want to start with the tutorial mode, which will teach you the basics of the game, such as how to place towers, upgrade them, use powers, and pop bloons. You can also play the easy maps and modes first, to get familiar with the game mechanics and strategies.
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Here are some basic tips and tricks for beginners:
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Try different combinations of monkey towers and heroes, and see what works best for you. Each tower and hero has its own strengths and weaknesses, and can synergize well with others.
-
Upgrade your monkey towers wisely, and don't spend all your money on one tower. You can choose from three upgrade paths for each tower, each with five tiers of upgrades. The higher tiers are more expensive but more powerful.
-
Use your activated powers and abilities sparingly, and save them for when you really need them. Activated powers are consumable items that can give you an edge in the game, such as extra lives, cash, or damage. Abilities are special skills that your towers or heroes can use once they reach a certain level.
-
Pop as many bloons as possible, and don't let them escape. Each bloon that escapes will cost you one life (or more, depending on the bloon type). If you lose all your lives, you will lose the game.
-
Have fun and experiment with different settings and modifiers. You can change the game speed, difficulty, mode, map, and more to suit your preference and challenge yourself.
-
-
The best strategies and builds for advanced players
-
If you are an experienced player of Bloons TD 6, you may want to try some of the harder maps and modes, such as impoppable, chimps, or expert. These modes will test your skills and knowledge of the game, and require you to use more advanced strategies and builds.
-
Here are some of the best strategies and builds for advanced players:
-
-
Use monkey knowledge points to unlock meta-upgrades that can boost your performance in the game. Monkey knowledge points are earned by leveling up in the game, and can be spent on various categories of upgrades, such as primary, military, magic, support, powers, heroes, or balance.
-
Use monkey money to buy premium items that can enhance your gameplay experience. Monkey money is earned by completing maps or achievements in the game, and can be used to buy skins, insta-monkeys, powers, heroes, or knowledge respecs.
-
Use trophies to buy cosmetic items that can customize your appearance in the game. Trophies are earned by completing events or challenges in the game, and can be used to buy decals,
Continuing the article:
-
Use trophies to buy cosmetic items that can customize your appearance in the game. Trophies are earned by completing events or challenges in the game, and can be used to buy decals, portraits, music, sound effects, and more.
-
Use the alchemist tower to buff your other towers with powerful potions. The alchemist tower can apply various effects to nearby towers, such as increased damage, range, pierce, attack speed, or cash per pop. The alchemist tower is especially effective with fast-firing or multi-shot towers, such as the ninja, dartling gunner, or super monkey.
-
Use the perma-spike tower to create a reliable backup defense. The perma-spike tower can produce spikes that last until they are used up, and can deal massive damage to bloons. The perma-spike tower is especially useful for dealing with late-game bloons, such as DDTs, ZOMGs, or BADs.
-
Use the sun avatar tower to unleash devastating beams of plasma. The sun avatar tower is one of the most powerful towers in the game, capable of popping almost any type of bloon with ease. The sun avatar tower can also be upgraded to the sun temple or the true sun god, which are even more powerful and can affect other towers in their range.
-
Use the banana farm tower to generate extra income. The banana farm tower can produce bananas that can be collected for cash, or automatically deposited into your account. The banana farm tower can also be upgraded to increase its production rate, value, or efficiency.
-
-
The fun and creative modes and challenges for everyone
-
Bloons TD 6 is not only a challenging game, but also a fun and creative one. You can play various modes and challenges that can spice up your gameplay and test your skills in different ways. Here are some of the fun and creative modes and challenges for everyone:
-
-
Sandbox mode: You can create your own scenarios and experiments using unlimited money, lives, towers, bloons, and powers. You can also modify the bloon properties, such as speed, health, immunity, or regrowth. Sandbox mode is a great way to test your strategies, learn new things, or just have fun.
-
Races mode: You can compete with other players online to see who can complete a map or a challenge faster. You can also create your own races and share them with others. Races mode is a great way to challenge yourself, improve your skills, or show off your achievements.
-
Daily challenges: You can play a new challenge every day that has different settings and modifiers. You can also vote for the next daily challenge from a list of options. Daily challenges are a great way to discover new possibilities, earn rewards, or join the community.
-
Advanced challenges: You can play a special challenge every week that has more difficult settings and modifiers. You can also submit your own advanced challenges for others to play. Advanced challenges are a great way to push your limits, earn trophies, or showcase your creativity.
-
Achievements: You can complete various achievements that have different objectives and criteria. You can also view your progress and statistics in the game. Achievements are a great way to track your goals, earn monkey money, or unlock new items.
-
-
Conclusion
-
Bloons TD 6 is a fun and challenging tower defense game that has something for everyone. Whether you want to pop some bloons, build some towers, or create some challenges, you can do it all in Bloons TD 6. You can also download and play Bloons TD 6 APK for free from various sources online.
-
However, you should also be careful and responsible when using Bloons TD 6 APK, as there are some risks and drawbacks involved. You should always backup your data, scan your APK file,
Continuing the article:
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However, you should also be careful and responsible when using Bloons TD 6 APK, as there are some risks and drawbacks involved. You should always backup your data, scan your APK file, check the reviews and ratings, update your APK version, and respect the rights and policies of Ninja Kiwi. By doing so, you can reduce the dangers and enhance the enjoyment of playing Bloons TD 6 APK.
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Bloons TD 6 is a game that can provide you with hours of fun and challenge. Whether you play it on your device or PC, with the official version or the APK version, you can experience the thrill and excitement of popping bloons and building towers. So what are you waiting for? Download Bloons TD 6 APK today and join the monkey madness!
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FAQs
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Here are some of the frequently asked questions about Bloons TD 6 APK:
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Q: Is Bloons TD 6 APK safe to use?
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A: Bloons TD 6 APK is safe to use as long as you download it from a reputable and trustworthy source, and scan it with an antivirus software before installing it. However, there is no guarantee that Bloons TD 6 APK will work perfectly or safely on your device or PC, so use it at your own risk.
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Q: Is Bloons TD 6 APK legal to use?
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A: Bloons TD 6 APK is not legal to use, as it violates the terms of service and privacy policy of Ninja Kiwi, the developer and publisher of Bloons TD 6. By using Bloons TD 6 APK, you are infringing on the intellectual property rights of Ninja Kiwi, and risk getting banned or suspended from the game.
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Q: Is Bloons TD 6 APK free to use?
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A: Bloons TD 6 APK is free to use, as you do not have to pay anything to download or play it. However, you may not get the full features and content of the game, as the APK version may not be updated as frequently as the official version. You may also encounter compatibility issues or bugs that affect the performance or stability of the game.
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Q: How to update Bloons TD 6 APK?
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A: To update Bloons TD 6 APK, you will need to download and install the latest version of the APK file from a reliable website. You may need to uninstall the previous version of Bloons TD 6 APK before installing the new one. You may also need to backup your data before updating Bloons TD 6 APK, as you may lose your progress or settings.
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Q: How to uninstall Bloons TD 6 APK?
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A: To uninstall Bloons TD 6 APK, you will need to delete the APK file from your device or PC storage. You may also need to remove any residual files or folders related to Bloons TD 6 APK. You may also need to restore your device or PC settings to their original state.
-
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Boss Domino APK A Local Indonesian Game with Various Slots.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Boss Domino APK A Local Indonesian Game with Various Slots.md
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--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Boss Domino APK A Local Indonesian Game with Various Slots.md
+++ /dev/null
@@ -1,132 +0,0 @@
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-
Boss Domino APK: A Fun and Exciting Casino Game for Android
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If you are looking for a casino game that offers a variety of local Indonesian games, such as Domino Gaple, Domino Qiu Qiu, and various slots, then you should try Boss Domino APK. Boss Domino APK is a free Android game that lets you play casino games online with your friends or other players. You can also win free coins, bonuses, jackpots, and prizes by playing Boss Domino APK. In this article, we will tell you everything you need to know about Boss Domino APK, including what it is, how to download and install it, how to play it, and why you should play it.
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What is Boss Domino APK?
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Boss Domino APK is an Android game that provides local Indonesian casino games, such as Domino Gaple , Domino Qiu Qiu, and various slots. Domino Gaple is a popular card game in Indonesia that is similar to poker, but uses domino tiles instead of cards. Domino Qiu Qiu is another domino game that is also known as 99 or Kiu Kiu, and involves forming the best combination of four tiles out of six tiles in your hand. Slots are games of chance that involve spinning reels and matching symbols to win prizes.
Boss Domino APK is not only a game, but also a social platform where you can chat with other players, send emoticons, and make friends. You can also join clubs, participate in tournaments, and rank on the leaderboard. Boss Domino APK is designed with attractive graphics, sound effects, and animations that make the gameplay more enjoyable and realistic. You can also customize your avatar, profile, and game settings according to your preferences.
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How to download and install Boss Domino APK?
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Boss Domino APK is not available on the official Google Play Store, but you can still download and install it from other sources. There are two ways to download and install Boss Domino APK: from APKCombo or from Google Play Store using a VPN.
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Steps to download Boss Domino APK from APKCombo
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APKCombo is a website that provides APK and XAPK files for Android apps and games. You can download Boss Domino APK from APKCombo by following these steps:
Choose the latest version of Boss Domino APK and download the XAPK file to your device.
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Install the XAPK file using APKCombo Installer, which is an app that can extract and install XAPK files.
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Open Boss Domino APK and enjoy playing.
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Steps to download Boss Domino APK from Google Play Store
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If you prefer to download Boss Domino APK from Google Play Store, you will need to use a VPN app that can change your location to Indonesia. This is because Boss Domino APK is only available in Indonesia on Google Play Store. You can download Boss Domino APK from Google Play Store using a VPN by following these steps:
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-
Open the VPN app on your Android device and connect to an Indonesian server.
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Open the Google Play Store app on your Android device and search for Boss Domino in the search bar.
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Tap on the Boss Domino icon and then tap on Install.
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Wait for the installation to complete and then open Boss Domino APK.
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Disconnect the VPN app and enjoy playing.
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How to play Boss Domino APK?
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Boss Domino APK offers three types of casino games: Domino Gaple, Domino Qiu Qiu, and slots. Each game has its own rules, tips, and features that you need to know before playing. Here are some basic guidelines for playing each game:
-
Rules and tips for playing Domino Gaple
-
Domino Gaple is a card game that uses domino tiles instead of cards. The objective of the game is to get rid of all your tiles before your opponents. Here are some rules and tips for playing Domino Gaple:
-
-
Each player starts with seven tiles and one tile is placed on the table as the starter.
-
On your turn, you can either place a tile that matches one of the ends of the chain on the table, or draw a tile from the stock if you have no matching tiles.
-
The game ends when one player runs out of tiles or when the stock is empty and no one can make a move.
-
The player with the lowest total value of tiles in their hand wins the game.
-
You can use strategies such as blocking, bluffing, or counting tiles to gain an advantage over your opponents.
-
-
Rules and tips for playing Domino Qiu Qiu
-
Domino Qiu Qiu is another domino game that is also known as 99 or Kiu Kiu. The objective of the game is to form the best combination of four tiles out of six tiles in your hand. Here are some rules and tips for playing Domino Qiu Qiu:
-
-
Each player starts with six tiles and four rounds of betting take place.
-
On each round, you can either check, raise, call, fold, or all-in depending on your tiles and the bets of other players.
-
The game ends when one player wins all the chips or when all players except one fold.
-
The ranking of tile combinations is as follows: six gods > four balak > pure big > pure small > three doubles > two doubles > one double > high card.
-
You can use strategies such as bluffing, folding, or all-in to influence the bets of other players.
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-
Rules and tips for playing slot games
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Slots are games of chance that involve spinning reels and matching symbols to win prizes. The objective of the game is to match symbols on the reels and win prizes. Here are some rules and tips for playing slot games:
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Each slot game has different themes, paylines, symbols, and features.
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You can adjust your bet size and number of paylines before spinning the reels.
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You can also use auto spin, turbo spin, or max bet options to speed up the gameplay.
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You can win free spins, bonus games, jackpots, or multipliers depending on the slot game.
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You can use strategies such as choosing the right slot game, managing your bankroll, or betting wisely to increase your chances of winning.
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Why should you play Boss Domino APK?
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Boss Domino APK is not just a game, but also a fun and exciting way to spend your time. There are many benefits of playing Boss Domino APK, such as:
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Benefits of playing Boss Domino APK
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You can enjoy various casino games in one app.
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You can play with your friends or other players online.
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You can improve your skills and strategies in domino games.
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You can have fun and relax with colorful graphics and sound effects.
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You can win real money or prizes by playing slot games.
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-
Conclusion
-
Boss Domino APK is a free Android game that offers a variety of local Indonesian casino games, such as Domino Gaple, Domino Qiu Qiu, and various slots. You can download and install Boss Domino APK from APKCombo or Google Play Store using a VPN. You can also play Boss Domino APK online with your friends or other players. You can also win free coins, bonuses, jackpots, and prizes by playing Boss Domino APK. Boss Domino APK is a fun and exciting casino game that you should try if you love casino games.
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FAQs
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What is the difference between APK and XAPK files?
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APK files are Android application packages that contain the app code and resources. XAPK files are extended APK files that contain additional data such as OBB files or split APKs.
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How can I get more free coins and bonuses in Boss Domino APK?
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You can get more free coins and bonuses in Boss Domino APK by logging in daily, completing tasks, inviting friends, joining clubs, participating in tournaments, or watching ads.
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Is Boss Domino APK safe to download and play?
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Boss Domino APK is safe to download and play as long as you download it from a trusted source such as APKCombo or Google Play Store using a VPN. You should also avoid using any modded or hacked versions of Boss Domino APK as they may contain viruses or malware.
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Can I play Boss Domino APK offline?
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No, you cannot play Boss Domino APK offline as it requires an internet connection to access the online features and games. You can only play Boss Domino APK online with your friends or other players.
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Can I play Boss Domino APK on PC?
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Yes, you can play Boss Domino APK on PC by using an Android emulator such as BlueStacks or NoxPlayer. You can download and install the emulator on your PC and then download and install Boss Domino APK from the emulator's app store.
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car Parking Multiplayer APK A Free Game with Realistic Cars and Maps.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car Parking Multiplayer APK A Free Game with Realistic Cars and Maps.md
deleted file mode 100644
index 77ab00947bc8d5848816f23d66770d9416058ca6..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car Parking Multiplayer APK A Free Game with Realistic Cars and Maps.md
+++ /dev/null
@@ -1,66 +0,0 @@
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-
Free Download Car Parking Multiplayer APK: A Realistic Driving Simulator for Android
-
Do you love driving and parking games? Do you want to experience a realistic simulation of driving various vehicles in a detailed city? Do you want to challenge yourself and other players in different parking scenarios? If you answered yes to any of these questions, then you should try Car Parking Multiplayer, a popular game for Android devices that lets you enjoy all these features and more. In this article, we will tell you what Car Parking Multiplayer is, how to download and install it on your device, and some tips and tricks for playing it.
Car Parking Multiplayer is a 3D driving simulator game developed by olzhass, a studio that specializes in creating realistic car games. The game has over 100 million downloads on Google Play Store and has received positive reviews from players and critics alike. The game offers more than just parking, as you can also explore an open-world city with various locations, such as airports, gas stations, car washes, garages, and more. You can also customize your car with different paint colors, stickers, rims, spoilers, and other accessories. Moreover, you can play online with other players in multiplayer mode, where you can chat, race, trade cars, or even prank each other.
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Features of Car Parking Multiplayer
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Realistic graphics and physics
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One of the main attractions of Car Parking Multiplayer is its realistic graphics and physics. The game uses high-quality 3D models and textures to create a lifelike environment that you can explore. The game also simulates the physics of different vehicles, such as their weight, speed, acceleration, braking, steering, suspension, and damage. You can also experience different weather conditions, such as rain, snow, fog, or night time. The game also has realistic sound effects that match the engine sounds, horn sounds, tire sounds, and collision sounds of each vehicle.
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Variety of vehicles and customization options
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The game features over 100 vehicles that you can drive and park in various scenarios. You can choose from different categories of vehicles, such as cars, trucks, buses, motorcycles, or even helicopters. Each vehicle has its own characteristics and performance that affect how it handles on the road. You can also customize your vehicle with different options, such as paint color, stickers, rims, spoilers, neon lights, license plates, and more. You can also modify your vehicle's engine power, transmission type, brake force, steering sensitivity, and other settings.
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Open-world exploration and multiplayer mode
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The game allows you to explore an open-world city with various locations that you can visit. You can drive around freely or follow the GPS directions to find your destination. You can also interact with different objects in the city, such as traffic lights, gas stations, car washes, garages, or even animals. You can also play online with other players in multiplayer mode, where you can join different servers based on your region or language. You can chat with other players, race with them, trade cars with them, or even prank them by blocking their way or honking at them.
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How to download and install Car Parking Multiplayer APK?
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Download from official sources
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The easiest way to download Car Parking Multiplayer APK is to use the official sources, such as Google Play Store or Upt
Enable unknown sources on your device
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If you download Car Parking Multiplayer APK from a third-party source, such as Uptodown, you will need to enable unknown sources on your device. This is because Android devices normally block the installation of apps that are not from the official sources, such as Google Play Store. To enable unknown sources, you need to go to your device's settings, then security, then toggle on the option that says "allow installation of apps from unknown sources". This will allow you to install Car Parking Multiplayer APK on your device.
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Install the APK file and launch the game
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Once you have downloaded Car Parking Multiplayer APK from your preferred source, you need to locate the file on your device's storage. You can use a file manager app to find the file, or you can check your download folder. Once you find the file, tap on it to start the installation process. Follow the instructions on the screen to complete the installation. After the installation is done, you can launch the game by tapping on its icon on your home screen or app drawer. Enjoy playing Car Parking Multiplayer on your Android device!
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Tips and tricks for playing Car Parking Multiplayer
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Learn the basics of parking and driving
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Before you start playing Car Parking Multiplayer, you should learn the basics of parking and driving in the game. The game has a tutorial mode that teaches you how to use the controls, such as the steering wheel, pedals, gearbox, handbrake, and camera. You should also practice parking in different situations, such as parallel parking, reverse parking, diagonal parking, and more. You can also adjust the difficulty level of the parking challenges, from easy to hard. The game also has a driving school mode that teaches you how to follow traffic rules, such as speed limits, stop signs, traffic lights, and more.
-
Use the map and GPS to find your destination
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The game has a large open-world city that you can explore freely. However, if you want to find a specific location or complete a mission, you will need to use the map and GPS features. The map shows you the layout of the city and the locations of different places, such as gas stations, car washes, garages, airports, and more. You can also see your current position and direction on the map. The GPS feature helps you navigate to your destination by showing you the best route and giving you voice directions. You can also set waypoints on the map to mark places that you want to visit.
-
Earn money and upgrade your car
-
The game allows you to earn money by completing missions, parking challenges, races, or other activities. You can use the money to buy new cars or upgrade your existing ones. You can also sell your cars or trade them with other players online. You can upgrade your car's performance by modifying its engine power, transmission type, brake force, steering sensitivity, and other settings. You can also customize your car's appearance by changing its paint color, stickers, rims, spoilers, neon lights, license plates, and more.
-
Join online servers and interact with other players
-
The game has a multiplayer mode that lets you play online with other players from around the world. You can join different servers based on your region or language. You can chat with other players, race with them, trade cars with them, or even prank them by blocking their way or honking at them. You can also create your own server and invite your friends to join. You can also join clans or groups of players who share similar interests or goals in the game.
-
Conclusion
-
Car Parking Multiplayer is a realistic driving simulator game for Android devices that offers more than just parking. You can explore an open-world city with various locations, customize your car with different options, and play online with other players in multiplayer mode. The game has realistic graphics and physics, a variety of vehicles and customization options, and an easy-to-use interface. If you want to download Car Parking Multiplayer APK for free, you can use the official sources, such as Google Play Store or Uptodown, or any other trusted third-party source. However, you will need to enable unknown sources on your device before installing the APK file. You can also follow our tips and tricks for playing Car Parking Multiplayer to improve your skills and enjoy the game more.
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FAQs
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Q: Is Car Parking Multiplayer safe to download?
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A: Yes, Car Parking Multiplayer is safe to download if you use official sources, such as Google Play Store or Uptodown[^1^ or any other trusted third-party source. However, you should always scan the APK file for viruses or malware before installing it on your device.
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Q: How much storage space does Car Parking Multiplayer require?
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A: Car Parking Multiplayer requires about 500 MB of storage space on your device. However, this may vary depending on the updates and additional content that you download in the game.
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Q: Can I play Car Parking Multiplayer offline?
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A: Yes, you can play Car Parking Multiplayer offline in single-player mode. However, you will need an internet connection to play online in multiplayer mode or to access some features, such as the car market or the clan system.
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Q: What are the minimum requirements to play Car Parking Multiplayer on Android?
-
A: The minimum requirements to play Car Parking Multiplayer on Android are as follows:
-
Android version: 4.4 or higher
-
RAM: 1 GB or higher
-
CPU: 1.5 GHz or higher
-
GPU: Mali-400 MP or higher
-
-
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Q: How can I contact the developers of Car Parking Multiplayer?
-
A: You can contact the developers of Car Parking Multiplayer by sending them an email at olzhass@yandex.com or by visiting their website at https://olzhass.com/. You can also follow them on Facebook, Instagram, YouTube, or Discord for the latest news and updates about the game.
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/College Romance Season 1 A Hilarious Story of Three BFFs and Their Love Lives.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/College Romance Season 1 A Hilarious Story of Three BFFs and Their Love Lives.md
deleted file mode 100644
index 213789b86bd9073cde478682e9362a16e585eb4d..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/College Romance Season 1 A Hilarious Story of Three BFFs and Their Love Lives.md
+++ /dev/null
@@ -1,111 +0,0 @@
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College Romance Season 1 Download Filmyzilla: Is It Legal and Safe?
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College Romance is a popular web series that depicts the lives and loves of three college friends. The first season of the show was released in 2018 and received rave reviews from the audience. The show is full of humor, drama, romance, and nostalgia. It is available to watch online on Sony Liv and TVF Play.
Filmyzilla is a notorious website that leaks movies and shows online for free. The website has a huge collection of pirated content from various genres and languages. One can find the latest movies, Bollywood movies, dubbed movies, Telugu movies, Tamil movies, Hollywood movies, web series, and more on this website.
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But is it legal and safe to download College Romance season 1 from Filmyzilla? In this article, we will answer this question and provide you with some alternatives and solutions to watch College Romance season 1 online legally and safely.
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College Romance Season 1: A Fun and Relatable Web Series
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The Plot and Characters of College Romance Season 1
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College Romance season 1 follows the adventures of three best friends - Naira, Trippy, and Karan - who are looking for love, laughs, and some lifelong memories while attending college together. Naira is a smart and confident girl who helps her friends with their relationship problems. Trippy is a fun-loving guy who has a crush on a senior girl named Raveena. Karan is a confused guy who is dating a girl named Deepika but is not sure about his feelings for her.
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The show explores the various aspects of college life such as parties, exams, friendships, breakups, hookups, and more. The show also features some hilarious situations and dialogues that will make you laugh out loud. The show has a total of five episodes, each lasting for about 20 minutes.
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The Popularity and Reception of College Romance Season 1
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College Romance season 1 was a huge hit among the viewers, especially the youth. The show received positive feedback from the critics and the audience alike. The show was praised for its realistic portrayal of college life, its relatable characters, its witty humor, its catchy music, and its engaging storyline. The show also won several awards and nominations, such as the Indian Television Academy Awards, the Indian Web Series Awards, the Streamy Awards India, and more.
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The show has a rating of 8.7 out of 10 on IMDb and a rating of 4.5 out of 5 on JustWatch. The show has also garnered a huge fan following on social media platforms such as YouTube, Instagram, Facebook, Twitter, etc. The show has over 100 million views on YouTube and over one million followers on Instagram.
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Filmyzilla: A Notorious Movie Piracy Website
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How Filmyzilla Leaks Movies and Shows Online
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Filmyzilla is one of the many torrent websites that illegally provide leaked and pirated movies and shows for free. The website has a large library of movies and shows of different genres and categories. One can find the latest movies, Bollywood movies, dubbed movies, Telugu movies, Tamil movies, Hollywood movies, web series, and more on this website. The website also offers various formats and resolutions to choose from, such as 360p, 480p, 720p, 1080p, HD, etc.
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Filmyzilla leaks movies and shows online by uploading them on its servers or by providing links to other sources. The website often uploads the movies and shows before or soon after their official release. The website also changes its domain name frequently to avoid detection and legal action. Some of the domain names used by Filmyzilla are filmyzilla.com, filmyzilla.in, filmyzilla.net, filmyzilla.vip, filmyzilla.pro, filmyzilla.me, etc.
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The Risks and Consequences of Using Filmyzilla
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Using Filmyzilla or any other torrent website is not only illegal but also risky and harmful. Here are some of the risks and consequences of using Filmyzilla:
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Legal trouble: Downloading or streaming movies and shows from Filmyzilla is a violation of the Indian Copyright Act of 1957 and the Information Technology Act of 2000. Anyone who is caught using Filmyzilla can face legal action such as fines, imprisonment, or both.
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Malware infection: Filmyzilla or any other torrent website may contain viruses, malware, spyware, ransomware, or other malicious software that can infect your device and compromise your data and security. These malware can also damage your device or make it vulnerable to hackers.
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Poor quality: Filmyzilla or any other torrent website may not provide you with the best quality of movies and shows. The movies and shows may be low in resolution, audio quality, subtitles, or synchronization. They may also have watermarks, advertisements, or other interruptions that can ruin your viewing experience.
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Unethical behavior: Using Filmyzilla or any other torrent website is an unethical and immoral act that harms the film industry and the artists. By downloading or streaming movies and shows from Filmyzilla, you are depriving the filmmakers and actors of their rightful earnings and recognition. You are also supporting the illegal and criminal activities of the piracy websites.
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College Romance Season 1 Download Filmyzilla: Why You Should Avoid It
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The Legal Issues of Downloading College Romance Season 1 from Filmyzilla
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Downloading College Romance season 1 from Filmyzilla is a clear case of piracy and infringement of intellectual property rights. College Romance season 1 is the original work of The Viral Fever (TVF) and Sony Liv, who own the exclusive rights to distribute and exhibit the show online. By downloading College Romance season 1 from Filmyzilla, you are violating their rights and breaking the law.
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The makers of College Romance season 1 have taken several measures to prevent the piracy of their show. They have filed complaints against Filmyzilla and other piracy websites that have leaked their show online. They have also urged the viewers to watch their show only on the official platforms such as Sony Liv and TVF Play. They have also warned the viewers about the legal consequences of using piracy websites such as Filmyzilla.
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The Quality and Security Issues of Downloading College Romance Season 1 from Filmyzilla
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Downloading College Romance season 1 from Filmyzilla is not only illegal but also unsafe and unsatisfactory. As mentioned earlier, Filmyzilla may expose you to various malware threats that can harm your device and data. Moreover, Filmyzilla may not provide you with the best quality of College Romance season 1. The show may be low in resolution, audio quality, subtitles, or synchronization. It may also have watermarks, advertisements, or other interruptions that can spoil your enjoyment.
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Downloading College Romance season 1 from Filmyzilla is also a waste of time and resources. You may have to spend a lot of time searching for a working link or a suitable format on Filmyzilla. You may also have to deal with slow download speed, broken links, corrupted files, or incomplete downloads on Filmyzilla. You may also end up consuming a lot of data bandwidth or storage space on your device by downloading College Romance season 1 from Filmyzilla.
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College Romance Season 1 Download Filmyzilla: Alternatives and Solutions
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The Legal and Safe Ways to Watch College Romance Season 1 Online
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The best way to watch College Romance season 1 online is to use the legal and safe platforms that have the official rights to stream the show online. These platforms are Sony Liv and TVF Play. Sony Liv is a premium video-on-demand service that offers a variety of movies, shows, sports, and live TV channels. Sony Liv has the exclusive streaming rights for College Romance season 1 in India. You can watch College Romance season 1 on Sony Liv by subscribing to one of its plans. The plans start from Rs. 299 per month or Rs. 999 per year. You can also get a free trial for 7 days before subscribing to Sony Liv. You can access Sony Liv on your web browser, mobile app, smart TV, or gaming console.
- TVF Play is a free online video platform that offers original and creative content from The Viral Fever (TVF). TVF Play has the co-production rights for College Romance season 1 along with Sony Liv. You can watch College Romance season 1 on TVF Play for free without any subscription or registration. You can access TVF Play on your web browser, mobile app, or YouTube channel.
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The Benefits and Advantages of Watching College Romance Season 1 Online
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Watching College Romance season 1 online on Sony Liv or TVF Play has many benefits and advantages over downloading it from Filmyzilla or any other piracy website. Here are some of them:
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Legal and ethical: Watching College Romance season 1 online on Sony Liv or TVF Play is a legal and ethical way to enjoy the show. You are respecting the rights and efforts of the makers and actors of the show. You are also supporting the growth and development of the Indian web series industry.
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Safe and secure: Watching College Romance season 1 online on Sony Liv or TVF Play is a safe and secure way to enjoy the show. You are not exposing yourself to any malware threats or legal troubles. You are also protecting your device and data from any damage or loss.
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High quality: Watching College Romance season 1 online on Sony Liv or TVF Play is a high-quality way to enjoy the show. You are getting the best resolution, audio quality, subtitles, and synchronization of the show. You are also getting a smooth and uninterrupted streaming experience without any watermarks, advertisements, or other distractions.
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Convenient and flexible: Watching College Romance season 1 online on Sony Liv or TVF Play is a convenient and flexible way to enjoy the show. You can watch the show anytime, anywhere, and on any device of your choice. You can also pause, resume, rewind, or fast-forward the show as per your preference. You can also watch the show offline by downloading it on your device.
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Conclusion
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College Romance season 1 is a fun and relatable web series that you should not miss. The show has a great plot, characters, humor, music, and message. The show is available to watch online on Sony Liv and TVF Play legally and safely.
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Downloading College Romance season 1 from Filmyzilla or any other piracy website is illegal, risky, harmful, and unethical. You should avoid using Filmyzilla or any other torrent website for downloading or streaming movies and shows online.
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Instead, you should use the legal and safe platforms such as Sony Liv and TVF Play to watch College Romance season 1 online. You will get many benefits and advantages by doing so.
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We hope this article has helped you understand why you should not download College Romance season 1 from Filmyzilla or any other piracy website. We also hope you have enjoyed reading this article as much as we have enjoyed writing it for you.
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Frequently Asked Questions
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Here are some of the frequently asked questions about College Romance season 1 download Filmyzilla:
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Is College Romance season 1 available on Netflix?
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No, College Romance season 1 is not available on Netflix. The show is only available on Sony Liv and TVF Play.
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Is College Romance season 1 based on a true story?
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No, College Romance season 1 is not based on a true story. The show is a fictional comedy-drama that depicts the lives and loves of three college friends.
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Is College Romance season 2 coming soon?
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Yes, College Romance season 2 is coming soon. The makers of the show have announced that they are working on the second season of the show and it will be released in 2023.
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Is Filmyzilla banned in India?
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Yes, Filmyzilla is banned in India by the government along with many other piracy websites. However, Filmyzilla keeps changing its domain name and proxy servers to evade the ban and continue to operate.
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Is there any penalty for using Filmyzilla?
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Yes, there is a penalty for using Filmyzilla or any other piracy website. According to the Indian law, anyone who is found using Filmyzilla or any other piracy website can face a fine of up to Rs. 3 lakhs or imprisonment of up to 3 years or both.
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diff --git a/spaces/1phancelerku/anime-remove-background/ARK Survival Evolved - Primal Fear Mod A Guide to the Best Dinos and Items.md b/spaces/1phancelerku/anime-remove-background/ARK Survival Evolved - Primal Fear Mod A Guide to the Best Dinos and Items.md
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How to Download and Install Primal Fear Mod for Ark: Survival Evolved
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If you are looking for a new and exciting way to play Ark: Survival Evolved, you might want to try out the Primal Fear mod. This mod adds over 200 different creatures, as well as new items, weapons, armor, and bosses. In this article, we will show you how to download and install Primal Fear mod for Ark, as well as some of its features and creatures.
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What is Primal Fear Mod?
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Primal Fear is a massive dino mod for Ark, developed by Steam user Pikkon38. The mod participated in the Ark Modding Contest of 2018 and placed in 4th. In June 2020, Primal Fear joined the Sponsored Mod program.
Primal Fear mod adds many features to enhance your gameplay, such as:
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Tame, breed, and ride all new creatures to expand on your gameplay
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Adds new taming mechanics for custom creatures
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Custom tiered kibble system
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New and more powerful weapons and armor
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Adds crafting stations designated for the mod
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Expansions for Scorched Earth, Aberration, Extinction, and Genesis maps
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Creatures of Primal Fear Mod
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Primal Fear mod currently has over 200 different creatures in it, and more creatures are added in the development process. The creatures are divided into different tiers, such as:
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Toxic Creatures: The first tier of creatures that have a 7% chance of spawning. They drop Toxic Blood and Toxic Hide when harvested.
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Alpha Creatures: The second tier of creatures that have a 5% chance of spawning. They require Alpha Kibble to tame, which requires Toxic Egg and Toxic Blood. They drop Alpha Blood and Alpha Hide when harvested.
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Elemental Creatures: The third tier of creatures that have a 3% chance of spawning. They require Elemental Kibble to tame, which requires Apex Egg, Apex Blood, and various Feathers. They drop Elemental Hide and Feathers when harvested.
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Apex Creatures: The fourth tier of creatures that have a 3.5% chance of spawning. They require Apex Kibble to tame, which requires Alpha Blood and Elemental Egg. They drop Apex Blood and Apex Hide when harvested.
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Fabled Creatures: The fifth tier of creatures that have a 2.75% chance of spawning. They require Fabled Kibble to tame, which requires Apex Blood and Apex Egg. They drop Fabled Blood and Fabled Hide when harvested.
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Celestial Creatures: The sixth tier of creatures that have a 2.25% chance of spawning. They require Celestial Kibble to tame, which requires Fabled Blood and Fabled Egg. They drop Celestial Soul when killed.
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Demonic Creatures: The seventh tier of creatures that have a 2% chance of spawning. They require Demonic Kibble to tame, which requires Celestial Soul and Demonic Soul. They drop Demonic Soul when killed.
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Primal Creatures: The eighth tier of creatures that are bosses in Primal Fear. They have a full black body, a red outline, a thick red aura, old Dragon OST playing while near them, as well as being noticeably larger than their vanilla counterparts.
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And many more...
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How to Find and Subscribe to Primal Fear Mod on Steam Workshop
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To install mods on Ark, you need to subscribe to them from the Steam Workshop. Here are the steps to find and subscribe to Primal Fear mod on Steam Workshop:
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Open Steam and go to the Library tab.
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Right-click on Ark: Survival Evolved and select Properties.
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Go to the Local Files tab and click on Browse Local Files.
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Open the ShooterGame folder and then the Content folder.
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Copy the Mods folder and paste it somewhere safe as a backup.
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Go back to Steam and click on the Community tab.
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Click on Workshop and search for Ark: Survival Evolved.
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In the search bar, type Primal Fear and press Enter.
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You will see a list of mods related to Primal Fear. The main mod is called Primal Fear and has over 1.5 million subscribers. You can also subscribe to other mods that are compatible with Primal Fear, such as Primal Fear Boss Expansion, Primal Fear Aberration Expansion, Primal Fear Genesis Expansion, etc.
-
To subscribe to a mod, click on it and then click on the green Subscribe button. The mod will start downloading automatically.
-
-
How to Install and Activate Primal Fear Mod on Ark
-
After subscribing to the mods, you need to install and activate them on Ark. Here are the steps to do that:
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How to install primal fear mod for ark survival evolved
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-
How to Copy and Extract the Mods
-
-
Go to the Mods folder that you copied earlier and open it.
-
You will see a bunch of folders with numbers as their names. These are the mods that you subscribed to. Each folder has a .mod file inside it.
-
Copy all the folders and paste them into the Mods folder inside the Content folder of Ark. This will overwrite the existing folders.
-
Open each folder and extract the .mod file using a program like WinRAR or 7-Zip. You will get a folder with the same name as the .mod file.
-
Delete the .mod file and keep the extracted folder.
-
-
How to Select and Load the Mods
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-
Launch Ark: Survival Evolved from Steam.
-
In the main menu, click on Host/Local.
-
Click on Play Single Player or Host Non-Dedicated Session, depending on your preference.
-
In the Game Settings tab, scroll down to Active Mods and click on Select Mod.
-
You will see a list of mods that you installed. Select Primal Fear as the first mod, followed by any other mods that you want to use. You can also change the order of the mods by dragging them up or down.
-
Click on Save Changes and then click on Play With Mods.
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-
How to Update and Backup the Mods
-
To update the mods, you need to unsubscribe and resubscribe to them from Steam Workshop. This will download the latest version of the mods. You can also check for updates manually by going to Steam Workshop and clicking on Updates in the left sidebar. To backup the mods, you need to copy the Mods folder from Ark's Content folder and paste it somewhere safe. You can also use a program like Ark Server Manager to manage your mods more easily.
-
Conclusion
-
Primal Fear is a great mod for Ark: Survival Evolved that adds a lot of new content and challenges to the game. It is easy to download and install from Steam Workshop, and you can customize your gameplay with different expansions and settings. If you are looking for a fresh and fun way to play Ark, you should definitely give Primal Fear a try!
-
FAQs
-
-
Q: What are the system requirements for Primal Fear mod?
-
A: Primal Fear mod is quite demanding on your system, especially if you use multiple expansions and high settings. You should have at least 8 GB of RAM, a quad-core processor, and a dedicated graphics card with 4 GB of VRAM or more.
-
Q: How do I uninstall Primal Fear mod?
-
A: To uninstall Primal Fear mod, you need to unsubscribe from it on Steam Workshop, delete its folders from Ark's Mods folder, and remove it from your Active Mods list in Ark's Game Settings.
-
Q: How do I spawn Primal Fear creatures using commands?
-
A: To spawn Primal Fear creatures using commands, you need to know their spawn codes. You can find them on Primal Fear's Wiki page. You can also use the Beacon app to generate spawn codes for Primal Fear creatures. To spawn a creature using commands, you need to open the console by pressing Tab, and then type cheat spawndino followed by the spawn code. For example, to spawn an Apex Reaper King, you can type: cheat spawndino "Blueprint'/Game/Mods/Primal_Fear/Dinos/Apex/Apex_Reaper/King/PFApexXenomorph_Character_BP_Male_Tamed_Child.PFApexXenomorph_Character_BP_Male_Tamed_Child'" 1 1 1 30 You can also change the numbers at the end to adjust the level, location, and quantity of the spawned creature.
-
Q: How do I tame Primal Fear creatures?
-
A: To tame Primal Fear creatures, you need to use different types of kibble depending on the tier of the creature. You can craft kibble using the Primal Smithy or the Primal Cooking Pot. You also need to use tranquilizers that are strong enough to knock out the creature. You can use the Primal Pike, the Primal Rifle, or the Primal Compound Bow with different types of arrows and darts. You can also use special items like Potent Narcotics, Tame Helpers, and Wake Up Stimulants to help with the taming process.
-
Q: How do I fight Primal Fear bosses?
-
A: To fight Primal Fear bosses, you need to summon them using special items called Summoners. You can craft Summoners using the Primal Smithy or the Primal Cooking Pot. You also need to have a strong team of creatures and weapons to face the bosses. Some bosses have special abilities and weaknesses that you need to be aware of. For example, Nova the Destroyer has three AOE attacks and is immune to fire damage, but is vulnerable to electric damage.
-
Q: How do I get Primal Fear expansions?
-
A: To get Primal Fear expansions, you need to subscribe to them on Steam Workshop, just like the main mod. You can find them by searching for Primal Fear on Workshop and looking for the ones that have Expansion in their name. You also need to install and activate them on Ark, just like the main mod. You can use them on any map that supports them.
-
Q: How do I get support for Primal Fear mod?
-
A: To get support for Primal Fear mod, you can join their Discord server, where you can ask questions, report bugs, give feedback, and chat with other players and developers. You can also check their Wiki page for more information and guides.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Business WhatsApp Sender 7.0.1.1 for Free and Boost Your Business.md b/spaces/1phancelerku/anime-remove-background/Download Business WhatsApp Sender 7.0.1.1 for Free and Boost Your Business.md
deleted file mode 100644
index b6359911ead3ee0721221546a23e3c1ca61e56f2..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Business WhatsApp Sender 7.0.1.1 for Free and Boost Your Business.md
+++ /dev/null
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Business WhatsApp Sender 7.0.1.1 Free Download: A Powerful Tool for WhatsApp Marketing
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WhatsApp is one of the most popular and widely used messaging apps in the world, with over 2 billion users as of 2020. It is not only a great tool for personal communication, but also a powerful platform for business marketing. With WhatsApp Business, you can create a professional profile for your business, communicate more efficiently with your customers, and grow your business.
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Business WhatsApp Sender 7.0.1.1 is a software that allows you to send unlimited messages to your potential and existing customers using WhatsApp Business. It is a 100% safe and reliable tool that secures your account and reduces the chances of getting blocked.
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You can send text, images, audio, document files, etc.
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You can import or extract contacts from various sources such as Google Maps, groups, files, etc.
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You can support multi-language functionality.
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You can view reports and statistics of your campaigns.
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How to download and install WhatsApp Business Sender 7.0.1.1
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-Run both WhatsApp Messenger and WhatsApp Business on the same phone [^4^]
-Use WhatsApp Web to respond to your customers from your computer [^4^]
-Send multimedia, free calls, free international messaging, group chat, offline messages, and more on WhatsApp Business [^4^]
-Contact smb@support.whatsapp.com or follow @WhatsApp on Twitter for feedback, questions, or concerns about WhatsApp Business [^4^]
-
Step 1: Download the software from the official website
-
The first step is to download the software from the official website. You can choose between the trial version and the full version. The trial version allows you to send up to 10 messages per day for free, while the full version costs $49 and allows you to send unlimited messages.
-
Step 2: Install the software on your PC
-
The next step is to install the software on your PC. You need to have Windows 7 or higher, .NET Framework 4.5 or higher, and WhatsApp Business installed on your PC. You also need to have a valid WhatsApp Business number and a QR code scanner. To install the software, follow these steps:
-
-
Run the setup file and follow the instructions.
-
Accept the terms and conditions and click Next.
-
Choose the destination folder and click Next.
-
Wait for the installation to complete and click Finish.
-
-
Step 3: Generate an order number and send it to the developer
-
The third step is to generate an order number and send it to the developer. This is required to activate the full version of the software. To generate an order number, follow these steps:
-
-
Open the software and click on Register.
-
Enter your name, email, phone number, and country.
-
Click on Generate Order Number and copy it.
-
Send the order number to the developer via email or WhatsApp.
-
-
Step 4: Receive the activation code and enter it in the software
-
The final step is to receive the activation code and enter it in the software. This will unlock all the features of the software and allow you to use it without any limitations. To activate the software, follow these steps:
-
-
Wait for the developer to send you the activation code via email or WhatsApp.
-
Open the software and click on Register.
-
Enter your name, email, phone number, country, and activation code.
-
Click on Activate and enjoy the software.
-
-
How to Use Business WhatsApp Sender 7.0.1.1 for WhatsApp Marketing?
-
Now that you have downloaded and activated Business WhatsApp Sender 7.0.1.1, you are ready to use it for your WhatsApp marketing campaigns. Here are some tips on how to use it effectively:
-
Import or extract contacts from various sources
-
The first thing you need to do is to import or extract contacts from various sources such as Google Maps, groups, files, etc. You can do this by clicking on Contacts > Import Contacts or Contacts > Extract Contacts. You can also filter contacts by country code, gender, name, etc.
-
Create and customize your messages with text, images, audio, documents, etc.
-
The next thing you need to do is to create and customize your messages with text, images, audio, documents, etc. You can do this by clicking on Messages > Create Message or Messages > Edit Message. You can also use variables, emojis, links, etc. to make your messages more personalized and engaging.
-
Set up dynamic chatbots and auto-reply options
-
The third thing you need to do is to set up dynamic chatbots and auto-reply options for different scenarios. You can do this by clicking on Settings > Chatbot Settings or Settings > Auto Reply Settings. You can also use keywords, conditions, actions, etc. to make your chatbots and auto-replies more intelligent and responsive.
-
Control the speed, delay, and sleep time of your campaigns
-
The last thing you need to do is to control the speed, delay, and sleep time of your campaigns. You can do this by clicking on Settings > General Settings or Settings > Campaign Settings. You can also use timers, schedulers, etc. to make your campaigns more efficient and effective.
-
Conclusion
-
Business WhatsApp Sender 7.0.1.1 is a powerful tool for WhatsApp marketing that allows you to send unlimited messages to your potential and existing customers using WhatsApp Business. It offers various features and benefits that make it a must-have tool for any WhatsApp marketer. To download and activate Business WhatsApp Sender 7.0.1.1, you need to follow four simple steps: download the software from the official website, install it on your PC, generate an order number and send it to the developer, receive the activation code and enter it in the software. To use Business WhatsApp Sender 7.0.1.1 for WhatsApp marketing, you need to follow some tips on how to import or extract contacts from various sources, create and customize your messages with text, images, audio, documents, etc., set up dynamic chatbots and auto-reply options, and control the speed, delay, and sleep time of your campaigns. By following these tips, you can make the most out of Business WhatsApp Sender 7.0.1.1 and achieve your marketing goals.
-
FAQs
-
Here are some frequently asked questions about Business WhatsApp Sender 7.0.1.1:
-
-
Is Business WhatsApp Sender 7.0.1.1 compatible with WhatsApp Web?
-
No, Business WhatsApp Sender 7.0.1.1 is not compatible with WhatsApp Web. You need to have WhatsApp Business installed on your PC to use the software.
-
How many messages can I send per day using Business WhatsApp Sender 7.0.1.1?
-
There is no limit on how many messages you can send per day using Business WhatsApp Sender 7.0.1.1. However, you should avoid sending too many messages to avoid getting blocked by WhatsApp.
-
Can I use Business WhatsApp Sender 7.0.1.1 on multiple PCs?
-
No, you can only use Business WhatsApp Sender 7.0.1.1 on one PC per license. If you want to use it on multiple PCs, you need to buy multiple licenses.
-
Does Business WhatsApp Sender 7.0.1.1 support multi-language functionality?
-
Yes, Business WhatsApp Sender 7.0.1.1 supports multi-language functionality. You can send messages in any language you want.
-
What is the refund policy of Business WhatsApp Sender 7.0.1.1?
-
Business WhatsApp Sender 7.0.1.1 offers a 30-day money-back guarantee if you are not satisfied with the software.
- 197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Experience the Epic Battle of Godzilla and Kong in PUBG Mobile 1.4 (APK).md b/spaces/1phancelerku/anime-remove-background/Experience the Epic Battle of Godzilla and Kong in PUBG Mobile 1.4 (APK).md
deleted file mode 100644
index 9cb7e0a4151f5abc19939a29d6208c34ed440251..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Experience the Epic Battle of Godzilla and Kong in PUBG Mobile 1.4 (APK).md
+++ /dev/null
@@ -1,103 +0,0 @@
-
-
PUBG Mobile Godzilla Modu Indir APK: How to Download and Play the New Update
-
PUBG Mobile is one of the most popular and addictive battle royale games on mobile devices. It offers a thrilling and immersive gameplay experience with realistic graphics, diverse maps, and various modes. But what if you could spice up your game with some giant monsters from the Godzilla vs Kong movie? Well, that's exactly what the latest update of PUBG Mobile offers. In this article, we will tell you everything you need to know about the PUBG Mobile Godzilla Modu, how to download it, and what are the new features and changes that it brings.
A brief introduction to the new mode and its features
-
PUBG Mobile Godzilla Modu is a new mode that is part of the 1.4 update of the game. It is also known as Titan Strikes mode, as it features three titans from the Godzilla vs Kong movie: Godzilla, Kong, and Mechagodzilla. These titans will appear on different maps (Erangel, Sanhok, and Livik) and will roam around, causing havoc and destruction. Players will have to avoid or fight them, while also dealing with other enemies.
-
The collaboration between PUBG Mobile and Godzilla vs Kong movie
-
The PUBG Mobile Godzilla Modu is a result of a unique collaboration between Tencent, the developer of PUBG Mobile, and Legendary Pictures, the producer of the Godzilla vs Kong movie. The movie is an action-packed blockbuster that pits two iconic monsters against each other in an epic battle for supremacy. The collaboration aims to bring some of the excitement and spectacle of the movie to the game, as well as to celebrate the third anniversary of PUBG Mobile.
-
How to Download PUBG Mobile Godzilla Modu APK?
-
The difference between regular and compact versions of the APK
-
To download and play the PUBG Mobile Godzilla Modu, you will need to update your game to the latest version (1.4). You can do this by using the Google Play Store or by downloading the APK file from the official website of PUBG Mobile. There are two variants of the APK file available: regular version and compact version. The regular version has a size of 990 MB and includes all the new content. The compact version has a size of 661 MB and requires additional resource packs to be downloaded in-game.
-
The step-by-step guide to download and install the APK file
-
Here are the steps that you need to follow to download and install the PUBG Mobile Godzilla Modu APK file:
-
-
Click on one of these links to download either the regular version or the compact version of the APK file.
-
Once the file is downloaded, locate it on your device and tap on it to install it. Make sure that you have enabled the "Install from Unknown Source" option in your settings.
-
After the installation is complete, open PUBG Mobile on your device. If you have downloaded the compact version, you will have to download some resource packs in-game.
-
Login to your account and enjoy playing PUBG Mobile Godzilla Modu.
-
-
The disclaimer for users from India and other banned countries
-
Disclaimer
Disclaimer: PUBG Mobile is banned in some countries, such as India, due to various reasons. Therefore, we do not recommend or endorse downloading or playing the game in those regions. Please follow the laws and regulations of your country and respect the rights of others.
-
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-
What are the New Features and Changes in PUBG Mobile Godzilla Modu?
-
The appearance of Titans (Godzilla, Kong, and Mechagodzilla) on the maps
-
One of the most exciting features of PUBG Mobile Godzilla Modu is the appearance of the three titans from the Godzilla vs Kong movie on different maps. Each titan has its own behavior, abilities, and impact on the environment. Here is a brief overview of each titan and its map:
-
-
Godzilla: The king of the monsters will appear on Erangel, the classic map of PUBG Mobile. He will spawn randomly on the map and will move towards specific locations, such as Mylta Power, School, Military Base, and others. He will also roar occasionally, which will alert nearby players of his presence. Godzilla can attack players with his tail, claws, and atomic breath. He can also destroy buildings and vehicles with his sheer size and strength.
-
Kong: The king of Skull Island will appear on Sanhok, the tropical map of PUBG Mobile. He will spawn at the ruins in the center of the map and will stay there for a while. He will then move to one of the four Apex Camps (more on that later) and will defend it from other players. Kong can attack players with his fists, feet, and roar. He can also throw rocks and trees at players and vehicles.
-
Mechagodzilla: The mechanical titan will appear on Livik, the smallest map of PUBG Mobile. He will spawn at a random location on the map and will patrol around it. He will also shoot lasers and missiles at players and vehicles. Mechagodzilla can also create an electromagnetic pulse that will disable all electronic devices in a certain radius.
-
-
The new Titan Crystals that grant special abilities to players
-
Another new feature of PUBG Mobile Godzilla Modu is the Titan Crystals. These are special items that can be found on the maps where the titans appear. They are dropped by the titans or by special helicopters that fly over the maps. There are two types of Titan Crystals: Erangel Titan Crystal and Sanhok Titan Crystal.
-
The Erangel Titan Crystal is a blue crystal that can be used to create a protective shield around the player for a short time. The shield can block bullets and other projectiles, but not melee attacks or explosions. The shield also has a cooldown time after each use.
-
The Sanhok Titan Crystal is a yellow crystal that can be used to enhance the player's abilities for a short time. The player can jump higher, run faster, and deal more damage with melee attacks. The player can also see footprints of nearby enemies on the mini-map. The effect also has a cooldown time after each use.
-
The new Apex Camps that offer high-quality loot and supplies
-
The Apex Camps are new locations that can be found on Sanhok, where Kong appears. There are four Apex Camps on the map: Alpha, Beta, Gamma, and Delta. Each camp has a different theme and layout, such as a temple, a cave, a village, or a factory. Each camp also has high-quality loot and supplies, such as weapons, armor, ammo, health kits, and more.
-
However, there is a catch: only one Apex Camp is active at a time, and it is guarded by Kong. Players will have to fight their way through Kong's attacks and other enemies to reach the camp and loot it. The active camp will change every few minutes, so players will have to keep an eye on the mini-map to know where to go next.
-
The new vehicle (Coupe RB) and the new shooting mode (OTS)
-
PUBG Mobile Godzilla Modu also introduces a new vehicle and a new shooting mode to the game. The new vehicle is called Coupe RB, and it is a sports car that can fit two players. It has a high speed and acceleration, but a low durability and stability. It can be found on Erangel, Miramar, Sanhok, and Livik.
-
The new shooting mode is called OTS (Over The Shoulder), and it is an alternative to TPP (Third Person Perspective) and FPP (First Person Perspective). OTS mode allows players to aim more accurately over their shoulder without using the scope or iron sight. It also reduces the recoil of weapons, but increases the weapon sway. OTS mode can be toggled on or off by pressing a button on the screen. OTS mode can be used on all maps and modes, except for FPP-only modes.
-
Conclusion
-
PUBG Mobile Godzilla Modu is a new and exciting mode that brings the epic monsters from the Godzilla vs Kong movie to the game. It offers a unique and thrilling gameplay experience, as players have to survive and fight against the titans, while also competing with other players. The mode also introduces new features and changes, such as Titan Crystals, Apex Camps, Coupe RB, and OTS mode. If you are a fan of PUBG Mobile and Godzilla vs Kong, you should definitely try out this mode and enjoy the action-packed adventure.
-
To download and play PUBG Mobile Godzilla Modu, you will need to update your game to the latest version (1.4) by using the Google Play Store or by downloading the APK file from the official website of PUBG Mobile. However, please note that PUBG Mobile is banned in some countries, such as India, and we do not recommend or endorse playing the game in those regions.
-
We hope that this article has helped you to learn more about PUBG Mobile Godzilla Modu and how to download and play it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy gaming!
-
FAQs
-
Q: How long will PUBG Mobile Godzilla Modu last?
-
A: According to the official announcement, PUBG Mobile Godzilla Modu will last until June 8, 2023. However, there might be extensions or changes depending on the feedback and popularity of the mode.
-
Q: Can I play PUBG Mobile Godzilla Modu with my friends?
-
A: Yes, you can play PUBG Mobile Godzilla Modu with your friends in squad mode or duo mode. You can also invite your friends to join your team or match with random players online.
-
Q: How can I get more Titan Crystals?
-
A: You can get more Titan Crystals by finding them on the maps where the titans appear. They are dropped by the titans or by special helicopters that fly over the maps. You can also get them by completing missions or events related to the mode.
-
Q: What are the benefits of playing PUBG Mobile Godzilla Modu?
-
A: Playing PUBG Mobile Godzilla Modu can give you several benefits, such as:
-
-
Enjoying a new and fun gameplay experience with giant monsters and special abilities.
-
Earning rewards and achievements related to the mode, such as skins, outfits, emotes, and more.
-
Improving your skills and strategies by facing different challenges and scenarios.
-
Supporting the collaboration between PUBG Mobile and Godzilla vs Kong movie.
-
-
Q: Is PUBG Mobile Godzilla Modu safe and legal to play?
-
A: PUBG Mobile Godzilla Modu is safe and legal to play in most countries where PUBG Mobile is available. However, there are some countries where PUBG Mobile is banned or restricted, such as India, Pakistan, China, and others. In those countries, playing PUBG Mobile Godzilla Modu might be risky or illegal, and we do not recommend or endorse doing so. Please follow the laws and regulations of your country and respect the rights of others.
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/optim/linear_warmup_lr_scheduler.py b/spaces/AIConsultant/MusicGen/audiocraft/optim/linear_warmup_lr_scheduler.py
deleted file mode 100644
index 03274a1ae52b6f20473973b77619f34b2bddd6a1..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/optim/linear_warmup_lr_scheduler.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import typing as tp
-
-from torch.optim import Optimizer
-from torch.optim.lr_scheduler import _LRScheduler
-
-
-class LinearWarmupLRScheduler(_LRScheduler):
- """Inverse square root LR scheduler.
-
- Args:
- optimizer (Optimizer): Torch optimizer.
- warmup_steps (int): Number of warmup steps.
- warmup_init_lr (tp.Optional[float]): Initial learning rate
- during warmup phase. When not set, use the provided learning rate.
- """
- def __init__(self, optimizer: Optimizer, warmup_steps: int, warmup_init_lr: tp.Optional[float] = 0):
- self.warmup_steps = warmup_steps
- self.warmup_init_lr = warmup_init_lr
- super().__init__(optimizer)
-
- def _get_sched_lr(self, lr: float, step: int):
- if step < self.warmup_steps:
- warmup_init_lr = self.warmup_init_lr or 0
- lr_step = (lr - warmup_init_lr) / self.warmup_steps
- lr = warmup_init_lr + step * lr_step
- return lr
-
- def get_lr(self):
- return [self._get_sched_lr(base_lr, self.last_epoch) for base_lr in self.base_lrs]
diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/wav_processors/base_processor.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/wav_processors/base_processor.py
deleted file mode 100644
index e8200dc58a9388ac94a5ec34b8a65f75e380255b..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/wav_processors/base_processor.py
+++ /dev/null
@@ -1,25 +0,0 @@
-REGISTERED_WAV_PROCESSORS = {}
-
-
-def register_wav_processors(name):
- def _f(cls):
- REGISTERED_WAV_PROCESSORS[name] = cls
- return cls
-
- return _f
-
-
-def get_wav_processor_cls(name):
- return REGISTERED_WAV_PROCESSORS.get(name, None)
-
-
-class BaseWavProcessor:
- @property
- def name(self):
- raise NotImplementedError
-
- def output_fn(self, input_fn):
- return f'{input_fn[:-4]}_{self.name}.wav'
-
- def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args):
- raise NotImplementedError
diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/vocoder/bigvgan/alias_free_torch/resample.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/vocoder/bigvgan/alias_free_torch/resample.py
deleted file mode 100644
index 750e6c3402cc5ac939c4b9d075246562e0e1d1a7..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/vocoder/bigvgan/alias_free_torch/resample.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
-# LICENSE is in incl_licenses directory.
-
-import torch.nn as nn
-from torch.nn import functional as F
-from .filter import LowPassFilter1d
-from .filter import kaiser_sinc_filter1d
-
-
-class UpSample1d(nn.Module):
- def __init__(self, ratio=2, kernel_size=None):
- super().__init__()
- self.ratio = ratio
- self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
- self.stride = ratio
- self.pad = self.kernel_size // ratio - 1
- self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
- self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
- filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,
- half_width=0.6 / ratio,
- kernel_size=self.kernel_size)
- self.register_buffer("filter", filter)
-
- # x: [B, C, T]
- def forward(self, x):
- _, C, _ = x.shape
-
- x = F.pad(x, (self.pad, self.pad), mode='replicate')
- x = self.ratio * F.conv_transpose1d(
- x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
- x = x[..., self.pad_left:-self.pad_right]
-
- return x
-
-
-class DownSample1d(nn.Module):
- def __init__(self, ratio=2, kernel_size=None):
- super().__init__()
- self.ratio = ratio
- self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
- self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
- half_width=0.6 / ratio,
- stride=ratio,
- kernel_size=self.kernel_size)
-
- def forward(self, x):
- xx = self.lowpass(x)
-
- return xx
\ No newline at end of file
diff --git a/spaces/AILab-CVC/EvalCrafter/app.py b/spaces/AILab-CVC/EvalCrafter/app.py
deleted file mode 100644
index de3784cb7d4648561c13cec0bbb64ec8631d23bd..0000000000000000000000000000000000000000
--- a/spaces/AILab-CVC/EvalCrafter/app.py
+++ /dev/null
@@ -1,121 +0,0 @@
-"""
-Adapted from the SEED-Bench Leaderboard by AILab-CVC
-Source: https://huggingface.co/spaces/AILab-CVC/SEED-Bench_Leaderboard
-"""
-
-__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions']
-
-import gradio as gr
-import pandas as pd
-import json
-import pdb
-import tempfile
-
-from constants import *
-from src.auto_leaderboard.model_metadata_type import ModelType
-
-global data_component, filter_component
-
-
-def upload_file(files):
- file_paths = [file.name for file in files]
- return file_paths
-
-def get_baseline_df():
- df = pd.read_csv(CSV_DIR)
- df = df.sort_values(by="Final Sum Score", ascending=False)
- present_columns = MODEL_INFO + checkbox_group.value
- df = df[present_columns]
- print(df)
- return df
-
-def get_all_df():
- df = pd.read_csv(CSV_DIR)
- df = df.sort_values(by="Final Sum Score", ascending=False)
- print(df)
- return df
-
-block = gr.Blocks()
-
-
-with block:
- gr.Markdown(
- LEADERBORAD_INTRODUCTION
- )
- with gr.Tabs(elem_classes="tab-buttons") as tabs:
- with gr.TabItem("🏅 EvalCrafter Benchmark", elem_id="evalcrafter-benchmark-tab-table", id=0):
-
- gr.Markdown(
- TABLE_INTRODUCTION
- )
-
- # selection for column part:
- checkbox_group = gr.CheckboxGroup(
- choices=TASK_INFO_v2,
- value=AVG_INFO,
- label="Select options",
- interactive=True,
- )
-
- # 创建数据帧组件
- # pdb.set_trace()
- data_component = gr.components.Dataframe(
- value=get_baseline_df,
- headers=COLUMN_NAMES,
- type="pandas",
- datatype=DATA_TITILE_TYPE,
- interactive=False,
- visible=True,
- )
-
- def on_checkbox_group_change(selected_columns):
- # pdb.set_trace()
- selected_columns = [item for item in TASK_INFO_v2 if item in selected_columns]
- present_columns = MODEL_INFO + selected_columns
- updated_data = get_all_df()[present_columns]
- updated_data = updated_data.sort_values(by=present_columns[3], ascending=False)
- updated_headers = present_columns
- update_datatype = [DATA_TITILE_TYPE[COLUMN_NAMES.index(x)] for x in updated_headers]
-
- # pdb.set_trace()
- filter_component = gr.components.Dataframe(
- value=updated_data,
- headers=updated_headers,
- type="pandas",
- datatype=update_datatype,
- interactive=False,
- visible=True,
- )
- # pdb.set_trace()
- return filter_component.value
-
- # 将复选框组关联到处理函数
- checkbox_group.change(fn=on_checkbox_group_change, inputs=checkbox_group, outputs=data_component)
-
-
- # table 2
- with gr.TabItem("📝 About", elem_id="evalcrafter-benchmark-tab-table", id=2):
- gr.Markdown(LEADERBORAD_INFO, elem_classes="markdown-text")
-
-
- with gr.Row():
- data_run = gr.Button("Refresh")
- data_run.click(
- get_baseline_df, outputs=data_component
- )
-
- gr.Markdown(r"""
- Please cite this paper if you find it useful ♥️:
-
- ```bibtex
- @inproceedings{Liu2023EvalCrafterBA,
- title={EvalCrafter: Benchmarking and Evaluating Large Video Generation Models},
- author={Yaofang Liu and Xiaodong Cun and Xuebo Liu and Xintao Wang and Yong Zhang and Haoxin Chen and Yang Liu and Tieyong Zeng and Raymond Chan and Ying Shan},
- year={2023},
- url={https://api.semanticscholar.org/CorpusID:264172222}
- }
- ```
- """)
- # block.load(get_baseline_df, outputs=data_title)
-
-block.launch(share=False)
\ No newline at end of file
diff --git a/spaces/ALSv/FSW/roop/processors/frame/core.py b/spaces/ALSv/FSW/roop/processors/frame/core.py
deleted file mode 100644
index 498169d34a00e0a2547940380afd69967a2eca8c..0000000000000000000000000000000000000000
--- a/spaces/ALSv/FSW/roop/processors/frame/core.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import os
-import sys
-import importlib
-import psutil
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from queue import Queue
-from types import ModuleType
-from typing import Any, List, Callable
-from tqdm import tqdm
-
-import roop
-
-FRAME_PROCESSORS_MODULES: List[ModuleType] = []
-FRAME_PROCESSORS_INTERFACE = [
- 'pre_check',
- 'pre_start',
- 'process_frame',
- 'process_frames',
- 'process_image',
- 'process_video',
- 'post_process'
-]
-
-
-def load_frame_processor_module(frame_processor: str) -> Any:
- try:
- frame_processor_module = importlib.import_module(f'roop.processors.frame.{frame_processor}')
- for method_name in FRAME_PROCESSORS_INTERFACE:
- if not hasattr(frame_processor_module, method_name):
- raise NotImplementedError
- except ModuleNotFoundError:
- sys.exit(f'Frame processor {frame_processor} not found.')
- except NotImplementedError:
- sys.exit(f'Frame processor {frame_processor} not implemented correctly.')
- return frame_processor_module
-
-
-def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType]:
- global FRAME_PROCESSORS_MODULES
-
- if not FRAME_PROCESSORS_MODULES:
- for frame_processor in frame_processors:
- frame_processor_module = load_frame_processor_module(frame_processor)
- FRAME_PROCESSORS_MODULES.append(frame_processor_module)
- return FRAME_PROCESSORS_MODULES
-
-
-def multi_process_frame(source_path: str, temp_frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None], update: Callable[[], None]) -> None:
- with ThreadPoolExecutor(max_workers=roop.globals.execution_threads) as executor:
- futures = []
- queue = create_queue(temp_frame_paths)
- queue_per_future = max(len(temp_frame_paths) // roop.globals.execution_threads, 1)
- while not queue.empty():
- future = executor.submit(process_frames, source_path, pick_queue(queue, queue_per_future), update)
- futures.append(future)
- for future in as_completed(futures):
- future.result()
-
-
-def create_queue(temp_frame_paths: List[str]) -> Queue[str]:
- queue: Queue[str] = Queue()
- for frame_path in temp_frame_paths:
- queue.put(frame_path)
- return queue
-
-
-def pick_queue(queue: Queue[str], queue_per_future: int) -> List[str]:
- queues = []
- for _ in range(queue_per_future):
- if not queue.empty():
- queues.append(queue.get())
- return queues
-
-
-def process_video(source_path: str, frame_paths: list[str], process_frames: Callable[[str, List[str], Any], None]) -> None:
- progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
- total = len(frame_paths)
- with tqdm(total=total, desc='Processing', unit='frame', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
- multi_process_frame(source_path, frame_paths, process_frames, lambda: update_progress(progress))
-
-
-def update_progress(progress: Any = None) -> None:
- process = psutil.Process(os.getpid())
- memory_usage = process.memory_info().rss / 1024 / 1024 / 1024
- progress.set_postfix({
- 'memory_usage': '{:.2f}'.format(memory_usage).zfill(5) + 'GB',
- 'execution_providers': roop.globals.execution_providers,
- 'execution_threads': roop.globals.execution_threads
- })
- progress.refresh()
- progress.update(1)
diff --git a/spaces/AONYLMR/White-box-Cartoonization/wbc/guided_filter.py b/spaces/AONYLMR/White-box-Cartoonization/wbc/guided_filter.py
deleted file mode 100644
index fd019d145efc7f308cd96de90f4e7b648f6820b4..0000000000000000000000000000000000000000
--- a/spaces/AONYLMR/White-box-Cartoonization/wbc/guided_filter.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import tensorflow as tf
-import numpy as np
-
-
-
-
-def tf_box_filter(x, r):
- k_size = int(2*r+1)
- ch = x.get_shape().as_list()[-1]
- weight = 1/(k_size**2)
- box_kernel = weight*np.ones((k_size, k_size, ch, 1))
- box_kernel = np.array(box_kernel).astype(np.float32)
- output = tf.nn.depthwise_conv2d(x, box_kernel, [1, 1, 1, 1], 'SAME')
- return output
-
-
-
-def guided_filter(x, y, r, eps=1e-2):
-
- x_shape = tf.shape(x)
- #y_shape = tf.shape(y)
-
- N = tf_box_filter(tf.ones((1, x_shape[1], x_shape[2], 1), dtype=x.dtype), r)
-
- mean_x = tf_box_filter(x, r) / N
- mean_y = tf_box_filter(y, r) / N
- cov_xy = tf_box_filter(x * y, r) / N - mean_x * mean_y
- var_x = tf_box_filter(x * x, r) / N - mean_x * mean_x
-
- A = cov_xy / (var_x + eps)
- b = mean_y - A * mean_x
-
- mean_A = tf_box_filter(A, r) / N
- mean_b = tf_box_filter(b, r) / N
-
- output = mean_A * x + mean_b
-
- return output
-
-
-
-def fast_guided_filter(lr_x, lr_y, hr_x, r=1, eps=1e-8):
-
- #assert lr_x.shape.ndims == 4 and lr_y.shape.ndims == 4 and hr_x.shape.ndims == 4
-
- lr_x_shape = tf.shape(lr_x)
- #lr_y_shape = tf.shape(lr_y)
- hr_x_shape = tf.shape(hr_x)
-
- N = tf_box_filter(tf.ones((1, lr_x_shape[1], lr_x_shape[2], 1), dtype=lr_x.dtype), r)
-
- mean_x = tf_box_filter(lr_x, r) / N
- mean_y = tf_box_filter(lr_y, r) / N
- cov_xy = tf_box_filter(lr_x * lr_y, r) / N - mean_x * mean_y
- var_x = tf_box_filter(lr_x * lr_x, r) / N - mean_x * mean_x
-
- A = cov_xy / (var_x + eps)
- b = mean_y - A * mean_x
-
- mean_A = tf.image.resize_images(A, hr_x_shape[1: 3])
- mean_b = tf.image.resize_images(b, hr_x_shape[1: 3])
-
- output = mean_A * hr_x + mean_b
-
- return output
-
-
-if __name__ == '__main__':
- import cv2
- from tqdm import tqdm
-
- input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
- #input_superpixel = tf.placeholder(tf.float32, [16, 256, 256, 3])
- output = guided_filter(input_photo, input_photo, 5, eps=1)
- image = cv2.imread('output_figure1/cartoon2.jpg')
- image = image/127.5 - 1
- image = np.expand_dims(image, axis=0)
-
- config = tf.ConfigProto()
- config.gpu_options.allow_growth = True
- sess = tf.Session(config=config)
- sess.run(tf.global_variables_initializer())
-
- out = sess.run(output, feed_dict={input_photo: image})
- out = (np.squeeze(out)+1)*127.5
- out = np.clip(out, 0, 255).astype(np.uint8)
- cv2.imwrite('output_figure1/cartoon2_filter.jpg', out)
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Acytoo.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Acytoo.py
deleted file mode 100644
index d36ca6da22ddfa43690abdd0db27e6f971320f93..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Acytoo.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from __future__ import annotations
-
-from aiohttp import ClientSession
-
-from ..typing import AsyncGenerator
-from .base_provider import AsyncGeneratorProvider
-
-
-class Acytoo(AsyncGeneratorProvider):
- url = 'https://chat.acytoo.com'
- working = True
- supports_gpt_35_turbo = True
-
- @classmethod
- async def create_async_generator(
- cls,
- model: str,
- messages: list[dict[str, str]],
- proxy: str = None,
- **kwargs
- ) -> AsyncGenerator:
-
- async with ClientSession(
- headers=_create_header()
- ) as session:
- async with session.post(
- cls.url + '/api/completions',
- proxy=proxy,
- json=_create_payload(messages, **kwargs)
- ) as response:
- response.raise_for_status()
- async for stream in response.content.iter_any():
- if stream:
- yield stream.decode()
-
-
-def _create_header():
- return {
- 'accept': '*/*',
- 'content-type': 'application/json',
- }
-
-
-def _create_payload(messages: list[dict[str, str]], temperature: float = 0.5, **kwargs):
- return {
- 'key' : '',
- 'model' : 'gpt-3.5-turbo',
- 'messages' : messages,
- 'temperature' : temperature,
- 'password' : ''
- }
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/input/PointerUpDownCell.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/input/PointerUpDownCell.js
deleted file mode 100644
index 00cb1480b93aa4c6ae6316d94579cb4818fe9b94..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/input/PointerUpDownCell.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import EmitCellEvent from './EmitCellEvent.js';
-
-var PointerUpDownCell = function (table, tableConfig) {
- table
- .on('pointerdown', function (pointer, localX, localY, event) {
- EmitCellEvent(this.eventEmitter, 'cell.down', table, pointer.worldX, pointer.worldY, pointer, event);
- }, this)
- .on('pointerup', function (pointer, localX, localY, event) {
- EmitCellEvent(this.eventEmitter, 'cell.up', table, pointer.worldX, pointer.worldY, pointer, event);
- }, this)
-}
-
-export default PointerUpDownCell;
\ No newline at end of file
diff --git a/spaces/Alpaca233/SadTalker/src/audio2pose_models/res_unet.py b/spaces/Alpaca233/SadTalker/src/audio2pose_models/res_unet.py
deleted file mode 100644
index f2611e1d1a9bf233507427b34928fca60e094224..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/src/audio2pose_models/res_unet.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn
-from src.audio2pose_models.networks import ResidualConv, Upsample
-
-
-class ResUnet(nn.Module):
- def __init__(self, channel=1, filters=[32, 64, 128, 256]):
- super(ResUnet, self).__init__()
-
- self.input_layer = nn.Sequential(
- nn.Conv2d(channel, filters[0], kernel_size=3, padding=1),
- nn.BatchNorm2d(filters[0]),
- nn.ReLU(),
- nn.Conv2d(filters[0], filters[0], kernel_size=3, padding=1),
- )
- self.input_skip = nn.Sequential(
- nn.Conv2d(channel, filters[0], kernel_size=3, padding=1)
- )
-
- self.residual_conv_1 = ResidualConv(filters[0], filters[1], stride=(2,1), padding=1)
- self.residual_conv_2 = ResidualConv(filters[1], filters[2], stride=(2,1), padding=1)
-
- self.bridge = ResidualConv(filters[2], filters[3], stride=(2,1), padding=1)
-
- self.upsample_1 = Upsample(filters[3], filters[3], kernel=(2,1), stride=(2,1))
- self.up_residual_conv1 = ResidualConv(filters[3] + filters[2], filters[2], stride=1, padding=1)
-
- self.upsample_2 = Upsample(filters[2], filters[2], kernel=(2,1), stride=(2,1))
- self.up_residual_conv2 = ResidualConv(filters[2] + filters[1], filters[1], stride=1, padding=1)
-
- self.upsample_3 = Upsample(filters[1], filters[1], kernel=(2,1), stride=(2,1))
- self.up_residual_conv3 = ResidualConv(filters[1] + filters[0], filters[0], stride=1, padding=1)
-
- self.output_layer = nn.Sequential(
- nn.Conv2d(filters[0], 1, 1, 1),
- nn.Sigmoid(),
- )
-
- def forward(self, x):
- # Encode
- x1 = self.input_layer(x) + self.input_skip(x)
- x2 = self.residual_conv_1(x1)
- x3 = self.residual_conv_2(x2)
- # Bridge
- x4 = self.bridge(x3)
-
- # Decode
- x4 = self.upsample_1(x4)
- x5 = torch.cat([x4, x3], dim=1)
-
- x6 = self.up_residual_conv1(x5)
-
- x6 = self.upsample_2(x6)
- x7 = torch.cat([x6, x2], dim=1)
-
- x8 = self.up_residual_conv2(x7)
-
- x8 = self.upsample_3(x8)
- x9 = torch.cat([x8, x1], dim=1)
-
- x10 = self.up_residual_conv3(x9)
-
- output = self.output_layer(x10)
-
- return output
\ No newline at end of file
diff --git a/spaces/Alycer/VITS-Umamusume-voice-synthesizer/hubert_model.py b/spaces/Alycer/VITS-Umamusume-voice-synthesizer/hubert_model.py
deleted file mode 100644
index 6c7f8716c268d0f371f5a9f7995f59bd4b9082d1..0000000000000000000000000000000000000000
--- a/spaces/Alycer/VITS-Umamusume-voice-synthesizer/hubert_model.py
+++ /dev/null
@@ -1,221 +0,0 @@
-import copy
-from typing import Optional, Tuple
-import random
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-class Hubert(nn.Module):
- def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
- super().__init__()
- self._mask = mask
- self.feature_extractor = FeatureExtractor()
- self.feature_projection = FeatureProjection()
- self.positional_embedding = PositionalConvEmbedding()
- self.norm = nn.LayerNorm(768)
- self.dropout = nn.Dropout(0.1)
- self.encoder = TransformerEncoder(
- nn.TransformerEncoderLayer(
- 768, 12, 3072, activation="gelu", batch_first=True
- ),
- 12,
- )
- self.proj = nn.Linear(768, 256)
-
- self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
- self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
- def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
- mask = None
- if self.training and self._mask:
- mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
- x[mask] = self.masked_spec_embed.to(x.dtype)
- return x, mask
-
- def encode(
- self, x: torch.Tensor, layer: Optional[int] = None
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- x = self.feature_extractor(x)
- x = self.feature_projection(x.transpose(1, 2))
- x, mask = self.mask(x)
- x = x + self.positional_embedding(x)
- x = self.dropout(self.norm(x))
- x = self.encoder(x, output_layer=layer)
- return x, mask
-
- def logits(self, x: torch.Tensor) -> torch.Tensor:
- logits = torch.cosine_similarity(
- x.unsqueeze(2),
- self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
- dim=-1,
- )
- return logits / 0.1
-
- def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
- x, mask = self.encode(x)
- x = self.proj(x)
- logits = self.logits(x)
- return logits, mask
-
-
-class HubertSoft(Hubert):
- def __init__(self):
- super().__init__()
-
- @torch.inference_mode()
- def units(self, wav: torch.Tensor) -> torch.Tensor:
- wav = F.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
- x, _ = self.encode(wav)
- return self.proj(x)
-
-
-class FeatureExtractor(nn.Module):
- def __init__(self):
- super().__init__()
- self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
- self.norm0 = nn.GroupNorm(512, 512)
- self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
- self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
- self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
- self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
- self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
- self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = F.gelu(self.norm0(self.conv0(x)))
- x = F.gelu(self.conv1(x))
- x = F.gelu(self.conv2(x))
- x = F.gelu(self.conv3(x))
- x = F.gelu(self.conv4(x))
- x = F.gelu(self.conv5(x))
- x = F.gelu(self.conv6(x))
- return x
-
-
-class FeatureProjection(nn.Module):
- def __init__(self):
- super().__init__()
- self.norm = nn.LayerNorm(512)
- self.projection = nn.Linear(512, 768)
- self.dropout = nn.Dropout(0.1)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.norm(x)
- x = self.projection(x)
- x = self.dropout(x)
- return x
-
-
-class PositionalConvEmbedding(nn.Module):
- def __init__(self):
- super().__init__()
- self.conv = nn.Conv1d(
- 768,
- 768,
- kernel_size=128,
- padding=128 // 2,
- groups=16,
- )
- self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.conv(x.transpose(1, 2))
- x = F.gelu(x[:, :, :-1])
- return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
- def __init__(
- self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
- ) -> None:
- super(TransformerEncoder, self).__init__()
- self.layers = nn.ModuleList(
- [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
- )
- self.num_layers = num_layers
-
- def forward(
- self,
- src: torch.Tensor,
- mask: torch.Tensor = None,
- src_key_padding_mask: torch.Tensor = None,
- output_layer: Optional[int] = None,
- ) -> torch.Tensor:
- output = src
- for layer in self.layers[:output_layer]:
- output = layer(
- output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
- )
- return output
-
-
-def _compute_mask(
- shape: Tuple[int, int],
- mask_prob: float,
- mask_length: int,
- device: torch.device,
- min_masks: int = 0,
-) -> torch.Tensor:
- batch_size, sequence_length = shape
-
- if mask_length < 1:
- raise ValueError("`mask_length` has to be bigger than 0.")
-
- if mask_length > sequence_length:
- raise ValueError(
- f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
- )
-
- # compute number of masked spans in batch
- num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
- num_masked_spans = max(num_masked_spans, min_masks)
-
- # make sure num masked indices <= sequence_length
- if num_masked_spans * mask_length > sequence_length:
- num_masked_spans = sequence_length // mask_length
-
- # SpecAugment mask to fill
- mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
- # uniform distribution to sample from, make sure that offset samples are < sequence_length
- uniform_dist = torch.ones(
- (batch_size, sequence_length - (mask_length - 1)), device=device
- )
-
- # get random indices to mask
- mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
- # expand masked indices to masked spans
- mask_indices = (
- mask_indices.unsqueeze(dim=-1)
- .expand((batch_size, num_masked_spans, mask_length))
- .reshape(batch_size, num_masked_spans * mask_length)
- )
- offsets = (
- torch.arange(mask_length, device=device)[None, None, :]
- .expand((batch_size, num_masked_spans, mask_length))
- .reshape(batch_size, num_masked_spans * mask_length)
- )
- mask_idxs = mask_indices + offsets
-
- # scatter indices to mask
- mask = mask.scatter(1, mask_idxs, True)
-
- return mask
-
-
-def hubert_soft(
- path: str
-) -> HubertSoft:
- r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
- Args:
- path (str): path of a pretrained model
- """
- hubert = HubertSoft()
- checkpoint = torch.load(path)
- consume_prefix_in_state_dict_if_present(checkpoint, "module.")
- hubert.load_state_dict(checkpoint)
- hubert.eval()
- return hubert
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md
deleted file mode 100644
index 7142e0ded5a7833fd61bcbc1ae7018e0472c6fde..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md
+++ /dev/null
@@ -1,20 +0,0 @@
-
-
-# Singlestep DPM-Solver
-
-## Overview
-
-Original paper can be found [here](https://arxiv.org/abs/2206.00927) and the [improved version](https://arxiv.org/abs/2211.01095). The original implementation can be found [here](https://github.com/LuChengTHU/dpm-solver).
-
-## DPMSolverSinglestepScheduler
-[[autodoc]] DPMSolverSinglestepScheduler
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py
deleted file mode 100644
index cad82cb71940a28e78e70419ed80ebb2f55cb144..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py
+++ /dev/null
@@ -1,494 +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 warnings
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-import torch.nn.functional as F
-from transformers import CLIPTextModel, CLIPTokenizer
-
-from ...image_processor import VaeImageProcessor
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...schedulers import EulerDiscreteScheduler
-from ...utils import logging, randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.preprocess
-def preprocess(image):
- warnings.warn(
- "The preprocess method is deprecated and will be removed in a future version. Please"
- " use VaeImageProcessor.preprocess instead",
- FutureWarning,
- )
- if isinstance(image, torch.Tensor):
- return image
- elif isinstance(image, PIL.Image.Image):
- image = [image]
-
- if isinstance(image[0], PIL.Image.Image):
- w, h = image[0].size
- w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64
-
- image = [np.array(i.resize((w, h)))[None, :] for i in image]
- image = np.concatenate(image, axis=0)
- image = np.array(image).astype(np.float32) / 255.0
- image = image.transpose(0, 3, 1, 2)
- image = 2.0 * image - 1.0
- image = torch.from_numpy(image)
- elif isinstance(image[0], torch.Tensor):
- image = torch.cat(image, dim=0)
- return image
-
-
-class StableDiffusionLatentUpscalePipeline(DiffusionPipeline):
- r"""
- Pipeline for upscaling Stable Diffusion output image resolution by a factor of 2.
-
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
- implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
- Args:
- vae ([`AutoencoderKL`]):
- Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
- text_encoder ([`~transformers.CLIPTextModel`]):
- Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
- tokenizer ([`~transformers.CLIPTokenizer`]):
- A `CLIPTokenizer` to tokenize text.
- unet ([`UNet2DConditionModel`]):
- A `UNet2DConditionModel` to denoise the encoded image latents.
- scheduler ([`SchedulerMixin`]):
- A [`EulerDiscreteScheduler`] to be used in combination with `unet` to denoise the encoded image latents.
- """
-
- def __init__(
- self,
- vae: AutoencoderKL,
- text_encoder: CLIPTextModel,
- tokenizer: CLIPTokenizer,
- unet: UNet2DConditionModel,
- scheduler: EulerDiscreteScheduler,
- ):
- super().__init__()
-
- self.register_modules(
- vae=vae,
- text_encoder=text_encoder,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- )
- self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
- self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, resample="bicubic")
-
- def _encode_prompt(self, prompt, device, do_classifier_free_guidance, negative_prompt):
- r"""
- Encodes the prompt into text encoder hidden states.
-
- Args:
- prompt (`str` or `list(int)`):
- prompt to be encoded
- device: (`torch.device`):
- torch device
- do_classifier_free_guidance (`bool`):
- whether to use classifier free guidance or not
- negative_prompt (`str` or `List[str]`):
- The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
- if `guidance_scale` is less than `1`).
- """
- batch_size = len(prompt) if isinstance(prompt, list) else 1
-
- text_inputs = self.tokenizer(
- prompt,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- return_length=True,
- return_tensors="pt",
- )
- text_input_ids = text_inputs.input_ids
-
- 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 CLIP can only handle sequences up to"
- f" {self.tokenizer.model_max_length} tokens: {removed_text}"
- )
-
- text_encoder_out = self.text_encoder(
- text_input_ids.to(device),
- output_hidden_states=True,
- )
- text_embeddings = text_encoder_out.hidden_states[-1]
- text_pooler_out = text_encoder_out.pooler_output
-
- # get unconditional embeddings for classifier free guidance
- if do_classifier_free_guidance:
- 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 = text_input_ids.shape[-1]
- uncond_input = self.tokenizer(
- uncond_tokens,
- padding="max_length",
- max_length=max_length,
- truncation=True,
- return_length=True,
- return_tensors="pt",
- )
-
- uncond_encoder_out = self.text_encoder(
- uncond_input.input_ids.to(device),
- output_hidden_states=True,
- )
-
- uncond_embeddings = uncond_encoder_out.hidden_states[-1]
- uncond_pooler_out = uncond_encoder_out.pooler_output
-
- # 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
- text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
- text_pooler_out = torch.cat([uncond_pooler_out, text_pooler_out])
-
- return text_embeddings, text_pooler_out
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
- def decode_latents(self, latents):
- warnings.warn(
- "The decode_latents method is deprecated and will be removed in a future version. Please"
- " use VaeImageProcessor instead",
- FutureWarning,
- )
- latents = 1 / self.vae.config.scaling_factor * latents
- image = self.vae.decode(latents, return_dict=False)[0]
- image = (image / 2 + 0.5).clamp(0, 1)
- # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
- image = image.cpu().permute(0, 2, 3, 1).float().numpy()
- return image
-
- def check_inputs(self, prompt, image, callback_steps):
- if 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 (
- not isinstance(image, torch.Tensor)
- and not isinstance(image, PIL.Image.Image)
- and not isinstance(image, list)
- ):
- raise ValueError(
- f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or `list` but is {type(image)}"
- )
-
- # verify batch size of prompt and image are same if image is a list or tensor
- if isinstance(image, list) or isinstance(image, torch.Tensor):
- if isinstance(prompt, str):
- batch_size = 1
- else:
- batch_size = len(prompt)
- if isinstance(image, list):
- image_batch_size = len(image)
- else:
- image_batch_size = image.shape[0] if image.ndim == 4 else 1
- if batch_size != image_batch_size:
- raise ValueError(
- f"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}."
- " Please make sure that passed `prompt` matches the batch size of `image`."
- )
-
- 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)}."
- )
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.prepare_latents
- def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
- shape = (batch_size, num_channels_latents, height, width)
- if latents is None:
- latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
- else:
- if latents.shape != shape:
- raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
- 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()
- def __call__(
- self,
- prompt: Union[str, List[str]],
- image: Union[
- torch.FloatTensor,
- PIL.Image.Image,
- np.ndarray,
- List[torch.FloatTensor],
- List[PIL.Image.Image],
- List[np.ndarray],
- ] = None,
- num_inference_steps: int = 75,
- guidance_scale: float = 9.0,
- negative_prompt: Optional[Union[str, List[str]]] = None,
- generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
- latents: Optional[torch.FloatTensor] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
- callback_steps: int = 1,
- ):
- r"""
- The call function to the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide image upscaling.
- image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
- `Image` or tensor representing an image batch to be upscaled. If it's a tensor, it can be either a
- latent output from a Stable Diffusion model or an image tensor in the range `[-1, 1]`. It is considered
- a `latent` if `image.shape[1]` is `4`; otherwise, it is considered to be an image representation and
- encoded using this pipeline's `vae` encoder.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- A higher guidance scale value encourages the model to generate images closely linked to the text
- `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts to guide what to not include in image generation. If not defined, you need to
- pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
- to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
- generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
- A [`torch.Generator`](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 image
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor is generated by sampling using the supplied random `generator`.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generated image. Choose between `PIL.Image` or `np.array`.
- 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 calls every `callback_steps` steps during inference. The function is 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 is called. If not specified, the callback is called at
- every step.
-
- Examples:
- ```py
- >>> from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline
- >>> import torch
-
-
- >>> pipeline = StableDiffusionPipeline.from_pretrained(
- ... "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
- ... )
- >>> pipeline.to("cuda")
-
- >>> model_id = "stabilityai/sd-x2-latent-upscaler"
- >>> upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
- >>> upscaler.to("cuda")
-
- >>> prompt = "a photo of an astronaut high resolution, unreal engine, ultra realistic"
- >>> generator = torch.manual_seed(33)
-
- >>> low_res_latents = pipeline(prompt, generator=generator, output_type="latent").images
-
- >>> with torch.no_grad():
- ... image = pipeline.decode_latents(low_res_latents)
- >>> image = pipeline.numpy_to_pil(image)[0]
-
- >>> image.save("../images/a1.png")
-
- >>> upscaled_image = upscaler(
- ... prompt=prompt,
- ... image=low_res_latents,
- ... num_inference_steps=20,
- ... guidance_scale=0,
- ... generator=generator,
- ... ).images[0]
-
- >>> upscaled_image.save("../images/a2.png")
- ```
-
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
- otherwise a `tuple` is returned where the first element is a list with the generated images.
- """
-
- # 1. Check inputs
- self.check_inputs(prompt, image, callback_steps)
-
- # 2. Define call parameters
- batch_size = 1 if isinstance(prompt, str) else len(prompt)
- 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
-
- if guidance_scale == 0:
- prompt = [""] * batch_size
-
- # 3. Encode input prompt
- text_embeddings, text_pooler_out = self._encode_prompt(
- prompt, device, do_classifier_free_guidance, negative_prompt
- )
-
- # 4. Preprocess image
- image = self.image_processor.preprocess(image)
- image = image.to(dtype=text_embeddings.dtype, device=device)
- if image.shape[1] == 3:
- # encode image if not in latent-space yet
- image = self.vae.encode(image).latent_dist.sample() * self.vae.config.scaling_factor
-
- # 5. set timesteps
- self.scheduler.set_timesteps(num_inference_steps, device=device)
- timesteps = self.scheduler.timesteps
-
- batch_multiplier = 2 if do_classifier_free_guidance else 1
- image = image[None, :] if image.ndim == 3 else image
- image = torch.cat([image] * batch_multiplier)
-
- # 5. Add noise to image (set to be 0):
- # (see below notes from the author):
- # "the This step theoretically can make the model work better on out-of-distribution inputs, but mostly just seems to make it match the input less, so it's turned off by default."
- noise_level = torch.tensor([0.0], dtype=torch.float32, device=device)
- noise_level = torch.cat([noise_level] * image.shape[0])
- inv_noise_level = (noise_level**2 + 1) ** (-0.5)
-
- image_cond = F.interpolate(image, scale_factor=2, mode="nearest") * inv_noise_level[:, None, None, None]
- image_cond = image_cond.to(text_embeddings.dtype)
-
- noise_level_embed = torch.cat(
- [
- torch.ones(text_pooler_out.shape[0], 64, dtype=text_pooler_out.dtype, device=device),
- torch.zeros(text_pooler_out.shape[0], 64, dtype=text_pooler_out.dtype, device=device),
- ],
- dim=1,
- )
-
- timestep_condition = torch.cat([noise_level_embed, text_pooler_out], dim=1)
-
- # 6. Prepare latent variables
- height, width = image.shape[2:]
- num_channels_latents = self.vae.config.latent_channels
- latents = self.prepare_latents(
- batch_size,
- num_channels_latents,
- height * 2, # 2x upscale
- width * 2,
- text_embeddings.dtype,
- device,
- generator,
- latents,
- )
-
- # 7. Check that sizes of image and latents match
- num_channels_image = image.shape[1]
- if num_channels_latents + num_channels_image != self.unet.config.in_channels:
- raise ValueError(
- f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
- f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
- f" `num_channels_image`: {num_channels_image} "
- f" = {num_channels_latents+num_channels_image}. Please verify the config of"
- " `pipeline.unet` or your `image` input."
- )
-
- # 9. Denoising loop
- num_warmup_steps = 0
-
- with self.progress_bar(total=num_inference_steps) as progress_bar:
- for i, t in enumerate(timesteps):
- sigma = self.scheduler.sigmas[i]
- # expand the latents if we are doing classifier free guidance
- latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
- scaled_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- scaled_model_input = torch.cat([scaled_model_input, image_cond], dim=1)
- # preconditioning parameter based on Karras et al. (2022) (table 1)
- timestep = torch.log(sigma) * 0.25
-
- noise_pred = self.unet(
- scaled_model_input,
- timestep,
- encoder_hidden_states=text_embeddings,
- timestep_cond=timestep_condition,
- ).sample
-
- # in original repo, the output contains a variance channel that's not used
- noise_pred = noise_pred[:, :-1]
-
- # apply preconditioning, based on table 1 in Karras et al. (2022)
- inv_sigma = 1 / (sigma**2 + 1)
- noise_pred = inv_sigma * latent_model_input + self.scheduler.scale_model_input(sigma, t) * noise_pred
-
- # 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).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)
-
- if not output_type == "latent":
- image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
- else:
- image = latents
-
- image = self.image_processor.postprocess(image, output_type=output_type)
-
- if not return_dict:
- return (image,)
-
- return ImagePipelineOutput(images=image)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_flax.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_flax.py
deleted file mode 100644
index 8db8ec7810068aab4517fe2066e3fab10a52f6f7..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_flax.py
+++ /dev/null
@@ -1,99 +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 gc
-import unittest
-
-from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
-from diffusers.utils import is_flax_available, slow
-from diffusers.utils.testing_utils import require_flax
-
-
-if is_flax_available():
- import jax
- import jax.numpy as jnp
- from flax.jax_utils import replicate
- from flax.training.common_utils import shard
-
-
-@slow
-@require_flax
-class FlaxStableDiffusion2PipelineIntegrationTests(unittest.TestCase):
- def tearDown(self):
- # clean up the VRAM after each test
- super().tearDown()
- gc.collect()
-
- def test_stable_diffusion_flax(self):
- sd_pipe, params = FlaxStableDiffusionPipeline.from_pretrained(
- "stabilityai/stable-diffusion-2",
- revision="bf16",
- dtype=jnp.bfloat16,
- )
-
- prompt = "A painting of a squirrel eating a burger"
- num_samples = jax.device_count()
- prompt = num_samples * [prompt]
- prompt_ids = sd_pipe.prepare_inputs(prompt)
-
- params = replicate(params)
- prompt_ids = shard(prompt_ids)
-
- prng_seed = jax.random.PRNGKey(0)
- prng_seed = jax.random.split(prng_seed, jax.device_count())
-
- images = sd_pipe(prompt_ids, params, prng_seed, num_inference_steps=25, jit=True)[0]
- assert images.shape == (jax.device_count(), 1, 768, 768, 3)
-
- images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
- image_slice = images[0, 253:256, 253:256, -1]
-
- output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
- expected_slice = jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512])
- print(f"output_slice: {output_slice}")
- assert jnp.abs(output_slice - expected_slice).max() < 1e-2
-
- def test_stable_diffusion_dpm_flax(self):
- model_id = "stabilityai/stable-diffusion-2"
- scheduler, scheduler_params = FlaxDPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
- sd_pipe, params = FlaxStableDiffusionPipeline.from_pretrained(
- model_id,
- scheduler=scheduler,
- revision="bf16",
- dtype=jnp.bfloat16,
- )
- params["scheduler"] = scheduler_params
-
- prompt = "A painting of a squirrel eating a burger"
- num_samples = jax.device_count()
- prompt = num_samples * [prompt]
- prompt_ids = sd_pipe.prepare_inputs(prompt)
-
- params = replicate(params)
- prompt_ids = shard(prompt_ids)
-
- prng_seed = jax.random.PRNGKey(0)
- prng_seed = jax.random.split(prng_seed, jax.device_count())
-
- images = sd_pipe(prompt_ids, params, prng_seed, num_inference_steps=25, jit=True)[0]
- assert images.shape == (jax.device_count(), 1, 768, 768, 3)
-
- images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
- image_slice = images[0, 253:256, 253:256, -1]
-
- output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
- expected_slice = jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297])
- print(f"output_slice: {output_slice}")
- assert jnp.abs(output_slice - expected_slice).max() < 1e-2
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py
deleted file mode 100644
index a620188807218a9c80ad89ac6002dda3ea4b830c..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py'
-model = dict(
- pretrained='open-mmlab://res2net101_v1d_26w_4s',
- backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26))
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/double_roi_head.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/double_roi_head.py
deleted file mode 100644
index a1aa6c8244a889fbbed312a89574c3e11be294f0..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/double_roi_head.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from ..builder import HEADS
-from .standard_roi_head import StandardRoIHead
-
-
-@HEADS.register_module()
-class DoubleHeadRoIHead(StandardRoIHead):
- """RoI head for Double Head RCNN.
-
- https://arxiv.org/abs/1904.06493
- """
-
- def __init__(self, reg_roi_scale_factor, **kwargs):
- super(DoubleHeadRoIHead, self).__init__(**kwargs)
- self.reg_roi_scale_factor = reg_roi_scale_factor
-
- def _bbox_forward(self, x, rois):
- """Box head forward function used in both training and testing time."""
- bbox_cls_feats = self.bbox_roi_extractor(
- x[:self.bbox_roi_extractor.num_inputs], rois)
- bbox_reg_feats = self.bbox_roi_extractor(
- x[:self.bbox_roi_extractor.num_inputs],
- rois,
- roi_scale_factor=self.reg_roi_scale_factor)
- if self.with_shared_head:
- bbox_cls_feats = self.shared_head(bbox_cls_feats)
- bbox_reg_feats = self.shared_head(bbox_reg_feats)
- cls_score, bbox_pred = self.bbox_head(bbox_cls_feats, bbox_reg_feats)
-
- bbox_results = dict(
- cls_score=cls_score,
- bbox_pred=bbox_pred,
- bbox_feats=bbox_cls_feats)
- return bbox_results
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/AutoGPTQ_loader.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/AutoGPTQ_loader.py
deleted file mode 100644
index 987f5ba7971b0d14bd94c9c9523c6a8ba2fecfe9..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/AutoGPTQ_loader.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from pathlib import Path
-
-from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
-
-import modules.shared as shared
-from modules.logging_colors import logger
-from modules.models import get_max_memory_dict
-
-
-def load_quantized(model_name):
- path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
- pt_path = None
-
- # Find the model checkpoint
- if shared.args.checkpoint:
- pt_path = Path(shared.args.checkpoint)
- else:
- for ext in ['.safetensors', '.pt', '.bin']:
- found = list(path_to_model.glob(f"*{ext}"))
- if len(found) > 0:
- if len(found) > 1:
- logger.warning(f'More than one {ext} model has been found. The last one will be selected. It could be wrong.')
-
- pt_path = found[-1]
- break
-
- if pt_path is None:
- logger.error("The model could not be loaded because its checkpoint file in .bin/.pt/.safetensors format could not be located.")
- return
-
- use_safetensors = pt_path.suffix == '.safetensors'
- if not (path_to_model / "quantize_config.json").exists():
- quantize_config = BaseQuantizeConfig(
- bits=bits if (bits := shared.args.wbits) > 0 else 4,
- group_size=gs if (gs := shared.args.groupsize) > 0 else -1,
- desc_act=shared.args.desc_act
- )
- else:
- quantize_config = None
-
- # Define the params for AutoGPTQForCausalLM.from_quantized
- params = {
- 'model_basename': pt_path.stem,
- 'device': "cuda:0" if not shared.args.cpu else "cpu",
- 'use_triton': shared.args.triton,
- 'inject_fused_attention': not shared.args.no_inject_fused_attention,
- 'inject_fused_mlp': not shared.args.no_inject_fused_mlp,
- 'use_safetensors': use_safetensors,
- 'trust_remote_code': shared.args.trust_remote_code,
- 'max_memory': get_max_memory_dict(),
- 'quantize_config': quantize_config,
- 'use_cuda_fp16': not shared.args.no_use_cuda_fp16,
- 'disable_exllama': shared.args.disable_exllama,
- }
-
- logger.info(f"The AutoGPTQ params are: {params}")
- model = AutoGPTQForCausalLM.from_quantized(path_to_model, **params)
-
- # These lines fix the multimodal extension when used with AutoGPTQ
- if hasattr(model, 'model'):
- if not hasattr(model, 'dtype'):
- if hasattr(model.model, 'dtype'):
- model.dtype = model.model.dtype
-
- if hasattr(model.model, 'model') and hasattr(model.model.model, 'embed_tokens'):
- if not hasattr(model, 'embed_tokens'):
- model.embed_tokens = model.model.model.embed_tokens
-
- if not hasattr(model.model, 'embed_tokens'):
- model.model.embed_tokens = model.model.model.embed_tokens
-
- return model
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/parse.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/parse.py
deleted file mode 100644
index f60f0d611b8d75692221d0edd7dc993b0a6445c9..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/parse.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-
-from io import StringIO
-
-from .file_client import FileClient
-
-
-def list_from_file(filename,
- prefix='',
- offset=0,
- max_num=0,
- encoding='utf-8',
- file_client_args=None):
- """Load a text file and parse the content as a list of strings.
-
- Note:
- In v1.3.16 and later, ``list_from_file`` supports loading a text file
- which can be storaged in different backends and parsing the content as
- a list for strings.
-
- Args:
- filename (str): Filename.
- prefix (str): The prefix to be inserted to the beginning of each item.
- offset (int): The offset of lines.
- max_num (int): The maximum number of lines to be read,
- zeros and negatives mean no limitation.
- encoding (str): Encoding used to open the file. Default utf-8.
- file_client_args (dict, optional): Arguments to instantiate a
- FileClient. See :class:`mmcv.fileio.FileClient` for details.
- Default: None.
-
- Examples:
- >>> list_from_file('/path/of/your/file') # disk
- ['hello', 'world']
- >>> list_from_file('s3://path/of/your/file') # ceph or petrel
- ['hello', 'world']
-
- Returns:
- list[str]: A list of strings.
- """
- cnt = 0
- item_list = []
- file_client = FileClient.infer_client(file_client_args, filename)
- with StringIO(file_client.get_text(filename, encoding)) as f:
- for _ in range(offset):
- f.readline()
- for line in f:
- if 0 < max_num <= cnt:
- break
- item_list.append(prefix + line.rstrip('\n\r'))
- cnt += 1
- return item_list
-
-
-def dict_from_file(filename,
- key_type=str,
- encoding='utf-8',
- file_client_args=None):
- """Load a text file and parse the content as a dict.
-
- Each line of the text file will be two or more columns split by
- whitespaces or tabs. The first column will be parsed as dict keys, and
- the following columns will be parsed as dict values.
-
- Note:
- In v1.3.16 and later, ``dict_from_file`` supports loading a text file
- which can be storaged in different backends and parsing the content as
- a dict.
-
- Args:
- filename(str): Filename.
- key_type(type): Type of the dict keys. str is user by default and
- type conversion will be performed if specified.
- encoding (str): Encoding used to open the file. Default utf-8.
- file_client_args (dict, optional): Arguments to instantiate a
- FileClient. See :class:`mmcv.fileio.FileClient` for details.
- Default: None.
-
- Examples:
- >>> dict_from_file('/path/of/your/file') # disk
- {'key1': 'value1', 'key2': 'value2'}
- >>> dict_from_file('s3://path/of/your/file') # ceph or petrel
- {'key1': 'value1', 'key2': 'value2'}
-
- Returns:
- dict: The parsed contents.
- """
- mapping = {}
- file_client = FileClient.infer_client(file_client_args, filename)
- with StringIO(file_client.get_text(filename, encoding)) as f:
- for line in f:
- items = line.rstrip('\n').split()
- assert len(items) >= 2
- key = key_type(items[0])
- val = items[1:] if len(items) > 2 else items[1]
- mapping[key] = val
- return mapping
diff --git a/spaces/Anustup/NS_AI_LABS/src/vad.py b/spaces/Anustup/NS_AI_LABS/src/vad.py
deleted file mode 100644
index 4d11c28eb32953f3829ff4f8a4e4030e22a22140..0000000000000000000000000000000000000000
--- a/spaces/Anustup/NS_AI_LABS/src/vad.py
+++ /dev/null
@@ -1,477 +0,0 @@
-from abc import ABC, abstractmethod
-from collections import Counter, deque
-
-from typing import Any, Deque, Iterator, List, Dict
-
-from pprint import pprint
-
-from src.segments import merge_timestamps
-
-# Workaround for https://github.com/tensorflow/tensorflow/issues/48797
-try:
- import tensorflow as tf
-except ModuleNotFoundError:
- # Error handling
- pass
-
-import torch
-
-import ffmpeg
-import numpy as np
-
-from src.utils import format_timestamp
-from enum import Enum
-
-class NonSpeechStrategy(Enum):
- """
- Ignore non-speech frames segments.
- """
- SKIP = 1
- """
- Just treat non-speech segments as speech.
- """
- CREATE_SEGMENT = 2
- """
- Expand speech segments into subsequent non-speech segments.
- """
- EXPAND_SEGMENT = 3
-
-# Defaults for Silero
-SPEECH_TRESHOLD = 0.3
-
-# Minimum size of segments to process
-MIN_SEGMENT_DURATION = 1
-
-# The maximum time for texts from old segments to be used in the next segment
-MAX_PROMPT_WINDOW = 0 # seconds (0 = disabled)
-PROMPT_NO_SPEECH_PROB = 0.1 # Do not pass the text from segments with a no speech probability higher than this
-
-VAD_MAX_PROCESSING_CHUNK = 60 * 60 # 60 minutes of audio
-
-class TranscriptionConfig(ABC):
- def __init__(self, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP,
- segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None,
- max_merge_size: float = None, max_prompt_window: float = None):
- self.non_speech_strategy = non_speech_strategy
- self.segment_padding_left = segment_padding_left
- self.segment_padding_right = segment_padding_right
- self.max_silent_period = max_silent_period
- self.max_merge_size = max_merge_size
- self.max_prompt_window = max_prompt_window
-
-class PeriodicTranscriptionConfig(TranscriptionConfig):
- def __init__(self, periodic_duration: float, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP,
- segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None,
- max_merge_size: float = None, max_prompt_window: float = None):
- super().__init__(non_speech_strategy, segment_padding_left, segment_padding_right, max_silent_period, max_merge_size, max_prompt_window)
- self.periodic_duration = periodic_duration
-
-class AbstractTranscription(ABC):
- def __init__(self, sampling_rate: int = 16000):
- self.sampling_rate = sampling_rate
-
- def get_audio_segment(self, str, start_time: str = None, duration: str = None):
- return load_audio(str, self.sampling_rate, start_time, duration)
-
- @abstractmethod
- def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig):
- """
- Get the start and end timestamps of the sections that should be transcribed by this VAD method.
-
- Parameters
- ----------
- audio: str
- The audio file.
- config: TranscriptionConfig
- The transcription configuration.
-
- Returns
- -------
- A list of start and end timestamps, in fractional seconds.
- """
- return
-
- def transcribe(self, audio: str, whisperCallable, config: TranscriptionConfig):
- """
- Transcribe the given audo file.
-
- Parameters
- ----------
- audio: str
- The audio file.
-
- whisperCallable: Callable[[Union[str, np.ndarray, torch.Tensor], int, str, str], dict[str, Union[dict, Any]]]
- The callback that is used to invoke Whisper on an audio file/buffer. The first parameter is the audio file/buffer,
- the second parameter is an optional text prompt, and the last is the current detected language. The return value is the result of the Whisper call.
-
- Returns
- -------
- A list of start and end timestamps, in fractional seconds.
- """
-
- # get speech timestamps from full audio file
- seconds_timestamps = self.get_transcribe_timestamps(audio, config)
-
- #for seconds_timestamp in seconds_timestamps:
- # print("VAD timestamp ", format_timestamp(seconds_timestamp['start']), " to ", format_timestamp(seconds_timestamp['end']))
-
- merged = merge_timestamps(seconds_timestamps, config.max_silent_period, config.max_merge_size, config.segment_padding_left, config.segment_padding_right)
-
- # A deque of transcribed segments that is passed to the next segment as a prompt
- prompt_window = deque()
-
- print("Timestamps:")
- pprint(merged)
-
- if config.non_speech_strategy != NonSpeechStrategy.SKIP:
- max_audio_duration = get_audio_duration(audio)
-
- # Expand segments to include the gaps between them
- if (config.non_speech_strategy == NonSpeechStrategy.CREATE_SEGMENT):
- # When we have a prompt window, we create speech segments betwen each segment if we exceed the merge size
- merged = self.fill_gaps(merged, total_duration=max_audio_duration, max_expand_size=config.max_merge_size)
- elif config.non_speech_strategy == NonSpeechStrategy.EXPAND_SEGMENT:
- # With no prompt window, it is better to just expand the segments (this effectively passes the prompt to the next segment)
- merged = self.expand_gaps(merged, total_duration=max_audio_duration)
- else:
- raise Exception("Unknown non-speech strategy: " + str(config.non_speech_strategy))
-
- print("Transcribing non-speech:")
- pprint(merged)
-
- result = {
- 'text': "",
- 'segments': [],
- 'language': ""
- }
- languageCounter = Counter()
- detected_language = None
-
- segment_index = -1
-
- # For each time segment, run whisper
- for segment in merged:
- segment_index += 1
- segment_start = segment['start']
- segment_end = segment['end']
- segment_expand_amount = segment.get('expand_amount', 0)
- segment_gap = segment.get('gap', False)
-
- segment_duration = segment_end - segment_start
-
- if segment_duration < MIN_SEGMENT_DURATION:
- continue;
-
- # Audio to run on Whisper
- segment_audio = self.get_audio_segment(audio, start_time = str(segment_start), duration = str(segment_duration))
- # Previous segments to use as a prompt
- segment_prompt = ' '.join([segment['text'] for segment in prompt_window]) if len(prompt_window) > 0 else None
-
- # Detected language
- detected_language = languageCounter.most_common(1)[0][0] if len(languageCounter) > 0 else None
-
- print("Running whisper from ", format_timestamp(segment_start), " to ", format_timestamp(segment_end), ", duration: ",
- segment_duration, "expanded: ", segment_expand_amount, "prompt: ", segment_prompt, "language: ", detected_language)
- segment_result = whisperCallable(segment_audio, segment_index, segment_prompt, detected_language)
-
- adjusted_segments = self.adjust_timestamp(segment_result["segments"], adjust_seconds=segment_start, max_source_time=segment_duration)
-
- # Propagate expand amount to the segments
- if (segment_expand_amount > 0):
- segment_without_expansion = segment_duration - segment_expand_amount
-
- for adjusted_segment in adjusted_segments:
- adjusted_segment_end = adjusted_segment['end']
-
- # Add expand amount if the segment got expanded
- if (adjusted_segment_end > segment_without_expansion):
- adjusted_segment["expand_amount"] = adjusted_segment_end - segment_without_expansion
-
- # Append to output
- result['text'] += segment_result['text']
- result['segments'].extend(adjusted_segments)
-
- # Increment detected language
- if not segment_gap:
- languageCounter[segment_result['language']] += 1
-
- # Update prompt window
- self.__update_prompt_window(prompt_window, adjusted_segments, segment_end, segment_gap, config)
-
- if detected_language is not None:
- result['language'] = detected_language
-
- return result
-
- def __update_prompt_window(self, prompt_window: Deque, adjusted_segments: List, segment_end: float, segment_gap: bool, config: TranscriptionConfig):
- if (config.max_prompt_window is not None and config.max_prompt_window > 0):
- # Add segments to the current prompt window (unless it is a speech gap)
- if not segment_gap:
- for segment in adjusted_segments:
- if segment.get('no_speech_prob', 0) <= PROMPT_NO_SPEECH_PROB:
- prompt_window.append(segment)
-
- while (len(prompt_window) > 0):
- first_end_time = prompt_window[0].get('end', 0)
- # Time expanded in the segments should be discounted from the prompt window
- first_expand_time = prompt_window[0].get('expand_amount', 0)
-
- if (first_end_time - first_expand_time < segment_end - config.max_prompt_window):
- prompt_window.popleft()
- else:
- break
-
- def include_gaps(self, segments: Iterator[dict], min_gap_length: float, total_duration: float):
- result = []
- last_end_time = 0
-
- for segment in segments:
- segment_start = float(segment['start'])
- segment_end = float(segment['end'])
-
- if (last_end_time != segment_start):
- delta = segment_start - last_end_time
-
- if (min_gap_length is None or delta >= min_gap_length):
- result.append( { 'start': last_end_time, 'end': segment_start, 'gap': True } )
-
- last_end_time = segment_end
- result.append(segment)
-
- # Also include total duration if specified
- if (total_duration is not None and last_end_time < total_duration):
- delta = total_duration - segment_start
-
- if (min_gap_length is None or delta >= min_gap_length):
- result.append( { 'start': last_end_time, 'end': total_duration, 'gap': True } )
-
- return result
-
- # Expand the end time of each segment to the start of the next segment
- def expand_gaps(self, segments: List[Dict[str, Any]], total_duration: float):
- result = []
-
- if len(segments) == 0:
- return result
-
- # Add gap at the beginning if needed
- if (segments[0]['start'] > 0):
- result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } )
-
- for i in range(len(segments) - 1):
- current_segment = segments[i]
- next_segment = segments[i + 1]
-
- delta = next_segment['start'] - current_segment['end']
-
- # Expand if the gap actually exists
- if (delta >= 0):
- current_segment = current_segment.copy()
- current_segment['expand_amount'] = delta
- current_segment['end'] = next_segment['start']
-
- result.append(current_segment)
-
- # Add last segment
- last_segment = segments[-1]
- result.append(last_segment)
-
- # Also include total duration if specified
- if (total_duration is not None):
- last_segment = result[-1]
-
- if (last_segment['end'] < total_duration):
- last_segment = last_segment.copy()
- last_segment['end'] = total_duration
- result[-1] = last_segment
-
- return result
-
- def fill_gaps(self, segments: List[Dict[str, Any]], total_duration: float, max_expand_size: float = None):
- result = []
-
- if len(segments) == 0:
- return result
-
- # Add gap at the beginning if needed
- if (segments[0]['start'] > 0):
- result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } )
-
- for i in range(len(segments) - 1):
- expanded = False
- current_segment = segments[i]
- next_segment = segments[i + 1]
-
- delta = next_segment['start'] - current_segment['end']
-
- if (max_expand_size is not None and delta <= max_expand_size):
- # Just expand the current segment
- current_segment = current_segment.copy()
- current_segment['expand_amount'] = delta
- current_segment['end'] = next_segment['start']
- expanded = True
-
- result.append(current_segment)
-
- # Add a gap to the next segment if needed
- if (delta >= 0 and not expanded):
- result.append({ 'start': current_segment['end'], 'end': next_segment['start'], 'gap': True } )
-
- # Add last segment
- last_segment = segments[-1]
- result.append(last_segment)
-
- # Also include total duration if specified
- if (total_duration is not None):
- last_segment = result[-1]
-
- delta = total_duration - last_segment['end']
-
- if (delta > 0):
- if (max_expand_size is not None and delta <= max_expand_size):
- # Expand the last segment
- last_segment = last_segment.copy()
- last_segment['expand_amount'] = delta
- last_segment['end'] = total_duration
- result[-1] = last_segment
- else:
- result.append({ 'start': last_segment['end'], 'end': total_duration, 'gap': True } )
-
- return result
-
- def adjust_timestamp(self, segments: Iterator[dict], adjust_seconds: float, max_source_time: float = None):
- result = []
-
- for segment in segments:
- segment_start = float(segment['start'])
- segment_end = float(segment['end'])
-
- # Filter segments?
- if (max_source_time is not None):
- if (segment_start > max_source_time):
- continue
- segment_end = min(max_source_time, segment_end)
-
- new_segment = segment.copy()
-
- # Add to start and end
- new_segment['start'] = segment_start + adjust_seconds
- new_segment['end'] = segment_end + adjust_seconds
- result.append(new_segment)
- return result
-
- def multiply_timestamps(self, timestamps: List[Dict[str, Any]], factor: float):
- result = []
-
- for entry in timestamps:
- start = entry['start']
- end = entry['end']
-
- result.append({
- 'start': start * factor,
- 'end': end * factor
- })
- return result
-
-class VadSileroTranscription(AbstractTranscription):
- def __init__(self, sampling_rate: int = 16000):
- super().__init__(sampling_rate=sampling_rate)
-
- self.model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad')
- (self.get_speech_timestamps, _, _, _, _) = utils
-
-
- def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig):
- audio_duration = get_audio_duration(audio)
- result = []
-
- # Divide procesisng of audio into chunks
- chunk_start = 0.0
-
- while (chunk_start < audio_duration):
- chunk_duration = min(audio_duration - chunk_start, VAD_MAX_PROCESSING_CHUNK)
-
- print("Processing VAD in chunk from {} to {}".format(format_timestamp(chunk_start), format_timestamp(chunk_start + chunk_duration)))
- wav = self.get_audio_segment(audio, str(chunk_start), str(chunk_duration))
-
- sample_timestamps = self.get_speech_timestamps(wav, self.model, sampling_rate=self.sampling_rate, threshold=SPEECH_TRESHOLD)
- seconds_timestamps = self.multiply_timestamps(sample_timestamps, factor=1 / self.sampling_rate)
- adjusted = self.adjust_timestamp(seconds_timestamps, adjust_seconds=chunk_start, max_source_time=chunk_start + chunk_duration)
-
- #pprint(adjusted)
-
- result.extend(adjusted)
- chunk_start += chunk_duration
-
- return result
-
-# A very simple VAD that just marks every N seconds as speech
-class VadPeriodicTranscription(AbstractTranscription):
- def __init__(self, sampling_rate: int = 16000):
- super().__init__(sampling_rate=sampling_rate)
-
- def get_transcribe_timestamps(self, audio: str, config: PeriodicTranscriptionConfig):
- # Get duration in seconds
- audio_duration = get_audio_duration(audio)
- result = []
-
- # Generate a timestamp every N seconds
- start_timestamp = 0
-
- while (start_timestamp < audio_duration):
- end_timestamp = min(start_timestamp + config.periodic_duration, audio_duration)
- segment_duration = end_timestamp - start_timestamp
-
- # Minimum duration is 1 second
- if (segment_duration >= 1):
- result.append( { 'start': start_timestamp, 'end': end_timestamp } )
-
- start_timestamp = end_timestamp
-
- return result
-
-def get_audio_duration(file: str):
- return float(ffmpeg.probe(file)["format"]["duration"])
-
-def load_audio(file: str, sample_rate: int = 16000,
- start_time: str = None, duration: str = None):
- """
- Open an audio file and read as mono waveform, resampling as necessary
-
- Parameters
- ----------
- file: str
- The audio file to open
-
- sr: int
- The sample rate to resample the audio if necessary
-
- start_time: str
- The start time, using the standard FFMPEG time duration syntax, or None to disable.
-
- duration: str
- The duration, using the standard FFMPEG time duration syntax, or None to disable.
-
- Returns
- -------
- A NumPy array containing the audio waveform, in float32 dtype.
- """
- try:
- inputArgs = {'threads': 0}
-
- if (start_time is not None):
- inputArgs['ss'] = start_time
- if (duration is not None):
- inputArgs['t'] = duration
-
- # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
- # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
- out, _ = (
- ffmpeg.input(file, **inputArgs)
- .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sample_rate)
- .run(cmd="ffmpeg", capture_stdout=True, capture_stderr=True)
- )
- except ffmpeg.Error as e:
- raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}")
-
- return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
\ No newline at end of file
diff --git a/spaces/Ariharasudhan/YoloV5/utils/loss.py b/spaces/Ariharasudhan/YoloV5/utils/loss.py
deleted file mode 100644
index 9b9c3d9f80181d1ad5b54d2700f32ba042368c31..0000000000000000000000000000000000000000
--- a/spaces/Ariharasudhan/YoloV5/utils/loss.py
+++ /dev/null
@@ -1,234 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Loss functions
-"""
-
-import torch
-import torch.nn as nn
-
-from utils.metrics import bbox_iou
-from utils.torch_utils import de_parallel
-
-
-def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
- # return positive, negative label smoothing BCE targets
- return 1.0 - 0.5 * eps, 0.5 * eps
-
-
-class BCEBlurWithLogitsLoss(nn.Module):
- # BCEwithLogitLoss() with reduced missing label effects.
- def __init__(self, alpha=0.05):
- super().__init__()
- self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss()
- self.alpha = alpha
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
- pred = torch.sigmoid(pred) # prob from logits
- dx = pred - true # reduce only missing label effects
- # dx = (pred - true).abs() # reduce missing label and false label effects
- alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
- loss *= alpha_factor
- return loss.mean()
-
-
-class FocalLoss(nn.Module):
- # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
- def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
- super().__init__()
- self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
- self.gamma = gamma
- self.alpha = alpha
- self.reduction = loss_fcn.reduction
- self.loss_fcn.reduction = 'none' # required to apply FL to each element
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
- # p_t = torch.exp(-loss)
- # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
-
- # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
- pred_prob = torch.sigmoid(pred) # prob from logits
- p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
- alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
- modulating_factor = (1.0 - p_t) ** self.gamma
- loss *= alpha_factor * modulating_factor
-
- if self.reduction == 'mean':
- return loss.mean()
- elif self.reduction == 'sum':
- return loss.sum()
- else: # 'none'
- return loss
-
-
-class QFocalLoss(nn.Module):
- # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
- def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
- super().__init__()
- self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
- self.gamma = gamma
- self.alpha = alpha
- self.reduction = loss_fcn.reduction
- self.loss_fcn.reduction = 'none' # required to apply FL to each element
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
-
- pred_prob = torch.sigmoid(pred) # prob from logits
- alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
- modulating_factor = torch.abs(true - pred_prob) ** self.gamma
- loss *= alpha_factor * modulating_factor
-
- if self.reduction == 'mean':
- return loss.mean()
- elif self.reduction == 'sum':
- return loss.sum()
- else: # 'none'
- return loss
-
-
-class ComputeLoss:
- sort_obj_iou = False
-
- # Compute losses
- def __init__(self, model, autobalance=False):
- device = next(model.parameters()).device # get model device
- h = model.hyp # hyperparameters
-
- # Define criteria
- BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
- BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-
- # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
- self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
-
- # Focal loss
- g = h['fl_gamma'] # focal loss gamma
- if g > 0:
- BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
- m = de_parallel(model).model[-1] # Detect() module
- self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
- self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index
- self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
- self.na = m.na # number of anchors
- self.nc = m.nc # number of classes
- self.nl = m.nl # number of layers
- self.anchors = m.anchors
- self.device = device
-
- def __call__(self, p, targets): # predictions, targets
- lcls = torch.zeros(1, device=self.device) # class loss
- lbox = torch.zeros(1, device=self.device) # box loss
- lobj = torch.zeros(1, device=self.device) # object loss
- tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
-
- # Losses
- for i, pi in enumerate(p): # layer index, layer predictions
- b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
- tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
-
- n = b.shape[0] # number of targets
- if n:
- # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0
- pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions
-
- # Regression
- pxy = pxy.sigmoid() * 2 - 0.5
- pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
- pbox = torch.cat((pxy, pwh), 1) # predicted box
- iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)
- lbox += (1.0 - iou).mean() # iou loss
-
- # Objectness
- iou = iou.detach().clamp(0).type(tobj.dtype)
- if self.sort_obj_iou:
- j = iou.argsort()
- b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
- if self.gr < 1:
- iou = (1.0 - self.gr) + self.gr * iou
- tobj[b, a, gj, gi] = iou # iou ratio
-
- # Classification
- if self.nc > 1: # cls loss (only if multiple classes)
- t = torch.full_like(pcls, self.cn, device=self.device) # targets
- t[range(n), tcls[i]] = self.cp
- lcls += self.BCEcls(pcls, t) # BCE
-
- # Append targets to text file
- # with open('targets.txt', 'a') as file:
- # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
- obji = self.BCEobj(pi[..., 4], tobj)
- lobj += obji * self.balance[i] # obj loss
- if self.autobalance:
- self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
- if self.autobalance:
- self.balance = [x / self.balance[self.ssi] for x in self.balance]
- lbox *= self.hyp['box']
- lobj *= self.hyp['obj']
- lcls *= self.hyp['cls']
- bs = tobj.shape[0] # batch size
-
- return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
-
- def build_targets(self, p, targets):
- # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
- na, nt = self.na, targets.shape[0] # number of anchors, targets
- tcls, tbox, indices, anch = [], [], [], []
- gain = torch.ones(7, device=self.device) # normalized to gridspace gain
- ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
- targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices
-
- g = 0.5 # bias
- off = torch.tensor(
- [
- [0, 0],
- [1, 0],
- [0, 1],
- [-1, 0],
- [0, -1], # j,k,l,m
- # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
- ],
- device=self.device).float() * g # offsets
-
- for i in range(self.nl):
- anchors, shape = self.anchors[i], p[i].shape
- gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
-
- # Match targets to anchors
- t = targets * gain # shape(3,n,7)
- if nt:
- # Matches
- r = t[..., 4:6] / anchors[:, None] # wh ratio
- j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare
- # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
- t = t[j] # filter
-
- # Offsets
- gxy = t[:, 2:4] # grid xy
- gxi = gain[[2, 3]] - gxy # inverse
- j, k = ((gxy % 1 < g) & (gxy > 1)).T
- l, m = ((gxi % 1 < g) & (gxi > 1)).T
- j = torch.stack((torch.ones_like(j), j, k, l, m))
- t = t.repeat((5, 1, 1))[j]
- offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
- else:
- t = targets[0]
- offsets = 0
-
- # Define
- bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
- a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class
- gij = (gxy - offsets).long()
- gi, gj = gij.T # grid indices
-
- # Append
- indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid
- tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
- anch.append(anchors[a]) # anchors
- tcls.append(c) # class
-
- return tcls, tbox, indices, anch
diff --git a/spaces/Arnx/MusicGenXvAKN/audiocraft/modules/conv.py b/spaces/Arnx/MusicGenXvAKN/audiocraft/modules/conv.py
deleted file mode 100644
index 972938ab84712eb06e1b10cea25444eee51d6637..0000000000000000000000000000000000000000
--- a/spaces/Arnx/MusicGenXvAKN/audiocraft/modules/conv.py
+++ /dev/null
@@ -1,245 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-import warnings
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn.utils import spectral_norm, weight_norm
-
-
-CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
- 'time_group_norm'])
-
-
-def apply_parametrization_norm(module: nn.Module, norm: str = 'none'):
- assert norm in CONV_NORMALIZATIONS
- if norm == 'weight_norm':
- return weight_norm(module)
- elif norm == 'spectral_norm':
- return spectral_norm(module)
- else:
- # We already check was in CONV_NORMALIZATION, so any other choice
- # doesn't need reparametrization.
- return module
-
-
-def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs):
- """Return the proper normalization module. If causal is True, this will ensure the returned
- module is causal, or return an error if the normalization doesn't support causal evaluation.
- """
- assert norm in CONV_NORMALIZATIONS
- if norm == 'time_group_norm':
- if causal:
- raise ValueError("GroupNorm doesn't support causal evaluation.")
- assert isinstance(module, nn.modules.conv._ConvNd)
- return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
- else:
- return nn.Identity()
-
-
-def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
- padding_total: int = 0) -> int:
- """See `pad_for_conv1d`.
- """
- length = x.shape[-1]
- n_frames = (length - kernel_size + padding_total) / stride + 1
- ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
- return ideal_length - length
-
-
-def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
- """Pad for a convolution to make sure that the last window is full.
- Extra padding is added at the end. This is required to ensure that we can rebuild
- an output of the same length, as otherwise, even with padding, some time steps
- might get removed.
- For instance, with total padding = 4, kernel size = 4, stride = 2:
- 0 0 1 2 3 4 5 0 0 # (0s are padding)
- 1 2 3 # (output frames of a convolution, last 0 is never used)
- 0 0 1 2 3 4 5 0 # (output of tr. conv., but pos. 5 is going to get removed as padding)
- 1 2 3 4 # once you removed padding, we are missing one time step !
- """
- extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
- return F.pad(x, (0, extra_padding))
-
-
-def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
- """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
- If this is the case, we insert extra 0 padding to the right before the reflection happen.
- """
- length = x.shape[-1]
- padding_left, padding_right = paddings
- assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
- if mode == 'reflect':
- max_pad = max(padding_left, padding_right)
- extra_pad = 0
- if length <= max_pad:
- extra_pad = max_pad - length + 1
- x = F.pad(x, (0, extra_pad))
- padded = F.pad(x, paddings, mode, value)
- end = padded.shape[-1] - extra_pad
- return padded[..., :end]
- else:
- return F.pad(x, paddings, mode, value)
-
-
-def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
- """Remove padding from x, handling properly zero padding. Only for 1d!
- """
- padding_left, padding_right = paddings
- assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
- assert (padding_left + padding_right) <= x.shape[-1]
- end = x.shape[-1] - padding_right
- return x[..., padding_left: end]
-
-
-class NormConv1d(nn.Module):
- """Wrapper around Conv1d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, causal: bool = False, norm: str = 'none',
- norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
- self.norm_type = norm
-
- def forward(self, x):
- x = self.conv(x)
- x = self.norm(x)
- return x
-
-
-class NormConv2d(nn.Module):
- """Wrapper around Conv2d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
- self.norm_type = norm
-
- def forward(self, x):
- x = self.conv(x)
- x = self.norm(x)
- return x
-
-
-class NormConvTranspose1d(nn.Module):
- """Wrapper around ConvTranspose1d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, causal: bool = False, norm: str = 'none',
- norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
- self.norm_type = norm
-
- def forward(self, x):
- x = self.convtr(x)
- x = self.norm(x)
- return x
-
-
-class NormConvTranspose2d(nn.Module):
- """Wrapper around ConvTranspose2d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
-
- def forward(self, x):
- x = self.convtr(x)
- x = self.norm(x)
- return x
-
-
-class StreamableConv1d(nn.Module):
- """Conv1d with some builtin handling of asymmetric or causal padding
- and normalization.
- """
- def __init__(self, in_channels: int, out_channels: int,
- kernel_size: int, stride: int = 1, dilation: int = 1,
- groups: int = 1, bias: bool = True, causal: bool = False,
- norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
- pad_mode: str = 'reflect'):
- super().__init__()
- # warn user on unusual setup between dilation and stride
- if stride > 1 and dilation > 1:
- warnings.warn('StreamableConv1d has been initialized with stride > 1 and dilation > 1'
- f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).')
- self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
- dilation=dilation, groups=groups, bias=bias, causal=causal,
- norm=norm, norm_kwargs=norm_kwargs)
- self.causal = causal
- self.pad_mode = pad_mode
-
- def forward(self, x):
- B, C, T = x.shape
- kernel_size = self.conv.conv.kernel_size[0]
- stride = self.conv.conv.stride[0]
- dilation = self.conv.conv.dilation[0]
- kernel_size = (kernel_size - 1) * dilation + 1 # effective kernel size with dilations
- padding_total = kernel_size - stride
- extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
- if self.causal:
- # Left padding for causal
- x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
- else:
- # Asymmetric padding required for odd strides
- padding_right = padding_total // 2
- padding_left = padding_total - padding_right
- x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
- return self.conv(x)
-
-
-class StreamableConvTranspose1d(nn.Module):
- """ConvTranspose1d with some builtin handling of asymmetric or causal padding
- and normalization.
- """
- def __init__(self, in_channels: int, out_channels: int,
- kernel_size: int, stride: int = 1, causal: bool = False,
- norm: str = 'none', trim_right_ratio: float = 1.,
- norm_kwargs: tp.Dict[str, tp.Any] = {}):
- super().__init__()
- self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
- causal=causal, norm=norm, norm_kwargs=norm_kwargs)
- self.causal = causal
- self.trim_right_ratio = trim_right_ratio
- assert self.causal or self.trim_right_ratio == 1., \
- "`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
- assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
-
- def forward(self, x):
- kernel_size = self.convtr.convtr.kernel_size[0]
- stride = self.convtr.convtr.stride[0]
- padding_total = kernel_size - stride
-
- y = self.convtr(x)
-
- # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
- # removed at the very end, when keeping only the right length for the output,
- # as removing it here would require also passing the length at the matching layer
- # in the encoder.
- if self.causal:
- # Trim the padding on the right according to the specified ratio
- # if trim_right_ratio = 1.0, trim everything from right
- padding_right = math.ceil(padding_total * self.trim_right_ratio)
- padding_left = padding_total - padding_right
- y = unpad1d(y, (padding_left, padding_right))
- else:
- # Asymmetric padding required for odd strides
- padding_right = padding_total // 2
- padding_left = padding_total - padding_right
- y = unpad1d(y, (padding_left, padding_right))
- return y
diff --git a/spaces/Ash123/stable-diffusion-nano/share_btn.py b/spaces/Ash123/stable-diffusion-nano/share_btn.py
deleted file mode 100644
index 4c9aa8a91b1d0f86746fb118c19b03df86d424a3..0000000000000000000000000000000000000000
--- a/spaces/Ash123/stable-diffusion-nano/share_btn.py
+++ /dev/null
@@ -1,60 +0,0 @@
-community_icon_html = """"""
-
-loading_icon_html = """"""
-
-share_js = """async () => {
- async function uploadFile(file){
- const UPLOAD_URL = 'https://huggingface.co/uploads';
- const response = await fetch(UPLOAD_URL, {
- method: 'POST',
- headers: {
- 'Content-Type': file.type,
- 'X-Requested-With': 'XMLHttpRequest',
- },
- body: file, /// <- File inherits from Blob
- });
- const url = await response.text();
- return url;
- }
- const gradioEl = document.querySelector('body > gradio-app');
- const imgEls = gradioEl.querySelectorAll('#gallery img');
- const promptTxt = gradioEl.querySelector('#prompt-text-input input').value;
- const shareBtnEl = gradioEl.querySelector('#share-btn');
- const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
- const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
- if(!imgEls.length){
- return;
- };
- shareBtnEl.style.pointerEvents = 'none';
- shareIconEl.style.display = 'none';
- loadingIconEl.style.removeProperty('display');
- const files = await Promise.all(
- [...imgEls].map(async (imgEl) => {
- const res = await fetch(imgEl.src);
- const blob = await res.blob();
- const imgId = Date.now() % 200;
- const fileName = `diffuse-the-rest-${{imgId}}.jpg`;
- return new File([blob], fileName, { type: 'image/jpeg' });
- })
- );
- const urls = await Promise.all(files.map((f) => uploadFile(f)));
- const htmlImgs = urls.map(url => ``);
- const descriptionMd = `
-${htmlImgs.join(`\n`)}
-
`;
- const params = new URLSearchParams({
- title: promptTxt,
- description: descriptionMd,
- });
- const paramsStr = params.toString();
- window.open(`https://huggingface.co/spaces/stabilityai/stable-diffusion/discussions/new?${paramsStr}`, '_blank');
- shareBtnEl.style.removeProperty('pointer-events');
- shareIconEl.style.removeProperty('display');
- loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/appdirs.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/appdirs.py
deleted file mode 100644
index 16933bf8afedcbe3e9d4fcc04e5f7246228c56fc..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/appdirs.py
+++ /dev/null
@@ -1,52 +0,0 @@
-"""
-This code wraps the vendored appdirs module to so the return values are
-compatible for the current pip code base.
-
-The intention is to rewrite current usages gradually, keeping the tests pass,
-and eventually drop this after all usages are changed.
-"""
-
-import os
-import sys
-from typing import List
-
-from pip._vendor import platformdirs as _appdirs
-
-
-def user_cache_dir(appname: str) -> str:
- return _appdirs.user_cache_dir(appname, appauthor=False)
-
-
-def _macos_user_config_dir(appname: str, roaming: bool = True) -> str:
- # Use ~/Application Support/pip, if the directory exists.
- path = _appdirs.user_data_dir(appname, appauthor=False, roaming=roaming)
- if os.path.isdir(path):
- return path
-
- # Use a Linux-like ~/.config/pip, by default.
- linux_like_path = "~/.config/"
- if appname:
- linux_like_path = os.path.join(linux_like_path, appname)
-
- return os.path.expanduser(linux_like_path)
-
-
-def user_config_dir(appname: str, roaming: bool = True) -> str:
- if sys.platform == "darwin":
- return _macos_user_config_dir(appname, roaming)
-
- return _appdirs.user_config_dir(appname, appauthor=False, roaming=roaming)
-
-
-# for the discussion regarding site_config_dir locations
-# see
-def site_config_dirs(appname: str) -> List[str]:
- if sys.platform == "darwin":
- return [_appdirs.site_data_dir(appname, appauthor=False, multipath=True)]
-
- dirval = _appdirs.site_config_dir(appname, appauthor=False, multipath=True)
- if sys.platform == "win32":
- return [dirval]
-
- # Unix-y system. Look in /etc as well.
- return dirval.split(os.pathsep) + ["/etc"]
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/tomli/__init__.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/tomli/__init__.py
deleted file mode 100644
index 4c6ec97ec6961bcf184b6e0b2437b9924db0b9de..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/tomli/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# SPDX-License-Identifier: MIT
-# SPDX-FileCopyrightText: 2021 Taneli Hukkinen
-# Licensed to PSF under a Contributor Agreement.
-
-__all__ = ("loads", "load", "TOMLDecodeError")
-__version__ = "2.0.1" # DO NOT EDIT THIS LINE MANUALLY. LET bump2version UTILITY DO IT
-
-from ._parser import TOMLDecodeError, load, loads
-
-# Pretend this exception was created here.
-TOMLDecodeError.__module__ = __name__
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py
deleted file mode 100644
index 8f369a2afedb6c6e69fd52ff9a9a6b1cdf965937..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from .mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ import (
- dataloader,
- lr_multiplier,
- model,
- optimizer,
- train,
-)
-
-train.max_iter *= 4 # 100ep -> 400ep
-
-lr_multiplier.scheduler.milestones = [
- milestone * 4 for milestone in lr_multiplier.scheduler.milestones
-]
-lr_multiplier.scheduler.num_updates = train.max_iter
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/modeling/test_backbone.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/modeling/test_backbone.py
deleted file mode 100644
index 3bb100f9bd5b4939e4646821c5a60d51c8ea65fd..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/modeling/test_backbone.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-import unittest
-import torch
-
-import detectron2.export.torchscript # apply patch # noqa
-from detectron2 import model_zoo
-from detectron2.config import get_cfg
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.backbone import build_resnet_backbone
-from detectron2.modeling.backbone.fpn import build_resnet_fpn_backbone
-
-
-class TestBackBone(unittest.TestCase):
- def test_resnet_scriptability(self):
- cfg = get_cfg()
- resnet = build_resnet_backbone(cfg, ShapeSpec(channels=3))
-
- scripted_resnet = torch.jit.script(resnet)
-
- inp = torch.rand(2, 3, 100, 100)
- out1 = resnet(inp)["res4"]
- out2 = scripted_resnet(inp)["res4"]
- self.assertTrue(torch.allclose(out1, out2))
-
- def test_fpn_scriptability(self):
- cfg = model_zoo.get_config("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml")
- bb = build_resnet_fpn_backbone(cfg, ShapeSpec(channels=3))
- bb_s = torch.jit.script(bb)
-
- inp = torch.rand(2, 3, 128, 128)
- out1 = bb(inp)["p5"]
- out2 = bb_s(inp)["p5"]
- self.assertTrue(torch.allclose(out1, out2))
diff --git a/spaces/Bart92/RVC_HF/demucs/__main__.py b/spaces/Bart92/RVC_HF/demucs/__main__.py
deleted file mode 100644
index 5148f20623bdaa827777558844796ded1876d7d0..0000000000000000000000000000000000000000
--- a/spaces/Bart92/RVC_HF/demucs/__main__.py
+++ /dev/null
@@ -1,317 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import json
-import math
-import os
-import sys
-import time
-from dataclasses import dataclass, field
-
-import torch as th
-from torch import distributed, nn
-from torch.nn.parallel.distributed import DistributedDataParallel
-
-from .augment import FlipChannels, FlipSign, Remix, Scale, Shift
-from .compressed import get_compressed_datasets
-from .model import Demucs
-from .parser import get_name, get_parser
-from .raw import Rawset
-from .repitch import RepitchedWrapper
-from .pretrained import load_pretrained, SOURCES
-from .tasnet import ConvTasNet
-from .test import evaluate
-from .train import train_model, validate_model
-from .utils import (human_seconds, load_model, save_model, get_state,
- save_state, sizeof_fmt, get_quantizer)
-from .wav import get_wav_datasets, get_musdb_wav_datasets
-
-
-@dataclass
-class SavedState:
- metrics: list = field(default_factory=list)
- last_state: dict = None
- best_state: dict = None
- optimizer: dict = None
-
-
-def main():
- parser = get_parser()
- args = parser.parse_args()
- name = get_name(parser, args)
- print(f"Experiment {name}")
-
- if args.musdb is None and args.rank == 0:
- print(
- "You must provide the path to the MusDB dataset with the --musdb flag. "
- "To download the MusDB dataset, see https://sigsep.github.io/datasets/musdb.html.",
- file=sys.stderr)
- sys.exit(1)
-
- eval_folder = args.evals / name
- eval_folder.mkdir(exist_ok=True, parents=True)
- args.logs.mkdir(exist_ok=True)
- metrics_path = args.logs / f"{name}.json"
- eval_folder.mkdir(exist_ok=True, parents=True)
- args.checkpoints.mkdir(exist_ok=True, parents=True)
- args.models.mkdir(exist_ok=True, parents=True)
-
- if args.device is None:
- device = "cpu"
- if th.cuda.is_available():
- device = "cuda"
- else:
- device = args.device
-
- th.manual_seed(args.seed)
- # Prevents too many threads to be started when running `museval` as it can be quite
- # inefficient on NUMA architectures.
- os.environ["OMP_NUM_THREADS"] = "1"
- os.environ["MKL_NUM_THREADS"] = "1"
-
- if args.world_size > 1:
- if device != "cuda" and args.rank == 0:
- print("Error: distributed training is only available with cuda device", file=sys.stderr)
- sys.exit(1)
- th.cuda.set_device(args.rank % th.cuda.device_count())
- distributed.init_process_group(backend="nccl",
- init_method="tcp://" + args.master,
- rank=args.rank,
- world_size=args.world_size)
-
- checkpoint = args.checkpoints / f"{name}.th"
- checkpoint_tmp = args.checkpoints / f"{name}.th.tmp"
- if args.restart and checkpoint.exists() and args.rank == 0:
- checkpoint.unlink()
-
- if args.test or args.test_pretrained:
- args.epochs = 1
- args.repeat = 0
- if args.test:
- model = load_model(args.models / args.test)
- else:
- model = load_pretrained(args.test_pretrained)
- elif args.tasnet:
- model = ConvTasNet(audio_channels=args.audio_channels,
- samplerate=args.samplerate, X=args.X,
- segment_length=4 * args.samples,
- sources=SOURCES)
- else:
- model = Demucs(
- audio_channels=args.audio_channels,
- channels=args.channels,
- context=args.context,
- depth=args.depth,
- glu=args.glu,
- growth=args.growth,
- kernel_size=args.kernel_size,
- lstm_layers=args.lstm_layers,
- rescale=args.rescale,
- rewrite=args.rewrite,
- stride=args.conv_stride,
- resample=args.resample,
- normalize=args.normalize,
- samplerate=args.samplerate,
- segment_length=4 * args.samples,
- sources=SOURCES,
- )
- model.to(device)
- if args.init:
- model.load_state_dict(load_pretrained(args.init).state_dict())
-
- if args.show:
- print(model)
- size = sizeof_fmt(4 * sum(p.numel() for p in model.parameters()))
- print(f"Model size {size}")
- return
-
- try:
- saved = th.load(checkpoint, map_location='cpu')
- except IOError:
- saved = SavedState()
-
- optimizer = th.optim.Adam(model.parameters(), lr=args.lr)
-
- quantizer = None
- quantizer = get_quantizer(model, args, optimizer)
-
- if saved.last_state is not None:
- model.load_state_dict(saved.last_state, strict=False)
- if saved.optimizer is not None:
- optimizer.load_state_dict(saved.optimizer)
-
- model_name = f"{name}.th"
- if args.save_model:
- if args.rank == 0:
- model.to("cpu")
- model.load_state_dict(saved.best_state)
- save_model(model, quantizer, args, args.models / model_name)
- return
- elif args.save_state:
- model_name = f"{args.save_state}.th"
- if args.rank == 0:
- model.to("cpu")
- model.load_state_dict(saved.best_state)
- state = get_state(model, quantizer)
- save_state(state, args.models / model_name)
- return
-
- if args.rank == 0:
- done = args.logs / f"{name}.done"
- if done.exists():
- done.unlink()
-
- augment = [Shift(args.data_stride)]
- if args.augment:
- augment += [FlipSign(), FlipChannels(), Scale(),
- Remix(group_size=args.remix_group_size)]
- augment = nn.Sequential(*augment).to(device)
- print("Agumentation pipeline:", augment)
-
- if args.mse:
- criterion = nn.MSELoss()
- else:
- criterion = nn.L1Loss()
-
- # Setting number of samples so that all convolution windows are full.
- # Prevents hard to debug mistake with the prediction being shifted compared
- # to the input mixture.
- samples = model.valid_length(args.samples)
- print(f"Number of training samples adjusted to {samples}")
- samples = samples + args.data_stride
- if args.repitch:
- # We need a bit more audio samples, to account for potential
- # tempo change.
- samples = math.ceil(samples / (1 - 0.01 * args.max_tempo))
-
- args.metadata.mkdir(exist_ok=True, parents=True)
- if args.raw:
- train_set = Rawset(args.raw / "train",
- samples=samples,
- channels=args.audio_channels,
- streams=range(1, len(model.sources) + 1),
- stride=args.data_stride)
-
- valid_set = Rawset(args.raw / "valid", channels=args.audio_channels)
- elif args.wav:
- train_set, valid_set = get_wav_datasets(args, samples, model.sources)
- elif args.is_wav:
- train_set, valid_set = get_musdb_wav_datasets(args, samples, model.sources)
- else:
- train_set, valid_set = get_compressed_datasets(args, samples)
-
- if args.repitch:
- train_set = RepitchedWrapper(
- train_set,
- proba=args.repitch,
- max_tempo=args.max_tempo)
-
- best_loss = float("inf")
- for epoch, metrics in enumerate(saved.metrics):
- print(f"Epoch {epoch:03d}: "
- f"train={metrics['train']:.8f} "
- f"valid={metrics['valid']:.8f} "
- f"best={metrics['best']:.4f} "
- f"ms={metrics.get('true_model_size', 0):.2f}MB "
- f"cms={metrics.get('compressed_model_size', 0):.2f}MB "
- f"duration={human_seconds(metrics['duration'])}")
- best_loss = metrics['best']
-
- if args.world_size > 1:
- dmodel = DistributedDataParallel(model,
- device_ids=[th.cuda.current_device()],
- output_device=th.cuda.current_device())
- else:
- dmodel = model
-
- for epoch in range(len(saved.metrics), args.epochs):
- begin = time.time()
- model.train()
- train_loss, model_size = train_model(
- epoch, train_set, dmodel, criterion, optimizer, augment,
- quantizer=quantizer,
- batch_size=args.batch_size,
- device=device,
- repeat=args.repeat,
- seed=args.seed,
- diffq=args.diffq,
- workers=args.workers,
- world_size=args.world_size)
- model.eval()
- valid_loss = validate_model(
- epoch, valid_set, model, criterion,
- device=device,
- rank=args.rank,
- split=args.split_valid,
- overlap=args.overlap,
- world_size=args.world_size)
-
- ms = 0
- cms = 0
- if quantizer and args.rank == 0:
- ms = quantizer.true_model_size()
- cms = quantizer.compressed_model_size(num_workers=min(40, args.world_size * 10))
-
- duration = time.time() - begin
- if valid_loss < best_loss and ms <= args.ms_target:
- best_loss = valid_loss
- saved.best_state = {
- key: value.to("cpu").clone()
- for key, value in model.state_dict().items()
- }
-
- saved.metrics.append({
- "train": train_loss,
- "valid": valid_loss,
- "best": best_loss,
- "duration": duration,
- "model_size": model_size,
- "true_model_size": ms,
- "compressed_model_size": cms,
- })
- if args.rank == 0:
- json.dump(saved.metrics, open(metrics_path, "w"))
-
- saved.last_state = model.state_dict()
- saved.optimizer = optimizer.state_dict()
- if args.rank == 0 and not args.test:
- th.save(saved, checkpoint_tmp)
- checkpoint_tmp.rename(checkpoint)
-
- print(f"Epoch {epoch:03d}: "
- f"train={train_loss:.8f} valid={valid_loss:.8f} best={best_loss:.4f} ms={ms:.2f}MB "
- f"cms={cms:.2f}MB "
- f"duration={human_seconds(duration)}")
-
- if args.world_size > 1:
- distributed.barrier()
-
- del dmodel
- model.load_state_dict(saved.best_state)
- if args.eval_cpu:
- device = "cpu"
- model.to(device)
- model.eval()
- evaluate(model, args.musdb, eval_folder,
- is_wav=args.is_wav,
- rank=args.rank,
- world_size=args.world_size,
- device=device,
- save=args.save,
- split=args.split_valid,
- shifts=args.shifts,
- overlap=args.overlap,
- workers=args.eval_workers)
- model.to("cpu")
- if args.rank == 0:
- if not (args.test or args.test_pretrained):
- save_model(model, quantizer, args, args.models / model_name)
- print("done")
- done.write_text("done")
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Benson/text-generation/Examples/Blockman Go Skyblock Hack Apk.md b/spaces/Benson/text-generation/Examples/Blockman Go Skyblock Hack Apk.md
deleted file mode 100644
index ffd30e7f784e1f50f3f218bc95434a3ff33fad75..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Blockman Go Skyblock Hack Apk.md
+++ /dev/null
@@ -1,86 +0,0 @@
-
-
Blockman Go Skyblock Hack Apk: Cómo obtener dinero ilimitado y más
-
¿Te encanta jugar Blockman Go Skyblock, pero te gustaría tener más recursos y opciones para disfrutar del juego? Si es así, usted podría estar interesado en Blockman Go Skyblock Hack Apk, una versión modificada del juego que le da dinero ilimitado, un menú de mod, y no hay anuncios. En este artículo, le diremos lo que es Blockman Go Skyblock, por qué necesita el hack apk, cómo descargar e instalar, y cómo usarlo. ¡Vamos a empezar!
Blockman Go Skyblock es un popular juego móvil que permite a los jugadores construir y gestionar sus propias ciudades virtuales. El juego se basa en el modo Skyblock de Minecraft, donde se comienza con una pequeña isla en el cielo y tiene que expandirse utilizando recursos limitados. También puedes interactuar con otros jugadores, unirte a minijuegos, intercambiar objetos y chatear con amigos.
-
El juego tiene muchas características que lo hacen divertido y adictivo, como:
-
-
Varios bloques y elementos para crear y usar
-
Pieles y trajes personalizables para tu personaje
-
Diferentes modos y mapas para explorar y jugar
-
Una comunidad amistosa y activa de jugadores
-
Actualizaciones y eventos regulares
-
-
¿Por qué necesita Blockman Go Skyblock Hack Apk?
-
Aunque Blockman Go Skyblock es un gran juego, también tiene algunos inconvenientes que pueden limitar su disfrute. Por ejemplo, necesitas gastar dinero real o ver anuncios para obtener más monedas, que se utilizan para comprar bloques, artículos, pieles y otras cosas. También tienes que lidiar con anuncios molestos que aparecen de vez en cuando. Y algunas de las funciones de mod están bloqueadas o restringidas a menos que pagues por ellas.
-
-
Por eso es posible que desee probar Blockman Go Skyblock Hack Apk, una versión modificada del juego que le da acceso a dinero ilimitado, un menú de mod, y sin anuncios. Con este hack apk, usted puede:
-
Dinero ilimitado
-
-
Menú de mods
-
Puedes acceder a un menú mod que te permite personalizar y controlar varios aspectos del juego. Por ejemplo, puedes activar o desactivar la gravedad, el modo volar, el modo velocidad, el modo dios, etc. También puedes cambiar el clima, el tiempo, la dificultad, etc. También puedes generar objetos, turbas, animales, etc. El menú mod te da más libertad y diversión en el juego.
-
No hay anuncios
-
Puedes disfrutar del juego sin interrupciones ni distracciones de los anuncios. No tienes que ver ningún anuncio para obtener más monedas o desbloquear funciones. Puedes jugar el juego sin problemas y en paz.
-
Cómo descargar e instalar Blockman Go Skyblock Hack Apk?
-
Si usted está interesado en la descarga y la instalación de Blockman Go Skyblock Hack Apk en su dispositivo, Estos son los pasos que debe seguir:
-
Requisitos
-
-
Un dispositivo Android con al menos 4 GB de RAM y 100 MB de espacio de almacenamiento gratuito
-
Una conexión a Internet estable
-
Una aplicación de administrador de archivos
-
Permitir la instalación de aplicaciones de fuentes desconocidas en la configuración del dispositivo
-
-
Enlace de descarga
-
Puede descargar el Blockman Go Skyblock Hack Apk archivo desde este enlace: Blockman Go Skyblock Hack Apk Download. Este enlace es seguro y seguro, y le dirigirá a un sitio de buena reputación donde se puede obtener la última versión del archivo apk modded.
-
Proceso de instalación
-
Una vez que haya descargado el archivo apk, siga estos pasos para instalarlo en su dispositivo:
-
-
Busque el archivo apk en su aplicación de administrador de archivos y toque en él.
-
Aparecerá una ventana emergente pidiéndole que confirme la instalación. Toque en "Instalar".
-
Espere a que se complete la instalación. Puede tardar unos segundos o minutos dependiendo del dispositivo.
-
Una vez realizada la instalación, toque en "Abrir" para iniciar el juego.
-
Disfrutar de Blockman Go Skyblock Hack Apk!
-
-
Cómo utilizar Blockman Go Skyblock Hack Apk?
-
-
Opciones de menú Mod
-
Para acceder al menú mod, toque en el icono que parece un engranaje en la esquina superior derecha de la pantalla. Aparecerá una lista de opciones, como:
-
-
gravedad: puede activar o desactivar la gravedad en el juego. Si lo apaga, puede volar libremente.
-
Modo mosca: Puede activar o desactivar el modo mosca en el juego. Si lo habilita, puede volar pulsando dos veces el botón de salto.
-
Modo de velocidad: Puedes aumentar o disminuir la velocidad de tu personaje en el juego. Puedes elegir entre normal, rápido o súper rápido.
-
Modo Dios: Puedes activar o desactivar el modo dios en el juego. Si lo activas, serás invencible e inmune a cualquier daño.
-
Tiempo: Puedes cambiar el tiempo en el juego. Puedes elegir entre soleado, lluvioso, nevado o tormentoso.
-
Tiempo: Puedes cambiar la hora en el juego. Puedes elegir entre día, noche, amanecer o atardecer.
-
Dificultad: Puedes cambiar el nivel de dificultad en el juego. Puedes elegir entre fácil, normal, duro o extremo.
-
Spawn: Puedes generar cualquier objeto, mafia, animal o NPC en el juego. Puede elegir entre una amplia gama de opciones y personalizar su cantidad y ubicación.
-
-
También puede cerrar el menú mod tocando el icono de nuevo.
-
Consejos y trucos
-
Aquí hay algunos consejos y trucos para sacar el máximo provecho de Blockman Go Skyblock Hack Apk:
-
-
Usa dinero ilimitado para comprar todo lo que quieras en el juego. También puedes usarlo para actualizar tu isla más rápido y fácil.
-
Utiliza las opciones de menú mod para personalizar y controlar tu experiencia de juego. También puedes usarlas para divertirte más y desafiarte a ti mismo.
-
No use anuncios para disfrutar del juego sin interrupciones ni distracciones. También puede guardar sus datos y duración de la batería evitando los anuncios.
-
-
Tenga cuidado de no abusar o abusar de las características de mod en el juego. Es posible que otros jugadores lo prohíban o lo denuncien.
-
-
Conclusión
-
En conclusión, Blockman Go Skyblock Hack Apk es una gran manera de disfrutar de Blockman Go Skyblock con más características y opciones. Puede obtener dinero ilimitado, un menú de mod, y no hay anuncios con esta versión modificada del juego. También puede descargarlo e instalarlo fácilmente en su dispositivo siguiendo nuestra guía. Y puedes usarlo de forma sencilla y segura siguiendo nuestros consejos y trucos. ¿Qué estás esperando? Descargar Blockman Go Skyblock Hack Apk ahora y divertirse!
-
Preguntas frecuentes
-
Aquí están algunas de las preguntas más comunes que la gente pregunta acerca de Blockman Go Skyblock Hack Apk:
-
-
¿Es seguro Blockman Go Skyblock Hack Apk?
-
Sí, Blockman Go Skyblock Hack Apk es seguro de usar, siempre y cuando se descarga desde una fuente de confianza y siga las instrucciones de instalación. El archivo apk modded no contiene ningún virus, malware o spyware que puede dañar su dispositivo o datos. Sin embargo, siempre debe tener cuidado al descargar e instalar cualquier archivo apk desde Internet, y escanearlo con una aplicación antivirus antes de abrirlo.
-
Es Blockman Go Skyblock Hack Apk libre?
-
Sí, Blockman Go Skyblock Hack Apk es libre de descargar y usar. No tienes que pagar dinero o ver ningún anuncio para obtener la versión modificada del juego. Usted puede disfrutar de todas las características y beneficios de la apk hack sin gastar un centavo.
-
¿Funciona Blockman Go Skyblock Hack Apk en dispositivos iOS?
-
No, Blockman Go Skyblock Hack Apk solo funciona en dispositivos Android. No es compatible con dispositivos iOS, como iPhones o iPads. Si quieres jugar a Blockman Go Skyblock en tu dispositivo iOS, tendrás que descargar la versión original del juego desde la App Store.
-
¿Puedo jugar Blockman Go Skyblock Hack Apk offline?
-
-
¿Puedo actualizar Blockman Go Skyblock Hack Apk?
-
Sí, puede actualizar Blockman Go Skyblock Hack Apk siempre que haya una nueva versión disponible. Sin embargo, usted tendrá que descargar e instalar el nuevo archivo apk manualmente, como el juego no se actualizará automáticamente. Puedes consultar las actualizaciones visitando el enlace de descarga o siguiendo nuestro blog.
- 64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Conseguir Sobre l Descarga Gratuita 2023 Apk.md b/spaces/Benson/text-generation/Examples/Conseguir Sobre l Descarga Gratuita 2023 Apk.md
deleted file mode 100644
index eeef76388d0950585c8d26bfefc2c7fd641ed4f8..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Conseguir Sobre l Descarga Gratuita 2023 Apk.md
+++ /dev/null
@@ -1,65 +0,0 @@
-
-
Cómo superar la descarga gratuita 2023 APK: Cómo jugar el juego más frustrante en su dispositivo Android
-
Si estás buscando un juego que ponga a prueba tu paciencia, habilidad y cordura, no busques más allá de Cómo superarlo con Bennett Foddy. Este juego es conocido por ser uno de los juegos más difíciles y provocadores de ira jamás creados. Y ahora, se puede jugar en su dispositivo Android de forma gratuita con la ayuda de un archivo APK. En este artículo, te contaremos todo lo que necesitas saber sobre Getting Over It, por qué querrías jugarlo en tu dispositivo móvil y cómo descargarlo e instalarlo gratis en 2023.
-
¿Qué es superar con Bennett Foddy?
-
Getting Over It with Bennett Foddy es un juego que fue lanzado en 2017 por el desarrollador indie Bennett Foddy. El juego es un homenaje a un juego de 2002 llamado Sexy Hiking, que también era conocido por su extrema dificultad y absurdo.
La jugabilidad de Getting Over es simple pero desafiante. Controlas a un hombre llamado Diógenes, que está atrapado en un caldero y sostiene un martillo. Su objetivo es utilizar el martillo para escalar varios obstáculos, como rocas, árboles, tuberías y muebles. El juego no tiene puntos de control, así que si te caes, tienes que empezar de nuevo desde el principio. El juego tampoco tiene fin, así que puedes seguir escalando todo el tiempo que quieras.
-
El juego está diseñado para ser frustrante e implacable. La física es realista pero impredecible, por lo que nunca se sabe cómo reaccionará el martillo al medio ambiente. Los controles también son difíciles de dominar, ya que tienes que usar el ratón o la pantalla táctil para balancear el martillo en diferentes direcciones. El juego también cuenta con un comentario de voz en off por el propio Bennett Foddy, quien se burlará, alentará o filosofará sobre su progreso (o falta de él).
-
La historia y la recepción de Cómo superarlo
-
-
A partir de junio de 2023, el juego ha vendido más de 5 millones de copias en varias plataformas, incluyendo Windows, Mac, iOS y Android. El juego también ha ganado varios premios, como el Nuovo Award en el Independent Games Festival en 2018 y el Best Design Award en los Game Developers Choice Awards en 2019.
-
¿Por qué quieres jugar a Getting Over It en tu dispositivo Android?
-
Superar Es un juego que se puede disfrutar (o sufrir) en cualquier plataforma, pero jugar en su dispositivo Android tiene algunas ventajas y desventajas.
-
Los beneficios de jugar Cómo superarlo en el móvil
-
-
Se puede jugar en cualquier lugar y en cualquier momento. No necesitas un PC o una consola para experimentar la emoción (o agonía) de Cómo superarlo - Puedes desafiarte a ti mismo con un esquema de control diferente. Usar la pantalla táctil para balancear el martillo puede ser más intuitivo o frustrante, dependiendo de tu preferencia. - Puedes ahorrar algo de dinero. El juego cuesta $7.99 en Steam, pero puedes descargarlo gratis en tu dispositivo Android con un archivo APK.
-
-
Los desafíos de jugar Cómo superarlo en el móvil
-
-
Necesitas un dispositivo compatible. No todos los dispositivos Android pueden ejecutar el juego sin problemas, por lo que debe verificar las especificaciones y la compatibilidad antes de descargar e instalar el juego. - Necesitas suficiente espacio de almacenamiento. El juego ocupa unos 200 MB de espacio en tu dispositivo, por lo que debes asegurarte de tener suficiente espacio para él. - Necesita una conexión a Internet estable. El juego requiere una conexión a Internet para verificar la licencia y acceder a algunas características, como las tablas de clasificación y el chat. - Usted puede encontrar algunos errores o fallos. El juego no es oficialmente compatible con el desarrollador en dispositivos Android, por lo que puede experimentar algunos problemas o errores al jugar el juego.
-
-
Cómo descargar e instalar Getting Over It APK gratis en 2023
-
-
Los requisitos para descargar e instalar Cómo superar APK
-
Antes de proceder, asegúrese de que tiene los siguientes requisitos:
-
-
Un dispositivo Android que se ejecuta en Android 5.0 o superior y tiene al menos 1 GB de RAM y 200 MB de espacio de almacenamiento libre. - Una conexión a Internet fiable para descargar el archivo APK y acceder a las características del juego. - Una aplicación de administrador de archivos que puede abrir e instalar archivos APK. - La voluntad de asumir el riesgo de descargar e instalar una versión no oficial del juego que puede contener malware o virus.
-
-
Los pasos para descargar e instalar Cómo superarlo APK
-
Una vez que haya cumplido con los requisitos, siga estos pasos para descargar e instalar Getting Over It APK:
-
-
Paso 1: Encontrar una fuente confiable para Cómo superarlo APK
-
El primer paso es encontrar un sitio web de confianza que ofrece Cómo superar APK gratis. Hay muchos sitios web que afirman proporcionar el juego, pero no todos ellos son seguros o legítimos. Algunos de ellos pueden contener archivos falsos o obsoletos, o peor, malware o virus que pueden dañar su dispositivo o robar sus datos.
-
Para evitar estos riesgos, usted debe hacer una investigación y comprobar las revisiones y calificaciones del sitio web antes de descargar nada de él. También debe buscar signos de credibilidad, como una URL segura (https), una política de privacidad clara y una información de contacto.
-
Un ejemplo de una fuente confiable para Getting Over It APK es [APKPure], que es un sitio web popular que ofrece varios archivos APK para los usuarios de Android. Puedes visitar su sitio web y buscar Cómo superarlo con Bennett Foddy en su barra de búsqueda.
-
Paso 2: Descargar el archivo APK a su dispositivo
-
El siguiente paso es descargar el archivo APK a su dispositivo. Una vez que haya encontrado una fuente confiable, haga clic en el botón de descarga y espere a que se descargue el archivo. El tamaño del archivo es de aproximadamente 200 MB, por lo que puede tomar algún tiempo dependiendo de su velocidad de Internet.
-
-
Paso 3: Habilitar fuentes desconocidas en la configuración del dispositivo
-
El tercer paso es habilitar fuentes desconocidas en la configuración del dispositivo. Esto es necesario porque los dispositivos Android normalmente bloquean la instalación de aplicaciones desde fuentes distintas de Google Play Store. Para permitir la instalación de Getting Over It APK, es necesario cambiar esta configuración.
-
Para hacer esto, vaya a la configuración del dispositivo y busque la opción de seguridad o privacidad. Entonces, encontrar la opción que dice "Fuentes desconocidas" o "Instalar aplicaciones desconocidas" y alternar en. Es posible que vea un mensaje de advertencia que dice "Su teléfono y los datos personales son más vulnerables a los ataques de aplicaciones de fuentes desconocidas. Usted acepta que usted es el único responsable de cualquier daño a su teléfono o pérdida de datos que pueda resultar del uso de estas aplicaciones." Toque en Aceptar o Permitir proceder.
Paso 4: Instalar el archivo APK y lanzar el juego
-
El paso final es instalar el archivo APK y lanzar el juego. Para hacer esto, toque en el archivo APK y siga las instrucciones en la pantalla. Usted puede ver un mensaje que dice "¿Desea instalar esta aplicación? No requiere ningún acceso especial." Toque en Instalar y espere a que termine la instalación.
-
Después de la instalación se ha completado, usted debe ver un mensaje que dice "App instalado". Toca Abrir para iniciar el juego, o ve al cajón de la aplicación y busca el icono Cómo superarlo. También puedes ver un acceso directo en la pantalla de inicio.
-
Felicitaciones, que ha descargado con éxito e instalado Cómo Sobre Ella APK de forma gratuita en 2023. Ahora, puedes disfrutar (o soportar) el juego más frustrante en tu dispositivo Android.
-
Conclusión
-
-
En este artículo, hemos explicado qué es Getting Over It, por qué querrías reproducirlo en tu dispositivo móvil y cómo descargarlo e instalarlo gratis en 2023. También le hemos proporcionado una fuente confiable para Getting Over It APK, así como los requisitos y pasos para descargarlo e instalarlo.
-
Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. Y si eres lo suficientemente valiente como para intentar superarlo en tu dispositivo Android, te deseamos buena suerte y que te diviertas (o no).
-
Preguntas frecuentes
-
Aquí hay algunas preguntas frecuentes sobre cómo superar APK:
-
-
Q: ¿Es Getting Over It APK seguro para descargar e instalar?
-- A: Cómo superarlo APK es seguro para descargar e instalar si lo obtiene de una fuente confiable, como [APKPure]. Sin embargo, siempre debe tener cuidado al descargar e instalar cualquier archivo APK de fuentes desconocidas, ya que pueden contener malware o virus que pueden dañar su dispositivo o robar sus datos. También debe escanear el archivo con una aplicación antivirus antes de instalarlo.
-
P: ¿Es legal descargar e instalar APK?
-- A: Getting Over It APK no es legal para descargar e instalar, ya que es una versión no oficial del juego que viola los derechos del desarrollador y los términos de servicio. Al descargar e instalar Getting Over It APK, usted está arriesgando la acción legal del desarrollador o las autoridades. También estás privando al desarrollador de sus ingresos legítimos de las ventas del juego. Por lo tanto, no recomendamos o avalar la descarga e instalación de Getting Over It APK.
-
Q: ¿Está superando APK compatible con todos los dispositivos Android?
-
-
Q: ¿Cómo puedo actualizar Cómo superarlo APK?
-- A: Getting Over It APK no tiene una función de actualización automática, por lo que tendrá que actualizar manualmente cada vez que una nueva versión está disponible. Para ello, tendrá que repetir los pasos para descargar e instalar Getting Over It APK desde una fuente confiable. También es posible que tenga que desinstalar la versión anterior del juego antes de instalar el nuevo.
-
Q: ¿Cómo puedo desinstalar Cómo superar APK?
-- A: Si desea desinstalar Getting Over It APK de su dispositivo, puede hacerlo siguiendo estos pasos: - Ir a la configuración de su dispositivo y buscar la opción de aplicaciones o aplicaciones. - Encuentra y toca en Cómo superarlo con Bennett Foddy en la lista de aplicaciones. - Toque en Desinstalar y confirmar su elección. - Espere a que la desinstalación termine y luego elimine el archivo APK de su dispositivo.
-
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/resources.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/resources.py
deleted file mode 100644
index fef52aa103ea369c96567b9af2a5a0ba14db5cb9..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/resources.py
+++ /dev/null
@@ -1,358 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2013-2017 Vinay Sajip.
-# Licensed to the Python Software Foundation under a contributor agreement.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-from __future__ import unicode_literals
-
-import bisect
-import io
-import logging
-import os
-import pkgutil
-import sys
-import types
-import zipimport
-
-from . import DistlibException
-from .util import cached_property, get_cache_base, Cache
-
-logger = logging.getLogger(__name__)
-
-
-cache = None # created when needed
-
-
-class ResourceCache(Cache):
- def __init__(self, base=None):
- if base is None:
- # Use native string to avoid issues on 2.x: see Python #20140.
- base = os.path.join(get_cache_base(), str('resource-cache'))
- super(ResourceCache, self).__init__(base)
-
- def is_stale(self, resource, path):
- """
- Is the cache stale for the given resource?
-
- :param resource: The :class:`Resource` being cached.
- :param path: The path of the resource in the cache.
- :return: True if the cache is stale.
- """
- # Cache invalidation is a hard problem :-)
- return True
-
- def get(self, resource):
- """
- Get a resource into the cache,
-
- :param resource: A :class:`Resource` instance.
- :return: The pathname of the resource in the cache.
- """
- prefix, path = resource.finder.get_cache_info(resource)
- if prefix is None:
- result = path
- else:
- result = os.path.join(self.base, self.prefix_to_dir(prefix), path)
- dirname = os.path.dirname(result)
- if not os.path.isdir(dirname):
- os.makedirs(dirname)
- if not os.path.exists(result):
- stale = True
- else:
- stale = self.is_stale(resource, path)
- if stale:
- # write the bytes of the resource to the cache location
- with open(result, 'wb') as f:
- f.write(resource.bytes)
- return result
-
-
-class ResourceBase(object):
- def __init__(self, finder, name):
- self.finder = finder
- self.name = name
-
-
-class Resource(ResourceBase):
- """
- A class representing an in-package resource, such as a data file. This is
- not normally instantiated by user code, but rather by a
- :class:`ResourceFinder` which manages the resource.
- """
- is_container = False # Backwards compatibility
-
- def as_stream(self):
- """
- Get the resource as a stream.
-
- This is not a property to make it obvious that it returns a new stream
- each time.
- """
- return self.finder.get_stream(self)
-
- @cached_property
- def file_path(self):
- global cache
- if cache is None:
- cache = ResourceCache()
- return cache.get(self)
-
- @cached_property
- def bytes(self):
- return self.finder.get_bytes(self)
-
- @cached_property
- def size(self):
- return self.finder.get_size(self)
-
-
-class ResourceContainer(ResourceBase):
- is_container = True # Backwards compatibility
-
- @cached_property
- def resources(self):
- return self.finder.get_resources(self)
-
-
-class ResourceFinder(object):
- """
- Resource finder for file system resources.
- """
-
- if sys.platform.startswith('java'):
- skipped_extensions = ('.pyc', '.pyo', '.class')
- else:
- skipped_extensions = ('.pyc', '.pyo')
-
- def __init__(self, module):
- self.module = module
- self.loader = getattr(module, '__loader__', None)
- self.base = os.path.dirname(getattr(module, '__file__', ''))
-
- def _adjust_path(self, path):
- return os.path.realpath(path)
-
- def _make_path(self, resource_name):
- # Issue #50: need to preserve type of path on Python 2.x
- # like os.path._get_sep
- if isinstance(resource_name, bytes): # should only happen on 2.x
- sep = b'/'
- else:
- sep = '/'
- parts = resource_name.split(sep)
- parts.insert(0, self.base)
- result = os.path.join(*parts)
- return self._adjust_path(result)
-
- def _find(self, path):
- return os.path.exists(path)
-
- def get_cache_info(self, resource):
- return None, resource.path
-
- def find(self, resource_name):
- path = self._make_path(resource_name)
- if not self._find(path):
- result = None
- else:
- if self._is_directory(path):
- result = ResourceContainer(self, resource_name)
- else:
- result = Resource(self, resource_name)
- result.path = path
- return result
-
- def get_stream(self, resource):
- return open(resource.path, 'rb')
-
- def get_bytes(self, resource):
- with open(resource.path, 'rb') as f:
- return f.read()
-
- def get_size(self, resource):
- return os.path.getsize(resource.path)
-
- def get_resources(self, resource):
- def allowed(f):
- return (f != '__pycache__' and not
- f.endswith(self.skipped_extensions))
- return set([f for f in os.listdir(resource.path) if allowed(f)])
-
- def is_container(self, resource):
- return self._is_directory(resource.path)
-
- _is_directory = staticmethod(os.path.isdir)
-
- def iterator(self, resource_name):
- resource = self.find(resource_name)
- if resource is not None:
- todo = [resource]
- while todo:
- resource = todo.pop(0)
- yield resource
- if resource.is_container:
- rname = resource.name
- for name in resource.resources:
- if not rname:
- new_name = name
- else:
- new_name = '/'.join([rname, name])
- child = self.find(new_name)
- if child.is_container:
- todo.append(child)
- else:
- yield child
-
-
-class ZipResourceFinder(ResourceFinder):
- """
- Resource finder for resources in .zip files.
- """
- def __init__(self, module):
- super(ZipResourceFinder, self).__init__(module)
- archive = self.loader.archive
- self.prefix_len = 1 + len(archive)
- # PyPy doesn't have a _files attr on zipimporter, and you can't set one
- if hasattr(self.loader, '_files'):
- self._files = self.loader._files
- else:
- self._files = zipimport._zip_directory_cache[archive]
- self.index = sorted(self._files)
-
- def _adjust_path(self, path):
- return path
-
- def _find(self, path):
- path = path[self.prefix_len:]
- if path in self._files:
- result = True
- else:
- if path and path[-1] != os.sep:
- path = path + os.sep
- i = bisect.bisect(self.index, path)
- try:
- result = self.index[i].startswith(path)
- except IndexError:
- result = False
- if not result:
- logger.debug('_find failed: %r %r', path, self.loader.prefix)
- else:
- logger.debug('_find worked: %r %r', path, self.loader.prefix)
- return result
-
- def get_cache_info(self, resource):
- prefix = self.loader.archive
- path = resource.path[1 + len(prefix):]
- return prefix, path
-
- def get_bytes(self, resource):
- return self.loader.get_data(resource.path)
-
- def get_stream(self, resource):
- return io.BytesIO(self.get_bytes(resource))
-
- def get_size(self, resource):
- path = resource.path[self.prefix_len:]
- return self._files[path][3]
-
- def get_resources(self, resource):
- path = resource.path[self.prefix_len:]
- if path and path[-1] != os.sep:
- path += os.sep
- plen = len(path)
- result = set()
- i = bisect.bisect(self.index, path)
- while i < len(self.index):
- if not self.index[i].startswith(path):
- break
- s = self.index[i][plen:]
- result.add(s.split(os.sep, 1)[0]) # only immediate children
- i += 1
- return result
-
- def _is_directory(self, path):
- path = path[self.prefix_len:]
- if path and path[-1] != os.sep:
- path += os.sep
- i = bisect.bisect(self.index, path)
- try:
- result = self.index[i].startswith(path)
- except IndexError:
- result = False
- return result
-
-
-_finder_registry = {
- type(None): ResourceFinder,
- zipimport.zipimporter: ZipResourceFinder
-}
-
-try:
- # In Python 3.6, _frozen_importlib -> _frozen_importlib_external
- try:
- import _frozen_importlib_external as _fi
- except ImportError:
- import _frozen_importlib as _fi
- _finder_registry[_fi.SourceFileLoader] = ResourceFinder
- _finder_registry[_fi.FileFinder] = ResourceFinder
- # See issue #146
- _finder_registry[_fi.SourcelessFileLoader] = ResourceFinder
- del _fi
-except (ImportError, AttributeError):
- pass
-
-
-def register_finder(loader, finder_maker):
- _finder_registry[type(loader)] = finder_maker
-
-
-_finder_cache = {}
-
-
-def finder(package):
- """
- Return a resource finder for a package.
- :param package: The name of the package.
- :return: A :class:`ResourceFinder` instance for the package.
- """
- if package in _finder_cache:
- result = _finder_cache[package]
- else:
- if package not in sys.modules:
- __import__(package)
- module = sys.modules[package]
- path = getattr(module, '__path__', None)
- if path is None:
- raise DistlibException('You cannot get a finder for a module, '
- 'only for a package')
- loader = getattr(module, '__loader__', None)
- finder_maker = _finder_registry.get(type(loader))
- if finder_maker is None:
- raise DistlibException('Unable to locate finder for %r' % package)
- result = finder_maker(module)
- _finder_cache[package] = result
- return result
-
-
-_dummy_module = types.ModuleType(str('__dummy__'))
-
-
-def finder_for_path(path):
- """
- Return a resource finder for a path, which should represent a container.
-
- :param path: The path.
- :return: A :class:`ResourceFinder` instance for the path.
- """
- result = None
- # calls any path hooks, gets importer into cache
- pkgutil.get_importer(path)
- loader = sys.path_importer_cache.get(path)
- finder = _finder_registry.get(type(loader))
- if finder:
- module = _dummy_module
- module.__file__ = os.path.join(path, '')
- module.__loader__ = loader
- result = finder(module)
- return result
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/segment.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/segment.py
deleted file mode 100644
index e125798463512ce4322a2cc139b4e5c1515e5c05..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/segment.py
+++ /dev/null
@@ -1,739 +0,0 @@
-from enum import IntEnum
-from functools import lru_cache
-from itertools import filterfalse
-from logging import getLogger
-from operator import attrgetter
-from typing import (
- TYPE_CHECKING,
- Dict,
- Iterable,
- List,
- NamedTuple,
- Optional,
- Sequence,
- Tuple,
- Type,
- Union,
-)
-
-from .cells import (
- _is_single_cell_widths,
- cached_cell_len,
- cell_len,
- get_character_cell_size,
- set_cell_size,
-)
-from .repr import Result, rich_repr
-from .style import Style
-
-if TYPE_CHECKING:
- from .console import Console, ConsoleOptions, RenderResult
-
-log = getLogger("rich")
-
-
-class ControlType(IntEnum):
- """Non-printable control codes which typically translate to ANSI codes."""
-
- BELL = 1
- CARRIAGE_RETURN = 2
- HOME = 3
- CLEAR = 4
- SHOW_CURSOR = 5
- HIDE_CURSOR = 6
- ENABLE_ALT_SCREEN = 7
- DISABLE_ALT_SCREEN = 8
- CURSOR_UP = 9
- CURSOR_DOWN = 10
- CURSOR_FORWARD = 11
- CURSOR_BACKWARD = 12
- CURSOR_MOVE_TO_COLUMN = 13
- CURSOR_MOVE_TO = 14
- ERASE_IN_LINE = 15
- SET_WINDOW_TITLE = 16
-
-
-ControlCode = Union[
- Tuple[ControlType],
- Tuple[ControlType, Union[int, str]],
- Tuple[ControlType, int, int],
-]
-
-
-@rich_repr()
-class Segment(NamedTuple):
- """A piece of text with associated style. Segments are produced by the Console render process and
- are ultimately converted in to strings to be written to the terminal.
-
- Args:
- text (str): A piece of text.
- style (:class:`~rich.style.Style`, optional): An optional style to apply to the text.
- control (Tuple[ControlCode], optional): Optional sequence of control codes.
-
- Attributes:
- cell_length (int): The cell length of this Segment.
- """
-
- text: str
- style: Optional[Style] = None
- control: Optional[Sequence[ControlCode]] = None
-
- @property
- def cell_length(self) -> int:
- """The number of terminal cells required to display self.text.
-
- Returns:
- int: A number of cells.
- """
- text, _style, control = self
- return 0 if control else cell_len(text)
-
- def __rich_repr__(self) -> Result:
- yield self.text
- if self.control is None:
- if self.style is not None:
- yield self.style
- else:
- yield self.style
- yield self.control
-
- def __bool__(self) -> bool:
- """Check if the segment contains text."""
- return bool(self.text)
-
- @property
- def is_control(self) -> bool:
- """Check if the segment contains control codes."""
- return self.control is not None
-
- @classmethod
- @lru_cache(1024 * 16)
- def _split_cells(cls, segment: "Segment", cut: int) -> Tuple["Segment", "Segment"]:
-
- text, style, control = segment
- _Segment = Segment
-
- cell_length = segment.cell_length
- if cut >= cell_length:
- return segment, _Segment("", style, control)
-
- cell_size = get_character_cell_size
-
- pos = int((cut / cell_length) * (len(text) - 1))
-
- before = text[:pos]
- cell_pos = cell_len(before)
- if cell_pos == cut:
- return (
- _Segment(before, style, control),
- _Segment(text[pos:], style, control),
- )
- while pos < len(text):
- char = text[pos]
- pos += 1
- cell_pos += cell_size(char)
- before = text[:pos]
- if cell_pos == cut:
- return (
- _Segment(before, style, control),
- _Segment(text[pos:], style, control),
- )
- if cell_pos > cut:
- return (
- _Segment(before[: pos - 1] + " ", style, control),
- _Segment(" " + text[pos:], style, control),
- )
-
- raise AssertionError("Will never reach here")
-
- def split_cells(self, cut: int) -> Tuple["Segment", "Segment"]:
- """Split segment in to two segments at the specified column.
-
- If the cut point falls in the middle of a 2-cell wide character then it is replaced
- by two spaces, to preserve the display width of the parent segment.
-
- Returns:
- Tuple[Segment, Segment]: Two segments.
- """
- text, style, control = self
-
- if _is_single_cell_widths(text):
- # Fast path with all 1 cell characters
- if cut >= len(text):
- return self, Segment("", style, control)
- return (
- Segment(text[:cut], style, control),
- Segment(text[cut:], style, control),
- )
-
- return self._split_cells(self, cut)
-
- @classmethod
- def line(cls) -> "Segment":
- """Make a new line segment."""
- return cls("\n")
-
- @classmethod
- def apply_style(
- cls,
- segments: Iterable["Segment"],
- style: Optional[Style] = None,
- post_style: Optional[Style] = None,
- ) -> Iterable["Segment"]:
- """Apply style(s) to an iterable of segments.
-
- Returns an iterable of segments where the style is replaced by ``style + segment.style + post_style``.
-
- Args:
- segments (Iterable[Segment]): Segments to process.
- style (Style, optional): Base style. Defaults to None.
- post_style (Style, optional): Style to apply on top of segment style. Defaults to None.
-
- Returns:
- Iterable[Segments]: A new iterable of segments (possibly the same iterable).
- """
- result_segments = segments
- if style:
- apply = style.__add__
- result_segments = (
- cls(text, None if control else apply(_style), control)
- for text, _style, control in result_segments
- )
- if post_style:
- result_segments = (
- cls(
- text,
- (
- None
- if control
- else (_style + post_style if _style else post_style)
- ),
- control,
- )
- for text, _style, control in result_segments
- )
- return result_segments
-
- @classmethod
- def filter_control(
- cls, segments: Iterable["Segment"], is_control: bool = False
- ) -> Iterable["Segment"]:
- """Filter segments by ``is_control`` attribute.
-
- Args:
- segments (Iterable[Segment]): An iterable of Segment instances.
- is_control (bool, optional): is_control flag to match in search.
-
- Returns:
- Iterable[Segment]: And iterable of Segment instances.
-
- """
- if is_control:
- return filter(attrgetter("control"), segments)
- else:
- return filterfalse(attrgetter("control"), segments)
-
- @classmethod
- def split_lines(cls, segments: Iterable["Segment"]) -> Iterable[List["Segment"]]:
- """Split a sequence of segments in to a list of lines.
-
- Args:
- segments (Iterable[Segment]): Segments potentially containing line feeds.
-
- Yields:
- Iterable[List[Segment]]: Iterable of segment lists, one per line.
- """
- line: List[Segment] = []
- append = line.append
-
- for segment in segments:
- if "\n" in segment.text and not segment.control:
- text, style, _ = segment
- while text:
- _text, new_line, text = text.partition("\n")
- if _text:
- append(cls(_text, style))
- if new_line:
- yield line
- line = []
- append = line.append
- else:
- append(segment)
- if line:
- yield line
-
- @classmethod
- def split_and_crop_lines(
- cls,
- segments: Iterable["Segment"],
- length: int,
- style: Optional[Style] = None,
- pad: bool = True,
- include_new_lines: bool = True,
- ) -> Iterable[List["Segment"]]:
- """Split segments in to lines, and crop lines greater than a given length.
-
- Args:
- segments (Iterable[Segment]): An iterable of segments, probably
- generated from console.render.
- length (int): Desired line length.
- style (Style, optional): Style to use for any padding.
- pad (bool): Enable padding of lines that are less than `length`.
-
- Returns:
- Iterable[List[Segment]]: An iterable of lines of segments.
- """
- line: List[Segment] = []
- append = line.append
-
- adjust_line_length = cls.adjust_line_length
- new_line_segment = cls("\n")
-
- for segment in segments:
- if "\n" in segment.text and not segment.control:
- text, segment_style, _ = segment
- while text:
- _text, new_line, text = text.partition("\n")
- if _text:
- append(cls(_text, segment_style))
- if new_line:
- cropped_line = adjust_line_length(
- line, length, style=style, pad=pad
- )
- if include_new_lines:
- cropped_line.append(new_line_segment)
- yield cropped_line
- line.clear()
- else:
- append(segment)
- if line:
- yield adjust_line_length(line, length, style=style, pad=pad)
-
- @classmethod
- def adjust_line_length(
- cls,
- line: List["Segment"],
- length: int,
- style: Optional[Style] = None,
- pad: bool = True,
- ) -> List["Segment"]:
- """Adjust a line to a given width (cropping or padding as required).
-
- Args:
- segments (Iterable[Segment]): A list of segments in a single line.
- length (int): The desired width of the line.
- style (Style, optional): The style of padding if used (space on the end). Defaults to None.
- pad (bool, optional): Pad lines with spaces if they are shorter than `length`. Defaults to True.
-
- Returns:
- List[Segment]: A line of segments with the desired length.
- """
- line_length = sum(segment.cell_length for segment in line)
- new_line: List[Segment]
-
- if line_length < length:
- if pad:
- new_line = line + [cls(" " * (length - line_length), style)]
- else:
- new_line = line[:]
- elif line_length > length:
- new_line = []
- append = new_line.append
- line_length = 0
- for segment in line:
- segment_length = segment.cell_length
- if line_length + segment_length < length or segment.control:
- append(segment)
- line_length += segment_length
- else:
- text, segment_style, _ = segment
- text = set_cell_size(text, length - line_length)
- append(cls(text, segment_style))
- break
- else:
- new_line = line[:]
- return new_line
-
- @classmethod
- def get_line_length(cls, line: List["Segment"]) -> int:
- """Get the length of list of segments.
-
- Args:
- line (List[Segment]): A line encoded as a list of Segments (assumes no '\\\\n' characters),
-
- Returns:
- int: The length of the line.
- """
- _cell_len = cell_len
- return sum(_cell_len(text) for text, style, control in line if not control)
-
- @classmethod
- def get_shape(cls, lines: List[List["Segment"]]) -> Tuple[int, int]:
- """Get the shape (enclosing rectangle) of a list of lines.
-
- Args:
- lines (List[List[Segment]]): A list of lines (no '\\\\n' characters).
-
- Returns:
- Tuple[int, int]: Width and height in characters.
- """
- get_line_length = cls.get_line_length
- max_width = max(get_line_length(line) for line in lines) if lines else 0
- return (max_width, len(lines))
-
- @classmethod
- def set_shape(
- cls,
- lines: List[List["Segment"]],
- width: int,
- height: Optional[int] = None,
- style: Optional[Style] = None,
- new_lines: bool = False,
- ) -> List[List["Segment"]]:
- """Set the shape of a list of lines (enclosing rectangle).
-
- Args:
- lines (List[List[Segment]]): A list of lines.
- width (int): Desired width.
- height (int, optional): Desired height or None for no change.
- style (Style, optional): Style of any padding added.
- new_lines (bool, optional): Padded lines should include "\n". Defaults to False.
-
- Returns:
- List[List[Segment]]: New list of lines.
- """
- _height = height or len(lines)
-
- blank = (
- [cls(" " * width + "\n", style)] if new_lines else [cls(" " * width, style)]
- )
-
- adjust_line_length = cls.adjust_line_length
- shaped_lines = lines[:_height]
- shaped_lines[:] = [
- adjust_line_length(line, width, style=style) for line in lines
- ]
- if len(shaped_lines) < _height:
- shaped_lines.extend([blank] * (_height - len(shaped_lines)))
- return shaped_lines
-
- @classmethod
- def align_top(
- cls: Type["Segment"],
- lines: List[List["Segment"]],
- width: int,
- height: int,
- style: Style,
- new_lines: bool = False,
- ) -> List[List["Segment"]]:
- """Aligns lines to top (adds extra lines to bottom as required).
-
- Args:
- lines (List[List[Segment]]): A list of lines.
- width (int): Desired width.
- height (int, optional): Desired height or None for no change.
- style (Style): Style of any padding added.
- new_lines (bool, optional): Padded lines should include "\n". Defaults to False.
-
- Returns:
- List[List[Segment]]: New list of lines.
- """
- extra_lines = height - len(lines)
- if not extra_lines:
- return lines[:]
- lines = lines[:height]
- blank = cls(" " * width + "\n", style) if new_lines else cls(" " * width, style)
- lines = lines + [[blank]] * extra_lines
- return lines
-
- @classmethod
- def align_bottom(
- cls: Type["Segment"],
- lines: List[List["Segment"]],
- width: int,
- height: int,
- style: Style,
- new_lines: bool = False,
- ) -> List[List["Segment"]]:
- """Aligns render to bottom (adds extra lines above as required).
-
- Args:
- lines (List[List[Segment]]): A list of lines.
- width (int): Desired width.
- height (int, optional): Desired height or None for no change.
- style (Style): Style of any padding added. Defaults to None.
- new_lines (bool, optional): Padded lines should include "\n". Defaults to False.
-
- Returns:
- List[List[Segment]]: New list of lines.
- """
- extra_lines = height - len(lines)
- if not extra_lines:
- return lines[:]
- lines = lines[:height]
- blank = cls(" " * width + "\n", style) if new_lines else cls(" " * width, style)
- lines = [[blank]] * extra_lines + lines
- return lines
-
- @classmethod
- def align_middle(
- cls: Type["Segment"],
- lines: List[List["Segment"]],
- width: int,
- height: int,
- style: Style,
- new_lines: bool = False,
- ) -> List[List["Segment"]]:
- """Aligns lines to middle (adds extra lines to above and below as required).
-
- Args:
- lines (List[List[Segment]]): A list of lines.
- width (int): Desired width.
- height (int, optional): Desired height or None for no change.
- style (Style): Style of any padding added.
- new_lines (bool, optional): Padded lines should include "\n". Defaults to False.
-
- Returns:
- List[List[Segment]]: New list of lines.
- """
- extra_lines = height - len(lines)
- if not extra_lines:
- return lines[:]
- lines = lines[:height]
- blank = cls(" " * width + "\n", style) if new_lines else cls(" " * width, style)
- top_lines = extra_lines // 2
- bottom_lines = extra_lines - top_lines
- lines = [[blank]] * top_lines + lines + [[blank]] * bottom_lines
- return lines
-
- @classmethod
- def simplify(cls, segments: Iterable["Segment"]) -> Iterable["Segment"]:
- """Simplify an iterable of segments by combining contiguous segments with the same style.
-
- Args:
- segments (Iterable[Segment]): An iterable of segments.
-
- Returns:
- Iterable[Segment]: A possibly smaller iterable of segments that will render the same way.
- """
- iter_segments = iter(segments)
- try:
- last_segment = next(iter_segments)
- except StopIteration:
- return
-
- _Segment = Segment
- for segment in iter_segments:
- if last_segment.style == segment.style and not segment.control:
- last_segment = _Segment(
- last_segment.text + segment.text, last_segment.style
- )
- else:
- yield last_segment
- last_segment = segment
- yield last_segment
-
- @classmethod
- def strip_links(cls, segments: Iterable["Segment"]) -> Iterable["Segment"]:
- """Remove all links from an iterable of styles.
-
- Args:
- segments (Iterable[Segment]): An iterable segments.
-
- Yields:
- Segment: Segments with link removed.
- """
- for segment in segments:
- if segment.control or segment.style is None:
- yield segment
- else:
- text, style, _control = segment
- yield cls(text, style.update_link(None) if style else None)
-
- @classmethod
- def strip_styles(cls, segments: Iterable["Segment"]) -> Iterable["Segment"]:
- """Remove all styles from an iterable of segments.
-
- Args:
- segments (Iterable[Segment]): An iterable segments.
-
- Yields:
- Segment: Segments with styles replace with None
- """
- for text, _style, control in segments:
- yield cls(text, None, control)
-
- @classmethod
- def remove_color(cls, segments: Iterable["Segment"]) -> Iterable["Segment"]:
- """Remove all color from an iterable of segments.
-
- Args:
- segments (Iterable[Segment]): An iterable segments.
-
- Yields:
- Segment: Segments with colorless style.
- """
-
- cache: Dict[Style, Style] = {}
- for text, style, control in segments:
- if style:
- colorless_style = cache.get(style)
- if colorless_style is None:
- colorless_style = style.without_color
- cache[style] = colorless_style
- yield cls(text, colorless_style, control)
- else:
- yield cls(text, None, control)
-
- @classmethod
- def divide(
- cls, segments: Iterable["Segment"], cuts: Iterable[int]
- ) -> Iterable[List["Segment"]]:
- """Divides an iterable of segments in to portions.
-
- Args:
- cuts (Iterable[int]): Cell positions where to divide.
-
- Yields:
- [Iterable[List[Segment]]]: An iterable of Segments in List.
- """
- split_segments: List["Segment"] = []
- add_segment = split_segments.append
-
- iter_cuts = iter(cuts)
-
- while True:
- cut = next(iter_cuts, -1)
- if cut == -1:
- return []
- if cut != 0:
- break
- yield []
- pos = 0
-
- segments_clear = split_segments.clear
- segments_copy = split_segments.copy
-
- _cell_len = cached_cell_len
- for segment in segments:
- text, _style, control = segment
- while text:
- end_pos = pos if control else pos + _cell_len(text)
- if end_pos < cut:
- add_segment(segment)
- pos = end_pos
- break
-
- if end_pos == cut:
- add_segment(segment)
- yield segments_copy()
- segments_clear()
- pos = end_pos
-
- cut = next(iter_cuts, -1)
- if cut == -1:
- if split_segments:
- yield segments_copy()
- return
-
- break
-
- else:
- before, segment = segment.split_cells(cut - pos)
- text, _style, control = segment
- add_segment(before)
- yield segments_copy()
- segments_clear()
- pos = cut
-
- cut = next(iter_cuts, -1)
- if cut == -1:
- if split_segments:
- yield segments_copy()
- return
-
- yield segments_copy()
-
-
-class Segments:
- """A simple renderable to render an iterable of segments. This class may be useful if
- you want to print segments outside of a __rich_console__ method.
-
- Args:
- segments (Iterable[Segment]): An iterable of segments.
- new_lines (bool, optional): Add new lines between segments. Defaults to False.
- """
-
- def __init__(self, segments: Iterable[Segment], new_lines: bool = False) -> None:
- self.segments = list(segments)
- self.new_lines = new_lines
-
- def __rich_console__(
- self, console: "Console", options: "ConsoleOptions"
- ) -> "RenderResult":
- if self.new_lines:
- line = Segment.line()
- for segment in self.segments:
- yield segment
- yield line
- else:
- yield from self.segments
-
-
-class SegmentLines:
- def __init__(self, lines: Iterable[List[Segment]], new_lines: bool = False) -> None:
- """A simple renderable containing a number of lines of segments. May be used as an intermediate
- in rendering process.
-
- Args:
- lines (Iterable[List[Segment]]): Lists of segments forming lines.
- new_lines (bool, optional): Insert new lines after each line. Defaults to False.
- """
- self.lines = list(lines)
- self.new_lines = new_lines
-
- def __rich_console__(
- self, console: "Console", options: "ConsoleOptions"
- ) -> "RenderResult":
- if self.new_lines:
- new_line = Segment.line()
- for line in self.lines:
- yield from line
- yield new_line
- else:
- for line in self.lines:
- yield from line
-
-
-if __name__ == "__main__": # pragma: no cover
- from pip._vendor.rich.console import Console
- from pip._vendor.rich.syntax import Syntax
- from pip._vendor.rich.text import Text
-
- code = """from rich.console import Console
-console = Console()
-text = Text.from_markup("Hello, [bold magenta]World[/]!")
-console.print(text)"""
-
- text = Text.from_markup("Hello, [bold magenta]World[/]!")
-
- console = Console()
-
- console.rule("rich.Segment")
- console.print(
- "A Segment is the last step in the Rich render process before generating text with ANSI codes."
- )
- console.print("\nConsider the following code:\n")
- console.print(Syntax(code, "python", line_numbers=True))
- console.print()
- console.print(
- "When you call [b]print()[/b], Rich [i]renders[/i] the object in to the following:\n"
- )
- fragments = list(console.render(text))
- console.print(fragments)
- console.print()
- console.print("The Segments are then processed to produce the following output:\n")
- console.print(text)
- console.print(
- "\nYou will only need to know this if you are implementing your own Rich renderables."
- )
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/text_file.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/text_file.py
deleted file mode 100644
index 7274d4b16e1bee16751515f42793ebefdd769b96..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/text_file.py
+++ /dev/null
@@ -1,287 +0,0 @@
-"""text_file
-
-provides the TextFile class, which gives an interface to text files
-that (optionally) takes care of stripping comments, ignoring blank
-lines, and joining lines with backslashes."""
-
-import sys
-
-
-class TextFile:
- """Provides a file-like object that takes care of all the things you
- commonly want to do when processing a text file that has some
- line-by-line syntax: strip comments (as long as "#" is your
- comment character), skip blank lines, join adjacent lines by
- escaping the newline (ie. backslash at end of line), strip
- leading and/or trailing whitespace. All of these are optional
- and independently controllable.
-
- Provides a 'warn()' method so you can generate warning messages that
- report physical line number, even if the logical line in question
- spans multiple physical lines. Also provides 'unreadline()' for
- implementing line-at-a-time lookahead.
-
- Constructor is called as:
-
- TextFile (filename=None, file=None, **options)
-
- It bombs (RuntimeError) if both 'filename' and 'file' are None;
- 'filename' should be a string, and 'file' a file object (or
- something that provides 'readline()' and 'close()' methods). It is
- recommended that you supply at least 'filename', so that TextFile
- can include it in warning messages. If 'file' is not supplied,
- TextFile creates its own using 'io.open()'.
-
- The options are all boolean, and affect the value returned by
- 'readline()':
- strip_comments [default: true]
- strip from "#" to end-of-line, as well as any whitespace
- leading up to the "#" -- unless it is escaped by a backslash
- lstrip_ws [default: false]
- strip leading whitespace from each line before returning it
- rstrip_ws [default: true]
- strip trailing whitespace (including line terminator!) from
- each line before returning it
- skip_blanks [default: true}
- skip lines that are empty *after* stripping comments and
- whitespace. (If both lstrip_ws and rstrip_ws are false,
- then some lines may consist of solely whitespace: these will
- *not* be skipped, even if 'skip_blanks' is true.)
- join_lines [default: false]
- if a backslash is the last non-newline character on a line
- after stripping comments and whitespace, join the following line
- to it to form one "logical line"; if N consecutive lines end
- with a backslash, then N+1 physical lines will be joined to
- form one logical line.
- collapse_join [default: false]
- strip leading whitespace from lines that are joined to their
- predecessor; only matters if (join_lines and not lstrip_ws)
- errors [default: 'strict']
- error handler used to decode the file content
-
- Note that since 'rstrip_ws' can strip the trailing newline, the
- semantics of 'readline()' must differ from those of the builtin file
- object's 'readline()' method! In particular, 'readline()' returns
- None for end-of-file: an empty string might just be a blank line (or
- an all-whitespace line), if 'rstrip_ws' is true but 'skip_blanks' is
- not."""
-
- default_options = {
- 'strip_comments': 1,
- 'skip_blanks': 1,
- 'lstrip_ws': 0,
- 'rstrip_ws': 1,
- 'join_lines': 0,
- 'collapse_join': 0,
- 'errors': 'strict',
- }
-
- def __init__(self, filename=None, file=None, **options):
- """Construct a new TextFile object. At least one of 'filename'
- (a string) and 'file' (a file-like object) must be supplied.
- They keyword argument options are described above and affect
- the values returned by 'readline()'."""
- if filename is None and file is None:
- raise RuntimeError(
- "you must supply either or both of 'filename' and 'file'"
- )
-
- # set values for all options -- either from client option hash
- # or fallback to default_options
- for opt in self.default_options.keys():
- if opt in options:
- setattr(self, opt, options[opt])
- else:
- setattr(self, opt, self.default_options[opt])
-
- # sanity check client option hash
- for opt in options.keys():
- if opt not in self.default_options:
- raise KeyError("invalid TextFile option '%s'" % opt)
-
- if file is None:
- self.open(filename)
- else:
- self.filename = filename
- self.file = file
- self.current_line = 0 # assuming that file is at BOF!
-
- # 'linebuf' is a stack of lines that will be emptied before we
- # actually read from the file; it's only populated by an
- # 'unreadline()' operation
- self.linebuf = []
-
- def open(self, filename):
- """Open a new file named 'filename'. This overrides both the
- 'filename' and 'file' arguments to the constructor."""
- self.filename = filename
- self.file = open(self.filename, errors=self.errors)
- self.current_line = 0
-
- def close(self):
- """Close the current file and forget everything we know about it
- (filename, current line number)."""
- file = self.file
- self.file = None
- self.filename = None
- self.current_line = None
- file.close()
-
- def gen_error(self, msg, line=None):
- outmsg = []
- if line is None:
- line = self.current_line
- outmsg.append(self.filename + ", ")
- if isinstance(line, (list, tuple)):
- outmsg.append("lines %d-%d: " % tuple(line))
- else:
- outmsg.append("line %d: " % line)
- outmsg.append(str(msg))
- return "".join(outmsg)
-
- def error(self, msg, line=None):
- raise ValueError("error: " + self.gen_error(msg, line))
-
- def warn(self, msg, line=None):
- """Print (to stderr) a warning message tied to the current logical
- line in the current file. If the current logical line in the
- file spans multiple physical lines, the warning refers to the
- whole range, eg. "lines 3-5". If 'line' supplied, it overrides
- the current line number; it may be a list or tuple to indicate a
- range of physical lines, or an integer for a single physical
- line."""
- sys.stderr.write("warning: " + self.gen_error(msg, line) + "\n")
-
- def readline(self): # noqa: C901
- """Read and return a single logical line from the current file (or
- from an internal buffer if lines have previously been "unread"
- with 'unreadline()'). If the 'join_lines' option is true, this
- may involve reading multiple physical lines concatenated into a
- single string. Updates the current line number, so calling
- 'warn()' after 'readline()' emits a warning about the physical
- line(s) just read. Returns None on end-of-file, since the empty
- string can occur if 'rstrip_ws' is true but 'strip_blanks' is
- not."""
- # If any "unread" lines waiting in 'linebuf', return the top
- # one. (We don't actually buffer read-ahead data -- lines only
- # get put in 'linebuf' if the client explicitly does an
- # 'unreadline()'.
- if self.linebuf:
- line = self.linebuf[-1]
- del self.linebuf[-1]
- return line
-
- buildup_line = ''
-
- while True:
- # read the line, make it None if EOF
- line = self.file.readline()
- if line == '':
- line = None
-
- if self.strip_comments and line:
-
- # Look for the first "#" in the line. If none, never
- # mind. If we find one and it's the first character, or
- # is not preceded by "\", then it starts a comment --
- # strip the comment, strip whitespace before it, and
- # carry on. Otherwise, it's just an escaped "#", so
- # unescape it (and any other escaped "#"'s that might be
- # lurking in there) and otherwise leave the line alone.
-
- pos = line.find("#")
- if pos == -1: # no "#" -- no comments
- pass
-
- # It's definitely a comment -- either "#" is the first
- # character, or it's elsewhere and unescaped.
- elif pos == 0 or line[pos - 1] != "\\":
- # Have to preserve the trailing newline, because it's
- # the job of a later step (rstrip_ws) to remove it --
- # and if rstrip_ws is false, we'd better preserve it!
- # (NB. this means that if the final line is all comment
- # and has no trailing newline, we will think that it's
- # EOF; I think that's OK.)
- eol = (line[-1] == '\n') and '\n' or ''
- line = line[0:pos] + eol
-
- # If all that's left is whitespace, then skip line
- # *now*, before we try to join it to 'buildup_line' --
- # that way constructs like
- # hello \\
- # # comment that should be ignored
- # there
- # result in "hello there".
- if line.strip() == "":
- continue
- else: # it's an escaped "#"
- line = line.replace("\\#", "#")
-
- # did previous line end with a backslash? then accumulate
- if self.join_lines and buildup_line:
- # oops: end of file
- if line is None:
- self.warn("continuation line immediately precedes " "end-of-file")
- return buildup_line
-
- if self.collapse_join:
- line = line.lstrip()
- line = buildup_line + line
-
- # careful: pay attention to line number when incrementing it
- if isinstance(self.current_line, list):
- self.current_line[1] = self.current_line[1] + 1
- else:
- self.current_line = [self.current_line, self.current_line + 1]
- # just an ordinary line, read it as usual
- else:
- if line is None: # eof
- return None
-
- # still have to be careful about incrementing the line number!
- if isinstance(self.current_line, list):
- self.current_line = self.current_line[1] + 1
- else:
- self.current_line = self.current_line + 1
-
- # strip whitespace however the client wants (leading and
- # trailing, or one or the other, or neither)
- if self.lstrip_ws and self.rstrip_ws:
- line = line.strip()
- elif self.lstrip_ws:
- line = line.lstrip()
- elif self.rstrip_ws:
- line = line.rstrip()
-
- # blank line (whether we rstrip'ed or not)? skip to next line
- # if appropriate
- if (line == '' or line == '\n') and self.skip_blanks:
- continue
-
- if self.join_lines:
- if line[-1] == '\\':
- buildup_line = line[:-1]
- continue
-
- if line[-2:] == '\\\n':
- buildup_line = line[0:-2] + '\n'
- continue
-
- # well, I guess there's some actual content there: return it
- return line
-
- def readlines(self):
- """Read and return the list of all logical lines remaining in the
- current file."""
- lines = []
- while True:
- line = self.readline()
- if line is None:
- return lines
- lines.append(line)
-
- def unreadline(self, line):
- """Push 'line' (a string) onto an internal buffer that will be
- checked by future 'readline()' calls. Handy for implementing
- a parser with line-at-a-time lookahead."""
- self.linebuf.append(line)
diff --git a/spaces/BrianL/CoE197-Fil-DialectTranslator/README.md b/spaces/BrianL/CoE197-Fil-DialectTranslator/README.md
deleted file mode 100644
index 66183047edd8baca4c70948ee676d9cc6f0f2043..0000000000000000000000000000000000000000
--- a/spaces/BrianL/CoE197-Fil-DialectTranslator/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Space2
-emoji: 🌍
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 2.8.14
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/CForGETaass/vits-uma-genshin-honkai/modules.py b/spaces/CForGETaass/vits-uma-genshin-honkai/modules.py
deleted file mode 100644
index 56ea4145eddf19dd330a3a41ab0183efc1686d83..0000000000000000000000000000000000000000
--- a/spaces/CForGETaass/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/ChandraMohanNayal/AutoGPT/autogpt/commands/web_playwright.py b/spaces/ChandraMohanNayal/AutoGPT/autogpt/commands/web_playwright.py
deleted file mode 100644
index 4e388ded203cefb5e24f9116f7fe5b8a94893413..0000000000000000000000000000000000000000
--- a/spaces/ChandraMohanNayal/AutoGPT/autogpt/commands/web_playwright.py
+++ /dev/null
@@ -1,80 +0,0 @@
-"""Web scraping commands using Playwright"""
-from __future__ import annotations
-
-try:
- from playwright.sync_api import sync_playwright
-except ImportError:
- print(
- "Playwright not installed. Please install it with 'pip install playwright' to use."
- )
-from bs4 import BeautifulSoup
-
-from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
-
-
-def scrape_text(url: str) -> str:
- """Scrape text from a webpage
-
- Args:
- url (str): The URL to scrape text from
-
- Returns:
- str: The scraped text
- """
- with sync_playwright() as p:
- browser = p.chromium.launch()
- page = browser.new_page()
-
- try:
- page.goto(url)
- html_content = page.content()
- soup = BeautifulSoup(html_content, "html.parser")
-
- for script in soup(["script", "style"]):
- script.extract()
-
- text = soup.get_text()
- lines = (line.strip() for line in text.splitlines())
- chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
- text = "\n".join(chunk for chunk in chunks if chunk)
-
- except Exception as e:
- text = f"Error: {str(e)}"
-
- finally:
- browser.close()
-
- return text
-
-
-def scrape_links(url: str) -> str | list[str]:
- """Scrape links from a webpage
-
- Args:
- url (str): The URL to scrape links from
-
- Returns:
- Union[str, List[str]]: The scraped links
- """
- with sync_playwright() as p:
- browser = p.chromium.launch()
- page = browser.new_page()
-
- try:
- page.goto(url)
- html_content = page.content()
- soup = BeautifulSoup(html_content, "html.parser")
-
- for script in soup(["script", "style"]):
- script.extract()
-
- hyperlinks = extract_hyperlinks(soup, url)
- formatted_links = format_hyperlinks(hyperlinks)
-
- except Exception as e:
- formatted_links = f"Error: {str(e)}"
-
- finally:
- browser.close()
-
- return formatted_links
diff --git a/spaces/Codecooker/rvcapi/src/infer_pack/modules.py b/spaces/Codecooker/rvcapi/src/infer_pack/modules.py
deleted file mode 100644
index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000
--- a/spaces/Codecooker/rvcapi/src/infer_pack/modules.py
+++ /dev/null
@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-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
-
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from infer_pack.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=0.0):
- 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
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-
-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=0.0)
- 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/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/ImageMorph.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/ImageMorph.py
deleted file mode 100644
index 6fccc315b3d25cf2cfe2dec952c938041f1d4531..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/ImageMorph.py
+++ /dev/null
@@ -1,254 +0,0 @@
-# A binary morphology add-on for the Python Imaging Library
-#
-# History:
-# 2014-06-04 Initial version.
-#
-# Copyright (c) 2014 Dov Grobgeld
-
-import re
-
-from . import Image, _imagingmorph
-
-LUT_SIZE = 1 << 9
-
-# fmt: off
-ROTATION_MATRIX = [
- 6, 3, 0,
- 7, 4, 1,
- 8, 5, 2,
-]
-MIRROR_MATRIX = [
- 2, 1, 0,
- 5, 4, 3,
- 8, 7, 6,
-]
-# fmt: on
-
-
-class LutBuilder:
- """A class for building a MorphLut from a descriptive language
-
- The input patterns is a list of a strings sequences like these::
-
- 4:(...
- .1.
- 111)->1
-
- (whitespaces including linebreaks are ignored). The option 4
- describes a series of symmetry operations (in this case a
- 4-rotation), the pattern is described by:
-
- - . or X - Ignore
- - 1 - Pixel is on
- - 0 - Pixel is off
-
- The result of the operation is described after "->" string.
-
- The default is to return the current pixel value, which is
- returned if no other match is found.
-
- Operations:
-
- - 4 - 4 way rotation
- - N - Negate
- - 1 - Dummy op for no other operation (an op must always be given)
- - M - Mirroring
-
- Example::
-
- lb = LutBuilder(patterns = ["4:(... .1. 111)->1"])
- lut = lb.build_lut()
-
- """
-
- def __init__(self, patterns=None, op_name=None):
- if patterns is not None:
- self.patterns = patterns
- else:
- self.patterns = []
- self.lut = None
- if op_name is not None:
- known_patterns = {
- "corner": ["1:(... ... ...)->0", "4:(00. 01. ...)->1"],
- "dilation4": ["4:(... .0. .1.)->1"],
- "dilation8": ["4:(... .0. .1.)->1", "4:(... .0. ..1)->1"],
- "erosion4": ["4:(... .1. .0.)->0"],
- "erosion8": ["4:(... .1. .0.)->0", "4:(... .1. ..0)->0"],
- "edge": [
- "1:(... ... ...)->0",
- "4:(.0. .1. ...)->1",
- "4:(01. .1. ...)->1",
- ],
- }
- if op_name not in known_patterns:
- msg = "Unknown pattern " + op_name + "!"
- raise Exception(msg)
-
- self.patterns = known_patterns[op_name]
-
- def add_patterns(self, patterns):
- self.patterns += patterns
-
- def build_default_lut(self):
- symbols = [0, 1]
- m = 1 << 4 # pos of current pixel
- self.lut = bytearray(symbols[(i & m) > 0] for i in range(LUT_SIZE))
-
- def get_lut(self):
- return self.lut
-
- def _string_permute(self, pattern, permutation):
- """string_permute takes a pattern and a permutation and returns the
- string permuted according to the permutation list.
- """
- assert len(permutation) == 9
- return "".join(pattern[p] for p in permutation)
-
- def _pattern_permute(self, basic_pattern, options, basic_result):
- """pattern_permute takes a basic pattern and its result and clones
- the pattern according to the modifications described in the $options
- parameter. It returns a list of all cloned patterns."""
- patterns = [(basic_pattern, basic_result)]
-
- # rotations
- if "4" in options:
- res = patterns[-1][1]
- for i in range(4):
- patterns.append(
- (self._string_permute(patterns[-1][0], ROTATION_MATRIX), res)
- )
- # mirror
- if "M" in options:
- n = len(patterns)
- for pattern, res in patterns[:n]:
- patterns.append((self._string_permute(pattern, MIRROR_MATRIX), res))
-
- # negate
- if "N" in options:
- n = len(patterns)
- for pattern, res in patterns[:n]:
- # Swap 0 and 1
- pattern = pattern.replace("0", "Z").replace("1", "0").replace("Z", "1")
- res = 1 - int(res)
- patterns.append((pattern, res))
-
- return patterns
-
- def build_lut(self):
- """Compile all patterns into a morphology lut.
-
- TBD :Build based on (file) morphlut:modify_lut
- """
- self.build_default_lut()
- patterns = []
-
- # Parse and create symmetries of the patterns strings
- for p in self.patterns:
- m = re.search(r"(\w*):?\s*\((.+?)\)\s*->\s*(\d)", p.replace("\n", ""))
- if not m:
- msg = 'Syntax error in pattern "' + p + '"'
- raise Exception(msg)
- options = m.group(1)
- pattern = m.group(2)
- result = int(m.group(3))
-
- # Get rid of spaces
- pattern = pattern.replace(" ", "").replace("\n", "")
-
- patterns += self._pattern_permute(pattern, options, result)
-
- # compile the patterns into regular expressions for speed
- for i, pattern in enumerate(patterns):
- p = pattern[0].replace(".", "X").replace("X", "[01]")
- p = re.compile(p)
- patterns[i] = (p, pattern[1])
-
- # Step through table and find patterns that match.
- # Note that all the patterns are searched. The last one
- # caught overrides
- for i in range(LUT_SIZE):
- # Build the bit pattern
- bitpattern = bin(i)[2:]
- bitpattern = ("0" * (9 - len(bitpattern)) + bitpattern)[::-1]
-
- for p, r in patterns:
- if p.match(bitpattern):
- self.lut[i] = [0, 1][r]
-
- return self.lut
-
-
-class MorphOp:
- """A class for binary morphological operators"""
-
- def __init__(self, lut=None, op_name=None, patterns=None):
- """Create a binary morphological operator"""
- self.lut = lut
- if op_name is not None:
- self.lut = LutBuilder(op_name=op_name).build_lut()
- elif patterns is not None:
- self.lut = LutBuilder(patterns=patterns).build_lut()
-
- def apply(self, image):
- """Run a single morphological operation on an image
-
- Returns a tuple of the number of changed pixels and the
- morphed image"""
- if self.lut is None:
- msg = "No operator loaded"
- raise Exception(msg)
-
- if image.mode != "L":
- msg = "Image mode must be L"
- raise ValueError(msg)
- outimage = Image.new(image.mode, image.size, None)
- count = _imagingmorph.apply(bytes(self.lut), image.im.id, outimage.im.id)
- return count, outimage
-
- def match(self, image):
- """Get a list of coordinates matching the morphological operation on
- an image.
-
- Returns a list of tuples of (x,y) coordinates
- of all matching pixels. See :ref:`coordinate-system`."""
- if self.lut is None:
- msg = "No operator loaded"
- raise Exception(msg)
-
- if image.mode != "L":
- msg = "Image mode must be L"
- raise ValueError(msg)
- return _imagingmorph.match(bytes(self.lut), image.im.id)
-
- def get_on_pixels(self, image):
- """Get a list of all turned on pixels in a binary image
-
- Returns a list of tuples of (x,y) coordinates
- of all matching pixels. See :ref:`coordinate-system`."""
-
- if image.mode != "L":
- msg = "Image mode must be L"
- raise ValueError(msg)
- return _imagingmorph.get_on_pixels(image.im.id)
-
- def load_lut(self, filename):
- """Load an operator from an mrl file"""
- with open(filename, "rb") as f:
- self.lut = bytearray(f.read())
-
- if len(self.lut) != LUT_SIZE:
- self.lut = None
- msg = "Wrong size operator file!"
- raise Exception(msg)
-
- def save_lut(self, filename):
- """Save an operator to an mrl file"""
- if self.lut is None:
- msg = "No operator loaded"
- raise Exception(msg)
- with open(filename, "wb") as f:
- f.write(self.lut)
-
- def set_lut(self, lut):
- """Set the lut from an external source"""
- self.lut = lut
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/_compat.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/_compat.py
deleted file mode 100644
index 2233fe33c72a3ac8888bb6d143922d031539c925..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/_compat.py
+++ /dev/null
@@ -1,616 +0,0 @@
-from collections import deque
-from copy import copy
-from dataclasses import dataclass, is_dataclass
-from enum import Enum
-from typing import (
- Any,
- Callable,
- Deque,
- Dict,
- FrozenSet,
- List,
- Mapping,
- Sequence,
- Set,
- Tuple,
- Type,
- Union,
-)
-
-from fastapi.exceptions import RequestErrorModel
-from fastapi.types import IncEx, ModelNameMap, UnionType
-from pydantic import BaseModel, create_model
-from pydantic.version import VERSION as PYDANTIC_VERSION
-from starlette.datastructures import UploadFile
-from typing_extensions import Annotated, Literal, get_args, get_origin
-
-PYDANTIC_V2 = PYDANTIC_VERSION.startswith("2.")
-
-
-sequence_annotation_to_type = {
- Sequence: list,
- List: list,
- list: list,
- Tuple: tuple,
- tuple: tuple,
- Set: set,
- set: set,
- FrozenSet: frozenset,
- frozenset: frozenset,
- Deque: deque,
- deque: deque,
-}
-
-sequence_types = tuple(sequence_annotation_to_type.keys())
-
-if PYDANTIC_V2:
- from pydantic import PydanticSchemaGenerationError as PydanticSchemaGenerationError
- from pydantic import TypeAdapter
- from pydantic import ValidationError as ValidationError
- from pydantic._internal._schema_generation_shared import ( # type: ignore[attr-defined]
- GetJsonSchemaHandler as GetJsonSchemaHandler,
- )
- from pydantic._internal._typing_extra import eval_type_lenient
- from pydantic._internal._utils import lenient_issubclass as lenient_issubclass
- from pydantic.fields import FieldInfo
- from pydantic.json_schema import GenerateJsonSchema as GenerateJsonSchema
- from pydantic.json_schema import JsonSchemaValue as JsonSchemaValue
- from pydantic_core import CoreSchema as CoreSchema
- from pydantic_core import MultiHostUrl as MultiHostUrl
- from pydantic_core import PydanticUndefined, PydanticUndefinedType
- from pydantic_core import Url as Url
- from pydantic_core.core_schema import (
- general_plain_validator_function as general_plain_validator_function,
- )
-
- Required = PydanticUndefined
- Undefined = PydanticUndefined
- UndefinedType = PydanticUndefinedType
- evaluate_forwardref = eval_type_lenient
- Validator = Any
-
- class BaseConfig:
- pass
-
- class ErrorWrapper(Exception):
- pass
-
- @dataclass
- class ModelField:
- field_info: FieldInfo
- name: str
- mode: Literal["validation", "serialization"] = "validation"
-
- @property
- def alias(self) -> str:
- a = self.field_info.alias
- return a if a is not None else self.name
-
- @property
- def required(self) -> bool:
- return self.field_info.is_required()
-
- @property
- def default(self) -> Any:
- return self.get_default()
-
- @property
- def type_(self) -> Any:
- return self.field_info.annotation
-
- def __post_init__(self) -> None:
- self._type_adapter: TypeAdapter[Any] = TypeAdapter(
- Annotated[self.field_info.annotation, self.field_info]
- )
-
- def get_default(self) -> Any:
- if self.field_info.is_required():
- return Undefined
- return self.field_info.get_default(call_default_factory=True)
-
- def validate(
- self,
- value: Any,
- values: Dict[str, Any] = {}, # noqa: B006
- *,
- loc: Tuple[Union[int, str], ...] = (),
- ) -> Tuple[Any, Union[List[Dict[str, Any]], None]]:
- try:
- return (
- self._type_adapter.validate_python(value, from_attributes=True),
- None,
- )
- except ValidationError as exc:
- return None, _regenerate_error_with_loc(
- errors=exc.errors(), loc_prefix=loc
- )
-
- def serialize(
- self,
- value: Any,
- *,
- mode: Literal["json", "python"] = "json",
- include: Union[IncEx, None] = None,
- exclude: Union[IncEx, None] = None,
- by_alias: bool = True,
- exclude_unset: bool = False,
- exclude_defaults: bool = False,
- exclude_none: bool = False,
- ) -> Any:
- # What calls this code passes a value that already called
- # self._type_adapter.validate_python(value)
- return self._type_adapter.dump_python(
- value,
- mode=mode,
- include=include,
- exclude=exclude,
- by_alias=by_alias,
- exclude_unset=exclude_unset,
- exclude_defaults=exclude_defaults,
- exclude_none=exclude_none,
- )
-
- def __hash__(self) -> int:
- # Each ModelField is unique for our purposes, to allow making a dict from
- # ModelField to its JSON Schema.
- return id(self)
-
- def get_annotation_from_field_info(
- annotation: Any, field_info: FieldInfo, field_name: str
- ) -> Any:
- return annotation
-
- def _normalize_errors(errors: Sequence[Any]) -> List[Dict[str, Any]]:
- return errors # type: ignore[return-value]
-
- def _model_rebuild(model: Type[BaseModel]) -> None:
- model.model_rebuild()
-
- def _model_dump(
- model: BaseModel, mode: Literal["json", "python"] = "json", **kwargs: Any
- ) -> Any:
- return model.model_dump(mode=mode, **kwargs)
-
- def _get_model_config(model: BaseModel) -> Any:
- return model.model_config
-
- def get_schema_from_model_field(
- *,
- field: ModelField,
- schema_generator: GenerateJsonSchema,
- model_name_map: ModelNameMap,
- field_mapping: Dict[
- Tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue
- ],
- ) -> Dict[str, Any]:
- # This expects that GenerateJsonSchema was already used to generate the definitions
- json_schema = field_mapping[(field, field.mode)]
- if "$ref" not in json_schema:
- # TODO remove when deprecating Pydantic v1
- # Ref: https://github.com/pydantic/pydantic/blob/d61792cc42c80b13b23e3ffa74bc37ec7c77f7d1/pydantic/schema.py#L207
- json_schema[
- "title"
- ] = field.field_info.title or field.alias.title().replace("_", " ")
- return json_schema
-
- def get_compat_model_name_map(fields: List[ModelField]) -> ModelNameMap:
- return {}
-
- def get_definitions(
- *,
- fields: List[ModelField],
- schema_generator: GenerateJsonSchema,
- model_name_map: ModelNameMap,
- ) -> Tuple[
- Dict[
- Tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue
- ],
- Dict[str, Dict[str, Any]],
- ]:
- inputs = [
- (field, field.mode, field._type_adapter.core_schema) for field in fields
- ]
- field_mapping, definitions = schema_generator.generate_definitions(
- inputs=inputs
- )
- return field_mapping, definitions # type: ignore[return-value]
-
- def is_scalar_field(field: ModelField) -> bool:
- from fastapi import params
-
- return field_annotation_is_scalar(
- field.field_info.annotation
- ) and not isinstance(field.field_info, params.Body)
-
- def is_sequence_field(field: ModelField) -> bool:
- return field_annotation_is_sequence(field.field_info.annotation)
-
- def is_scalar_sequence_field(field: ModelField) -> bool:
- return field_annotation_is_scalar_sequence(field.field_info.annotation)
-
- def is_bytes_field(field: ModelField) -> bool:
- return is_bytes_or_nonable_bytes_annotation(field.type_)
-
- def is_bytes_sequence_field(field: ModelField) -> bool:
- return is_bytes_sequence_annotation(field.type_)
-
- def copy_field_info(*, field_info: FieldInfo, annotation: Any) -> FieldInfo:
- return type(field_info).from_annotation(annotation)
-
- def serialize_sequence_value(*, field: ModelField, value: Any) -> Sequence[Any]:
- origin_type = (
- get_origin(field.field_info.annotation) or field.field_info.annotation
- )
- assert issubclass(origin_type, sequence_types) # type: ignore[arg-type]
- return sequence_annotation_to_type[origin_type](value) # type: ignore[no-any-return]
-
- def get_missing_field_error(loc: Tuple[str, ...]) -> Dict[str, Any]:
- error = ValidationError.from_exception_data(
- "Field required", [{"type": "missing", "loc": loc, "input": {}}]
- ).errors()[0]
- error["input"] = None
- return error # type: ignore[return-value]
-
- def create_body_model(
- *, fields: Sequence[ModelField], model_name: str
- ) -> Type[BaseModel]:
- field_params = {f.name: (f.field_info.annotation, f.field_info) for f in fields}
- BodyModel: Type[BaseModel] = create_model(model_name, **field_params) # type: ignore[call-overload]
- return BodyModel
-
-else:
- from fastapi.openapi.constants import REF_PREFIX as REF_PREFIX
- from pydantic import AnyUrl as Url # noqa: F401
- from pydantic import ( # type: ignore[assignment]
- BaseConfig as BaseConfig, # noqa: F401
- )
- from pydantic import ValidationError as ValidationError # noqa: F401
- from pydantic.class_validators import ( # type: ignore[no-redef]
- Validator as Validator, # noqa: F401
- )
- from pydantic.error_wrappers import ( # type: ignore[no-redef]
- ErrorWrapper as ErrorWrapper, # noqa: F401
- )
- from pydantic.errors import MissingError
- from pydantic.fields import ( # type: ignore[attr-defined]
- SHAPE_FROZENSET,
- SHAPE_LIST,
- SHAPE_SEQUENCE,
- SHAPE_SET,
- SHAPE_SINGLETON,
- SHAPE_TUPLE,
- SHAPE_TUPLE_ELLIPSIS,
- )
- from pydantic.fields import FieldInfo as FieldInfo
- from pydantic.fields import ( # type: ignore[no-redef,attr-defined]
- ModelField as ModelField, # noqa: F401
- )
- from pydantic.fields import ( # type: ignore[no-redef,attr-defined]
- Required as Required, # noqa: F401
- )
- from pydantic.fields import ( # type: ignore[no-redef,attr-defined]
- Undefined as Undefined,
- )
- from pydantic.fields import ( # type: ignore[no-redef, attr-defined]
- UndefinedType as UndefinedType, # noqa: F401
- )
- from pydantic.networks import ( # type: ignore[no-redef]
- MultiHostDsn as MultiHostUrl, # noqa: F401
- )
- from pydantic.schema import (
- field_schema,
- get_flat_models_from_fields,
- get_model_name_map,
- model_process_schema,
- )
- from pydantic.schema import ( # type: ignore[no-redef] # noqa: F401
- get_annotation_from_field_info as get_annotation_from_field_info,
- )
- from pydantic.typing import ( # type: ignore[no-redef]
- evaluate_forwardref as evaluate_forwardref, # noqa: F401
- )
- from pydantic.utils import ( # type: ignore[no-redef]
- lenient_issubclass as lenient_issubclass, # noqa: F401
- )
-
- GetJsonSchemaHandler = Any # type: ignore[assignment,misc]
- JsonSchemaValue = Dict[str, Any] # type: ignore[misc]
- CoreSchema = Any # type: ignore[assignment,misc]
-
- sequence_shapes = {
- SHAPE_LIST,
- SHAPE_SET,
- SHAPE_FROZENSET,
- SHAPE_TUPLE,
- SHAPE_SEQUENCE,
- SHAPE_TUPLE_ELLIPSIS,
- }
- sequence_shape_to_type = {
- SHAPE_LIST: list,
- SHAPE_SET: set,
- SHAPE_TUPLE: tuple,
- SHAPE_SEQUENCE: list,
- SHAPE_TUPLE_ELLIPSIS: list,
- }
-
- @dataclass
- class GenerateJsonSchema: # type: ignore[no-redef]
- ref_template: str
-
- class PydanticSchemaGenerationError(Exception): # type: ignore[no-redef]
- pass
-
- def general_plain_validator_function( # type: ignore[misc]
- function: Callable[..., Any],
- *,
- ref: Union[str, None] = None,
- metadata: Any = None,
- serialization: Any = None,
- ) -> Any:
- return {}
-
- def get_model_definitions(
- *,
- flat_models: Set[Union[Type[BaseModel], Type[Enum]]],
- model_name_map: Dict[Union[Type[BaseModel], Type[Enum]], str],
- ) -> Dict[str, Any]:
- definitions: Dict[str, Dict[str, Any]] = {}
- for model in flat_models:
- m_schema, m_definitions, m_nested_models = model_process_schema(
- model, model_name_map=model_name_map, ref_prefix=REF_PREFIX
- )
- definitions.update(m_definitions)
- model_name = model_name_map[model]
- if "description" in m_schema:
- m_schema["description"] = m_schema["description"].split("\f")[0]
- definitions[model_name] = m_schema
- return definitions
-
- def is_pv1_scalar_field(field: ModelField) -> bool:
- from fastapi import params
-
- field_info = field.field_info
- if not (
- field.shape == SHAPE_SINGLETON # type: ignore[attr-defined]
- and not lenient_issubclass(field.type_, BaseModel)
- and not lenient_issubclass(field.type_, dict)
- and not field_annotation_is_sequence(field.type_)
- and not is_dataclass(field.type_)
- and not isinstance(field_info, params.Body)
- ):
- return False
- if field.sub_fields: # type: ignore[attr-defined]
- if not all(
- is_pv1_scalar_field(f)
- for f in field.sub_fields # type: ignore[attr-defined]
- ):
- return False
- return True
-
- def is_pv1_scalar_sequence_field(field: ModelField) -> bool:
- if (field.shape in sequence_shapes) and not lenient_issubclass( # type: ignore[attr-defined]
- field.type_, BaseModel
- ):
- if field.sub_fields is not None: # type: ignore[attr-defined]
- for sub_field in field.sub_fields: # type: ignore[attr-defined]
- if not is_pv1_scalar_field(sub_field):
- return False
- return True
- if _annotation_is_sequence(field.type_):
- return True
- return False
-
- def _normalize_errors(errors: Sequence[Any]) -> List[Dict[str, Any]]:
- use_errors: List[Any] = []
- for error in errors:
- if isinstance(error, ErrorWrapper):
- new_errors = ValidationError( # type: ignore[call-arg]
- errors=[error], model=RequestErrorModel
- ).errors()
- use_errors.extend(new_errors)
- elif isinstance(error, list):
- use_errors.extend(_normalize_errors(error))
- else:
- use_errors.append(error)
- return use_errors
-
- def _model_rebuild(model: Type[BaseModel]) -> None:
- model.update_forward_refs()
-
- def _model_dump(
- model: BaseModel, mode: Literal["json", "python"] = "json", **kwargs: Any
- ) -> Any:
- return model.dict(**kwargs)
-
- def _get_model_config(model: BaseModel) -> Any:
- return model.__config__ # type: ignore[attr-defined]
-
- def get_schema_from_model_field(
- *,
- field: ModelField,
- schema_generator: GenerateJsonSchema,
- model_name_map: ModelNameMap,
- field_mapping: Dict[
- Tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue
- ],
- ) -> Dict[str, Any]:
- # This expects that GenerateJsonSchema was already used to generate the definitions
- return field_schema( # type: ignore[no-any-return]
- field, model_name_map=model_name_map, ref_prefix=REF_PREFIX
- )[0]
-
- def get_compat_model_name_map(fields: List[ModelField]) -> ModelNameMap:
- models = get_flat_models_from_fields(fields, known_models=set())
- return get_model_name_map(models) # type: ignore[no-any-return]
-
- def get_definitions(
- *,
- fields: List[ModelField],
- schema_generator: GenerateJsonSchema,
- model_name_map: ModelNameMap,
- ) -> Tuple[
- Dict[
- Tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue
- ],
- Dict[str, Dict[str, Any]],
- ]:
- models = get_flat_models_from_fields(fields, known_models=set())
- return {}, get_model_definitions(
- flat_models=models, model_name_map=model_name_map
- )
-
- def is_scalar_field(field: ModelField) -> bool:
- return is_pv1_scalar_field(field)
-
- def is_sequence_field(field: ModelField) -> bool:
- return field.shape in sequence_shapes or _annotation_is_sequence(field.type_) # type: ignore[attr-defined]
-
- def is_scalar_sequence_field(field: ModelField) -> bool:
- return is_pv1_scalar_sequence_field(field)
-
- def is_bytes_field(field: ModelField) -> bool:
- return lenient_issubclass(field.type_, bytes)
-
- def is_bytes_sequence_field(field: ModelField) -> bool:
- return field.shape in sequence_shapes and lenient_issubclass(field.type_, bytes) # type: ignore[attr-defined]
-
- def copy_field_info(*, field_info: FieldInfo, annotation: Any) -> FieldInfo:
- return copy(field_info)
-
- def serialize_sequence_value(*, field: ModelField, value: Any) -> Sequence[Any]:
- return sequence_shape_to_type[field.shape](value) # type: ignore[no-any-return,attr-defined]
-
- def get_missing_field_error(loc: Tuple[str, ...]) -> Dict[str, Any]:
- missing_field_error = ErrorWrapper(MissingError(), loc=loc) # type: ignore[call-arg]
- new_error = ValidationError([missing_field_error], RequestErrorModel)
- return new_error.errors()[0] # type: ignore[return-value]
-
- def create_body_model(
- *, fields: Sequence[ModelField], model_name: str
- ) -> Type[BaseModel]:
- BodyModel = create_model(model_name)
- for f in fields:
- BodyModel.__fields__[f.name] = f # type: ignore[index]
- return BodyModel
-
-
-def _regenerate_error_with_loc(
- *, errors: Sequence[Any], loc_prefix: Tuple[Union[str, int], ...]
-) -> List[Dict[str, Any]]:
- updated_loc_errors: List[Any] = [
- {**err, "loc": loc_prefix + err.get("loc", ())}
- for err in _normalize_errors(errors)
- ]
-
- return updated_loc_errors
-
-
-def _annotation_is_sequence(annotation: Union[Type[Any], None]) -> bool:
- if lenient_issubclass(annotation, (str, bytes)):
- return False
- return lenient_issubclass(annotation, sequence_types)
-
-
-def field_annotation_is_sequence(annotation: Union[Type[Any], None]) -> bool:
- return _annotation_is_sequence(annotation) or _annotation_is_sequence(
- get_origin(annotation)
- )
-
-
-def value_is_sequence(value: Any) -> bool:
- return isinstance(value, sequence_types) and not isinstance(value, (str, bytes)) # type: ignore[arg-type]
-
-
-def _annotation_is_complex(annotation: Union[Type[Any], None]) -> bool:
- return (
- lenient_issubclass(annotation, (BaseModel, Mapping, UploadFile))
- or _annotation_is_sequence(annotation)
- or is_dataclass(annotation)
- )
-
-
-def field_annotation_is_complex(annotation: Union[Type[Any], None]) -> bool:
- origin = get_origin(annotation)
- if origin is Union or origin is UnionType:
- return any(field_annotation_is_complex(arg) for arg in get_args(annotation))
-
- return (
- _annotation_is_complex(annotation)
- or _annotation_is_complex(origin)
- or hasattr(origin, "__pydantic_core_schema__")
- or hasattr(origin, "__get_pydantic_core_schema__")
- )
-
-
-def field_annotation_is_scalar(annotation: Any) -> bool:
- # handle Ellipsis here to make tuple[int, ...] work nicely
- return annotation is Ellipsis or not field_annotation_is_complex(annotation)
-
-
-def field_annotation_is_scalar_sequence(annotation: Union[Type[Any], None]) -> bool:
- origin = get_origin(annotation)
- if origin is Union or origin is UnionType:
- at_least_one_scalar_sequence = False
- for arg in get_args(annotation):
- if field_annotation_is_scalar_sequence(arg):
- at_least_one_scalar_sequence = True
- continue
- elif not field_annotation_is_scalar(arg):
- return False
- return at_least_one_scalar_sequence
- return field_annotation_is_sequence(annotation) and all(
- field_annotation_is_scalar(sub_annotation)
- for sub_annotation in get_args(annotation)
- )
-
-
-def is_bytes_or_nonable_bytes_annotation(annotation: Any) -> bool:
- if lenient_issubclass(annotation, bytes):
- return True
- origin = get_origin(annotation)
- if origin is Union or origin is UnionType:
- for arg in get_args(annotation):
- if lenient_issubclass(arg, bytes):
- return True
- return False
-
-
-def is_uploadfile_or_nonable_uploadfile_annotation(annotation: Any) -> bool:
- if lenient_issubclass(annotation, UploadFile):
- return True
- origin = get_origin(annotation)
- if origin is Union or origin is UnionType:
- for arg in get_args(annotation):
- if lenient_issubclass(arg, UploadFile):
- return True
- return False
-
-
-def is_bytes_sequence_annotation(annotation: Any) -> bool:
- origin = get_origin(annotation)
- if origin is Union or origin is UnionType:
- at_least_one = False
- for arg in get_args(annotation):
- if is_bytes_sequence_annotation(arg):
- at_least_one = True
- continue
- return at_least_one
- return field_annotation_is_sequence(annotation) and all(
- is_bytes_or_nonable_bytes_annotation(sub_annotation)
- for sub_annotation in get_args(annotation)
- )
-
-
-def is_uploadfile_sequence_annotation(annotation: Any) -> bool:
- origin = get_origin(annotation)
- if origin is Union or origin is UnionType:
- at_least_one = False
- for arg in get_args(annotation):
- if is_uploadfile_sequence_annotation(arg):
- at_least_one = True
- continue
- return at_least_one
- return field_annotation_is_sequence(annotation) and all(
- is_uploadfile_or_nonable_uploadfile_annotation(sub_annotation)
- for sub_annotation in get_args(annotation)
- )
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/colorLib/builder.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/colorLib/builder.py
deleted file mode 100644
index 442bc20e4223827d8e28c9fbb0290dac6f1553dc..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/colorLib/builder.py
+++ /dev/null
@@ -1,659 +0,0 @@
-"""
-colorLib.builder: Build COLR/CPAL tables from scratch
-
-"""
-import collections
-import copy
-import enum
-from functools import partial
-from math import ceil, log
-from typing import (
- Any,
- Dict,
- Generator,
- Iterable,
- List,
- Mapping,
- Optional,
- Sequence,
- Tuple,
- Type,
- TypeVar,
- Union,
-)
-from fontTools.misc.arrayTools import intRect
-from fontTools.misc.fixedTools import fixedToFloat
-from fontTools.misc.treeTools import build_n_ary_tree
-from fontTools.ttLib.tables import C_O_L_R_
-from fontTools.ttLib.tables import C_P_A_L_
-from fontTools.ttLib.tables import _n_a_m_e
-from fontTools.ttLib.tables import otTables as ot
-from fontTools.ttLib.tables.otTables import ExtendMode, CompositeMode
-from .errors import ColorLibError
-from .geometry import round_start_circle_stable_containment
-from .table_builder import BuildCallback, TableBuilder
-
-
-# TODO move type aliases to colorLib.types?
-T = TypeVar("T")
-_Kwargs = Mapping[str, Any]
-_PaintInput = Union[int, _Kwargs, ot.Paint, Tuple[str, "_PaintInput"]]
-_PaintInputList = Sequence[_PaintInput]
-_ColorGlyphsDict = Dict[str, Union[_PaintInputList, _PaintInput]]
-_ColorGlyphsV0Dict = Dict[str, Sequence[Tuple[str, int]]]
-_ClipBoxInput = Union[
- Tuple[int, int, int, int, int], # format 1, variable
- Tuple[int, int, int, int], # format 0, non-variable
- ot.ClipBox,
-]
-
-
-MAX_PAINT_COLR_LAYER_COUNT = 255
-_DEFAULT_ALPHA = 1.0
-_MAX_REUSE_LEN = 32
-
-
-def _beforeBuildPaintRadialGradient(paint, source):
- x0 = source["x0"]
- y0 = source["y0"]
- r0 = source["r0"]
- x1 = source["x1"]
- y1 = source["y1"]
- r1 = source["r1"]
-
- # TODO apparently no builder_test confirms this works (?)
-
- # avoid abrupt change after rounding when c0 is near c1's perimeter
- c = round_start_circle_stable_containment((x0, y0), r0, (x1, y1), r1)
- x0, y0 = c.centre
- r0 = c.radius
-
- # update source to ensure paint is built with corrected values
- source["x0"] = x0
- source["y0"] = y0
- source["r0"] = r0
- source["x1"] = x1
- source["y1"] = y1
- source["r1"] = r1
-
- return paint, source
-
-
-def _defaultColorStop():
- colorStop = ot.ColorStop()
- colorStop.Alpha = _DEFAULT_ALPHA
- return colorStop
-
-
-def _defaultVarColorStop():
- colorStop = ot.VarColorStop()
- colorStop.Alpha = _DEFAULT_ALPHA
- return colorStop
-
-
-def _defaultColorLine():
- colorLine = ot.ColorLine()
- colorLine.Extend = ExtendMode.PAD
- return colorLine
-
-
-def _defaultVarColorLine():
- colorLine = ot.VarColorLine()
- colorLine.Extend = ExtendMode.PAD
- return colorLine
-
-
-def _defaultPaintSolid():
- paint = ot.Paint()
- paint.Alpha = _DEFAULT_ALPHA
- return paint
-
-
-def _buildPaintCallbacks():
- return {
- (
- BuildCallback.BEFORE_BUILD,
- ot.Paint,
- ot.PaintFormat.PaintRadialGradient,
- ): _beforeBuildPaintRadialGradient,
- (
- BuildCallback.BEFORE_BUILD,
- ot.Paint,
- ot.PaintFormat.PaintVarRadialGradient,
- ): _beforeBuildPaintRadialGradient,
- (BuildCallback.CREATE_DEFAULT, ot.ColorStop): _defaultColorStop,
- (BuildCallback.CREATE_DEFAULT, ot.VarColorStop): _defaultVarColorStop,
- (BuildCallback.CREATE_DEFAULT, ot.ColorLine): _defaultColorLine,
- (BuildCallback.CREATE_DEFAULT, ot.VarColorLine): _defaultVarColorLine,
- (
- BuildCallback.CREATE_DEFAULT,
- ot.Paint,
- ot.PaintFormat.PaintSolid,
- ): _defaultPaintSolid,
- (
- BuildCallback.CREATE_DEFAULT,
- ot.Paint,
- ot.PaintFormat.PaintVarSolid,
- ): _defaultPaintSolid,
- }
-
-
-def populateCOLRv0(
- table: ot.COLR,
- colorGlyphsV0: _ColorGlyphsV0Dict,
- glyphMap: Optional[Mapping[str, int]] = None,
-):
- """Build v0 color layers and add to existing COLR table.
-
- Args:
- table: a raw ``otTables.COLR()`` object (not ttLib's ``table_C_O_L_R_``).
- colorGlyphsV0: map of base glyph names to lists of (layer glyph names,
- color palette index) tuples. Can be empty.
- glyphMap: a map from glyph names to glyph indices, as returned from
- ``TTFont.getReverseGlyphMap()``, to optionally sort base records by GID.
- """
- if glyphMap is not None:
- colorGlyphItems = sorted(
- colorGlyphsV0.items(), key=lambda item: glyphMap[item[0]]
- )
- else:
- colorGlyphItems = colorGlyphsV0.items()
- baseGlyphRecords = []
- layerRecords = []
- for baseGlyph, layers in colorGlyphItems:
- baseRec = ot.BaseGlyphRecord()
- baseRec.BaseGlyph = baseGlyph
- baseRec.FirstLayerIndex = len(layerRecords)
- baseRec.NumLayers = len(layers)
- baseGlyphRecords.append(baseRec)
-
- for layerGlyph, paletteIndex in layers:
- layerRec = ot.LayerRecord()
- layerRec.LayerGlyph = layerGlyph
- layerRec.PaletteIndex = paletteIndex
- layerRecords.append(layerRec)
-
- table.BaseGlyphRecordArray = table.LayerRecordArray = None
- if baseGlyphRecords:
- table.BaseGlyphRecordArray = ot.BaseGlyphRecordArray()
- table.BaseGlyphRecordArray.BaseGlyphRecord = baseGlyphRecords
- if layerRecords:
- table.LayerRecordArray = ot.LayerRecordArray()
- table.LayerRecordArray.LayerRecord = layerRecords
- table.BaseGlyphRecordCount = len(baseGlyphRecords)
- table.LayerRecordCount = len(layerRecords)
-
-
-def buildCOLR(
- colorGlyphs: _ColorGlyphsDict,
- version: Optional[int] = None,
- *,
- glyphMap: Optional[Mapping[str, int]] = None,
- varStore: Optional[ot.VarStore] = None,
- varIndexMap: Optional[ot.DeltaSetIndexMap] = None,
- clipBoxes: Optional[Dict[str, _ClipBoxInput]] = None,
- allowLayerReuse: bool = True,
-) -> C_O_L_R_.table_C_O_L_R_:
- """Build COLR table from color layers mapping.
-
- Args:
-
- colorGlyphs: map of base glyph name to, either list of (layer glyph name,
- color palette index) tuples for COLRv0; or a single ``Paint`` (dict) or
- list of ``Paint`` for COLRv1.
- version: the version of COLR table. If None, the version is determined
- by the presence of COLRv1 paints or variation data (varStore), which
- require version 1; otherwise, if all base glyphs use only simple color
- layers, version 0 is used.
- glyphMap: a map from glyph names to glyph indices, as returned from
- TTFont.getReverseGlyphMap(), to optionally sort base records by GID.
- varStore: Optional ItemVarationStore for deltas associated with v1 layer.
- varIndexMap: Optional DeltaSetIndexMap for deltas associated with v1 layer.
- clipBoxes: Optional map of base glyph name to clip box 4- or 5-tuples:
- (xMin, yMin, xMax, yMax) or (xMin, yMin, xMax, yMax, varIndexBase).
-
- Returns:
- A new COLR table.
- """
- self = C_O_L_R_.table_C_O_L_R_()
-
- if varStore is not None and version == 0:
- raise ValueError("Can't add VarStore to COLRv0")
-
- if version in (None, 0) and not varStore:
- # split color glyphs into v0 and v1 and encode separately
- colorGlyphsV0, colorGlyphsV1 = _split_color_glyphs_by_version(colorGlyphs)
- if version == 0 and colorGlyphsV1:
- raise ValueError("Can't encode COLRv1 glyphs in COLRv0")
- else:
- # unless explicitly requested for v1 or have variations, in which case
- # we encode all color glyph as v1
- colorGlyphsV0, colorGlyphsV1 = {}, colorGlyphs
-
- colr = ot.COLR()
-
- populateCOLRv0(colr, colorGlyphsV0, glyphMap)
-
- colr.LayerList, colr.BaseGlyphList = buildColrV1(
- colorGlyphsV1,
- glyphMap,
- allowLayerReuse=allowLayerReuse,
- )
-
- if version is None:
- version = 1 if (varStore or colorGlyphsV1) else 0
- elif version not in (0, 1):
- raise NotImplementedError(version)
- self.version = colr.Version = version
-
- if version == 0:
- self.ColorLayers = self._decompileColorLayersV0(colr)
- else:
- colr.ClipList = buildClipList(clipBoxes) if clipBoxes else None
- colr.VarIndexMap = varIndexMap
- colr.VarStore = varStore
- self.table = colr
-
- return self
-
-
-def buildClipList(clipBoxes: Dict[str, _ClipBoxInput]) -> ot.ClipList:
- clipList = ot.ClipList()
- clipList.Format = 1
- clipList.clips = {name: buildClipBox(box) for name, box in clipBoxes.items()}
- return clipList
-
-
-def buildClipBox(clipBox: _ClipBoxInput) -> ot.ClipBox:
- if isinstance(clipBox, ot.ClipBox):
- return clipBox
- n = len(clipBox)
- clip = ot.ClipBox()
- if n not in (4, 5):
- raise ValueError(f"Invalid ClipBox: expected 4 or 5 values, found {n}")
- clip.xMin, clip.yMin, clip.xMax, clip.yMax = intRect(clipBox[:4])
- clip.Format = int(n == 5) + 1
- if n == 5:
- clip.VarIndexBase = int(clipBox[4])
- return clip
-
-
-class ColorPaletteType(enum.IntFlag):
- USABLE_WITH_LIGHT_BACKGROUND = 0x0001
- USABLE_WITH_DARK_BACKGROUND = 0x0002
-
- @classmethod
- def _missing_(cls, value):
- # enforce reserved bits
- if isinstance(value, int) and (value < 0 or value & 0xFFFC != 0):
- raise ValueError(f"{value} is not a valid {cls.__name__}")
- return super()._missing_(value)
-
-
-# None, 'abc' or {'en': 'abc', 'de': 'xyz'}
-_OptionalLocalizedString = Union[None, str, Dict[str, str]]
-
-
-def buildPaletteLabels(
- labels: Iterable[_OptionalLocalizedString], nameTable: _n_a_m_e.table__n_a_m_e
-) -> List[Optional[int]]:
- return [
- nameTable.addMultilingualName(l, mac=False)
- if isinstance(l, dict)
- else C_P_A_L_.table_C_P_A_L_.NO_NAME_ID
- if l is None
- else nameTable.addMultilingualName({"en": l}, mac=False)
- for l in labels
- ]
-
-
-def buildCPAL(
- palettes: Sequence[Sequence[Tuple[float, float, float, float]]],
- paletteTypes: Optional[Sequence[ColorPaletteType]] = None,
- paletteLabels: Optional[Sequence[_OptionalLocalizedString]] = None,
- paletteEntryLabels: Optional[Sequence[_OptionalLocalizedString]] = None,
- nameTable: Optional[_n_a_m_e.table__n_a_m_e] = None,
-) -> C_P_A_L_.table_C_P_A_L_:
- """Build CPAL table from list of color palettes.
-
- Args:
- palettes: list of lists of colors encoded as tuples of (R, G, B, A) floats
- in the range [0..1].
- paletteTypes: optional list of ColorPaletteType, one for each palette.
- paletteLabels: optional list of palette labels. Each lable can be either:
- None (no label), a string (for for default English labels), or a
- localized string (as a dict keyed with BCP47 language codes).
- paletteEntryLabels: optional list of palette entry labels, one for each
- palette entry (see paletteLabels).
- nameTable: optional name table where to store palette and palette entry
- labels. Required if either paletteLabels or paletteEntryLabels is set.
-
- Return:
- A new CPAL v0 or v1 table, if custom palette types or labels are specified.
- """
- if len({len(p) for p in palettes}) != 1:
- raise ColorLibError("color palettes have different lengths")
-
- if (paletteLabels or paletteEntryLabels) and not nameTable:
- raise TypeError(
- "nameTable is required if palette or palette entries have labels"
- )
-
- cpal = C_P_A_L_.table_C_P_A_L_()
- cpal.numPaletteEntries = len(palettes[0])
-
- cpal.palettes = []
- for i, palette in enumerate(palettes):
- colors = []
- for j, color in enumerate(palette):
- if not isinstance(color, tuple) or len(color) != 4:
- raise ColorLibError(
- f"In palette[{i}][{j}]: expected (R, G, B, A) tuple, got {color!r}"
- )
- if any(v > 1 or v < 0 for v in color):
- raise ColorLibError(
- f"palette[{i}][{j}] has invalid out-of-range [0..1] color: {color!r}"
- )
- # input colors are RGBA, CPAL encodes them as BGRA
- red, green, blue, alpha = color
- colors.append(
- C_P_A_L_.Color(*(round(v * 255) for v in (blue, green, red, alpha)))
- )
- cpal.palettes.append(colors)
-
- if any(v is not None for v in (paletteTypes, paletteLabels, paletteEntryLabels)):
- cpal.version = 1
-
- if paletteTypes is not None:
- if len(paletteTypes) != len(palettes):
- raise ColorLibError(
- f"Expected {len(palettes)} paletteTypes, got {len(paletteTypes)}"
- )
- cpal.paletteTypes = [ColorPaletteType(t).value for t in paletteTypes]
- else:
- cpal.paletteTypes = [C_P_A_L_.table_C_P_A_L_.DEFAULT_PALETTE_TYPE] * len(
- palettes
- )
-
- if paletteLabels is not None:
- if len(paletteLabels) != len(palettes):
- raise ColorLibError(
- f"Expected {len(palettes)} paletteLabels, got {len(paletteLabels)}"
- )
- cpal.paletteLabels = buildPaletteLabels(paletteLabels, nameTable)
- else:
- cpal.paletteLabels = [C_P_A_L_.table_C_P_A_L_.NO_NAME_ID] * len(palettes)
-
- if paletteEntryLabels is not None:
- if len(paletteEntryLabels) != cpal.numPaletteEntries:
- raise ColorLibError(
- f"Expected {cpal.numPaletteEntries} paletteEntryLabels, "
- f"got {len(paletteEntryLabels)}"
- )
- cpal.paletteEntryLabels = buildPaletteLabels(paletteEntryLabels, nameTable)
- else:
- cpal.paletteEntryLabels = [
- C_P_A_L_.table_C_P_A_L_.NO_NAME_ID
- ] * cpal.numPaletteEntries
- else:
- cpal.version = 0
-
- return cpal
-
-
-# COLR v1 tables
-# See draft proposal at: https://github.com/googlefonts/colr-gradients-spec
-
-
-def _is_colrv0_layer(layer: Any) -> bool:
- # Consider as COLRv0 layer any sequence of length 2 (be it tuple or list) in which
- # the first element is a str (the layerGlyph) and the second element is an int
- # (CPAL paletteIndex).
- # https://github.com/googlefonts/ufo2ft/issues/426
- try:
- layerGlyph, paletteIndex = layer
- except (TypeError, ValueError):
- return False
- else:
- return isinstance(layerGlyph, str) and isinstance(paletteIndex, int)
-
-
-def _split_color_glyphs_by_version(
- colorGlyphs: _ColorGlyphsDict,
-) -> Tuple[_ColorGlyphsV0Dict, _ColorGlyphsDict]:
- colorGlyphsV0 = {}
- colorGlyphsV1 = {}
- for baseGlyph, layers in colorGlyphs.items():
- if all(_is_colrv0_layer(l) for l in layers):
- colorGlyphsV0[baseGlyph] = layers
- else:
- colorGlyphsV1[baseGlyph] = layers
-
- # sanity check
- assert set(colorGlyphs) == (set(colorGlyphsV0) | set(colorGlyphsV1))
-
- return colorGlyphsV0, colorGlyphsV1
-
-
-def _reuse_ranges(num_layers: int) -> Generator[Tuple[int, int], None, None]:
- # TODO feels like something itertools might have already
- for lbound in range(num_layers):
- # Reuse of very large #s of layers is relatively unlikely
- # +2: we want sequences of at least 2
- # otData handles single-record duplication
- for ubound in range(
- lbound + 2, min(num_layers + 1, lbound + 2 + _MAX_REUSE_LEN)
- ):
- yield (lbound, ubound)
-
-
-class LayerReuseCache:
- reusePool: Mapping[Tuple[Any, ...], int]
- tuples: Mapping[int, Tuple[Any, ...]]
- keepAlive: List[ot.Paint] # we need id to remain valid
-
- def __init__(self):
- self.reusePool = {}
- self.tuples = {}
- self.keepAlive = []
-
- def _paint_tuple(self, paint: ot.Paint):
- # start simple, who even cares about cyclic graphs or interesting field types
- def _tuple_safe(value):
- if isinstance(value, enum.Enum):
- return value
- elif hasattr(value, "__dict__"):
- return tuple(
- (k, _tuple_safe(v)) for k, v in sorted(value.__dict__.items())
- )
- elif isinstance(value, collections.abc.MutableSequence):
- return tuple(_tuple_safe(e) for e in value)
- return value
-
- # Cache the tuples for individual Paint instead of the whole sequence
- # because the seq could be a transient slice
- result = self.tuples.get(id(paint), None)
- if result is None:
- result = _tuple_safe(paint)
- self.tuples[id(paint)] = result
- self.keepAlive.append(paint)
- return result
-
- def _as_tuple(self, paints: Sequence[ot.Paint]) -> Tuple[Any, ...]:
- return tuple(self._paint_tuple(p) for p in paints)
-
- def try_reuse(self, layers: List[ot.Paint]) -> List[ot.Paint]:
- found_reuse = True
- while found_reuse:
- found_reuse = False
-
- ranges = sorted(
- _reuse_ranges(len(layers)),
- key=lambda t: (t[1] - t[0], t[1], t[0]),
- reverse=True,
- )
- for lbound, ubound in ranges:
- reuse_lbound = self.reusePool.get(
- self._as_tuple(layers[lbound:ubound]), -1
- )
- if reuse_lbound == -1:
- continue
- new_slice = ot.Paint()
- new_slice.Format = int(ot.PaintFormat.PaintColrLayers)
- new_slice.NumLayers = ubound - lbound
- new_slice.FirstLayerIndex = reuse_lbound
- layers = layers[:lbound] + [new_slice] + layers[ubound:]
- found_reuse = True
- break
- return layers
-
- def add(self, layers: List[ot.Paint], first_layer_index: int):
- for lbound, ubound in _reuse_ranges(len(layers)):
- self.reusePool[self._as_tuple(layers[lbound:ubound])] = (
- lbound + first_layer_index
- )
-
-
-class LayerListBuilder:
- layers: List[ot.Paint]
- cache: LayerReuseCache
- allowLayerReuse: bool
-
- def __init__(self, *, allowLayerReuse=True):
- self.layers = []
- if allowLayerReuse:
- self.cache = LayerReuseCache()
- else:
- self.cache = None
-
- # We need to intercept construction of PaintColrLayers
- callbacks = _buildPaintCallbacks()
- callbacks[
- (
- BuildCallback.BEFORE_BUILD,
- ot.Paint,
- ot.PaintFormat.PaintColrLayers,
- )
- ] = self._beforeBuildPaintColrLayers
- self.tableBuilder = TableBuilder(callbacks)
-
- # COLR layers is unusual in that it modifies shared state
- # so we need a callback into an object
- def _beforeBuildPaintColrLayers(self, dest, source):
- # Sketchy gymnastics: a sequence input will have dropped it's layers
- # into NumLayers; get it back
- if isinstance(source.get("NumLayers", None), collections.abc.Sequence):
- layers = source["NumLayers"]
- else:
- layers = source["Layers"]
-
- # Convert maps seqs or whatever into typed objects
- layers = [self.buildPaint(l) for l in layers]
-
- # No reason to have a colr layers with just one entry
- if len(layers) == 1:
- return layers[0], {}
-
- if self.cache is not None:
- # Look for reuse, with preference to longer sequences
- # This may make the layer list smaller
- layers = self.cache.try_reuse(layers)
-
- # The layer list is now final; if it's too big we need to tree it
- is_tree = len(layers) > MAX_PAINT_COLR_LAYER_COUNT
- layers = build_n_ary_tree(layers, n=MAX_PAINT_COLR_LAYER_COUNT)
-
- # We now have a tree of sequences with Paint leaves.
- # Convert the sequences into PaintColrLayers.
- def listToColrLayers(layer):
- if isinstance(layer, collections.abc.Sequence):
- return self.buildPaint(
- {
- "Format": ot.PaintFormat.PaintColrLayers,
- "Layers": [listToColrLayers(l) for l in layer],
- }
- )
- return layer
-
- layers = [listToColrLayers(l) for l in layers]
-
- # No reason to have a colr layers with just one entry
- if len(layers) == 1:
- return layers[0], {}
-
- paint = ot.Paint()
- paint.Format = int(ot.PaintFormat.PaintColrLayers)
- paint.NumLayers = len(layers)
- paint.FirstLayerIndex = len(self.layers)
- self.layers.extend(layers)
-
- # Register our parts for reuse provided we aren't a tree
- # If we are a tree the leaves registered for reuse and that will suffice
- if self.cache is not None and not is_tree:
- self.cache.add(layers, paint.FirstLayerIndex)
-
- # we've fully built dest; empty source prevents generalized build from kicking in
- return paint, {}
-
- def buildPaint(self, paint: _PaintInput) -> ot.Paint:
- return self.tableBuilder.build(ot.Paint, paint)
-
- def build(self) -> Optional[ot.LayerList]:
- if not self.layers:
- return None
- layers = ot.LayerList()
- layers.LayerCount = len(self.layers)
- layers.Paint = self.layers
- return layers
-
-
-def buildBaseGlyphPaintRecord(
- baseGlyph: str, layerBuilder: LayerListBuilder, paint: _PaintInput
-) -> ot.BaseGlyphList:
- self = ot.BaseGlyphPaintRecord()
- self.BaseGlyph = baseGlyph
- self.Paint = layerBuilder.buildPaint(paint)
- return self
-
-
-def _format_glyph_errors(errors: Mapping[str, Exception]) -> str:
- lines = []
- for baseGlyph, error in sorted(errors.items()):
- lines.append(f" {baseGlyph} => {type(error).__name__}: {error}")
- return "\n".join(lines)
-
-
-def buildColrV1(
- colorGlyphs: _ColorGlyphsDict,
- glyphMap: Optional[Mapping[str, int]] = None,
- *,
- allowLayerReuse: bool = True,
-) -> Tuple[Optional[ot.LayerList], ot.BaseGlyphList]:
- if glyphMap is not None:
- colorGlyphItems = sorted(
- colorGlyphs.items(), key=lambda item: glyphMap[item[0]]
- )
- else:
- colorGlyphItems = colorGlyphs.items()
-
- errors = {}
- baseGlyphs = []
- layerBuilder = LayerListBuilder(allowLayerReuse=allowLayerReuse)
- for baseGlyph, paint in colorGlyphItems:
- try:
- baseGlyphs.append(buildBaseGlyphPaintRecord(baseGlyph, layerBuilder, paint))
-
- except (ColorLibError, OverflowError, ValueError, TypeError) as e:
- errors[baseGlyph] = e
-
- if errors:
- failed_glyphs = _format_glyph_errors(errors)
- exc = ColorLibError(f"Failed to build BaseGlyphList:\n{failed_glyphs}")
- exc.errors = errors
- raise exc from next(iter(errors.values()))
-
- layers = layerBuilder.build()
- glyphs = ot.BaseGlyphList()
- glyphs.BaseGlyphCount = len(baseGlyphs)
- glyphs.BaseGlyphPaintRecord = baseGlyphs
- return (layers, glyphs)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/_writers.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/_writers.py
deleted file mode 100644
index 939cdb912a9debaea07fbf3a9ac04549c44d077c..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/_writers.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# Code to read HTTP data
-#
-# Strategy: each writer takes an event + a write-some-bytes function, which is
-# calls.
-#
-# WRITERS is a dict describing how to pick a reader. It maps states to either:
-# - a writer
-# - or, for body writers, a dict of framin-dependent writer factories
-
-from typing import Any, Callable, Dict, List, Tuple, Type, Union
-
-from ._events import Data, EndOfMessage, Event, InformationalResponse, Request, Response
-from ._headers import Headers
-from ._state import CLIENT, IDLE, SEND_BODY, SEND_RESPONSE, SERVER
-from ._util import LocalProtocolError, Sentinel
-
-__all__ = ["WRITERS"]
-
-Writer = Callable[[bytes], Any]
-
-
-def write_headers(headers: Headers, write: Writer) -> None:
- # "Since the Host field-value is critical information for handling a
- # request, a user agent SHOULD generate Host as the first header field
- # following the request-line." - RFC 7230
- raw_items = headers._full_items
- for raw_name, name, value in raw_items:
- if name == b"host":
- write(b"%s: %s\r\n" % (raw_name, value))
- for raw_name, name, value in raw_items:
- if name != b"host":
- write(b"%s: %s\r\n" % (raw_name, value))
- write(b"\r\n")
-
-
-def write_request(request: Request, write: Writer) -> None:
- if request.http_version != b"1.1":
- raise LocalProtocolError("I only send HTTP/1.1")
- write(b"%s %s HTTP/1.1\r\n" % (request.method, request.target))
- write_headers(request.headers, write)
-
-
-# Shared between InformationalResponse and Response
-def write_any_response(
- response: Union[InformationalResponse, Response], write: Writer
-) -> None:
- if response.http_version != b"1.1":
- raise LocalProtocolError("I only send HTTP/1.1")
- status_bytes = str(response.status_code).encode("ascii")
- # We don't bother sending ascii status messages like "OK"; they're
- # optional and ignored by the protocol. (But the space after the numeric
- # status code is mandatory.)
- #
- # XX FIXME: could at least make an effort to pull out the status message
- # from stdlib's http.HTTPStatus table. Or maybe just steal their enums
- # (either by import or copy/paste). We already accept them as status codes
- # since they're of type IntEnum < int.
- write(b"HTTP/1.1 %s %s\r\n" % (status_bytes, response.reason))
- write_headers(response.headers, write)
-
-
-class BodyWriter:
- def __call__(self, event: Event, write: Writer) -> None:
- if type(event) is Data:
- self.send_data(event.data, write)
- elif type(event) is EndOfMessage:
- self.send_eom(event.headers, write)
- else: # pragma: no cover
- assert False
-
- def send_data(self, data: bytes, write: Writer) -> None:
- pass
-
- def send_eom(self, headers: Headers, write: Writer) -> None:
- pass
-
-
-#
-# These are all careful not to do anything to 'data' except call len(data) and
-# write(data). This allows us to transparently pass-through funny objects,
-# like placeholder objects referring to files on disk that will be sent via
-# sendfile(2).
-#
-class ContentLengthWriter(BodyWriter):
- def __init__(self, length: int) -> None:
- self._length = length
-
- def send_data(self, data: bytes, write: Writer) -> None:
- self._length -= len(data)
- if self._length < 0:
- raise LocalProtocolError("Too much data for declared Content-Length")
- write(data)
-
- def send_eom(self, headers: Headers, write: Writer) -> None:
- if self._length != 0:
- raise LocalProtocolError("Too little data for declared Content-Length")
- if headers:
- raise LocalProtocolError("Content-Length and trailers don't mix")
-
-
-class ChunkedWriter(BodyWriter):
- def send_data(self, data: bytes, write: Writer) -> None:
- # if we encoded 0-length data in the naive way, it would look like an
- # end-of-message.
- if not data:
- return
- write(b"%x\r\n" % len(data))
- write(data)
- write(b"\r\n")
-
- def send_eom(self, headers: Headers, write: Writer) -> None:
- write(b"0\r\n")
- write_headers(headers, write)
-
-
-class Http10Writer(BodyWriter):
- def send_data(self, data: bytes, write: Writer) -> None:
- write(data)
-
- def send_eom(self, headers: Headers, write: Writer) -> None:
- if headers:
- raise LocalProtocolError("can't send trailers to HTTP/1.0 client")
- # no need to close the socket ourselves, that will be taken care of by
- # Connection: close machinery
-
-
-WritersType = Dict[
- Union[Tuple[Type[Sentinel], Type[Sentinel]], Type[Sentinel]],
- Union[
- Dict[str, Type[BodyWriter]],
- Callable[[Union[InformationalResponse, Response], Writer], None],
- Callable[[Request, Writer], None],
- ],
-]
-
-WRITERS: WritersType = {
- (CLIENT, IDLE): write_request,
- (SERVER, IDLE): write_any_response,
- (SERVER, SEND_RESPONSE): write_any_response,
- SEND_BODY: {
- "chunked": ChunkedWriter,
- "content-length": ContentLengthWriter,
- "http/1.0": Http10Writer,
- },
-}
diff --git a/spaces/Datasculptor/MusicGen/MODEL_CARD.md b/spaces/Datasculptor/MusicGen/MODEL_CARD.md
deleted file mode 100644
index 6c2c9f883969eb905e74ad3376966d156cc5ca00..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/MusicGen/MODEL_CARD.md
+++ /dev/null
@@ -1,81 +0,0 @@
-# MusicGen Model Card
-
-## Model details
-
-**Organization developing the model:** The FAIR team of Meta AI.
-
-**Model date:** MusicGen was trained between April 2023 and May 2023.
-
-**Model version:** This is the version 1 of the model.
-
-**Model type:** MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation.
-
-**Paper or resources for more information:** More information can be found in the paper [Simple and Controllable Music Generation][arxiv].
-
-**Citation details** See [our paper][arxiv]
-
-**License** Code is released under MIT, model weights are released under CC-BY-NC 4.0.
-
-**Where to send questions or comments about the model:** Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue.
-
-## Intended use
-**Primary intended use:** The primary use of MusicGen is research on AI-based music generation, including:
-
-- Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science
-- Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs
-
-**Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models.
-
-**Out-of-scope use cases** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
-
-## Metrics
-
-**Models performance measures:** We used the following objective measure to evaluate the model on a standard music benchmark:
-
-- Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish)
-- Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST)
-- CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model
-
-Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes:
-
-- Overall quality of the music samples;
-- Text relevance to the provided text input;
-- Adherence to the melody for melody-guided music generation.
-
-More details on performance measures and human studies can be found in the paper.
-
-**Decision thresholds:** Not applicable.
-
-## Evaluation datasets
-
-The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set.
-
-## Training datasets
-
-The model was trained on licensed data using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound), [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing.
-
-## Quantitative analysis
-
-More information can be found in the paper [Simple and Controllable Music Generation][arxiv], in the Experimental Setup section.
-
-## Limitations and biases
-
-**Data:** The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model.
-
-**Mitigations:** Vocals have been removed from the data source using corresponding tags, and then using using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs).
-
-**Limitations:**
-
-- The model is not able to generate realistic vocals.
-- The model has been trained with English descriptions and will not perform as well in other languages.
-- The model does not perform equally well for all music styles and cultures.
-- The model sometimes generates end of songs, collapsing to silence.
-- It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results.
-
-**Biases:** The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive.
-
-**Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data.
-
-**Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.
-
-[arxiv]: https://arxiv.org/abs/2306.05284
diff --git a/spaces/Datasculptor/StyleGAN-NADA/e4e/scripts/inference.py b/spaces/Datasculptor/StyleGAN-NADA/e4e/scripts/inference.py
deleted file mode 100644
index 185b9b34db85dcd97b9793bd5dbfc9d1ca046549..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/StyleGAN-NADA/e4e/scripts/inference.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import argparse
-
-import torch
-import numpy as np
-import sys
-import os
-import dlib
-
-sys.path.append(".")
-sys.path.append("..")
-
-from configs import data_configs, paths_config
-from datasets.inference_dataset import InferenceDataset
-from torch.utils.data import DataLoader
-from utils.model_utils import setup_model
-from utils.common import tensor2im
-from utils.alignment import align_face
-from PIL import Image
-
-
-def main(args):
- net, opts = setup_model(args.ckpt, device)
- is_cars = 'cars_' in opts.dataset_type
- generator = net.decoder
- generator.eval()
- args, data_loader = setup_data_loader(args, opts)
-
- # Check if latents exist
- latents_file_path = os.path.join(args.save_dir, 'latents.pt')
- if os.path.exists(latents_file_path):
- latent_codes = torch.load(latents_file_path).to(device)
- else:
- latent_codes = get_all_latents(net, data_loader, args.n_sample, is_cars=is_cars)
- torch.save(latent_codes, latents_file_path)
-
- if not args.latents_only:
- generate_inversions(args, generator, latent_codes, is_cars=is_cars)
-
-
-def setup_data_loader(args, opts):
- dataset_args = data_configs.DATASETS[opts.dataset_type]
- transforms_dict = dataset_args['transforms'](opts).get_transforms()
- images_path = args.images_dir if args.images_dir is not None else dataset_args['test_source_root']
- print(f"images path: {images_path}")
- align_function = None
- if args.align:
- align_function = run_alignment
- test_dataset = InferenceDataset(root=images_path,
- transform=transforms_dict['transform_test'],
- preprocess=align_function,
- opts=opts)
-
- data_loader = DataLoader(test_dataset,
- batch_size=args.batch,
- shuffle=False,
- num_workers=2,
- drop_last=True)
-
- print(f'dataset length: {len(test_dataset)}')
-
- if args.n_sample is None:
- args.n_sample = len(test_dataset)
- return args, data_loader
-
-
-def get_latents(net, x, is_cars=False):
- codes = net.encoder(x)
- if net.opts.start_from_latent_avg:
- if codes.ndim == 2:
- codes = codes + net.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :]
- else:
- codes = codes + net.latent_avg.repeat(codes.shape[0], 1, 1)
- if codes.shape[1] == 18 and is_cars:
- codes = codes[:, :16, :]
- return codes
-
-
-def get_all_latents(net, data_loader, n_images=None, is_cars=False):
- all_latents = []
- i = 0
- with torch.no_grad():
- for batch in data_loader:
- if n_images is not None and i > n_images:
- break
- x = batch
- inputs = x.to(device).float()
- latents = get_latents(net, inputs, is_cars)
- all_latents.append(latents)
- i += len(latents)
- return torch.cat(all_latents)
-
-
-def save_image(img, save_dir, idx):
- result = tensor2im(img)
- im_save_path = os.path.join(save_dir, f"{idx:05d}.jpg")
- Image.fromarray(np.array(result)).save(im_save_path)
-
-
-@torch.no_grad()
-def generate_inversions(args, g, latent_codes, is_cars):
- print('Saving inversion images')
- inversions_directory_path = os.path.join(args.save_dir, 'inversions')
- os.makedirs(inversions_directory_path, exist_ok=True)
- for i in range(args.n_sample):
- imgs, _ = g([latent_codes[i].unsqueeze(0)], input_is_latent=True, randomize_noise=False, return_latents=True)
- if is_cars:
- imgs = imgs[:, :, 64:448, :]
- save_image(imgs[0], inversions_directory_path, i + 1)
-
-
-def run_alignment(image_path):
- predictor = dlib.shape_predictor(paths_config.model_paths['shape_predictor'])
- aligned_image = align_face(filepath=image_path, predictor=predictor)
- print("Aligned image has shape: {}".format(aligned_image.size))
- return aligned_image
-
-
-if __name__ == "__main__":
- device = "cuda"
-
- parser = argparse.ArgumentParser(description="Inference")
- parser.add_argument("--images_dir", type=str, default=None,
- help="The directory of the images to be inverted")
- parser.add_argument("--save_dir", type=str, default=None,
- help="The directory to save the latent codes and inversion images. (default: images_dir")
- parser.add_argument("--batch", type=int, default=1, help="batch size for the generator")
- parser.add_argument("--n_sample", type=int, default=None, help="number of the samples to infer.")
- parser.add_argument("--latents_only", action="store_true", help="infer only the latent codes of the directory")
- parser.add_argument("--align", action="store_true", help="align face images before inference")
- parser.add_argument("ckpt", metavar="CHECKPOINT", help="path to generator checkpoint")
-
- args = parser.parse_args()
- main(args)
diff --git a/spaces/Datasculptor/stabilityai-stable-diffusion-2-1/app.py b/spaces/Datasculptor/stabilityai-stable-diffusion-2-1/app.py
deleted file mode 100644
index 0160420876923d89f2ab5fccb9f4d13725e29972..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/stabilityai-stable-diffusion-2-1/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stabilityai/stable-diffusion-2-1").launch()
\ No newline at end of file
diff --git a/spaces/Deepak107/NSFW-Detection/README.md b/spaces/Deepak107/NSFW-Detection/README.md
deleted file mode 100644
index 21d3bd0a38405e337e8e686c74ad421c881415a0..0000000000000000000000000000000000000000
--- a/spaces/Deepak107/NSFW-Detection/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: NSFW Detection
-emoji: 🌍
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.8
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Dilmurat/bingo/README.md b/spaces/Dilmurat/bingo/README.md
deleted file mode 100644
index 5d6936218874c647b5d22e13ad4be7edb8936f92..0000000000000000000000000000000000000000
--- a/spaces/Dilmurat/bingo/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-title: bingo
-emoji: 😊
-colorFrom: red
-colorTo: red
-sdk: docker
-license: mit
-duplicated_from: hf4all/bingo
----
-
-
-
-# Bingo
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-
-
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://github.com/weaigc/bingo/blob/main/license)
-
-问题反馈请前往 https://github.com/weaigc/bingo/issues
-
-
-
diff --git a/spaces/ECCV2022/bytetrack/tools/convert_crowdhuman_to_coco.py b/spaces/ECCV2022/bytetrack/tools/convert_crowdhuman_to_coco.py
deleted file mode 100644
index 62e0b66788f7625e2fbb5ba420794abf1125aa84..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/tools/convert_crowdhuman_to_coco.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import os
-import numpy as np
-import json
-from PIL import Image
-
-DATA_PATH = 'datasets/crowdhuman/'
-OUT_PATH = DATA_PATH + 'annotations/'
-SPLITS = ['val', 'train']
-DEBUG = False
-
-def load_func(fpath):
- print('fpath', fpath)
- assert os.path.exists(fpath)
- with open(fpath,'r') as fid:
- lines = fid.readlines()
- records =[json.loads(line.strip('\n')) for line in lines]
- return records
-
-if __name__ == '__main__':
- if not os.path.exists(OUT_PATH):
- os.mkdir(OUT_PATH)
- for split in SPLITS:
- data_path = DATA_PATH + split
- out_path = OUT_PATH + '{}.json'.format(split)
- out = {'images': [], 'annotations': [], 'categories': [{'id': 1, 'name': 'person'}]}
- ann_path = DATA_PATH + 'annotation_{}.odgt'.format(split)
- anns_data = load_func(ann_path)
- image_cnt = 0
- ann_cnt = 0
- video_cnt = 0
- for ann_data in anns_data:
- image_cnt += 1
- file_path = DATA_PATH + 'CrowdHuman_{}/'.format(split) + '{}.jpg'.format(ann_data['ID'])
- im = Image.open(file_path)
- image_info = {'file_name': '{}.jpg'.format(ann_data['ID']),
- 'id': image_cnt,
- 'height': im.size[1],
- 'width': im.size[0]}
- out['images'].append(image_info)
- if split != 'test':
- anns = ann_data['gtboxes']
- for i in range(len(anns)):
- ann_cnt += 1
- fbox = anns[i]['fbox']
- ann = {'id': ann_cnt,
- 'category_id': 1,
- 'image_id': image_cnt,
- 'track_id': -1,
- 'bbox_vis': anns[i]['vbox'],
- 'bbox': fbox,
- 'area': fbox[2] * fbox[3],
- 'iscrowd': 1 if 'extra' in anns[i] and \
- 'ignore' in anns[i]['extra'] and \
- anns[i]['extra']['ignore'] == 1 else 0}
- out['annotations'].append(ann)
- print('loaded {} for {} images and {} samples'.format(split, len(out['images']), len(out['annotations'])))
- json.dump(out, open(out_path, 'w'))
\ No newline at end of file
diff --git a/spaces/EcoCy/LoRA-DreamBooth-Training-UI/inference.py b/spaces/EcoCy/LoRA-DreamBooth-Training-UI/inference.py
deleted file mode 100644
index ce0f2b08df75e6d62f06c4119f1dc859930de032..0000000000000000000000000000000000000000
--- a/spaces/EcoCy/LoRA-DreamBooth-Training-UI/inference.py
+++ /dev/null
@@ -1,94 +0,0 @@
-from __future__ import annotations
-
-import gc
-import pathlib
-
-import gradio as gr
-import PIL.Image
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-from huggingface_hub import ModelCard
-
-
-class InferencePipeline:
- def __init__(self, hf_token: str | None = None):
- self.hf_token = hf_token
- self.pipe = None
- self.device = torch.device(
- 'cuda:0' if torch.cuda.is_available() else 'cpu')
- self.lora_model_id = None
- self.base_model_id = None
-
- def clear(self) -> None:
- self.lora_model_id = None
- self.base_model_id = None
- del self.pipe
- self.pipe = None
- torch.cuda.empty_cache()
- gc.collect()
-
- @staticmethod
- def check_if_model_is_local(lora_model_id: str) -> bool:
- return pathlib.Path(lora_model_id).exists()
-
- @staticmethod
- def get_model_card(model_id: str,
- hf_token: str | None = None) -> ModelCard:
- if InferencePipeline.check_if_model_is_local(model_id):
- card_path = (pathlib.Path(model_id) / 'README.md').as_posix()
- else:
- card_path = model_id
- return ModelCard.load(card_path, token=hf_token)
-
- @staticmethod
- def get_base_model_info(lora_model_id: str,
- hf_token: str | None = None) -> str:
- card = InferencePipeline.get_model_card(lora_model_id, hf_token)
- return card.data.base_model
-
- def load_pipe(self, lora_model_id: str) -> None:
- if lora_model_id == self.lora_model_id:
- return
- base_model_id = self.get_base_model_info(lora_model_id, self.hf_token)
- if base_model_id != self.base_model_id:
- if self.device.type == 'cpu':
- pipe = DiffusionPipeline.from_pretrained(
- base_model_id, use_auth_token=self.hf_token)
- else:
- pipe = DiffusionPipeline.from_pretrained(
- base_model_id,
- torch_dtype=torch.float16,
- use_auth_token=self.hf_token)
- pipe = pipe.to(self.device)
- pipe.scheduler = DPMSolverMultistepScheduler.from_config(
- pipe.scheduler.config)
- self.pipe = pipe
- self.pipe.unet.load_attn_procs( # type: ignore
- lora_model_id, use_auth_token=self.hf_token)
-
- self.lora_model_id = lora_model_id # type: ignore
- self.base_model_id = base_model_id # type: ignore
-
- def run(
- self,
- lora_model_id: str,
- prompt: str,
- lora_scale: float,
- seed: int,
- n_steps: int,
- guidance_scale: float,
- ) -> PIL.Image.Image:
- if not torch.cuda.is_available():
- raise gr.Error('CUDA is not available.')
-
- self.load_pipe(lora_model_id)
-
- generator = torch.Generator(device=self.device).manual_seed(seed)
- out = self.pipe(
- prompt,
- num_inference_steps=n_steps,
- guidance_scale=guidance_scale,
- generator=generator,
- cross_attention_kwargs={'scale': lora_scale},
- ) # type: ignore
- return out.images[0]
diff --git a/spaces/Epoching/3D_Photo_Inpainting/MiDaS/run.py b/spaces/Epoching/3D_Photo_Inpainting/MiDaS/run.py
deleted file mode 100644
index a483d2850a81b3520b80097eff4bb9367ef6a144..0000000000000000000000000000000000000000
--- a/spaces/Epoching/3D_Photo_Inpainting/MiDaS/run.py
+++ /dev/null
@@ -1,81 +0,0 @@
-"""Compute depth maps for images in the input folder.
-"""
-import os
-import glob
-import torch
-# from monodepth_net import MonoDepthNet
-# import utils
-import matplotlib.pyplot as plt
-import numpy as np
-import cv2
-import imageio
-
-
-def run_depth(img_names, input_path, output_path, model_path, Net, utils, target_w=None):
- """Run MonoDepthNN to compute depth maps.
-
- Args:
- input_path (str): path to input folder
- output_path (str): path to output folder
- model_path (str): path to saved model
- """
- print("initialize")
-
- # select device
- device = torch.device("cpu")
- print("device: %s" % device)
-
- # load network
- model = Net(model_path)
- model.to(device)
- model.eval()
-
- # get input
- # img_names = glob.glob(os.path.join(input_path, "*"))
- num_images = len(img_names)
-
- # create output folder
- os.makedirs(output_path, exist_ok=True)
-
- print("start processing")
-
- for ind, img_name in enumerate(img_names):
-
- print(" processing {} ({}/{})".format(img_name, ind + 1, num_images))
-
- # input
- img = utils.read_image(img_name)
- w = img.shape[1]
- scale = 640. / max(img.shape[0], img.shape[1])
- target_height, target_width = int(round(img.shape[0] * scale)), int(round(img.shape[1] * scale))
- img_input = utils.resize_image(img)
- print(img_input.shape)
- img_input = img_input.to(device)
- # compute
- with torch.no_grad():
- out = model.forward(img_input)
-
- depth = utils.resize_depth(out, target_width, target_height)
- img = cv2.resize((img * 255).astype(np.uint8), (target_width, target_height), interpolation=cv2.INTER_AREA)
-
- filename = os.path.join(
- output_path, os.path.splitext(os.path.basename(img_name))[0]
- )
- np.save(filename + '.npy', depth)
- utils.write_depth(filename, depth, bits=2)
-
- print("finished")
-
-
-# if __name__ == "__main__":
-# # set paths
-# INPUT_PATH = "image"
-# OUTPUT_PATH = "output"
-# MODEL_PATH = "model.pt"
-
-# # set torch options
-# torch.backends.cudnn.enabled = True
-# torch.backends.cudnn.benchmark = True
-
-# # compute depth maps
-# run_depth(INPUT_PATH, OUTPUT_PATH, MODEL_PATH, Net, target_w=640)
diff --git a/spaces/EronSamez/RVC_HFmeu/infer/modules/vc/modules.py b/spaces/EronSamez/RVC_HFmeu/infer/modules/vc/modules.py
deleted file mode 100644
index 458cfbe860b23bdd8f07abc2934443e6b8b01c3a..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/infer/modules/vc/modules.py
+++ /dev/null
@@ -1,526 +0,0 @@
-import os, sys
-import traceback
-import logging
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-logger = logging.getLogger(__name__)
-import lib.globals.globals as rvc_globals
-import numpy as np
-import soundfile as sf
-import torch
-from io import BytesIO
-from infer.lib.audio import load_audio
-from infer.lib.audio import wav2
-from infer.lib.infer_pack.models import (
- SynthesizerTrnMs256NSFsid,
- SynthesizerTrnMs256NSFsid_nono,
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono,
-)
-from infer.modules.vc.pipeline import Pipeline
-from infer.modules.vc.utils import *
-import time
-import scipy.io.wavfile as wavfile
-
-def note_to_hz(note_name):
- SEMITONES = {'C': -9, 'C#': -8, 'D': -7, 'D#': -6, 'E': -5, 'F': -4, 'F#': -3, 'G': -2, 'G#': -1, 'A': 0, 'A#': 1, 'B': 2}
- pitch_class, octave = note_name[:-1], int(note_name[-1])
- semitone = SEMITONES[pitch_class]
- note_number = 12 * (octave - 4) + semitone
- frequency = 440.0 * (2.0 ** (1.0/12)) ** note_number
- return frequency
-
-class VC:
- def __init__(self, config):
- self.n_spk = None
- self.tgt_sr = None
- self.net_g = None
- self.pipeline = None
- self.cpt = None
- self.version = None
- self.if_f0 = None
- self.version = None
- self.hubert_model = None
-
- self.config = config
-
- def get_vc(self, sid, *to_return_protect):
- logger.info("Get sid: " + sid)
-
- to_return_protect0 = {
- "visible": self.if_f0 != 0,
- "value": to_return_protect[0]
- if self.if_f0 != 0 and to_return_protect
- else 0.5,
- "__type__": "update",
- }
- to_return_protect1 = {
- "visible": self.if_f0 != 0,
- "value": to_return_protect[1]
- if self.if_f0 != 0 and to_return_protect
- else 0.33,
- "__type__": "update",
- }
-
- if not sid:
- if self.hubert_model is not None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
- logger.info("Clean model cache")
- del (
- self.net_g,
- self.n_spk,
- self.vc,
- self.hubert_model,
- self.tgt_sr,
- ) # ,cpt
- self.hubert_model = (
- self.net_g
- ) = self.n_spk = self.vc = self.hubert_model = self.tgt_sr = None
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- ###楼下不这么折腾清理不干净
- self.if_f0 = self.cpt.get("f0", 1)
- self.version = self.cpt.get("version", "v1")
- if self.version == "v1":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs256NSFsid(
- *self.cpt["config"], is_half=self.config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs256NSFsid_nono(*self.cpt["config"])
- elif self.version == "v2":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs768NSFsid(
- *self.cpt["config"], is_half=self.config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs768NSFsid_nono(*self.cpt["config"])
- del self.net_g, self.cpt
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- return (
- {"visible": False, "__type__": "update"},
- {
- "visible": True,
- "value": to_return_protect0,
- "__type__": "update",
- },
- {
- "visible": True,
- "value": to_return_protect1,
- "__type__": "update",
- },
- "",
- "",
- )
- #person = f'{os.getenv("weight_root")}/{sid}'
- person = f'{sid}'
- #logger.info(f"Loading: {person}")
- logger.info(f"Loading...")
- self.cpt = torch.load(person, map_location="cpu")
- self.tgt_sr = self.cpt["config"][-1]
- self.cpt["config"][-3] = self.cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- self.if_f0 = self.cpt.get("f0", 1)
- self.version = self.cpt.get("version", "v1")
-
- synthesizer_class = {
- ("v1", 1): SynthesizerTrnMs256NSFsid,
- ("v1", 0): SynthesizerTrnMs256NSFsid_nono,
- ("v2", 1): SynthesizerTrnMs768NSFsid,
- ("v2", 0): SynthesizerTrnMs768NSFsid_nono,
- }
-
- self.net_g = synthesizer_class.get(
- (self.version, self.if_f0), SynthesizerTrnMs256NSFsid
- )(*self.cpt["config"], is_half=self.config.is_half)
-
- del self.net_g.enc_q
-
- self.net_g.load_state_dict(self.cpt["weight"], strict=False)
- self.net_g.eval().to(self.config.device)
- if self.config.is_half:
- self.net_g = self.net_g.half()
- else:
- self.net_g = self.net_g.float()
-
- self.pipeline = Pipeline(self.tgt_sr, self.config)
- n_spk = self.cpt["config"][-3]
- index = {"value": get_index_path_from_model(sid), "__type__": "update"}
- logger.info("Select index: " + index["value"])
-
- return (
- (
- {"visible": False, "maximum": n_spk, "__type__": "update"},
- to_return_protect0,
- to_return_protect1
- )
- if to_return_protect
- else {"visible": False, "maximum": n_spk, "__type__": "update"}
- )
-
-
- def vc_single(
- self,
- sid,
- input_audio_path0,
- input_audio_path1,
- f0_up_key,
- f0_file,
- f0_method,
- file_index,
- file_index2,
- index_rate,
- filter_radius,
- resample_sr,
- rms_mix_rate,
- protect,
- crepe_hop_length,
- f0_min,
- note_min,
- f0_max,
- note_max,
- f0_autotune,
- ):
- global total_time
- total_time = 0
- start_time = time.time()
- if not input_audio_path0 and not input_audio_path1:
- return "You need to upload an audio", None
-
- if (not os.path.exists(input_audio_path0)) and (not os.path.exists(os.path.join(now_dir, input_audio_path0))):
- return "Audio was not properly selected or doesn't exist", None
-
- input_audio_path1 = input_audio_path1 or input_audio_path0
- print(f"\nStarting inference for '{os.path.basename(input_audio_path1)}'")
- print("-------------------")
- f0_up_key = int(f0_up_key)
- if rvc_globals.NotesOrHertz and f0_method != 'rmvpe':
- f0_min = note_to_hz(note_min) if note_min else 50
- f0_max = note_to_hz(note_max) if note_max else 1100
- print(f"Converted Min pitch: freq - {f0_min}\n"
- f"Converted Max pitch: freq - {f0_max}")
- else:
- f0_min = f0_min or 50
- f0_max = f0_max or 1100
- try:
- input_audio_path1 = input_audio_path1 or input_audio_path0
- print(f"Attempting to load {input_audio_path1}....")
- audio = load_audio(file=input_audio_path1,
- sr=16000,
- DoFormant=rvc_globals.DoFormant,
- Quefrency=rvc_globals.Quefrency,
- Timbre=rvc_globals.Timbre)
-
- audio_max = np.abs(audio).max() / 0.95
- if audio_max > 1:
- audio /= audio_max
- times = [0, 0, 0]
-
- if self.hubert_model is None:
- self.hubert_model = load_hubert(self.config)
-
- try:
- self.if_f0 = self.cpt.get("f0", 1)
- except NameError:
- message = "Model was not properly selected"
- print(message)
- return message, None
-
- file_index = (
- (
- file_index.strip(" ")
- .strip('"')
- .strip("\n")
- .strip('"')
- .strip(" ")
- .replace("trained", "added")
- )
- if file_index != ""
- else file_index2
- ) # 防止小白写错，自动帮他替换掉
-
- try:
- audio_opt = self.pipeline.pipeline(
- self.hubert_model,
- self.net_g,
- sid,
- audio,
- input_audio_path1,
- times,
- f0_up_key,
- f0_method,
- file_index,
- index_rate,
- self.if_f0,
- filter_radius,
- self.tgt_sr,
- resample_sr,
- rms_mix_rate,
- self.version,
- protect,
- crepe_hop_length,
- f0_autotune,
- f0_file=f0_file,
- f0_min=f0_min,
- f0_max=f0_max
- )
- except AssertionError:
- message = "Mismatching index version detected (v1 with v2, or v2 with v1)."
- print(message)
- return message, None
- except NameError:
- message = "RVC libraries are still loading. Please try again in a few seconds."
- print(message)
- return message, None
-
- if self.tgt_sr != resample_sr >= 16000:
- self.tgt_sr = resample_sr
- index_info = (
- "Index:\n%s." % file_index
- if os.path.exists(file_index)
- else "Index not used."
- )
- end_time = time.time()
- total_time = end_time - start_time
-
- output_folder = "audio-outputs"
- os.makedirs(output_folder, exist_ok=True)
- output_filename = "generated_audio_{}.wav"
- output_count = 1
- while True:
- current_output_path = os.path.join(output_folder, output_filename.format(output_count))
- if not os.path.exists(current_output_path):
- break
- output_count += 1
-
- wavfile.write(current_output_path, self.tgt_sr, audio_opt)
- print(f"Generated audio saved to: {current_output_path}")
- return f"Success.\n {index_info}\nTime:\n npy:{times[0]}, f0:{times[1]}, infer:{times[2]}\nTotal Time: {total_time} seconds", (self.tgt_sr, audio_opt)
- except:
- info = traceback.format_exc()
- logger.warn(info)
- return info, (None, None)
-
- def vc_single_dont_save(
- self,
- sid,
- input_audio_path0,
- input_audio_path1,
- f0_up_key,
- f0_file,
- f0_method,
- file_index,
- file_index2,
- index_rate,
- filter_radius,
- resample_sr,
- rms_mix_rate,
- protect,
- crepe_hop_length,
- f0_min,
- note_min,
- f0_max,
- note_max,
- f0_autotune,
- ):
- global total_time
- total_time = 0
- start_time = time.time()
- if not input_audio_path0 and not input_audio_path1:
- return "You need to upload an audio", None
-
- if (not os.path.exists(input_audio_path0)) and (not os.path.exists(os.path.join(now_dir, input_audio_path0))):
- return "Audio was not properly selected or doesn't exist", None
-
- input_audio_path1 = input_audio_path1 or input_audio_path0
- print(f"\nStarting inference for '{os.path.basename(input_audio_path1)}'")
- print("-------------------")
- f0_up_key = int(f0_up_key)
- if rvc_globals.NotesOrHertz and f0_method != 'rmvpe':
- f0_min = note_to_hz(note_min) if note_min else 50
- f0_max = note_to_hz(note_max) if note_max else 1100
- print(f"Converted Min pitch: freq - {f0_min}\n"
- f"Converted Max pitch: freq - {f0_max}")
- else:
- f0_min = f0_min or 50
- f0_max = f0_max or 1100
- try:
- input_audio_path1 = input_audio_path1 or input_audio_path0
- print(f"Attempting to load {input_audio_path1}....")
- audio = load_audio(file=input_audio_path1,
- sr=16000,
- DoFormant=rvc_globals.DoFormant,
- Quefrency=rvc_globals.Quefrency,
- Timbre=rvc_globals.Timbre)
-
- audio_max = np.abs(audio).max() / 0.95
- if audio_max > 1:
- audio /= audio_max
- times = [0, 0, 0]
-
- if self.hubert_model is None:
- self.hubert_model = load_hubert(self.config)
-
- try:
- self.if_f0 = self.cpt.get("f0", 1)
- except NameError:
- message = "Model was not properly selected"
- print(message)
- return message, None
-
- file_index = (
- (
- file_index.strip(" ")
- .strip('"')
- .strip("\n")
- .strip('"')
- .strip(" ")
- .replace("trained", "added")
- )
- if file_index != ""
- else file_index2
- ) # 防止小白写错，自动帮他替换掉
-
- try:
- audio_opt = self.pipeline.pipeline(
- self.hubert_model,
- self.net_g,
- sid,
- audio,
- input_audio_path1,
- times,
- f0_up_key,
- f0_method,
- file_index,
- index_rate,
- self.if_f0,
- filter_radius,
- self.tgt_sr,
- resample_sr,
- rms_mix_rate,
- self.version,
- protect,
- crepe_hop_length,
- f0_autotune,
- f0_file=f0_file,
- f0_min=f0_min,
- f0_max=f0_max
- )
- except AssertionError:
- message = "Mismatching index version detected (v1 with v2, or v2 with v1)."
- print(message)
- return message, None
- except NameError:
- message = "RVC libraries are still loading. Please try again in a few seconds."
- print(message)
- return message, None
-
- if self.tgt_sr != resample_sr >= 16000:
- self.tgt_sr = resample_sr
- index_info = (
- "Index:\n%s." % file_index
- if os.path.exists(file_index)
- else "Index not used."
- )
- end_time = time.time()
- total_time = end_time - start_time
-
- return f"Success.\n {index_info}\nTime:\n npy:{times[0]}, f0:{times[1]}, infer:{times[2]}\nTotal Time: {total_time} seconds", (self.tgt_sr, audio_opt)
- except:
- info = traceback.format_exc()
- logger.warn(info)
- return info, (None, None)
-
-
- def vc_multi(
- self,
- sid,
- dir_path,
- opt_root,
- paths,
- f0_up_key,
- f0_method,
- file_index,
- file_index2,
- index_rate,
- filter_radius,
- resample_sr,
- rms_mix_rate,
- protect,
- format1,
- crepe_hop_length,
- f0_min,
- note_min,
- f0_max,
- note_max,
- f0_autotune,
- ):
- if rvc_globals.NotesOrHertz and f0_method != 'rmvpe':
- f0_min = note_to_hz(note_min) if note_min else 50
- f0_max = note_to_hz(note_max) if note_max else 1100
- print(f"Converted Min pitch: freq - {f0_min}\n"
- f"Converted Max pitch: freq - {f0_max}")
- else:
- f0_min = f0_min or 50
- f0_max = f0_max or 1100
- try:
- dir_path = (
- dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
- ) # 防止小白拷路径头尾带了空格和"和回车
- opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
- os.makedirs(opt_root, exist_ok=True)
- try:
- if dir_path != "":
- paths = [
- os.path.join(dir_path, name) for name in os.listdir(dir_path)
- ]
- else:
- paths = [path.name for path in paths]
- except:
- traceback.print_exc()
- paths = [path.name for path in paths]
- infos = []
- for path in paths:
- info, opt = self.vc_single(
- sid,
- path,
- f0_up_key,
- None,
- f0_method,
- file_index,
- file_index2,
- # file_big_npy,
- index_rate,
- filter_radius,
- resample_sr,
- rms_mix_rate,
- protect,
- )
- if "Success" in info:
- try:
- tgt_sr, audio_opt = opt
- if format1 in ["wav", "flac"]:
- sf.write(
- "%s/%s.%s"
- % (opt_root, os.path.basename(path), format1),
- audio_opt,
- tgt_sr,
- )
- else:
- path = "%s/%s.%s" % (opt_root, os.path.basename(path), format1)
- with BytesIO() as wavf:
- sf.write(
- wavf,
- audio_opt,
- tgt_sr,
- format="wav"
- )
- wavf.seek(0, 0)
- with open(path, "wb") as outf:
- wav2(wavf, outf, format1)
- except:
- info += traceback.format_exc()
- infos.append("%s->%s" % (os.path.basename(path), info))
- yield "\n".join(infos)
- yield "\n".join(infos)
- except:
- yield traceback.format_exc()
diff --git a/spaces/GT6242Causion/Causion/src/pred_plot.py b/spaces/GT6242Causion/Causion/src/pred_plot.py
deleted file mode 100644
index a3f8667230675939404bc72df1cd58fce39d5f33..0000000000000000000000000000000000000000
--- a/spaces/GT6242Causion/Causion/src/pred_plot.py
+++ /dev/null
@@ -1,269 +0,0 @@
-from datetime import date, datetime, timedelta
-from sklearn.model_selection import train_test_split
-from sklearn.neural_network import MLPClassifier
-import pandas as pd
-import plotly.graph_objects as go
-import streamlit as st
-from plotly.subplots import make_subplots
-
-def hour_rounder(t):
- if int(t.minute)>= 30:
- time_1 = str(int(t.hour)+1)
- if len(time_1) == 1:
- return "0"+time_1+":00"
- else:
- return str(time_1)+":00"
- else:
- if len(str(t.hour)) == 1:
- return "0"+str(t.hour)+":00"
- else:
- return str(t.hour)+":00"
-
-def peak_hours(t):
- peak = ['07:00', "08:00", '09:00', "17:00", "18:00", "19:00"]
- if t in peak:
- return 1
- else:
- return 0
-
-def weekend(w):
- end = ['Saturday', 'Sunday']
- if w in end:
- return 1
- else:
- return 0
-
-def vehicle_cat(v):
- if v >= 0 and v < 2:
- return 0
- elif v >= 2 and v < 4:
- return 1
- elif v >= 4 and v < 6:
- return 2
- elif v >= 6 and v < 8:
- return 3
- else:
- return 4
-
-def data_split(final_table):
- X = final_table.loc[:,['day', 'hour','view']]
- Y = final_table.loc[:,'cat']
-
- X = pd.get_dummies(X)
- X.loc[:,['peak', 'weekend']] = final_table.loc[:,['peak', 'weekend']]
-
-
-
- x_train, x_test, y_train, y_test = train_test_split(X, Y, train_size=0.7,
- test_size=0.3,
- shuffle=True, random_state=13)
-
- return x_train, x_test, y_train, y_test
-
-def convert_date(date):
- return datetime.strptime(date, "%Y-%m-%d").strftime('%A')
-
-def create_row(x_train, date_d, hour, view):
- if date_d is None:
- date_d = "2023-04-11"
- if hour is None:
- hour = "09:00"
- if view is None:
- view = "Johor-Tuas"
-
- features = x_train.columns
- d_dict = {}
- day = datetime.strptime(date_d, "%Y-%m-%d").strftime('%A')
- hour = str(hour)
- view = str(view)
- col_day = "day_" + day
- col_hour = 'hour_'+ hour
- col_view = 'view_'+view
-
- for i in features:
- if i == col_day or i == col_hour or i == col_view:
- d_dict[i] = [1]
- else:
- d_dict[i] = [0]
- end = ['Saturday', 'Sunday']
- peak = ['07:00', "08:00", '09:00', "17:00", "18:00", "19:00"]
-
- if day in end:
- d_dict['weekend'] = 1
- if hour in peak:
- d_dict['peak'] = 1
- result = pd.DataFrame.from_dict(d_dict, orient='columns')
- for i in features:
- result[i] = result[i].astype('category')
- return result
-
-def prep_data_pred_plot(df):
- df = df.sort_values(by=['date']).reset_index(drop=True)
- df['date'] = pd.to_datetime(df['date'], format = "%Y-%m-%d")
- df['day'] = df['date'].dt.day_name()
- df.drop(columns=['motorcycle'], axis=1, inplace=True)
- df['vehicle'] = df['car'] + df['large_vehicle']
-
- transfer = {"View_from_Second_Link_at_Tuas_to_sg": 'Johor-Tuas',
- "View_from_Second_Link_at_Tuas_to_jh": 'Tuas-Johor',
- "View_from_Tuas_Checkpoint_to_sg": 'Johor-Tuas',
- "View_from_Tuas_Checkpoint_to_jh": 'Tuas-Johor',
- "View_from_Woodlands_Causeway_Towards_Johor_to_sg": 'Johor-Woodlands',
- "View_from_Woodlands_Causeway_Towards_Johor_to_jh": 'Woodlands-Johor',
- "View_from_Woodlands_Checkpoint_Towards_BKE_to_sg": 'Johor-Woodlands',
- "View_from_Woodlands_Checkpoint_Towards_BKE_to_jh": 'Woodlands-Johor'}
-
- new_table = df.replace({'view':transfer})
- options = ['Johor-Woodlands','Woodlands-Johor','Johor-Tuas','Tuas-Johor']
- final_df = new_table[new_table['view'].isin(options)]
- final_df.loc[:, 'time'] = pd.to_datetime(final_df.loc[:,'time'], format='%H:%M:%S')
- final_df.loc[:,'hour'] = final_df.loc[:,'time'].apply(hour_rounder)
-
- final_table = final_df.groupby(['view', 'day', 'hour']).mean().reset_index().loc[:,['day', 'hour','view', 'vehicle']]
- final_table['vehicle'] = final_table['vehicle'].apply(lambda x: round(x))
- final_table.loc[:,'peak'] = final_table.loc[:,'hour'].apply(peak_hours)
- final_table.loc[:,'peak'] = final_table.loc[:,'peak'].astype('category')
- final_table.loc[:,'weekend'] = final_table.loc[:,'day'].apply(weekend)
- final_table.loc[:,'weekend'] = final_table.loc[:,'weekend'].astype('category')
- final_table.loc[:,'cat'] = final_table.loc[:,'vehicle'].apply(vehicle_cat)
- final_table.loc[:,'cat'] = final_table.loc[:,'cat'].astype('category')
-
- return final_table
-
-def gen_fig():
-
- paths = ["M 0.2 0.35 L 0.48 0.52 L 0.52 0.50",
- "M 0.25 0.75 L 0.475 0.52 L 0.52 0.52",
- "M 0.5 0.9 L 0.485 0.52 L 0.515 0.52",
- "M 0.75 0.75 L 0.485 0.52 L 0.52 0.51",
- "M 0.8 0.35 L 0.48 0.50 L 0.52 0.52"]
-
- figs = []
- values_ = ["No Traffic on Johor-Singapore Causeway", "Low Traffic on Johor-Singapore Causeway", "Johor-Singapore Causeway Slightly Busy",
- "Johor-Singapore Causeway Moderately Busy", "Busiest Time to Travel on Johor-Singapore Causeway"]
-
- for i in range(5):
- plot_bgcolor = "#def"
- colors = ["#f25829", "#f2a529", "#eff229", "#85e043", "#2bad4e","rgba(0,0,0,0)"]
- quadrant_text = ["Heavy", "Moderate", "Mild", "Low", "None",""]
- n_quadrants = len(colors) - 1
- figure_1 = go.Figure(
- data=[
- go.Pie(
- values=[14,14,14,14,14,30],
- rotation=130,
- hole=0.75,
- marker_colors=colors,
- marker_line={"width":2, "color":"white"},
- textinfo="none",
- text=quadrant_text,
- hoverinfo="text"
- ),
- ],
- layout=go.Layout(
- showlegend=False,
- margin=dict(b=0,t=30,l=10,r=10),
- width=500,
- height=350,
- paper_bgcolor="rgba(0,0,0,0)",
- annotations=[
- go.layout.Annotation(
- text=f"{values_[i]}",
- x=0.5, xanchor="center", xref="paper",
- y= 0.1, yanchor="bottom", yref="paper",
- showarrow=False,
- font= {"size":15, "color":"#333"}
- )
- ]
- )
- )
- figure_1.update_layout(shapes=[dict(type='path',
- path=paths[i],
- fillcolor="#333"),
- go.layout.Shape(
- type="circle",
- x0=0.48, x1=0.52,
- y0=0.48, y1=0.54,
- fillcolor="#333",
- line_color="#333",
- )])
- figs.append(figure_1)
-
- return figs
-
-def predicted_figure(clf, x, figs):
-
- result = create_row(x[0], x[1], x[2], x[3])
-
- pred_val = clf.predict(result)[0]
-
- return figs[pred_val]
-
-def get_today():
- t = str(date.today()).split('-')
- today = []
-
- for i in t:
- if t[0] =='0':
- today.append(int(t[1:]))
- else:
- today.append(int(i))
- return today
-
-def update_output(date_value):
- string_prefix = 'Travel Day: '
- if date_value is not None:
- date_string = convert_date(date_value)
- return string_prefix + date_string
-
-def update_final_output_hour(starter_variables, my_date_picker_single, hours_dropdown_id, direction_id):
- # starter_variables = [clf, str(date.today()), "07:00", "Tuas-Johor"]
- starter_variables[1] = str(my_date_picker_single)
- starter_variables[2] = str(hours_dropdown_id)
- starter_variables[3] = str(direction_id)
- fig = predicted_figure(starter_variables)
- return fig
-
-def train_model(x_train, y_train):
- clf = MLPClassifier(solver='lbfgs', alpha=3, hidden_layer_sizes=(5,4), random_state=2, max_iter=3000)
- clf.fit(x_train, y_train)
-
- return clf
-
-def pred_bars(my_date_picker_single, final_table):
- day_today = convert_date(str(my_date_picker_single))
- df_filter = final_table[final_table['day']==day_today]
-
- color_map = {0:"#2bad4e", 1:"#85e043", 2:"#eff229", 3:"#f2a529", 4:"#f25829"}
-
-
- bar_day = make_subplots(shared_yaxes="all", rows=2, cols=2, start_cell="bottom-left", subplot_titles=("Johor-Tuas",
- "Tuas-Johor",
- "Johor-Woodlands",
- "Woodlands-Johor"))
- f1 = df_filter[df_filter['view']=='Johor-Tuas']
- c1 = pd.Series(f1['cat']).map(color_map)
- bar_day.add_trace(go.Bar(x=f1['hour'], y=f1['vehicle'], name='Johor-Tuas', showlegend=False, marker={'color':c1}),
- row=1, col=1)
-
- f2 = df_filter[df_filter['view']=='Tuas-Johor']
- c2 = pd.Series(f2['cat']).map(color_map)
- bar_day.add_trace(go.Bar(x=f2['hour'], y=f2['vehicle'], name='Tuas-Johor', showlegend=False, marker={'color':c2}),
- row=1, col=2)
- f3 = df_filter[df_filter['view']=='Johor-Woodlands']
- c3 = pd.Series(f3['cat']).map(color_map)
- bar_day.add_trace(go.Bar(x=f3['hour'], y=f3['vehicle'], name='Johor-Woodlands', showlegend=False, marker={'color':c3}),
- row=2, col=1)
- f4 = df_filter[df_filter['view']=='Woodlands-Johor']
- c4 = pd.Series(f4['cat']).map(color_map)
- bar_day.add_trace(go.Bar(x=f4['hour'], y=f4['vehicle'], name='Woodlands-Johor', showlegend=False, marker={'color':c4}),
- row=2, col=2)
-
- val_d = my_date_picker_single.strftime("%d %B, %Y")
- day_d = my_date_picker_single.strftime("%A")
- tex = "Predicted 24 Hour Traffic Trend on: " + day_d + ", " + str(val_d)
-
-
- bar_day.update_layout(title_text=tex, paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)")
- bar_day.update_xaxes(tickangle=45)
- return bar_day
\ No newline at end of file
diff --git a/spaces/Gauri54damle/sdxl-lora-multi-object/style.css b/spaces/Gauri54damle/sdxl-lora-multi-object/style.css
deleted file mode 100644
index 9bfa78cc983f84693cf7cbab1e3bfd0e0d36c944..0000000000000000000000000000000000000000
--- a/spaces/Gauri54damle/sdxl-lora-multi-object/style.css
+++ /dev/null
@@ -1,24 +0,0 @@
-.finetuned-diffusion-div div{
- display:inline-flex;
- align-items:center;
- gap:.8rem;
- font-size:1.75rem
-}
-.finetuned-diffusion-div div h1{
- font-weight:900;
- margin-bottom:7px
-}
-.finetuned-diffusion-div p{
- margin-bottom:10px;
- font-size:94%
-}
-a{
- text-decoration:underline
-}
-.tabs{
- margin-top:0;
- margin-bottom:0
-}
-#gallery{
- min-height:20rem
-}
diff --git a/spaces/Gen-Sim/Gen-Sim/scripts/metascripts/train10_gptmixcliport3.sh b/spaces/Gen-Sim/Gen-Sim/scripts/metascripts/train10_gptmixcliport3.sh
deleted file mode 100644
index 0728981a26bd2e5c0113bbd80ddd15e76dddd84d..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/scripts/metascripts/train10_gptmixcliport3.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/bin/bash
-#SBATCH -c 10
-#SBATCH -n 1
-#SBATCH -o logs/%j.out
-#SBATCH --exclusive
-STEPS=${1-'50000'}
-
-
-sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \
- "[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq,color-sorted-container-stack,color-sorted-block-race,Four-corner-pyramid-challenge,triangle-block-arrangement,sort-and-stack-clr-blocks,color-coordinated-sphere-insertion,rainbow-stack,align-pair-colored-blocks-along-line,vertical-insertion-blocks,stack-blocks-in-container]" \
- "[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq]" \
- gpt5_mixcliport3_task $STEPS
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index 132787db98d3fc9df5ed62e31738c82da8c279bf..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = [
- '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/cityscapes.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
deleted file mode 100644
index a3c86e18ea65c6aaa36a4fb6e2708f08c7ae1698..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,35 +0,0 @@
-_base_ = [
- '../_base_/models/ocrnet_hr18.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
-]
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(decode_head=[
- dict(
- type='FCNHead',
- in_channels=[18, 36, 72, 144],
- channels=sum([18, 36, 72, 144]),
- in_index=(0, 1, 2, 3),
- input_transform='resize_concat',
- kernel_size=1,
- num_convs=1,
- concat_input=False,
- dropout_ratio=-1,
- num_classes=150,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
- dict(
- type='OCRHead',
- in_channels=[18, 36, 72, 144],
- in_index=(0, 1, 2, 3),
- input_transform='resize_concat',
- channels=512,
- ocr_channels=256,
- dropout_ratio=-1,
- num_classes=150,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-])
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/denoising_dataset.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/denoising_dataset.py
deleted file mode 100644
index bdb62c8d5db9c8755c72db4d0d8083c936f18dc8..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/denoising_dataset.py
+++ /dev/null
@@ -1,436 +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 math
-
-import numpy as np
-import torch
-
-from . import FairseqDataset, data_utils
-
-
-def collate(
- samples,
- pad_idx,
- eos_idx,
- vocab,
- left_pad_source=False,
- left_pad_target=False,
- input_feeding=True,
- pad_to_length=None,
-):
- assert input_feeding
- if len(samples) == 0:
- return {}
-
- def merge(key, left_pad, move_eos_to_beginning=False, pad_to_length=None):
- return data_utils.collate_tokens(
- [s[key] for s in samples],
- pad_idx,
- eos_idx=None, # use eos_idx of each sample instead of vocab.eos()
- left_pad=left_pad,
- move_eos_to_beginning=move_eos_to_beginning,
- pad_to_length=pad_to_length,
- )
-
- id = torch.LongTensor([s["id"] for s in samples])
- src_tokens = merge(
- "source",
- left_pad=left_pad_source,
- pad_to_length=pad_to_length["source"] if pad_to_length is not None else None,
- )
- # sort by descending source length
- src_lengths = torch.LongTensor([s["source"].numel() for s in samples])
- src_lengths, sort_order = src_lengths.sort(descending=True)
- id = id.index_select(0, sort_order)
- src_tokens = src_tokens.index_select(0, sort_order)
-
- prev_output_tokens = None
- target = None
- if samples[0].get("target", None) is not None:
- target = merge(
- "target",
- left_pad=left_pad_target,
- pad_to_length=pad_to_length["target"]
- if pad_to_length is not None
- else None,
- )
- target = target.index_select(0, sort_order)
- ntokens = sum(len(s["target"]) for s in samples)
-
- if input_feeding:
- # we create a shifted version of targets for feeding the
- # previous output token(s) into the next decoder step
- prev_output_tokens = merge(
- "target",
- left_pad=left_pad_target,
- move_eos_to_beginning=True,
- pad_to_length=pad_to_length["target"]
- if pad_to_length is not None
- else None,
- )
- prev_output_tokens = prev_output_tokens.index_select(0, sort_order)
- else:
- ntokens = sum(len(s["source"]) for s in samples)
-
- batch = {
- "id": id,
- "ntokens": ntokens,
- "net_input": {
- "src_tokens": src_tokens,
- "src_lengths": src_lengths,
- },
- "target": target,
- "nsentences": samples[0]["source"].size(0),
- "sort_order": sort_order,
- }
- if prev_output_tokens is not None:
- batch["net_input"]["prev_output_tokens"] = prev_output_tokens
-
- return batch
-
-
-class DenoisingDataset(FairseqDataset):
- """
- A wrapper around TokenBlockDataset for BART dataset.
-
- Args:
- dataset (TokenBlockDataset): dataset to wrap
- sizes (List[int]): sentence lengths
- vocab (~fairseq.data.Dictionary): vocabulary
- mask_idx (int): dictionary index used for masked token
- mask_whole_words: only mask whole words. This should be a byte mask
- over vocab indices, indicating whether it is the beginning of a
- word. We will extend any mask to encompass the whole word.
- shuffle (bool, optional): shuffle the elements before batching.
- Default: ``True``
- seed: Seed for random number generator for reproducibility.
- args: argparse arguments.
- """
-
- def __init__(
- self,
- dataset,
- sizes,
- vocab,
- mask_idx,
- mask_whole_words,
- shuffle,
- seed,
- args,
- eos=None,
- item_transform_func=None,
- ):
- self.dataset = dataset
-
- self.sizes = sizes
-
- self.vocab = vocab
- self.shuffle = shuffle
- self.seed = seed
- self.mask_idx = mask_idx
- self.mask_whole_word = mask_whole_words
- self.mask_ratio = args.mask
- self.random_ratio = args.mask_random
- self.insert_ratio = args.insert
- self.rotate_ratio = args.rotate
- self.permute_sentence_ratio = args.permute_sentences
- self.eos = eos if eos is not None else vocab.eos()
- self.item_transform_func = item_transform_func
-
- if args.bpe != "gpt2":
- self.full_stop_index = self.vocab.eos()
- else:
- assert args.bpe == "gpt2"
- self.full_stop_index = self.vocab.index("13")
-
- self.replace_length = args.replace_length
- if self.replace_length not in [-1, 0, 1]:
- raise ValueError(f"invalid arg: replace_length={self.replace_length}")
- if args.mask_length not in ["subword", "word", "span-poisson"]:
- raise ValueError(f"invalid arg: mask-length={args.mask_length}")
- if args.mask_length == "subword" and args.replace_length not in [0, 1]:
- raise ValueError(f"if using subwords, use replace-length=1 or 0")
-
- self.mask_span_distribution = None
- if args.mask_length == "span-poisson":
- _lambda = args.poisson_lambda
-
- lambda_to_the_k = 1
- e_to_the_minus_lambda = math.exp(-_lambda)
- k_factorial = 1
- ps = []
- for k in range(0, 128):
- ps.append(e_to_the_minus_lambda * lambda_to_the_k / k_factorial)
- lambda_to_the_k *= _lambda
- k_factorial *= k + 1
- if ps[-1] < 0.0000001:
- break
- ps = torch.FloatTensor(ps)
- self.mask_span_distribution = torch.distributions.Categorical(ps)
-
- self.epoch = 0
-
- @property
- def can_reuse_epoch_itr_across_epochs(self):
- return True # only the noise changes, not item sizes
-
- def set_epoch(self, epoch, **unused):
- self.epoch = epoch
-
- def __getitem__(self, index):
- with data_utils.numpy_seed(self.seed, self.epoch, index):
- tokens = self.dataset[index]
- assert tokens[-1] == self.eos
- source, target = tokens, tokens.clone()
-
- if self.permute_sentence_ratio > 0.0:
- source = self.permute_sentences(source, self.permute_sentence_ratio)
-
- if self.mask_ratio > 0:
- source = self.add_whole_word_mask(source, self.mask_ratio)
-
- if self.insert_ratio > 0:
- source = self.add_insertion_noise(source, self.insert_ratio)
-
- if self.rotate_ratio > 0.0 and np.random.random() < self.rotate_ratio:
- source = self.add_rolling_noise(source)
- # there can additional changes to make:
- if self.item_transform_func is not None:
- source, target = self.item_transform_func(source, target)
-
- assert (source >= 0).all()
- assert (source[1:-1] >= 1).all()
- assert (source <= len(self.vocab)).all()
- assert source[0] == self.vocab.bos()
- assert source[-1] == self.eos
- return {
- "id": index,
- "source": source,
- "target": target,
- }
-
- def __len__(self):
- return len(self.dataset)
-
- def permute_sentences(self, source, p=1.0):
- full_stops = source == self.full_stop_index
- # Pretend it ends with a full stop so last span is a sentence
- full_stops[-2] = 1
-
- # Tokens that are full stops, where the previous token is not
- sentence_ends = (full_stops[1:] * ~full_stops[:-1]).nonzero(as_tuple=False) + 2
- result = source.clone()
-
- num_sentences = sentence_ends.size(0)
- num_to_permute = math.ceil((num_sentences * 2 * p) / 2.0)
- substitutions = torch.randperm(num_sentences)[:num_to_permute]
- ordering = torch.arange(0, num_sentences)
- ordering[substitutions] = substitutions[torch.randperm(num_to_permute)]
-
- # Ignore at start
- index = 1
- for i in ordering:
- sentence = source[(sentence_ends[i - 1] if i > 0 else 1) : sentence_ends[i]]
- result[index : index + sentence.size(0)] = sentence
- index += sentence.size(0)
- return result
-
- def word_starts(self, source):
- if self.mask_whole_word is not None:
- is_word_start = self.mask_whole_word.gather(0, source)
- else:
- is_word_start = torch.ones(source.size())
- is_word_start[0] = 0
- is_word_start[-1] = 0
- return is_word_start
-
- def add_whole_word_mask(self, source, p):
- is_word_start = self.word_starts(source)
- num_to_mask = int(math.ceil(is_word_start.float().sum() * p))
- num_inserts = 0
- if num_to_mask == 0:
- return source
-
- if self.mask_span_distribution is not None:
- lengths = self.mask_span_distribution.sample(sample_shape=(num_to_mask,))
-
- # Make sure we have enough to mask
- cum_length = torch.cumsum(lengths, 0)
- while cum_length[-1] < num_to_mask:
- lengths = torch.cat(
- [
- lengths,
- self.mask_span_distribution.sample(sample_shape=(num_to_mask,)),
- ],
- dim=0,
- )
- cum_length = torch.cumsum(lengths, 0)
-
- # Trim to masking budget
- i = 0
- while cum_length[i] < num_to_mask:
- i += 1
- lengths[i] = num_to_mask - (0 if i == 0 else cum_length[i - 1])
- num_to_mask = i + 1
- lengths = lengths[:num_to_mask]
-
- # Handle 0-length mask (inserts) separately
- lengths = lengths[lengths > 0]
- num_inserts = num_to_mask - lengths.size(0)
- num_to_mask -= num_inserts
- if num_to_mask == 0:
- return self.add_insertion_noise(source, num_inserts / source.size(0))
-
- assert (lengths > 0).all()
- else:
- lengths = torch.ones((num_to_mask,)).long()
- assert is_word_start[-1] == 0
- word_starts = is_word_start.nonzero(as_tuple=False)
- indices = word_starts[
- torch.randperm(word_starts.size(0))[:num_to_mask]
- ].squeeze(1)
- mask_random = torch.FloatTensor(num_to_mask).uniform_() < self.random_ratio
-
- source_length = source.size(0)
- assert source_length - 1 not in indices
- to_keep = torch.ones(source_length, dtype=torch.bool)
- is_word_start[
- -1
- ] = 255 # acts as a long length, so spans don't go over the end of doc
- if self.replace_length == 0:
- to_keep[indices] = 0
- else:
- # keep index, but replace it with [MASK]
- source[indices] = self.mask_idx
- source[indices[mask_random]] = torch.randint(
- 1, len(self.vocab), size=(mask_random.sum(),)
- )
-
- if self.mask_span_distribution is not None:
- assert len(lengths.size()) == 1
- assert lengths.size() == indices.size()
- lengths -= 1
- while indices.size(0) > 0:
- assert lengths.size() == indices.size()
- lengths -= is_word_start[indices + 1].long()
- uncompleted = lengths >= 0
- indices = indices[uncompleted] + 1
- mask_random = mask_random[uncompleted]
- lengths = lengths[uncompleted]
- if self.replace_length != -1:
- # delete token
- to_keep[indices] = 0
- else:
- # keep index, but replace it with [MASK]
- source[indices] = self.mask_idx
- source[indices[mask_random]] = torch.randint(
- 1, len(self.vocab), size=(mask_random.sum(),)
- )
- else:
- # A bit faster when all lengths are 1
- while indices.size(0) > 0:
- uncompleted = is_word_start[indices + 1] == 0
- indices = indices[uncompleted] + 1
- mask_random = mask_random[uncompleted]
- if self.replace_length != -1:
- # delete token
- to_keep[indices] = 0
- else:
- # keep index, but replace it with [MASK]
- source[indices] = self.mask_idx
- source[indices[mask_random]] = torch.randint(
- 1, len(self.vocab), size=(mask_random.sum(),)
- )
-
- assert source_length - 1 not in indices
-
- source = source[to_keep]
-
- if num_inserts > 0:
- source = self.add_insertion_noise(source, num_inserts / source.size(0))
-
- return source
-
- def add_permuted_noise(self, tokens, p):
- num_words = len(tokens)
- num_to_permute = math.ceil(((num_words * 2) * p) / 2.0)
- substitutions = torch.randperm(num_words - 2)[:num_to_permute] + 1
- tokens[substitutions] = tokens[substitutions[torch.randperm(num_to_permute)]]
- return tokens
-
- def add_rolling_noise(self, tokens):
- offset = np.random.randint(1, max(1, tokens.size(-1) - 1) + 1)
- tokens = torch.cat(
- (tokens[0:1], tokens[offset:-1], tokens[1:offset], tokens[-1:]),
- dim=0,
- )
- return tokens
-
- def add_insertion_noise(self, tokens, p):
- if p == 0.0:
- return tokens
-
- num_tokens = len(tokens)
- n = int(math.ceil(num_tokens * p))
-
- noise_indices = torch.randperm(num_tokens + n - 2)[:n] + 1
- noise_mask = torch.zeros(size=(num_tokens + n,), dtype=torch.bool)
- noise_mask[noise_indices] = 1
- result = torch.LongTensor(n + len(tokens)).fill_(-1)
-
- num_random = int(math.ceil(n * self.random_ratio))
- result[noise_indices[num_random:]] = self.mask_idx
- result[noise_indices[:num_random]] = torch.randint(
- low=1, high=len(self.vocab), size=(num_random,)
- )
-
- result[~noise_mask] = tokens
-
- assert (result >= 0).all()
- return result
-
- def collater(self, samples, pad_to_length=None):
- """Merge a list of samples to form a mini-batch.
- Args:
- samples (List[dict]): samples to collate
- Returns:
- dict: a mini-batch of data
- """
- return collate(
- samples, self.vocab.pad(), self.eos, self.vocab, pad_to_length=pad_to_length
- )
-
- def num_tokens(self, index):
- """Return the number of tokens in a sample. This value is used to
- enforce ``--max-tokens`` during batching."""
- return self.sizes[index]
-
- def size(self, index):
- """Return an example's size as a float or tuple. This value is used when
- filtering a dataset with ``--max-positions``."""
- return self.sizes[index]
-
- def ordered_indices(self):
- """Return an ordered list of indices. Batches will be constructed based
- on this order."""
- if self.shuffle:
- indices = np.random.permutation(len(self))
- else:
- indices = np.arange(len(self))
- return indices[np.argsort(self.sizes[indices], kind="mergesort")]
-
- def prefetch(self, indices):
- self.src.prefetch(indices)
- self.tgt.prefetch(indices)
-
- @property
- def supports_prefetch(self):
- return (
- hasattr(self.src, "supports_prefetch")
- and self.src.supports_prefetch
- and hasattr(self.tgt, "supports_prefetch")
- and self.tgt.supports_prefetch
- )
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/sort_dataset.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/sort_dataset.py
deleted file mode 100644
index b3890e7279e1f26db2e48ec0a91c639e9299d60f..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/sort_dataset.py
+++ /dev/null
@@ -1,21 +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 numpy as np
-
-from . import BaseWrapperDataset
-
-
-class SortDataset(BaseWrapperDataset):
- def __init__(self, dataset, sort_order):
- super().__init__(dataset)
- if not isinstance(sort_order, (list, tuple)):
- sort_order = [sort_order]
- self.sort_order = sort_order
-
- assert all(len(so) == len(dataset) for so in sort_order)
-
- def ordered_indices(self):
- return np.lexsort(self.sort_order)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/utils/cider/pyciderevalcap/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/utils/cider/pyciderevalcap/__init__.py
deleted file mode 100644
index 3f7d85bba884ea8f83fc6ab2a1e6ade80d98d4d9..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/utils/cider/pyciderevalcap/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-__author__ = 'tylin'
diff --git a/spaces/HarshulNanda/HARM_ML_App_ludwig/statsViewer.py b/spaces/HarshulNanda/HARM_ML_App_ludwig/statsViewer.py
deleted file mode 100644
index e9bbad8035966d4b0b33b9f92c18aab5dfe75408..0000000000000000000000000000000000000000
--- a/spaces/HarshulNanda/HARM_ML_App_ludwig/statsViewer.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import os
-from pytube import YouTube
-import pytube
-from stqdm import stqdm
-import pandas as pd
-from youtubesearchpython import Video, ResultMode
-import streamlit as st
-import scrapetube
-from categoryPredictor import predictCategoryFor
-import pandas as pd
-
-@st.experimental_memo
-def convert_df(df):
- return df.to_csv(index=False).encode('utf-8')
-
-def generate_channel_video_data(of_channel, with_number_of_videos):
- video_urls = []
- c_id = Video.get(of_channel, mode=ResultMode.json, get_upload_date=True)["channel"]["id"]
- videos = scrapetube.get_channel(c_id)
- i = 0
- for video in videos:
- video_urls.append("https://www.youtube.com/watch?v="+str(video['videoId']))
- i += 1
- if i == with_number_of_videos:
- break
-
- data = {
- "Title": [],
- "Description": [],
- "Category": [],
- "Is Educational?": [],
- "Beyond Exams Category": [],
- }
-
- timer = stqdm(video_urls)
-
- for video in timer:
- timer.set_description("☕️ Have a coffee, while we are generating your dataset. ")
- try:
- v = Video.get(video, mode = ResultMode.json, get_upload_date=True)
- t = v["title"]
- d = v["description"]
- c = v["category"]
- isEdu, isCat, cat_array, sub_array = predictCategoryFor(video)
- data["Description"].append(d)
- data["Category"].append(c)
- data["Title"].append(t)
- data["Is Educational?"].append(isEdu)
- data["Beyond Exams Category"].append(isCat)
- except Exception as e:
- print(e)
- continue
-
- df = pd.DataFrame(data)
- st.dataframe(df)
- csv = convert_df(df)
-
- st.download_button(
- "Download this dataframe",
- csv,
- "file.csv",
- "text/csv",
- key='download-csv'
- )
\ No newline at end of file
diff --git a/spaces/Harveenchadha/Hindi_TTS/app.py b/spaces/Harveenchadha/Hindi_TTS/app.py
deleted file mode 100644
index c4723bc3b7b1edccd7b4002510c4ff9f2041e390..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Hindi_TTS/app.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import os
-os.system("pip uninstall -y gradio")
-os.system("pip install gradio==2.7")
-import gradio as gr
-
-#os.system('git clone https://github.com/Open-Speech-EkStep/vakyansh-tts')
-os.chdir('vakyansh_tts')
-os.system('bash install.sh')
-os.system('python setup.py bdist_wheel')
-os.system('pip install -e .')
-os.chdir('tts_infer')
-os.system('mkdir translit_models')
-os.chdir('translit_models')
-os.system('wget -q https://storage.googleapis.com/vakyaansh-open-models/translit_models/default_lineup.json')
-os.system('mkdir hindi')
-os.chdir('hindi')
-os.system('wget -q https://storage.googleapis.com/vakyaansh-open-models/translit_models/hindi/hindi_transliteration.zip')
-os.system('unzip hindi_transliteration')
-
-os.system('wget -q https://storage.googleapis.com/vakyansh-open-models/tts/hindi/hi-IN/female_voice_0/glow.zip')
-os.system('unzip glow.zip')
-
-os.system('wget -q https://storage.googleapis.com/vakyansh-open-models/tts/hindi/hi-IN/female_voice_0/hifi.zip')
-os.system('unzip hifi.zip')
-os.system('rm glow.zip')
-os.system('rm hifi.zip')
-
-os.system('mkdir male')
-os.chdir('male')
-os.system('wget -q https://storage.googleapis.com/vakyansh-open-models/tts/hindi/hi-IN/male_voice_1/glow.zip')
-os.system('unzip glow.zip')
-
-os.system('wget -q https://storage.googleapis.com/vakyansh-open-models/tts/hindi/hi-IN/male_voice_1/hifi.zip')
-os.system('unzip hifi.zip')
-
-
-os.system('pwd')
-os.system('rm glow.zip')
-os.system('rm hifi.zip')
-os.system('pip uninstall -y numpy')
-os.system('pip install numpy==1.19.5')
-os.system('pip uninstall -y numba')
-os.system('pip install numba==0.53')
-
-os.chdir('/home/user/app/')
-os.system('pwd')
-#print('hello')
-
-from vakyansh_tts.tts_infer.tts import TextToMel, MelToWav
-from vakyansh_tts.tts_infer.transliterate import XlitEngine
-from vakyansh_tts.tts_infer.num_to_word_on_sent import normalize_nums
-
-import re
-from scipy.io.wavfile import write
-device = 'cpu'
-
-text_to_mel_f = TextToMel(glow_model_dir='/home/user/app/vakyansh_tts/tts_infer/translit_models/hindi/glow_ckp', device=device)
-mel_to_wav_f = MelToWav(hifi_model_dir='/home/user/app/vakyansh_tts/tts_infer/translit_models/hindi/hifi_ckp', device=device)
-text_to_mel_m = TextToMel(glow_model_dir='/home/user/app/vakyansh_tts/tts_infer/translit_models/hindi/male/glow_ckp', device=device)
-mel_to_wav_m = MelToWav(hifi_model_dir='/home/user/app/vakyansh_tts/tts_infer/translit_models/hindi/male/hifi_ckp', device=device)
-
-
-def translit(text, lang):
- reg = re.compile(r'[a-zA-Z]')
- engine = XlitEngine(lang)
- words = [engine.translit_word(word, topk=1)[lang][0] if reg.match(word) else word for word in text.split()]
- updated_sent = ' '.join(words)
- return updated_sent
-
-def run_tts(text, gender):
- print("Original Text from user: ", text)
- lang='hi'
- text = text.replace('।', '.') # only for hindi models
- text_num_to_word = normalize_nums(text, lang) # converting numbers to words in lang
- text_num_to_word_and_transliterated = translit(text_num_to_word, lang) # transliterating english words to lang
- print("Text after preprocessing: ", text_num_to_word_and_transliterated)
- if gender == 'female':
- mel = text_to_mel_f.generate_mel(text_num_to_word_and_transliterated)
- audio, sr = mel_to_wav_f.generate_wav(mel)
- else:
- mel = text_to_mel_m.generate_mel(text_num_to_word_and_transliterated)
- audio, sr = mel_to_wav_m.generate_wav(mel)
- #write(filename='temp.wav', rate=sr, data=audio) # for saving wav file, if needed
- return (sr, audio)
-
-#_, audio = run_tts('hello my name is harveen')
-
-
-textbox = gr.inputs.Textbox(
- placeholder="Enter Hindi text here", default="", label="TTS"
-)
-
-choices = ['male', 'female']
-radioBtns = gr.inputs.Radio(choices, type="value", default='male', label=None)
-
-op = gr.outputs.Audio(type="numpy", label=None)
-examples = [['क्रिप्टो करेंसी दरअसल, वित्तीय लेन-देन का एक जरिया है। बिल्कुल भारतीय रुपये और अमेरिकी डॉलर के समान, अंतर सिर्फ इतना है कि यह आभाषी है और दिखाई नहीं देती, न ही आप इसे छू सकते हैं।', 'male'],
- ['mujhe abhi bhi yakeen nai aa raha ki yeh aise bhi chal sakta hai', 'male'],
- ['मुझे 26 रुपए दे दो, फिर मेरे पास 50 रुपए हो जाएंगे', 'male']]
-
-iface = gr.Interface(fn=run_tts, examples=examples, inputs=[textbox,radioBtns], outputs=op, title='Vakyansh Text To Speech (TTS): Hindi Demo', description = 'Glow TTS + hifi gan. Training Code: https://github.com/Open-Speech-EkStep/vakyansh-tts ' , article = ' Note: This space is running on CPU, inference times will be higher. Please report issues to @harveenchadha twitter. ')
-iface.launch(enable_queue=True, cache_examples=True)
diff --git a/spaces/Hexamind/QnA/src/model/block.py b/spaces/Hexamind/QnA/src/model/block.py
deleted file mode 100644
index da01e982a6ce409a4bdfbe6e64abaf20d0e32755..0000000000000000000000000000000000000000
--- a/spaces/Hexamind/QnA/src/model/block.py
+++ /dev/null
@@ -1,44 +0,0 @@
-class Block:
- def __init__(self, doc: str = '', title: str = '', content: str = '', content_fr: str = '',
- index: str = '', rank: int = 0, level: int = 0, distance: float = 99999):
- self.doc = doc
- self.title = title
- self.title_fr = ""
- self.content = content
- self.content_fr = content_fr
- self.specials = []
- self.index = index
- self.rank = rank
- self.level = level
- self.distance = distance
-
- def to_dict(self) -> {}:
- block_dict = {'doc': self.doc, 'title': self.title, 'title_fr': self.title_fr, 'content': self.content,
- 'content_fr': self.content_fr, 'index': self.index, 'rank': self.rank, 'level': self.level,
- 'distance': self.distance}
- for i, s in enumerate(self.specials):
- special_key = 'special_'+str(i)
- block_dict[special_key] = s
- block_dict['specials_len'] = len(self.specials)
- return block_dict
-
- def from_dict(self, block_dict: {}):
- self.doc = block_dict['doc']
- self.title = block_dict['title']
- self.title_fr = block_dict['title_fr']
- self.content = block_dict['content']
- self.content_fr = block_dict['content_fr']
- self.index = block_dict['index']
- self.rank = block_dict['rank']
- self.level = block_dict['level']
- self.distance = block_dict['distance']
- self.specials = []
- for i in range(block_dict['specials_len']):
- special_key = 'special_' + str(i)
- self.specials.append(block_dict[special_key])
- return self
-
- @property
- def distance_str(self) -> str:
- return format(self.distance, '.2f')
-
diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/test_data/blocks_configs.py b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/test_data/blocks_configs.py
deleted file mode 100644
index 11dfa3a8da5efe45127f8e967ce5a0530f82b771..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/test_data/blocks_configs.py
+++ /dev/null
@@ -1,653 +0,0 @@
-XRAY_CONFIG = {
- "version": "3.4b3\n",
- "mode": "blocks",
- "dev_mode": True,
- "components": [
- {
- "id": 27,
- "type": "markdown",
- "props": {
- "value": "
- Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search
- LaTeX rendering / Table rendering / Code highlighting
- Auto dark mode / Adaptive web interface / WeChat-like theme
- Multi-parameters tuning / Multi-API-Key support / Multi-user support
- Compatible with GPT-4 / Local deployment for LLMs
-
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Download The Tarzan A New York Full !FREE! Movie Italian Dubbed In Torrent.md b/spaces/bioriAsaeru/text-to-voice/Download The Tarzan A New York Full !FREE! Movie Italian Dubbed In Torrent.md
deleted file mode 100644
index 271c89f96843e13e914c08892737af47e36ef5fa..0000000000000000000000000000000000000000
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diff --git a/spaces/breadlicker45/the-jam-machine-app/familizer.py b/spaces/breadlicker45/the-jam-machine-app/familizer.py
deleted file mode 100644
index a55b29c4612a4541162fbb1cd1d7e3d1795758dc..0000000000000000000000000000000000000000
--- a/spaces/breadlicker45/the-jam-machine-app/familizer.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import random
-from joblib import Parallel, delayed
-from pathlib import Path
-from constants import INSTRUMENT_CLASSES, INSTRUMENT_TRANSFER_CLASSES
-from utils import get_files, timeit, FileCompressor
-
-
-class Familizer:
- def __init__(self, n_jobs=-1, arbitrary=False):
- self.n_jobs = n_jobs
- self.reverse_family(arbitrary)
-
- def get_family_number(self, program_number):
- """
- Given a MIDI instrument number, return its associated instrument family number.
- """
- for instrument_class in INSTRUMENT_CLASSES:
- if program_number in instrument_class["program_range"]:
- return instrument_class["family_number"]
-
- def reverse_family(self, arbitrary):
- """
- Create a dictionary of family numbers to randomly assigned program numbers.
- This is used to reverse the family number tokens back to program number tokens.
- """
-
- if arbitrary is True:
- int_class = INSTRUMENT_TRANSFER_CLASSES
- else:
- int_class = INSTRUMENT_CLASSES
-
- self.reference_programs = {}
- for family in int_class:
- self.reference_programs[family["family_number"]] = random.choice(
- family["program_range"]
- )
-
- def get_program_number(self, family_number):
- """
- Given given a family number return a random program number in the respective program_range.
- This is the reverse operation of get_family_number.
- """
- assert family_number in self.reference_programs
- return self.reference_programs[family_number]
-
- # Replace instruments in text files
- def replace_instrument_token(self, token):
- """
- Given a MIDI program number in a word token, replace it with the family or program
- number token depending on the operation.
- e.g. INST=86 -> INST=10
- """
- inst_number = int(token.split("=")[1])
- if self.operation == "family":
- return "INST=" + str(self.get_family_number(inst_number))
- elif self.operation == "program":
- return "INST=" + str(self.get_program_number(inst_number))
-
- def replace_instrument_in_text(self, text):
- """Given a text piece, replace all instrument tokens with family number tokens."""
- return " ".join(
- [
- self.replace_instrument_token(token)
- if token.startswith("INST=") and not token == "INST=DRUMS"
- else token
- for token in text.split(" ")
- ]
- )
-
- def replace_instruments_in_file(self, file):
- """Given a text file, replace all instrument tokens with family number tokens."""
- text = file.read_text()
- file.write_text(self.replace_instrument_in_text(text))
-
- @timeit
- def replace_instruments(self):
- """
- Given a directory of text files:
- Replace all instrument tokens with family number tokens.
- """
- files = get_files(self.output_directory, extension="txt")
- Parallel(n_jobs=self.n_jobs)(
- delayed(self.replace_instruments_in_file)(file) for file in files
- )
-
- def replace_tokens(self, input_directory, output_directory, operation):
- """
- Given a directory and an operation, perform the operation on all text files in the directory.
- operation can be either 'family' or 'program'.
- """
- self.input_directory = input_directory
- self.output_directory = output_directory
- self.operation = operation
-
- # Uncompress files, replace tokens, compress files
- fc = FileCompressor(self.input_directory, self.output_directory, self.n_jobs)
- fc.unzip()
- self.replace_instruments()
- fc.zip()
- print(self.operation + " complete.")
-
- def to_family(self, input_directory, output_directory):
- """
- Given a directory containing zip files, replace all instrument tokens with
- family number tokens. The output is a directory of zip files.
- """
- self.replace_tokens(input_directory, output_directory, "family")
-
- def to_program(self, input_directory, output_directory):
- """
- Given a directory containing zip files, replace all instrument tokens with
- program number tokens. The output is a directory of zip files.
- """
- self.replace_tokens(input_directory, output_directory, "program")
-
-
-if __name__ == "__main__":
-
- # Choose number of jobs for parallel processing
- n_jobs = -1
-
- # Instantiate Familizer
- familizer = Familizer(n_jobs)
-
- # Choose directory to process for program
- input_directory = Path("midi/dataset/first_selection/validate").resolve() # fmt: skip
- output_directory = input_directory / "family"
-
- # familize files
- familizer.to_family(input_directory, output_directory)
-
- # Choose directory to process for family
- # input_directory = Path("../data/music_picks/encoded_samples/validate/family").resolve() # fmt: skip
- # output_directory = input_directory.parent / "program"
-
- # # programize files
- # familizer.to_program(input_directory, output_directory)
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/inference.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/inference.py
deleted file mode 100644
index 81049649edddb23aeebeac4085514da838f1463b..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/inference.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from dataclasses import fields
-from typing import Any, List
-import torch
-
-from detectron2.structures import Instances
-
-
-def densepose_inference(densepose_predictor_output: Any, detections: List[Instances]) -> None:
- """
- Splits DensePose predictor outputs into chunks, each chunk corresponds to
- detections on one image. Predictor output chunks are stored in `pred_densepose`
- attribute of the corresponding `Instances` object.
-
- Args:
- densepose_predictor_output: a dataclass instance (can be of different types,
- depending on predictor used for inference). Each field can be `None`
- (if the corresponding output was not inferred) or a tensor of size
- [N, ...], where N = N_1 + N_2 + .. + N_k is a total number of
- detections on all images, N_1 is the number of detections on image 1,
- N_2 is the number of detections on image 2, etc.
- detections: a list of objects of type `Instance`, k-th object corresponds
- to detections on k-th image.
- """
- k = 0
- for detection_i in detections:
- if densepose_predictor_output is None:
- # don't add `pred_densepose` attribute
- continue
- n_i = detection_i.__len__()
-
- PredictorOutput = type(densepose_predictor_output)
- output_i_dict = {}
- # we assume here that `densepose_predictor_output` is a dataclass object
- for field in fields(densepose_predictor_output):
- field_value = getattr(densepose_predictor_output, field.name)
- # slice tensors
- if isinstance(field_value, torch.Tensor):
- output_i_dict[field.name] = field_value[k : k + n_i]
- # leave others as is
- else:
- output_i_dict[field.name] = field_value
- detection_i.pred_densepose = PredictorOutput(**output_i_dict)
- k += n_i
diff --git a/spaces/bubbletea98/Neo4J_Integration/app.py b/spaces/bubbletea98/Neo4J_Integration/app.py
deleted file mode 100644
index 68c13f6c979721057cf4f403613f8710c19fca97..0000000000000000000000000000000000000000
--- a/spaces/bubbletea98/Neo4J_Integration/app.py
+++ /dev/null
@@ -1,255 +0,0 @@
-drop_down_l=['Autoencoders',
- 'Dirichlet Processes',
- 'Gaussian graphical models',
- 'Manifold Learning',
- 'Markov Random Fields',
- 'Markov chains',
- 'Mean Field Approximation',
- 'Message Passing',
- 'Meta-Learning',
- 'Mixture Models',
- 'Naive Bayes',
- 'Principal Component Analysis',
- 'ResNet',
- 'Sampling',
- 'Sequence to sequence',
- 'State Space Models',
- 'Unsupervised learning',
- 'Variations of GANs',
- 'Visual QA',
- 'a* search',
- 'adversarial search',
- 'agent-based view of ai',
- 'automated essay scoring',
- 'autonomous cars',
- 'backpropagation',
- 'bag of words model',
- 'bayes theorem',
- 'bayesian network',
- 'beam search',
- 'bio text mining',
- 'character level language models',
- 'chinese nlp',
- 'chomsky hierarchy',
- 'citation networks',
- 'cky parsing',
- 'classic parsing methods',
- 'classification',
- 'clustering',
- 'computation theory',
- 'computational phonology',
- 'computer vision',
- 'context free grammar',
- 'context free grammars',
- 'context sensitive grammar',
- 'convolutional neural network',
- 'convolutional neural networks',
- 'course introduction',
- 'deep learning introduction',
- 'dependency parsing',
- 'dependency syntax',
- 'dialog systems',
- 'dimensionality reduction',
- 'discourse analysis',
- 'discourse parsing',
- 'document ranking',
- 'document representation',
- 'dual decomposition',
- 'dynamic programming',
- 'edit distance',
- 'entailment',
- 'evaluation of dependency parsing',
- 'evaluation of language modeling',
- 'evaluation of question answering',
- 'evaluation of text classification',
- 'event detection',
- 'expectation maximization algorithm',
- 'expert systems',
- 'feature learning',
- 'feature selection',
- 'finite state machines',
- 'finite state transducers',
- 'first order logic',
- 'first-order logic',
- 'game playing in ai',
- 'gated recurrent units',
- 'generative adversarial networks',
- 'generative and discriminative models',
- 'gibbs sampling',
- 'grammar checker',
- 'graph convolutional networks',
- 'graph theory',
- 'graph-based nlp',
- 'graphical models',
- 'harmonic functions',
- 'heuristic search',
- 'hidden markov models',
- 'image retrieval',
- 'information extraction',
- 'information retrieval',
- 'informed search',
- 'kernel function',
- 'kernels',
- 'knowledge representation',
- 'language identification',
- 'language modeling',
- 'latent dirichlet allocation',
- 'latent semantic indexing',
- 'latent variable models',
- 'lexical semantics',
- 'lexicalized parsing',
- 'lexicography',
- 'linear algebra',
- 'linear discriminant analysis',
- 'linear regression',
- 'linguistics basics',
- 'log-linear models',
- 'logic and logical agents',
- 'logic and reasoning',
- 'logistic regression',
- 'long short term memory networks',
- 'loss function',
- 'machine learning resources',
- 'machine translation',
- 'machine translation techniques',
- 'markov chain monte carlo',
- 'markov decision processes',
- 'mathematical models',
- 'matrix factorization',
- 'matrix multiplication',
- 'maximum likelihood estimation',
- 'memory networks',
- 'monte carlo methods',
- 'monte carlo tree search',
- 'morphology and lexicon',
- 'morphology and semantics in machine translation',
- 'multi-agent systems',
- 'multilingual word embedding',
- 'n-gram models',
- 'natural language processing intro',
- 'neural language modeling',
- 'neural machine translation',
- 'neural networks',
- 'neural parsing',
- 'neural question answering',
- 'neural summarization',
- 'neural turing machine',
- 'newton method',
- 'nlp and vision',
- 'nlp for biology',
- 'nlp for the humanities',
- 'noisy channel model',
- 'optimization',
- 'pagerank',
- 'parsing',
- 'parsing evaluation',
- 'part of speech tagging',
- 'particle filter',
- 'parts of speech',
- 'penn treebank',
- 'phonetics',
- 'planning',
- 'pointer networks',
- 'predicate logic',
- 'preprocessing',
- 'probabilistic context free grammars',
- 'probabilistic grammars',
- 'probabilities',
- 'problem solving and search',
- 'programming languages',
- 'propositional logic',
- 'prosody',
- 'python',
- 'question answering',
- 'random walks',
- 'random walks and harmonic functions',
- 'recommendation system',
- 'recurrent neural networks',
- 'recursive neural network',
- 'recursive neural networks',
- 'regular expressions',
- 'reinforcement learning',
- 'relation extraction',
- 'robotic locomotion',
- 'robotics',
- 'scientific article summarization',
- 'search',
- 'search engines',
- 'semantic parsing',
- 'semantic role labeling',
- 'semantic similarity',
- 'semi supervised learning',
- 'semi-supervised learning',
- 'sentence boundary recognition',
- 'sentence representations',
- 'sentence simplification',
- 'sentiment analysis',
- 'seq2seq',
- 'sequence classification and conditional random fields',
- 'shallow parsing',
- 'shift-reduce parsing',
- 'singular value decomposition',
- 'social media analysis',
- 'social network extraction',
- 'spectral clustering',
- 'spectral methods',
- 'speech processing',
- 'speech signal analysis',
- 'speech synthesis',
- 'spelling correction',
- 'stack lstm',
- 'statistical machine translation',
- 'statistical parsing',
- 'statistical part of speech tagging',
- 'structured learning',
- 'structured sparsity',
- 'summarization evaluation',
- 'syntax',
- 'syntax based machine translation',
- 'syntaxnet',
- 'text generation',
- 'text mining',
- 'text similarity',
- 'text summarization',
- 'text to speech generation',
- 'the ibm models',
- 'thesaurus-based similarity',
- 'tokenization',
- 'toolkits for information retrieval',
- 'tools for dl',
- 'topic modeling',
- 'training neural networks',
- 'transition based dependency parsing',
- 'tree adjoining grammar',
- 'uncertainty',
- 'variational bayes models',
- 'vector representations',
- 'vector semantics',
- 'weakly-supervised learning',
- 'word distributions',
- 'word embedding',
- 'word embedding variations',
- 'word segmentation',
- 'word sense disambiguation',
- 'wordnet']
-domain_list=['ml', 'nlp', 'dl', 'deep-rl', 'pr', 'ai']
-import gradio as gr
-from py2neo import Graph
-
-def greet(topic_name, lecture_name, URL_link,author,year,domain):
- graph = Graph("neo4j+s://e4991d17.databases.neo4j.io", auth=("neo4j", "ohM5LvdhcutqyRQTqk6PpuiDofvpA4o3pXLpC9kb4_g"),routing=True)
- qs="MATCH (T:topic)\
- Where T.id_topic=$topic\
- CREATE (test:lecture{topic:$topic,name:$lecture,id:$URL,year:$year,author:$author,domain:$domain})-[:lecture_of]->(T);"
- graph.run(qs,topic=topic_name,lecture=lecture_name,URL=URL_link,author=author,year=year,domain=domain)
- return 'Finished Adding'
-
-iface = gr.Interface(
- fn=greet,
- inputs=[
- gr.inputs.Dropdown(drop_down_l, label="Topic belonging"),
- 'text','text','text','text',
- gr.inputs.Radio(domain_list, label="Which Domain this lecture is belonging ?"),
- ],
- outputs=['text'])
-iface.launch()
\ No newline at end of file
diff --git a/spaces/cakewalk/splat/index.html b/spaces/cakewalk/splat/index.html
deleted file mode 100644
index 4be79e8cea9e02371af6f75fc516aa570c6f07e5..0000000000000000000000000000000000000000
--- a/spaces/cakewalk/splat/index.html
+++ /dev/null
@@ -1,262 +0,0 @@
-
-
-
- WebGL Gaussian Splat Viewer
-
-
-
-
-
-
-
-
-
movement (arrow keys)
-- left/right arrow keys to strafe side to side
-- up/down arrow keys to move forward/back
-- space to jump
-
-camera angle (wasd)
-- a/d to turn camera left/right
-- w/s to tilt camera up/down
-- q/e to roll camera counterclockwise/clockwise
-- i/k and j/l to orbit
-
-trackpad
-- scroll up/down/left/right to orbit
-- pinch to move forward/back
-- ctrl key + scroll to move forward/back
-- shift + scroll to move up/down or strafe
-
-mouse
-- click and drag to orbit
-- right click (or ctrl/cmd key) and drag up/down to move
-
-touch (mobile)
-- one finger to orbit
-- two finger pinch to move forward/back
-- two finger rotate to rotate camera clockwise/counterclockwise
-- two finger pan to move side-to-side and up-down
-
-other
-- press 0-9 to switch to one of the pre-loaded camera views
-- press p to resume default animation
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/modules.py b/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/modules.py
deleted file mode 100644
index 9c7fd9cd6eb8b7e0ec0e08957e970744a374a924..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/modules.py
+++ /dev/null
@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-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/camenduru-com/audioldm-text-to-audio-generation/audioldm/__init__.py b/spaces/camenduru-com/audioldm-text-to-audio-generation/audioldm/__init__.py
deleted file mode 100644
index 2f93cab80ded8e7239bb96eb6e364c3fd4fb46d9..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/audioldm-text-to-audio-generation/audioldm/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .ldm import LatentDiffusion
-from .utils import seed_everything
-from .pipeline import *
\ No newline at end of file
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/docs/tutorials/README.md b/spaces/carlosalonso/Detection-video/carpeta_deteccion/docs/tutorials/README.md
deleted file mode 100644
index 1ca9c94d042ef838143a45490fe6b4556c19f3c9..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/docs/tutorials/README.md
+++ /dev/null
@@ -1,4 +0,0 @@
-# Read the docs:
-
-The latest documentation built from this directory is available at [detectron2.readthedocs.io](https://detectron2.readthedocs.io/).
-Documents in this directory are not meant to be read on github.
diff --git a/spaces/cbr/swp/utils.py b/spaces/cbr/swp/utils.py
deleted file mode 100644
index 2a74e9e795af9f6e7f78e28520617753beee36ef..0000000000000000000000000000000000000000
--- a/spaces/cbr/swp/utils.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import os
-import cv2
-import time
-import glob
-import shutil
-import platform
-import datetime
-import subprocess
-from threading import Thread
-from moviepy.editor import VideoFileClip, ImageSequenceClip
-from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip
-
-
-def trim_video(video_path, output_path, start_frame, stop_frame):
- video_name, _ = os.path.splitext(os.path.basename(video_path))
- trimmed_video_filename = video_name + "_trimmed" + ".mp4"
- temp_path = os.path.join(output_path, "trim")
- os.makedirs(temp_path, exist_ok=True)
- trimmed_video_file_path = os.path.join(temp_path, trimmed_video_filename)
-
- video = VideoFileClip(video_path)
- fps = video.fps
- start_time = start_frame / fps
- duration = (stop_frame - start_frame) / fps
-
- trimmed_video = video.subclip(start_time, start_time + duration)
- trimmed_video.write_videofile(
- trimmed_video_file_path, codec="libx264", audio_codec="aac"
- )
- trimmed_video.close()
- video.close()
-
- return trimmed_video_file_path
-
-
-def open_directory(path=None):
- if path is None:
- return
- try:
- os.startfile(path)
- except:
- subprocess.Popen(["xdg-open", path])
-
-
-class StreamerThread(object):
- def __init__(self, src=0):
- self.capture = cv2.VideoCapture(src)
- self.capture.set(cv2.CAP_PROP_BUFFERSIZE, 2)
- self.FPS = 1 / 30
- self.FPS_MS = int(self.FPS * 1000)
- self.thread = None
- self.stopped = False
- self.frame = None
-
- def start(self):
- self.thread = Thread(target=self.update, args=())
- self.thread.daemon = True
- self.thread.start()
-
- def stop(self):
- self.stopped = True
- self.thread.join()
- print("stopped")
-
- def update(self):
- while not self.stopped:
- if self.capture.isOpened():
- (self.status, self.frame) = self.capture.read()
- time.sleep(self.FPS)
-
-
-class ProcessBar:
- def __init__(self, bar_length, total, before="⬛", after="🟨"):
- self.bar_length = bar_length
- self.total = total
- self.before = before
- self.after = after
- self.bar = [self.before] * bar_length
- self.start_time = time.time()
-
- def get(self, index):
- total = self.total
- elapsed_time = time.time() - self.start_time
- average_time_per_iteration = elapsed_time / (index + 1)
- remaining_iterations = total - (index + 1)
- estimated_remaining_time = remaining_iterations * average_time_per_iteration
-
- self.bar[int(index / total * self.bar_length)] = self.after
- info_text = f"({index+1}/{total}) {''.join(self.bar)} "
- info_text += f"(ETR: {int(estimated_remaining_time // 60)} min {int(estimated_remaining_time % 60)} sec)"
- return info_text
-
-
-logo_image = cv2.imread("./assets/images/logo.png", cv2.IMREAD_UNCHANGED)
-
-
-def add_logo_to_image(img, logo=logo_image):
- logo_size = int(img.shape[1] * 0.1)
- logo = cv2.resize(logo, (logo_size, logo_size))
- if logo.shape[2] == 4:
- alpha = logo[:, :, 3]
- else:
- alpha = np.ones_like(logo[:, :, 0]) * 255
- padding = int(logo_size * 0.1)
- roi = img.shape[0] - logo_size - padding, img.shape[1] - logo_size - padding
- for c in range(0, 3):
- img[roi[0] : roi[0] + logo_size, roi[1] : roi[1] + logo_size, c] = (
- alpha / 255.0
- ) * logo[:, :, c] + (1 - alpha / 255.0) * img[
- roi[0] : roi[0] + logo_size, roi[1] : roi[1] + logo_size, c
- ]
- return img
diff --git a/spaces/ccolas/TastyPiano/src/cocktails/pipeline/get_affect2affective_cluster.py b/spaces/ccolas/TastyPiano/src/cocktails/pipeline/get_affect2affective_cluster.py
deleted file mode 100644
index 6b0cd8cc37195869643cd591b9cf4585d7ff3c4a..0000000000000000000000000000000000000000
--- a/spaces/ccolas/TastyPiano/src/cocktails/pipeline/get_affect2affective_cluster.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from src.music.config import CHECKPOINTS_PATH
-import pickle
-import numpy as np
-
-# can be computed from cocktail2affect
-cluster_model_path = CHECKPOINTS_PATH + "/music2cocktails/affects2affect_cluster/cluster_model.pickle"
-
-def get_affect2affective_cluster():
- with open(cluster_model_path, 'rb') as f:
- data = pickle.load(f)
- model = data['cluster_model']
- dimensions_weights = data['dimensions_weights']
- def find_cluster(aff_coord):
- if aff_coord.ndim == 1:
- aff_coord = aff_coord.reshape(1, -1)
- return model.predict(aff_coord * np.array(dimensions_weights))
- return find_cluster
-
-def get_affective_cluster_centers():
- with open(cluster_model_path, 'rb') as f:
- data = pickle.load(f)
- return data['cluster_centers']
-
diff --git a/spaces/chansung/zero2story/interfaces/story_gen_ui.py b/spaces/chansung/zero2story/interfaces/story_gen_ui.py
deleted file mode 100644
index 83132505c3301a37ab8d4ab25872210f257afd81..0000000000000000000000000000000000000000
--- a/spaces/chansung/zero2story/interfaces/story_gen_ui.py
+++ /dev/null
@@ -1,451 +0,0 @@
-import re
-import copy
-import random
-import gradio as gr
-from gradio_client import Client
-from pathlib import Path
-
-from modules import (
- ImageMaker, MusicMaker, merge_video
-)
-from modules.llms import get_llm_factory
-from interfaces import utils
-
-from pingpong import PingPong
-from pingpong.context import CtxLastWindowStrategy
-
-img_maker = ImageMaker('https://huggingface.co/jphan32/Zero2Story/landscapeAnimePro_v20Inspiration.safetensors',
- vae="https://huggingface.co/jphan32/Zero2Story/cute20vae.safetensors")
-
-bgm_maker = MusicMaker(model_size='small', output_format='mp3')
-
-video_gen_client_url = None # e.g. "https://0447df3cf5f7c49c46.gradio.live"
-
-
-async def update_story_gen(
- cursors, cur_cursor_idx,
- genre, place, mood,
- main_char_name, main_char_age, main_char_personality, main_char_job,
- side_char_enable1, side_char_name1, side_char_age1, side_char_personality1, side_char_job1,
- side_char_enable2, side_char_name2, side_char_age2, side_char_personality2, side_char_job2,
- side_char_enable3, side_char_name3, side_char_age3, side_char_personality3, side_char_job3,
-):
- if len(cursors) == 1:
- return await first_story_gen(
- cursors,
- genre, place, mood,
- main_char_name, main_char_age, main_char_personality, main_char_job,
- side_char_enable1, side_char_name1, side_char_age1, side_char_personality1, side_char_job1,
- side_char_enable2, side_char_name2, side_char_age2, side_char_personality2, side_char_job2,
- side_char_enable3, side_char_name3, side_char_age3, side_char_personality3, side_char_job3,
- cur_cursor_idx=cur_cursor_idx
- )
- else:
- return await next_story_gen(
- cursors,
- None,
- genre, place, mood,
- main_char_name, main_char_age, main_char_personality, main_char_job,
- side_char_enable1, side_char_name1, side_char_age1, side_char_personality1, side_char_job1,
- side_char_enable2, side_char_name2, side_char_age2, side_char_personality2, side_char_job2,
- side_char_enable3, side_char_name3, side_char_age3, side_char_personality3, side_char_job3,
- cur_cursor_idx=cur_cursor_idx
- )
-
-async def next_story_gen(
- cursors,
- action,
- genre, place, mood,
- main_char_name, main_char_age, main_char_personality, main_char_job,
- side_char_enable1, side_char_name1, side_char_age1, side_char_personality1, side_char_job1,
- side_char_enable2, side_char_name2, side_char_age2, side_char_personality2, side_char_job2,
- side_char_enable3, side_char_name3, side_char_age3, side_char_personality3, side_char_job3,
- cur_cursor_idx=None,
- llm_type="PaLM"
-):
- factory = get_llm_factory(llm_type)
- prompts = factory.create_prompt_manager().prompts
- llm_service = factory.create_llm_service()
-
- stories = ""
- cur_side_chars = 1
-
- action = cursors[cur_cursor_idx]["action"] if cur_cursor_idx is not None else action
- end_idx = len(cursors) if cur_cursor_idx is None else len(cursors)-1
-
- for cursor in cursors[:end_idx]:
- stories = stories + cursor["story"]
-
- side_char_prompt = utils.add_side_character(
- [side_char_enable1, side_char_enable2, side_char_enable3],
- [side_char_name1, side_char_name2, side_char_name3],
- [side_char_job1, side_char_job2, side_char_job3],
- [side_char_age1, side_char_age2, side_char_age3],
- [side_char_personality1, side_char_personality2, side_char_personality3],
- )
-
- prompt = prompts['story_gen']['next_story_gen'].format(
- genre=genre, place=place, mood=mood,
- main_char_name=main_char_name,
- main_char_job=main_char_job,
- main_char_age=main_char_age,
- main_char_personality=main_char_personality,
- side_char_placeholder=side_char_prompt,
- stories=stories, action=action,
- )
-
- print(f"generated prompt:\n{prompt}")
- parameters = llm_service.make_params(mode="text", temperature=1.0, top_k=40, top_p=0.9, max_output_tokens=4096)
- try:
- response_json = await utils.retry_until_valid_json(prompt, parameters=parameters)
- except Exception as e:
- print(e)
- raise gr.Error(e)
-
- story = response_json["paragraphs"]
- if isinstance(story, list):
- story = "\n\n".join(story)
-
- if cur_cursor_idx is None:
- cursors.append({
- "title": "",
- "story": story,
- "action": action
- })
- else:
- cursors[cur_cursor_idx]["story"] = story
- cursors[cur_cursor_idx]["action"] = action
-
- return (
- cursors, len(cursors)-1,
- story,
- gr.update(
- maximum=len(cursors), value=len(cursors),
- label=f"{len(cursors)} out of {len(cursors)} stories",
- visible=True, interactive=True
- ),
- gr.update(interactive=True),
- gr.update(interactive=True),
- gr.update(value=None, visible=False, interactive=True),
- gr.update(value=None, visible=False, interactive=True),
- gr.update(value=None, visible=False, interactive=True),
- )
-
-async def actions_gen(
- cursors,
- genre, place, mood,
- main_char_name, main_char_age, main_char_personality, main_char_job,
- side_char_enable1, side_char_name1, side_char_age1, side_char_personality1, side_char_job1,
- side_char_enable2, side_char_name2, side_char_age2, side_char_personality2, side_char_job2,
- side_char_enable3, side_char_name3, side_char_age3, side_char_personality3, side_char_job3,
- cur_cursor_idx=None,
- llm_type="PaLM"
-):
- factory = get_llm_factory(llm_type)
- prompts = factory.create_prompt_manager().prompts
- llm_service = factory.create_llm_service()
-
- stories = ""
- cur_side_chars = 1
- end_idx = len(cursors) if cur_cursor_idx is None else len(cursors)-1
-
- for cursor in cursors[:end_idx]:
- stories = stories + cursor["story"]
-
- summary_prompt = prompts['story_gen']['summarize'].format(stories=stories)
-
- print(f"generated prompt:\n{summary_prompt}")
- parameters = llm_service.make_params(mode="text", temperature=1.0, top_k=40, top_p=1.0, max_output_tokens=4096)
-
- try:
- _, summary = await llm_service.gen_text(summary_prompt, mode="text", parameters=parameters)
- except Exception as e:
- print(e)
- raise gr.Error(e)
-
- side_char_prompt = utils.add_side_character(
- [side_char_enable1, side_char_enable2, side_char_enable3],
- [side_char_name1, side_char_name2, side_char_name3],
- [side_char_job1, side_char_job2, side_char_job3],
- [side_char_age1, side_char_age2, side_char_age3],
- [side_char_personality1, side_char_personality2, side_char_personality3],
- )
- prompt = prompts['story_gen']['actions_gen'].format(
- genre=genre, place=place, mood=mood,
- main_char_name=main_char_name,
- main_char_job=main_char_job,
- main_char_age=main_char_age,
- main_char_personality=main_char_personality,
- side_char_placeholder=side_char_prompt,
- summary=summary,
- )
-
- print(f"generated prompt:\n{prompt}")
- parameters = llm_service.make_params(mode="text", temperature=1.0, top_k=40, top_p=1.0, max_output_tokens=4096)
- try:
- response_json = await utils.retry_until_valid_json(prompt, parameters=parameters)
- except Exception as e:
- print(e)
- raise gr.Error(e)
- actions = response_json["options"]
-
- random_actions = random.sample(actions, 3)
-
- return (
- gr.update(value=random_actions[0], interactive=True),
- gr.update(value=random_actions[1], interactive=True),
- gr.update(value=random_actions[2], interactive=True),
- " "
- )
-
-async def first_story_gen(
- cursors,
- genre, place, mood,
- main_char_name, main_char_age, main_char_personality, main_char_job,
- side_char_enable1, side_char_name1, side_char_age1, side_char_personality1, side_char_job1,
- side_char_enable2, side_char_name2, side_char_age2, side_char_personality2, side_char_job2,
- side_char_enable3, side_char_name3, side_char_age3, side_char_personality3, side_char_job3,
- cur_cursor_idx=None,
- llm_type="PaLM"
-):
- factory = get_llm_factory(llm_type)
- prompts = factory.create_prompt_manager().prompts
- llm_service = factory.create_llm_service()
-
- cur_side_chars = 1
-
- side_char_prompt = utils.add_side_character(
- [side_char_enable1, side_char_enable2, side_char_enable3],
- [side_char_name1, side_char_name2, side_char_name3],
- [side_char_job1, side_char_job2, side_char_job3],
- [side_char_age1, side_char_age2, side_char_age3],
- [side_char_personality1, side_char_personality2, side_char_personality3],
- )
- prompt = prompts['story_gen']['first_story_gen'].format(
- genre=genre, place=place, mood=mood,
- main_char_name=main_char_name,
- main_char_job=main_char_job,
- main_char_age=main_char_age,
- main_char_personality=main_char_personality,
- side_char_placeholder=side_char_prompt,
- )
-
- print(f"generated prompt:\n{prompt}")
- parameters = llm_service.make_params(mode="text", temperature=1.0, top_k=40, top_p=1.0, max_output_tokens=4096)
- try:
- response_json = await utils.retry_until_valid_json(prompt, parameters=parameters)
- except Exception as e:
- print(e)
- raise gr.Error(e)
-
- story = response_json["paragraphs"]
- if isinstance(story, list):
- story = "\n\n".join(story)
-
- if cur_cursor_idx is None:
- cursors.append({
- "title": "",
- "story": story
- })
- else:
- cursors[cur_cursor_idx]["story"] = story
-
- return (
- cursors, len(cursors)-1,
- story,
- gr.update(
- maximum=len(cursors), value=len(cursors),
- label=f"{len(cursors)} out of {len(cursors)} stories",
- visible=False if len(cursors) == 1 else True, interactive=True
- ),
- gr.update(interactive=True),
- gr.update(interactive=True),
- gr.update(value=None, visible=False, interactive=True),
- gr.update(value=None, visible=False, interactive=True),
- gr.update(value=None, visible=False, interactive=True),
- )
-
-def video_gen(
- image, audio, title, cursors, cur_cursor, use_ffmpeg=True
-):
- if use_ffmpeg:
- output_filename = merge_video(image, audio, story_title="")
-
- if not use_ffmpeg or not output_filename:
- client = Client(video_gen_client_url)
- result = client.predict(
- "",
- audio,
- image,
- f"{utils.id_generator()}.mp4",
- api_name="/predict"
- )
- output_filename = result[0]
-
- cursors[cur_cursor]["video"] = output_filename
-
- return (
- gr.update(visible=False),
- gr.update(visible=False),
- gr.update(visible=True, value=output_filename),
- cursors,
- " "
- )
-
-
-def image_gen(
- genre, place, mood, title, story_content, cursors, cur_cursor, llm_type="PaLM"
-):
- # generate prompts for background image with LLM
- for _ in range(3):
- try:
- prompt, neg_prompt = img_maker.generate_background_prompts(genre, place, mood, title, "", story_content, llm_type)
- print(f"Image Prompt: {prompt}")
- print(f"Negative Prompt: {neg_prompt}")
- break
- except Exception as e:
- print(e)
- raise gr.Error(e)
-
- if not prompt:
- raise ValueError("Failed to generate prompts for background image.")
-
- # generate image
- try:
- img_filename = img_maker.text2image(prompt, neg_prompt=neg_prompt, ratio='16:9', cfg=6.5)
- except ValueError as e:
- print(e)
- img_filename = str(Path('.') / 'assets' / 'nsfw_warning_wide.png')
-
- cursors[cur_cursor]["img"] = img_filename
-
- return (
- gr.update(visible=True, value=img_filename),
- cursors,
- " "
- )
-
-
-def audio_gen(
- genre, place, mood, title, story_content, cursors, cur_cursor, llm_type="PaLM"
-):
- # generate prompt for background music with LLM
- for _ in range(3):
- try:
- prompt = bgm_maker.generate_prompt(genre, place, mood, title, "", story_content, llm_type)
- print(f"Music Prompt: {prompt}")
- break
- except Exception as e:
- print(e)
- raise gr.Error(e)
-
- if not prompt:
- raise ValueError("Failed to generate prompt for background music.")
-
- # generate music
- bgm_filename = bgm_maker.text2music(prompt, length=60)
- cursors[cur_cursor]["audio"] = bgm_filename
-
- return (
- gr.update(visible=True, value=bgm_filename),
- cursors,
- " "
- )
-
-def move_story_cursor(moved_cursor, cursors):
- cursor_content = cursors[moved_cursor-1]
- max_cursor = len(cursors)
-
- action_btn = (
- gr.update(interactive=False),
- gr.update(interactive=False),
- gr.update(interactive=False)
- )
-
- if moved_cursor == max_cursor:
- action_btn = (
- gr.update(interactive=True),
- gr.update(interactive=True),
- gr.update(interactive=True)
- )
-
- if "video" in cursor_content:
- outputs = (
- moved_cursor-1,
- gr.update(label=f"{moved_cursor} out of {len(cursors)} chapters"),
- cursor_content["story"],
- gr.update(value=None, visible=False),
- gr.update(value=None, visible=False),
- gr.update(value=cursor_content["video"], visible=True),
- )
-
- else:
- image_container = gr.update(value=None, visible=False)
- audio_container = gr.update(value=None, visible=False)
-
- if "img" in cursor_content:
- image_container = gr.update(value=cursor_content["img"], visible=True)
-
- if "audio" in cursor_content:
- audio_container = gr.update(value=cursor_content["audio"], visible=True)
-
- outputs = (
- moved_cursor-1,
- gr.update(label=f"{moved_cursor} out of {len(cursors)} stories"),
- cursor_content["story"],
- image_container,
- audio_container,
- gr.update(value=None, visible=False),
- )
-
- return outputs + action_btn
-
-def update_story_content(story_content, cursors, cur_cursor):
- cursors[cur_cursor]["story"] = story_content
- return cursors
-
-def disable_btns():
- return (
- gr.update(interactive=False), # image_gen_btn
- gr.update(interactive=False), # audio_gen_btn
- gr.update(interactive=False), # img_audio_combine_btn
-
- gr.update(interactive=False), # regen_actions_btn
- gr.update(interactive=False), # regen_story_btn
- gr.update(interactive=False), # custom_prompt_txt
-
- gr.update(interactive=False), # action_btn1
- gr.update(interactive=False), # action_btn2
- gr.update(interactive=False), # action_btn3
-
- gr.update(interactive=False), # custom_action_txt
-
- gr.update(interactive=False), # restart_from_story_generation_btn
- gr.update(interactive=False), # story_writing_done_btn
- )
-
-def enable_btns(story_image, story_audio):
- video_gen_btn_state = gr.update(interactive=False)
-
- if story_image is not None and \
- story_audio is not None:
- video_gen_btn_state = gr.update(interactive=True)
-
- return (
- gr.update(interactive=True), # image_gen_btn
- gr.update(interactive=True), # audio_gen_btn
- video_gen_btn_state, # img_audio_combine_btn
-
- gr.update(interactive=True), # regen_actions_btn
- gr.update(interactive=True), # regen_story_btn
- gr.update(interactive=True), # custom_prompt_txt
-
- gr.update(interactive=True), # action_btn1
- gr.update(interactive=True), # action_btn2
- gr.update(interactive=True), # action_btn3
-
- gr.update(interactive=True), # custom_action_txt
-
- gr.update(interactive=True), # restart_from_story_generation_btn
- gr.update(interactive=True), # story_writing_done_btn
- )
\ No newline at end of file
diff --git a/spaces/chasemcdo/hf_localai/examples/query_data/store.py b/spaces/chasemcdo/hf_localai/examples/query_data/store.py
deleted file mode 100644
index 0d628c81967f2a7829f0597c1681c427d4914643..0000000000000000000000000000000000000000
--- a/spaces/chasemcdo/hf_localai/examples/query_data/store.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import os
-
-# Uncomment to specify your OpenAI API key here (local testing only, not in production!), or add corresponding environment variable (recommended)
-# os.environ['OPENAI_API_KEY']= ""
-
-from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, LLMPredictor, PromptHelper, ServiceContext
-from langchain.llms.openai import OpenAI
-from llama_index import StorageContext, load_index_from_storage
-
-base_path = os.environ.get('OPENAI_API_BASE', 'http://localhost:8080/v1')
-
-# This example uses text-davinci-003 by default; feel free to change if desired
-llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo", openai_api_base=base_path))
-
-# Configure prompt parameters and initialise helper
-max_input_size = 400
-num_output = 400
-max_chunk_overlap = 30
-
-prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)
-
-# Load documents from the 'data' directory
-documents = SimpleDirectoryReader('data').load_data()
-service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper, chunk_size_limit = 400)
-index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context)
-index.storage_context.persist(persist_dir="./storage")
-
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/docs/freeze_module.md b/spaces/chendl/compositional_test/multimodal/YOLOX/docs/freeze_module.md
deleted file mode 100644
index 421d95cd96d0a876f17ad57af899b2e06f0addbd..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/docs/freeze_module.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Freeze module
-
-This page guide users to freeze module in YOLOX.
-Exp controls everything in YOLOX, so let's start from creating an Exp object.
-
-## 1. Create your own expermiment object
-
-We take an example of YOLOX-S model on COCO dataset to give a more clear guide.
-
-Import the config you want (or write your own Exp object inherit from `yolox.exp.BaseExp`).
-```python
-from yolox.exp.default.yolox_s import Exp as MyExp
-```
-
-## 2. Override `get_model` method
-
-Here is a simple code to freeze backbone (FPN not included) of module.
-```python
-class Exp(MyExp):
-
- def get_model(self):
- from yolox.utils import freeze_module
- model = super().get_model()
- freeze_module(model.backbone.backbone)
- return model
-```
-if you only want to freeze FPN, `freeze_module(model.backbone)` might help.
-
-## 3. Train
-Suppose that the path of your Exp is `/path/to/my_exp.py`, use the following command to train your model.
-```bash
-python3 -m yolox.tools.train -f /path/to/my_exp.py
-```
-For more details of training, run the following command.
-```bash
-python3 -m yolox.tools.train --help
-```
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/tests/utils/test_model_utils.py b/spaces/chendl/compositional_test/multimodal/YOLOX/tests/utils/test_model_utils.py
deleted file mode 100644
index abfc3446f06974998c8ab25b5ded52e1327e2363..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/tests/utils/test_model_utils.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-import unittest
-
-import torch
-from torch import nn
-
-from yolox.utils import adjust_status, freeze_module
-from yolox.exp import get_exp
-
-
-class TestModelUtils(unittest.TestCase):
-
- def setUp(self):
- self.model: nn.Module = get_exp(exp_name="yolox-s").get_model()
-
- def test_model_state_adjust_status(self):
- data = torch.ones(1, 10, 10, 10)
- # use bn since bn changes state during train/val
- model = nn.BatchNorm2d(10)
- prev_state = model.state_dict()
-
- modes = [False, True]
- results = [True, False]
-
- # test under train/eval mode
- for mode, result in zip(modes, results):
- with adjust_status(model, training=mode):
- model(data)
- model_state = model.state_dict()
- self.assertTrue(len(model_state) == len(prev_state))
- self.assertEqual(
- result,
- all([torch.allclose(v, model_state[k]) for k, v in prev_state.items()])
- )
-
- # test recurrsive context case
- prev_state = model.state_dict()
- with adjust_status(model, training=False):
- with adjust_status(model, training=False):
- model(data)
- model_state = model.state_dict()
- self.assertTrue(len(model_state) == len(prev_state))
- self.assertTrue(
- all([torch.allclose(v, model_state[k]) for k, v in prev_state.items()])
- )
-
- def test_model_effect_adjust_status(self):
- # test context effect
- self.model.train()
- with adjust_status(self.model, training=False):
- for module in self.model.modules():
- self.assertFalse(module.training)
- # all training after exit
- for module in self.model.modules():
- self.assertTrue(module.training)
-
- # only backbone set to eval
- self.model.backbone.eval()
- with adjust_status(self.model, training=False):
- for module in self.model.modules():
- self.assertFalse(module.training)
-
- for name, module in self.model.named_modules():
- if "backbone" in name:
- self.assertFalse(module.training)
- else:
- self.assertTrue(module.training)
-
- def test_freeze_module(self):
- model = nn.Sequential(
- nn.Conv2d(3, 10, 1),
- nn.BatchNorm2d(10),
- nn.ReLU(),
- )
- data = torch.rand(1, 3, 10, 10)
- model.train()
- assert isinstance(model[1], nn.BatchNorm2d)
- before_states = model[1].state_dict()
- freeze_module(model[1])
- model(data)
- after_states = model[1].state_dict()
- self.assertTrue(
- all([torch.allclose(v, after_states[k]) for k, v in before_states.items()])
- )
-
- # yolox test
- self.model.train()
- for module in self.model.modules():
- self.assertTrue(module.training)
-
- freeze_module(self.model, "backbone")
- for module in self.model.backbone.modules():
- self.assertFalse(module.training)
- for p in self.model.backbone.parameters():
- self.assertFalse(p.requires_grad)
-
- for module in self.model.head.modules():
- self.assertTrue(module.training)
- for p in self.model.head.parameters():
- self.assertTrue(p.requires_grad)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/chendl/compositional_test/transformers/docker/transformers-pytorch-tpu/docker-entrypoint.sh b/spaces/chendl/compositional_test/transformers/docker/transformers-pytorch-tpu/docker-entrypoint.sh
deleted file mode 100644
index fbe59566fdcdfd2e61d23288d8da6273003ff9ab..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/docker/transformers-pytorch-tpu/docker-entrypoint.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/bin/bash
-source ~/.bashrc
-echo "running docker-entrypoint.sh"
-conda activate container
-echo $KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS
-echo "printed TPU info"
-export XRT_TPU_CONFIG="tpu_worker;0;${KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS:7}"
-exec "$@"#!/bin/bash
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/rag-end2end-retriever/use_own_knowledge_dataset.py b/spaces/chendl/compositional_test/transformers/examples/research_projects/rag-end2end-retriever/use_own_knowledge_dataset.py
deleted file mode 100644
index e0aa86a3a65ba91089c9b363b226e3b5ca343631..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/rag-end2end-retriever/use_own_knowledge_dataset.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import logging
-import os
-from dataclasses import dataclass, field
-from functools import partial
-from pathlib import Path
-from tempfile import TemporaryDirectory
-from typing import List, Optional
-
-import faiss
-import torch
-from datasets import Features, Sequence, Value, load_dataset
-
-from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
-
-
-logger = logging.getLogger(__name__)
-torch.set_grad_enabled(False)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-def split_text(text: str, n=100, character=" ") -> List[str]:
- """Split the text every ``n``-th occurrence of ``character``"""
- text = text.split(character)
- return [character.join(text[i : i + n]).strip() for i in range(0, len(text), n)]
-
-
-def split_documents(documents: dict) -> dict:
- """Split documents into passages"""
- titles, texts = [], []
- for title, text in zip(documents["title"], documents["text"]):
- if text is not None:
- for passage in split_text(text):
- titles.append(title if title is not None else "")
- texts.append(passage)
- return {"title": titles, "text": texts}
-
-
-def embed(documents: dict, ctx_encoder: DPRContextEncoder, ctx_tokenizer: DPRContextEncoderTokenizerFast) -> dict:
- """Compute the DPR embeddings of document passages"""
- input_ids = ctx_tokenizer(
- documents["title"], documents["text"], truncation=True, padding="longest", return_tensors="pt"
- )["input_ids"]
- embeddings = ctx_encoder(input_ids.to(device=device), return_dict=True).pooler_output
- return {"embeddings": embeddings.detach().cpu().numpy()}
-
-
-def main(
- rag_example_args: "RagExampleArguments",
- processing_args: "ProcessingArguments",
- index_hnsw_args: "IndexHnswArguments",
-):
- ######################################
- logger.info("Step 1 - Create the dataset")
- ######################################
-
- # The dataset needed for RAG must have three columns:
- # - title (string): title of the document
- # - text (string): text of a passage of the document
- # - embeddings (array of dimension d): DPR representation of the passage
- # Let's say you have documents in tab-separated csv files with columns "title" and "text"
- assert os.path.isfile(rag_example_args.csv_path), "Please provide a valid path to a csv file"
-
- # You can load a Dataset object this way
- dataset = load_dataset(
- "csv", data_files=[rag_example_args.csv_path], split="train", delimiter="\t", column_names=["title", "text"]
- )
-
- # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
-
- # Then split the documents into passages of 100 words
- dataset = dataset.map(split_documents, batched=True, num_proc=processing_args.num_proc)
-
- # And compute the embeddings
- ctx_encoder = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=device)
- ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name)
- new_features = Features(
- {"text": Value("string"), "title": Value("string"), "embeddings": Sequence(Value("float32"))}
- ) # optional, save as float32 instead of float64 to save space
- dataset = dataset.map(
- partial(embed, ctx_encoder=ctx_encoder, ctx_tokenizer=ctx_tokenizer),
- batched=True,
- batch_size=processing_args.batch_size,
- features=new_features,
- )
-
- # And finally save your dataset
- passages_path = os.path.join(rag_example_args.output_dir, "my_knowledge_dataset")
- dataset.save_to_disk(passages_path)
- # from datasets import load_from_disk
- # dataset = load_from_disk(passages_path) # to reload the dataset
-
- ######################################
- logger.info("Step 2 - Index the dataset")
- ######################################
-
- # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
- index = faiss.IndexHNSWFlat(index_hnsw_args.d, index_hnsw_args.m, faiss.METRIC_INNER_PRODUCT)
- dataset.add_faiss_index("embeddings", custom_index=index)
-
- # And save the index
- index_path = os.path.join(rag_example_args.output_dir, "my_knowledge_dataset_hnsw_index.faiss")
- dataset.get_index("embeddings").save(index_path)
- # dataset.load_faiss_index("embeddings", index_path) # to reload the index
-
-
-@dataclass
-class RagExampleArguments:
- csv_path: str = field(
- default=str(Path(__file__).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"),
- metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"},
- )
- question: Optional[str] = field(
- default=None,
- metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."},
- )
- rag_model_name: str = field(
- default="facebook/rag-sequence-nq",
- metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"},
- )
- dpr_ctx_encoder_model_name: str = field(
- default="facebook/dpr-ctx_encoder-multiset-base",
- metadata={
- "help": (
- "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"
- " 'facebook/dpr-ctx_encoder-multiset-base'"
- )
- },
- )
- output_dir: Optional[str] = field(
- default=str(Path(__file__).parent / "test_run" / "dummy-kb"),
- metadata={"help": "Path to a directory where the dataset passages and the index will be saved"},
- )
-
-
-@dataclass
-class ProcessingArguments:
- num_proc: Optional[int] = field(
- default=None,
- metadata={
- "help": "The number of processes to use to split the documents into passages. Default is single process."
- },
- )
- batch_size: int = field(
- default=16,
- metadata={
- "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
- },
- )
-
-
-@dataclass
-class IndexHnswArguments:
- d: int = field(
- default=768,
- metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."},
- )
- m: int = field(
- default=128,
- metadata={
- "help": (
- "The number of bi-directional links created for every new element during the HNSW index construction."
- )
- },
- )
-
-
-if __name__ == "__main__":
- logging.basicConfig(level=logging.WARNING)
- logger.setLevel(logging.INFO)
-
- parser = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
- rag_example_args, processing_args, index_hnsw_args = parser.parse_args_into_dataclasses()
- with TemporaryDirectory() as tmp_dir:
- rag_example_args.output_dir = rag_example_args.output_dir or tmp_dir
- main(rag_example_args, processing_args, index_hnsw_args)
diff --git a/spaces/chilge/Fushimi/inference_main.py b/spaces/chilge/Fushimi/inference_main.py
deleted file mode 100644
index 825e791db86d37e955f42e8cb34323dbb248ed32..0000000000000000000000000000000000000000
--- a/spaces/chilge/Fushimi/inference_main.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import io
-import logging
-import time
-from pathlib import Path
-
-import librosa
-import numpy as np
-import soundfile
-
-from inference import infer_tool
-from inference import slicer
-from inference.infer_tool import Svc
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-chunks_dict = infer_tool.read_temp("inference/chunks_temp.json")
-
-model_path = "logs/48k/G_174000-Copy1.pth"
-config_path = "configs/config.json"
-svc_model = Svc(model_path, config_path)
-infer_tool.mkdir(["raw", "results"])
-
-# 支持多个wav文件，放在raw文件夹下
-clean_names = ["君の知らない物語-src"]
-trans = [-5] # 音高调整，支持正负（半音）
-spk_list = ['yunhao'] # 每次同时合成多语者音色
-slice_db = -40 # 默认-40，嘈杂的音频可以-30，干声保留呼吸可以-50
-wav_format = 'flac' # 音频输出格式
-
-infer_tool.fill_a_to_b(trans, clean_names)
-for clean_name, tran in zip(clean_names, trans):
- raw_audio_path = f"raw/{clean_name}"
- if "." not in raw_audio_path:
- raw_audio_path += ".wav"
- infer_tool.format_wav(raw_audio_path)
- wav_path = Path(raw_audio_path).with_suffix('.wav')
- audio, sr = librosa.load(wav_path, mono=True, sr=None)
- wav_hash = infer_tool.get_md5(audio)
- if wav_hash in chunks_dict.keys():
- print("load chunks from temp")
- chunks = chunks_dict[wav_hash]["chunks"]
- else:
- chunks = slicer.cut(wav_path, db_thresh=slice_db)
- print(chunks)
- chunks_dict[wav_hash] = {"chunks": chunks, "time": int(time.time())}
- infer_tool.write_temp("inference/chunks_temp.json", chunks_dict)
- audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
-
- for spk in spk_list:
- audio = []
- for (slice_tag, data) in audio_data:
- print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
- length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample))
- raw_path = io.BytesIO()
- soundfile.write(raw_path, data, audio_sr, format="wav")
- raw_path.seek(0)
- if slice_tag:
- print('jump empty segment')
- _audio = np.zeros(length)
- else:
- out_audio, out_sr = svc_model.infer(spk, tran, raw_path)
- _audio = out_audio.cpu().numpy()
- audio.extend(list(_audio))
-
- res_path = f'./results/{clean_name}_{tran}key_{spk}.{wav_format}'
- soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/ImageOps.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/ImageOps.py
deleted file mode 100644
index 17702778c134abcb51d7632367fbbf1a2f3048fa..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/ImageOps.py
+++ /dev/null
@@ -1,628 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# standard image operations
-#
-# History:
-# 2001-10-20 fl Created
-# 2001-10-23 fl Added autocontrast operator
-# 2001-12-18 fl Added Kevin's fit operator
-# 2004-03-14 fl Fixed potential division by zero in equalize
-# 2005-05-05 fl Fixed equalize for low number of values
-#
-# Copyright (c) 2001-2004 by Secret Labs AB
-# Copyright (c) 2001-2004 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import functools
-import operator
-import re
-
-from . import ExifTags, Image, ImagePalette
-
-#
-# helpers
-
-
-def _border(border):
- if isinstance(border, tuple):
- if len(border) == 2:
- left, top = right, bottom = border
- elif len(border) == 4:
- left, top, right, bottom = border
- else:
- left = top = right = bottom = border
- return left, top, right, bottom
-
-
-def _color(color, mode):
- if isinstance(color, str):
- from . import ImageColor
-
- color = ImageColor.getcolor(color, mode)
- return color
-
-
-def _lut(image, lut):
- if image.mode == "P":
- # FIXME: apply to lookup table, not image data
- msg = "mode P support coming soon"
- raise NotImplementedError(msg)
- elif image.mode in ("L", "RGB"):
- if image.mode == "RGB" and len(lut) == 256:
- lut = lut + lut + lut
- return image.point(lut)
- else:
- msg = "not supported for this image mode"
- raise OSError(msg)
-
-
-#
-# actions
-
-
-def autocontrast(image, cutoff=0, ignore=None, mask=None, preserve_tone=False):
- """
- Maximize (normalize) image contrast. This function calculates a
- histogram of the input image (or mask region), removes ``cutoff`` percent of the
- lightest and darkest pixels from the histogram, and remaps the image
- so that the darkest pixel becomes black (0), and the lightest
- becomes white (255).
-
- :param image: The image to process.
- :param cutoff: The percent to cut off from the histogram on the low and
- high ends. Either a tuple of (low, high), or a single
- number for both.
- :param ignore: The background pixel value (use None for no background).
- :param mask: Histogram used in contrast operation is computed using pixels
- within the mask. If no mask is given the entire image is used
- for histogram computation.
- :param preserve_tone: Preserve image tone in Photoshop-like style autocontrast.
-
- .. versionadded:: 8.2.0
-
- :return: An image.
- """
- if preserve_tone:
- histogram = image.convert("L").histogram(mask)
- else:
- histogram = image.histogram(mask)
-
- lut = []
- for layer in range(0, len(histogram), 256):
- h = histogram[layer : layer + 256]
- if ignore is not None:
- # get rid of outliers
- try:
- h[ignore] = 0
- except TypeError:
- # assume sequence
- for ix in ignore:
- h[ix] = 0
- if cutoff:
- # cut off pixels from both ends of the histogram
- if not isinstance(cutoff, tuple):
- cutoff = (cutoff, cutoff)
- # get number of pixels
- n = 0
- for ix in range(256):
- n = n + h[ix]
- # remove cutoff% pixels from the low end
- cut = n * cutoff[0] // 100
- for lo in range(256):
- if cut > h[lo]:
- cut = cut - h[lo]
- h[lo] = 0
- else:
- h[lo] -= cut
- cut = 0
- if cut <= 0:
- break
- # remove cutoff% samples from the high end
- cut = n * cutoff[1] // 100
- for hi in range(255, -1, -1):
- if cut > h[hi]:
- cut = cut - h[hi]
- h[hi] = 0
- else:
- h[hi] -= cut
- cut = 0
- if cut <= 0:
- break
- # find lowest/highest samples after preprocessing
- for lo in range(256):
- if h[lo]:
- break
- for hi in range(255, -1, -1):
- if h[hi]:
- break
- if hi <= lo:
- # don't bother
- lut.extend(list(range(256)))
- else:
- scale = 255.0 / (hi - lo)
- offset = -lo * scale
- for ix in range(256):
- ix = int(ix * scale + offset)
- if ix < 0:
- ix = 0
- elif ix > 255:
- ix = 255
- lut.append(ix)
- return _lut(image, lut)
-
-
-def colorize(image, black, white, mid=None, blackpoint=0, whitepoint=255, midpoint=127):
- """
- Colorize grayscale image.
- This function calculates a color wedge which maps all black pixels in
- the source image to the first color and all white pixels to the
- second color. If ``mid`` is specified, it uses three-color mapping.
- The ``black`` and ``white`` arguments should be RGB tuples or color names;
- optionally you can use three-color mapping by also specifying ``mid``.
- Mapping positions for any of the colors can be specified
- (e.g. ``blackpoint``), where these parameters are the integer
- value corresponding to where the corresponding color should be mapped.
- These parameters must have logical order, such that
- ``blackpoint <= midpoint <= whitepoint`` (if ``mid`` is specified).
-
- :param image: The image to colorize.
- :param black: The color to use for black input pixels.
- :param white: The color to use for white input pixels.
- :param mid: The color to use for midtone input pixels.
- :param blackpoint: an int value [0, 255] for the black mapping.
- :param whitepoint: an int value [0, 255] for the white mapping.
- :param midpoint: an int value [0, 255] for the midtone mapping.
- :return: An image.
- """
-
- # Initial asserts
- assert image.mode == "L"
- if mid is None:
- assert 0 <= blackpoint <= whitepoint <= 255
- else:
- assert 0 <= blackpoint <= midpoint <= whitepoint <= 255
-
- # Define colors from arguments
- black = _color(black, "RGB")
- white = _color(white, "RGB")
- if mid is not None:
- mid = _color(mid, "RGB")
-
- # Empty lists for the mapping
- red = []
- green = []
- blue = []
-
- # Create the low-end values
- for i in range(0, blackpoint):
- red.append(black[0])
- green.append(black[1])
- blue.append(black[2])
-
- # Create the mapping (2-color)
- if mid is None:
- range_map = range(0, whitepoint - blackpoint)
-
- for i in range_map:
- red.append(black[0] + i * (white[0] - black[0]) // len(range_map))
- green.append(black[1] + i * (white[1] - black[1]) // len(range_map))
- blue.append(black[2] + i * (white[2] - black[2]) // len(range_map))
-
- # Create the mapping (3-color)
- else:
- range_map1 = range(0, midpoint - blackpoint)
- range_map2 = range(0, whitepoint - midpoint)
-
- for i in range_map1:
- red.append(black[0] + i * (mid[0] - black[0]) // len(range_map1))
- green.append(black[1] + i * (mid[1] - black[1]) // len(range_map1))
- blue.append(black[2] + i * (mid[2] - black[2]) // len(range_map1))
- for i in range_map2:
- red.append(mid[0] + i * (white[0] - mid[0]) // len(range_map2))
- green.append(mid[1] + i * (white[1] - mid[1]) // len(range_map2))
- blue.append(mid[2] + i * (white[2] - mid[2]) // len(range_map2))
-
- # Create the high-end values
- for i in range(0, 256 - whitepoint):
- red.append(white[0])
- green.append(white[1])
- blue.append(white[2])
-
- # Return converted image
- image = image.convert("RGB")
- return _lut(image, red + green + blue)
-
-
-def contain(image, size, method=Image.Resampling.BICUBIC):
- """
- Returns a resized version of the image, set to the maximum width and height
- within the requested size, while maintaining the original aspect ratio.
-
- :param image: The image to resize and crop.
- :param size: The requested output size in pixels, given as a
- (width, height) tuple.
- :param method: Resampling method to use. Default is
- :py:attr:`~PIL.Image.Resampling.BICUBIC`.
- See :ref:`concept-filters`.
- :return: An image.
- """
-
- im_ratio = image.width / image.height
- dest_ratio = size[0] / size[1]
-
- if im_ratio != dest_ratio:
- if im_ratio > dest_ratio:
- new_height = round(image.height / image.width * size[0])
- if new_height != size[1]:
- size = (size[0], new_height)
- else:
- new_width = round(image.width / image.height * size[1])
- if new_width != size[0]:
- size = (new_width, size[1])
- return image.resize(size, resample=method)
-
-
-def pad(image, size, method=Image.Resampling.BICUBIC, color=None, centering=(0.5, 0.5)):
- """
- Returns a resized and padded version of the image, expanded to fill the
- requested aspect ratio and size.
-
- :param image: The image to resize and crop.
- :param size: The requested output size in pixels, given as a
- (width, height) tuple.
- :param method: Resampling method to use. Default is
- :py:attr:`~PIL.Image.Resampling.BICUBIC`.
- See :ref:`concept-filters`.
- :param color: The background color of the padded image.
- :param centering: Control the position of the original image within the
- padded version.
-
- (0.5, 0.5) will keep the image centered
- (0, 0) will keep the image aligned to the top left
- (1, 1) will keep the image aligned to the bottom
- right
- :return: An image.
- """
-
- resized = contain(image, size, method)
- if resized.size == size:
- out = resized
- else:
- out = Image.new(image.mode, size, color)
- if resized.palette:
- out.putpalette(resized.getpalette())
- if resized.width != size[0]:
- x = round((size[0] - resized.width) * max(0, min(centering[0], 1)))
- out.paste(resized, (x, 0))
- else:
- y = round((size[1] - resized.height) * max(0, min(centering[1], 1)))
- out.paste(resized, (0, y))
- return out
-
-
-def crop(image, border=0):
- """
- Remove border from image. The same amount of pixels are removed
- from all four sides. This function works on all image modes.
-
- .. seealso:: :py:meth:`~PIL.Image.Image.crop`
-
- :param image: The image to crop.
- :param border: The number of pixels to remove.
- :return: An image.
- """
- left, top, right, bottom = _border(border)
- return image.crop((left, top, image.size[0] - right, image.size[1] - bottom))
-
-
-def scale(image, factor, resample=Image.Resampling.BICUBIC):
- """
- Returns a rescaled image by a specific factor given in parameter.
- A factor greater than 1 expands the image, between 0 and 1 contracts the
- image.
-
- :param image: The image to rescale.
- :param factor: The expansion factor, as a float.
- :param resample: Resampling method to use. Default is
- :py:attr:`~PIL.Image.Resampling.BICUBIC`.
- See :ref:`concept-filters`.
- :returns: An :py:class:`~PIL.Image.Image` object.
- """
- if factor == 1:
- return image.copy()
- elif factor <= 0:
- msg = "the factor must be greater than 0"
- raise ValueError(msg)
- else:
- size = (round(factor * image.width), round(factor * image.height))
- return image.resize(size, resample)
-
-
-def deform(image, deformer, resample=Image.Resampling.BILINEAR):
- """
- Deform the image.
-
- :param image: The image to deform.
- :param deformer: A deformer object. Any object that implements a
- ``getmesh`` method can be used.
- :param resample: An optional resampling filter. Same values possible as
- in the PIL.Image.transform function.
- :return: An image.
- """
- return image.transform(
- image.size, Image.Transform.MESH, deformer.getmesh(image), resample
- )
-
-
-def equalize(image, mask=None):
- """
- Equalize the image histogram. This function applies a non-linear
- mapping to the input image, in order to create a uniform
- distribution of grayscale values in the output image.
-
- :param image: The image to equalize.
- :param mask: An optional mask. If given, only the pixels selected by
- the mask are included in the analysis.
- :return: An image.
- """
- if image.mode == "P":
- image = image.convert("RGB")
- h = image.histogram(mask)
- lut = []
- for b in range(0, len(h), 256):
- histo = [_f for _f in h[b : b + 256] if _f]
- if len(histo) <= 1:
- lut.extend(list(range(256)))
- else:
- step = (functools.reduce(operator.add, histo) - histo[-1]) // 255
- if not step:
- lut.extend(list(range(256)))
- else:
- n = step // 2
- for i in range(256):
- lut.append(n // step)
- n = n + h[i + b]
- return _lut(image, lut)
-
-
-def expand(image, border=0, fill=0):
- """
- Add border to the image
-
- :param image: The image to expand.
- :param border: Border width, in pixels.
- :param fill: Pixel fill value (a color value). Default is 0 (black).
- :return: An image.
- """
- left, top, right, bottom = _border(border)
- width = left + image.size[0] + right
- height = top + image.size[1] + bottom
- color = _color(fill, image.mode)
- if image.palette:
- palette = ImagePalette.ImagePalette(palette=image.getpalette())
- if isinstance(color, tuple):
- color = palette.getcolor(color)
- else:
- palette = None
- out = Image.new(image.mode, (width, height), color)
- if palette:
- out.putpalette(palette.palette)
- out.paste(image, (left, top))
- return out
-
-
-def fit(image, size, method=Image.Resampling.BICUBIC, bleed=0.0, centering=(0.5, 0.5)):
- """
- Returns a resized and cropped version of the image, cropped to the
- requested aspect ratio and size.
-
- This function was contributed by Kevin Cazabon.
-
- :param image: The image to resize and crop.
- :param size: The requested output size in pixels, given as a
- (width, height) tuple.
- :param method: Resampling method to use. Default is
- :py:attr:`~PIL.Image.Resampling.BICUBIC`.
- See :ref:`concept-filters`.
- :param bleed: Remove a border around the outside of the image from all
- four edges. The value is a decimal percentage (use 0.01 for
- one percent). The default value is 0 (no border).
- Cannot be greater than or equal to 0.5.
- :param centering: Control the cropping position. Use (0.5, 0.5) for
- center cropping (e.g. if cropping the width, take 50% off
- of the left side, and therefore 50% off the right side).
- (0.0, 0.0) will crop from the top left corner (i.e. if
- cropping the width, take all of the crop off of the right
- side, and if cropping the height, take all of it off the
- bottom). (1.0, 0.0) will crop from the bottom left
- corner, etc. (i.e. if cropping the width, take all of the
- crop off the left side, and if cropping the height take
- none from the top, and therefore all off the bottom).
- :return: An image.
- """
-
- # by Kevin Cazabon, Feb 17/2000
- # kevin@cazabon.com
- # https://www.cazabon.com
-
- # ensure centering is mutable
- centering = list(centering)
-
- if not 0.0 <= centering[0] <= 1.0:
- centering[0] = 0.5
- if not 0.0 <= centering[1] <= 1.0:
- centering[1] = 0.5
-
- if not 0.0 <= bleed < 0.5:
- bleed = 0.0
-
- # calculate the area to use for resizing and cropping, subtracting
- # the 'bleed' around the edges
-
- # number of pixels to trim off on Top and Bottom, Left and Right
- bleed_pixels = (bleed * image.size[0], bleed * image.size[1])
-
- live_size = (
- image.size[0] - bleed_pixels[0] * 2,
- image.size[1] - bleed_pixels[1] * 2,
- )
-
- # calculate the aspect ratio of the live_size
- live_size_ratio = live_size[0] / live_size[1]
-
- # calculate the aspect ratio of the output image
- output_ratio = size[0] / size[1]
-
- # figure out if the sides or top/bottom will be cropped off
- if live_size_ratio == output_ratio:
- # live_size is already the needed ratio
- crop_width = live_size[0]
- crop_height = live_size[1]
- elif live_size_ratio >= output_ratio:
- # live_size is wider than what's needed, crop the sides
- crop_width = output_ratio * live_size[1]
- crop_height = live_size[1]
- else:
- # live_size is taller than what's needed, crop the top and bottom
- crop_width = live_size[0]
- crop_height = live_size[0] / output_ratio
-
- # make the crop
- crop_left = bleed_pixels[0] + (live_size[0] - crop_width) * centering[0]
- crop_top = bleed_pixels[1] + (live_size[1] - crop_height) * centering[1]
-
- crop = (crop_left, crop_top, crop_left + crop_width, crop_top + crop_height)
-
- # resize the image and return it
- return image.resize(size, method, box=crop)
-
-
-def flip(image):
- """
- Flip the image vertically (top to bottom).
-
- :param image: The image to flip.
- :return: An image.
- """
- return image.transpose(Image.Transpose.FLIP_TOP_BOTTOM)
-
-
-def grayscale(image):
- """
- Convert the image to grayscale.
-
- :param image: The image to convert.
- :return: An image.
- """
- return image.convert("L")
-
-
-def invert(image):
- """
- Invert (negate) the image.
-
- :param image: The image to invert.
- :return: An image.
- """
- lut = []
- for i in range(256):
- lut.append(255 - i)
- return image.point(lut) if image.mode == "1" else _lut(image, lut)
-
-
-def mirror(image):
- """
- Flip image horizontally (left to right).
-
- :param image: The image to mirror.
- :return: An image.
- """
- return image.transpose(Image.Transpose.FLIP_LEFT_RIGHT)
-
-
-def posterize(image, bits):
- """
- Reduce the number of bits for each color channel.
-
- :param image: The image to posterize.
- :param bits: The number of bits to keep for each channel (1-8).
- :return: An image.
- """
- lut = []
- mask = ~(2 ** (8 - bits) - 1)
- for i in range(256):
- lut.append(i & mask)
- return _lut(image, lut)
-
-
-def solarize(image, threshold=128):
- """
- Invert all pixel values above a threshold.
-
- :param image: The image to solarize.
- :param threshold: All pixels above this greyscale level are inverted.
- :return: An image.
- """
- lut = []
- for i in range(256):
- if i < threshold:
- lut.append(i)
- else:
- lut.append(255 - i)
- return _lut(image, lut)
-
-
-def exif_transpose(image, *, in_place=False):
- """
- If an image has an EXIF Orientation tag, other than 1, transpose the image
- accordingly, and remove the orientation data.
-
- :param image: The image to transpose.
- :param in_place: Boolean. Keyword-only argument.
- If ``True``, the original image is modified in-place, and ``None`` is returned.
- If ``False`` (default), a new :py:class:`~PIL.Image.Image` object is returned
- with the transposition applied. If there is no transposition, a copy of the
- image will be returned.
- """
- image_exif = image.getexif()
- orientation = image_exif.get(ExifTags.Base.Orientation)
- method = {
- 2: Image.Transpose.FLIP_LEFT_RIGHT,
- 3: Image.Transpose.ROTATE_180,
- 4: Image.Transpose.FLIP_TOP_BOTTOM,
- 5: Image.Transpose.TRANSPOSE,
- 6: Image.Transpose.ROTATE_270,
- 7: Image.Transpose.TRANSVERSE,
- 8: Image.Transpose.ROTATE_90,
- }.get(orientation)
- if method is not None:
- transposed_image = image.transpose(method)
- if in_place:
- image.im = transposed_image.im
- image.pyaccess = None
- image._size = transposed_image._size
- exif_image = image if in_place else transposed_image
-
- exif = exif_image.getexif()
- if ExifTags.Base.Orientation in exif:
- del exif[ExifTags.Base.Orientation]
- if "exif" in exif_image.info:
- exif_image.info["exif"] = exif.tobytes()
- elif "Raw profile type exif" in exif_image.info:
- exif_image.info["Raw profile type exif"] = exif.tobytes().hex()
- elif "XML:com.adobe.xmp" in exif_image.info:
- for pattern in (
- r'tiff:Orientation="([0-9])"',
- r"([0-9])",
- ):
- exif_image.info["XML:com.adobe.xmp"] = re.sub(
- pattern, "", exif_image.info["XML:com.adobe.xmp"]
- )
- if not in_place:
- return transposed_image
- elif not in_place:
- return image.copy()
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/functorch/dim/magic_trace.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/functorch/dim/magic_trace.py
deleted file mode 100644
index 8d4e5ec31ef897bacffae4371b18b441293a21f0..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/functorch/dim/magic_trace.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-from contextlib import contextmanager
-import os
-import subprocess
-import signal
-
-@contextmanager
-def magic_trace(output='trace.fxt', magic_trace_cache='/tmp/magic-trace'):
- pid = os.getpid()
- if not os.path.exists(magic_trace_cache):
- print(f"Downloading magic_trace to: {magic_trace_cache}")
- subprocess.run(['wget', '-O', magic_trace_cache, '-q',
- 'https://github.com/janestreet/magic-trace/releases/download/v1.0.2/magic-trace'])
- subprocess.run(['chmod', '+x', magic_trace_cache])
- args = [magic_trace_cache, 'attach', '-pid', str(pid), '-o', output]
- p = subprocess.Popen(args, stderr=subprocess.PIPE, encoding='utf-8')
- while True:
- x = p.stderr.readline()
- print(x)
- if 'Attached' in x:
- break
- try:
- yield
- finally:
- p.send_signal(signal.SIGINT)
- r = p.wait()
- print(p.stderr.read())
- p.stderr.close()
- if r != 0:
- raise ValueError(f'magic_trace exited abnormally: {r}')
diff --git a/spaces/cihyFjudo/fairness-paper-search/Disk Drill Pro 2 Activation Code Mac Recover Any File Type from Any Storage Device.md b/spaces/cihyFjudo/fairness-paper-search/Disk Drill Pro 2 Activation Code Mac Recover Any File Type from Any Storage Device.md
deleted file mode 100644
index 05f7f86cf09fc343e77a229c1a81ad8304efaaa5..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Disk Drill Pro 2 Activation Code Mac Recover Any File Type from Any Storage Device.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
-
Once your order for Disk Drill PRO or Enterprise is processed, we immediately send you the activation code to enter into the free Basic edition of Disk Drill. If it's entered correctly, your copy of Disk Drill will be upgraded right away.
aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/Stalker For Mac How to Play the Legendary Survival Horror Game on Your Apple Device.md b/spaces/cihyFjudo/fairness-paper-search/Stalker For Mac How to Play the Legendary Survival Horror Game on Your Apple Device.md
deleted file mode 100644
index 00e415c7e20d76e5ae145ddd0020c6519623290e..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Stalker For Mac How to Play the Legendary Survival Horror Game on Your Apple Device.md
+++ /dev/null
@@ -1,10 +0,0 @@
-
-
When holding the PDA in the lowered position, stalkers can track their position on the map while walking around. This will make it easier to navigate towards map markers like hidden stashes or mission objectives.
-
And to interact with the PDA it can be held closer to your eyes so it will fill almost the whole screen and the text on it is more readable. If you prefer the old style 2D PDA we even added an option to switch back to it, but real stalkers would not do that.
Special characters and common stalkers offer more unique tasks, along with expanded dialogue. Stalkers can sell and buy items from traders, or even visit local mechanics to upgrade their favorite weapons. In addition: Loners, Mercs and a few others can offer escort services for a fee. Many special characters also have connections around the Zone: you can offer them a payment to bribe an enemy faction, temporarily preventing them from attacking on sight.
-
The disguise system has improved. Your equipment and behavior affects the way stalkers observe and remember you. And a new experimental feature will make stealth kills more viable to kill enemies sneakily. Your rank and reputation counts. Exceptionally high or low values respectively will attract attention, possibly even from your enemies...
-
i'd just like to thank you for the abundance of various items in this mod. my entire life i mostly just played the vanilla versions of stalker and only tried out around 5-10 mods briefly. most of them were mediocre and poorly-built. but anomaly is obviously high-quality. since everyone already knows how good anomaly generally is, in my comment i'd like to thank you for something else which i don't think most people mention. for instance when playing the vanilla version of shadow of chernobyl, it was so nice to have two versions of the ecologist suit - a regular one and an improved one. and i kept thinking to myself: WHY NOT ADD MORE VARIATIONS? and this mod is so awesome - there are like 8 different versions of the ecologist suit! each with its own unique properties! this makes me so happy. oh and also the abundance of armor types and weapons in general is so cool. and it's awesome to have ecologists also wear actual body armor protecting from bullets. there's so much variety in this game, factions are so different, character models vary greatly and npcs utilize tons and tons of armor and weapon types, this is amazingly refreshing and im so happy to see variety and versatility in this game. thank you for adding dozens of new armor suits, thank you for adding dozens of new guns and thank you for making npcs actually have different models and utilize those items! this is just awesome, i love the huge amount of various items you have in this mod, this is exactly what vanilla stalker lacks and it's like somebody heard my thoughts and implemented this. thank you! have a good day
-
See Also: 'The Thing About Heroes' Episode GuideSynopsis:With Mac in Chicago hunting the 333 caller, the rest of the team heads to the subway to investigate the death of train operator Kevin Carmichael, whose body was found slumped in the broken window of his train. The CSIs and Flack are in for a surprise when the doors slam shut and the train springs into action, taking them all for a ride save for Danny, who stepped briefly onto the platform to get his kit. Danny runs to a control box only to find an MP3 player strapped to it, controlling the train. Danny hurls a rock at it and disables it just before the train crashes into a sitting car. When the CSIs get off the train, Flack notices they're at 33rd Street--on the 3 train. Stella realizes it's the work of Mac's stalker. In Chicago, Mac searches the Tribune Building and finds a hanging decomposed body in one of the rooms, along with a hangman puzzle, with the letters of the alphabet above it--save for the letters that spell out "coward." He fills in Chicago PD Detective Brennan on the 333 caller and the clues that led him to Chicago, but she reminds him he has no jurisdiction in the Windy City. While the CSIs work the Carmichael case, Chief Sinclair sends Flack to Chicago to keep an eye on Mac. In the lab, Adam shows Danny how the MP3 player worked, and points out a site on the internet where the saboteur picked up the technical know-how to program the MP3 player to do hijack the train.In the morgue in Chicago, Brennan tells Mac the dead man was in his mid-twenties and died thirty years ago of a gunshot wound to the stomach. His body was buried and dug up. Mac calls an old friend, Jimmy and meets up with him. He recalls Bobby O'Toole, the man who beat Jimmy's brother Will to death. Mac accuses Jimmy of being the 333 stalker, but Jimmy denies it and tells Mac to stay away from him. After Jimmy leaves, Mac picks up his discarded cigarette butt and Flack arrives. Mac fills him on what happened thirty years ago: sixteen-year-old Will was making deliveries for a mobster, allowing his fourteen-year-old brother Jimmy and his friend--Mac--to tag along. But a delivery to Bobby O'Toole--who lived in apartment 333--went terribly wrong, and Jimmy and Mac witnessed Bobby beating Will to death. Jimmy pulled a gun out of Bobby's drawer but it got knocked out of his hand; Mac picked it up but was unable to shoot Bobby. Mac gets the DNA report on the cigarette from Stella: the blood is a filial match to the blood on the puzzle pieces and the DNA on the MP3 player. Mac recalls Will and Jimmy had a younger brother: Andy. Back at the lab, Stella studies the puzzles, disturbed. Suddenly, she recalls a puzzle piece she found at Drew Bedford's apartment after she brought the first puzzle to him matched not the first puzzle but the second. She puts it together: Drew is the 333 stalker. She confirms it when partial prints off the gifts he's given her match the prints on the MP3 player.Mac and Flack return to New York and, along with Stella and Danny, prepare to storm Drew's apartment. Sinclair joins them. While Mac scours the wine racks, Drew knocks him unconscious. Mac awakens in a chair surrounded by lasers. Drew tells him if he trips the lasers, a gun will fire at his head, and promises the same will happen to whoever walks through the door to save him. Drew calls him a coward, saying that he could have saved his brother if he'd fired the gun. He shows Mac a newspaper article on his heroics after the drug bust, questioning Mac's status as a "hero." The team works frantically to find Mac, consulting a playlist on the MP3 player, which leads them to a forgotten subway tunnel. Flack pulls an ace out of his sleeve: Jimmy. Jimmy calls Drew and bursts into the room. The shotgun fires into his chest and Drew runs for him. Mac sets off the laser as Drew crosses in front of the gun and it hits Drew. Mac takes him down with a shot to the arm. Jimmy is unharmed because he was wearing a bulletproof vest, and Drew's wound isn't fatal. The family has lost enough, Mac tells Flack.Analysis:After ten episodes worth of build up, "The Thing About Heroes" delivers an exciting conclusion to the 333 storyline. Mac journeys to Chicago and finally connects the 333 caller to an incident from his past, Stella puts it together that Drew Bedford is the stalker and Mac finally has his showdown with the guy. We also learn what apparently set Drew off: after Mac took down the Irish mob in "Snow Day", the newspapers picked up on the story and started referring to Mac as a "hero," an assessment Drew clearly doesn't agree with.It's a rather daring move to label the show's hero a coward, even if it's a brand the audience will ultimately dismiss. Mac's inaction is imminently forgivable because he was a child, but Drew, though deranged, doesn't come off as entirely unsympathetic, either in the eyes of the audience or Mac. Indeed, Mac makes the risky move of shooting to wound rather than kill Drew, saying that Drew and his brother have lost enough. Though Mac might disagree, the move is a heroic one. Gary Sinise acquits himself well during the episode, toning down Mac's persecution complex and showing the CSI finally putting all the pieces together and confronting his past head on.Drew Bedford may have been an obvious suspect early on--he did show up in Stella's life right around the time the 333 caller starting bothering Mac--but he doesn't disappoint in the final showdown, in large part due to Kerr Smith's impassioned performance. Smith, who up until this point has had little to do but unsuccessfully woo Stella, turns up the intensity and makes us wish we'd seen more of him. He makes Drew's anger both real and sympathetic, and even though he's misguided, it's hard not to feel sorry for the man whose life was shaped by watching his older brother get beaten to death and who has become obsessed with getting revenge on the family friend who, as a child, was unable to fire a gun at the man beating his brother.Thankfully, Stella is the one to figure out Drew is the 333 caller when she recalls a puzzle piece she found at his apartment matched a puzzle the CSIs recovered after her visit, not before. It's gratifying that Stella never let her guard down with Drew, given that he came on way too strong and given her previous bad experience with perfect-boyfriend-turned-psycho Frankie back in season two. I didn't think much of Stella going to confront Drew alone twice in the last episode, "One Wedding and a Funeral", so it's nice to see her exhibiting sharper thinking here.It's too bad that the CSIs pretty much walk into Drew's trap and hand him Mac on a platter. I know Drew needed to get a hold of Mac to advance the plot along, but really did it need to be so easy for him? Stella, Flack and Danny are all with him, but none of them see fit to stick by Mac's side. Flack, Mac's confidant throughout the entire 333 saga, follows Danny into some back room rather than sticking with Mac. Flack's protectiveness of Danny is well documented, but this may have been the one case where Flack should have worried more about Mac than Danny. Stella accompanies Mac into the wine cellar, but is (somewhat understandably) sidetracked when she comes across pictures of herself that Drew must have taken. Mac unwisely wanders into the rows of wine racks by himself and unsurprisingly falls into Drew's trap.I did like Drew's laser set up; it reminded me of the one Mac used in "Snow Day" when he captured one of the mobsters and tied him up in a chair behind lasers rigged to set off an explosive. I wonder if Drew perhaps read about Mac's laser set up in that newspaper article and decided to mimic it. It was cool, even if it was a tad elaborate. But, then, hasn't that been Drew's thing all along? His whole ploy with the calls and romancing Stella was long and involved. I'm still not sure we needed all of the build up; I can't help but wonder if the storyline would have unfolded in a more interesting way if Drew had just approached Stella and then Mac had been drawn in with the puzzles. The whole "calling at 3:33am" thing got a little out of hand, and I loved Lindsay's reaction to it when she said, "No woman would make anonymous calls at 3:33 in the morning."It was fun to see Mac revisit his old Chicago stomping grounds, and watching him go through the Tribune Building while the frustrated cop tried to halt his progress earned a laugh. I wish the Chicago detective he worked with hadn't been so downright insufferable; Brennan's every other word was essentially to remind Mac that he didn't have jurisdiction in Chicago. She was so irritatingly obnoxious that I was hoping she'd cop the attitude in front of Flack so he could put her in her place. Mac might have a commanding presence, but some situations require a sharp tongue, and Flack's got that in spades.The opening scene with the out-of-control train was quite exciting, allowing the episode to begin on a note as thrilling as the one it ends up closing with. Perennial "damsel in distress" Danny actually gets to save someone else for the second time this season (after he rescued Hawkes in "The Deep") when he steps off the train to grab his kit and ends up being the only one of the team not stuck on the runaway train. Danny stops the train by doing what he does best in stressful situations: throwing something. This time, it's not a liquid solution but a rock, which disables the MP3 player controlling the train. Just as in "The Deep," much is made of Danny's heroics: both Stella and Mac feel the need to make note of the fact that the train didn't crash "thanks to Danny." It's rather endearing to see both of Danny's superiors are still aware of his desire for affirmation, and furthers the idea that Danny is in serious need of self-esteem boosting.And so the 333 saga ends, with Drew Bedford alive and clearly unrepentant. Will Mac come to regret his act of mercy, or have we seen the last of the 333 stalker? Time will tell, but I hope the door is closed on the saga. I'm glad it wasn't stretched out past the midway point in the season; there's only so much ammunition that can be got out of creepy phone calls and elaborate cat-and-mouse games. That being said, the final chapter in the saga certainly did pack a punch.
aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/Stronghold 3 Gold Trainer 1.10 27781 Boost Your Performance and Enjoyment.md b/spaces/cihyFjudo/fairness-paper-search/Stronghold 3 Gold Trainer 1.10 27781 Boost Your Performance and Enjoyment.md
deleted file mode 100644
index 3fb729986909fc2606dbeae0db99f2c9b1942315..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Stronghold 3 Gold Trainer 1.10 27781 Boost Your Performance and Enjoyment.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/The History and Trivia of Sierra Charriba the Italian Version of the American Western Film Major Dundee.md b/spaces/cihyFjudo/fairness-paper-search/The History and Trivia of Sierra Charriba the Italian Version of the American Western Film Major Dundee.md
deleted file mode 100644
index 34a80e3d4246a8bd08a47a8ac473e2606bfdc2d6..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/The History and Trivia of Sierra Charriba the Italian Version of the American Western Film Major Dundee.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
", unsafe_allow_html=True)
-st.markdown('##')
-
-col1, col2 = st.columns(2)
-
-col1.subheader("Edit code")
-code = col1.text_area(label="", value=default_code, height=220,).strip()
-inputs = tokenizer(code, return_tensors='pt')
-token_list = [tokenizer.decode(t) for t in inputs["input_ids"][0]]
-
-with torch.no_grad():
- logits = model(input_ids=inputs["input_ids"]).logits[0]
- probs = softmax(logits, dim=-1)
-
-loss = calculate_loss(logits, inputs["input_ids"][0])
-norm_probs, sorted_token_ids = calculate_scores(probs.numpy(), inputs["input_ids"][0].numpy())
-
-if len(inputs['input_ids'])>1024:
- st.warning("Your input is longer than the maximum 1024 tokens and will be truncated.")
-st.sidebar.title("Info:")
-st.sidebar.markdown("This demo uses CodeParrot to highlight the parts of code with low probability. Since CodeParrot is an autoregressive model the tokens at the beginning tend to have a lower probability. E.g. the model can't know what you want to import because it has no access to information later in the code. However, as you can see in the example on the right it still can highlight bugs or unconventional naming.\n\nAt the bottom of the page is an example of how a better solution might look like. Try to copy paste it and press **CMD + Enter** to update the highlighting.")
-st.sidebar.title("Settings:")
-if st.sidebar.radio("Highlight mode:", ["Probability heuristics", "Scaled loss per token"]) == "Probability heuristics":
- scores = norm_probs
-else:
- scores = loss
-
-suggestion_threshold = st.sidebar.slider("Suggestion threshold", 0.0, 1.0, 0.2)
-
-col2.subheader("Highlighted code")
-col2.markdown('##')
-html_string = highlight_token_scores(token_list, scores, sep="")
-col2.markdown(html_string, unsafe_allow_html=True)
-col2.markdown('##')
-
-st.subheader("Model suggestions")
-top_k = {}
-for i in range(5):
- top_k[f"top-{i+1}"] = ["No prediction for first token"] + [repr(tokenizer.decode(idx)) for idx in sorted_token_ids[:, i]]
-df = pd.DataFrame({"tokens": [repr(t) for t in token_list], "scores": scores, **top_k})
-df.index.name = "position"
-df_filter = df.loc[df["scores"]<=suggestion_threshold]
-df_filter.reset_index(inplace=True)
-df_filter = df_filter[["tokens", "scores", "position", "top-1", "top-2", "top-3", "top-4", "top-5",]]
-df_filter = df_filter.style.apply(color_dataframe, axis=1)
-st.dataframe(df_filter)
-
-st.markdown('##')
-
-st.subheader("Possible solution")
-st.code(solution_code)
\ No newline at end of file
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/amrwbdec.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/amrwbdec.c
deleted file mode 100644
index 9d75b972fa796c35e60f6ee6d0f26c48be00027a..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/amrwbdec.c
+++ /dev/null
@@ -1,1309 +0,0 @@
-/*
- * AMR wideband decoder
- * Copyright (c) 2010 Marcelo Galvao Povoa
- *
- * 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
- * AMR wideband decoder
- */
-
-#include "config.h"
-
-#include "libavutil/channel_layout.h"
-#include "libavutil/common.h"
-#include "libavutil/lfg.h"
-
-#include "avcodec.h"
-#include "lsp.h"
-#include "celp_filters.h"
-#include "celp_math.h"
-#include "acelp_filters.h"
-#include "acelp_vectors.h"
-#include "acelp_pitch_delay.h"
-#include "codec_internal.h"
-#include "decode.h"
-
-#define AMR_USE_16BIT_TABLES
-#include "amr.h"
-
-#include "amrwbdata.h"
-#if ARCH_MIPS
-#include "mips/amrwbdec_mips.h"
-#endif /* ARCH_MIPS */
-
-typedef struct AMRWBContext {
- AMRWBFrame frame; ///< AMRWB parameters decoded from bitstream
- enum Mode fr_cur_mode; ///< mode index of current frame
- uint8_t fr_quality; ///< frame quality index (FQI)
- float isf_cur[LP_ORDER]; ///< working ISF vector from current frame
- float isf_q_past[LP_ORDER]; ///< quantized ISF vector of the previous frame
- float isf_past_final[LP_ORDER]; ///< final processed ISF vector of the previous frame
- double isp[4][LP_ORDER]; ///< ISP vectors from current frame
- double isp_sub4_past[LP_ORDER]; ///< ISP vector for the 4th subframe of the previous frame
-
- float lp_coef[4][LP_ORDER]; ///< Linear Prediction Coefficients from ISP vector
-
- uint8_t base_pitch_lag; ///< integer part of pitch lag for the next relative subframe
- uint8_t pitch_lag_int; ///< integer part of pitch lag of the previous subframe
-
- float excitation_buf[AMRWB_P_DELAY_MAX + LP_ORDER + 2 + AMRWB_SFR_SIZE]; ///< current excitation and all necessary excitation history
- float *excitation; ///< points to current excitation in excitation_buf[]
-
- float pitch_vector[AMRWB_SFR_SIZE]; ///< adaptive codebook (pitch) vector for current subframe
- float fixed_vector[AMRWB_SFR_SIZE]; ///< algebraic codebook (fixed) vector for current subframe
-
- float prediction_error[4]; ///< quantified prediction errors {20log10(^gamma_gc)} for previous four subframes
- float pitch_gain[6]; ///< quantified pitch gains for the current and previous five subframes
- float fixed_gain[2]; ///< quantified fixed gains for the current and previous subframes
-
- float tilt_coef; ///< {beta_1} related to the voicing of the previous subframe
-
- float prev_sparse_fixed_gain; ///< previous fixed gain; used by anti-sparseness to determine "onset"
- uint8_t prev_ir_filter_nr; ///< previous impulse response filter "impNr": 0 - strong, 1 - medium, 2 - none
- float prev_tr_gain; ///< previous initial gain used by noise enhancer for threshold
-
- float samples_az[LP_ORDER + AMRWB_SFR_SIZE]; ///< low-band samples and memory from synthesis at 12.8kHz
- float samples_up[UPS_MEM_SIZE + AMRWB_SFR_SIZE]; ///< low-band samples and memory processed for upsampling
- float samples_hb[LP_ORDER_16k + AMRWB_SFR_SIZE_16k]; ///< high-band samples and memory from synthesis at 16kHz
-
- float hpf_31_mem[2], hpf_400_mem[2]; ///< previous values in the high pass filters
- float demph_mem[1]; ///< previous value in the de-emphasis filter
- float bpf_6_7_mem[HB_FIR_SIZE]; ///< previous values in the high-band band pass filter
- float lpf_7_mem[HB_FIR_SIZE]; ///< previous values in the high-band low pass filter
-
- AVLFG prng; ///< random number generator for white noise excitation
- uint8_t first_frame; ///< flag active during decoding of the first frame
- ACELPFContext acelpf_ctx; ///< context for filters for ACELP-based codecs
- ACELPVContext acelpv_ctx; ///< context for vector operations for ACELP-based codecs
- CELPFContext celpf_ctx; ///< context for filters for CELP-based codecs
- CELPMContext celpm_ctx; ///< context for fixed point math operations
-
-} AMRWBContext;
-
-typedef struct AMRWBChannelsContext {
- AMRWBContext ch[2];
-} AMRWBChannelsContext;
-
-static av_cold int amrwb_decode_init(AVCodecContext *avctx)
-{
- AMRWBChannelsContext *s = avctx->priv_data;
- int i;
-
- if (avctx->ch_layout.nb_channels > 2) {
- avpriv_report_missing_feature(avctx, ">2 channel AMR");
- return AVERROR_PATCHWELCOME;
- }
-
- if (!avctx->ch_layout.nb_channels) {
- av_channel_layout_uninit(&avctx->ch_layout);
- avctx->ch_layout = (AVChannelLayout)AV_CHANNEL_LAYOUT_MONO;
- }
- if (!avctx->sample_rate)
- avctx->sample_rate = 16000;
- avctx->sample_fmt = AV_SAMPLE_FMT_FLTP;
-
- for (int ch = 0; ch < avctx->ch_layout.nb_channels; ch++) {
- AMRWBContext *ctx = &s->ch[ch];
-
- av_lfg_init(&ctx->prng, 1);
-
- ctx->excitation = &ctx->excitation_buf[AMRWB_P_DELAY_MAX + LP_ORDER + 1];
- ctx->first_frame = 1;
-
- for (i = 0; i < LP_ORDER; i++)
- ctx->isf_past_final[i] = isf_init[i] * (1.0f / (1 << 15));
-
- for (i = 0; i < 4; i++)
- ctx->prediction_error[i] = MIN_ENERGY;
-
- ff_acelp_filter_init(&ctx->acelpf_ctx);
- ff_acelp_vectors_init(&ctx->acelpv_ctx);
- ff_celp_filter_init(&ctx->celpf_ctx);
- ff_celp_math_init(&ctx->celpm_ctx);
- }
-
- return 0;
-}
-
-/**
- * Decode the frame header in the "MIME/storage" format. This format
- * is simpler and does not carry the auxiliary frame information.
- *
- * @param[in] ctx The Context
- * @param[in] buf Pointer to the input buffer
- *
- * @return The decoded header length in bytes
- */
-static int decode_mime_header(AMRWBContext *ctx, const uint8_t *buf)
-{
- /* Decode frame header (1st octet) */
- ctx->fr_cur_mode = buf[0] >> 3 & 0x0F;
- ctx->fr_quality = (buf[0] & 0x4) == 0x4;
-
- return 1;
-}
-
-/**
- * Decode quantized ISF vectors using 36-bit indexes (6K60 mode only).
- *
- * @param[in] ind Array of 5 indexes
- * @param[out] isf_q Buffer for isf_q[LP_ORDER]
- */
-static void decode_isf_indices_36b(uint16_t *ind, float *isf_q)
-{
- int i;
-
- for (i = 0; i < 9; i++)
- isf_q[i] = dico1_isf[ind[0]][i] * (1.0f / (1 << 15));
-
- for (i = 0; i < 7; i++)
- isf_q[i + 9] = dico2_isf[ind[1]][i] * (1.0f / (1 << 15));
-
- for (i = 0; i < 5; i++)
- isf_q[i] += dico21_isf_36b[ind[2]][i] * (1.0f / (1 << 15));
-
- for (i = 0; i < 4; i++)
- isf_q[i + 5] += dico22_isf_36b[ind[3]][i] * (1.0f / (1 << 15));
-
- for (i = 0; i < 7; i++)
- isf_q[i + 9] += dico23_isf_36b[ind[4]][i] * (1.0f / (1 << 15));
-}
-
-/**
- * Decode quantized ISF vectors using 46-bit indexes (except 6K60 mode).
- *
- * @param[in] ind Array of 7 indexes
- * @param[out] isf_q Buffer for isf_q[LP_ORDER]
- */
-static void decode_isf_indices_46b(uint16_t *ind, float *isf_q)
-{
- int i;
-
- for (i = 0; i < 9; i++)
- isf_q[i] = dico1_isf[ind[0]][i] * (1.0f / (1 << 15));
-
- for (i = 0; i < 7; i++)
- isf_q[i + 9] = dico2_isf[ind[1]][i] * (1.0f / (1 << 15));
-
- for (i = 0; i < 3; i++)
- isf_q[i] += dico21_isf[ind[2]][i] * (1.0f / (1 << 15));
-
- for (i = 0; i < 3; i++)
- isf_q[i + 3] += dico22_isf[ind[3]][i] * (1.0f / (1 << 15));
-
- for (i = 0; i < 3; i++)
- isf_q[i + 6] += dico23_isf[ind[4]][i] * (1.0f / (1 << 15));
-
- for (i = 0; i < 3; i++)
- isf_q[i + 9] += dico24_isf[ind[5]][i] * (1.0f / (1 << 15));
-
- for (i = 0; i < 4; i++)
- isf_q[i + 12] += dico25_isf[ind[6]][i] * (1.0f / (1 << 15));
-}
-
-/**
- * Apply mean and past ISF values using the prediction factor.
- * Updates past ISF vector.
- *
- * @param[in,out] isf_q Current quantized ISF
- * @param[in,out] isf_past Past quantized ISF
- */
-static void isf_add_mean_and_past(float *isf_q, float *isf_past)
-{
- int i;
- float tmp;
-
- for (i = 0; i < LP_ORDER; i++) {
- tmp = isf_q[i];
- isf_q[i] += isf_mean[i] * (1.0f / (1 << 15));
- isf_q[i] += PRED_FACTOR * isf_past[i];
- isf_past[i] = tmp;
- }
-}
-
-/**
- * Interpolate the fourth ISP vector from current and past frames
- * to obtain an ISP vector for each subframe.
- *
- * @param[in,out] isp_q ISPs for each subframe
- * @param[in] isp4_past Past ISP for subframe 4
- */
-static void interpolate_isp(double isp_q[4][LP_ORDER], const double *isp4_past)
-{
- int i, k;
-
- for (k = 0; k < 3; k++) {
- float c = isfp_inter[k];
- for (i = 0; i < LP_ORDER; i++)
- isp_q[k][i] = (1.0 - c) * isp4_past[i] + c * isp_q[3][i];
- }
-}
-
-/**
- * Decode an adaptive codebook index into pitch lag (except 6k60, 8k85 modes).
- * Calculate integer lag and fractional lag always using 1/4 resolution.
- * In 1st and 3rd subframes the index is relative to last subframe integer lag.
- *
- * @param[out] lag_int Decoded integer pitch lag
- * @param[out] lag_frac Decoded fractional pitch lag
- * @param[in] pitch_index Adaptive codebook pitch index
- * @param[in,out] base_lag_int Base integer lag used in relative subframes
- * @param[in] subframe Current subframe index (0 to 3)
- */
-static void decode_pitch_lag_high(int *lag_int, int *lag_frac, int pitch_index,
- uint8_t *base_lag_int, int subframe)
-{
- if (subframe == 0 || subframe == 2) {
- if (pitch_index < 376) {
- *lag_int = (pitch_index + 137) >> 2;
- *lag_frac = pitch_index - (*lag_int << 2) + 136;
- } else if (pitch_index < 440) {
- *lag_int = (pitch_index + 257 - 376) >> 1;
- *lag_frac = (pitch_index - (*lag_int << 1) + 256 - 376) * 2;
- /* the actual resolution is 1/2 but expressed as 1/4 */
- } else {
- *lag_int = pitch_index - 280;
- *lag_frac = 0;
- }
- /* minimum lag for next subframe */
- *base_lag_int = av_clip(*lag_int - 8 - (*lag_frac < 0),
- AMRWB_P_DELAY_MIN, AMRWB_P_DELAY_MAX - 15);
- // XXX: the spec states clearly that *base_lag_int should be
- // the nearest integer to *lag_int (minus 8), but the ref code
- // actually always uses its floor, I'm following the latter
- } else {
- *lag_int = (pitch_index + 1) >> 2;
- *lag_frac = pitch_index - (*lag_int << 2);
- *lag_int += *base_lag_int;
- }
-}
-
-/**
- * Decode an adaptive codebook index into pitch lag for 8k85 and 6k60 modes.
- * The description is analogous to decode_pitch_lag_high, but in 6k60 the
- * relative index is used for all subframes except the first.
- */
-static void decode_pitch_lag_low(int *lag_int, int *lag_frac, int pitch_index,
- uint8_t *base_lag_int, int subframe, enum Mode mode)
-{
- if (subframe == 0 || (subframe == 2 && mode != MODE_6k60)) {
- if (pitch_index < 116) {
- *lag_int = (pitch_index + 69) >> 1;
- *lag_frac = (pitch_index - (*lag_int << 1) + 68) * 2;
- } else {
- *lag_int = pitch_index - 24;
- *lag_frac = 0;
- }
- // XXX: same problem as before
- *base_lag_int = av_clip(*lag_int - 8 - (*lag_frac < 0),
- AMRWB_P_DELAY_MIN, AMRWB_P_DELAY_MAX - 15);
- } else {
- *lag_int = (pitch_index + 1) >> 1;
- *lag_frac = (pitch_index - (*lag_int << 1)) * 2;
- *lag_int += *base_lag_int;
- }
-}
-
-/**
- * Find the pitch vector by interpolating the past excitation at the
- * pitch delay, which is obtained in this function.
- *
- * @param[in,out] ctx The context
- * @param[in] amr_subframe Current subframe data
- * @param[in] subframe Current subframe index (0 to 3)
- */
-static void decode_pitch_vector(AMRWBContext *ctx,
- const AMRWBSubFrame *amr_subframe,
- const int subframe)
-{
- int pitch_lag_int, pitch_lag_frac;
- int i;
- float *exc = ctx->excitation;
- enum Mode mode = ctx->fr_cur_mode;
-
- if (mode <= MODE_8k85) {
- decode_pitch_lag_low(&pitch_lag_int, &pitch_lag_frac, amr_subframe->adap,
- &ctx->base_pitch_lag, subframe, mode);
- } else
- decode_pitch_lag_high(&pitch_lag_int, &pitch_lag_frac, amr_subframe->adap,
- &ctx->base_pitch_lag, subframe);
-
- ctx->pitch_lag_int = pitch_lag_int;
- pitch_lag_int += pitch_lag_frac > 0;
-
- /* Calculate the pitch vector by interpolating the past excitation at the
- pitch lag using a hamming windowed sinc function */
- ctx->acelpf_ctx.acelp_interpolatef(exc,
- exc + 1 - pitch_lag_int,
- ac_inter, 4,
- pitch_lag_frac + (pitch_lag_frac > 0 ? 0 : 4),
- LP_ORDER, AMRWB_SFR_SIZE + 1);
-
- /* Check which pitch signal path should be used
- * 6k60 and 8k85 modes have the ltp flag set to 0 */
- if (amr_subframe->ltp) {
- memcpy(ctx->pitch_vector, exc, AMRWB_SFR_SIZE * sizeof(float));
- } else {
- for (i = 0; i < AMRWB_SFR_SIZE; i++)
- ctx->pitch_vector[i] = 0.18 * exc[i - 1] + 0.64 * exc[i] +
- 0.18 * exc[i + 1];
- memcpy(exc, ctx->pitch_vector, AMRWB_SFR_SIZE * sizeof(float));
- }
-}
-
-/** Get x bits in the index interval [lsb,lsb+len-1] inclusive */
-#define BIT_STR(x,lsb,len) av_mod_uintp2((x) >> (lsb), (len))
-
-/** Get the bit at specified position */
-#define BIT_POS(x, p) (((x) >> (p)) & 1)
-
-/**
- * The next six functions decode_[i]p_track decode exactly i pulses
- * positions and amplitudes (-1 or 1) in a subframe track using
- * an encoded pulse indexing (TS 26.190 section 5.8.2).
- *
- * The results are given in out[], in which a negative number means
- * amplitude -1 and vice versa (i.e., ampl(x) = x / abs(x) ).
- *
- * @param[out] out Output buffer (writes i elements)
- * @param[in] code Pulse index (no. of bits varies, see below)
- * @param[in] m (log2) Number of potential positions
- * @param[in] off Offset for decoded positions
- */
-static inline void decode_1p_track(int *out, int code, int m, int off)
-{
- int pos = BIT_STR(code, 0, m) + off; ///code: m+1 bits
-
- out[0] = BIT_POS(code, m) ? -pos : pos;
-}
-
-static inline void decode_2p_track(int *out, int code, int m, int off) ///code: 2m+1 bits
-{
- int pos0 = BIT_STR(code, m, m) + off;
- int pos1 = BIT_STR(code, 0, m) + off;
-
- out[0] = BIT_POS(code, 2*m) ? -pos0 : pos0;
- out[1] = BIT_POS(code, 2*m) ? -pos1 : pos1;
- out[1] = pos0 > pos1 ? -out[1] : out[1];
-}
-
-static void decode_3p_track(int *out, int code, int m, int off) ///code: 3m+1 bits
-{
- int half_2p = BIT_POS(code, 2*m - 1) << (m - 1);
-
- decode_2p_track(out, BIT_STR(code, 0, 2*m - 1),
- m - 1, off + half_2p);
- decode_1p_track(out + 2, BIT_STR(code, 2*m, m + 1), m, off);
-}
-
-static void decode_4p_track(int *out, int code, int m, int off) ///code: 4m bits
-{
- int half_4p, subhalf_2p;
- int b_offset = 1 << (m - 1);
-
- switch (BIT_STR(code, 4*m - 2, 2)) { /* case ID (2 bits) */
- case 0: /* 0 pulses in A, 4 pulses in B or vice versa */
- half_4p = BIT_POS(code, 4*m - 3) << (m - 1); // which has 4 pulses
- subhalf_2p = BIT_POS(code, 2*m - 3) << (m - 2);
-
- decode_2p_track(out, BIT_STR(code, 0, 2*m - 3),
- m - 2, off + half_4p + subhalf_2p);
- decode_2p_track(out + 2, BIT_STR(code, 2*m - 2, 2*m - 1),
- m - 1, off + half_4p);
- break;
- case 1: /* 1 pulse in A, 3 pulses in B */
- decode_1p_track(out, BIT_STR(code, 3*m - 2, m),
- m - 1, off);
- decode_3p_track(out + 1, BIT_STR(code, 0, 3*m - 2),
- m - 1, off + b_offset);
- break;
- case 2: /* 2 pulses in each half */
- decode_2p_track(out, BIT_STR(code, 2*m - 1, 2*m - 1),
- m - 1, off);
- decode_2p_track(out + 2, BIT_STR(code, 0, 2*m - 1),
- m - 1, off + b_offset);
- break;
- case 3: /* 3 pulses in A, 1 pulse in B */
- decode_3p_track(out, BIT_STR(code, m, 3*m - 2),
- m - 1, off);
- decode_1p_track(out + 3, BIT_STR(code, 0, m),
- m - 1, off + b_offset);
- break;
- }
-}
-
-static void decode_5p_track(int *out, int code, int m, int off) ///code: 5m bits
-{
- int half_3p = BIT_POS(code, 5*m - 1) << (m - 1);
-
- decode_3p_track(out, BIT_STR(code, 2*m + 1, 3*m - 2),
- m - 1, off + half_3p);
-
- decode_2p_track(out + 3, BIT_STR(code, 0, 2*m + 1), m, off);
-}
-
-static void decode_6p_track(int *out, int code, int m, int off) ///code: 6m-2 bits
-{
- int b_offset = 1 << (m - 1);
- /* which half has more pulses in cases 0 to 2 */
- int half_more = BIT_POS(code, 6*m - 5) << (m - 1);
- int half_other = b_offset - half_more;
-
- switch (BIT_STR(code, 6*m - 4, 2)) { /* case ID (2 bits) */
- case 0: /* 0 pulses in A, 6 pulses in B or vice versa */
- decode_1p_track(out, BIT_STR(code, 0, m),
- m - 1, off + half_more);
- decode_5p_track(out + 1, BIT_STR(code, m, 5*m - 5),
- m - 1, off + half_more);
- break;
- case 1: /* 1 pulse in A, 5 pulses in B or vice versa */
- decode_1p_track(out, BIT_STR(code, 0, m),
- m - 1, off + half_other);
- decode_5p_track(out + 1, BIT_STR(code, m, 5*m - 5),
- m - 1, off + half_more);
- break;
- case 2: /* 2 pulses in A, 4 pulses in B or vice versa */
- decode_2p_track(out, BIT_STR(code, 0, 2*m - 1),
- m - 1, off + half_other);
- decode_4p_track(out + 2, BIT_STR(code, 2*m - 1, 4*m - 4),
- m - 1, off + half_more);
- break;
- case 3: /* 3 pulses in A, 3 pulses in B */
- decode_3p_track(out, BIT_STR(code, 3*m - 2, 3*m - 2),
- m - 1, off);
- decode_3p_track(out + 3, BIT_STR(code, 0, 3*m - 2),
- m - 1, off + b_offset);
- break;
- }
-}
-
-/**
- * Decode the algebraic codebook index to pulse positions and signs,
- * then construct the algebraic codebook vector.
- *
- * @param[out] fixed_vector Buffer for the fixed codebook excitation
- * @param[in] pulse_hi MSBs part of the pulse index array (higher modes only)
- * @param[in] pulse_lo LSBs part of the pulse index array
- * @param[in] mode Mode of the current frame
- */
-static void decode_fixed_vector(float *fixed_vector, const uint16_t *pulse_hi,
- const uint16_t *pulse_lo, const enum Mode mode)
-{
- /* sig_pos stores for each track the decoded pulse position indexes
- * (1-based) multiplied by its corresponding amplitude (+1 or -1) */
- int sig_pos[4][6];
- int spacing = (mode == MODE_6k60) ? 2 : 4;
- int i, j;
-
- switch (mode) {
- case MODE_6k60:
- for (i = 0; i < 2; i++)
- decode_1p_track(sig_pos[i], pulse_lo[i], 5, 1);
- break;
- case MODE_8k85:
- for (i = 0; i < 4; i++)
- decode_1p_track(sig_pos[i], pulse_lo[i], 4, 1);
- break;
- case MODE_12k65:
- for (i = 0; i < 4; i++)
- decode_2p_track(sig_pos[i], pulse_lo[i], 4, 1);
- break;
- case MODE_14k25:
- for (i = 0; i < 2; i++)
- decode_3p_track(sig_pos[i], pulse_lo[i], 4, 1);
- for (i = 2; i < 4; i++)
- decode_2p_track(sig_pos[i], pulse_lo[i], 4, 1);
- break;
- case MODE_15k85:
- for (i = 0; i < 4; i++)
- decode_3p_track(sig_pos[i], pulse_lo[i], 4, 1);
- break;
- case MODE_18k25:
- for (i = 0; i < 4; i++)
- decode_4p_track(sig_pos[i], (int) pulse_lo[i] +
- ((int) pulse_hi[i] << 14), 4, 1);
- break;
- case MODE_19k85:
- for (i = 0; i < 2; i++)
- decode_5p_track(sig_pos[i], (int) pulse_lo[i] +
- ((int) pulse_hi[i] << 10), 4, 1);
- for (i = 2; i < 4; i++)
- decode_4p_track(sig_pos[i], (int) pulse_lo[i] +
- ((int) pulse_hi[i] << 14), 4, 1);
- break;
- case MODE_23k05:
- case MODE_23k85:
- for (i = 0; i < 4; i++)
- decode_6p_track(sig_pos[i], (int) pulse_lo[i] +
- ((int) pulse_hi[i] << 11), 4, 1);
- break;
- }
-
- memset(fixed_vector, 0, sizeof(float) * AMRWB_SFR_SIZE);
-
- for (i = 0; i < 4; i++)
- for (j = 0; j < pulses_nb_per_mode_tr[mode][i]; j++) {
- int pos = (FFABS(sig_pos[i][j]) - 1) * spacing + i;
-
- fixed_vector[pos] += sig_pos[i][j] < 0 ? -1.0 : 1.0;
- }
-}
-
-/**
- * Decode pitch gain and fixed gain correction factor.
- *
- * @param[in] vq_gain Vector-quantized index for gains
- * @param[in] mode Mode of the current frame
- * @param[out] fixed_gain_factor Decoded fixed gain correction factor
- * @param[out] pitch_gain Decoded pitch gain
- */
-static void decode_gains(const uint8_t vq_gain, const enum Mode mode,
- float *fixed_gain_factor, float *pitch_gain)
-{
- const int16_t *gains = (mode <= MODE_8k85 ? qua_gain_6b[vq_gain] :
- qua_gain_7b[vq_gain]);
-
- *pitch_gain = gains[0] * (1.0f / (1 << 14));
- *fixed_gain_factor = gains[1] * (1.0f / (1 << 11));
-}
-
-/**
- * Apply pitch sharpening filters to the fixed codebook vector.
- *
- * @param[in] ctx The context
- * @param[in,out] fixed_vector Fixed codebook excitation
- */
-// XXX: Spec states this procedure should be applied when the pitch
-// lag is less than 64, but this checking seems absent in reference and AMR-NB
-static void pitch_sharpening(AMRWBContext *ctx, float *fixed_vector)
-{
- int i;
-
- /* Tilt part */
- for (i = AMRWB_SFR_SIZE - 1; i != 0; i--)
- fixed_vector[i] -= fixed_vector[i - 1] * ctx->tilt_coef;
-
- /* Periodicity enhancement part */
- for (i = ctx->pitch_lag_int; i < AMRWB_SFR_SIZE; i++)
- fixed_vector[i] += fixed_vector[i - ctx->pitch_lag_int] * 0.85;
-}
-
-/**
- * Calculate the voicing factor (-1.0 = unvoiced to 1.0 = voiced).
- *
- * @param[in] p_vector, f_vector Pitch and fixed excitation vectors
- * @param[in] p_gain, f_gain Pitch and fixed gains
- * @param[in] ctx The context
- */
-// XXX: There is something wrong with the precision here! The magnitudes
-// of the energies are not correct. Please check the reference code carefully
-static float voice_factor(float *p_vector, float p_gain,
- float *f_vector, float f_gain,
- CELPMContext *ctx)
-{
- double p_ener = (double) ctx->dot_productf(p_vector, p_vector,
- AMRWB_SFR_SIZE) *
- p_gain * p_gain;
- double f_ener = (double) ctx->dot_productf(f_vector, f_vector,
- AMRWB_SFR_SIZE) *
- f_gain * f_gain;
-
- return (p_ener - f_ener) / (p_ener + f_ener + 0.01);
-}
-
-/**
- * Reduce fixed vector sparseness by smoothing with one of three IR filters,
- * also known as "adaptive phase dispersion".
- *
- * @param[in] ctx The context
- * @param[in,out] fixed_vector Unfiltered fixed vector
- * @param[out] buf Space for modified vector if necessary
- *
- * @return The potentially overwritten filtered fixed vector address
- */
-static float *anti_sparseness(AMRWBContext *ctx,
- float *fixed_vector, float *buf)
-{
- int ir_filter_nr;
-
- if (ctx->fr_cur_mode > MODE_8k85) // no filtering in higher modes
- return fixed_vector;
-
- if (ctx->pitch_gain[0] < 0.6) {
- ir_filter_nr = 0; // strong filtering
- } else if (ctx->pitch_gain[0] < 0.9) {
- ir_filter_nr = 1; // medium filtering
- } else
- ir_filter_nr = 2; // no filtering
-
- /* detect 'onset' */
- if (ctx->fixed_gain[0] > 3.0 * ctx->fixed_gain[1]) {
- if (ir_filter_nr < 2)
- ir_filter_nr++;
- } else {
- int i, count = 0;
-
- for (i = 0; i < 6; i++)
- if (ctx->pitch_gain[i] < 0.6)
- count++;
-
- if (count > 2)
- ir_filter_nr = 0;
-
- if (ir_filter_nr > ctx->prev_ir_filter_nr + 1)
- ir_filter_nr--;
- }
-
- /* update ir filter strength history */
- ctx->prev_ir_filter_nr = ir_filter_nr;
-
- ir_filter_nr += (ctx->fr_cur_mode == MODE_8k85);
-
- if (ir_filter_nr < 2) {
- int i;
- const float *coef = ir_filters_lookup[ir_filter_nr];
-
- /* Circular convolution code in the reference
- * decoder was modified to avoid using one
- * extra array. The filtered vector is given by:
- *
- * c2(n) = sum(i,0,len-1){ c(i) * coef( (n - i + len) % len ) }
- */
-
- memset(buf, 0, sizeof(float) * AMRWB_SFR_SIZE);
- for (i = 0; i < AMRWB_SFR_SIZE; i++)
- if (fixed_vector[i])
- ff_celp_circ_addf(buf, buf, coef, i, fixed_vector[i],
- AMRWB_SFR_SIZE);
- fixed_vector = buf;
- }
-
- return fixed_vector;
-}
-
-/**
- * Calculate a stability factor {teta} based on distance between
- * current and past isf. A value of 1 shows maximum signal stability.
- */
-static float stability_factor(const float *isf, const float *isf_past)
-{
- int i;
- float acc = 0.0;
-
- for (i = 0; i < LP_ORDER - 1; i++)
- acc += (isf[i] - isf_past[i]) * (isf[i] - isf_past[i]);
-
- // XXX: This part is not so clear from the reference code
- // the result is more accurate changing the "/ 256" to "* 512"
- return FFMAX(0.0, 1.25 - acc * 0.8 * 512);
-}
-
-/**
- * Apply a non-linear fixed gain smoothing in order to reduce
- * fluctuation in the energy of excitation.
- *
- * @param[in] fixed_gain Unsmoothed fixed gain
- * @param[in,out] prev_tr_gain Previous threshold gain (updated)
- * @param[in] voice_fac Frame voicing factor
- * @param[in] stab_fac Frame stability factor
- *
- * @return The smoothed gain
- */
-static float noise_enhancer(float fixed_gain, float *prev_tr_gain,
- float voice_fac, float stab_fac)
-{
- float sm_fac = 0.5 * (1 - voice_fac) * stab_fac;
- float g0;
-
- // XXX: the following fixed-point constants used to in(de)crement
- // gain by 1.5dB were taken from the reference code, maybe it could
- // be simpler
- if (fixed_gain < *prev_tr_gain) {
- g0 = FFMIN(*prev_tr_gain, fixed_gain + fixed_gain *
- (6226 * (1.0f / (1 << 15)))); // +1.5 dB
- } else
- g0 = FFMAX(*prev_tr_gain, fixed_gain *
- (27536 * (1.0f / (1 << 15)))); // -1.5 dB
-
- *prev_tr_gain = g0; // update next frame threshold
-
- return sm_fac * g0 + (1 - sm_fac) * fixed_gain;
-}
-
-/**
- * Filter the fixed_vector to emphasize the higher frequencies.
- *
- * @param[in,out] fixed_vector Fixed codebook vector
- * @param[in] voice_fac Frame voicing factor
- */
-static void pitch_enhancer(float *fixed_vector, float voice_fac)
-{
- int i;
- float cpe = 0.125 * (1 + voice_fac);
- float last = fixed_vector[0]; // holds c(i - 1)
-
- fixed_vector[0] -= cpe * fixed_vector[1];
-
- for (i = 1; i < AMRWB_SFR_SIZE - 1; i++) {
- float cur = fixed_vector[i];
-
- fixed_vector[i] -= cpe * (last + fixed_vector[i + 1]);
- last = cur;
- }
-
- fixed_vector[AMRWB_SFR_SIZE - 1] -= cpe * last;
-}
-
-/**
- * Conduct 16th order linear predictive coding synthesis from excitation.
- *
- * @param[in] ctx Pointer to the AMRWBContext
- * @param[in] lpc Pointer to the LPC coefficients
- * @param[out] excitation Buffer for synthesis final excitation
- * @param[in] fixed_gain Fixed codebook gain for synthesis
- * @param[in] fixed_vector Algebraic codebook vector
- * @param[in,out] samples Pointer to the output samples and memory
- */
-static void synthesis(AMRWBContext *ctx, float *lpc, float *excitation,
- float fixed_gain, const float *fixed_vector,
- float *samples)
-{
- ctx->acelpv_ctx.weighted_vector_sumf(excitation, ctx->pitch_vector, fixed_vector,
- ctx->pitch_gain[0], fixed_gain, AMRWB_SFR_SIZE);
-
- /* emphasize pitch vector contribution in low bitrate modes */
- if (ctx->pitch_gain[0] > 0.5 && ctx->fr_cur_mode <= MODE_8k85) {
- int i;
- float energy = ctx->celpm_ctx.dot_productf(excitation, excitation,
- AMRWB_SFR_SIZE);
-
- // XXX: Weird part in both ref code and spec. A unknown parameter
- // {beta} seems to be identical to the current pitch gain
- float pitch_factor = 0.25 * ctx->pitch_gain[0] * ctx->pitch_gain[0];
-
- for (i = 0; i < AMRWB_SFR_SIZE; i++)
- excitation[i] += pitch_factor * ctx->pitch_vector[i];
-
- ff_scale_vector_to_given_sum_of_squares(excitation, excitation,
- energy, AMRWB_SFR_SIZE);
- }
-
- ctx->celpf_ctx.celp_lp_synthesis_filterf(samples, lpc, excitation,
- AMRWB_SFR_SIZE, LP_ORDER);
-}
-
-/**
- * Apply to synthesis a de-emphasis filter of the form:
- * H(z) = 1 / (1 - m * z^-1)
- *
- * @param[out] out Output buffer
- * @param[in] in Input samples array with in[-1]
- * @param[in] m Filter coefficient
- * @param[in,out] mem State from last filtering
- */
-static void de_emphasis(float *out, float *in, float m, float mem[1])
-{
- int i;
-
- out[0] = in[0] + m * mem[0];
-
- for (i = 1; i < AMRWB_SFR_SIZE; i++)
- out[i] = in[i] + out[i - 1] * m;
-
- mem[0] = out[AMRWB_SFR_SIZE - 1];
-}
-
-/**
- * Upsample a signal by 5/4 ratio (from 12.8kHz to 16kHz) using
- * a FIR interpolation filter. Uses past data from before *in address.
- *
- * @param[out] out Buffer for interpolated signal
- * @param[in] in Current signal data (length 0.8*o_size)
- * @param[in] o_size Output signal length
- * @param[in] ctx The context
- */
-static void upsample_5_4(float *out, const float *in, int o_size, CELPMContext *ctx)
-{
- const float *in0 = in - UPS_FIR_SIZE + 1;
- int i, j, k;
- int int_part = 0, frac_part;
-
- i = 0;
- for (j = 0; j < o_size / 5; j++) {
- out[i] = in[int_part];
- frac_part = 4;
- i++;
-
- for (k = 1; k < 5; k++) {
- out[i] = ctx->dot_productf(in0 + int_part,
- upsample_fir[4 - frac_part],
- UPS_MEM_SIZE);
- int_part++;
- frac_part--;
- i++;
- }
- }
-}
-
-/**
- * Calculate the high-band gain based on encoded index (23k85 mode) or
- * on the low-band speech signal and the Voice Activity Detection flag.
- *
- * @param[in] ctx The context
- * @param[in] synth LB speech synthesis at 12.8k
- * @param[in] hb_idx Gain index for mode 23k85 only
- * @param[in] vad VAD flag for the frame
- */
-static float find_hb_gain(AMRWBContext *ctx, const float *synth,
- uint16_t hb_idx, uint8_t vad)
-{
- int wsp = (vad > 0);
- float tilt;
- float tmp;
-
- if (ctx->fr_cur_mode == MODE_23k85)
- return qua_hb_gain[hb_idx] * (1.0f / (1 << 14));
-
- tmp = ctx->celpm_ctx.dot_productf(synth, synth + 1, AMRWB_SFR_SIZE - 1);
-
- if (tmp > 0) {
- tilt = tmp / ctx->celpm_ctx.dot_productf(synth, synth, AMRWB_SFR_SIZE);
- } else
- tilt = 0;
-
- /* return gain bounded by [0.1, 1.0] */
- return av_clipf((1.0 - tilt) * (1.25 - 0.25 * wsp), 0.1, 1.0);
-}
-
-/**
- * Generate the high-band excitation with the same energy from the lower
- * one and scaled by the given gain.
- *
- * @param[in] ctx The context
- * @param[out] hb_exc Buffer for the excitation
- * @param[in] synth_exc Low-band excitation used for synthesis
- * @param[in] hb_gain Wanted excitation gain
- */
-static void scaled_hb_excitation(AMRWBContext *ctx, float *hb_exc,
- const float *synth_exc, float hb_gain)
-{
- int i;
- float energy = ctx->celpm_ctx.dot_productf(synth_exc, synth_exc,
- AMRWB_SFR_SIZE);
-
- /* Generate a white-noise excitation */
- for (i = 0; i < AMRWB_SFR_SIZE_16k; i++)
- hb_exc[i] = 32768.0 - (uint16_t) av_lfg_get(&ctx->prng);
-
- ff_scale_vector_to_given_sum_of_squares(hb_exc, hb_exc,
- energy * hb_gain * hb_gain,
- AMRWB_SFR_SIZE_16k);
-}
-
-/**
- * Calculate the auto-correlation for the ISF difference vector.
- */
-static float auto_correlation(float *diff_isf, float mean, int lag)
-{
- int i;
- float sum = 0.0;
-
- for (i = 7; i < LP_ORDER - 2; i++) {
- float prod = (diff_isf[i] - mean) * (diff_isf[i - lag] - mean);
- sum += prod * prod;
- }
- return sum;
-}
-
-/**
- * Extrapolate a ISF vector to the 16kHz range (20th order LP)
- * used at mode 6k60 LP filter for the high frequency band.
- *
- * @param[out] isf Buffer for extrapolated isf; contains LP_ORDER
- * values on input
- */
-static void extrapolate_isf(float isf[LP_ORDER_16k])
-{
- float diff_isf[LP_ORDER - 2], diff_mean;
- float corr_lag[3];
- float est, scale;
- int i, j, i_max_corr;
-
- isf[LP_ORDER_16k - 1] = isf[LP_ORDER - 1];
-
- /* Calculate the difference vector */
- for (i = 0; i < LP_ORDER - 2; i++)
- diff_isf[i] = isf[i + 1] - isf[i];
-
- diff_mean = 0.0;
- for (i = 2; i < LP_ORDER - 2; i++)
- diff_mean += diff_isf[i] * (1.0f / (LP_ORDER - 4));
-
- /* Find which is the maximum autocorrelation */
- i_max_corr = 0;
- for (i = 0; i < 3; i++) {
- corr_lag[i] = auto_correlation(diff_isf, diff_mean, i + 2);
-
- if (corr_lag[i] > corr_lag[i_max_corr])
- i_max_corr = i;
- }
- i_max_corr++;
-
- for (i = LP_ORDER - 1; i < LP_ORDER_16k - 1; i++)
- isf[i] = isf[i - 1] + isf[i - 1 - i_max_corr]
- - isf[i - 2 - i_max_corr];
-
- /* Calculate an estimate for ISF(18) and scale ISF based on the error */
- est = 7965 + (isf[2] - isf[3] - isf[4]) / 6.0;
- scale = 0.5 * (FFMIN(est, 7600) - isf[LP_ORDER - 2]) /
- (isf[LP_ORDER_16k - 2] - isf[LP_ORDER - 2]);
-
- for (i = LP_ORDER - 1, j = 0; i < LP_ORDER_16k - 1; i++, j++)
- diff_isf[j] = scale * (isf[i] - isf[i - 1]);
-
- /* Stability insurance */
- for (i = 1; i < LP_ORDER_16k - LP_ORDER; i++)
- if (diff_isf[i] + diff_isf[i - 1] < 5.0) {
- if (diff_isf[i] > diff_isf[i - 1]) {
- diff_isf[i - 1] = 5.0 - diff_isf[i];
- } else
- diff_isf[i] = 5.0 - diff_isf[i - 1];
- }
-
- for (i = LP_ORDER - 1, j = 0; i < LP_ORDER_16k - 1; i++, j++)
- isf[i] = isf[i - 1] + diff_isf[j] * (1.0f / (1 << 15));
-
- /* Scale the ISF vector for 16000 Hz */
- for (i = 0; i < LP_ORDER_16k - 1; i++)
- isf[i] *= 0.8;
-}
-
-/**
- * Spectral expand the LP coefficients using the equation:
- * y[i] = x[i] * (gamma ** i)
- *
- * @param[out] out Output buffer (may use input array)
- * @param[in] lpc LP coefficients array
- * @param[in] gamma Weighting factor
- * @param[in] size LP array size
- */
-static void lpc_weighting(float *out, const float *lpc, float gamma, int size)
-{
- int i;
- float fac = gamma;
-
- for (i = 0; i < size; i++) {
- out[i] = lpc[i] * fac;
- fac *= gamma;
- }
-}
-
-/**
- * Conduct 20th order linear predictive coding synthesis for the high
- * frequency band excitation at 16kHz.
- *
- * @param[in] ctx The context
- * @param[in] subframe Current subframe index (0 to 3)
- * @param[in,out] samples Pointer to the output speech samples
- * @param[in] exc Generated white-noise scaled excitation
- * @param[in] isf Current frame isf vector
- * @param[in] isf_past Past frame final isf vector
- */
-static void hb_synthesis(AMRWBContext *ctx, int subframe, float *samples,
- const float *exc, const float *isf, const float *isf_past)
-{
- float hb_lpc[LP_ORDER_16k];
- enum Mode mode = ctx->fr_cur_mode;
-
- if (mode == MODE_6k60) {
- float e_isf[LP_ORDER_16k]; // ISF vector for extrapolation
- double e_isp[LP_ORDER_16k];
-
- ctx->acelpv_ctx.weighted_vector_sumf(e_isf, isf_past, isf, isfp_inter[subframe],
- 1.0 - isfp_inter[subframe], LP_ORDER);
-
- extrapolate_isf(e_isf);
-
- e_isf[LP_ORDER_16k - 1] *= 2.0;
- ff_acelp_lsf2lspd(e_isp, e_isf, LP_ORDER_16k);
- ff_amrwb_lsp2lpc(e_isp, hb_lpc, LP_ORDER_16k);
-
- lpc_weighting(hb_lpc, hb_lpc, 0.9, LP_ORDER_16k);
- } else {
- lpc_weighting(hb_lpc, ctx->lp_coef[subframe], 0.6, LP_ORDER);
- }
-
- ctx->celpf_ctx.celp_lp_synthesis_filterf(samples, hb_lpc, exc, AMRWB_SFR_SIZE_16k,
- (mode == MODE_6k60) ? LP_ORDER_16k : LP_ORDER);
-}
-
-/**
- * Apply a 15th order filter to high-band samples.
- * The filter characteristic depends on the given coefficients.
- *
- * @param[out] out Buffer for filtered output
- * @param[in] fir_coef Filter coefficients
- * @param[in,out] mem State from last filtering (updated)
- * @param[in] in Input speech data (high-band)
- *
- * @remark It is safe to pass the same array in in and out parameters
- */
-
-#ifndef hb_fir_filter
-static void hb_fir_filter(float *out, const float fir_coef[HB_FIR_SIZE + 1],
- float mem[HB_FIR_SIZE], const float *in)
-{
- int i, j;
- float data[AMRWB_SFR_SIZE_16k + HB_FIR_SIZE]; // past and current samples
-
- memcpy(data, mem, HB_FIR_SIZE * sizeof(float));
- memcpy(data + HB_FIR_SIZE, in, AMRWB_SFR_SIZE_16k * sizeof(float));
-
- for (i = 0; i < AMRWB_SFR_SIZE_16k; i++) {
- out[i] = 0.0;
- for (j = 0; j <= HB_FIR_SIZE; j++)
- out[i] += data[i + j] * fir_coef[j];
- }
-
- memcpy(mem, data + AMRWB_SFR_SIZE_16k, HB_FIR_SIZE * sizeof(float));
-}
-#endif /* hb_fir_filter */
-
-/**
- * Update context state before the next subframe.
- */
-static void update_sub_state(AMRWBContext *ctx)
-{
- memmove(&ctx->excitation_buf[0], &ctx->excitation_buf[AMRWB_SFR_SIZE],
- (AMRWB_P_DELAY_MAX + LP_ORDER + 1) * sizeof(float));
-
- memmove(&ctx->pitch_gain[1], &ctx->pitch_gain[0], 5 * sizeof(float));
- memmove(&ctx->fixed_gain[1], &ctx->fixed_gain[0], 1 * sizeof(float));
-
- memmove(&ctx->samples_az[0], &ctx->samples_az[AMRWB_SFR_SIZE],
- LP_ORDER * sizeof(float));
- memmove(&ctx->samples_up[0], &ctx->samples_up[AMRWB_SFR_SIZE],
- UPS_MEM_SIZE * sizeof(float));
- memmove(&ctx->samples_hb[0], &ctx->samples_hb[AMRWB_SFR_SIZE_16k],
- LP_ORDER_16k * sizeof(float));
-}
-
-static int amrwb_decode_frame(AVCodecContext *avctx, AVFrame *frame,
- int *got_frame_ptr, AVPacket *avpkt)
-{
- AMRWBChannelsContext *s = avctx->priv_data;
- const uint8_t *buf = avpkt->data;
- int buf_size = avpkt->size;
- int sub, i, ret;
-
- /* get output buffer */
- frame->nb_samples = 4 * AMRWB_SFR_SIZE_16k;
- if ((ret = ff_get_buffer(avctx, frame, 0)) < 0)
- return ret;
-
- for (int ch = 0; ch < avctx->ch_layout.nb_channels; ch++) {
- AMRWBContext *ctx = &s->ch[ch];
- AMRWBFrame *cf = &ctx->frame;
- int expected_fr_size, header_size;
- float spare_vector[AMRWB_SFR_SIZE]; // extra stack space to hold result from anti-sparseness processing
- float fixed_gain_factor; // fixed gain correction factor (gamma)
- float *synth_fixed_vector; // pointer to the fixed vector that synthesis should use
- float synth_fixed_gain; // the fixed gain that synthesis should use
- float voice_fac, stab_fac; // parameters used for gain smoothing
- float synth_exc[AMRWB_SFR_SIZE]; // post-processed excitation for synthesis
- float hb_exc[AMRWB_SFR_SIZE_16k]; // excitation for the high frequency band
- float hb_samples[AMRWB_SFR_SIZE_16k]; // filtered high-band samples from synthesis
- float hb_gain;
- float *buf_out = (float *)frame->extended_data[ch];
-
- header_size = decode_mime_header(ctx, buf);
- expected_fr_size = ((cf_sizes_wb[ctx->fr_cur_mode] + 7) >> 3) + 1;
-
- if (!ctx->fr_quality)
- av_log(avctx, AV_LOG_ERROR, "Encountered a bad or corrupted frame\n");
-
- if (ctx->fr_cur_mode == NO_DATA || !ctx->fr_quality) {
- /* The specification suggests a "random signal" and
- "a muting technique" to "gradually decrease the output level". */
- av_samples_set_silence(&frame->extended_data[ch], 0, frame->nb_samples, 1, AV_SAMPLE_FMT_FLT);
- buf += expected_fr_size;
- buf_size -= expected_fr_size;
- continue;
- }
- if (ctx->fr_cur_mode > MODE_SID) {
- av_log(avctx, AV_LOG_ERROR,
- "Invalid mode %d\n", ctx->fr_cur_mode);
- return AVERROR_INVALIDDATA;
- }
-
- if (buf_size < expected_fr_size) {
- av_log(avctx, AV_LOG_ERROR,
- "Frame too small (%d bytes). Truncated file?\n", buf_size);
- *got_frame_ptr = 0;
- return AVERROR_INVALIDDATA;
- }
-
- if (ctx->fr_cur_mode == MODE_SID) { /* Comfort noise frame */
- avpriv_request_sample(avctx, "SID mode");
- return AVERROR_PATCHWELCOME;
- }
-
- ff_amr_bit_reorder((uint16_t *) &ctx->frame, sizeof(AMRWBFrame),
- buf + header_size, amr_bit_orderings_by_mode[ctx->fr_cur_mode]);
-
- /* Decode the quantized ISF vector */
- if (ctx->fr_cur_mode == MODE_6k60) {
- decode_isf_indices_36b(cf->isp_id, ctx->isf_cur);
- } else {
- decode_isf_indices_46b(cf->isp_id, ctx->isf_cur);
- }
-
- isf_add_mean_and_past(ctx->isf_cur, ctx->isf_q_past);
- ff_set_min_dist_lsf(ctx->isf_cur, MIN_ISF_SPACING, LP_ORDER - 1);
-
- stab_fac = stability_factor(ctx->isf_cur, ctx->isf_past_final);
-
- ctx->isf_cur[LP_ORDER - 1] *= 2.0;
- ff_acelp_lsf2lspd(ctx->isp[3], ctx->isf_cur, LP_ORDER);
-
- /* Generate a ISP vector for each subframe */
- if (ctx->first_frame) {
- ctx->first_frame = 0;
- memcpy(ctx->isp_sub4_past, ctx->isp[3], LP_ORDER * sizeof(double));
- }
- interpolate_isp(ctx->isp, ctx->isp_sub4_past);
-
- for (sub = 0; sub < 4; sub++)
- ff_amrwb_lsp2lpc(ctx->isp[sub], ctx->lp_coef[sub], LP_ORDER);
-
- for (sub = 0; sub < 4; sub++) {
- const AMRWBSubFrame *cur_subframe = &cf->subframe[sub];
- float *sub_buf = buf_out + sub * AMRWB_SFR_SIZE_16k;
-
- /* Decode adaptive codebook (pitch vector) */
- decode_pitch_vector(ctx, cur_subframe, sub);
- /* Decode innovative codebook (fixed vector) */
- decode_fixed_vector(ctx->fixed_vector, cur_subframe->pul_ih,
- cur_subframe->pul_il, ctx->fr_cur_mode);
-
- pitch_sharpening(ctx, ctx->fixed_vector);
-
- decode_gains(cur_subframe->vq_gain, ctx->fr_cur_mode,
- &fixed_gain_factor, &ctx->pitch_gain[0]);
-
- ctx->fixed_gain[0] =
- ff_amr_set_fixed_gain(fixed_gain_factor,
- ctx->celpm_ctx.dot_productf(ctx->fixed_vector,
- ctx->fixed_vector,
- AMRWB_SFR_SIZE) /
- AMRWB_SFR_SIZE,
- ctx->prediction_error,
- ENERGY_MEAN, energy_pred_fac);
-
- /* Calculate voice factor and store tilt for next subframe */
- voice_fac = voice_factor(ctx->pitch_vector, ctx->pitch_gain[0],
- ctx->fixed_vector, ctx->fixed_gain[0],
- &ctx->celpm_ctx);
- ctx->tilt_coef = voice_fac * 0.25 + 0.25;
-
- /* Construct current excitation */
- for (i = 0; i < AMRWB_SFR_SIZE; i++) {
- ctx->excitation[i] *= ctx->pitch_gain[0];
- ctx->excitation[i] += ctx->fixed_gain[0] * ctx->fixed_vector[i];
- ctx->excitation[i] = truncf(ctx->excitation[i]);
- }
-
- /* Post-processing of excitation elements */
- synth_fixed_gain = noise_enhancer(ctx->fixed_gain[0], &ctx->prev_tr_gain,
- voice_fac, stab_fac);
-
- synth_fixed_vector = anti_sparseness(ctx, ctx->fixed_vector,
- spare_vector);
-
- pitch_enhancer(synth_fixed_vector, voice_fac);
-
- synthesis(ctx, ctx->lp_coef[sub], synth_exc, synth_fixed_gain,
- synth_fixed_vector, &ctx->samples_az[LP_ORDER]);
-
- /* Synthesis speech post-processing */
- de_emphasis(&ctx->samples_up[UPS_MEM_SIZE],
- &ctx->samples_az[LP_ORDER], PREEMPH_FAC, ctx->demph_mem);
-
- ctx->acelpf_ctx.acelp_apply_order_2_transfer_function(&ctx->samples_up[UPS_MEM_SIZE],
- &ctx->samples_up[UPS_MEM_SIZE], hpf_zeros, hpf_31_poles,
- hpf_31_gain, ctx->hpf_31_mem, AMRWB_SFR_SIZE);
-
- upsample_5_4(sub_buf, &ctx->samples_up[UPS_FIR_SIZE],
- AMRWB_SFR_SIZE_16k, &ctx->celpm_ctx);
-
- /* High frequency band (6.4 - 7.0 kHz) generation part */
- ctx->acelpf_ctx.acelp_apply_order_2_transfer_function(hb_samples,
- &ctx->samples_up[UPS_MEM_SIZE], hpf_zeros, hpf_400_poles,
- hpf_400_gain, ctx->hpf_400_mem, AMRWB_SFR_SIZE);
-
- hb_gain = find_hb_gain(ctx, hb_samples,
- cur_subframe->hb_gain, cf->vad);
-
- scaled_hb_excitation(ctx, hb_exc, synth_exc, hb_gain);
-
- hb_synthesis(ctx, sub, &ctx->samples_hb[LP_ORDER_16k],
- hb_exc, ctx->isf_cur, ctx->isf_past_final);
-
- /* High-band post-processing filters */
- hb_fir_filter(hb_samples, bpf_6_7_coef, ctx->bpf_6_7_mem,
- &ctx->samples_hb[LP_ORDER_16k]);
-
- if (ctx->fr_cur_mode == MODE_23k85)
- hb_fir_filter(hb_samples, lpf_7_coef, ctx->lpf_7_mem,
- hb_samples);
-
- /* Add the low and high frequency bands */
- for (i = 0; i < AMRWB_SFR_SIZE_16k; i++)
- sub_buf[i] = (sub_buf[i] + hb_samples[i]) * (1.0f / (1 << 15));
-
- /* Update buffers and history */
- update_sub_state(ctx);
- }
-
- /* update state for next frame */
- memcpy(ctx->isp_sub4_past, ctx->isp[3], LP_ORDER * sizeof(ctx->isp[3][0]));
- memcpy(ctx->isf_past_final, ctx->isf_cur, LP_ORDER * sizeof(float));
-
- buf += expected_fr_size;
- buf_size -= expected_fr_size;
- }
-
- *got_frame_ptr = 1;
-
- return buf - avpkt->data;
-}
-
-const FFCodec ff_amrwb_decoder = {
- .p.name = "amrwb",
- CODEC_LONG_NAME("AMR-WB (Adaptive Multi-Rate WideBand)"),
- .p.type = AVMEDIA_TYPE_AUDIO,
- .p.id = AV_CODEC_ID_AMR_WB,
- .priv_data_size = sizeof(AMRWBChannelsContext),
- .init = amrwb_decode_init,
- FF_CODEC_DECODE_CB(amrwb_decode_frame),
- .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_CHANNEL_CONF,
- .p.sample_fmts = (const enum AVSampleFormat[]){ AV_SAMPLE_FMT_FLTP,
- AV_SAMPLE_FMT_NONE },
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hevc_data.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hevc_data.c
deleted file mode 100644
index 1633a41c136f0403175e8c65d88d4228a6a272af..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hevc_data.c
+++ /dev/null
@@ -1,75 +0,0 @@
-/*
- * HEVC shared tables
- *
- * 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
- */
-
-#include
-
-#include "hevc_data.h"
-
-const uint8_t ff_hevc_diag_scan4x4_x[16] = {
- 0, 0, 1, 0,
- 1, 2, 0, 1,
- 2, 3, 1, 2,
- 3, 2, 3, 3,
-};
-
-const uint8_t ff_hevc_diag_scan4x4_y[16] = {
- 0, 1, 0, 2,
- 1, 0, 3, 2,
- 1, 0, 3, 2,
- 1, 3, 2, 3,
-};
-
-const uint8_t ff_hevc_diag_scan8x8_x[64] = {
- 0, 0, 1, 0,
- 1, 2, 0, 1,
- 2, 3, 0, 1,
- 2, 3, 4, 0,
- 1, 2, 3, 4,
- 5, 0, 1, 2,
- 3, 4, 5, 6,
- 0, 1, 2, 3,
- 4, 5, 6, 7,
- 1, 2, 3, 4,
- 5, 6, 7, 2,
- 3, 4, 5, 6,
- 7, 3, 4, 5,
- 6, 7, 4, 5,
- 6, 7, 5, 6,
- 7, 6, 7, 7,
-};
-
-const uint8_t ff_hevc_diag_scan8x8_y[64] = {
- 0, 1, 0, 2,
- 1, 0, 3, 2,
- 1, 0, 4, 3,
- 2, 1, 0, 5,
- 4, 3, 2, 1,
- 0, 6, 5, 4,
- 3, 2, 1, 0,
- 7, 6, 5, 4,
- 3, 2, 1, 0,
- 7, 6, 5, 4,
- 3, 2, 1, 7,
- 6, 5, 4, 3,
- 2, 7, 6, 5,
- 4, 3, 7, 6,
- 5, 4, 7, 6,
- 5, 7, 6, 7,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/idctdsp_lasx.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/idctdsp_lasx.c
deleted file mode 100644
index 1cfab0e028216f0f86fb63dbc228a05bcb8c5379..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/idctdsp_lasx.c
+++ /dev/null
@@ -1,124 +0,0 @@
-/*
- * Copyright (c) 2021 Loongson Technology Corporation Limited
- * Contributed by Hao Chen
- *
- * 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
- */
-
-#include "idctdsp_loongarch.h"
-#include "libavutil/loongarch/loongson_intrinsics.h"
-
-void ff_put_pixels_clamped_lasx(const int16_t *block,
- uint8_t *av_restrict pixels,
- ptrdiff_t stride)
-{
- __m256i b0, b1, b2, b3;
- __m256i temp0, temp1;
- ptrdiff_t stride_2x = stride << 1;
- ptrdiff_t stride_4x = stride << 2;
- ptrdiff_t stride_3x = stride_2x + stride;
-
- DUP4_ARG2(__lasx_xvld, block, 0, block, 32, block, 64, block, 96,
- b0, b1, b2, b3);
- DUP4_ARG1(__lasx_xvclip255_h, b0, b1, b2, b3, b0, b1, b2, b3);
- DUP2_ARG2(__lasx_xvpickev_b, b1, b0, b3, b2, temp0, temp1);
- __lasx_xvstelm_d(temp0, pixels, 0, 0);
- __lasx_xvstelm_d(temp0, pixels + stride, 0, 2);
- __lasx_xvstelm_d(temp0, pixels + stride_2x, 0, 1);
- __lasx_xvstelm_d(temp0, pixels + stride_3x, 0, 3);
- pixels += stride_4x;
- __lasx_xvstelm_d(temp1, pixels, 0, 0);
- __lasx_xvstelm_d(temp1, pixels + stride, 0, 2);
- __lasx_xvstelm_d(temp1, pixels + stride_2x, 0, 1);
- __lasx_xvstelm_d(temp1, pixels + stride_3x, 0, 3);
-}
-
-void ff_put_signed_pixels_clamped_lasx(const int16_t *block,
- uint8_t *av_restrict pixels,
- ptrdiff_t stride)
-{
- __m256i b0, b1, b2, b3;
- __m256i temp0, temp1;
- __m256i const_128 = {0x0080008000800080, 0x0080008000800080,
- 0x0080008000800080, 0x0080008000800080};
- ptrdiff_t stride_2x = stride << 1;
- ptrdiff_t stride_4x = stride << 2;
- ptrdiff_t stride_3x = stride_2x + stride;
-
- DUP4_ARG2(__lasx_xvld, block, 0, block, 32, block, 64, block, 96,
- b0, b1, b2, b3);
- DUP4_ARG2(__lasx_xvadd_h, b0, const_128, b1, const_128, b2, const_128,
- b3, const_128, b0, b1, b2, b3);
- DUP4_ARG1(__lasx_xvclip255_h, b0, b1, b2, b3, b0, b1, b2, b3);
- DUP2_ARG2(__lasx_xvpickev_b, b1, b0, b3, b2, temp0, temp1);
- __lasx_xvstelm_d(temp0, pixels, 0, 0);
- __lasx_xvstelm_d(temp0, pixels + stride, 0, 2);
- __lasx_xvstelm_d(temp0, pixels + stride_2x, 0, 1);
- __lasx_xvstelm_d(temp0, pixels + stride_3x, 0, 3);
- pixels += stride_4x;
- __lasx_xvstelm_d(temp1, pixels, 0, 0);
- __lasx_xvstelm_d(temp1, pixels + stride, 0, 2);
- __lasx_xvstelm_d(temp1, pixels + stride_2x, 0, 1);
- __lasx_xvstelm_d(temp1, pixels + stride_3x, 0, 3);
-}
-
-void ff_add_pixels_clamped_lasx(const int16_t *block,
- uint8_t *av_restrict pixels,
- ptrdiff_t stride)
-{
- __m256i b0, b1, b2, b3;
- __m256i p0, p1, p2, p3, p4, p5, p6, p7;
- __m256i temp0, temp1, temp2, temp3;
- uint8_t *pix = pixels;
- ptrdiff_t stride_2x = stride << 1;
- ptrdiff_t stride_4x = stride << 2;
- ptrdiff_t stride_3x = stride_2x + stride;
-
- DUP4_ARG2(__lasx_xvld, block, 0, block, 32, block, 64, block, 96,
- b0, b1, b2, b3);
- p0 = __lasx_xvldrepl_d(pix, 0);
- pix += stride;
- p1 = __lasx_xvldrepl_d(pix, 0);
- pix += stride;
- p2 = __lasx_xvldrepl_d(pix, 0);
- pix += stride;
- p3 = __lasx_xvldrepl_d(pix, 0);
- pix += stride;
- p4 = __lasx_xvldrepl_d(pix, 0);
- pix += stride;
- p5 = __lasx_xvldrepl_d(pix, 0);
- pix += stride;
- p6 = __lasx_xvldrepl_d(pix, 0);
- pix += stride;
- p7 = __lasx_xvldrepl_d(pix, 0);
- DUP4_ARG3(__lasx_xvpermi_q, p1, p0, 0x20, p3, p2, 0x20, p5, p4, 0x20,
- p7, p6, 0x20, temp0, temp1, temp2, temp3);
- DUP4_ARG2(__lasx_xvaddw_h_h_bu, b0, temp0, b1, temp1, b2, temp2, b3, temp3,
- temp0, temp1, temp2, temp3);
- DUP4_ARG1(__lasx_xvclip255_h, temp0, temp1, temp2, temp3,
- temp0, temp1, temp2, temp3);
- DUP2_ARG2(__lasx_xvpickev_b, temp1, temp0, temp3, temp2, temp0, temp1);
- __lasx_xvstelm_d(temp0, pixels, 0, 0);
- __lasx_xvstelm_d(temp0, pixels + stride, 0, 2);
- __lasx_xvstelm_d(temp0, pixels + stride_2x, 0, 1);
- __lasx_xvstelm_d(temp0, pixels + stride_3x, 0, 3);
- pixels += stride_4x;
- __lasx_xvstelm_d(temp1, pixels, 0, 0);
- __lasx_xvstelm_d(temp1, pixels + stride, 0, 2);
- __lasx_xvstelm_d(temp1, pixels + stride_2x, 0, 1);
- __lasx_xvstelm_d(temp1, pixels + stride_3x, 0, 3);
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Chapters Interactive Stories MOD APK and Get Free Premium Choices.md b/spaces/congsaPfin/Manga-OCR/logs/Download Chapters Interactive Stories MOD APK and Get Free Premium Choices.md
deleted file mode 100644
index db7842bb22f746d1eafa5f56c74f1fe9f8b81935..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Chapters Interactive Stories MOD APK and Get Free Premium Choices.md
+++ /dev/null
@@ -1,99 +0,0 @@
-
-
Chapters: Interactive Stories Mod Apk Free Premium Choices
-
Do you love reading interactive stories that let you choose your own path and outcome? Do you want to experience different genres of stories, from romance to thriller, from comedy to fantasy? Do you wish you had unlimited resources to make the best choices and unlock all the stories in the game? If you answered yes to any of these questions, then you might be interested in Chapters: Interactive Stories Mod Apk.
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What is a mod apk?
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A mod apk is a modified version of an original app that has been altered to provide additional features or benefits that are not available in the official version. A mod apk can also remove some limitations or restrictions that are imposed by the original app.
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No ads and no root required: Ads can be annoying and distracting when you are playing an interactive story. They can also consume your data and battery. With a mod apk, you can remove all the ads from the game and enjoy a smooth and uninterrupted gameplay. Moreover, you don't need to root your device to use a mod apk for Chapters: Interactive Stories.
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How to Download and Install Chapters: Interactive Stories Mod Apk
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If you want to download and install Chapters: Interactive Stories Mod Apk, you need to follow these simple steps:
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Enable unknown sources on your device: To do this, go to your device settings and look for the security or privacy option. Then, find the unknown sources option and enable it. This will allow you to install apps from sources other than the Google Play Store.
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Locate and install the mod apk file on your device: Once you have downloaded the mod apk file, you need to find it on 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.
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Conclusion
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Chapters: Interactive Stories is a fun and engaging app that lets you play interactive stories in various genres. You can make choices that affect the story and the outcome, customize your character, and create your own stories. However, if you want to enjoy the game to the fullest, you might want to use a mod apk that gives you unlimited diamonds and tickets, access to all stories and genres, no ads, and no root required. To use a mod apk for Chapters: Interactive Stories, you just need to download and install it on your device following some simple steps. Then, you can launch the app and enjoy playing your favorite stories with premium choices.
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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 mod apks for popular games and apps. Thank you for reading!
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Calendar Girl: A comedy story where you get to work as an escort for 12 different clients who have different needs and quirks.
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Q5: How can I create my own story in Chapters: Interactive Stories?
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A5: To create your own story in Chapters: Interactive Stories, you need to register as an author on the app. Then, you can use the story editor tool to write your story, add choices, customize characters, and upload images. You can also preview your story before publishing it on the app.
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Fantasea APK Mod The Ultimate Seafaring Game with Customizable Battleships.md b/spaces/congsaPfin/Manga-OCR/logs/Fantasea APK Mod The Ultimate Seafaring Game with Customizable Battleships.md
deleted file mode 100644
index 49b596f770f73f92f01d9cd4ca5ea3541e12c982..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Fantasea APK Mod The Ultimate Seafaring Game with Customizable Battleships.md
+++ /dev/null
@@ -1,118 +0,0 @@
-
-
Fantasea APK Mod: A Classic Seafaring Game with Unlimited Possibilities
-
If you are a fan of seafaring games, you might have heard of Fantasea, a role-playing game developed by Green Mushroom. In this game, you can design your own battleship, explore the ocean, dive for relics, and fight against sea creatures and other forces. But what if you want to enjoy the game without any limitations or restrictions? That's where Fantasea APK Mod comes in. In this article, we will tell you what Fantasea APK Mod is, what features it offers, how to download and install it, and what are its pros and cons. Read on to find out more.
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What is Fantasea APK Mod?
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Fantasea APK Mod is a modified version of the original Fantasea game that allows you to access all the features and content of the game for free. You don't need to spend any money or watch any ads to play the game. You can also enjoy unlimited resources, such as gold, gems, and energy, to upgrade your battleship and unlock new items. With Fantasea APK Mod, you can experience the full potential of the game and have more fun.
One of the main features of Fantasea APK Mod is that you can customize your own battleship from thousands of possible combinations. You can choose from different parts, such as sails, bows, weapons, and cannons, to create a unique ship that suits your style and strategy. You can also change the color and appearance of your ship to make it stand out from the rest.
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Explore the ocean and discover treasures
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Another feature of Fantasea APK Mod is that you can explore the ocean and discover treasures hidden in various islands. You can use your sea chart to navigate through different regions and encounter different challenges and events. You can also collect resources and materials from the islands to craft new items and improve your ship.
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Dive into the depths and hunt for relics
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A third feature of Fantasea APK Mod is that you can dive into the depths and hunt for relics from ancient civilizations. You can use your diving equipment to explore underwater caves and ruins and find rare artifacts and secrets. You can also face dangerous sea creatures and monsters that guard the relics and try to stop you.
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Fight against sea creatures and other forces
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A fourth feature of Fantasea APK Mod is that you can fight against sea creatures and other forces that threaten the ocean. You can use your weapons and cannons to attack your enemies and defend your ship. You can also use special skills and abilities to gain an advantage in battle. You can also join forces with other players online and cooperate or compete with them in various modes.
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How to download and install Fantasea APK Mod
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Download the APK file from a trusted source
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The first step to download and install Fantasea APK Mod is to find a reliable source that offers the latest version of the modded file. You can search online for websites that provide free downloads of Fantasea APK Mod or use this link to get it directly.
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Enable unknown sources on your device
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The second step to download and install Fantasea APK Mod is to enable unknown sources on your device. This will allow you to install apps that are not from the official Google Play Store. To do this , follow these steps:
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Go to your device's settings and tap on security.
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Find the option that says unknown sources and toggle it on.
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Confirm your choice by tapping on OK.
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Install the APK file and launch the game
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The third and final step to download and install Fantasea APK Mod is to install the APK file and launch the game. To do this, follow these steps:
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Locate the downloaded APK file on your device's storage and tap on it.
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Follow the instructions on the screen to install the app.
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Once the installation is complete, tap on open to launch the game.
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Enjoy playing Fantasea APK Mod with unlimited features and resources.
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Pros and cons of Fantasea APK Mod
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Pros
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Some of the advantages of using Fantasea APK Mod are:
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You can access all the features and content of the game for free.
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You can enjoy unlimited resources, such as gold, gems, and energy, to upgrade your battleship and unlock new items.
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You can customize your own battleship from thousands of possible combinations.
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You can explore the ocean and discover treasures hidden in various islands.
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You can dive into the depths and hunt for relics from ancient civilizations.
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You can fight against sea creatures and other forces that threaten the ocean.
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You can join forces with other players online and cooperate or compete with them in various modes.
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Cons
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Some of the disadvantages of using Fantasea APK Mod are:
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You may encounter some bugs and glitches that affect the game performance and stability.
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You may risk getting banned from the game if you use the modded file in online modes.
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You may not be able to update the game to the latest version without losing the modded features and resources.
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You may compromise your device's security and privacy by installing apps from unknown sources.
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Conclusion
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Fantasea APK Mod is a modified version of the original Fantasea game that allows you to access all the features and content of the game for free. You can also enjoy unlimited resources, such as gold, gems, and energy, to upgrade your battleship and unlock new items. With Fantasea APK Mod, you can experience the full potential of the game and have more fun. However, you should also be aware of the risks and drawbacks of using Fantasea APK Mod, such as bugs, glitches, bans, updates, and security issues. Therefore, you should use Fantasea APK Mod at your own discretion and responsibility. We hope this article has helped you learn more about Fantasea APK Mod and how to download and install it. If you have any questions or feedback, feel free to leave a comment below.
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FAQs
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Here are some frequently asked questions about Fantasea APK Mod:
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What is Fantasea?
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Fantasea is a role-playing game developed by Green Mushroom that lets you design your own battleship, explore the ocean, dive for relics, and fight against sea creatures and other forces.
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What is Fantasea APK Mod?
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Fantasea APK Mod is a modified version of the original Fantasea game that allows you to access all the features and content of the game for free. You can also enjoy unlimited resources, such as gold, gems, and energy, to upgrade your battleship and unlock new items.
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How to download and install Fantasea APK Mod?
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To download and install Fantasea APK Mod, you need to follow these steps:
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Download the APK file from a trusted source or use this link .
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Enable unknown sources on your device by going to settings > security > unknown sources > toggle on > OK.
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Install the APK file by locating it on your device's storage > tapping on it > following the instructions > open.
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What are the pros and cons of Fantasea APK Mod?
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The pros of Fantasea APK Mod are that you can access all the features and content of the game for free, enjoy unlimited resources, customize your own battleship, explore the ocean, dive for relics, fight against sea creatures and other forces, and join forces with other players online. The cons of Fantasea APK Mod are that you may encounter some bugs and glitches, risk getting banned from the game, not be able to update the game, and compromise your device's security and privacy by installing apps from unknown sources.
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Is Fantasea APK Mod safe to use?
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Fantasea APK Mod is not an official app from the game developer, so it may not be safe to use. You may expose your device to malware, viruses, spyware, or other harmful software by installing apps from unknown sources. You may also violate the game's terms of service and risk getting banned from the game if you use the modded file in online modes. Therefore, you should use Fantasea APK Mod at your own discretion and responsibility.
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Is Fantasea APK Mod compatible with my device?
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Fantasea APK Mod is compatible with most Android devices that run on Android 4.4 or higher. However, some devices may not support the modded file or may experience some issues with the game performance and stability. You can check the compatibility of your device by reading the app's description and reviews before downloading and installing it.
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How to Download 20 Percent ft Mr Blue - A Guide for Tanzanian Music Lovers
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If you are a fan of Tanzanian music, you have probably heard of 20 Percent ft Mr Blue, one of the hottest songs in the country right now. The catchy tune, featuring two of the most talented and respected artists in the industry, has been topping the charts and trending on social media since its release in 2020. But how can you download this song legally and safely, without risking your device or breaking the law? In this article, we will show you how to do just that, as well as give you some background information on the song and its creators. Read on to find out more.
20 Percent ft Mr Blue is a collaboration between two Tanzanian musicians, 20 Percent (also known as 20 Power) and Mr Blue (also known as Byser). The song is a fusion of bongo flava, a genre of Tanzanian urban music that blends hip hop, R&B, reggae, dancehall, and traditional elements, and taarab, a genre of sung poetry that originated in Zanzibar and is influenced by Arabic, Indian, and Swahili cultures. The song is about overcoming challenges and criticism, staying true to oneself, and expressing one's feelings through music.
-
Downloading music legally and safely is important for several reasons. First, it supports the artists who work hard to create their music and deserve to be compensated for their efforts. Second, it protects your device from malware, viruses, spyware, and other harmful software that can damage your data or compromise your security. Third, it respects the intellectual property rights of the creators and avoids legal consequences such as fines or lawsuits.
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In this article, we will cover three main ways to download 20 Percent ft Mr Blue legally and safely: buying music on desktop or mobile devices, using free music sites or blogs, and using YouTube or other streaming services. We will also provide you with some tips and tricks to make the most out of your download experience.
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What is 20 Percent ft Mr Blue?
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Who are 20 Percent and Mr Blue?
-
20 Percent is a Tanzanian rapper, singer, songwriter, producer, and entrepreneur who rose to fame in the early 2000s with his hit songs such as Ya Nini Malumbano, Tamaa Mbaya, Nyerere Nyerere, Bangi Bangi, Sauti Yangu, Nyumba Ya Milele, Wan
Wanakwenda, and many more. He is known for his versatile and unique style, blending different genres and languages, and addressing social and political issues in his lyrics. He has won several awards, including the Kilimanjaro Music Awards, the Tanzania Music Awards, and the East Africa Music Awards. He is also the founder and CEO of 20 Percent Entertainment, a record label and media company that promotes and supports upcoming artists.
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Mr Blue is a Tanzanian rapper, singer, songwriter, and actor who started his career in the late 1990s as a member of the group East Coast Team. He later went solo and released his debut album Mr Blue in 2001, followed by other albums such as The Voice, Blue Forever, The Legend, and Money Maker. He is regarded as one of the pioneers and legends of bongo flava, and has collaborated with many other artists such as AY, Lady Jaydee, Mwana FA, Ali Kiba, Diamond Platnumz, Nandy, Rosa Ree, and more. He has also appeared in several movies and TV shows, such as Bongo Star Search, Bongo Movie Awards, and Nipe Nafasi.
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What is the song about?
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20 Percent ft Mr Blue is a song that celebrates the power of music and the passion of the artists. The song is divided into three verses, each sung by one of the artists, and a chorus that is repeated four times. The song starts with 20 Percent singing about how he has faced many challenges and criticism in his musical journey, but he has never given up or changed his style. He says that music is his life and his voice, and he expresses his feelings through it. He also praises Mr Blue for being his friend and mentor, and for inspiring him to keep going.
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The chorus is sung by both artists, and it is a catchy hook that invites the listeners to join them in their musical celebration. They say that they are not afraid of anyone or anything, and they are ready to face any obstacle or enemy. They also say that they are proud of their music and their identity, and they are not ashamed of their roots or their culture. They say that they are here to stay and to make history.
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The second verse is sung by Mr Blue, who echoes 20 Percent's sentiments and adds his own perspective. He says that he has been in the game for a long time, and he has seen many changes and trends in the industry. He says that he has always stayed true to himself and his fans, and he has never compromised his quality or integrity. He says that he loves music more than anything else, and he is grateful for the opportunities and recognition that he has received. He also acknowledges 20 Percent for being his brother and partner, and for sharing his vision and passion.
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The third verse is sung by both artists together, who rap in a fast-paced and energetic style. They say that they are unstoppable and unbeatable, and they challenge anyone who doubts them or tries to stop them. They say that they are confident and talented, and they have nothing to prove or lose. They say that they are happy and satisfied with their music and their lives, and they are not interested in fame or money. They say that they are loyal to their fans and their country, and they are ready to represent Tanzania on the world stage.
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How did the song perform on the charts and social media?
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20 Percent ft Mr Blue was an instant hit among Tanzanian music lovers, as well as fans from other African countries and beyond. The song reached the number one spot on several Tanzanian radio and TV charts, such as Clouds FM, Radio One, TBC, and EATV. It also received positive reviews from critics and fans alike, who praised the song's production, lyrics, message, and performance. The song also generated a lot of buzz on social media platforms, such as Twitter, Instagram, Facebook, and TikTok, where users shared their reactions, opinions, videos, and memes about the song. The song also inspired many covers, remixes, and parodies by other artists and fans.
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How to Download 20 Percent ft Mr Blue Legally and Safely?
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Now that you know more about the song and its creators, you might be wondering how to download it to your device so that you can enjoy it anytime and anywhere. There are many ways to do that, but not all of them are legal or safe. Some methods might expose you to malware, viruses, spyware, or other harmful software that can damage your device or compromise your security. Some methods might also violate the intellectual property rights of the artists and expose you to legal consequences such as fines or lawsuits. Therefore, it is important to choose a method that is legal and safe, and that respects the artists and their work. Here are three main ways to download 20 Percent ft Mr Blue legally and safely:
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Buying Music on Desktop or Mobile Devices
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One of the easiest and safest ways to download 20 Percent ft Mr Blue is to buy it from an online music store or platform, such as iTunes or Google Play. These platforms allow you to purchase and download the song for a small fee, usually less than a dollar. You can also buy the whole album or other songs by the same artists if you want. Buying music from these platforms has several advantages:
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You support the artists financially and help them continue making music.
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You get a high-quality audio file that you can play on any device.
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You get access to additional features such as lyrics, artwork, metadata, etc.
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You avoid malware, viruses, spyware, or other harmful software that can damage your device or compromise your security.
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You respect the intellectual property rights of the artists and avoid legal consequences such as fines or lawsuits.
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To buy music from these platforms, you need to have an account and a payment method (such as a credit card or a mobile money service). You also need to have enough storage space on your device to save the file. Here are the steps to buy 20 Percent ft Mr Blue from iTunes or Google Play:
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-
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iTunes
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Google Play
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Open the iTunes app on your desktop or mobile device.
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Search for 20 Percent ft Mr Blue in the search bar.
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Select the song from the results and click on the price button.
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Enter your Apple ID and password if prompted.
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Confirm your purchase and wait for the download to complete.
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Enjoy your song!
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Open the Google Play app on your desktop or mobile device.
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Search for 20 Percent ft Mr Blue in the search bar.
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Select the song from the results and click on the price button.
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Enter your Google account and password if prompted.
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Choose your payment method and confirm your purchase.
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Wait for the download to complete.
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Enjoy your song!
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Using Free Music Sites or Blogs
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Another way to download 20 Percent ft Mr Blue is to use free music sites or blogs that offer the song for download. These sites or blogs are usually run by fans or music enthusiasts who want to share their favorite songs with others. They usually provide a link to download the song as an MP3 file, sometimes along with other information such as lyrics, artwork, metadata, etc. Using free music sites or blogs has some advantages:
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You can download the song for free and save your money.
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You can find and download other songs by the same artists or similar genres.
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You can discover new music from Tanzania and other African countries.
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However, using free music sites or blogs also has some disadvantages:
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You might not support the artists financially and affect their income and livelihood.
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You might get a low-quality audio file that has poor sound or glitches.
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You might encounter malware, viruses, spyware, or other harmful software that can damage your device or compromise your security.
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You might violate the intellectual property rights of the artists and face legal consequences such as fines or lawsuits.
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To use free music sites or blogs, you need to have a reliable internet connection and a browser that can access the site or blog. You also need to have enough storage space on your device to save the file. Here are some tips to use free music sites or blogs safely and legally:
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Choose a reputable and legal site or blog that has positive reviews and feedback from other users.
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Check the source and quality of the file before downloading it. Avoid files that have suspicious names, extensions, sizes, or dates.
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Scan the file with an antivirus or anti-malware software before opening it. Delete the file if it contains any threats or errors.
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Acknowledge and respect the artists and their work. Give them credit and appreciation for their music. Do not claim the music as your own or use it for commercial purposes without their permission.
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Using YouTube or Other Streaming Services
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A third way to download 20 Percent ft Mr Blue is to use YouTube or other streaming services that offer the song for streaming or download. These services allow you to listen to the song online or offline, depending on your preference and subscription. They also provide you with other features such as lyrics, artwork, metadata, playlists, recommendations, etc. Using YouTube or other streaming services has some advantages:
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You can stream or download the song in high-quality audio and video formats.
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You can access a large library of music from different artists, genres, countries, and eras.
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You can enjoy other content related to the song, such as official videos, live performances, interviews, behind-the-scenes, etc.
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However, using YouTube or other streaming services also has some disadvantages:
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You might need to pay a subscription fee to access some features or content.
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You might need a stable internet connection and enough data to stream or download the song.
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You might not be able to save the song as an MP3 file on your device without using converters or third-party apps.
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To use YouTube or other streaming services, you need to have an account and a subscription (if required) for the service. You also need to have a compatible device and app that can play the song. Here are some steps to stream or download 20 Percent ft Mr Blue from YouTube:
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Open the YouTube app on your device.
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Search for 20 Percent ft Mr Blue in the search bar.
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Select the song from the results and tap on it to play it.
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If you want to stream the song online, you can enjoy it as long as you have an internet connection and data.
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If you want to download the song offline, you need to have a YouTube Premium subscription. Tap on the download button below the video and choose your preferred quality. Wait for the download to complete and enjoy your song offline.
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If you want to save the song as an MP3 file on your device, you need to use a converter or a third-party app that can extract the audio from the video. There are many online converters and apps available for this purpose, but not all of them are safe or legal. Be careful when choosing a converter or an app, and follow these tips:
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Choose a reputable and legal converter or app that has positive reviews and feedback from other users.
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Check the source and quality of the file before converting or downloading it. Avoid files that have suspicious names, extensions, sizes, or dates.
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Scan the file with an antivirus or anti-malware software before opening it. Delete the file if it contains any threats or errors.
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Acknowledge and respect the artists and their work. Give them credit and appreciation for their music. Do not claim the music as your own or use it for commercial purposes without their permission.
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Conclusion
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20 Percent ft Mr Blue is a great song that showcases the talent and diversity of Tanzanian music. It is a song that you can enjoy and relate to, whether you are a fan of bongo flava, taarab, or both. It is also a song that you can download legally and safely, without risking your device or breaking the law. You can choose from three main ways to download the song: buying music on desktop or mobile devices, using free music sites or blogs, and using YouTube or other streaming services. Each method has its pros and cons, and you should choose the one that suits your preferences and needs. Whichever method you choose, make sure to follow the tips and tricks that we have provided to make the most out of your download experience.
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We hope that this article has helped you learn more about 20 Percent ft Mr Blue and how to download it. We also hope that you enjoy the song and support the artists who created it. 20 Percent and Mr Blue are two of the best artists in Tanzania, and they deserve your recognition and appreciation. If you like their music, you can also check out their other songs, albums, videos, and projects. You can also explore other genres of Tanzanian music, such as bongo rap, singeli, kwaito, zouk, etc. Tanzania has a rich and vibrant musical culture that you can discover and enjoy.
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Thank you for reading this article. If you have any questions or feedback, please feel free to share them with us in the comments section below. We would love to hear from you. Happy downloading!
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FAQs
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What are some other popular songs by 20 Percent and Mr Blue?
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Some other popular songs by 20 Percent are:
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Mama Neema
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Nakupenda Sana
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Maisha Ya Bongo
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Nia Yako
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Tukutane
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Some other popular songs by Mr Blue are:
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Baki na Mimi
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Mboga Saba
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Pesa
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Baby
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Mama La Mama
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Where can I watch the official video of 20 Percent ft Mr Blue?
-
You can watch the official video of 20 Percent ft Mr Blue on YouTube or on the artists' official websites or social media pages. Here is the link to the YouTube video:
-
What are some other genres of Tanzanian music that I can explore?
-
Some other genres of Tanzanian music that you can explore are:
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Bongo rap: A genre of hip hop that originated in Tanzania in the late 1980s and early 1990s. It features rapping in Swahili, English, or other languages over beats that incorporate traditional or modern elements. Some of the pioneers and legends of bongo rap are Fid Q, Professor Jay, Juma Nature, Dully Sykes, etc.
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Singeli: A genre of electronic music that emerged in Tanzania in the 2000s. It features fast-paced beats that range from 180 to 300 beats per minute, often accompanied by vocals that are sung or rapped in Swahili or other languages. Some of the popular artists of singeli are S Kide, Man Fongo, Sholo Mwamba, etc.
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Kwaito: A genre of dance music that originated in South Africa in the 1990s and spread to other African countries, including Tanzania. It features slow-tempo beats that are influenced by house music, disco, funk, etc., often mixed with vocals that are sung or rapped in local languages. Some of the popular artists of kwaito are TID, Mr Nice, Chege, etc.
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Zouk: A genre of music that originated in the French Caribbean islands in the 1980s and became popular in Africa in the 1990s. It features smooth and sensual rhythms that are influenced by kompa, cadence-lypso, salsa, etc., often sung in French Creole or other languages. Some of the popular artists of zouk are Ali Kiba, Lady Jaydee, Ray C, etc.
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How can I discover new music from Tanzania and other African countries?
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There are many ways to discover new music from Tanzania and other African countries, such as:
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Listening to radio stations or podcasts that play music from different regions and genres.
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Following music blogs or websites that review and recommend music from different artists and scenes.
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Subscribing to music streaming services or platforms that offer curated playlists and suggestions based on your preferences and tastes.
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Joining online communities or forums that discuss and share music from different cultures and backgrounds.
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Attending live concerts or festivals that showcase music from different performers and styles.
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How can I share my feedback or opinions on 20 Percent ft Mr Blue?
-
If you want to share your feedback or opinions on 20 Percent ft Mr Blue, you can do so by:
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Leaving a comment or rating on the platform where you downloaded or streamed the song.
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Posting a review or a video on your blog, website, or social media page.
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Creating a cover, a remix, or a parody of the song and uploading it online.
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Contacting the artists directly through their official websites or social media pages.
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Participating in online polls or surveys that ask for your opinion on the song.
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/SD Card Cleaner How to Keep Your SD Card Clean and Tidy.md b/spaces/congsaPfin/Manga-OCR/logs/SD Card Cleaner How to Keep Your SD Card Clean and Tidy.md
deleted file mode 100644
index c83dbf8de67286d5237d5ba0abbbd00309b6843a..0000000000000000000000000000000000000000
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How to Use SD Card Cleaner APK to Free Up Space on Your Android Device
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Do you have an Android device with an SD card that is full of junk large files? Do you want to get rid of them and free up some space? If yes, then you may want to try SD Card Cleaner APK, a tool that can help you scan your SD cards and delete large files that you don't need anymore. In this article, we will show you what SD Card Cleaner APK is, how to download and install it, how to use it, and what are its benefits and drawbacks.
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What is SD Card Cleaner APK and Why You Need It
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SD Card Cleaner APK is a tool that scans your SD cards and deletes large files that you don't need anymore
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SD Card Cleaner APK is an app that can help you clean your SD cards and keep them tidy. It can easily scan your SD cards and identify large files that are taking up space. These files may include photos, videos, music, documents, apps, cache, or other data. You can view the results by categories and preview the files before deleting them. You can also select which files you want to delete and which ones you want to keep.
SD Card Cleaner APK can help you save space, improve performance, and avoid errors on your device
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By using SD Card Cleaner APK, you can free up some space on your SD cards and make room for new files. This can also improve the performance of your device, as it will run faster and smoother. Moreover, deleting large files can help you avoid errors or problems on your device, such as slow loading, crashing, or freezing. You can also prevent data corruption or loss by keeping your SD cards clean.
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How to Download and Install SD Card Cleaner APK on Your Android Device
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You can download SD Card Cleaner APK from APKCombo or other trusted sources
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One of the ways to get SD Card Cleaner APK is to download it from APKCombo, a website that offers free and safe downloads of various apps. You can also search for other sources online, but make sure they are reliable and secure. Avoid downloading from unknown or suspicious websites, as they may contain malware or viruses.
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You need to enable unknown sources in your settings to install SD Card Cleaner APK
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Before you can install SD Card Cleaner APK on your device, you need to enable unknown sources in your settings. This will allow you to install apps from sources other than the Google Play Store. To do this, follow these steps:
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Go to your device's settings and tap on security or privacy.
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Find the option that says unknown sources or install unknown apps and toggle it on.
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You may see a warning message that says installing from unknown sources may harm your device. Tap on OK or allow to proceed.
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You can follow the instructions on the screen to complete the installation process
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Once you have enabled unknown sources, you can proceed to install SD Card Cleaner APK on your device. To do this, follow these steps:
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Locate the SD Card Cleaner APK file that you have downloaded and tap on it.
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You may see a pop-up window that asks you to confirm the installation. Tap on install or next to continue.
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Wait for the installation to finish. You may see a progress bar or a notification that says installing.
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When the installation is done, you may see a message that says app installed or done. Tap on open or launch to start using SD Card Cleaner APK.
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How to Use SD Card Cleaner APK to Scan and Delete Large Files on Your SD Cards
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You can launch SD Card Cleaner APK from your app drawer or home screen
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After you have installed SD Card Cleaner APK, you can easily access it from your app drawer or home screen. You may see an icon that looks like a blue SD card with a broom on it. Tap on it to open SD Card Cleaner APK.
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You can select the SD cards that you want to scan and tap on the scan button
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When you open SD Card Cleaner APK, you will see a list of all the SD cards that are connected to your device. You can select one or more SD cards that you want to scan by tapping on them. You will see a check mark next to the selected SD cards. You can also tap on select all to scan all the SD cards at once. When you are done selecting, tap on the scan button at the bottom of the screen.
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You can view the results by categories and preview the files before deleting them
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After you tap on the scan button, SD Card Cleaner APK will start scanning your selected SD cards and show you the results by categories. You will see how much space each category is taking up and how many files are in each category. The categories may include photos, videos, music, documents, apps, cache, or other data. You can tap on each category to view the files in it. You can also preview the files by tapping on them and see their details such as name, size, date, and path.
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You can select the files that you want to delete and tap on the delete button
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When you view the files in each category, you can select the ones that you want to delete by tapping on them. You will see a check mark next to the selected files. You can also tap on select all to delete all the files in that category. When you are done selecting, tap on the delete button at the bottom of the screen.
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You can also use the settings menu to customize your preferences and options
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If you want to change some settings or options of SD Card Cleaner APK, you can tap on the menu icon at the top right corner of the screen and select settings. You will see a list of options that you can customize, such as:
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Language: You can choose the language of the app from English, Spanish, French, German, Italian, Portuguese, Russian, Turkish, Arabic, Chinese, Japanese, Korean, Hindi, Indonesian, or Vietnamese.
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Theme: You can choose the theme of the app from light or dark.
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Scan mode: You can choose how deep or fast you want the app to scan your SD cards from normal or advanced.
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Delete mode: You can choose how secure or quick you want the app to delete your files from normal or shred.
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Notification: You can choose whether you want to receive notifications from the app or not.
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Feedback: You can send feedback or suggestions to the developers of the app.
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About: You can view information about the app such as version, developer, website, email, privacy policy, and terms of service.
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Benefits and Drawbacks of Using SD Card Cleaner APK
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Benefits of using SD Card Cleaner APK include:
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It's easy, fast, and elegant: SD Card Cleaner APK has a simple and user-friendly interface that makes it easy to use. It can scan and delete large files quickly and efficiently. It also has a sleek and stylish design that makes it pleasing to the eye.
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It's free and has no ads: SD Card Cleaner APK is completely free to download and use. It has no ads or in-app purchases that may annoy or distract you.
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It's compatible with most Android devices and SD cards: SD Card Cleaner APK can work with most Android devices that have SD card slots. It can also support various types of SD cards, such as microSD, miniSD, SDHC, SDXC, etc.
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It can help you free up space, boost performance, and prevent errors on your device: SD Card Cleaner APK can help you get rid of large files that are taking up space on your SD cards. This can also improve the performance of your device, as it will run faster and smoother. Moreover, deleting large files can help you avoid errors or problems on your device, such as slow loading, crashing, or freezing. You can also prevent data corruption or loss by keeping your SD cards clean.
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Drawbacks of using SD Card Cleaner APK include:
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It requires internet connection for some features: SD Card Cleaner APK needs internet connection to download and install the app. It also needs internet connection to access some features, such as language selection, feedback, or about.
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It may not detect all large files on your SD cards: SD Card Cleaner APK may not be able to scan or delete all large files on your SD cards. Some files may be hidden, protected, or encrypted. Some files may also be part of system or app data that cannot be deleted.
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It may delete some files that you may need later: SD Card Cleaner APK may delete some files that you may not want to delete. These files may include important documents, photos, videos, music, or other data. You may also accidentally delete some files that you did not intend to delete. Once you delete the files, you may not be able to recover them.
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Conclusion and FAQs
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In conclusion, SD Card Cleaner APK is a tool that can help you scan your SD cards and delete large files that you don't need anymore. It can help you save space, improve performance, and avoid errors on your device. However, it also has some drawbacks, such as requiring internet connection for some features, not detecting all large files on your SD cards, and deleting some files that you may need later. Therefore, you should use it with caution and discretion.
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Here are some FAQs that you may have about SD Card Cleaner APK:
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Q:
Is SD Card Cleaner APK safe to use?
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A:
SD Card Cleaner APK is safe to use if you download it from trusted sources and enable unknown sources in your settings. It does not contain any malware or viruses that may harm your device. However, you should always scan the app with an antivirus before installing it.
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Q:
How much space can I save by using SD Card Cleaner APK?
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A:
The amount of space that you can save by using SD Card Cleaner APK depends on how many large files you have on your SD cards and how many of them you delete. You can see how much space each category is taking up and how much space you can free up by deleting them.
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Q:
Can I undo the deletion of the files by using SD Card Cleaner APK?
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A:
No, you cannot undo the deletion of the files by using SD Card Cleaner APK. Once you delete the files, they are gone forever. You may not be able to recover them by using any recovery tools or methods. Therefore, you should be careful and selective when deleting the files.
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Q:
Can I use SD Card Cleaner APK to scan and delete large files on my internal storage?
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A:
No, you cannot use SD Card Cleaner APK to scan and delete large files on your internal storage. The app only works with external SD cards that are connected to your device. If you want to clean your internal storage, you may need to use other apps or methods.
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Q:
How can I contact the developers of SD Card Cleaner APK?
-
A:
If you have any questions, feedback, or suggestions for the developers of SD Card Cleaner APK, you can contact them by using the feedback option in the settings menu of the app. You can also visit their website or email them at the following addresses:
I hope this article has helped you learn how to use SD Card Cleaner APK to free up space on your Android device. If you liked this article, please share it with your friends and family. Thank you for reading!
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Sniper Elite V2 Download: How to Get the Ultimate WWII Sniping Experience
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If you are a fan of sniping games, you might have heard of Sniper Elite V2, an award-winning and authentic World War II sniping experience. In this game, you play as elite US sniper Karl Fairburne, who is parachuted into Berlin amidst the Germans’ final stand. Your mission is to prevent Nazi V2 rocket program technology from falling into the hands of the besieging Red Army. Sounds exciting, right? But how can you get this game on your PC or console? In this article, we will tell you everything you need to know about Sniper Elite V2 download, including what the game is about, how to download it, and why you should download it. Let’s get started!
-
What is Sniper Elite V2?
-
Sniper Elite V2 is a sniping game developed by Rebellion and released in 2012. It is a remake of the 2005 game Sniper Elite, with improved graphics, gameplay, and features. The game is set in the final days of World War II, in the war-torn city of Berlin. You will have to use your skills, stealth, and strategy to complete your objectives and survive the enemy fire. Here are some of the aspects that make Sniper Elite V2 a great sniping game:
Sniper Elite V2 features a detailed sniping simulation with advanced ballistics, taking into account gravity, wind, velocity, bullet penetration, aim stability, and more. You will have to adjust your scope, hold your breath, and time your shot carefully to hit your target. You will also have to deal with realistic weapon behavior, such as recoil, reload time, and ammo management. The game also features an amazing ‘kill cam’ technology that showcases what really happens when a bullet enters an enemy’s body, allowing you to see hearts and lungs tear, livers burst, and bones shatter in X-Ray vision.
-
A thrilling and immersive story
-
Sniper Elite V2 follows the story of Karl Fairburne, a US OSS agent who is sent to Berlin to stop the Nazis from launching their V2 rockets. You will have to infiltrate enemy bases, sabotage their plans, assassinate their leaders, and uncover their secrets. The game has a cinematic presentation, with realistic cutscenes, voice acting, and sound effects. The game also has a historical accuracy, featuring authentic World War II locations, vehicles, weapons, and uniforms.
-
A variety of modes and content
-
Sniper Elite V2 offers a lot of content for you to enjoy. The game has a single-player campaign mode that consists of 11 missions that can be played on different difficulty levels. The game also has a co-op mode that allows you to play with a friend online or locally in two modes: Kill Tally (survive waves of enemies) and Overwatch (one player snipes while the other spots). The game also has a multiplayer mode that supports up to 12 players online in six modes: Team Deathmatch, Deathmatch, Team Distance King, Distance King, Team Dogtag Harvest, Dogtag Harvest and Capture the Flag. Additionally, the game has four DLC packs that add new missions (including the ultimate sniping mission - Kill Hitler) and new weapons for you to use.
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How to Download Sniper Elite V2?
-
Now that you know what Sniper Elite V2 is about, you might be wondering how to download it on your device. Here are some of the steps you need to follow:
-
System requirements
-
Before you download Sniper Elite V2, you need to make sure that your device meets the minimum system requirements for the game. According to the official website, these are: - A dual-core CPU with SSE3 or better (Intel Pentium D 3 GHz / AMD Athlon 64 X2 4200 / Intel Core i3-2100 / AMD A8-5600K) or better - At least 2 GB of RAM (4 GB for the remastered version) - A video card with at least 2 GB of VRAM and DirectX 11 support (NVIDIA GeForce 8800 / GTX 650 / GT 720 or ATI Radeon HD 3870 / 5570) or better - Windows Vista SP2, Windows 7 SP1, or Windows 10 (64-bit) - 15 GB of free disk space If your device meets these requirements, you can proceed to the next step.
-
Platforms and sources
-
Sniper Elite V2 is available for various platforms, including Microsoft Windows, PlayStation 3, Xbox 360, Wii U, Nintendo Switch, PlayStation 4, and Xbox One. However, the original version of the game is no longer for sale on Steam, as it has been replaced by the remastered version, which was released in 2019 and features enhanced graphics, new playable characters, a photo mode, and all the DLC content. The remastered version also supports crossplay across Steam (PC), Windows PC and Xbox One, meaning you can play online with your friends who use different devices. You can purchase Sniper Elite V2 Remastered from various sources, depending on your platform of choice. For PC users, you can buy the game from Steam, GOG, or the Rebellion store. For console users, you can buy the game from the PlayStation Store, the Microsoft Store, or the Nintendo eShop. The prices may vary depending on your region and currency, but generally, the game costs around $35 USD.
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Installation and activation
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Once you have purchased Sniper Elite V2 Remastered from your preferred source, you can download and install it on your device. The download size may vary depending on your platform and version, but it should be around 15 GB. The installation process may take a few minutes to complete, depending on your device's performance and internet speed. After the installation is done, you can launch the game from your device's menu or library. You may need to create or log in to your Rebellion account to activate the game and access its online features. You may also need to update the game to its latest version before playing. Once everything is set up, you can start enjoying Sniper Elite V2 Remastered!
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Why Download Sniper Elite V2?
-
Now that you know how to download Sniper Elite V2 Remastered, you might be wondering why you should download it in the first place. What makes this game worth playing? Here are some of the reasons why Sniper Elite V2 Remastered is a great sniping game:
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The benefits of playing Sniper Elite V2
-
Sniper Elite V2 Remastered is not just a mindless shooter game. It is a game that challenges your skills, stealth, and strategy as a sniper. Playing this game can have some benefits for you, such as: - Improving your concentration and focus: Sniping requires you to pay attention to every detail in your environment, such as wind direction, bullet drop, enemy movement, and more. You also need to time your shots carefully and avoid being detected by your enemies. Playing Sniper Elite V2 Remastered can help you improve your concentration and focus skills in real life. - Enhancing your creativity and problem-solving: Sniping also requires you to think creatively and solve problems in different situations. You need to find the best vantage point, use the environment to your advantage, set traps and diversions for your enemies, and choose the best weapon for each scenario. Playing Sniper Elite V2 Remastered can help you enhance your creativity and problem-solving skills in real life. - Having fun and relaxation: Sniping can also be fun and relaxing. You can enjoy the thrill of taking down your enemies with precise shots, watching the kill-cam animations in X-Ray vision, exploring the historical locations of Berlin, and playing with your friends online or locally. Playing Sniper Elite V2 Remastered can help you have fun and relaxation in real life.
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The features that make Sniper Elite V2 stand out
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Sniper Elite V2 Remastered is not just another sniping game. It is a game that has some unique features that make it stand out from other games in the genre. Some of these features are: - The realistic sniping simulation: Sniper Elite V 2 Remastered features a detailed sniping simulation with advanced ballistics, taking into account gravity, wind, velocity, bullet penetration, aim stability, and more. You will have to adjust your scope, hold your breath, and time your shot carefully to hit your target. You will also have to deal with realistic weapon behavior, such as recoil, reload time, and ammo management. The game also features an amazing ‘kill cam’ technology that showcases what really happens when a bullet enters an enemy’s body, allowing you to see hearts and lungs tear, livers burst, and bones shatter in X-Ray vision. - The thrilling and immersive story: Sniper Elite V2 Remastered follows the story of Karl Fairburne, a US OSS agent who is sent to Berlin to stop the Nazis from launching their V2 rockets. You will have to infiltrate enemy bases, sabotage their plans, assassinate their leaders, and uncover their secrets. The game has a cinematic presentation, with realistic cutscenes, voice acting, and sound effects. The game also has a historical accuracy, featuring authentic World War II locations, vehicles, weapons, and uniforms. - The variety of modes and content: Sniper Elite V2 Remastered offers a lot of content for you to enjoy. The game has a single-player campaign mode that consists of 11 missions that can be played on different difficulty levels. The game also has a co-op mode that allows you to play with a friend online or locally in two modes: Kill Tally (survive waves of enemies) and Overwatch (one player snipes while the other spots). The game also has a multiplayer mode that supports up to 12 players online in six modes: Team Deathmatch, Deathmatch, Team Distance King, Distance King, Team Dogtag Harvest, Dogtag Harvest and Capture the Flag. Additionally, the game has four DLC packs that add new missions (including the ultimate sniping mission - Kill Hitler) and new weapons for you to use.
-
The reviews and ratings of Sniper Elite V2
-
Sniper Elite V2 Remastered has received positive reviews and ratings from critics and players alike. The game has an average score of 7.5/10 on Metacritic, based on 32 critic reviews and 64 user ratings. The game has also received positive feedback on Steam, where it has over 1,600 reviews with an overall rating of 'Very Positive'. Some of the common praises for the game are: - The realistic and satisfying sniping mechanics - The stunning and enhanced graphics - The fun and varied gameplay modes - The interesting and immersive story - The value for money Some of the common criticisms for the game are: - The occasional bugs and glitches - The lack of innovation from the original version - The repetitive and linear level design - The mediocre AI and stealth system - The limited customization options
-
Conclusion
-
Sniper Elite V2 Remastered is a sniping game that offers you the ultimate World War II sniping experience. You will have to use your skills, stealth, and strategy to complete your objectives and survive the enemy fire. You will also enjoy the realistic sniping simulation, the thrilling and immersive story, and the variety of modes and content. If you are looking for a sniping game that is challenging, fun, and authentic, you should definitely download Sniper Elite V2 Remastered today!
-
FAQs
-
Here are some of the frequently asked questions about Sniper Elite V2 Remastered:
-
Q: How long is Sniper Elite V2 Remastered?
-
A: According to HowLongToBeat, the average time to beat Sniper Elite V2 Remastered is around 8 hours for the main story mode, 10 hours for the main story plus extras mode, and 16 hours for the completionist mode.
-
Q: Is Sniper Elite V2 Remastered cross-platform?
-
A: Yes, Sniper Elite V2 Remastered supports crossplay across Steam (PC), Windows PC and Xbox One. However, it does not support crossplay with PlayStation 4 or Nintendo Switch.
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Q: Is Sniper Elite V2 Remastered multiplayer?
-
A: Yes, Sniper Elite V2 Remastered has a multiplayer mode that supports up to 12 players online in six modes: Team Deathmatch, Deathmatch, Team Distance King, Distance King, Team Dogtag Harvest, Dogtag Harvest and Capture the Flag. The game also has a co-op mode that allows you to play with a friend online or locally in two modes: Kill Tally (survive waves of enemies) and Overwatch (one player snipes while the other spots).
-
Q: Is Sniper Elite V2 Remastered worth it?
-
A: Sniper Elite V2 Remastered is worth it if you are looking for a sniping game that is realistic, authentic, and fun. The game has improved graphics, new playable characters, a photo mode, and all the DLC content from the original version. The game also has a lot of replay value, as you can play the game on different difficulty levels, with different weapons, and with different modes. The game also has a reasonable price, especially if you already own the original version, as you can get a 70% discount on Steam.
-
Q: How to get Sniper Elite V2 Remastered for free?
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A: Sniper Elite V2 Remastered is not a free game, and you will have to purchase it from your preferred source to play it. However, there are some ways to get the game for free or at a lower price, such as: - Waiting for a sale or a discount: Sniper Elite V2 Remastered may go on sale or have a discount on various platforms and sources from time to time. You can check the official website or the social media accounts of Rebellion for any news or updates on this. - Using a coupon or a voucher: Sniper Elite V2 Remastered may have some coupons or vouchers that you can use to get the game for free or at a lower price. You can check the official website or the social media accounts of Rebellion for any news or updates on this. - Participating in a giveaway or a contest: Sniper Elite V2 Remastered may have some giveaways or contests that you can enter to win the game for free. You can check the official website or the social media accounts of Rebellion for any news or updates on this. - Downloading a cracked version: Sniper Elite V2 Remastered may have some cracked versions that you can download for free from some websites or torrents. However, this is not recommended, as it is illegal, unethical, and risky. You may face legal consequences, lose your account, damage your device, or expose your personal information by doing this.
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-
How to Download 3D Objects for Windows 10
-
Have you ever wanted to create, view, or use 3D objects on your computer? If you have Windows 10, you can do that easily with some built-in apps and online resources. In this article, we will show you how to download 3D objects for Windows 10 and what you can do with them.
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What are 3D Objects and Why They Are Useful
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3D objects are digital models that represent physical things in three dimensions. They can have different shapes, colors, textures, and animations. You can use them for various purposes, such as:
What are the Benefits of Using 3D Objects in Windows 10
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Windows 10 is a great platform for working with 3D objects. Here are some of the benefits of using them in Windows 10:
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You can access a variety of free and paid apps from the Microsoft Store that let you create, view, edit, and share 3D objects.
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You can use the built-in Paint 3D app to draw, paint, or sculpt your own 3D objects or import them from other sources.
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You can use the built-in Mixed Reality Viewer app to place your 3D objects in your real environment and take mixed reality photos.
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You can use the built-in Remix 3D community to browse, download, remix, and upload thousands of 3D objects created by other users.
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You can use the built-in File Explorer to manage your 3D objects in a dedicated folder called "3D Objects".
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How to Download 3D Objects for Windows 10
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There are two main ways to download 3D objects for Windows 10: from the Microsoft Store or from other websites. Let's see how to do both.
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From the Microsoft Store
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The Microsoft Store offers several apps that let you download or create your own 3D objects. Here are some of the most popular ones:
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How to Use 3D Builder App
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The 3D Builder app is a free app that lets you view, create, edit, and print your own or downloaded models. You can also scan real objects with your webcam and turn them into digital models. To use it, follow these steps:
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-
Open the Microsoft Store app on your PC and search for "3D Builder".
-
Click on "Get" or "Install" to download and install the app.
-
Open the app from the Start menu or the taskbar.
-
Select "New scene" to start a new project or "Load model" to open an existing one.
-
Use the toolbar on the left to add shapes, text, stickers, or custom models.
-
Use the toolbar on the top to modify, rotate, scale, group, or split your models.
-
Select "Save as" from the menu bar to save your project as a 3MF, STL, OBJ, PLY, or VRML file.
-
Select "Print" from the menu bar to send your model to a 3D printer or an online printing service.
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-
How to Use Paint 3D App
-
The Paint 3D app is a free app that lets you draw, paint, or sculpt your own 3D objects or import them from other sources. You can also add stickers, effects, text, or backgrounds to your creations. To use it, follow these steps:
-
-
Open the Paint 3D app from the Start menu or the taskbar.
-
Select "New" to start a new project or "Open" to open an existing one.
-
Use the toolbar on the top to switch between 2D and 3D modes.
-
Use the tools on the right to draw, paint, or sculpt your models.
-
Use the tools on the left to add stickers, effects, text, or backgrounds.
-
Select "Menu" from the top left corner to save your project as a 3MF, PNG, JPEG, GIF, or BMP file.
-
Select "Share" from the top right corner to share your project via email, social media, or Remix 3D.
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-
How to Use Mixed Reality Viewer App
-
The Mixed Reality Viewer app is a free app that lets you view your 3D objects in your real environment using your device's camera. You can also take mixed reality photos or videos and share them with others. To use it, follow these steps:
-
-
Open the Mixed Reality Viewer app from the Start menu or the taskbar.
-
Select "Browse" to choose a 3D object from your PC or Remix 3D.
-
Select "Mixed reality" to turn on your device's camera and place your 3D object in your real environment.
-
Use the buttons on the bottom to move, rotate, scale, or animate your 3D object.
-
Select "Capture" to take a mixed reality photo or video and save it to your PC.
-
Select "Share" to share your mixed reality photo or video via email, social media, or Remix 3D.
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-
From Other Websites
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If you want more options for downloading 3D objects for Windows 10, you can also visit some of the websites that offer free or paid models. Here are some of the most popular ones:
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How to Use Sketchfab Website
-
The Sketchfab website is a platform that hosts over 4 million 3D models created by artists and professionals. You can browse, download, remix, and upload models in various formats and categories. To use it, follow these steps:
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-
Visit the Sketchfab website and sign up for a free account.
-
Browse the models by categories, tags, formats, licenses, or collections.
-
Click on a model that you like and view it in 3D on your browser.
-
Select "Download" from the bottom right corner if the model is available for free download.
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Select "Buy" from the bottom right corner if the model is available for purchase.
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Select "Add to collection" from the bottom right corner if you want to save the model for later use.
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Select "Share" from the bottom right corner if you want to share the model via email, social media, or embed code.
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-
How to Use Tinkercad Website
-
The Tinkercad website is a platform that lets you create your own 3D models using simple shapes and tools. You can also browse, download, remix, and upload models created by other users. To use it, follow these steps:
-
-
Visit the Tinkercad website and sign up for a free account.
-
Select "Create new design" to start a new project or "Learn" to take some tutorials.
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Use the toolbar on the right to add shapes, text, numbers, symbols, or imported models.
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Use the toolbar on the top to modify, rotate, scale, group, align, or duplicate your models.
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Select "Export" from the top right corner to save your project as a STL, stories.
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YouTube: A video-sharing platform that lets you upload and view 3D videos in various formats and resolutions.
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WordPress: A website-building platform that lets you embed 3D models in your posts or pages using plugins or shortcodes.
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Google Poly: A platform that lets you browse, download, remix, and upload 3D models for virtual and augmented reality.
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How can I print 3D objects with a 3D printer?
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If you want to print your downloaded or created 3D objects with a 3D printer, you need to make sure that your models are compatible with your printer and that you have the right software and settings. Here are some general steps to follow:
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-
Save your 3D model as a STL, OBJ, or 3MF file, which are the most common formats for 3D printing.
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Open your 3D model in a slicing software, such as Cura, Slic3r, or PrusaSlicer, which will convert your model into layers and instructions for your printer.
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Adjust the settings for your printer, such as the nozzle size, layer height, infill density, print speed, and temperature.
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Preview the sliced model and check for any errors or issues.
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Save the sliced model as a G-code file, which is the language that your printer understands.
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Transfer the G-code file to your printer via USB, SD card, or Wi-Fi.
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Start the printing process and monitor the progress.
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Remove the printed object from the printer and clean it up if needed.
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Treasure Of Nadia Final Apk Download.md b/spaces/congsaPfin/Manga-OCR/logs/Treasure Of Nadia Final Apk Download.md
deleted file mode 100644
index 0c8e44178baf83440aac37d2ea7f4ecf002cc584..0000000000000000000000000000000000000000
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+++ /dev/null
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-
-
Treasure of Nadia: A Guide to Downloading the Final APK Version
-
Treasure of Nadia is a popular adventure game for Android devices that follows the story of a young treasure hunter who inherits his father's legacy and meets 12 beautiful women along the way. The game is the sequel to Lust Epidemic, and it features improved graphics, animations, and gameplay. If you are looking for a way to download the final APK version of Treasure of Nadia for free, you have come to the right place. In this article, I will show you how to find, install, and enjoy this game on your Android device.
An APK file is a package file format that contains all the files and data needed to run an Android application. APK stands for Android Package Kit, and it is similar to an EXE file on Windows or a DMG file on Mac. APK files can be downloaded from various sources online, such as official websites, app stores, or third-party platforms. However, not all APK files are safe and reliable, so you need to be careful when downloading them.
-
Why download an APK file?
-
There are several reasons why you might want to download an APK file instead of using the Google Play Store or other app stores. Some of them are:
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-
You want to access an app that is not available in your region or country.
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You want to update an app that is not yet updated on the app store.
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You want to try a beta or modded version of an app that has extra features or content.
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You want to save bandwidth or storage space by downloading a smaller or compressed file.
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You want to backup or share an app with someone else.
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-
How to download an APK file?
-
To download an APK file, you need to find a trustworthy source that offers the file you are looking for. You can use a web browser or a file manager app on your Android device to search for and download the APK file. Some of the most popular sources for APK files are:
-
-
Source
Description
-
[Malavida](^1^)
A website that offers free downloads of Android apps and games, including Treasure of Nadia.
-
[APKCombo](^2^)
A website that provides fast and easy downloads of APK files for various Android devices and versions.
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[APKPure](https://apkpure.com/)
A website that hosts safe and verified APK files for thousands of Android apps and games.
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[Aptoide](https://www.aptoide.com/)
An alternative app store that allows users to download and install apps without any restrictions.
-
[Uptodown](https://www.uptodown.com/android)
A website that offers downloads of Android apps and games in different languages and regions.
-
-
How to install an APK file?
-
To install an APK file on your Android device, you need to follow these steps:
-
-
Enable the installation of apps from unknown sources on your device settings. This option is usually found under Security or Privacy settings.
-
Locate the downloaded APK file on your device storage using a file manager app or your web browser.
-
Tap on the APK file and follow the instructions on the screen to install it.
-
Wait for the installation process to finish and launch the app from your app drawer or home screen.
-
-
How to enjoy Treasure of Nadia?
-
To enjoy Treasure of Nadia on your Android device, you need to have at least 4 GB of free storage space and Android 4.0.3 or higher. The game has a rating of 18+ and contains explicit scenes and content that may not be suitable for everyone. The game also requires an internet connection to play and update. Here are some tips on how to enjoy Treasure of Nadia:
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Explore the island of Newhaven and discover its secrets and treasures.
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Interact with the characters and build relationships with them.
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Collect items and craft tools to help you in your quest.
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Complete puzzles and mini-games to unlock new scenes and rewards.
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Customize your appearance and wardrobe to suit your style.
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-
Conclusion
-
Treasure of Nadia is a fun and exciting game that will keep you entertained for hours. It has a captivating story, stunning graphics, and engaging gameplay. If you want to download the final APK version of Treasure of Nadia for free, you can use one of the sources mentioned above. Just make sure to follow the steps on how to install and enjoy the game on your Android device. I hope you found this article helpful and informative. Happy treasure hunting!
-
FAQs
-
What is the difference between the final APK version and the previous versions of Treasure of Nadia?
-
The final APK version of Treasure of Nadia is the latest and most complete version of the game. It contains all the updates, bug fixes, and content that the developers have released so far. It also has a better performance and compatibility with different Android devices and versions.
-
Is Treasure of Nadia safe to download and play?
-
Treasure of Nadia is safe to download and play as long as you use a reliable source for the APK file. However, you should always be careful when downloading any file from the internet, as some sources may contain malware or viruses that can harm your device or steal your data. You should also scan the APK file with an antivirus app before installing it.
-
How can I update Treasure of Nadia?
-
Treasure of Nadia requires an internet connection to play and update. The game will automatically check for updates when you launch it and prompt you to download them if available. You can also manually check for updates by going to the settings menu in the game and tapping on the update button.
-
How can I save my progress in Treasure of Nadia?
-
Treasure of Nadia has an auto-save feature that saves your progress every time you exit the game or change locations. You can also manually save your progress by going to the settings menu in the game and tapping on the save button. You can load your saved progress by tapping on the load button in the same menu.
-
How can I contact the developers of Treasure of Nadia?
-
If you have any questions, feedback, or issues with Treasure of Nadia, you can contact the developers by visiting their official website at [NLT Media](https://nlt-media.com/). You can also follow them on their social media accounts on [Twitter](https://twitter.com/nltmedia), [Patreon](https://www.patreon.com/nlt), or [Discord](https://discord.gg/nlt).
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Unlock Max Level and Unlimited Coins in Plant vs Zombie 1 with MOD APK.md b/spaces/congsaPfin/Manga-OCR/logs/Unlock Max Level and Unlimited Coins in Plant vs Zombie 1 with MOD APK.md
deleted file mode 100644
index b230677ea206624a1c89f49c2f334512b846d2bf..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Unlock Max Level and Unlimited Coins in Plant vs Zombie 1 with MOD APK.md
+++ /dev/null
@@ -1,92 +0,0 @@
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-
Plant vs Zombie 1 Mod APK Max Level: A Guide for Beginners
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Do you love playing Plant vs Zombie 1, the classic tower defense game where you have to plant various plants to protect your lawn from waves of zombies? If yes, then you might want to try playing it with mod apk max level, a modified version of the game that gives you unlimited coins, suns, and max level for all plants and zombies. Sounds awesome, right?
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In this article, we will show you how to download and install Plant vs Zombie 1 Mod APK Max Level, how to play it, what are the features and benefits of it, what are the drawbacks and risks of it, and whether it is worth it or not. By the end of this article, you will have a clear idea of whether you should play Plant vs Zombie 1 with mod apk max level or not. Let's get started!
How to download and install Plant vs Zombie 1 Mod APK Max Level
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Before you can play Plant vs Zombie 1 with mod apk max level, you need to download and install the mod apk file on your device. Here are the steps you need to follow:
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Step 1: Find a reliable source for the mod apk file
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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 or viruses that can harm your device or steal your personal information. Therefore, you need to be careful when choosing a source for the mod apk file.
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One of the reliable sources that we recommend is [GetModsAPK](^i^), where you can find the latest version of Plant vs Zombie 1 Mod APK Max Level. This website has a good reputation and provides safe and secure downloads. You can also read the reviews and ratings from other users before downloading the mod apk file.
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Step 2: Enable unknown sources on your device
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Since the mod apk file is not from the official Google Play Store, you need to enable unknown sources on your device to allow the installation of third-party apps. To do this, go to your device settings, then security, then unknown sources, and toggle it on. You may see a warning message that says installing apps from unknown sources may harm your device, but don
Step 3: Download and install the mod apk file
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Now that you have enabled unknown sources on your device, you can proceed to download and install the mod apk file. To do this, go to the website where you found the mod apk file, such as [GetModsAPK](^i^), and click on the download button. You may see a pop-up window that asks you to confirm the download, just click on OK.
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Once the download is complete, you will see a notification that says the mod apk file is ready to install. Tap on the notification and follow the instructions on the screen to install the mod apk file. You may see another warning message that says the app may harm your device, but don't worry, just click on install anyway.
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After the installation is done, you will see a message that says the app has been installed successfully. You can now open the app and enjoy playing Plant vs Zombie 1 with mod apk max level.
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How to play Plant vs Zombie 1 Mod APK Max Level
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Playing Plant vs Zombie 1 with mod apk max level is not much different from playing the original game, except that you have more coins, suns, and levels to use. Here are the steps you need to follow:
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Step 1: Choose your game mode and difficulty level
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When you open the app, you will see a menu where you can choose your game mode and difficulty level. There are four game modes to choose from: Adventure, Survival, Puzzle, and Mini-Games. Each game mode has its own challenges and objectives. You can also choose your difficulty level from Easy, Normal, or Hard.
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We recommend starting with Adventure mode, which is the main story mode of the game. In this mode, you have to complete 50 levels across five different worlds: Day, Night, Pool, Fog, and Roof. Each world has its own unique plants, zombies, and obstacles. You can also unlock more plants and zombies as you progress through the levels.
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Step 2: Plant your plants and defend your lawn from zombies
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The core gameplay of Plant vs Zombie 1 is simple but addictive. You have to plant various plants on your lawn to defend it from waves of zombies that want to eat your brains. Each plant has its own abilities and costs a certain amount of suns to plant. Suns are generated by sunflowers or falling from the sky.
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You have to strategically place your plants on different lanes and columns to maximize their effectiveness and prevent the zombies from reaching your house. You can also dig up or replace your plants if needed. You have to be careful of some zombies that have special abilities or weapons that can destroy or bypass your plants.
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Step 3: Use coins, suns, and power-ups to boost your gameplay
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With mod apk max level, you have unlimited coins and suns to use in the game. Coins are used to buy items from Crazy Dave's shop, such as extra seed slots, plant upgrades, mini-games, and more. Suns are used to plant more plants during the game.
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You can also use power-ups to enhance your gameplay. Power-ups are special abilities that can be activated by tapping on them. There are three types of power-ups: Pinch, Flick, and Zap. Pinch allows you to pinch zombies' heads off with your fingers. Flick allows you to flick zombies off the screen with your fingers. Zap allows you to electrocute zombies with a lightning bolt.
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Power-ups cost coins to use and have a cooldown time before they can be used again. They can be very useful when you are facing a large horde of zombies or a tough boss.
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What are the features and benefits of Plant vs Zombie 1 Mod APK Max Level
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Plant vs Zombie 1 Mod APK Max Level has many features and benefits that make it more fun and enjoyable than the original game. Here are some of them:
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Feature 1: Unlimited coins and suns
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With mod apk max level, you never have to worry about running out of coins or suns in the game. You can buy anything you want from Crazy Dave's shop without any limitations. You can also plant as many plants as you want without any restrictions.
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This feature gives you more freedom and flexibility in your gameplay. You can experiment with different combinations of plants and items to find the best strategy for each level. You can also try out different game modes and difficulty levels without any fear of losing.
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Feature 2: Max level for all plants and zombies
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With mod apk max level, all your plants and zombies are at their maximum level. This means that they have the highest stats and abilities possible. For example, your peashooters can shoot faster and farther, your wall-nuts can withstand more damage, and your zombies can move faster and stronger.
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This feature makes your gameplay more exciting and challenging. You can face more powerful enemies and bosses that require more strategy and skill to defeat. You can also enjoy the full potential of your plants and zombies and see how they perform in different situations.
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Feature 3: No ads and no root required
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With mod apk max level, you don't have to deal with annoying ads that interrupt your gameplay or waste your time. You can play the game without any distractions or interruptions.
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Also, you don't need to root your device to use mod apk max level. Rooting is a process that gives you full access to your device's system, but it also voids your warranty and exposes you to security risks. With mod apk max level, you can enjoy the game without any risks or hassles.
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What are the drawbacks and risks of Plant vs Zombie 1 Mod APK Max Level
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Plant vs Zombie 1 Mod APK Max Level is not perfect, however. It also has some drawbacks and risks that you should be aware of before using it. Here are some of them:
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Drawback 1: Possible compatibility issues with some devices
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Since mod apk max level is a modified version of the game, it may not be compatible with some devices or operating systems. Some users have reported that the game crashes or freezes on their devices, or that some features don't work properly.
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To avoid this problem, you should check the compatibility of the mod apk file with your device before downloading and installing it. You should also make sure that your device has enough storage space and memory to run the game smoothly.
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Drawback 2: Possible security risks from malware or viruses
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As mentioned earlier, not all sources for mod apk files are trustworthy. Some of them may contain malware or viruses that can infect your device or steal your personal information. This can cause serious damage to your device or compromise your privacy and security.
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To avoid this problem, you should only download mod apk files from reliable sources, such as [GetModsAPK](^i^). You should also scan the mod apk file with a reputable antivirus software before installing it. You should also be careful of granting permissions to the app that may seem suspicious or unnecessary.
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Drawback 3: Possible loss of game progress or data
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Another risk of using mod apk max level is that you may lose your game progress or data if something goes wrong. For example, if the game crashes or updates, you may lose all your coins, suns, levels, plants, zombies, and items. This can be very frustrating and disappointing, especially if you have spent a lot of time and effort on the game.
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To avoid this problem, you should backup your game data before using mod apk max level. You can do this by using a cloud service, such as Google Drive or Dropbox, or by using a third-party app, such as Titanium Backup or Helium Backup. You should also avoid updating the game unless there is a new version of mod apk max level available.
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Conclusion: Is Plant vs Zombie 1 Mod APK Max Level worth it?
-
Plant vs Zombie 1 Mod APK Max Level is a fun and exciting way to play Plant vs Zombie 1, the classic tower defense game. It gives you unlimited coins, suns, and max level for all plants and zombies, which makes your gameplay more enjoyable and challenging. It also removes ads and does not require root access, which makes it more convenient and safe to use.
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However, it also has some drawbacks and risks that you should consider before using it. It may not be compatible with some devices or operating systems, it may contain malware or viruses that can harm your device or steal your personal information, and it may cause loss of game progress or data if something goes wrong.
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Therefore, we recommend using Plant vs Zombie 1 Mod APK Max Level only if you are willing to take these risks and if you are looking for a new way to experience the game. Otherwise, you may want to stick with the original game or look for other alternatives.
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FAQs
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Here are some frequently asked questions about Plant vs Zombie 1 Mod APK Max Level:
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Q1: Is Plant vs Zombie 1 Mod APK Max Level safe to use?
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A1: Plant vs Zombie 1 Mod APK Max Level is safe to use if you download it from a reliable source, such as [GetModsAPK](^i^), and if you scan it with a reputable antivirus software before installing it. You should also be careful of granting permissions to the app that may seem suspicious or unnecessary. However, there is always a risk of malware or viruses when downloading and installing mod apk files from unknown sources, so you should use it at your own discretion and responsibility.
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Q2: How can I update Plant vs Zombie 1 Mod APK Max Level?
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A2: Plant vs Zombie 1 Mod APK Max Level is not updated automatically, unlike the original game. You have to manually check for updates from the website where you downloaded the mod apk file, such as [GetModsAPK](^i^), and download and install the new version of the mod apk file. You should also backup your game data before updating, as you may lose your game progress or data if something goes wrong.
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Q3: How can I backup my game data before using Plant vs Zombie 1 Mod APK Max Level?
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A3: You can backup your game data by using a cloud service, such as Google Drive or Dropbox, or by using a third-party app, such as Titanium Backup or Helium Backup. You should backup your game data regularly, especially before using mod apk max level, updating the game, or uninstalling the app. This way, you can restore your game data if you lose it or if you want to switch back to the original game.
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Q4: How can I uninstall Plant vs Zombie 1 Mod APK Max Level?
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A4: You can uninstall Plant vs Zombie 1 Mod APK Max Level by following the same steps as uninstalling any other app on your device. Go to your device settings, then apps, then Plant vs Zombie 1, then uninstall. You may see a message that says the app has been uninstalled successfully. You can also delete the mod apk file from your device storage if you want to free up some space.
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Q5: Where can I find more tips and tricks for Plant vs Zombie 1 Mod APK Max Level?
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A5: You can find more tips and tricks for Plant vs Zombie 1 Mod APK Max Level by visiting online forums, blogs, or YouTube channels that are dedicated to the game. There, you can learn from other players who have used mod apk max level and share your own experiences and feedback. You can also ask questions and get answers from experts and enthusiasts.
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Warpath MOD APK The Best Way to Experience the Story-Based War Against the Black Crow.md b/spaces/congsaPfin/Manga-OCR/logs/Warpath MOD APK The Best Way to Experience the Story-Based War Against the Black Crow.md
deleted file mode 100644
index ba9206860eb5cbdf19616cfb1ce1d77ecf4c8c76..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Warpath MOD APK The Best Way to Experience the Story-Based War Against the Black Crow.md
+++ /dev/null
@@ -1,121 +0,0 @@
-
-
Warpath Mod APK Blackmod: How to Download and Play the Ultimate War Game
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Introduction
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If you are a fan of war games, you might have heard of Warpath, a real-time strategy game that lets you command your army in a massive world war. In this game, you can choose from hundreds of historical units, customize your weapons, and join forces with other players to fight against the evil Raven faction.
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But what if you want to enjoy the game without any limitations or restrictions? What if you want to have unlimited resources, unlocked units, and free upgrades? Well, there is a way to do that, and it is called Warpath Mod APK Blackmod.
In this article, we will show you what Warpath Mod APK Blackmod is, why you should use it, and how to download and play it on your device. So, without further ado, let's get started!
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What is Warpath?
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Warpath is a mobile war game developed by Lilith Games, the same company behind popular titles like Rise of Kingdoms and AFK Arena. Warpath was released in November 2020 and has since gained millions of downloads and positive reviews from players around the world.
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Warpath is set in an alternate history where World War II never ended. You play as a commander of one of the three factions: The Allies, The Axis, or The Legion. Your goal is to build your base, train your troops, and lead them into battle against the Raven faction, a mysterious enemy that threatens to destroy the world.
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Warpath features stunning graphics, realistic physics, and immersive sound effects that make you feel like you are in the middle of a war zone. You can also interact with other players through chat, alliance, and diplomacy systems. You can cooperate or compete with them in various modes such as campaign, PvP, and alliance wars.
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What is Blackmod?
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Blackmod is a website that provides modded versions of various Android games. A modded version is a modified version that has some features or functions that are not available in the original version. For example, a modded version might have unlimited money, unlocked items, or free premium features.
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Blackmod is one of the most popular and trusted sources of modded games on the internet. It has a large collection of games from different genres and categories. You can find action, adventure, role-playing, simulation, strategy, puzzle, casual, and more games on Blackmod. You can also request for mods that are not available on the website.
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Blackmod is easy to use and safe to download. You don't need to root your device or sign up for an account to access the mods. You just need to visit the website, search for the game you want, download the mod file, and install it on your device. You can also check the comments section for feedback from other users.
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Why use Warpath Mod APK Blackmod?
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Warpath Mod APK Blackmod is a modded version of Warpath that has several advantages over the original version. Here are some of the reasons why you should use Warpath Mod APK Blackmod:
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You can get unlimited gold, oil, steel, and other resources that you need to build your base and upgrade your units and weapons. You don't have to worry about running out of resources or waiting for them to generate.
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You can unlock all the units, weapons, and commanders that are available in the game. You don't have to spend real money or complete missions to get them. You can choose from tanks, planes, artillery, infantry, and more. You can also customize your units with different skins and attachments.
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You can get free upgrades for your units, weapons, and buildings. You don't have to spend resources or time to level up your army and base. You can instantly improve your combat power and efficiency.
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You can enjoy the game without any ads or pop-ups. You don't have to watch videos or click on banners to get rewards or access features. You can have a smooth and uninterrupted gaming experience.
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With Warpath Mod APK Blackmod, you can have more fun and excitement in playing Warpath. You can dominate the battlefield and crush your enemies with ease. You can also explore more features and content that the game has to offer.
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How to Download and Install Warpath Mod APK Blackmod
-
If you are interested in using Warpath Mod APK Blackmod, you need to follow these simple steps to download and install it on your device:
-
Step 1: Visit the Blackmod website
-
The first thing you need to do is to visit the Blackmod website at https://blackmod.net/. This is where you can find the mod file for Warpath and other games. You can use any browser that you prefer, such as Chrome, Firefox, or Safari.
-
Step 2: Search for Warpath Mod APK
-
Once you are on the Blackmod website, you need to search for Warpath Mod APK in the search bar. You can also browse through the categories or use the filters to find the game. You will see a list of results that match your query. You need to select the one that has the latest version and the most downloads.
-
Step 3: Download the mod file
-
After you select the Warpath Mod APK that you want, you will be redirected to a page that contains more information about the mod, such as the features, screenshots, installation instructions, and comments. You need to scroll down to the bottom of the page and click on the download button. You will then see a pop-up window that asks you to verify that you are not a robot. You need to complete a simple captcha test and then click on continue. You will then see another pop-up window that shows you a countdown timer. You need to wait for a few seconds until the timer reaches zero and then click on get link. You will then be taken to another page where you can finally download the mod file.
-
Step 4: Enable unknown sources on your device
-
Before you install the mod file on your device, you need to make sure that you have enabled unknown sources on your device. This is a security setting that allows you to install apps from sources other than the Google Play Store. To enable unknown sources, you need to go to your device settings, then security, then unknown sources, and then toggle it on. You might see a warning message that tells you about the risks of installing apps from unknown sources. You need to ignore it and click on OK.
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Step 5: Install the mod file
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Now that you have downloaded the mod file and enabled unknown sources on your device, you are ready to install it on your device. To do that, you need to locate the mod file in your device storage, usually in the downloads folder. You need to tap on it and then click on install. You might see another warning message that tells you about the permissions that the app requires. You need to accept them and click on next. The installation process will take a few seconds or minutes depending on your device speed. Once it is done, you will see a message that says app installed. You can then click on open or done.
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How to Play Warpath Mod APK Blackmod
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Congratulations! You have successfully downloaded and installed Warpath Mod APK Blackmod on your device. Now you can enjoy playing the game with unlimited features and benefits. Here are some tips on how to play Warpath Mod APK Blackmod:
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Step 1: Launch the game and choose your server
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When you launch the game for the first time, you will be asked to choose your server region. You can choose from Asia, Europe, America, or Oceania. It is recommended that you choose the server that is closest to your location for better performance and connection. You can also change your server later if you want to join a different alliance or play with your friends.
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Step 2: Create your commander and join an alliance
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After you choose your server, you will be asked to create your commander name and avatar. You can choose from different styles and colors, or you can use a random generator. You can also change your name and avatar later if you want. You will then be introduced to the game tutorial, which will guide you through the basics of the game. You will also be given some rewards and tips to help you start your journey.
-
One of the most important things you need to do in Warpath is to join an alliance. An alliance is a group of players who share the same goals and interests. You can cooperate with your alliance members in various ways, such as chatting, trading, donating, helping, and fighting. You can also participate in alliance events and missions that can give you more rewards and benefits. You can join an existing alliance or create your own alliance if you have enough resources.
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Step 3: Build your base and train your troops
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Your base is your main headquarters in Warpath. It is where you can manage your resources, units, buildings, and research. You need to build and upgrade different buildings in your base, such as barracks, factories, warehouses, hospitals, command center, and more. Each building has a different function and benefit that can improve your army and base.
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Your troops are your main force in Warpath. They are divided into four types: infantry, tank, artillery, and air force. Each type has its own strengths and weaknesses, as well as different units and weapons that you can choose from. You need to train and upgrade your troops in your barracks and factories, as well as equip them with the best weapons and attachments that you can find or craft. You can also customize your troops with different skins and formations.
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Step 4: Explore the map and fight enemies
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The map is the main battlefield in Warpath. It is where you can see the whole world of Warpath, as well as other players' bases and alliances. You can also find various resources, items, enemies, and events on the map. You need to explore the map and fight enemies to gain more resources, experience, and rewards. You can also capture or occupy different territories on the map to expand your influence and power.
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The enemies in Warpath are mainly the Raven faction, a rogue group that wants to destroy the world with their advanced technology and weapons. They have different bases and outposts on the map that you need to attack and destroy. They also have different units and weapons that you need to counter and defeat. You can also encounter other players on the map who might be your allies or enemies depending on your alliance and diplomacy status.
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Step 5: Enjoy the unlimited features of the mod
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The best part of playing Warpath Mod APK Blackmod is that you can enjoy the unlimited features of the mod that make the game more fun and easy. You can have unlimited resources that you can use to build your base and upgrade your units and weapons. You can unlock all the units, weapons, commanders, skins, attachments, and more that are available in the game. You can get free upgrades for your units, weapons, buildings, researches, etc. You can enjoy the game without any ads or pop-ups that might interrupt or annoy you.
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With Warpath Mod APK Blackmod, you can have the ultimate war game experience that you have always dreamed of. You can dominate the battlefield and crush your enemies with ease. You can also explore more features and content that the game has to offer.
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Conclusion
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Warpath is a great war game that lets you command your army in a massive world war. It has stunning graphics, realistic physics, immersive sound effects, interactive gameplay, and social features that make it one of the best war games on mobile devices.
-
But if you want to have more fun and excitement in playing Warpath, you should try Warpath Mod APK Blackmod. It is a modded version of Warpath that has unlimited features and benefits that make the game more enjoyable and easy. You can have unlimited resources, unlocked units, free upgrades, and more. You can also download and install it easily and safely from the Blackmod website.
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If you are interested in Warpath Mod APK Blackmod, you can follow the steps that we have provided in this article. You can also check out the Blackmod website for more modded games that you might like. We hope that this article has helped you and that you have fun playing Warpath Mod APK Blackmod!
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FAQs
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Here are some of the frequently asked questions that you might have about Warpath Mod APK Blackmod:
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-
Is Warpath Mod APK Blackmod safe to use?
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Yes, Warpath Mod APK Blackmod is safe to use as long as you download it from the Blackmod website, which is a trusted and reliable source of modded games. You don't need to root your device or sign up for an account to use the mod. You also don't need to worry about viruses or malware that might harm your device or data.
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Is Warpath Mod APK Blackmod compatible with my device?
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Warpath Mod APK Blackmod is compatible with most Android devices that have Android 4.4 or higher versions. However, some devices might have different specifications or settings that might affect the performance or compatibility of the mod. If you encounter any issues or errors while using the mod, you can try to clear the cache, restart your device, or reinstall the mod.
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Can I play Warpath Mod APK Blackmod online with other players?
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Yes, you can play Warpath Mod APK Blackmod online with other players who are using the same mod or server as you. You can chat, trade, donate, help, and fight with them in various modes and events. However, you might not be able to play with players who are using the original version of Warpath or a different mod or server than you.
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Can I update Warpath Mod APK Blackmod to the latest version?
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Yes, you can update Warpath Mod APK Blackmod to the latest version whenever there is a new update available on the Blackmod website. You just need to download the new mod file and install it over the old one. You don't need to uninstall or delete the old one. However, you might lose some of your progress or data if you update the mod without backing it up first.
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Can I request for a new feature or function in Warpath Mod APK Blackmod?
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Yes, you can request for a new feature or function in Warpath Mod APK Blackmod if you have any suggestions or ideas that can improve the mod. You can leave a comment on the Blackmod website or contact the mod developer directly. However, there is no guarantee that your request will be granted or implemented.
- 401be4b1e0
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diff --git a/spaces/contluForse/HuggingGPT/assets/Advance Steel 2018 Crack Xforce 32 Tips and Tricks for Using the Software.md b/spaces/contluForse/HuggingGPT/assets/Advance Steel 2018 Crack Xforce 32 Tips and Tricks for Using the Software.md
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index 6480ee37cf4bca8020867d72fb8feb8235031eb7..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Advance Steel 2018 Crack Xforce 32 Tips and Tricks for Using the Software.md
+++ /dev/null
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-
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- aaccfb2cb3
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diff --git a/spaces/contluForse/HuggingGPT/assets/Arnold 2012 X Force 2012 X32.exe.iso A Comprehensive Review and Comparison.md b/spaces/contluForse/HuggingGPT/assets/Arnold 2012 X Force 2012 X32.exe.iso A Comprehensive Review and Comparison.md
deleted file mode 100644
index e9207d6d17b797afe4b5cddac6de67c8901d6939..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Arnold 2012 X Force 2012 X32.exe.iso A Comprehensive Review and Comparison.md
+++ /dev/null
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I have a 2012 MacBook Pro with a built in DVD/CD player. My hard drive crashed so I had to get a new one and my computer was upgraded to Catalina. I am no longer able to use my DVD/CD player. Any suggestions. The CD, DVD, iPod is checked. I am at a loss.
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- aaccfb2cb3
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diff --git a/spaces/contluForse/HuggingGPT/assets/Detective Byomkesh Bakshy! Hd 720p Downloadl - Uncover the Conspiracy to Unsettle Calcutta in this Movie.md b/spaces/contluForse/HuggingGPT/assets/Detective Byomkesh Bakshy! Hd 720p Downloadl - Uncover the Conspiracy to Unsettle Calcutta in this Movie.md
deleted file mode 100644
index 8094778832b3fb2c14f3e0d6a469366df9490526..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Detective Byomkesh Bakshy! Hd 720p Downloadl - Uncover the Conspiracy to Unsettle Calcutta in this Movie.md
+++ /dev/null
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-
-
- aaccfb2cb3
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diff --git a/spaces/contluForse/HuggingGPT/assets/Enzo Ferrari la storia del fondatore della Scuderia Ferrari nel film completo 20.md b/spaces/contluForse/HuggingGPT/assets/Enzo Ferrari la storia del fondatore della Scuderia Ferrari nel film completo 20.md
deleted file mode 100644
index be1ca49f331878fb1e1da3ef31bf1b7b2b0c5cb5..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Enzo Ferrari la storia del fondatore della Scuderia Ferrari nel film completo 20.md
+++ /dev/null
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diff --git a/spaces/Mahiruoshi/MyGO_VIts-bert/bert_gen.py b/spaces/Mahiruoshi/MyGO_VIts-bert/bert_gen.py
deleted file mode 100644
index fb9b550f78dde279ce21e9f3accd84d507bcc8f0..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/MyGO_VIts-bert/bert_gen.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import torch
-from multiprocessing import Pool
-import commons
-import utils
-from tqdm import tqdm
-from text import cleaned_text_to_sequence, get_bert
-import argparse
-import torch.multiprocessing as mp
-
-
-def process_line(line):
- rank = mp.current_process()._identity
- rank = rank[0] if len(rank) > 0 else 0
- if torch.cuda.is_available():
- gpu_id = rank % torch.cuda.device_count()
- device = torch.device(f"cuda:{gpu_id}")
- wav_path, _, language_str, text, phones, tone, word2ph = line.strip().split("|")
- phone = phones.split(" ")
- tone = [int(i) for i in tone.split(" ")]
- word2ph = [int(i) for i in word2ph.split(" ")]
- word2ph = [i for i in word2ph]
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
- if hps.data.add_blank:
- phone = commons.intersperse(phone, 0)
- tone = commons.intersperse(tone, 0)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
-
- bert_path = wav_path.replace(".wav", ".bert.pt")
-
- try:
- bert = torch.load(bert_path)
- assert bert.shape[-1] == len(phone)
- except Exception:
- bert = get_bert(text, word2ph, language_str, device)
- assert bert.shape[-1] == len(phone)
- torch.save(bert, bert_path)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("-c", "--config", type=str, default="configs/config.json")
- parser.add_argument("--num_processes", type=int, default=2)
- args = parser.parse_args()
- config_path = args.config
- hps = utils.get_hparams_from_file(config_path)
- lines = []
- with open(hps.data.training_files, encoding="utf-8") as f:
- lines.extend(f.readlines())
-
- with open(hps.data.validation_files, encoding="utf-8") as f:
- lines.extend(f.readlines())
-
- num_processes = args.num_processes
- with Pool(processes=num_processes) as pool:
- for _ in tqdm(pool.imap_unordered(process_line, lines), total=len(lines)):
- pass
diff --git a/spaces/MathysL/AutoGPT4/tests/unit/test_commands.py b/spaces/MathysL/AutoGPT4/tests/unit/test_commands.py
deleted file mode 100644
index ecbac9b73bd9ad872931d77e144dd853b3d8ef64..0000000000000000000000000000000000000000
--- a/spaces/MathysL/AutoGPT4/tests/unit/test_commands.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""Unit tests for the commands module"""
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-import autogpt.agent.agent_manager as agent_manager
-from autogpt.app import execute_command, list_agents, start_agent
-
-
-@pytest.mark.integration_test
-def test_make_agent() -> None:
- """Test the make_agent command"""
- with patch("openai.ChatCompletion.create") as mock:
- obj = MagicMock()
- obj.response.choices[0].messages[0].content = "Test message"
- mock.return_value = obj
- start_agent("Test Agent", "chat", "Hello, how are you?", "gpt2")
- agents = list_agents()
- assert "List of agents:\n0: chat" == agents
- start_agent("Test Agent 2", "write", "Hello, how are you?", "gpt2")
- agents = list_agents()
- assert "List of agents:\n0: chat\n1: write" == agents
diff --git a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/tools/fix_o365_names.py b/spaces/MattyWhite/ChatGPT-ImageCaptioner2/tools/fix_o365_names.py
deleted file mode 100644
index c6730eacecb646bfef67a869dc9a93de6e55b6f2..0000000000000000000000000000000000000000
--- a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/tools/fix_o365_names.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import argparse
-import json
-import copy
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument("--ann", default='datasets/objects365/annotations/zhiyuan_objv2_val.json')
- parser.add_argument("--fix_name_map", default='datasets/metadata/Objects365_names_fix.csv')
- args = parser.parse_args()
-
- new_names = {}
- old_names = {}
- with open(args.fix_name_map, 'r') as f:
- for line in f:
- tmp = line.strip().split(',')
- old_names[int(tmp[0])] = tmp[1]
- new_names[int(tmp[0])] = tmp[2]
- data = json.load(open(args.ann, 'r'))
-
- cat_info = copy.deepcopy(data['categories'])
-
- for x in cat_info:
- if old_names[x['id']].strip() != x['name'].strip():
- print('{} {} {}'.format(x, old_names[x['id']], new_names[x['id']]))
- import pdb; pdb.set_trace()
- if old_names[x['id']] != new_names[x['id']]:
- print('Renaming', x['id'], x['name'], new_names[x['id']])
- x['name'] = new_names[x['id']]
-
- data['categories'] = cat_info
- out_name = args.ann[:-5] + '_fixname.json'
- print('Saving to', out_name)
- json.dump(data, open(out_name, 'w'))
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/logger/wandb.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/logger/wandb.py
deleted file mode 100644
index 9f6808462eb79ab2b04806a5d9f0d3dd079b5ea9..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/logger/wandb.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ...dist_utils import master_only
-from ..hook import HOOKS
-from .base import LoggerHook
-
-
-@HOOKS.register_module()
-class WandbLoggerHook(LoggerHook):
-
- def __init__(self,
- init_kwargs=None,
- interval=10,
- ignore_last=True,
- reset_flag=False,
- commit=True,
- by_epoch=True,
- with_step=True):
- super(WandbLoggerHook, self).__init__(interval, ignore_last,
- reset_flag, by_epoch)
- self.import_wandb()
- self.init_kwargs = init_kwargs
- self.commit = commit
- self.with_step = with_step
-
- def import_wandb(self):
- try:
- import wandb
- except ImportError:
- raise ImportError(
- 'Please run "pip install wandb" to install wandb')
- self.wandb = wandb
-
- @master_only
- def before_run(self, runner):
- super(WandbLoggerHook, self).before_run(runner)
- if self.wandb is None:
- self.import_wandb()
- if self.init_kwargs:
- self.wandb.init(**self.init_kwargs)
- else:
- self.wandb.init()
-
- @master_only
- def log(self, runner):
- tags = self.get_loggable_tags(runner)
- if tags:
- if self.with_step:
- self.wandb.log(
- tags, step=self.get_iter(runner), commit=self.commit)
- else:
- tags['global_step'] = self.get_iter(runner)
- self.wandb.log(tags, commit=self.commit)
-
- @master_only
- def after_run(self, runner):
- self.wandb.join()
diff --git a/spaces/MiSuku/Suku8008m/app.py b/spaces/MiSuku/Suku8008m/app.py
deleted file mode 100644
index a362dcc7d0ddd1eee86961f1bc3db6d894fbd3d5..0000000000000000000000000000000000000000
--- a/spaces/MiSuku/Suku8008m/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-def get_text_response(user_message,history):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/gatherers/base.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/gatherers/base.py
deleted file mode 100644
index f982a1a5d62e5071646d621865e6e9fd1dad674f..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/gatherers/base.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-from typing import List, Optional, Tuple, Union
-
-
-class BaseGatherer:
- """Base class for gatherer.
-
- Note: Gatherer assumes that all the annotation file is in the same
- directory and all the image files are in the same directory.
-
- Args:
- img_dir(str): The directory of the images. It is usually set
- automatically to f'text{task}_imgs/split' and users do not need to
- set it manually in config file in most cases. When the image files
- is not in 'text{task}_imgs/split' directory, users should set it.
- Defaults to ''.
- ann_dir (str): The directory of the annotation files. It is usually set
- automatically to 'annotations' and users do not need to set it
- manually in config file in most cases. When the annotation files
- is not in 'annotations' directory, users should set it. Defaults to
- 'annotations'.
- split (str, optional): List of splits to gather. It' s the partition of
- the datasets. Options are 'train', 'val' or 'test'. It is usually
- set automatically and users do not need to set it manually in
- config file in most cases. Defaults to None.
- data_root (str, optional): The root directory of the image and
- annotation. It is usually set automatically and users do not need
- to set it manually in config file in most cases. Defaults to None.
- """
-
- def __init__(self,
- img_dir: str = '',
- ann_dir: str = 'annotations',
- split: Optional[str] = None,
- data_root: Optional[str] = None) -> None:
- self.split = split
- self.data_root = data_root
- self.ann_dir = osp.join(data_root, ann_dir)
- self.img_dir = osp.join(data_root, img_dir)
-
- def __call__(self) -> Union[Tuple[List[str], List[str]], Tuple[str, str]]:
- """The return value of the gatherer is a tuple of two lists or strings.
-
- The first element is the list of image paths or the directory of the
- images. The second element is the list of annotation paths or the path
- of the annotation file which contains all the annotations.
- """
- raise NotImplementedError
diff --git a/spaces/MyGenAIchatBot/Puji/README.md b/spaces/MyGenAIchatBot/Puji/README.md
deleted file mode 100644
index ffdbad0b97a9d78643aafedbfb5062981e1b1d93..0000000000000000000000000000000000000000
--- a/spaces/MyGenAIchatBot/Puji/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Puji
-emoji: 🏢
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/MyGenAiUser/MyGenAiChat/README.md b/spaces/MyGenAiUser/MyGenAiChat/README.md
deleted file mode 100644
index 21e407cecb7c687f39e5e84ff00efaaef0f8527f..0000000000000000000000000000000000000000
--- a/spaces/MyGenAiUser/MyGenAiChat/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MyGenAiChat
-emoji: 🐠
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/xlnet/training_utils.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/xlnet/training_utils.py
deleted file mode 100644
index 293e4633d8f4ae0f00fc5fbabb3a3996827ced81..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/xlnet/training_utils.py
+++ /dev/null
@@ -1,310 +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.
-# ==============================================================================
-"""XLNet training utils."""
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
-
-import os
-import re
-
-from absl import logging
-
-# pytype: disable=attribute-error
-# pylint: disable=g-bare-generic,unused-import
-import tensorflow as tf
-from typing import Any, Callable, Dict, Text, Optional
-
-from official.nlp.bert import model_training_utils
-from official.nlp.xlnet import data_utils
-from official.nlp.xlnet import xlnet_modeling as modeling
-
-_MIN_SUMMARY_STEPS = 10
-
-
-def _save_checkpoint(checkpoint, model_dir, checkpoint_prefix):
- """Saves model to with provided checkpoint prefix."""
-
- checkpoint_path = os.path.join(model_dir, checkpoint_prefix)
- saved_path = checkpoint.save(checkpoint_path)
- logging.info("Saving model as TF checkpoint: %s", saved_path)
- return
-
-
-def _float_metric_value(metric):
- """Gets the value of a float-value keras metric."""
- return metric.result().numpy().astype(float)
-
-
-def train(
- strategy: tf.distribute.Strategy,
- model_fn: Callable,
- input_meta_data: Dict,
- train_input_fn: Callable,
- total_training_steps: int,
- steps_per_loop: int,
- optimizer: tf.keras.optimizers.Optimizer,
- learning_rate_fn: tf.keras.optimizers.schedules.LearningRateSchedule,
- eval_fn: Optional[Callable[[tf.keras.Model, int, tf.summary.SummaryWriter],
- Any]] = None,
- metric_fn: Optional[Callable[[], tf.keras.metrics.Metric]] = None,
- init_checkpoint: Optional[Text] = None,
- init_from_transformerxl: Optional[bool] = False,
- model_dir: Optional[Text] = None,
- save_steps: Optional[int] = None,
- run_eagerly: Optional[bool] = False):
- """Runs customized training.
-
- Args:
- strategy: Distribution strategy on which to run low level training loop.
- model_fn: The function returns a keras.Model.
- input_meta_data: A dictionary of params: `mem_len`, `lr_layer_decay_rate`,
- `n_layer`, `batch_size_per_core` and `d_model`.
- train_input_fn: Function returns a tf.data.Dataset used for training.
- total_training_steps: Number of steps to train in total.
- steps_per_loop: Number of steps per graph-mode loop. In order to reduce
- communication in eager context, training logs are printed every
- steps_per_loop.
- optimizer: The optimizer for model.
- learning_rate_fn: the learning rate schedule.
- eval_fn: A callback of evaluation function, that takes a keras.Model,
- current step and evaluation summary writer.
- metric_fn: A metrics function returns a Keras Metric object to record
- evaluation result using evaluation dataset or with training dataset
- after every epoch.
- init_checkpoint: Optional checkpoint to load to `sub_model` returned by
- `model_fn`.
- init_from_transformerxl: Whether to load to `transformerxl_model` of
- `model_fn`.
- model_dir: The directory of model (checkpoints, summaries).
- save_steps: The frequency to save checkpoints. Every save_steps, we save a
- model checkpoint. Model checkpoint will be saved and evaluation will be
- conducted if evaluation dataset is provided.
- run_eagerly: Whether to run training eagerly.
-
- Returns:
- Last training step logits if training happens, otherwise returns None.
- Raises:
- TypeError: if model directory is not specified.
- """
- required_arguments = [
- train_input_fn, total_training_steps, steps_per_loop, optimizer,
- learning_rate_fn, save_steps
- ]
- if [arg for arg in required_arguments if arg is None]:
- raise ValueError("`train_input_fn`, `total_training_steps`, "
- "`steps_per_loop`, `optimizer`, `save_steps` and "
- "`learning_rate_fn` are required parameters.")
- if not model_dir:
- raise TypeError("Model directory must be specified.")
- train_iterator = data_utils.get_input_iterator(train_input_fn, strategy)
- if not tf.io.gfile.exists(model_dir):
- tf.io.gfile.mkdir(model_dir)
- # Create summary writers
- summary_dir = os.path.join(model_dir, "summaries")
- if not tf.io.gfile.exists(summary_dir):
- tf.io.gfile.mkdir(summary_dir)
- train_summary_writer = None
- eval_summary_writer = None
- if eval_fn:
- eval_summary_writer = tf.summary.create_file_writer(
- os.path.join(summary_dir, "eval"))
- if steps_per_loop >= _MIN_SUMMARY_STEPS:
- # Only writes summary when the stats are collected sufficiently over
- # enough steps.
- train_summary_writer = tf.summary.create_file_writer(
- os.path.join(summary_dir, "train"))
-
- with strategy.scope():
- model = model_fn()
-
- if init_checkpoint:
- logging.info("restore from %s", init_checkpoint)
- if init_from_transformerxl:
- checkpoint = tf.train.Checkpoint(
- transformer_xl=model.transformerxl_model)
- else:
- checkpoint = tf.train.Checkpoint(model=model)
- checkpoint.restore(init_checkpoint)
-
- model.optimizer = optimizer
-
- if not hasattr(model, "optimizer"):
- raise ValueError("User should set optimizer attribute to model.")
-
- train_loss_metric = tf.keras.metrics.Mean("training_loss", dtype=tf.float32)
- train_metric = None
- if metric_fn:
- train_metric = metric_fn()
-
- def _replicated_step(inputs, mem=None):
- """Replicated training step."""
-
- inputs["mems"] = mem
- with tf.GradientTape() as tape:
- mem, logits = model(inputs, training=True)
- loss = model.losses
- train_loss_metric.update_state(loss)
- if train_metric:
- train_metric.update_state(inputs["label_ids"], logits)
- scaled_loss = loss[0] * 1.0 / float(strategy.num_replicas_in_sync)
-
- # Collects training variables.
- tvars = model.trainable_variables
- grads = tape.gradient(scaled_loss, tvars)
- clipped, _ = tf.clip_by_global_norm(grads, clip_norm=1.0)
-
- if input_meta_data["lr_layer_decay_rate"] != 1.0:
- n_layer = 0
- for i in range(len(clipped)):
- m = re.search(r"model/transformer/layer_(\d+?)/", tvars[i].name)
- if not m:
- continue
- n_layer = max(n_layer, int(m.group(1)) + 1)
-
- for i in range(len(clipped)):
- for l in range(n_layer):
- if "model/transformer/layer_{}/".format(l) in tvars[i].name:
- abs_rate = input_meta_data["lr_layer_decay_rate"]**(
- n_layer - 1 - l)
- clipped[i] *= abs_rate
- logging.info("Apply mult {:.4f} to layer-{} grad of {}".format(
- abs_rate, l, tvars[i].name))
- break
-
- optimizer.apply_gradients(zip(clipped, tvars))
- if input_meta_data["mem_len"] > 0:
- return mem
-
- def train_steps(iterator, steps):
- """Performs distributed training steps in a loop.
-
- Args:
- iterator: the distributed iterator of training datasets.
- steps: an tf.int32 integer tensor to specify number of steps to run
- inside host training loop.
-
- Raises:
- ValueError: Any of the arguments or tensor shapes are invalid.
-
- Returns:
- logits: logits computed.
- """
- if not isinstance(steps, tf.Tensor):
- raise ValueError("steps should be an Tensor. Python object may cause "
- "retracing.")
-
- def cache_fn():
- """Initializes memory tensor used in XLNet pretraining."""
- mems = []
- if input_meta_data["mem_len"] > 0:
- for _ in range(input_meta_data["n_layer"]):
- zeros = tf.zeros([
- input_meta_data["mem_len"],
- input_meta_data["batch_size_per_core"],
- input_meta_data["d_model"]
- ],
- dtype=tf.float32)
- mems.append(zeros)
- return mems
-
- if input_meta_data["mem_len"] > 0:
- mem = strategy.run(cache_fn)
- for _ in tf.range(steps):
- mem = strategy.run(
- _replicated_step, args=(
- next(iterator),
- mem,
- ))
- else:
- for _ in tf.range(steps):
- strategy.run(_replicated_step, args=(next(iterator),))
-
- if not run_eagerly:
- train_steps = tf.function(train_steps)
-
- logging.info("Start training...")
- checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
- latest_checkpoint_file = tf.train.latest_checkpoint(model_dir)
- if latest_checkpoint_file:
- logging.info("Checkpoint file %s found and restoring from checkpoint",
- latest_checkpoint_file)
- checkpoint.restore(latest_checkpoint_file)
- logging.info("Loading from checkpoint file completed")
-
- current_step = optimizer.iterations.numpy()
- checkpoint_name = "xlnet_step_{step}.ckpt"
-
- while current_step < total_training_steps:
- train_loss_metric.reset_states()
- if train_metric:
- train_metric.reset_states()
-
- steps = model_training_utils.steps_to_run(current_step, save_steps,
- steps_per_loop)
- train_steps(train_iterator, tf.convert_to_tensor(steps, dtype=tf.int32))
- current_step += steps
- train_loss = _float_metric_value(train_loss_metric)
- log_stream = "Train step: %d/%d / lr = %.9f / loss = %.7f" % (
- current_step, total_training_steps, learning_rate_fn(current_step),
- train_loss)
- if train_metric:
- log_stream += " / %s = %f" % (train_metric.name,
- _float_metric_value(train_metric))
- logging.info(log_stream)
- if train_summary_writer:
- with train_summary_writer.as_default():
- tf.summary.scalar(
- "learning_rate",
- learning_rate_fn(current_step),
- step=current_step)
- tf.summary.scalar(
- train_loss_metric.name, train_loss, step=current_step)
- if train_metric:
- tf.summary.scalar(
- train_metric.name,
- _float_metric_value(train_metric),
- step=current_step)
- train_summary_writer.flush()
- if model_dir and current_step % save_steps == 0:
- _save_checkpoint(checkpoint, model_dir,
- checkpoint_name.format(step=current_step))
-
- if eval_fn and current_step % save_steps == 0:
-
- logging.info("Running evaluation after step: %s.", current_step)
-
- eval_fn(model, current_step, eval_summary_writer)
- if model_dir:
- _save_checkpoint(checkpoint, model_dir,
- checkpoint_name.format(step=current_step))
- if eval_fn:
- logging.info("Running final evaluation after training is complete.")
- eval_metric = eval_fn(model, current_step, eval_summary_writer)
-
- training_summary = {
- "total_training_steps": total_training_steps,
- "train_loss": _float_metric_value(train_loss_metric),
- }
- if train_metric:
- training_summary["last_train_metrics"] = _float_metric_value(train_metric)
- if eval_fn:
- # eval_metric is supposed to be a float.
- training_summary["eval_metrics"] = eval_metric
-
- model_training_utils.write_txt_summary(training_summary, summary_dir)
-
- return model
diff --git a/spaces/Namit2111/id_verfiy/README.md b/spaces/Namit2111/id_verfiy/README.md
deleted file mode 100644
index 540a3c68ab21ff7a58e43414954c3a27b6d8f8a6..0000000000000000000000000000000000000000
--- a/spaces/Namit2111/id_verfiy/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Id Verfiy
-emoji: 😻
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NimaBoscarino/climategan/climategan/deeplab/deeplab_v2.py b/spaces/NimaBoscarino/climategan/climategan/deeplab/deeplab_v2.py
deleted file mode 100644
index 7c097ccf4181588f2f814d2baa564a2213899450..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/climategan/deeplab/deeplab_v2.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from climategan.blocks import InterpolateNearest2d
-from climategan.utils import find_target_size
-
-
-class _ASPPModule(nn.Module):
- # https://github.com/jfzhang95/pytorch-deeplab-xception/blob/master/modeling/aspp.py
- def __init__(
- self, inplanes, planes, kernel_size, padding, dilation, BatchNorm, no_init
- ):
- super().__init__()
- self.atrous_conv = nn.Conv2d(
- inplanes,
- planes,
- kernel_size=kernel_size,
- stride=1,
- padding=padding,
- dilation=dilation,
- bias=False,
- )
- self.bn = BatchNorm(planes)
- self.relu = nn.ReLU()
- if not no_init:
- self._init_weight()
-
- def forward(self, x):
- x = self.atrous_conv(x)
- x = self.bn(x)
-
- return self.relu(x)
-
- def _init_weight(self):
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- torch.nn.init.kaiming_normal_(m.weight)
- elif isinstance(m, nn.BatchNorm2d):
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
-
-class ASPP(nn.Module):
- # https://github.com/jfzhang95/pytorch-deeplab-xception/blob/master/modeling/aspp.py
- def __init__(self, backbone, output_stride, BatchNorm, no_init):
- super().__init__()
-
- if backbone == "mobilenet":
- inplanes = 320
- else:
- inplanes = 2048
-
- if output_stride == 16:
- dilations = [1, 6, 12, 18]
- elif output_stride == 8:
- dilations = [1, 12, 24, 36]
- else:
- raise NotImplementedError
-
- self.aspp1 = _ASPPModule(
- inplanes,
- 256,
- 1,
- padding=0,
- dilation=dilations[0],
- BatchNorm=BatchNorm,
- no_init=no_init,
- )
- self.aspp2 = _ASPPModule(
- inplanes,
- 256,
- 3,
- padding=dilations[1],
- dilation=dilations[1],
- BatchNorm=BatchNorm,
- no_init=no_init,
- )
- self.aspp3 = _ASPPModule(
- inplanes,
- 256,
- 3,
- padding=dilations[2],
- dilation=dilations[2],
- BatchNorm=BatchNorm,
- no_init=no_init,
- )
- self.aspp4 = _ASPPModule(
- inplanes,
- 256,
- 3,
- padding=dilations[3],
- dilation=dilations[3],
- BatchNorm=BatchNorm,
- no_init=no_init,
- )
-
- self.global_avg_pool = nn.Sequential(
- nn.AdaptiveAvgPool2d((1, 1)),
- nn.Conv2d(inplanes, 256, 1, stride=1, bias=False),
- BatchNorm(256),
- nn.ReLU(),
- )
- self.conv1 = nn.Conv2d(1280, 256, 1, bias=False)
- self.bn1 = BatchNorm(256)
- self.relu = nn.ReLU()
- self.dropout = nn.Dropout(0.5)
- if not no_init:
- self._init_weight()
-
- def forward(self, x):
- x1 = self.aspp1(x)
- x2 = self.aspp2(x)
- x3 = self.aspp3(x)
- x4 = self.aspp4(x)
- x5 = self.global_avg_pool(x)
- x5 = F.interpolate(x5, size=x4.size()[2:], mode="bilinear", align_corners=True)
- x = torch.cat((x1, x2, x3, x4, x5), dim=1)
-
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
-
- return self.dropout(x)
-
- def _init_weight(self):
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- # m.weight.data.normal_(0, math.sqrt(2. / n))
- torch.nn.init.kaiming_normal_(m.weight)
- elif isinstance(m, nn.BatchNorm2d):
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
-
-class DeepLabV2Decoder(nn.Module):
- # https://github.com/jfzhang95/pytorch-deeplab-xception/blob/master/modeling/decoder.py
- # https://github.com/jfzhang95/pytorch-deeplab-xception/blob/master/modeling/deeplab.py
- def __init__(self, opts, no_init=False):
- super().__init__()
- self.aspp = ASPP("resnet", 16, nn.BatchNorm2d, no_init)
- self.use_dada = ("d" in opts.tasks) and opts.gen.s.use_dada
-
- conv_modules = [
- nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Dropout(0.5),
- nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Dropout(0.1),
- ]
- if opts.gen.s.upsample_featuremaps:
- conv_modules = [InterpolateNearest2d(scale_factor=2)] + conv_modules
-
- conv_modules += [
- nn.Conv2d(256, opts.gen.s.output_dim, kernel_size=1, stride=1),
- ]
- self.conv = nn.Sequential(*conv_modules)
-
- self._target_size = find_target_size(opts, "s")
- print(
- " - {}: setting target size to {}".format(
- self.__class__.__name__, self._target_size
- )
- )
-
- def set_target_size(self, size):
- """
- Set final interpolation's target size
-
- Args:
- size (int, list, tuple): target size (h, w). If int, target will be (i, i)
- """
- if isinstance(size, (list, tuple)):
- self._target_size = size[:2]
- else:
- self._target_size = (size, size)
-
- def forward(self, z, z_depth=None):
- if self._target_size is None:
- error = "self._target_size should be set with self.set_target_size()"
- error += "to interpolate logits to the target seg map's size"
- raise Exception(error)
- if isinstance(z, (list, tuple)):
- z = z[0]
- if z.shape[1] != 2048:
- raise Exception(
- "Segmentation decoder will only work with 2048 channels for z"
- )
-
- if z_depth is not None and self.use_dada:
- z = z * z_depth
-
- y = self.aspp(z)
- y = self.conv(y)
- return F.interpolate(y, self._target_size, mode="bilinear", align_corners=True)
diff --git a/spaces/Nyashi/rvc-models-epic/infer_pack/models.py b/spaces/Nyashi/rvc-models-epic/infer_pack/models.py
deleted file mode 100644
index 96165f73644e6fb92d0ffedb4a3c9e1a457cb989..0000000000000000000000000000000000000000
--- a/spaces/Nyashi/rvc-models-epic/infer_pack/models.py
+++ /dev/null
@@ -1,982 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(256, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- self.encoder = attentions.Encoder(
- hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(256, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- self.encoder = attentions.Encoder(
- hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- x = self.proj(x) * x_mask
- return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(
- self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0,
- ):
- 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.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(
- modules.ResidualCouplingLayer(
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- mean_only=True,
- )
- )
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
- def remove_weight_norm(self):
- for i in range(self.n_flows):
- self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- 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.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=0,
- ):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(
- initial_channel, upsample_initial_channel, 7, 1, padding=3
- )
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(
- zip(resblock_kernel_sizes, resblock_dilation_sizes)
- ):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
- """Definition of sine generator
- SineGen(samp_rate, harmonic_num = 0,
- sine_amp = 0.1, noise_std = 0.003,
- voiced_threshold = 0,
- flag_for_pulse=False)
- samp_rate: sampling rate in Hz
- harmonic_num: number of harmonic overtones (default 0)
- sine_amp: amplitude of sine-wavefrom (default 0.1)
- noise_std: std of Gaussian noise (default 0.003)
- voiced_thoreshold: F0 threshold for U/V classification (default 0)
- flag_for_pulse: this SinGen is used inside PulseGen (default False)
- Note: when flag_for_pulse is True, the first time step of a voiced
- segment is always sin(np.pi) or cos(0)
- """
-
- def __init__(
- self,
- samp_rate,
- harmonic_num=0,
- sine_amp=0.1,
- noise_std=0.003,
- voiced_threshold=0,
- flag_for_pulse=False,
- ):
- super(SineGen, self).__init__()
- self.sine_amp = sine_amp
- self.noise_std = noise_std
- self.harmonic_num = harmonic_num
- self.dim = self.harmonic_num + 1
- self.sampling_rate = samp_rate
- self.voiced_threshold = voiced_threshold
-
- def _f02uv(self, f0):
- # generate uv signal
- uv = torch.ones_like(f0)
- uv = uv * (f0 > self.voiced_threshold)
- return uv
-
- def forward(self, f0, upp):
- """sine_tensor, uv = forward(f0)
- input F0: tensor(batchsize=1, length, dim=1)
- f0 for unvoiced steps should be 0
- output sine_tensor: tensor(batchsize=1, length, dim)
- output uv: tensor(batchsize=1, length, 1)
- """
- with torch.no_grad():
- f0 = f0[:, None].transpose(1, 2)
- f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
- # fundamental component
- f0_buf[:, :, 0] = f0[:, :, 0]
- for idx in np.arange(self.harmonic_num):
- f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
- idx + 2
- ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
- rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
- rand_ini = torch.rand(
- f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
- )
- rand_ini[:, 0] = 0
- rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
- tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化
- tmp_over_one *= upp
- tmp_over_one = F.interpolate(
- tmp_over_one.transpose(2, 1),
- scale_factor=upp,
- mode="linear",
- align_corners=True,
- ).transpose(2, 1)
- rad_values = F.interpolate(
- rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(
- 2, 1
- ) #######
- tmp_over_one %= 1
- tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
- cumsum_shift = torch.zeros_like(rad_values)
- cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
- sine_waves = torch.sin(
- torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
- )
- sine_waves = sine_waves * self.sine_amp
- uv = self._f02uv(f0)
- uv = F.interpolate(
- uv.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(2, 1)
- noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
- noise = noise_amp * torch.randn_like(sine_waves)
- sine_waves = sine_waves * uv + noise
- return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
- """SourceModule for hn-nsf
- SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
- add_noise_std=0.003, voiced_threshod=0)
- sampling_rate: sampling_rate in Hz
- harmonic_num: number of harmonic above F0 (default: 0)
- sine_amp: amplitude of sine source signal (default: 0.1)
- add_noise_std: std of additive Gaussian noise (default: 0.003)
- note that amplitude of noise in unvoiced is decided
- by sine_amp
- voiced_threshold: threhold to set U/V given F0 (default: 0)
- Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
- F0_sampled (batchsize, length, 1)
- Sine_source (batchsize, length, 1)
- noise_source (batchsize, length 1)
- uv (batchsize, length, 1)
- """
-
- def __init__(
- self,
- sampling_rate,
- harmonic_num=0,
- sine_amp=0.1,
- add_noise_std=0.003,
- voiced_threshod=0,
- is_half=True,
- ):
- super(SourceModuleHnNSF, self).__init__()
-
- self.sine_amp = sine_amp
- self.noise_std = add_noise_std
- self.is_half = is_half
- # to produce sine waveforms
- self.l_sin_gen = SineGen(
- sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
- )
-
- # to merge source harmonics into a single excitation
- self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
- self.l_tanh = torch.nn.Tanh()
-
- def forward(self, x, upp=None):
- sine_wavs, uv, _ = self.l_sin_gen(x, upp)
- if self.is_half:
- sine_wavs = sine_wavs.half()
- sine_merge = self.l_tanh(self.l_linear(sine_wavs))
- return sine_merge, None, None # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- sr,
- is_half=False,
- ):
- super(GeneratorNSF, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
-
- self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
- self.m_source = SourceModuleHnNSF(
- sampling_rate=sr, harmonic_num=0, is_half=is_half
- )
- self.noise_convs = nn.ModuleList()
- self.conv_pre = Conv1d(
- initial_channel, upsample_initial_channel, 7, 1, padding=3
- )
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- c_cur = upsample_initial_channel // (2 ** (i + 1))
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
- if i + 1 < len(upsample_rates):
- stride_f0 = np.prod(upsample_rates[i + 1 :])
- self.noise_convs.append(
- Conv1d(
- 1,
- c_cur,
- kernel_size=stride_f0 * 2,
- stride=stride_f0,
- padding=stride_f0 // 2,
- )
- )
- else:
- self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(
- zip(resblock_kernel_sizes, resblock_dilation_sizes)
- ):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- self.upp = np.prod(upsample_rates)
-
- def forward(self, x, f0, g=None):
- har_source, noi_source, uv = self.m_source(f0, self.upp)
- har_source = har_source.transpose(1, 2)
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- x_source = self.noise_convs[i](har_source)
- x = x + x_source
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
- return x
-
- def remove_weight_norm(self):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-sr2sr = {
- "32k": 32000,
- "40k": 40000,
- "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr,
- **kwargs
- ):
- super().__init__()
- if type(sr) == type("strr"):
- sr = sr2sr[sr]
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- sr=sr,
- is_half=kwargs["is_half"],
- )
- self.enc_q = PosteriorEncoder(
- spec_channels,
- inter_channels,
- hidden_channels,
- 5,
- 1,
- 16,
- gin_channels=gin_channels,
- )
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(
- self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
- ): # 这里ds是id，[bs,1]
- # print(1,pitch.shape)#[bs,t]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
- pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
- # print(-2,pitchf.shape,z_slice.shape)
- o = self.dec(z_slice, pitchf, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
- return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr=None,
- **kwargs
- ):
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=False,
- )
- self.dec = Generator(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- )
- self.enc_q = PosteriorEncoder(
- spec_channels,
- inter_channels,
- hidden_channels,
- 5,
- 1,
- 16,
- gin_channels=gin_channels,
- )
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- o = self.dec(z_slice, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, sid, max_len=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec((z * x_mask)[:, :, :max_len], g=g)
- return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- # hop_length,
- gin_channels=0,
- use_sdp=True,
- **kwargs
- ):
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256Sim(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- is_half=kwargs["is_half"],
- )
-
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(
- self, phone, phone_lengths, pitch, pitchf, y_lengths, ds
- ): # y是spec不需要了现在
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- x, x_mask = self.enc_p(phone, pitch, phone_lengths)
- x = self.flow(x, x_mask, g=g, reverse=True)
- z_slice, ids_slice = commons.rand_slice_segments(
- x, y_lengths, self.segment_size
- )
-
- pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
- o = self.dec(z_slice, pitchf, g=g)
- return o, ids_slice
-
- def infer(
- self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
- ): # y是spec不需要了现在
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- x, x_mask = self.enc_p(phone, pitch, phone_lengths)
- x = self.flow(x, x_mask, g=g, reverse=True)
- o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
- return o, o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2, 3, 5, 7, 11, 17]
- # periods = [3, 5, 7, 11, 17, 23, 37]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [
- DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
- ]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = [] #
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- # for j in range(len(fmap_r)):
- # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ]
- )
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(
- Conv2d(
- 1,
- 32,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 32,
- 128,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 128,
- 512,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 512,
- 1024,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 1024,
- 1024,
- (kernel_size, 1),
- 1,
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- ]
- )
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/multilingual/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/multilingual/__init__.py
deleted file mode 100644
index 6264236915a7269a4d920ee8213004374dd86a9a..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/multilingual/__init__.py
+++ /dev/null
@@ -1,4 +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.
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/unify_transformer.py b/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/unify_transformer.py
deleted file mode 100644
index d03a5d332e5fcd7ff0085b026b2eb79e8acfd4f8..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/unify_transformer.py
+++ /dev/null
@@ -1,1510 +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 math
-import random
-from typing import Any, Dict, List, Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.distributed import fsdp_wrap
-from fairseq.models import (
- FairseqEncoder,
- FairseqEncoderDecoderModel,
- FairseqIncrementalDecoder,
- register_model,
- register_model_architecture,
-)
-from fairseq.modules import (
- AdaptiveSoftmax,
- BaseLayer,
- FairseqDropout,
- LayerDropModuleList,
- LayerNorm,
- SinusoidalPositionalEmbedding,
- GradMultiply
-)
-from fairseq.modules.checkpoint_activations import checkpoint_wrapper
-from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
-from torch import Tensor
-
-from .unify_transformer_layer import TransformerEncoderLayer, TransformerDecoderLayer
-from .resnet import ResNet
-
-
-DEFAULT_MAX_SOURCE_POSITIONS = 1024
-DEFAULT_MAX_TARGET_POSITIONS = 1024
-
-
-DEFAULT_MIN_PARAMS_TO_WRAP = int(1e8)
-
-
-def BatchNorm2d(out_chan, momentum=0.1, eps=1e-3):
- return nn.SyncBatchNorm.convert_sync_batchnorm(
- nn.BatchNorm2d(out_chan, momentum=momentum, eps=eps)
- )
-
-
-def make_token_bucket_position(bucket_size, max_position=DEFAULT_MAX_SOURCE_POSITIONS):
- context_pos = torch.arange(max_position, dtype=torch.long)[:, None]
- memory_pos = torch.arange(max_position, dtype=torch.long)[None, :]
- relative_pos = context_pos - memory_pos
- sign = torch.sign(relative_pos)
- mid = bucket_size // 2
- abs_pos = torch.where((relative_pos
- 
- 
-